Ecology

Memorial University of Newfoundland, St. John’s, Newfoundland and Labrador, CanadaK. K. S. Layton & P. V. R. SnelgroveFisheries and Oceans Canada, St. John’s, Newfoundland and Labrador, CanadaK. K. S. Layton, J. B. Dempson, T. Kess, S. J. Lehnert, S. J. Duffy, A. M. Messmer, R. R. E. Stanley, C. DiBacco & I. R. BradburyUniversity of Aberdeen, Aberdeen, UKK. K. S. LaytonDalhousie University, Halifax, Nova Scotia, CanadaP. Bentzen, S. J. Salisbury & D. E. RuzzanteUniversity of Guelph, Guelph, Ontario, CanadaC. M. Nugent & M. M. FergusonUniversity of Victoria, Victoria, British Columbia, CanadaJ. S. Leong & B. F. Koop More

The polysaccharide xylan limits the growth of C. crescentus cells compared to the monomer xylose in well-mixed environmentsWe first tested our hypothesis that in well-mixed conditions the polymer xylan would limit the productivity of microbial populations relative to the monomer xylose. To determine if this was the case, we grew C. crescentus cells in the same concentration (weight/volume) of either the polymer xylan or its monomeric constituent xylose, both provided as the sole carbon source (Fig. 1a). We then compared the maximum growth rate and the maximal population size over the course of a 54 h growth cycle (Fig. 1b). In line with expectations, populations growing on the monomer xylose achieved higher growth rates and a higher maximum population size (Fig. 1b–d). This was true for all concentrations (0.01–0.1%) of monomer and polymer tested (Supplementary Fig. 2). These findings suggest that in well-mixed environments of equal carbon concentration, the complexity of the growth substrate governs the growth of C. crescentus populations.Cells engage in colonial behaviors on xylan whereas they exhibit solitary behaviors on xyloseGroup formation could be a key mechanism through which cells could overcome polymer-induced growth limitations that exist in well-mixed environments. Collective behavior would allow cells to increase their local cell density, which leads to higher local concentrations of the monomeric products of polymer degradation. To test this prediction, we tested whether xylose and xylan elicit different behavioral responses in C. crescentus. We used microfluidic growth chambers in which cells were forced to grow as a monolayer. Our expectation was that growth within these devices would provide the spatial structure to overcome the growth limitations observed in well-mixed conditions (Supplementary Fig. 1). We tracked and quantified movement, and growth of individual cells using time-lapse microscopy and image analysis. Chambers were constantly replenished with minimal medium containing either xylose or xylan through a main nutrient feeding channel, as described elsewhere [20, 23, 24].We found that C. crescentus displayed strikingly disparate behaviors in xylan and xylose: cells formed microcolonies on the polymer xylan (Fig. 2a, Supplementary Video 1), whereas on the monomer xylose they did not (Fig. 2b, Supplementary Video 2). We analyzed the temporal dynamics of cell growth and movement in the two carbon sources by following individual cells using cell segmentation and tracking. Mapping the lineages based on division events for all the cells in a chamber revealed that the microcolonies on the polymer xylan originated from a single progenitor cell (Fig. 2d, Supplementary Fig. 3a–c; Supplementary Video 3). This finding indicates that microcolonies were a result of swarmer cells not dispersing after division, rather than a product of secondary aggregation by planktonic cells. In contrast, in the monomer xylose only the stalked cells remained in the same position after cell division, whereas the presumably flagellated swarmer cells moved away (Fig. 2e, Supplementary Fig. 4a–c). As a consequence of this difference in behavior, the number of sessile cells increased much more rapidly in xylan. The number of cells in a growth chamber doubled on average every 3.6 ± 0.54 h in xylan (mean ± 95% CI, Fig. 2c) but took 15.50 ± 7.55 h to double in xylose (mean ± 95% CI, Fig. 2c). These differences occurred despite a similar propensity to produce offspring per sessile cell in the two substrates (Supplementary Fig. 5), and thus were driven by the reduced rate at which cells dispersed in xylan.Fig. 2: Cells display solitary behavior on xylose and aggregative behavior on xylan.Representative images of C. crescentus CB15 cells (labeled with constitutively expressed mKate2, false colored as magenta) at different time points within the microfluidic growth chambers supplied with either xylan (a) or xylose (b) as the sole source of carbon. c On xylan (yellow), the number of sessile cells in the growth chamber increases with time, whereas on xylose (blue) it remains nearly constant. Squares indicate the number of cells present at a given time point in each chamber (nchambers = 9), with a linear or exponential regression line for each chamber (xylose, linear regression model, R2 = 0.69–0.92, slope = 1.22–3.27, P  More

A 2-scale ensemble machine learningPredictions of soil nutrients are based on a fully automated and fully optimized 2-scale Ensemble Machine Learning (EML) framework as implemented in the mlr package for Machine Learning (https://mlr.mlr-org.com/). The entire process can be summarized in the following eight steps (Fig. 7):

1.

Prepare point data, quality control all values and remove any artifacts or types.

2.

Upload to Google Earth Engine, overlay the point data with the key covariates of interest and test fitting random forest or similar to get an initial estimate of relative variable importance and pre-select features of interest.

3.

Decide on a final list of all covariates to use in predictions, prepare covariates for predictive modeling—either using Amazon AWS or similar. Quality control all 250 m and 30 m resolution covariates and prepare Analysis-Ready data in a tiling system to speed up overlay and prediction.

4.

Run spatial overlay using 250 m and 30 m resolution covariates and generate regression matrices.

5.

Fit 250 m and 30 m resolution Ensemble Machine Learning models independently per soil property using spatial blocks of 30–100 km. Run sequentially: model fine-tuning, feature selection and stacking. Generate summary accuracy assessment, variable importance, and revise if necessary.

6.

Predict 250 m and 30 m resolution tiles independently using the optimized models. Downscale the 250 m predictions to 30 m resolution using Cubicsplines (GDAL).

7.

Combine predictions using Eq. (3) and generate pooled variance/s.d. using Eq. (4).

8.

Generate all final predictions as Cloud-Optimized GeoTIFFs. Upload to the server and share through API/Geoserver.

Figure 7Scheme: a two-scale framework for Predictive Soil Mapping based on Ensemble Machine Learning (as implemented in the mlr and mlr3 frameworks for Machine Learning28 and based on the SuperLearner algorithm). This process is applied for a bulk of soil samples, the individual models per soil variable are then fitted using automated fine-tuning, feature selection and stacking. The map is showing distribution of training points used in this work. Part of the training points that are publicly available are available for use from https://gitlab.com/openlandmap/compiled-ess-point-data-sets/.Full size imageFor the majority of soil properties, excluding depth to bedrock, we also use soil depth as one of the covariates so that the final models for the two scales are in the form5:$$begin{aligned} y(phi ,theta ,d) = d + x_1 (phi ,theta ) + x_2 (phi ,theta ) + cdots + X_p (phi ,theta ) end{aligned}$$
(1)
where y is the target variable, d is the soil sampling depth, (phi theta) are geographical coordinates (northing and easting), and (X_p) are the covariates. Adding soil depth as a covariate allows for directly producing 3D predictions35, which is our preferred approach as prediction can be then produced at any depth within the standard depth interval (e.g. 0–50 cm).Ensemble machine learningEnsembles are predictive models that combine predictions from two or more learners36. We implement ensembling within the mlr package by fitting a ‘meta-learner’ i.e. a learner that combines all individual learners. mlr has extensive functionality, especially for model ‘stacking’ i.e. to generate ensemble predictions, and also incorporates spatial Cross-Validation37. It also provides wrapper functions to automate hyper-parameter fine-tuning and feature selection, which can all be combined into fully-automated functions to fit and optimize models and produce predictions. Parallelisation can be initiated by using the parallelMap package, which automatically determines available resources and cleans-up all temporary sessions38.For stacking multiple base learners we use the SuperLearner method39, which is the most computational method but allows for an independent assessment of all individual learners through k-fold cross validation with refitting. To speed up computing we typically use a linear model (predict.lm) as the meta-learner, so that in fact the final formula to derive the final ensemble prediction can be directly interpreted by printing the model summary.The predictions in the Ensemble models described in Fig. 7 are in principle based on using the following five Machine Learning libraries common for many soil mapping projects5.

1.

Ranger: fully scalable implementation of Random Forest23.

2.

XGboost: extreme gradient boosting40.

3.

Deepnet: the Open Source implementation of deep learning26.

4.

Cubist: the Open Source implementation of Cubist regression trees41.

5.

Glmnet: GLM with Lasso or Elasticnet Regularization24.

These Open source libraries, with the exception of the Cubist, are available through a variety of programming environments including R, Python and also as standalone C++ libraries.Merging coarse and fine-scale predictionsThe idea of modeling soil spatial variation at different scales can be traced back to the work of McBratney42. In a multiscale model, soil variation can be considered a composite signal (Fig. 8):$$begin{aligned} y({mathbf{s}}_{mathtt {B}}) = S_4({mathbf{s}}_{mathtt {B}}) + S_3({mathbf{s}}_{mathtt {B}}) + S_2({mathbf{s}}_{mathtt {B}}) + S_1({mathbf{s}}_{mathtt {B}}) + varepsilon end{aligned}$$
(2)
where (S_4) is the value of the target variable estimated at the coarsest scale, (S_3), (S_2) and (S_1) are the higher order components, ({mathbf{s}}_{mathtt {B}}) is the location or block of land, and (varepsilon) is the residual soil variation i.e. pure noise.Figure 8Decomposition of a signal of spatial variation into four components plus noise. Based on McBratney42. See also Fig. 13 in Hengl et al.21.Full size imageIn this work we used a somewhat simplified version of Eq. (2) with only two scale-components: coarse ((S_2); 250 m) and fine ((S_1); 30 m). We produce the coarse-scale and fine-scale predictions independently, then merge using a weighted average43:$$begin{aligned} {hat{y}}({mathbf{s}}_{mathtt {B}}) = frac{sum _{i=1}^{2}{ w_i cdot S_i({mathbf{s}}_{mathtt {B}})}}{sum _{i=1}^{2}{ w_i }}, ; ; w_i = frac{1}{sigma _{i,mathrm{CV}}^2} end{aligned}$$
(3)
where ({hat{y}}({mathbf{s}}_{mathtt {B}})) is the ensemble prediction, (w_i) is the model weight and (sigma _{i,mathrm{CV}}^2) is the model squared prediction error obtained using cross-validation. This is an example of Ensemble Models fitted for coarse-scale model for soil pH:and the fine-scale model for soil pH:Note that in this case the coarse-scale model is somewhat more accurate with (mathrm {RMSE}=0.463), while the 30 m covariates achieve at best (mathrm {RMSE}=0.661), hence the weights for 250 m model are about 2(times) higher than for the 30 m resolution models. A step-by-step procedure explaining in detail how the 2-scale predictions are derived and merged is available at https://gitlab.com/openlandmap/spatial-predictions-using-eml. An R package landmap44 that implements the procedure in a few lines of code is also available.Transformation of log-normally distributed nutrients and propertiesFor the majority of log-normal distributed (right-skewed) variables we model and predict the ln-transformed values ((log _e(x+1))), then provide back-transformed predictions ((e^{x}-1)) to users via iSDAsoil. Note that also pH is a log-transformed variable of the hydrogen ion concentrations.Although ln-transformation is not required for non-linear models such as Random Forest or Gradient Boosting, we decided to apply it to give proportionally higher weights to lower values. This is, in principle, a biased decision by us the modelers as our interest is in improving predictions of critical values for agriculture i.e. producing maps of nutrient deficiencies and similar (hence focus on smaller values). If the objective of mapping was to produce soil organic carbon of peatlands or similar, then the ln-transformation could have decreased the overall accuracy, although with Machine Learning models sometimes it is impossible to predict effects as they are highly non-linear.Derivation of prediction errorsWe also provide per-pixel uncertainty in terms of prediction errors or prediction intervals (e.g. 50%, 68% and/or 90% probability intervals)45. Because stacking of learners is based on repeated resampling, the prediction errors (per pixel) can be determined using either:

1.

Quantile Regression Random Forest46, in our case by using the 4–5 base learners,

2.

Simplified procedure using Bootstraping, then deriving prediction errors as standard deviation from multiple independently fitted learners1.

Both are non-parametric techniques and the prediction errors do not require any assumptions or initial parameters, but come at a cost of extra computing. By default, we provide prediction errors with a probability of 67%, which is the 1 standard deviation upper and lower prediction interval. Prediction errors indicate extrapolation areas and should help users minimize risks of taking decisions.For derivation of prediction interval via either Quantile Regression RF or bootstrapping, it is important to note that the individual learners must be derived using randomized subsets of data (e.g. fivefold) which are spatially separated using block Cross-Validation or similar, otherwise the results might be over-optimistic and prediction errors too narrow.Figure 9Schematic example of the derivation of a pooled variance ((sigma _{mathtt {250m+30m}})) using the 250 m and 30 m predictions and predictions errors with (a) larger and (b) smaller differences in independent predictions.Full size imageFurther, the pooled variance (({hat{sigma }}_E)) from the two independent models (250 m and 100 m scales in Fig. 7) can be derived using47:$$begin{aligned} {hat{sigma }}_E = sqrt{sum _{j=1}^{s}{w_j cdot (hat{sigma }_j^2+{hat{mu }}_j^2 )} – left( sum _{j=1}^{s}{w_j cdot {hat{mu }}_j} right) ^2 }, ; ; sum _{j=1}^{s}{w_j} = 1 end{aligned}$$
(4)
where (sigma _j^2) is the prediction error for the independent components, ({hat{mu }}_j) is the predicted value, and w are the weights per predicted component (need to sum up to 1). If the two independent models (250 m and 30 m) produce very similar predictions so that ({hat{mu }}_{mathtt {250}} approx {hat{mu }}_{mathtt {30}}), then the pooled variance approaches the geometric mean of the two variances; if the independent predictions are different (({hat{mu }}_{mathtt {250}} – {hat{mu }}_{mathtt {30}} > 0)) than the pooled variances increase proportionally to this additional difference (Fig. 9).Accuracy assessment of final mapsWe report overall average accuracy in Table 1 and Fig. 4 using spatial fivefold Cross-Validation with model refitting1,48. For each variable we then compute the following three metrics: (1) Root Mean Square Error, (2) R-square from the meta-learner, and (3) Concordance Correlation Coefficient (Fig. 4), which is derived using49:$$begin{aligned} rho _c = frac{2 cdot rho cdot sigma _{{{hat{y}}}} cdot sigma _y }{ sigma _{{{hat{y}}}}^2 + sigma _y^2 + (mu _{{{hat{y}}}} – mu _y)^2} end{aligned}$$
(5)
where ({{hat{y}}}) are the predicted values and y are actual values at cross-validation points, (mu _{{{hat{y}}}}) and (mu _y) are predicted and observed means and (rho) is the correlation coefficient between predicted and observed values. CCC is the most appropriate performance criteria when it comes to measuring agreement between predictions and observations.For Cross-validation we use the spatial tile ID produced in the equal-area projection system for Africa (Lambert Azimuthal EPSG:42106) as the blocking parameter in the training function in mlr. This ensures that points falling in close proximity ( More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

1.Wilson, E. O. Sociobiology (Harvard Press, 1975).
Google Scholar 
2.Hölldobler, B. & Lumsden, C. J. Territorial strategies in ants. Science 210, 732–739 (1980).MathSciNet 
PubMed 
MATH 
Article 
ADS 
PubMed Central 

Google Scholar 
3.Baker, R. R. Insect territoriality. Annu. Rev. Entomol. 28, 65–89 (1983).Article 

Google Scholar 
4.Christensen, C. & Radford, A. N. Dear enemies or nasty neighbors? Causes and consequences of variation in the responses of group-living species to territorial intrusions. Behav. Ecol. 29, 1004–1013 (2018).Article 

Google Scholar 
5.Fisher, J. B. Evolution and bird sociality. In Evolution as a process (eds. Huxley, J., Hardy, A. C. & Ford, E. B.) 71–83. (Allen & Unwin, Australia, 1954).6.Temeles, E. J. The role of neighbours in territorial systems: when are they “dear enemies”?. Anim. Behav. 47, 339–350 (1994).Article 

Google Scholar 
7.Adams, E. S. Territoriality in ants (Hymenoptera: Formicidae): a review. Myrmecol. News 23, 101–118 (2016).
Google Scholar 
8.Müller, C. A. & Manser, M. B. “Nasty neighbours” rather than “dear enemies” in a social carnivore. Proc. R Soc. B Biol. Sci. 274, 959–965 (2007).Article 

Google Scholar 
9.Tanner, C. J. & Adler, F. R. To fight or not to fight: context-dependent interspecific aggression in competing ants. Anim. Behav. 77, 297–305 (2009).Article 

Google Scholar 
10.Mabelis, A. A. Wood ant wars. Neth. J. Zool. 29, 451–620 (1979).Article 

Google Scholar 
11.Hölldobler, B. Recruitment behavior, home range orientation and territoriality in harvester ants, Pogonomyrmex. Behav. Ecol. Sociobiol. 1, 3–44 (1976).Article 

Google Scholar 
12.Hölldobler, B. Tournaments and slavery in a desert ant. Science 80(192), 912–914 (1976).Article 
ADS 

Google Scholar 
13.Carlin, N. F. & Hölldobler, B. The kin recognition system of carpenter ants (Camponotus spp.) – I. Hierarchical cues in small colonies. Behav. Ecol. Sociobiol. 19, 123–134 (1986).14.Carlin, N. F. & Hölldobler, B. The kin recognition system of carpenter ants (Camponotus spp.)—II. Larger colonies. Behav. Ecol. Sociobiol. 20, 209–217 (1987).Article 

Google Scholar 
15.Langen, T. A., Tripet, F. & Nonacs, P. The red and the black: habituation and the dear-enemy phenomenon in two desert Pheidole ants. Behav. Ecol. Sociobiol. 48, 285–292 (2000).Article 

Google Scholar 
16.Dimarco, R. D., Farji-Brener, A. G. & Premoli, A. C. Dear enemy phenomenon in the leaf-cutting ant Acromyrmex lobicornis: behavioral and genetic evidence. Behav. Ecol. 21, 304–310 (2010).Article 

Google Scholar 
17.Yagound, B., Crowet, M., Leroy, C., Poteaux, C. & Châline, N. Interspecific variation in neighbour–stranger discrimination in ants of the Neoponera apicalis complex. Ecol. Entomol. 42, 125–136 (2017).Article 

Google Scholar 
18.Benedek, K. & Kóbori, O. T. “Nasty neighbour” effect in Formica pratensis retz. (Hymenoptera: Formicidae). N. West J. Zool. 10, 245–250 (2014).
Google Scholar 
19.Newey, P. S., Robson, S. K. A. & Crozier, R. H. Know thine enemy: why some weaver ants do but others do not. Behav. Ecol. 21, 381–386 (2010).Article 

Google Scholar 
20.Sanada-Morimura, S. et al. Encounter-induced hostility to neighbors in the ant Pristomyrmex pungens. Behav. Ecol. 14, 713–718 (2003).Article 

Google Scholar 
21.Boulay, R., Cerdá, X., Simon, T., Roldan, M. & Hefetz, A. Intraspecific competition in the ant Camponotus cruentatus: should we expect the “dear enemy” effect?. Anim. Behav. 74, 985–993 (2007).Article 

Google Scholar 
22.Frizzi, F. et al. The rules of aggression: How genetic, chemical and spatial factors affect intercolony fights in a dominant species, the mediterranean acrobat ant Crematogaster scutellaris. PLoS ONE 10, 1–16 (2015).Article 
CAS 

Google Scholar 
23.Crosland, M. W. Kin recognition in the ant Rhytidoponera confusa I. Environmental odour. Anim. Behav. 37, 912–919 (1989).Article 

Google Scholar 
24.Beye, M., Neumann, P. & Moritz, R. F. A. Nestmate recognition and the genetic gestalt in the mound-building ant Formica polyctena. Insectes Soc. 44, 49–58 (1997).Article 

Google Scholar 
25.Beye, M., Neumann, P., Chapuisat, M., Pamilo, P. & Moritz, R. F. A. Nestmate recognition and the genetic relatedness of nests in the ant Formica pratensis. Behav. Ecol. Soc. 43, 67–72 (1998).Article 

Google Scholar 
26.Martin, S. & Drijfhout, F. A review of ant cuticular hydrocarbons. J. Chem. Ecol. 35, 1151–1161 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Rico-Gray, V., Oliveira, P. S. & Oliveira, P. S. The Ecology and Evolution of Ant-plant Interactions (University of Chicago Press, 2007).
Google Scholar 
28.Adams, E. S. Boundary disputes in the territorial ant Azteca trigona: effects of asymmetries in colony size. Anim. Behav. 39, 321–328 (1990).Article 

Google Scholar 
29.Adams, E. S. Territory defense by the ant Azteca trigona: maintenance of an arboreal ant mosaic. Oecologia 97, 202–208 (1994).PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
30.Frederickson, M. E. & Gordon, D. M. The intertwined population biology of two Amazonian myrmecophytes and their symbiotic ants. Ecology 90, 1595–1607 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
31.Heil, M. & McKey, D. Protective and in ecological model systems in ecological and evolutionary research. Annu. Rev. Ecol. Evol. Syst. 34, 425–453 (2003).Article 

Google Scholar 
32.Hölldobler, B. The chemistry of social regulation: Multicomponent signals in ant societies. Proc. Natl. Acad. Sci. USA 92, 19–22 (1995).PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
33.Howard, R. W. & Blomquist, G. J. Ecological, behavioral, and biochemical aspects of insect hydrocarbons. Annu. Rev. Entomol. 50, 371–393 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Boulay, R., Hefetz, A., Soroker, V. & Lenoir, A. Camponotus fellah colony integration: worker individuality necessitates frequent hydrocarbon exchanges. Anim. Behav. 59, 1127–1133 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Errard, C., Hefetz, A. & Jaisson, P. Social discrimination tuning in ants: template formation and chemical similarity. Behav. Ecol. Sociobiol. 59, 353–363 (2006).Article 

Google Scholar 
36.Brandstaetter, A. S., Rössler, W. & Kleineidam, C. J. Friends and foes from an ant brain’s point of view—neuronal correlates of Colony Odors in a social insect. PLoS ONE 6, e21383 (2011).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
37.Leonhardt, S. D., Brandstaetter, A. S. & Kleineidam, C. J. Reformation process of the neuronal template for nestmate-recognition cues in the carpenter ant Camponotus floridanus. J. Comp. Physiol. 193, 993–1000 (2007).Article 

Google Scholar 
38.Guerrieri, F. J. et al. Ants recognize foes and not friends. Proc. R Soc. B Biol. Sci. 276, 2461–2468 (2009).CAS 
Article 

Google Scholar 
39.Newey, P. Not one odour but two: a new model for nestmate recognition. J. Theor. Biol. 270, 7–12 (2011).PubMed 
Article 

Google Scholar 
40.Martin, S. J., Vitikainen, E., Drijfhout, F. P. & Jackson, D. Conspecific ant aggression is correlated with chemical distance, but not with genetic or spatial distance. Behav. Gen. 42, 323–331 (2012).Article 

Google Scholar 
41.Longino, J. T. Azteca ants in Cecropia trees: taxonomy, colony structure, and behavior. In Ant-Plant Interactions (eds Huxley, C. R. & Cutler, D. F.) 271–288 (Oxford University Press, 1991).
Google Scholar 
42.Schupp, E. W. Azteca protection of Cecropia: ant occupation benefits juvenile trees. Oecologia 70, 379–385 (1986).PubMed 
Article 
ADS 

Google Scholar 
43.Oliveira, K. N. et al. The effect of symbiotic ant colonies on plant growth: a test using an Azteca-Cecropia system. PLoS ONE 10, 1–13 (2015).
Google Scholar 
44.Silva, C. A., Vieira, M. F. & Amaral, C. H. Floral attributes, ornithophily and reproductive success of Palicourea longepedunculata (Rubiaceae), a distylous shrub in southeastern Brazil. Rev. Bras. Bot. 33, 207–210 (2010).Article 

Google Scholar 
45.Veloso, H. P., Rangel Filho, A. L. R. & Lima, J. C. A. Classificação da Vegetação Brasileira Adaptada a um Sistema Universal (Ibge, 1991).46.Berg, C. C., Rosselli, P. F. & Davidson, D. W. Cecropia. Flora Neotropica. 94, 1–230 (2005). Retrieved April 22, 2020, from www.jstor.org/stable/439393847.Emery, C. & de Voyage, M. M. Bedot et Pictel dans l’Archipel Malais. Formicides de l’Archipel Malais [Travel of MM. Bedot and Pictel in the Malaysian Archipelago. Formicides from the Malaysian Archipelago]. Rev. Suisse. Zool. 1, 187–229 (1893).Article 

Google Scholar 
48.Davidson, D. W. & Fisher, B. L. Symbiosis of ants with Cecropia as a function of light regime. In Ant-Plant Interactions (eds. Huxley, C. R. & Cutler, D. F.) 289–309 (Oxford University Press, UK, 1991).49.Davidson, D. W. & McKey, D. Ant-plant symbioses: stalking the chuyachaqui. Trends Ecol. Evol. 8, 326–332 (1993).CAS 
PubMed 
Article 

Google Scholar 
50.Fonseca, C. R. & Ganade, G. Asymmetries, compartments and null interactions in an Amazonian ant-plant community. J. Anim. Ecol. 65, 339–347 (1996).Article 

Google Scholar 
51.Fonseca, C. R. Amazonian ant-plant interactions and the nesting space limitation hypothesis. J. Trop. Ecol. 15, 807–825 (1999).Article 

Google Scholar 
52.Longino, J. T. Geographic variation and community structure in an ant-plant mutualism: Azteca and Cecropia in Costa Rica. Biotropica 21, 126–132 (1989).Article 

Google Scholar 
53.Bruna, E. M., Izzo, T. J., Inouye, B. D., Uriarte, M. & Vasconcelos, H. L. Asymmetric dispersal and colonization success of Amazonian plant-ants queens. PLoS ONE 6, e22937 (2011).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
54.Yu, D. W. et al. Experimental demonstration of species coexistence enabled by dispersal limitation. J. Anim. Ecol. 73, 1102–1114 (2004).Article 

Google Scholar 
55.Rocha, C. F. D. & Bergallo, H. G. Bigger ant colonies reduce herbivory and herbivore residence time on leaves of an ant-plant: Azteca muelleri vs. Coelomera ruficornis on Cecropia pachystachya. Oecologia 91, 249–252 (1992).PubMed 
Article 
ADS 

Google Scholar 
56.Campbell, H., Fellowes, M. D. E. & Cook, J. M. Arboreal thorn-dwelling ants coexisting on the savannah ant-plant, Vachellia erioloba, use domatia morphology to select nest sites. Insectes Soc. 60, 373–382 (2013).Article 

Google Scholar 
57.Marting, P. R., Wcislo, W. T. & Pratt, S. C. Colony personality and plant health in the Azteca-Cecropia mutualism. Behav. Ecol. 29, 264–271 (2018).Article 

Google Scholar 
58.Tschinkel, W. R. Sociometry and sociogenesis of colonies of the fire ant Solenopsis invicta during one annual cycle: ecological archives M063–002. Ecol. Monogr. 63, 425–457 (1993).Article 

Google Scholar 
59.Wills, B. D., Powell, S., Rivera, M. D. & Suarez, A. V. Correlates and consequences of worker polymorphism in ants. Ann. Rev. Entomol. 63, 575–598 (2018).CAS 
Article 

Google Scholar 
60.Holway, D. A., Suarez, A. V. & Case, T. J. Loss of intraspecific aggression in the success of a widespread invasive social insect. Science 282, 949–952 (1998).CAS 
PubMed 
Article 
ADS 

Google Scholar 
61.Giraud, T., Pedersen, J. S. & Keller, L. Evolution of supercolonies: the Argentine ants of southern Europe. PNAS 99, 6075–6079 (2002).CAS 
PubMed 
Article 
ADS 

Google Scholar 
62.Fischer, D. C. Fundamentos de cromatografia. Rev. Bras. Cienc. Farm. 42, 308–308 (2006).
Google Scholar 
63.Koo, I., Shi, X., Kim, S. & Zhang, X. IMatch2: Compound identification using retention index for analysis of gas chromatography-mass spectrometry data. J. Chromatogr. A 1337, 202–210 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.El-Sayed, A. M. The Pherobase: Database of Pheromones and Semiochemicals. https://www.pherobase.com. Accessed 11 July 2020 (2020).65.NIST Livro de Química na Web. Base de dados de Referência padrão do NIST número 69. http://webbook.nist.gov/chemistry/. Accessed 13 July 2020 (2016).66.Vidal, D. M., Fávaro, C. F., Guimaraes, M. M. & Zarbin, P. H. Identification and synthesis of the male-produced sex pheromone of the soldier beetle Chauliognathus fallax (Coleoptera: Cantharidae). J. Brazil. Chem. Soc. 27, 1506–1511 (2016).CAS 

Google Scholar 
67.Carlson, D. A., Bernier, U. R. & Sutton, B. D. Elution patterns from capillary GC for methyl-branched alkanes. J. Chem. Ecol. 24, 1845–1865 (1998).CAS 
Article 

Google Scholar 
68.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org. Accessed 16 June 2020 (2017).69.Lanan, M. C. & Bronstein, J. L. An ant’s-eye view of an ant-plant protection mutualism. Oecologia 172, 779–790 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
70.Briefer, E., Rybak, F. & Aubin, T. When to be a dear enemy: flexible acoustic relationships of neighbouring skylarks Alauda arvensis. Anim. Behav. 76, 1319–1325 (2008).Article 

Google Scholar 
71.Hyman, J. Seasonal variation in response to neighbors and strangers by a territorial songbird. Ethology 111, 951–961 (2005).Article 

Google Scholar 
72.Sturgis, S. J. & Gordon, D. M. Nestmate recognition in ants (Hymenoptera: Formicidae): a review. Myrmecol. News 16, 101–110 (2012).
Google Scholar 
73.Matthews, R. W. & Matthews, J. R. Insect Behavior (Springer, 2009).
Google Scholar 
74.Boucher, D. H., James, S. & Keeler, K. H. The ecology of mutualism. Annu. Rev. Ecol. Evol. Syst. 13, 315–347 (1982).Article 

Google Scholar 
75.Connor, R. C. The benefits of mutualism: a conceptual framework. Biol. Rev. 70, 427–457 (1995).Article 

Google Scholar 
76.Bronstein, J. L. The costs of mutualism. Am. Zool. 41, 825–839 (2001).
Google Scholar 
77.Hölldobler, B. & Wilson, E. O. The Ants (Harvard University Press, 1990).
Google Scholar 
78.Dejean, A., Corbara, B., Orivel, J. & Leponce, M. Rainforest canopy ants: the implications of territoriality and predatory behavior. Funct. Ecol. Commun. 1, 105–120 (2007).
Google Scholar 
79.Dejean, A., Grangier, J., Leroy, C. & Orivel, J. Predation and aggressiveness in host plant protection: a generalization using ants from the genus Azteca. Naturwissenschaften 96, 57–63 (2009).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
80.Tripovich, J. S., Charrier, I., Rogers, T. L., Canfield, R. & Arnould, J. P. Acoustic features involved in the neighbour-stranger vocal recognition process in male Australian fur seals. Behav. Process. 79, 74–80 (2008).CAS 
Article 

Google Scholar 
81.Favaro, L., Gamba, M., Gili, C. & Pessani, D. Acoustic correlates of body size and individual identity in banded penguins. PLoS ONE 12, e0170001 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
82.Heinze, J., Foitzik, S., Hippert, A. & Hölldobler, B. Apparent dear-enemy phenomenon and environment-based recognition cues in the ant Leptothorax nylanderi. Ethology 102, 510–522 (1996).Article 

Google Scholar 
83.Vander Meer, R. K. & Morel, L. Nestmate Recognition in Ants. 79–103 (Pheromone communication in Soc. Insects, 1998).84.Provost, E., Blight, O., Tirard, A. & Renucci, M. Hydrocarbons and insects’ social physiology. Insect Physiology: New Research 19–72 (2008).85.Crozier, R. H. & Dix, M. W. Analysis of two genetic models for the innate components of colony odor in social Hymenoptera. Behav. Ecol. Sociobiol. 4, 217–224 (1979).Article 

Google Scholar 
86.Ozaki, M. et al. Behavior: ant nestmate and non-nestmate discrimination by a chemosensory sensillum. Science 309, 311–314 (2005).CAS 
PubMed 
Article 
ADS 

Google Scholar 
87.Starks, P. T. Recognition systems: from components to conservation. Ann. Zool. Fennici. 41, 689–690 (2004).
Google Scholar 
88.Franks, N., Blum, M., Smith, R. K. & Allies, A. B. Behavior and chemical disguise of cuckoo ant Leptothorax kutteri in relation to its host Leptothorax acervorum. J. Chem. Ecol. 16, 1431–1444 (1990).CAS 
PubMed 
Article 

Google Scholar 
89.Hernández, J. V. et al. Leaf-cutter ant species (Hymenoptera: Atta) differ in the types of cues used to differentiate between self and others. Anim. Behav. 71, 945–952 (2006).Article 

Google Scholar 
90.Nehring, V. et al. Chemical disguise of myrmecophilous cockroaches and its implications for understanding nestmate recognition mechanisms in leaf-cutting ants. BMC Ecol. 16, 1–11 (2016).Article 
CAS 

Google Scholar 
91.Hernández, J. V., López, H. & Jaffe, K. Nestmate recognition signals of the leaf-cutting ant Atta laevigata. J. Insect. Physiol. 48, 287–295 (2002).PubMed 
Article 
PubMed Central 

Google Scholar 
92.Howard, R. W. & Blomquist, G. J. Chemical ecology and biochemistry of insect hydrocarbons. Ann. Rev. Entomol. 27, 149–172 (1982).CAS 
Article 

Google Scholar 
93.Sturgis, S. J., Greene, M. J. & Gordon, D. M. Hydrocarbons on harvester ant (Pogonomyrmex barbatus) Middens Guide Foragers to the Nest. J. Chem. Ecol. 37, 514–524 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Greene, M. J. & Gordon, D. M. Cuticular hydrocarbons inform task decisions. Nature 423, 32–32 (2003).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
95.Sano, K., Bannon, N. & Greene, M. J. Pavement ant workers (Tetramorium caespitum) assess cues coded in cuticular hydrocarbons to recognize conspecific and heterospecific non-nestmate ants. J. Insect. Behav. 31, 186–199 (2018).Article 

Google Scholar 
96.Guillem, R. M., Drijfhout, F. P. & Martin, S. J. Species-specific cuticular hydrocarbon stability within European Myrmica Ants. J. Chem. Ecol. 42, 1052–1062 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
97.Sprenger, P. P. & Menzel, F. Cuticular hydrocarbons in ants (Hymenoptera: Formicidae) and other insects: how and why they differ among individuals, colonies, and species. Myrmec. News 30, 1–26 (2020).
98.Dahbi, A., Cerdá, X., Hefetz, A. & Lenoir, A. Social closure, aggressive behavior, and cuticular hydrocarbon profiles in the polydomous ant Cataglyphis iberica (Hymenoptera, Formicidae). J. Chem. Ecol. 22, 2173–2186 (1996).CAS 
PubMed 
Article 

Google Scholar 
99.Boulay, R., Katzav-Gozansky, T., Hefetz, A. & Lenoir, A. Odour convergence and tolerance between nestmates through trophallaxis and grooming in the ant Camponotus fellah (Dalla Torre). Insectes Soc. 51, 55–61 (2004).Article 

Google Scholar 
100.Dunn, R. R. & Messier, S. H. Evidence for the opposite of the dear enemy phenomenon in termites. J. Insect. Behav. 12, 461–464 (1999).Article 

Google Scholar 
101.Temeles, E. J., Muir, A. B., Slutsky, E. B. & Vitousek, M. N. Effect of food reductions on territorial behavior of purple-throated caribs. Condor 106, 691 (2004).Article 

Google Scholar 
102.Pacheco, P. S. M. & Del-Claro, K. Pseudomyrmex concolor Smith (Formicidae: Pseudomyrmecinae) as induced biotic defence for host plant Tachigali myrmecophila Ducke (Fabaceae: Caesalpinioideae). Ecol. Entomol. 43, 782–793 (2018).Article 

Google Scholar 
103.Hager, F. A. & Krausa, K. Acacia ants respond to plant-borne vibrations caused by mammalian browsers. Curr. Biol. 29, 717-725.e3 (2019).CAS 
PubMed 
Article 

Google Scholar  More

1.Wolfe, L. M. Why alien invaders succeed: Support for the escape-from-enemy hypothesis. Am. Nat. 160, 705–711. https://doi.org/10.1086/343872 (2002).Article 
PubMed 

Google Scholar 
2.Facon, B. et al. A general eco-evolutionary framework for understanding bioinvasions. Trends Ecol. Evol. 21, 130–135. https://doi.org/10.1016/j.tree.2005.10.012 (2006).Article 
PubMed 

Google Scholar 
3.Van Driesche, R. G. et al. Classical biological control for the protection of natural ecosystems. Biol. Control 54, S2–S33. https://doi.org/10.1016/j.biocontrol.2010.03.003 (2010).Article 

Google Scholar 
4.Hajek, A. E. et al. Exotic biological control agents: A solution or contribution to arthropod invasions?. Biol. Invasions 18, 953–969. https://doi.org/10.1007/s10530-016-1075-8 (2016).Article 

Google Scholar 
5.Schwarzlander, M., Hinz, H. L., Winston, R. L. & Day, M. D. Biological control of weeds: An analysis of introductions, rates of establishment and estimates of success, worldwide. Biocontrol 63, 319–331. https://doi.org/10.1007/s10526-018-9890-8 (2018).Article 

Google Scholar 
6.Naranjo, S. E., Ellsworth, P. C. & Frisvold, G. B. Economic value of biological control in integrated pest management of managed plant systems. Annu. Rev. Entomol. 60, 621–645. https://doi.org/10.1146/annurev-ento-010814-021005 (2015).CAS 
Article 
PubMed 

Google Scholar 
7.Hoddle, M. S., Lake, E. C., Minteer, C. R. & Daane, K. M. In Biological Control: A Global Initiative (eds Mason, P. G. & Dennis, N.) 69–92 (CSIRO Publishing, 2021).
Google Scholar 
8.Heimpel, G. E. & Cock, M. J. W. Shifting paradigms in the history of classical biological control. Biocontrol 63, 27–37. https://doi.org/10.1007/s10526-017-9841-9 (2018).Article 

Google Scholar 
9.Hoelmer, K. A. & Kirk, A. A. Selecting arthropod biological control agents against arthropod pests: Can the science be improved to decrease the risk of releasing ineffective agents?. Biol. Control 34, 255–264 (2005).Article 

Google Scholar 
10.Wang, X. G., Johnson, M. W., Daane, K. M. & Yokoyama, V. Y. Larger olive fruit size reduces the efficiency of Psyttalia concolor, as a parasitoid of the olive fruit fly. Biol. Control 49, 45–51. https://doi.org/10.1016/j.biocontrol.2009.01.004 (2009).Article 

Google Scholar 
11.Wang, X.-G., Levy, K., Son, Y., Johnson, M. W. & Daane, K. M. Comparison of the thermal performance between a population of the olive fruit fly and its co-adapted parasitoids. Biol. Control 60, 247–254. https://doi.org/10.1016/j.biocontrol.2011.11.012 (2012).Article 

Google Scholar 
12.Wharton, R. A. In Fruit flies: Their Biology, Natural Enemies and Control (eds Robinson, A. S. & Hooper, G.) 303–313 (Elsevier, 1989).
Google Scholar 
13.Purcell, M. F. Contribution of biological control to integrated pest management of tephritid fruit flies in the tropics and subtropics. Integr. Pest Manag. Rev. 3, 63–83 (1998).Article 

Google Scholar 
14.Ovruski, S. M., Aluja, M., Sivinski, J. & Wharton, R. A. Hymenopteran parasitoids on fruit-infesting Tephritidae (Diptera) in Latin America and the southern United States: Diversity, distribution, taxonomic status and their use in fruit fly biological control. Integr. Pest Manag. Rev. 5, 81–107 (2000).Article 

Google Scholar 
15.Mohamed, S. A., Ramadan, M. M. & Ekesi, S. In Fruit Fly Research and Development in Africa—Towards a Sustainable Management Strategy to Improve Horticulture (eds Ekesi, S. et al.) 325–368 (Springer International Publishing, 2006).
Google Scholar 
16.Garcia, F. R. M., Ovruski, S. M., Suarez, L., Cancino, J. & Liburd, O. E. Biological control of tephritid fruit flies in the Americas and Hawaii: A review of the use of parasitoids and predators. Insects https://doi.org/10.3390/insects11100662 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
17.Wharton, R. A. & Yoder, M. J. Wharton RA, Yoder MJ. 2017. Parasitoids of fruit-infesting tephritidae. http://paroffit.org. Accessed on November 15, 2020. (2017).18.Daane, K. M. & Johnson, M. W. Olive fruit fly: Managing an ancient pest in modern times. Annu. Rev. Entomol. 55, 155–169. https://doi.org/10.1146/annurev.ento.54.110807.090553 (2010).CAS 
Article 

Google Scholar 
19.Tzanakakis, M. E. Seasonal development and dormancy of insects and mites feeding on olive: A review. Neth. J. Zool. 52, 87–224 (2003).Article 

Google Scholar 
20.Green, P. S. A revision of Olea L. (Oleaceae). Kew Bull. 57, 91–140 (2002).Article 

Google Scholar 
21.Bon, M. C. et al. Populations of Bactrocera oleae (Diptera: Tephritidae) and its parasitoids in Himalayan Asia. Ann. Entomol. Soc. Am. 109, 81–91. https://doi.org/10.1093/aesa/sav114 (2016).Article 

Google Scholar 
22.Zygouridis, N. E., Augustinos, A. A., Zalom, F. G. & Mathiopoulos, K. D. Analysis of olive fly invasion in California based on microsatellite markers. Heredity 102, 402–412 (2009).CAS 
Article 

Google Scholar 
23.Augustinos, A. A. et al. Microsatellite analysis of olive fly populations in the Mediterranean indicates a westward expansion of the species. Genetica 125, 231–241 (2005).CAS 
Article 

Google Scholar 
24.Nardi, F. et al. Domestication of olive fly through a multi-regional host shift to cultivated olives: Comparative dating using complete mitochondrial genomes. Mol. Phylogenet. Evol. 57, 678–686. https://doi.org/10.1016/j.ympev.2010.08.008 (2010).CAS 
Article 
PubMed 

Google Scholar 
25.Neuenschwander, P., Bigler, F., Delucchi, V. & Michelakis, S. E. Natural enemies of preimaginal stages of Dacus oleae Gmel. (Dipt., Tephritidae) in Western Crete. I. Bionomics and phenologies. Boll Lab. Entomol Agrar Filippo Silvestri 40, 3–32 (1983).
Google Scholar 
26.Boccaccio, L. & Petacchi, R. Landscape effects on the complex of Bactrocera oleae parasitoids and implications for conservation biological control. Biocontrol 54, 607–616. https://doi.org/10.1007/s10526-009-9214-0 (2009).Article 

Google Scholar 
27.Borowiec, N. et al. Diversity and geographic distribution of the indigenous and exotic parasitoids of the olive fruit fly, Bactrocera oleae (Diptera: Tephritidae) Southern France. IOBC/WPRS Bull. 79, 71–78 (2012).
Google Scholar 
28.Al Khatib, F. et al. An integrative approach to species discrimination in the Eupelmus urozonus complex (Hymenoptera, Eupelmidae), with the description of 11 new species from the Western Palaearctic. Syst. Entomol. 39, 806–862. https://doi.org/10.1111/syen.12089 (2014).Article 

Google Scholar 
29.Kapaun, T., Nadel, H., Headrick, D. & Vredevoe, L. Biology and parasitism rates of Pteromalus nr. myopitae (Hymenoptera: Pteromalidae), a newly discovered parasitoid of olive fruit fly Bactrocera oleae (Diptera: Tephritidae) in coastal California. Biol. Control 53, 76–85. https://doi.org/10.1016/j.biocontrol.2009.11.002 (2010).Article 

Google Scholar 
30.Silvestri, F. Report on an expedition to Africa in search of natural enemies of fruit flies (Trupaneidae) with descriptions, observations and biological notes. Hawaii Board Agric. For. Div. Entomol. Bull. 3, 1–146 (1914).
Google Scholar 
31.Hoelmer, K. A., Kirk, A. A., Pickett, C. H., Daane, K. M. & Johnson, M. W. Prospects for improving biological control of olive fruit fly, Bactrocera oleae (Diptera: Tephritidae), with introduced parasitoids (Hymenoptera). Biocontrol Sci. Technol. 21, 1005–1025. https://doi.org/10.1080/09583157.2011.594951 (2011).Article 

Google Scholar 
32.Greathead, D. J. & Greathead, A. H. Biological control of insect pests by insect parasitoids and predators: The BIOCAT database. Biocontrol News Inf. 13, 61N-68N (1992).
Google Scholar 
33.Neuenschwander, P. Searching parasitoids of Dacus oleae (Gmel) (Dipt., Tephritidae) in South Africa. J. Appl. Entomol. 94, 509–522 (1982).
Google Scholar 
34.Loni, A. Developmental rate of Opius concolor (Hym.: Braconidae) at various constant temperatures. Entomophaga 42, 359–366 (1997).Article 

Google Scholar 
35.Miranda, M. A., Miquel, M., Terrassa, J., Melis, N. & Monerris, M. Parasitism of Bactrocera oleae (Diptera, Tephritidae) by Psyttalia concolor (Hymenoptera, Braconidae) in the Balearic Islands (Spain). J. Appl. Entomol. 132, 798–805 (2008).Article 

Google Scholar 
36.Muller, F. A., Dias, N. P., Gottschalk, M. S., Garcia, F. R. M. & Nava, D. E. Potential distribution of Bactrocera oleae and the parasitoids Fopius arisanus and Psyttalia concolor, aiming at classical biological control. Biol. Control 132, 144–151. https://doi.org/10.1016/j.biocontrol.2019.02.014 (2019).Article 

Google Scholar 
37.Chardonnet, F., Blanchet, A., Hurtrel, B., Marini, F. & Smith, L. Mass-rearing optimization of the parasitoid Psyttalia lounsburyi for biological control of the olive fruit fly. J. Appl. Entomol. 143, 277–288. https://doi.org/10.1111/jen.12573 (2019).Article 

Google Scholar 
38.La-Spina, M. et al. Effect of exposure time on mass-rearing production of the olive fruit fly parasitoid, Psyttalia lounsburyi (Hymenoptera: Braconidae). J. Appl. Entomol. 142, 319–326. https://doi.org/10.1111/jen.12478 (2018).CAS 
Article 

Google Scholar 
39.Malausa, J. C. et al. Introductions of the African parasitoid Psyttalia lounsburyi in South of France for classical biological control of Bactrocera oleae. IOBC/WPRS Bull. 59, 163–170 (2010).
Google Scholar 
40.Daane, K. M. et al. Classic biological control of olive fruit fly in California, USA: Release and recovery of introduced parasitoids. Biocontrol 60, 317–330. https://doi.org/10.1007/s10526-015-9652-9 (2015).Article 

Google Scholar 
41.Wharton, R. A. & Gilstrap, F. Key to and status of opiine braconid (Hymenoptera) parasitoids used in biological control of Ceratitis and Dacus s.l. (Diptera: Tephritidae). Ann. Entomol. Soc. Am. 76, 721–742 (1983).Article 

Google Scholar 
42.Sime, K. R. et al. Psyttalia ponerophaga (Hymenoptera: Braconidae) as a potential biological control agent of olive fruit fly Bactrocera oleae (Diptera: Tephritidae) in California. Bull. Entomol. Res. 97, 233–242. https://doi.org/10.1017/S0007485307004865 (2007).CAS 
Article 
PubMed 

Google Scholar 
43.Sime, K. R. et al. The biology of Bracon celer as a parasitoid of the olive fruit fly. Biocontrol 51, 553–567. https://doi.org/10.1007/s10526-005-6079-8 (2006).Article 

Google Scholar 
44.Sime, K. R. et al. Diachasmimorpha longicaudata and D. kraussii (Hymenoptera: Braconidae), potential parasitoids of the olive fruit fly. Biocontrol Sci. Technol. 16, 169–179. https://doi.org/10.1080/09583150500188445 (2006).Article 

Google Scholar 
45.Sime, K. R., Daane, K. M., Messing, R. H. & Johnson, M. W. Comparison of two laboratory cultures of Psyttalia concolor (Hymenoptera: Braconidae), as a parasitoid of the olive fruit fly. Biol. Control 39, 248–255. https://doi.org/10.1016/j.biocontrol.2006.06.007 (2006).Article 

Google Scholar 
46.Mkize, N., Hoelmer, K. A. & Villet, M. H. A survey of fruit-feeding insects and their parasitoids occurring on wild olives, Olea europaea ssp cuspidata, in the Eastern Cape of South Africa. Biocontrol Sci. Technol. 18, 991–1004 (2008).Article 

Google Scholar 
47.Sime, K. R., Daane, K. M., Wang, X.-G., Johnson, M. W. & Messing, R. H. Evaluation of Fopius arisanus as a biological control agent for the olive fruit fly in California. Agric. For. Entomol. 10, 423–431. https://doi.org/10.1111/j.1461-9563.2008.00401.x (2008).Article 

Google Scholar 
48.Wang, X. G. & Messing, R. H. Potential interactions between pupal and egg- or larval-pupal parasitoids of tephritid fruit flies. Environ. Entomol. 33, 1313–1320. https://doi.org/10.1603/0046-225x-33.5.1313 (2004).Article 

Google Scholar 
49.Wang, X. G. & Messing, R. H. The ectoparasitic pupal parasitoid, Pachycrepoideus vindemmiae (Hymenoptera: Pteromalidae), attacks other primary tephritid fruit fly parasitoids: Host expansion and potential non-target impact. Biol. Control 31, 227–236 (2004).Article 

Google Scholar 
50.Wang, X.-G., Johnson, M. W., Yokoyama, V. Y., Pickett, C. H. & Daane, K. M. Comparative evaluation of two olive fruit fly parasitoids under varying abiotic conditions. Biocontrol 56, 283–293. https://doi.org/10.1007/s10526-010-9332-8 (2011).CAS 
Article 

Google Scholar 
51.Daane, K. M. et al. Biological control of the olive fruit fly in California. Calif. Agric. 65, 21–28 (2011).Article 

Google Scholar 
52.Wang, X. G. et al. Crop domestication relaxes both top-down and bottom-up effects on a specialist herbivore. Basic Appl. Ecol. 10, 216–227. https://doi.org/10.1016/j.baae.2008.06.003 (2009).Article 

Google Scholar 
53.Nadel, H., Daane, K. M., Hoelmer, K. A., Pickett, C. H. & Johnson, M. W. Non-target host risk assessment of the idiobiont parasitoid, Bracon celer (Hymenoptera: Braconidae), for biological control of olive fruit fly in California. Biocontrol Sci. Technol. 19, 701–715. https://doi.org/10.1080/09583150902974384 (2009).Article 

Google Scholar 
54.Wharton, R. A. et al. Parasitoids of medfly, Ceratitis capitata, and related tephritids in Kenyan coffee: A predominantly koinobiont assemblage. Bull. Entomol. Res. 90, 517–526 (2000).CAS 
Article 

Google Scholar 
55.Kimani-Njogu, S. W., Trostle, M. K., Wharton, R. A., Woolley, J. B. & Raspi, A. Biosystematics of the Psyttalia concolor species complex (Hymenoptera: Braconidae: Opiinae): the identity of populations attacking Ceratitis capitata (Diptera: Tephritidae) in coffee in Kenya. Biol. Control 20, 167–174 (2001).Article 

Google Scholar 
56.Rugman-Jones, P. F., Wharton, R., van Noort, T. & Stouthamer, R. Molecular differentiation of the Psyttalia concolor (Szépligeti) species complex (Hymenoptera: Braconidae) associated with olive fly, Bactrocera oleae (Rossi) (Diptera: Tephritidae), Africa. Biol. Control 49, 17–26. https://doi.org/10.1016/j.biocontrol.2008.12.005 (2009).CAS 
Article 

Google Scholar 
57.Billah, M. K. et al. Cross mating studies among five fruit fly parasitoid populations: Potential biological control implications for tephritid pests. Biocontrol 53, 709–724 (2008).Article 

Google Scholar 
58.Narayanan, E. S. & Chawla, S. S. Parasites of fruit fly pests of the world. Beitrage zur Entomologie 12, 437–476 (1962).
Google Scholar 
59.Neuenschwander, P. Searching parasitoids of Dacus oleae in South Africa. Zeitschrift fur Angewandte Entomologie 94, 509–522 (1982).Article 

Google Scholar 
60.Daane, K. M. et al. Psyttalia lounsburyi (Hymenoptera: Braconidae), potential biological control agent for the olive fruit fly in California. Biol. Control 44, 78–89. https://doi.org/10.1016/j.biocontrol.2007.08.010 (2008).Article 

Google Scholar 
61.Benelli, G. et al. Behavioral and electrophysiological responses of the parasitic wasp Psyttalia concolor (Szepligeti) (Hymenoptera: Braconidae) to Ceratitis capitata-induced fruit volatiles. Biol. Control 64, 116–124. https://doi.org/10.1016/j.biocontrol.2012.10.010 (2013).CAS 
Article 

Google Scholar 
62.Raspi, A. & Loni, A. Alcune note sull’allevamento massale di Opius concolor Szépligeti (Hym.: Braconidae) e su recnti tentative d’introduzione della specie in Toscana e Liguria. Frustula Entomol. 30, 135–145 (1994).
Google Scholar 
63.Johnson, M. W. et al. High temperature impacts olive fruit fly population dynamics in California’s Central Valley. Calif. Agric. 65, 29–33 (2011).Article 

Google Scholar 
64.Yokoyama, V. Y. et al. Performance of Psyttalia humilis (Hymenoptera: Braconidae) reared from irradiated host on olive fruit fly (Diptera: Tephritidae) in California. Environ. Entomol. 41, 497–507. https://doi.org/10.1603/en11252 (2012).Article 
PubMed 

Google Scholar 
65.Yokoyama, V. Y. et al. Response of Psyttalia cf. concolor to olive fruit fly (Diptera: Tephritidae), high temperature, food, and bait sprays in California. Environ. Entomol. 40, 315–323 (2010).Article 

Google Scholar 
66.Yokoyama, V. Y. et al. Field performance and fitness of an olive fruit fly parasitoid, Psyttalia humilis (Hymenoptera: Braconidae), mass reared on irradiated Medfly. Biol. Control 54, 90–99. https://doi.org/10.1016/j.biocontrol.2010.04.008 (2010).Article 

Google Scholar 
67.Wang, X. G. et al. Overwintering survival of olive fruit Fly (Diptera: Tephritidae) and two introduced parasitoids in California. Environ. Entomol. 42, 467–476. https://doi.org/10.1603/en12299 (2013).Article 
PubMed 

Google Scholar 
68.Daane, K. M., Wang, X. G., Johnson, M. W. & Cooper, M. L. Low temperature storage effects on two olive fruit fly parasitoids. Biocontrol 58, 175–185. https://doi.org/10.1007/s10526-012-9481-z (2013).CAS 
Article 

Google Scholar 
69.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. (accessed 20 Dec 2020); https://www.r-project.org/index.htm (2020). More

Coral reef shoals in the south Pacific, part of which is a marine protected area.Credit: Pete Niesen/Alamy

After a year of pandemic-induced delays, 2021 is set to be a big year for biodiversity, climate and the ocean. Later this year, world leaders are expected to gather for meetings of the United Nations conventions on biological diversity and climate to set future agendas. Ocean policies will be a priority for both.Momentum is building for what is called the 30 × 30 campaign — a goal to protect 30% of the planet (both land and sea) by 2030. Last December, the 30% ocean goal was backed by the High Level Panel for a Sustainable Ocean Economy, which comprises the heads of state of 14 coastal nations, including some of the largest countries, such as Indonesia, and the smallest, like Palau. This is an important step.But this target is ambitious. At present, 15% of terrestrial surfaces are classed as protected, and only about 7% of the oceans have been designated or proposed as marine protected areas — so named because, within them, fishing and other industrial activities are prohibited or restricted. Just 2.6% of the oceans are either fully or highly protected. Although these numbers have been improving, they are behind schedule — a previous global target was to protect 17% of land and 10% of the oceans by 2020.Achieving the ocean’s full potential for helping humanity will require genuinely sustainable fishing practices, investments in renewable technologies such as offshore wind farms, and zero-emissions shipping. Carbon-hungry seagrasses and mangroves must also be restored. But efforts to achieve these goals inevitably create conflicts, because governments, the conservation community and industry tend to have different priorities. Such disagreements are impeding progress.
Read the paper: Protecting the global ocean for biodiversity, food and climate
Research published in Nature this week could help to resolve some of these tensions when establishing protected areas. Conservationist and National Geographic explorer-in-residence Enric Sala and his colleagues present a model showing how the ocean could be protected in a way that optimizes both environmental and fishing-industry benefits1. This model needs to be studied carefully as talks progress, because it could help nations to see where compromises are possible.The researchers assessed data on the distribution of ocean biodiversity (taking in 4,242 species); 1,150 commercially exploited seafood stocks; and carbon in marine sediments. They used these data to model the spaces where marine protected areas could be situated to achieve particular outcomes across three main goals. For example, a plan that protects 71% of the ocean could yield 91% of the maximum biodiversity benefits and 48% of the carbon benefits, but with no change to existing fisheries catches. In another scenario, 28% of the ocean could be protected to obtain a maximum increase in seafood catches while securing 35% of the maximum biodiversity benefits and 27% of the maximum carbon benefits.The model makes it clear that achieving the best outcome on all three goals will require give and take. Nations and stakeholder groups will need to weigh up each goal. That will be hard, but necessary; some countries will have to give a little of their profitable fisheries, for example. And under this model, nations will need to commit to reducing bottom trawling, a fishing practice that stirs up carbon-rich sediments on the sea floor, potentially releasing that carbon. According to one estimate2, the impact of this process on the ocean’s carbon-storage capacity is greater than that of other problems that receive more attention, such as the loss of biological carbon storage when mangroves are cleared.
Read the paper: Enabling conditions for an equitable and sustainable blue economy
Countries must also pay attention to equity and access, and ensure that decisions to create protected areas are made in consultation with affected and often vulnerable communities. December’s high-level panel report estimates that the economic opportunities provided by marine genetic resources, ecotourism, fisheries, renewable energy and carbon credits could reel in a net benefit of US$15.5 trillion by 2050. But, as Andrés Cisneros-Montemayor at the University of British Columbia in Vancouver, Canada, and his colleagues point out in this issue3, many coastal nations lack access to the infrastructure or governance needed to promote what is called a ‘sustainable blue economy’. As might be expected, some nations aren’t equipped to ensure that, say, their local fish stocks are protected from being used in farm feed; or that construction of new ports doesn’t unreasonably affect local communities or ecosystems.At present, most of the ocean economy isn’t exactly blue. A study of the 100 largest companies in the ocean economy (which together account for 60% of around US$2 trillion in annual revenue) showed that the majority profit from oil and gas. Even Norway, which co-chaired the high-level panel, recently announced 61 new offshore oil and gas licences, as well as its intention to grant sea-bed mining licences as early as 2023. Such moves are disappointing. Green groups and researchers must continue to put pressure on countries to live up to their promises.World leaders at the upcoming biodiversity and climate meetings have a big task. Expanding the blue economy is difficult given the economic consequences of protecting more of the ocean. But there is now not only momentum in this direction, but also research to show that it can be done. If humanity looks after the ocean, it will look after us. More

1.Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444:1027–31.PubMed 
PubMed Central 
Article 

Google Scholar 
2.Monachese M, Burton JP, Reid G. Bioremediation and tolerance of humans to heavy metals through microbial processes: a potential role for probiotics?. Appl Environ Microbiol. 2012;78:6397–404.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Chevalier C, Stojanovi O, Colin DJ, Suarez-Zamorano N, Tarallo V, Veyrat-Durebex C, et al. Gut microbiota orchestrates energy homeostasis during cold. Cell. 2015;163:1360–74.CAS 
PubMed 
Article 

Google Scholar 
4.Kamada N, Kim Y-G, Sham HP, Vallance BA, Puente JL, Martens EC, et al. Regulated virulence controls the ability of a pathogen to compete with the gut microbiota. Science. 2012;336:1325–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Zhang N, He Q-S. Commensal microbiome promotes resistance to local and systemic infections. Chin Med J. 2015;128:2250–5.PubMed 
PubMed Central 
Article 

Google Scholar 
6.Hooper LV, Littman DR, Macpherson AJ. Interactions between the microbiota and the immune system. Science. 2012;336:1268–73.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Honda K, Littman DR. The microbiota in adaptive immune homeostasis and disease. Nature 2016;535:75–84.CAS 
PubMed 
Article 

Google Scholar 
8.Thaiss CA, Zmora N, Levy M, Elinav E. The microbiome and innate immunity. Nature. 2016;535:65–74.CAS 
PubMed 
Article 

Google Scholar 
9.Hanski I, von Hertzen L, Fyhrquist N, Koskinen K, Torppa K, Laatikainen T, et al. Environmental biodiversity, human microbiota, and allergy are interrelated. Proc Natl Acad Sci USA. 2012;109:8334–9.CAS 
PubMed 
Article 

Google Scholar 
10.De Luca F, Shoenfeld Y. The microbiome in autoimmune diseases. Clin Exp Immuno l. 2019;195:74–85.Article 
CAS 

Google Scholar 
11.Costello EK, Stagaman K, Dethlefsen L, Bohannan BJM, Relman DA. The application of ecological theory toward an understanding of the human microbiome. Science. 2012;336:1255–62.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Dominguez-Bello MG, Costello EK, Contreras M, Magris M, Hidalgo G, Fierer N, et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc Natl Acad Sci USA. 2010;107:11971–5.PubMed 
Article 

Google Scholar 
13.Ferretti P, Pasolli E, Tett A, Asnicar F, Gorfer V, Fedi S, et al. Mother-to-infant microbial transmission from different body sites shapes the developing infant gut microbiome. Cell Host Microbe. 2018;24:133–45.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Lane AA, McGuire MK, McGuire MA, Williams JE, Lackey KA, Hagen EH, et al. Household composition and the infant fecal microbiome: the INSPIRE study. Am J Phys Anthropol. 2019;169:526–39.PubMed 
Article 

Google Scholar 
15.Lehtimäki J, Karkman A, Laatikainen T, Paalanen L, von Hertzen L, Haahtela T, et al. Patterns in the skin microbiota differ in children and teenagers between rural and urban environments. Sci Rep. 2017;7:45651.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
16.Tamburini S, Shen N, Wu H, medicine JC-N. The microbiome in early life: implications for health outcomes. Nat Med. 2016;22:713–22.CAS 
Article 

Google Scholar 
17.Turnbaugh PJ, Ridaura VK, Faith JJ, Rey FE, Knight R, Gordon JI. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci Transl Med. 2009;1:6ra14.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
18.David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2014;505:559–63.CAS 
PubMed 
Article 

Google Scholar 
19.Ottman N, Ruokolainen L, Suomalainen A, Sinkko H, Karisola P, Lehtimäki J, et al. Soil exposure modifies the gut microbiota and supports immune tolerance in a mouse model. J Allergy Clin Immunol. 2019;143:1198–206.CAS 
PubMed 
Article 

Google Scholar 
20.Grieneisen LE, Charpentier MJE, Alberts SC, Blekhman R, Bradburd G, Tung J, et al. Genes, geology and germs: gut microbiota across a primate hybrid zone are explained by site soil properties, not host species. Proc R Soc B Biol Sci. 2019;286:20190431.Article 

Google Scholar 
21.Sarkar A, Harty S, Johnson KV-A, Moeller AH, Archie EA, Schell LD, et al. Microbial transmission in animal social networks and the social microbiome. Nat Ecol Evol. 2020;4:1020–35.PubMed 
Article 

Google Scholar 
22.Moeller AH, Suzuki TA, Phifer-Rixey M, Nachman MW. Transmission modes of the mammalian gut microbiota. Science. 2018;362:453–7.CAS 
PubMed 
Article 

Google Scholar 
23.Hufeldt MR, Nielsen DS, Vogensen FK, Midtvedt T, Hansen AK. Variation in the gut microbiota of laboratory mice is related to both genetic and environmental factors. Comp Med. 2010;60:336–47.CAS 
PubMed 
PubMed Central 

Google Scholar 
24.Hildebrand F, Nguyen TLA, Brinkman B, Yunta R, Cauwe B, Vandenabeele P, et al. Inflammation-associated enterotypes, host genotype, cage and inter-individual effects drive gut microbiota variation in common laboratory mice. Genome Biol. 2013;14:R4.PubMed 
PubMed Central 
Article 

Google Scholar 
25.Lees H, Swann J, Poucher SM, Nicholson JK, Holmes E, Wilson ID, et al. Age and microenvironment outweigh genetic influence on the Zucker rat microbiome. PloS One. 2014;9:e100916.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
26.Tung J, Barreiro LB, Burns MB, Grenier JC, Lynch J, Grieneisen LE, et al. Social networks predict gut microbiome composition in wild baboons. Elife. 2015;4:e05224.PubMed Central 
Article 
PubMed 

Google Scholar 
27.Raulo A, Ruokolainen L, Lane A, Amato K, Knight R, Leigh S, et al. Social behaviour and gut microbiota in red-bellied lemurs (Eulemur rubriventer): In search of the role of immunity in the evolution of sociality. J Anim Ecol. 2018;87:388–99.PubMed 
Article 

Google Scholar 
28.Perofsky AC, Lewis RJ, Abondano LA, Di Fiore A, Meyers LA. Hierarchical social networks shape gut microbial composition in wild Verreaux’s sifaka. Proc Biol Sci. 2017;284:20172274.PubMed 
PubMed Central 

Google Scholar 
29.Moeller AH, Foerster S, Wilson ML, Pusey AE, Hahn BH, Ochman H. Social behavior shapes the chimpanzee pan-microbiome. Sci Adv. 2016;2:e1500997.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
30.Wikberg EC, Christie D, Sicotte P, Ting N. Interactions between social groups of colobus monkeys (Colobus vellerosus) explain similarities in their gut microbiomes. Anim Behav. 2020;163:17–31.Article 

Google Scholar 
31.Bennett G, Malone M, Sauther ML, Cuozzo FP, White B, Nelson KE, et al. Host age, social group, and habitat type influence the gut microbiota of wild ring-tailed lemurs (Lemur catta). Am J Primatol. 2016;78:883–92.CAS 
PubMed 
Article 

Google Scholar 
32.Theis KR, Schmidt TM, Holekamp KE. Evidence for a bacterial mechanism for group-specific social odors among hyenas. Sci Rep. 2012;2:615.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
33.Leclaire S, Nielsen JF, Drea CM. Bacterial communities in meerkat anal scent secretions vary with host sex, age, and group membership. Behav Ecol. 2014;25:996–1004.Article 

Google Scholar 
34.Antwis RE, Lea JMD, Unwin B, Shultz S. Gut microbiome composition is associated with spatial structuring and social interactions in semi-feral Welsh Mountain ponies. Microbiome. 2018;6:207.PubMed 
PubMed Central 
Article 

Google Scholar 
35.Song SJ, Lauber C, Costello EK, Lozupone CA, Humphrey G, Berg-Lyons D, et al. Cohabiting family members share microbiota with one another and with their dogs. Elife. 2013;2:e00458.PubMed 
PubMed Central 
Article 

Google Scholar 
36.Grieneisen LE, Livermore J, Alberts S, Tung J, Archie EA. Group living and male dispersal predict the core gut microbiome in wild baboons. Integr Comp Biol. 2017;57:770–85.PubMed 
PubMed Central 
Article 

Google Scholar 
37.Dill-McFarland KA, Tang Z-Z, Kemis JH, Kerby RL, Chen G, Palloni A, et al. Close social relationships correlate with human gut microbiota composition. Sci Rep. 2019;9:703.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
38.Wilson EO. Elementary concepts in sociobiology. In: Wilson EO, editors. Sociobiology: The New Synthesis. 25th ed. Cambridge, Massachutes, USA: Harvard University Press; 2000. p. 8.
Google Scholar 
39.Godsall B. Mechanisms of space use in the wood mouse, Apodemus sylvaticus. Doctoral Thesis, London: Imperial College; 2015.40.Wolton RJ. The ranging and nesting behaviour of Wood mice, Apodemus sylvaticus (Rodentia: Muridae), as revealed by radio-tracking. J Zool. 2009;206:203–22.Article 

Google Scholar 
41.Stopka P, Macdonald DW. The market effect in the Wood mouse, Apodemus sylvaticus: selling information on reproductive status. Ethology. 2001;105:969–82.42.Walton JB, Andrews JF. Torpor induced by food deprivation in the Wood mouse Apodemus sylvaticus. J Zool. 2009;194:260–3.Article 

Google Scholar 
43.Wolton RJ. A possible role for faeces in range-marking by the Wood mouse, Apodemus sylvaticus. J Zool. 2009;206:286–91.Article 

Google Scholar 
44.Wolton RJ. Individual recognition by olfaction in the Wood Mouse, Apodemus sylvaticus. Behaviour. 1984;88:191–9.Article 

Google Scholar 
45.Godsall B, Coulson T, Malo AF. From physiology to space use: energy reserves and androgenization explain home-range size variation in a woodland rodent. J Anim Ecol. 2014;83:126–35.PubMed 
Article 

Google Scholar 
46.Wang J, Santure AW. Parentage and sibship inference from multilocus genotype data under polygamy. Genetics. 2009;181:1579–94.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Farine DR. Animal social network inference and permutations for ecologists in R using asnipe. Methods Ecol Evol. 2013;4:1187–94.Article 

Google Scholar 
48.Csardi G, Nepusz T. The igraph software package for complex network research. Inter J Complex Syst. 2006;1695:1–9.
Google Scholar 
49.Firth JA, Sheldon BC. Social carry-over effects underpin trans-seasonally linked structure in a wild bird population. Ecol Lett. 2016;19:1324–32.PubMed 
PubMed Central 
Article 

Google Scholar 
50.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:e61217.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.McKnight DT, Huerlimann R, Bower DS, Schwarzkopf L, Alford RA, Zenger KR. Methods for normalizing microbiome data: an ecological perspective. Methods. Ecol Evol. 2019;10:389–400.
Google Scholar 
53.Hsieh TC, Ma KH, Chao A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods. Ecol Evol. 2016;7:1451–6.
Google Scholar 
54.Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MH, Oksanen MJ, et al. The vegan package. Community Ecol Package. 2007;10:719.
Google Scholar 
55.Bürkner PC. brms: an R package for Bayesian multilevel models using stan. J Stat Softw. 2017;80:1–28.Article 

Google Scholar 
56.Bürkner PC. Advanced Bayesian multilevel modeling with the R package brms. R J. 2018;10:395–411.Article 

Google Scholar 
57.Hadfield JD. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J Stat Softw. 2010;33:1–22.Article 

Google Scholar 
58.Dekker D, Krackhardt D, Snijders TA. Sensitivity of MRQAP tests to collinearity and autocorrelation conditions. Psychometrika. 2007;72:563–81.PubMed 
PubMed Central 
Article 

Google Scholar 
59.Ormerod KL, Wood DLA, Lachner N, Gellatly SL, Daly JN, Parsons JD, et al. Genomic characterization of the uncultured Bacteroidales family S24-7 inhabiting the guts of homeothermic animals. Microbiome. 2016;4:36.PubMed 
PubMed Central 
Article 

Google Scholar 
60.Wieczorek AS, Schmidt O, Chatzinotas A, von Bergen M, Gorissen A, Kolb S. Ecological functions of agricultural soil bacteria and microeukaryotes in chitin degradation: a case study. Front Microbiol. 2019;10:1293.PubMed 
PubMed Central 
Article 

Google Scholar 
61.Huang X, Liu L, Zhao J, Zhang J, Cai Z. The families Ruminococcaceae, Lachnospiraceae, and Clostridiaceae are the dominant bacterial groups during reductive soil disinfestation with incorporated plant residues. Appl Soil Ecol. 2019; 135:65–72.62.Moeller AH, Caro-Quintero A, Mjungu D, Georgiev AV, Lonsdorf EV, Muller MN, et al. Cospeciation of gut microbiota with hominids. Science. 2016;353:380–2.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Tew TE, Macdonald DW. Dynamics of space use and male vigour amongst wood mice, Apodemus sylvaticus, in the cereal ecosystem. Behav Ecol Sociobiol. 1994;34:337–45.Article 

Google Scholar 
64.Erazo D, Pedersen AB, Gallagher K, Fenton A. Who acquires infection from whom? Estimating herpesvirus transmission rates between wild rodent host groups. 2020, https://www.biorxiv.org/content/10.1101/2020.09.18.302489v1.65.Taylor SE, Klein LC, Lewis BP, Gruenewald TL, Gurung RAR, Updegraff JA. Biobehavioral responses to stress in females: tend-and-befriend, not fight-or-flight. Psychol Rev. 2000;107:411–29.CAS 
PubMed 
Article 

Google Scholar 
66.Amato KR, Van Belle S, Di Fiore A, Estrada A, Stumpf R, White B, et al. Patterns in gut microbiota similarity associated with degree of sociality among sex classes of a neotropical primate. Micro Ecol. 2017;74:250–8.Article 

Google Scholar 
67.Brito IL, Gurry T, Zhao S, Huang K, Young SK, Shea TP, et al. Transmission of human-associated microbiota along family and social networks. Nat Microbiol. 2019;4:964–71.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Johnson KV-A. Gut microbiome composition and diversity are related to human personality traits. Hum Microbiome J. 2020;15:100069.Article 

Google Scholar 
69.Levin II, Zonana DM, Fosdick BK, Song SJ, Knight R, Safran RJ. Stress response, gut microbial diversity and sexual signals correlate with social interactions. Biol Lett. 2016;12:20160352.PubMed 
PubMed Central 
Article 

Google Scholar 
70.Mouquet N, Loreau M. Community patterns in source‐sink metacommunities. Am Nat. 2003;162:544–57.PubMed 
Article 

Google Scholar 
71.Altizer S, Nunn CL, Thrall PH, Gittleman JL, Antonovics J, Cunningham AA, et al. Social organization and parasite risk in mammals: Integrating theory and empirical studies. Annu Rev Ecol Evol Syst. 2003;34:517–47.Article 

Google Scholar 
72.Loehle C. Social barriers to pathogen transmission in wild animal populations. Ecology. 1995;76:326–35.Article 

Google Scholar 
73.Moeller A, Dufva R, Allander K. Parasites and the evolution of host social behavior. Adv Study Behav. 1993;22:65–102.Article 

Google Scholar 
74.Reese AT, Dunn RR. Drivers of microbiome biodiversity: a review of general rules, feces, and ignorance. MBio. 2018;9:4.Article 

Google Scholar 
75.Shade A. Diversity is the question, not the answer. ISME J. 2017;11:1–6.PubMed 
Article 

Google Scholar 
76.Pallen MJ. The human microbiome and host-pathogen interactions. In: Metagenomics of the human body. New York, NY: Springer; 2011; p 43–61.77.Amato KR, Leigh SR, Kent A, Mackie RI, Yeoman CJ, Stumpf RM, et al. The gut microbiota appears to compensate for seasonal diet variation in the wild black howler monkey (Alouatta pigra). Micro Ecol. 2014;69:434–43.Article 
CAS 

Google Scholar 
78.Barribeau SM, Villinger J, Waldman B. Ecological immunogenetics of life-history traits in a model amphibian. Biol Lett. 2012;8:405–7.PubMed 
Article 

Google Scholar 
79.Feng T, Elson CO. Adaptive immunity in the host-microbiota dialog. Mucosal Immunol. 2011;4:15–21.CAS 
PubMed 
Article 

Google Scholar 
80.Amato KR. Incorporating the gut microbiota into models of human and non-human primate ecology and evolution. Am J Phys Anthropol. 2016;159:196–215.Article 

Google Scholar 
81.Knowles SCL, Eccles RM, Baltrūnaitė L. Species identity dominates over environment in shaping the microbiota of small mammals. Ecol Lett. 2019;22:826–37.CAS 
PubMed 
Article 

Google Scholar  More