Ecology

Verdura, J. et al. Biodiversity loss in a Mediterranean ecosystem due to an extreme warming event unveils the role of an engineering gorgonian species. Sci. Rep. 9, 1–11 (2019).CAS 

Google Scholar 
Pandori, L. L. M. & Sorte, C. J. B. The weakest link: sensitivity to climate extremes across life stages of marine invertebrates. Oikos 128, 621–629 (2019).
Google Scholar 
Palmer, G. et al. Climate change, climatic variation and extreme biological responses. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160144 (2017).Altwegg, R., Visser, V., Bailey, L. D. & Erni, B. Learning from single extreme events. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160141 (2017).
Google Scholar 
McDermott Long, O. et al. Sensitivity of UK butterflies to local climatic extremes: which life stages are most at risk? J. Anim. Ecol. 86, 108–116 (2017).PubMed 

Google Scholar 
Jentsch, A., Kreyling, J. & Beierkuhnlein, C. A new generation of climate‐change experiments: events, not trends. Front. Ecol. Environ. 5, 365–374 (2007).
Google Scholar 
Suggitt, A. J. et al. Habitat associations of species show consistent but weak responses to climate. Biol. Lett. 8, 590–593 (2012).PubMed 
PubMed Central 

Google Scholar 
Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).CAS 
PubMed 

Google Scholar 
Valladares, F. et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014).PubMed 

Google Scholar 
Bush, A. et al. Incorporating evolutionary adaptation in species distribution modelling reduces projected vulnerability to climate change. Ecol. Lett. 19, 1468–1478 (2016).PubMed 

Google Scholar 
Stephens, P. A. et al. Consistent response of bird populations to climate change on two continents. Science 352, 84–87 (2016).CAS 
PubMed 

Google Scholar 
Kerr, J. T. et al. Climate change impacts on bumblebees converge across continents. Science 349, 177–180 (2015).CAS 
PubMed 

Google Scholar 
Roy, D. B. et al. Similarities in butterfly emergence dates among populations suggest local adaptation to climate. Glob. Chang. Biol. 21, 3313–3322 (2015).PubMed 
PubMed Central 

Google Scholar 
Titeux, N. et al. The need for large-scale distribution data to estimate regional changes in species richness under future climate change. Divers. Distrib. 23, 1393–1407 (2017).
Google Scholar 
Haeler, E., Fiedler, K. & Grill, A. What prolongs a butterfly’s life?: trade-offs between dormancy, fecundity and body size. PLoS One 9, e111955 (2014).PubMed 
PubMed Central 

Google Scholar 
Gonzalez-Suarez, M., Gomez, A. & Revilla, E. Which intrinsic traits predict vulnerability to extinction depends on the actual threatening processes. Ecosphere 4, 1–16 (2013).
Google Scholar 
Pacifici, M. et al. Species’ traits influenced their response to recent climate change. Nat. Clim. Chang. 7, 205–208 (2017).
Google Scholar 
Kingsolver, J. G. & Watt, W. B. Thermoregulatory strategies in Colias butterflies: thermal stress and the limits to adaptation in temporally varying environments (Colorado). Am. Nat. 121, 32–55 (1983).
Google Scholar 
MacLean, H. J., Higgins, J. K., Buckley, L. B. & Kingsolver, J. G. Morphological and physiological determinants of local adaptation to climate in Rocky Mountain butterflies. Conserv. Physiol. 4, 1 (2016).Kingsolver, J. G. & Wiernasz, D. C. Seasonal polyphenism in wing-melanin pattern and thermoregulatory adaptation in Pieris butterflies. Am. Nat. 137, 816–830 (1991).
Google Scholar 
Herrando, S. et al. Contrasting impacts of precipitation on Mediterranean birds and butterflies. Sci. Rep. 9, 1–7 (2019).CAS 

Google Scholar 
Thomas, J. A. Monitoring change in the abundance and distribution of insects using butterflies and other indicator groups. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 360, 339–357 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
Roy, D. B., Rothery, P., Moss, D., Pollard, E. & Thomas, J. A. Butterfly numbers and weather: predicting historical trends in abundance and the future effects of climate change. J. Anim. Ecol. 70, 201–217 (2008).
Google Scholar 
Pöyry, J., Luoto, M., Heikkinen, R. K., Kuussaari, M. & Saarinen, K. Species traits explain recent range shifts of Finnish butterflies. Glob. Chang. Biol. 15, 732–743 (2009).
Google Scholar 
Devictor, V. et al. Differences in the climatic debts of birds and butterflies at a continental scale. Nat. Clim. Chang. 2, 121–124 (2012).
Google Scholar 
Krauss, J. et al. Habitat fragmentation causes immediate and time-delayed biodiversity loss at different trophic levels. Ecol. Lett. 13, 597–605 (2010).PubMed 
PubMed Central 

Google Scholar 
Eskildsen, A. et al. Ecological specialization matters: long-term trends in butterfly species richness and assemblage composition depend on multiple functional traits. Divers. Distrib. 21, 792–802 (2015).
Google Scholar 
Pollard, E. A method for assessing changes in the abundance of butterflies. Biol. Conserv. 12, 115–134 (1977).
Google Scholar 
Schmucki, R. et al. A regionally informed abundance index for supporting integrative analyses across butterfly monitoring schemes. J. Appl. Ecol. 53, 501–510 (2016).
Google Scholar 
Pollard, E., Lakhani, K. H. & Rothery, P. The detection of density-dependence from a series of annual censuses. Ecology 68, 2046–2055 (1987).CAS 
PubMed 

Google Scholar 
Dooley, C. A., Bonsall, M. B., Brereton, T. & Oliver, T. Spatial variation in the magnitude and functional form of density-dependent processes on the large skipper butterfly Ochlodes sylvanus. Ecol. Entomol. 38, 608–616 (2013).
Google Scholar 
Rothery, P., Newton, I., Dale, L. & Wesolowski, T. Testing for density dependence allowing for weather effects. Oecologia 112, 518–523 (1997).PubMed 

Google Scholar 
Oliver, T. H. et al. Interacting effects of climate change and habitat fragmentation on drought-sensitive butterflies. Nat. Clim. Chang. 5, 941–946 (2015).
Google Scholar 
Stefanescu, C., Carnicer, J. & Peñuelas, J. Determinants of species richness in generalist and specialist Mediterranean butterflies: the negative synergistic forces of climate and habitat change. Ecography 34, 353–363 (2011).
Google Scholar 
Essens, T., van Langevelde, F., Vos, R. A., Van Swaay, C. A. M. & WallisDeVries, M. F. Ecological determinants of butterfly vulnerability across the European continent. J. Insect Conserv. 21, 439–450 (2017).
Google Scholar 
Tolman, T. & Lewington, R. Butterflies of Europe (Harper Collins, 2008).Dapporto, L. et al. Integrating three comprehensive data sets shows that mitochondrial DNA variation is linked to species traits and paleogeographic events in European butterflies. Mol. Ecol. Resour. 19, 1623–1636 (2019).CAS 
PubMed 

Google Scholar 
Hewitt, G. M. Post-glacial re-colonization of European biota. Biol. J. Linn. Soc. Lond. 68, 87–112 (2008).
Google Scholar 
Dincă, V. et al. High resolution DNA barcode library for European butterflies reveals continental patterns of mitochondrial genetic diversity. Commun. Biol. 4, 1–11 (2021).
Google Scholar 
Fei, S. et al. Divergence of species responses to climate change. Sci. Adv. 3, e1603055 (2017).PubMed 
PubMed Central 

Google Scholar 
Macgregor, C. J. et al. Climate-induced phenology shifts linked to range expansions in species with multiple reproductive cycles per year. Nat. Commun. 10, 1–10 (2019).CAS 

Google Scholar 
Dapporto, L. & Dennis, R. L. H. The generalist–specialist continuum: testing predictions for distribution and trends in British butterflies. Biol. Conserv. 157, 229–236 (2013).
Google Scholar 
MacLean, S. A. & Beissinger, S. R. Species’ traits as predictors of range shifts under contemporary climate change: a review and meta-analysis. Glob. Chang. Biol. 23, 4094–4105 (2017).PubMed 

Google Scholar 
Morlon, H. et al. Spatial patterns of phylogenetic diversity. Ecol. Lett. 14, 141–149 (2011).PubMed 
PubMed Central 

Google Scholar 
Kraft, N. J. B. et al. Community assembly, coexistence and the environmental filtering metaphor. Funct. Ecol. 29, 592–599 (2015).
Google Scholar 
Razgour, O. et al. Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proc. Natl Acad. Sci. USA 116, 10418–10423 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vanden Broeck, A. et al. Gene flow and effective population sizes of the butterfly Maculinea alcon in a highly fragmented, anthropogenic landscape. Biol. Conserv. 209, 89–97 (2017).
Google Scholar 
Haldane, J. B. S. Theoretical genetics of autopolyploids. J. Genet. 22, 359–372 (1930).
Google Scholar 
Tigano, A. & Friesen, V. L. Genomics of local adaptation with gene flow. Mol. Ecol. 25, 2144–2164 (2016).PubMed 

Google Scholar 
Pfeifer, S. P. et al. The evolutionary history of Nebraska deer mice: local adaptation in the face of strong gene flow. Mol. Biol. Evol. 35, 792–806 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Reusch, T. B. H. & Wood, T. E. Molecular ecology of global change. Mol. Ecol. 16, 3973–3992 (2007).CAS 
PubMed 

Google Scholar 
DeLong, J. P. & Gibert, J. P. Gillespie eco-evolutionary models (GEMs) reveal the role of heritable trait variation in eco-evolutionary dynamics. Ecol. Evol. 6, 935–945 (2016).PubMed 
PubMed Central 

Google Scholar 
Atkins, K. E. & Travis, J. M. J. Local adaptation and the evolution of species’ ranges under climate change. J. Theor. Biol. 266, 449–457 (2010).CAS 
PubMed 

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

Google Scholar 
Mills, S. C. et al. European butterfly populations vary in sensitivity to weather across their geographical ranges. Glob. Ecol. Biogeogr. 26, 1374–1385 (2017).
Google Scholar 
Van Dyck, H., Bonte, D., Puls, R., Gotthard, K. & Maes, D. The lost generation hypothesis: could climate change drive ectotherms into a developmental trap? Oikos 124, 54–61 (2015).
Google Scholar 
Hu, G. et al. Environmental drivers of annual population fluctuations in a trans-Saharan insect migrant. Proc. Natl Acad. Sci. USA 118, 2102762118 (2021).
Google Scholar 
Merlin, C. & Liedvogel, M. The genetics and epigenetics of animal migration and orientation: birds, butterflies and beyond. J. Exp. Biol. 222, jeb191890 (2019).Wiemers, M. et al. An updated checklist of the European butterflies (Lepidoptera, Papilionoideae). Zookeys 2018, 9–45 (2018).
Google Scholar 
Dennis, E. B., Freeman, S. N., Brereton, T. & Roy, D. B. Indexing butterfly abundance whilst accounting for missing counts and variability in seasonal pattern. Methods Ecol. Evol. 4, 637–645 (2013).
Google Scholar 
Radchuk, V., Turlure, C. & Schtickzelle, N. Each life stage matters: the importance of assessing the response to climate change over the complete life cycle in butterflies. J. Anim. Ecol. 82, 275–285 (2013).PubMed 

Google Scholar 
Metzger, M. J. et al. A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. Glob. Ecol. Biogeogr. 22, 630–638 (2013).
Google Scholar 
Carnicer, J. et al. A unified framework for diversity gradients: the adaptive trait continuum. Glob. Ecol. Biogeogr. 22, 6–18 (2013).
Google Scholar 
Klok, E. J. & Klein Tank, A. M. G. Updated and extended European dataset of daily climate observations. Int. J. Climatol. 29, 1182–1191 (2009).
Google Scholar 
Haylock, M. R. et al. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. J. Geophys. Res. Atmos. 113, D20119 (2008).
Google Scholar 
Marsh, T. J. The UK drought of 2003: a hydrological review. Weather 59, 224–230 (2004).
Google Scholar 
Voyer, A. G. & Garamszegi, L. Z. An introduction to phylogenetic path analysis. in Modern Phylogenetic Comparative Methods and their Application in Evolutionary Biology (eds Garamszegi, L. Z. & Mundry, R.) 201–229 (Springer Berlin Heidelberg, 2014).Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).CAS 
PubMed 

Google Scholar 
Pöyry, J. et al. The effects of soil eutrophication propagate to higher trophic levels. Glob. Ecol. Biogeogr. 26, 18–30 (2017).
Google Scholar 
Münkemüller, T. et al. How to measure and test phylogenetic signal. Methods Ecol. Evol. 3, 743–756 (2012).
Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
Bartoń, K. MuMIn: Multi-model inference. R package version 1.10.5. (2014).Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). MEE. 3, 217–223 (2012).
Google Scholar 
Briere, J. F., Pracros, P., Le Roux, A. Y. & Pierre, J. S. A novel rate model of temperature-dependent development for arthropods. Environ. Entomol. 28, 22–29 (1999).
Google Scholar 
Shi, P. & Ge, F. A comparison of different thermal performance functions describing temperature-dependent development rates. J. Therm. Biol. 35, 225–231 (2010).
Google Scholar 
Angilletta, M. J., Wilson, R. S., Navas, C. A. & James, R. S. Tradeoffs and the evolution of thermal reaction norms. Trends Ecol. Evol. 18, 234–240 (2003).
Google Scholar 
Zeuss, D., Brandl, R., Brändle, M., Rahbek, C. & Brunzel, S. Global warming favours light-coloured insects in Europe. Nat. Commun. 5, 1–9 (2014).
Google Scholar  More

Stork, N. E. How many species of insects and other terrestrial arthropods are there on earth? (2017). https://doi.org/10.1146/annurev-ento-020117.Scudder, G. Insect Biodiversity: Science and Society—Google Books (Wiley-Blackwell, 2009).
Google Scholar 
Lami, F., Boscutti, F., Masin, R., Sigura, M. & Marini, L. Seed predation intensity and stability in agro-ecosystems: Role of predator diversity and soil disturbance. Agric. Ecosyst. Environ. 288, 106720 (2020).
Google Scholar 
Gallai, N., Salles, J. M., Settele, J. & Vaissière, B. E. Economic valuation of the vulnerability of world agriculture confronted with pollinator decline. Ecol. Econ. 68, 810–821 (2009).
Google Scholar 
Consoli, F. L., Parra, J. R. P. & Zucchi, R. A. Egg Parasitoids in Agroecosystems with Emphasis on Trichogramma (Springer Science, 2010).
Google Scholar 
Sánchez-Guillén, R. A., Córdoba-Aguilar, A., Hansson, B., Ott, J. & Wellenreuther, M. Evolutionary consequences of climate-induced range shifts in insects. Biol. Rev. 91, 1050–1064 (2016).PubMed 

Google Scholar 
Zalucki, M. P. et al. Estimating the economic cost of one of the world’s major insect pests, Plutella xylostella (Lepidoptera: Plutellidae): Just how long is a piece of string?. J. Econ. Entomol. 105, 1115–1129 (2012).PubMed 

Google Scholar 
Dornelas, M. & Daskalova, G. N. Nuanced changes in insect abundance. Science (80-). 368, 368–369 (2020).CAS 
ADS 

Google Scholar 
Didham, R. K. et al. Interpreting insect declines: Seven challenges and a way forward. Insect Conserv. Divers. 13, 103–114 (2020).
Google Scholar 
Greenwood, B. M., Bojang, K. & Whitty, C. J. M. Malaria. Lancet 365, 98 (2005).
Google Scholar 
Dangles, O. & Casas, J. Ecosystem services provided by insects for achieving sustainable development goals. Ecosyst. Serv. 35, 109–115 (2019).
Google Scholar 
Burkholder, W. E. & Ma, M. Pheromones for monitoring and control of stored-product insects. Annu. Rev. Entomol. 30, 257–272 (1985).CAS 

Google Scholar 
Morris, R. F. Sampling insect populations. Annu. Rev. Entomol. 5, 243–264 (1960).
Google Scholar 
Strickland, A. H. Sampling crop pests and their hosts. Annu. Rev. Entomol. 6, 201–220 (1961).
Google Scholar 
Bannerman, J. A., Costamagna, A. C., McCornack, B. P. & Ragsdale, D. W. Comparison of relative bias, precision, and efficiency of sampling methods for natural enemies of soybean aphid (Hemiptera: Aphididae). J. Econ. Entomol. 108, 1381–1397 (2015).CAS 
PubMed 

Google Scholar 
Osborne, J. L. et al. Harmonic radar: A new technique for investigating bumblebee and honey bee foraging flight. VII Int. Symp. Pollinat. 437, 159–164 (1996).
Google Scholar 
Zink, A. G. & Rosenheim, J. A. State-dependent sampling bias in insects: Implications for monitoring western tarnished plant bugs. Entomol. Exp. Appl. 113, 117–123 (2004).
Google Scholar 
Rancourt, B., Vincent, C. & De Oliveira, A. D. Circadian activity of Lygus lineolaris (Hemiptera: Miridae) and effectiveness of sampling techniques in strawberry fields. J. Econ. Entomol 93, 1160–1166 (2000).CAS 
PubMed 

Google Scholar 
Binns, M. R. & Nyrop, J. P. Sampling insect populations for the purpose of IPM decision making. Annu. Rev. Entomol. 37, 427–453. https://doi.org/10.1146/annurev.ento.37.1.427 (1992).Article 

Google Scholar 
Portman, Z. M., Bruninga-Socolar, B. & Cariveau, D. P. The state of bee monitoring in the United States: A call to refocus away from bowl traps and towards more effective methods. Ann. Entomol. Soc. Am. 113, 337–342 (2020).
Google Scholar 
Montgomery, G. A., Belitz, M. W., Guralnick, R. P. & Tingley, M. W. Standards and best practices for monitoring and benchmarking insects. Front. Ecol. Evolut. 8, 579193 (2021).
Google Scholar 
Bick, E., Dryden, D. M., Nguyen, H. D. & Kim, H. A novel CO2-based insect sampling device and associated field method evaluated in a strawberry agroecosystem. J. Econ. Entomol. 113, 1037–1042 (2020).CAS 
PubMed 

Google Scholar 
Wen, C. & Guyer, D. Image-based orchard insect automated identification and classification method. Comput. Electron. Agric. 89, 110–115 (2012).
Google Scholar 
Chen, Y., Why, A., Batista, G., Mafra-Neto, A. & Keogh, E. Flying insect classification with inexpensive sensors. J. Insect Behav. 27, 657–677 (2014).
Google Scholar 
Potamitis, I. & Rigakis, I. Novel noise-robust optoacoustic sensors to identify insects through wingbeats. IEEE Sens. J. 15, 4621–4631 (2015).CAS 
ADS 

Google Scholar 
Eliopoulos, P. A., Potamitis, I., Kontodimas, D. C. & Givropoulou, E. G. Detection of adult beetles inside the stored wheat mass based on their acoustic emissions. J. Econ. Entomol. 108, 2808–2814 (2015).CAS 
PubMed 

Google Scholar 
Ärje, J. et al. Automatic image-based identification and biomass estimation of invertebrates. Methods Ecol. Evol. 11, 922–931 (2020).
Google Scholar 
Hobbs, S. E. & Hodges, G. An optical method for automatic classification and recording of a suction trap catch. Bull. Entomol. Res. 83, 47–51 (1993).
Google Scholar 
O’Neill, M. A., Gauld, I. D., Gaston, K. J. & Weeks, P. Daisy: An automated invertebrate identification system using holistic vision techniques. in Proceedings of the Inaugural Meeting BioNET-INTERNATIONAL Group for Computer-Aided Taxonomy (BIGCAT) 13–22 (1997).Chesmore, E. D. Methodologies for automating the identification of species. in First BioNet-International Work. Gr. Autom. Taxon. 3–12 (2000).Martineau, M. et al. A survey on image-based insect classification. Pattern Recognit. 65, 273–284 (2017).ADS 

Google Scholar 
Silva, D. F., De Souza, V. M. A., Batista Geapa, K. E. & Ellis, D. P. W. Applying machine learning and audio analysis techniques to insect recognition in intelligent traps. in Proceedings—2013 12th International Conference on Machine Learning and Applications, ICMLA 2013. (2013).Capinera, J. L. & Walmsley, M. R. Visual responses of some sugarbeet insects to sticky traps and water pan traps of various colors. J. Econ. Entomol., 71(6), 926–927 (1978).
Google Scholar 
Moore, A., Miller, J. R., Tabashnik, B. E. & Gage, S. H. Automated identification of flying insects by analysis of wingbeat frequencies. J. Econ. Entomol. 79, 1703–1706 (1986).
Google Scholar 
Riley, J. R. Angular and temporal variations in the radar cross-sections of insects. Proc. Inst. Electr. Eng. (IET) 120, 1229–1232 (1973).
Google Scholar 
Reed, S. C., Williams, C. M. & Chadwick, L. E. Frequency of wing-beat as a character for separating species races and geographic varieties of Drosophila. Genetics 27, 349 (1942).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mankin, R. W., Hagstrum, D. W., Smith, M. T., Roda, A. L. & Kairo, M. T. K. Perspective and promise: a century of insect acoustic detection and monitoring. Am. Entomol. 57(1), 30–44 (2011).
Google Scholar 
Drake, V. A. & Reynolds, D. R. Radar Entomology: Observing Insect Flight and Migration (Cabi, 2012).
Google Scholar 
Long, T. et al. Entomological radar overview: System and signal processing. IEEE Aerosp. Electron. Syst. Mag. 35, 20–32 (2020).
Google Scholar 
Drake, V. A., Hatty, S., Symons, C. & Wang, H. Insect monitoring radar: Maximizing performance and utility. Remote Sens. 12, 596 (2020).ADS 

Google Scholar 
Brydegaard, M. & Jansson, S. Advances in entomological laser radar. IET Int. Radar Conf. https://doi.org/10.1049/joe.2019.0598 (2018).Article 

Google Scholar 
Jansson, S. Entomological Lidar: Target Characterization and Field Applications (Department of Physics, Lund University, 2020).
Google Scholar 
Malmqvist, E. From Fauna to Flames: Remote Sensing with Scheimpflug-Lidar (Department of Physics, Lund University, 2019).
Google Scholar 
Mankin, R. W., Hagstrum, D. W., Smith, M. T., Roda, A. L. & Kairo, M. T. K. Perspective and promise: A century of insect acoustic detection and monitoring. Am. Entomol. 57, 30–44 (2011).
Google Scholar 
Miller-Struttmann, N. E., Heise, D., Schul, J., Geib, J. C. & Galen, C. Flight of the bumble bee: Buzzes predict pollination services. PLoS ONE 12, 1–14 (2017).
Google Scholar 
Li, Y. et al. Mosquito detection with low-cost smartphones: Data acquisition for malaria research. arXiv:1711.06346 [stat.ML] (2017).Mukundarajan, H., Hol, F. J. H., Castillo, E. A., Newby, C. & Prakash, M. Using mobile phones as acoustic sensors for high-throughput mosquito surveillance. Elife 6, 1–26 (2017).
Google Scholar 
Osborne, J. L. et al. A landscape-scale study of bumble bee foraging range and constancy, using harmonic radar. J. Appl. Ecol. 36, 519–533 (1999).
Google Scholar 
Smith, A. D., Riley, J. R. & Gregory, R. D. A method for routine monitoring of the aerial migration of insects by using a vertical-looking radar. Philos. Trans. R. Soc. London. Ser. B Biol. Sci. 340, 393–404 (1993).
Google Scholar 
Chapman, J. W., Smith, A. D., Woiwod, I. P., Reynolds, D. R. & Riley, J. R. Development of vertical-looking radar technology for monitoring insect migration. Comput. Electron. Agric. 35(2–3), 95–110 (2002).
Google Scholar 
Schaefer, G. W. & Bent, G. A. An infra-red remote sensing system for the active detection and automatic determination of insect flight trajectories (IRADIT). Bull. Entomol. Res. 74, 261–278 (1984).
Google Scholar 
Farmery, M. J. Optical studies of insect flight at low altitude. (Doctoral dissertation, University of York, 1981).Farmery, M. J. The effect of air temperature on wingbeat frequency of naturally flying armyworm moth (Spodoptera exempta). Entomol. Exp. Appl. 32, 193–194 (1982).
Google Scholar 
Malmqvist, E. & Brydegaard, M. Applications of KHZ-CW lidar in ecological entomology. EPJ Web Conf. 119, 25016. https://doi.org/10.1051/epjconf/2016I11925016 (2016).Article 

Google Scholar 
Brydegaard, M. et al. Lidar reveals activity anomaly of malaria vectors during pan-African eclipse. Sci. Adv. 6, eaay5487 (2020).PubMed 
PubMed Central 
ADS 

Google Scholar 
Malmqvist, E. et al. The bat–bird–bug battle: Daily flight activity of insects and their predators over a rice field revealed by high-resolution Scheimpflug Lidar. Roy. Soc. Open Sci. 5(4), 172303 (2018).ADS 

Google Scholar 
Fristrup, K. M., Shaw, J. A. & Tauc, M. J. Development of a wing-beat-modulation scanning lidar system for insect studies. Lidar Remote Sens. Environ. Monit. 2017, 15. https://doi.org/10.1117/12.2274656 (2017).Article 

Google Scholar 
Hoffman, D. S., Nehrir, A. R., Repasky, K. S., Shaw, J. A. & Carlsten, J. L. Range-resolved optical detection of honeybees by use of wing-beat modulation of scattered light for locating land mines. Appl. Opt. 46, 3007–3012 (2007).PubMed 
ADS 

Google Scholar 
Jansson, S., Malmqvist, E. & Mlacha, Y. Real-time dispersal of malaria vectors in rural Africa monitored with lidar. Plos one. 16(3), e0247803 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jansson, S. & Brydegaard, M. Passive kHz lidar for the quantification of insect activity and dispersal. Anim. Biotelemet. 6, 6 (2018).
Google Scholar 
Jansson, S. P. & Sørensen, M. B. An optical remote sensing system for detection of aerial and aquatic fauna. U.S. Patent Application No. 16/346,322 (2019).Malmqvist, E., Jansson, S., Török, S. & Brydegaard, M. Effective parameterization of laser radar observations of atmospheric fauna. IEEE J. Sel. Top. Quant. Electron. 22, 1 (2015).
Google Scholar 
Drake, V. A., Wang, H. K. & Harman, I. T. Insect Monitoring Radar: Remote and network operation. Comput. Electron. Agric. 35, 77–94 (2002).
Google Scholar 
Kirkeby, C. et al. Advances in automatic identification of flying insects using optical sensors and machine learning. Sci. Rep. 11, 1555 (2021).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Jacques, S. L. Erratum: Optical properties of biological tissues: A review (Physics in Medicine and Biology (2013) 58). Phys. Med. Biol. 58, 5007–5008 (2013).
Google Scholar 
Li, M. et al. Bark beetles as lidar targets and prospects of photonic surveillance. J. Biophoton. https://doi.org/10.1002/jbio.202000420 (2020).Article 

Google Scholar 
Brydegaard, M. Advantages of shortwave infrared LIDAR entomology. in Laser Applications to Chemical, Security and Environmental Analysis LW2D-6 (Optical Society of America, 2014).
Google Scholar 
Brydegaard, M., Jansson, S., Schulz, M. & Runemark, A. Can the narrow red bands of dragonflies be used to perceive wing interference patterns? Ecol. Evol. 8(11), 5369–5384 (2018).PubMed 
PubMed Central 

Google Scholar 
Gebru, A. et al. Multiband modulation spectroscopy for the determination of sex and species of mosquitoes in flight. J. Biophotonics 11(8), e201800014 (2018).PubMed 

Google Scholar 
Potamitis, I. Classifying insects on the fly. Ecol. Inform. 21, 40–49 (2014).
Google Scholar 
Heathcote, G. D. The comparison of yellow cylindrical, flat and water traps, and of Johnson suction traps, for sampling aphids. Ann. Appl. Biol. 45, 133–139 (1957).
Google Scholar 
Vaishampayan, S. M., Kogan, M., Waldbauer, G. P. & Woolley, J. Spectral specific responses in the visual behavior of the greenhouse whitefly, Trialeurodes vaporariorum (Homoptera: Aleyrodidae). Entomol. Exp. Appl. 18, 344–356 (1975).
Google Scholar 
Mound, L. A. Studies on the olfaction and colour sensitivity of Bemisia tabaci (Genn.) (Homoptera, Aleyrodidae). Entomol. Exp. Appl. 5, 99–104 (1962).
Google Scholar 
Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Van Der Kooi, C. J., Stavenga, D. G., Arikawa, K., Belušič, G. & Kelber, A. Evolution of insect color vision: From spectral sensitivity to visual ecology. Annu. Rev. Entomol. 66, 435–461 (2021).PubMed 

Google Scholar  More

Site descriptionThis experiment was conducted in two long-term P-trial grassland sites (Site A and Site B) situated in proximity (~ 350 m) to each other in the dairy farm at Johnstown Castle, Wexford, Co. Wexford, Ireland (6°49′ W, 52°29′ N). The sites were grazed permanent grasslands before establishment. When the experiment was established in 1995, 16 (10 m × 2 m) plots were formed in each site in a fully randomised block design with four replicates. The two sites established were selected to represent different soil types and drainage classes. Site A is a moderately drained brown earth and site B is an imperfectly drained gley soil31. Each year in February, each plot received one of the four phosphorous (P) fertilization rates (16% P superphosphate): 0 (P0), 15 (P15), 30 (P30), and 45 (P45) kg P ha−1 year−1. All plots were initially sown with Lolium perenne and reseeded in 2016 with the same species. However, plant species such as Poa trivialis, Agropyron repens, Trifolium repens were present to a lesser extent. Above-ground biomass is harvested each month between February and August followed by 40 kg N ha−1 fertilizer applications. In the year (2019) of this experiment and the years before, SulCAN as a solid was applied at the first or second week of each month during February-August and potassium (K) as muriate of potash (KCl) was applied in February at a rate of 125 kg K ha−1. SulCAN contains 26.7% N in the form of nitric and ammoniacal nitrogen and 5% water soluble Sulphur. For this study plots receiving P0, P15 and P45 at the two field sites were set up to carry out this experiment. The two sites were selected as they had slightly different soil properties and thus there was an opportunity to consider a soil × treatment effect in the experiment.Experimental designFertilizer N and substrate C were applied on 8 May and 12 June in the experiment undertaken between May and July 2019, which represents the main growing season in Ireland. Within each plot, an area of 1 m × 1 m was selected. Following N fertilizer application (40 kg N ha−1) to all plots, carbon substrate [mixture of glucose (40%), sodium acetate (30%) and methanol (30%)] was applied once within the selected area using a sprayer watering can. Labile C available in animal excreta usually contains carbohydrates, volatile fatty acids, and alcohols32; as such different carbon substrates were applied to mimic this. Our review of the literature also indicated that C source types could differentially affect denitrifying communities and consequently denitrification rate. Thus, a mixture of three C sources was used to decrease bias of one microbial group over another as a result of single substrate use. Carbon was supplied to alleviate C-limitations of denitrification and nitrification processes as observed by O’Neill et al.29 in soils from this trial and to ensure equal substrate availability across all soil P levels. Equivalent C input rate of 0.63 g C m−2 day−1 was added to represent a daily rate of plant carbon input from Lolium perenne dominated ecosystem33. Soil samples were collected on eight occasions throughout the experimental period. Soil was sampled from across each selected area to a depth of 10 cm, sieved through 4 mm sieve and analysed for soil mineral N and microbial biomass.Soil properties, plant biomass and climate parametersPhysico-chemical soil properties were characterized by taking samples from 10 cm depth from each plot in the two sites before the commencement of the experiment. Soil pH was measured in water (2:1, water volume:soil mass) using Sally pH Auto analyser Dilution System (Gilson 215, Gilson, Dunstable, England). Soil organic matter (SOM) content was determined from mass loss on ignition at 550 °C for 7 h. Total C and total N concentrations were measured using a TrueSpec C/N analyser (TruSpec, LECO Corporation, Michigan, USA). Plant available P, potassium (K), and magnesium (Mg) were estimated using Morgan’s extraction34 and analysed using a Lachat QuickChem 8500 Series 2 Flow injection Analyzer (Lachat, QuickChem, 5600 Loveland, Colorado, USA). Particle size analysis was performed using the Pipette method35, where 2 mm sieved dry soil (20 g) was pre-treated with 6% H2O2, 3% NH4OH, and 5% sodium hexametaphosphate before separating soil aliquots into particle sizes. Water Holding Capacity (WHC) was determined from the mass difference between water-saturated and then overnight dried (105 °C) soil. Bulk density was determined by dividing weight of oven-dried soil by the total soil volume.To determine the mineral N concentrations, ten gram fresh soil was extracted with 50 mL 2 M KCl (5:1 solution to soil ratio). The supernatant was filtered through Whatman No. 1 filter paper and filtrates were stored in a cold room at 4 °C for about a week until analysis. Ammonium (NH4+) and nitrate (NO3−) concentrations in the extracts were analysed by the Aquakem 600 discrete analyser.Above-ground plant biomass from each plot of both sites was harvested twice during the experiment period (June 10 and July 11, 2019) to a height of ~ 5 cm using a Haldrup plot harvester. The total harvested biomass weight from each plot was recorded and a 100 g sub-sample was taken for dry matter (DM) analysis. Each fresh herbage sub-sample was weighed and placed in an oven at 70 °C for 3 days, and dry weight of the biomass was determined after re-weighing.Rainfall records for the experiment period were obtained from a Met Éireann weather observing station located in Teagasc dairy farm in Johnstown Castle, Co. Wexford., situated within a 100 m distance from the experimental sites. Volumetric soil moisture content and temperature was measured to 5 cm depth on individual plots on each gas sampling occasion using a handheld theta probe (WET-2 WET Sensor, Delta-T Devices, Cambridge, England). Water-filled pore space (WFPS) were calculated from the soil moisture values, bulk density of the soils, and soil particle density (2.65 g cm−3).Microbial biomass, glomalin-related soil protein and potential denitrification activitySoils were analysed for microbial biomass nitrogen (MBN), phosphorus (MBP) and carbon (MBC) using the fumigation extraction method as described respectively in (Brooks et al.36,37, and Vance et al.38). Five gram fumigated (24 h) and non-fumigated soil samples were extracted with 100 mL 0.5 M NaHCO3 and analysed for P colorimetrically using an Aquakem 600 discrete analyser (Thermo Electron OY, Vantaa, Finland). In order to avoid the spike readings by the instrument due to the effervescent nature of NaHCO3, one millilitre of 10% HCl was added to 10 mL extracts and diluted to 50 mL using distilled water. Microbial P was calculated by subtracting the P concentration of non-fumigated samples from fumigated samples, and dividing the result by an extraction factor of 0.437.Microbial biomass C and N were determined similarly using chloroform fumigation method with extraction period of 48 h with 0.5 M K2SO438. The extracts of the fumigated and non-fumigated samples were analysed for total C and N using a TOC-L CPH/CPN analyser (Shimadzu, Tokyo, Japan), and the differences, divided by correction factors of 0.45 and 0.54, were used to estimate the microbial biomass C and N, respectively.Glomalin is a glycoprotein produced by AMF and can be used as an indicator of mycorrhizal colonization in the plant root-soil interface39. Total glomalin-related soil protein (GRPS) was extracted by 90 min of autoclaving (121 °C) of 1 g air-dried soil in 8 mL of 50 mM sodium citrate adjusted to pH 8.0 with HCl40. Three additional sequential extractions were performed with the sodium citrate solution by autoclaving for 60 min until no red-brown color was visible in the last supernatant. After autoclaving, the samples were centrifuged at 10,000 revolutions per minute (rpm) for 5 min. The amounts of glomalin in the extracts were quantified using the Bradford dye-binding assay with bovine serum albumin (BSA) as the standard (2 mg mL−1). In a 96-well plate, replicated 200 µL of standard or extracts and 50 µL of dye reagent were added in each well and mixed using a microplate mixer. The Bradford-reactive substance was determined by measuring absorbance at 600 nm using Microplate Reader (Modulus Microplate Multimode Reader, Turner BioSystems, Sunnyvale, California, USA). Sample concentrations were determined using the standard curve. Potential denitrification activity (PDA) was determined using the acetylene inhibition method, modified from Pell et al.41. Briefly, replicated 20 g fresh soils were added into two identical flasks from a sample of soil. The flasks were then sealed with a rubber stopper and flushed and filled with helium after evacuating the headspace air. In one of the replicas, 10% of the headspaces were removed and replaced by acetylene. All flasks were incubated at 15 °C on an orbital shaker at 175 rpm for 30 min followed by the addition of a nutrient solution containing 75 mmol L−1 KNO3, 37.5 mmol L−1 Na-succinate, 25 mmol L−1 glucose, and 75 mM Na-acetate. Gas samples were taken from the headspace every 1 h for 5 h. N2O concentrations were determined using a gas chromatograph (Bruker, Scion 456-GC, Livingston, Scotland), and PDA was calculated from the rate of change of N2O concentrations over time from acetylene amended flasks.N2O and CO2 flux measurementsGas samples (N2O and CO2 fluxes) were measured before and after the application of N fertilizer and C substrates, with a daily sampling for 10 days directly after C + N additions and 3–4 times a week in the third and fourth week and 2–3 times a week in the subsequent weeks. A rectangular (40 × 40 cm) static collar, made of stainless steel (opaque), was anchored 5 cm deep into the soil within the marked area of 1 m × 1 m in each of the selected plots. During gas sampling, a 10 cm tall chamber lid fitted with two septa on top was placed on the collar lined with neoprene rubber band. To ensure hermetic sealing of the headspace during sampling, the ring area of the collar was half-filled with water, and a 10 kg weight was placed on the top of the lid to compress the seal. Gas samples were collected between 09:30 and 11:30 local time using a 10 mL Luer lock syringe fitted with a hypodermic needle via one of the septa at 0, 20, and 40 min after chamber closure. Prior to transferring the final sample into a pre-evacuated 7 mL glass vial, air in the chamber headspace was mixed by flushing the syringe three times. Gas samples were analysed using a gas chromatograph (Bruker, Scion 456-GC, Livingston, Scotland) fitted with an electron capture detector to analyse for N2O concentrations and a thermal conductivity detector to analyse for CO2 concentrations. Daily Fluxes (F) were calculated for each plot using the following equation:$$ F = left( {frac{Delta C}{{Delta t}}} right) times left( {frac{M times P}{{T times R}}} right) times left( frac{V}{A} right) $$where ∆C is the change in gas concentration in the chamber headspace during chamber enclosure period in ppbv, ∆t is chamber closing period in minutes, so ∆C/∆t is the slope of the gas concentration with time. M is the molar mass of N2O-N (28 g mol−1) and CO2-C (12 g mol−1), P and T are the atmospheric pressure (Pa) and temperature (K). Atmospheric pressure values were obtained from the nearby weather station whereas for T, wet sensor values were used. V is the headspace volume of the closed chamber (m3) and A is surface are of the chamber (m3). R is the ideal gas constant (8.314 J K−1 mol−1). Daily flux for each treatment is reported as the average of the replicates.Cumulative N2O and CO2 emissions were calculated over each application period by multiplying the daily N2O and CO2 fluxes by the number of days between two consecutive measurements. A summation of the cumulative flux of each application period is reported as the total cumulative flux.Statistical analysisANOVAs with repeated measures were used to test for the C + N addition effect on N2O and CO2 emissions, MBC, MBN, MBP, NO3−, and NH4+ with P treatment, site, and day of measurement as fixed effects, and individual plots as a random effect. Two-way ANOVA was applied to test for main and interaction effects of P treatment and site on cumulative N2O and CO2 emissions, soil property parameters (Table 1), plant biomass, and GRSP. Prior to analysis, response variables were checked for normality (sphericity for repeated ANOVA) and homogeneity of variance, and log transformed when required. Tukey’s HSD post-hoc tests were conducted to identify pair-wise comparisons of significant effects at P  More

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doi: https://doi.org/10.1038/d41586-022-00435-6

ReferencesIzdebski, A. et al. Nature Ecol. Evol. https://doi.org/10.1038/s41559-021-01652-4 (2022).
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Breed, M.D., Cook, C. & Krasnec, M.O. Cleptobiosis in social insects. Psyche 484765 (2012).Sakagami, S., Roubik, D. & Zucchi, R. Ethology of the robber stingless bee, Lestrimelitta limao (Hymenoptera: Apidae). Sociobiology 21, 237–277 (1993).
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Rasmussen, C. & Cameron, S. A. Global stingless bee phylogeny supports ancient divergence, vicariance, and long distance dispersal. Biol. J. Lin. Soc. 99, 206–232 (2010).
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Roubik, D. W. Ecology and Natural History of Tropical Bees (Cambridge University Press, 1989).
Google Scholar 
Camargo, J. M. F. & Pedro, S. R. M. Meliponini Lepeletier, 1836. in Catalogue of the Bees (Hymenoptera, Apoidea) in the Neotropical Region (ed Moure, J. S.). 272–578. (Sociedade Brasileira de Entomologia, 2007).Nogueira-Neto. P. Behavior problems related to the pillages made by some parasitic stingless bees (Meliponinae, Apidae). in Development and Evolution of Behavior: Essays in Memory of T.C. Schneirla (ed. Aronson, L.R.). 416–434. (W. H. Freeman, 1970).Quezada-Euán, J. J. G. & González-Acereto, J. Notes on the nest habits and host range of cleptobiotic Lestrimelitta niitkib (Ayala 1999) (Hymenoptera: Meliponini) from the Yucatan Peninsula, Mexico. Acta Zool. Mexicana 86, 245–249 (2002).
Google Scholar 
Rech, A. R., Schwade, M. A. & Schwade, M. R. M. Abelhas-sem-ferrão amazônicas defendem meliponarios contra saques de outras abelhas. Acta Amazon. 43, 389–394 (2013).
Google Scholar 
Grüter, C., von Zuben, L. G., Segers, F. H. I. D. & Cunningham, J. P. Warfare in stingless bees. Insect. Soc. 63, 223–236 (2016).
Google Scholar 
Cini, A., Bruschini, C., Poggi, L. & Cervo, R. Fight or fool? Physical strength, instead of sensory deception, matters in host nest invasion by a wasp social parasite. Anim. Behav. 81, 1139–1145 (2011).
Google Scholar 
Quezada-Euán, J. J. G. et al. Does sensory deception matter in eusocial obligate food robber systems? A study of Lestrimelitta and stingless bee hosts. Anim. Behav. 85, 817–823 (2013).
Google Scholar 
van Zweden, J. S. & D’Ettorre, P. Nestmate recognition in social insects and the role of hydrocarbons. In Insect hydrocarbons: biology, biochemistry, and chemical ecology (eds Blomquist, G. J. & Bagnères, A. G.) 222–243 (Cambridge University Press, 2010).
Google Scholar 
Blomquist, G. J. & Bagnères, A. G. Insect Hydrocarbons: Biology, Biochemistry and Chemical Ecology (Cambridge University Press, 2010).
Google Scholar 
Nash, D. R. & Boomsma, J. J. Communication between hosts and social parasites. In Sociobiology of Communication: An Interdisciplinary Perspective (eds d’Etorre, P. & Hughes, D. P.) 55–79 (Oxford University Press, 2008).
Google Scholar 
Martin, S. J., Shemilt, S., da S Lima, C. B. & de Carvalho, C. A. L. Are isomeric alkenes used in species recognition among neo-tropical stingless bees (Melipona spp). J. Chem. Ecol. 43, 1066–1072 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Chung, H. & Carroll, S. B. Wax, sex and the origin of species: Dual roles of insect cuticular hydrocarbons in adaptation and mating. BioEssays 37, 822–830 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Finck, J., Berdan, E. L., Mayer, F., Ronacher, B. & Geiselhardt, S. Divergence of cuticular hydrocarbons in two sympatric grasshopper species and the evolution of fatty acid synthases and elongases across insects. Nat. Sci. Rep. 6, 33695 (2016).ADS 
CAS 

Google Scholar 
Buellesback, J., Vetter, S. G. & Schmitt, T. Differences in the reliance on cuticular hydrocarbons as sexual signaling and species discrimination cues in parasitoid wasps. Front. Zool. 15, 22 (2018).
Google Scholar 
Medina-Medina, L.A. & Gonzalez-Acereto, J.A. La respuesta defensiva de Scaptotrigona pectoralis como un contundente escudo de protección contra las incursiones de Lestrimelitta niitkib dirigidas a otras especies de abejas sin aguijón. in VI Congreso Iberoamericano de Apicultura. 171–173. (1998).National Institute of Standards and Technology. Mass Spectral Library. (NIST/EPA/NIH, 2011).Tabachnick, B. G. & Fidell, L. S. Using Multivariate Statistics (Harper Collins College, 1996).
Google Scholar 
SAS Institute. SAS/STAT 9.2 User’s Guide. (SAS Institute Cary, 2008).Rasband, W.S. ImageJ. (U.S. National Institutes of Health, 1997–2012).Quezada-Euán, J.J.G., Paxton, R.J., Palmer, K.A., May-Itzá, W.D.J., Tay, W.T. & Oldroyd, B.P. Morphological and molecular characters reveal differentiation in a Neotropical social bee, Melipona beecheii (Apidae: Meliponini). Apidologie 38, 247–258 (2007).Rohlf, F. J. TPSDIG: Version 2.12. (New York State University, 2008).Klingenberg, C. P. MorphoJ: An integrated software package for geometric morphometrics. Mol. Ecol. Resour. 11, 353–357 (2011).PubMed 

Google Scholar 
Francoy, T.M., Grassi, M.L., Imperatriz-Fonseca, V.L., May-Itzá, W.D.J. & Quezada-Euán, J.J.G. Geometric morphometrics of the wing as a tool for assigning genetic lineages and geographic origin to Melipona beecheii (Hymenoptera: Meliponini). Apidologie 42, 499–507 (2011).Ratnasingham, S. & Hebert, P. D. N. A DNA-based registry for all animal species: the Barcode Index Number (BIN) system. PLoS ONE 8, e66213 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hurtado-Burillo, M., Ruiz, C., May-Itzá, W.D.J., Quezada-Eúan, J.J.G., & De la Rúa, P. Barcoding stingless bees: Genetic diversity of the economically important genus Scaptotrigona in Mesoamerica. Apidologie 44, 1–10 (2013).Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrigenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotech. 3, 294–299 (1994).CAS 

Google Scholar 
Packer, L., Sheffield, C.S., Gibbs, J., de Silva, N., Best, L.R. et al. The campaign to barcode the bees of the world: progress, problems and prognosis. in Memorias VI Congreso Mesoamericano Sobre Abejas Nativas, Guatemala. 178–180. (2009).Tamura, K., Stecher, G., & Kumar, S. MEGA11: Molecular evolutionary genetics analysis version 11. Mol. Biol. Evolut. https://doi.org/10.1093/molbev/msab120. (2021)Oliveira, F.F.d. & Marchi, P. Três espécies novas de Lestrimelitta Friese (Hymenoptera, Apidae) da Costa Rica, Panamá e Guiana Francesa. Rev. Bras. Entomol. 49, 1–6 (2005).González, V. H. & Griswold, T. L. New species and previously unknown males of neotropical cleptobiotic stingless bees (Hymenoptera, Apidae, Lestrimelitta). Caldasia 34, 227–245 (2012).
Google Scholar 
Marchi, P. & Melo, G.A.R. Revisão taxonômica das espêcies brasileiras do gênero Lestrimelitta Friese (Hymenoptera, Apidae, Meliponina). Rev. Bras. Entomol. 50, 6–30 (2006).Gonzalez, V., Rasmussen, C. & Velasquez, A. Una especie nueva de Lestrimelitta y un cambio de nombre en Lasioglossum (Hymenoptera: Apidae, Halictidae). Rev. Colomb. Entomol. 36, 319–324 (2010).
Google Scholar 
Ratnasingham, S. & Hebert, P. D. N. BOLD: The barcode of life data system (https://www.barcodinglife.org). Mol. Ecol. Notes 7, 355–364 (2007).Ayala, R. Revisión de las abejas sin aguijón de México (Hymenoptera: Apidae: Meliponini). Folia Entomol. Mexicana 106, 1–123 (1999).
Google Scholar 
Ruano, F. & Tinaut, A. The assault process of the slave-making ant Rossomyrmex minuchae (Hymenoptera: Formicidae). Sociobiology 43, 201–209 (2004).
Google Scholar 
Errard, C. et al. Coevolution-driven cuticular hydrocarbon variation between the slave-making ant Rossomyrmex minuchae and its host Proformica longiseta (Hymenoptera: Formicidae). Chemoecology 16, 235–240 (2006).CAS 

Google Scholar 
Dettner, K. & Liepert, C. Chemical mimicry and camouflage. Annu. Rev. Entomol. 39, 129–154 (1994).CAS 

Google Scholar 
Lenoir, A., d’Ettorre, P. & Errard, C. Chemical ecology and social parasitism in ants. Annu. Rev. Entomol. 46, 573–599 (2001).CAS 
PubMed 

Google Scholar 
Von Beeren, C., Pohl, S. & Witte, V. On the use of adaptive resemblance terms in chemical ecology. Psyche 2012, 635761 (2012).Lambardi, D., Dani, F. R., Turillazzi, S. & Boomsma, J. J. Chemical mimicry in an incipient leaf-cutting ant social parasite. Behav. Ecol. Sociobiol. 61, 843–851 (2007).
Google Scholar 
Uboni, A., Bagnères, A. G., Christidès, J. P. & Lorenzi, M. C. Cleptoparasites, social parasites and a common host: Chemical insignificance for visiting host nests, chemical mimicry for living in. J. Insect Physiol. 58, 1259–1264 (2012).CAS 
PubMed 

Google Scholar 
Quezada-Euán, J. J. G. et al. Body size differs in workers produced across time and is associated with variation in the quantity and composition of larval food in Nannotrigona perilampoides (Hymenoptera, Meliponini). Insect. Soc. 58, 31–38 (2011).
Google Scholar 
Nunes, T. M., Mateus, S., Turatti, I. C., Morgan, E. & Zucchi, R. Nestmate recognition in the stingless bee Frieseomelitta varia (Hymenoptera, Apidae, Meliponini): Sources of chemical signals. Anim. Behav. 81, 463–467 (2011).
Google Scholar 
Gutiérrez, E., Ruiz, D., Solís, T., May-Itzá, W.d.J., Moo-Valle, H. & Quezada-Euán, J.J.G. Does larval food affect cuticular profiles and recognition in eusocial bees? A test on Scaptotrigona gynes (Hymenoptera: Meliponini). Behav. Ecol. Sociobiol. 70, 871–879 (2016).Jones, S. M. et al. The role of wax and resin in the nestmate recognition system of a stingless bee, Tetragonisca angustula. Behav. Ecol. Sociobiol. 66, 1–12 (2012).
Google Scholar 
Leonhardt, S.D. Chemical ecology of stingless bees. J. Chem. Ecol. 43, 385–402 (2021).Akino, T. Chemical strategies to deal with ants: a review of mimicry, camouflage, propaganda, and phytomimesis by ants (Hymenoptera: Formicidae) and other arthropods. Myrmecol. News 11, 173–181 (2008).
Google Scholar 
Lenoir, A., Hefetz, A., Simon, T. & Soroker, V. Comparative dynamics of gestalt odour formation in two ant species Camponotus fellah and Aphaenogaster senilis (Hymenoptera: Formicidae). Physiol. Entomol. 26, 275–283 (2001).
Google Scholar 
Von Beeren, C. et al. Chemical and behavioral integration of army ant-associated rove beetles—A comparison between specialists and generalists. Front. Zool. 15, 8 (2018).
Google Scholar 
Kather, R. & Martin, S. J. Cuticular hydrocarbon profiles as a taxonomic tool: Advantages, limitations and technical aspects. Physiol. Entomol. 37, 25–32 (2012).CAS 

Google Scholar 
Menzel, F., Blaimer, B. B. & Schmitt, T. How do cuticular hydrocarbons evolve? Physiological constraints and climatic and biotic selection pressures act on a complex functional trait. Proc. R. Soc. B-Biol. Sci. 284, 20161727 (2017).
Google Scholar 
Savolainen, R. & Vepsäläinen, K. Sympatric speciation through intraspecific social parasitism. Proc. Nat. Acad. Sci. 100, 7169–7174 (2003).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hartke, J., Sprenger, P.P., Sahm, J, Winterberg, H., Orivel, J. et al. Cuticular hydrocarbons as potential mediators of cryptic species divergence in a mutualistic ant association. Ecol. Evolut. 9, 9160–9176 (2019).Doebeli, M. & Dieckmann, U. Evolutionary branching and sympatric speciation caused by different types of ecological interactions. Am. Nat. 156, S77–S101 (2000).PubMed 

Google Scholar 
Thibert-Plante, X. & Gavrilets, S. Evolution of mate choice and the so-called magic traits in ecological speciation. Ecol. Lett. 16, 1004–1013 (2013).PubMed 
PubMed Central 

Google Scholar 
Cabej, N. R. Epigenetic Principles of Evolution (Elsevier, 2012).
Google Scholar 
Quezada-Euán, J. J. G. Stingless Bees of Mexico: The Biology, Management and Conservation of an Ancient Heritage (Springer, 2018).
Google Scholar 
Von Zuben, L. G. et al. Interspecific chemical communication in raids of the robber bee Lestrimelitta limao. Insect. Soc. 63, 339–347 (2016).
Google Scholar  More

Ley, R. E. et al. Evolution of mammals and their gut microbes. Science 320, 1647–1651 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Thaiss, C. A., Zmora, N., Levy, M. & Elinav, E. The microbiome and innate immunity. Nature 535, 65–74 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Ezenwa, V. O., Gerardo, N. M., Inouye, D. W., Medina, M. & Xavier, J. B. Microbiology. Animal behavior and the microbiome. Science 338, 198–199 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sampson, T. R. & Mazmanian, S. K. Control of brain development, function, and behavior by the microbiome. Cell Host Microbe. 17, 565–576 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Voigt, R. M., Forsyth, C. B., Green, S. J., Engen, P. A. & Keshavarzian, A. Circadian rhythm and the gut microbiome. Int. Rev. Neurobiol. 131, 193–205 (2016).CAS 
PubMed 

Google Scholar 
Backhed, F. Programming of host metabolism by the gut microbiota. Endocr. Abstr. https://doi.org/10.1530/endoabs.32.s20.2 (2013).Article 

Google Scholar 
Mallott, E. K., Borries, C., Koenig, A., Amato, K. R. & Lu, A. Reproductive hormones mediate changes in the gut microbiome during pregnancy and lactation in Phayre’s leaf monkeys. Sci. Rep. 10, 9961 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Miller, E. A., Livermore, J. A., Alberts, S. C., Tung, J. & Archie, E. A. Ovarian cycling and reproductive state shape the vaginal microbiota in wild baboons. Microbiome. 5, 8 (2017).PubMed 
PubMed Central 

Google Scholar 
Gomez-Arango, L. F. et al. Connections between the gut microbiome and metabolic hormones in early pregnancy in overweight and obese women. Diabetes 65, 2214–2223 (2016).CAS 
PubMed 

Google Scholar 
Shin, J.-H. et al. Serum level of sex steroid hormone is associated with diversity and profiles of human gut microbiome. Res. Microbiol. 170, 192–201 (2019).CAS 
PubMed 

Google Scholar 
Burokas, A., Moloney, R. D., Dinan, T. G. & Cryan, J. F. Microbiota regulation of the mammalian gut–brain axis. Adv. Appl. Microbiol. 91, 1–62. (2015).Sudo, N. The hypothalamic–pituitary–adrenal axis and gut microbiota. Gut–Brain Axis. https://doi.org/10.1016/b978-0-12-802304-4.00013-x (2016).Article 

Google Scholar 
Sapolsky, R. M., Romero, L. M. & Munck, A. U. How do glucocorticoids influence stress responses? Integrating permissive, suppressive, stimulatory, and preparative actions. Endocr. Rev. 21, 55–89 (2000).CAS 
PubMed 

Google Scholar 
Hau, M., Casagrande, S., Ouyang, J. Q. & Baugh, A. T. Glucocorticoid-mediated phenotypes in vertebrates: Multilevel variation and evolution. Adv. Stud. Behav. 48, 41–115 (2016).
Google Scholar 
Sprague, R. S. & Breuner, C. W. Timing of fledging is influenced by glucocorticoid physiology in Laysan Albatross chicks. Horm. Behav. 58, 297–305 (2010).CAS 
PubMed 

Google Scholar 
Fletcher, Q. E., Dantzer, B. & Boonstra, R. The impact of reproduction on the stress axis of free-living male northern red backed voles (Myodes rutilus). Gen. Comp. Endocrinol. 224, 136–147 (2015).CAS 
PubMed 

Google Scholar 
Romero, L. M. & Wikelski, M. Corticosterone levels predict survival probabilities of Galapagos marine iguanas during El Nino events. Proc. Natl. Acad. Sci. USA. 98, 7366–7370 (2001).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chevalier, C. et al. Gut microbiota orchestrates energy homeostasis during cold. Cell 163, 1360–1374 (2015).CAS 
PubMed 

Google Scholar 
Amato, K. R. et al. Habitat degradation impacts black howler monkey (Alouatta pigra) gastrointestinal microbiomes. ISME J. 7, 1344–1353 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Baniel, A. et al. Seasonal shifts in the gut microbiome indicate plastic responses to diet in wild geladas. Microbiome. 9, 26 (2021).PubMed 
PubMed Central 

Google Scholar 
Kohl, K. D., Amaya, J., Passement, C. A., Dearing, M. D. & McCue, M. D. Unique and shared responses of the gut microbiota to prolonged fasting: A comparative study across five classes of vertebrate hosts. FEMS Microbiol. Ecol. 90, 883–894 (2014).CAS 
PubMed 

Google Scholar 
Stecher, B. et al. Like will to like: Abundances of closely related species can predict susceptibility to intestinal colonization by pathogenic and commensal bacteria. PLoS Pathog. https://doi.org/10.1371/journal.ppat.1000711 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Das, B. & Nair, G. B. Homeostasis and dysbiosis of the gut microbiome in health and disease. J. Biosci. 44(5), 1–8 (2019). https://www.ncbi.nlm.nih.gov/pubmed/31719226.CAS 

Google Scholar 
Noguera, J. C., Aira, M., Pérez-Losada, M., Domínguez, J. & Velando, A. Glucocorticoids modulate gastrointestinal microbiome in a wild bird. R. Soc. Open Sci. https://doi.org/10.1098/rsos.171743 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
UrenWebster, T. M., Rodriguez-Barreto, D., Consuegra, S. & GarciadeLeaniz, C. Cortisol-related signatures of stress in the fish microbiome. Front. Microbiol. 11, 1621 (2020).
Google Scholar 
Stothart, M. R., Palme, R. & Newman, A. E. M. It’s what’s on the inside that counts: stress physiology and the bacterial microbiome of a wild urban mammal. Proc. Biol. Sci. 286, 20192111 (2019).PubMed 
PubMed Central 

Google Scholar 
Vlčková, K. et al. Impact of stress on the gut microbiome of free-ranging western lowland gorillas. Microbiology 164, 40–44 (2018).PubMed 

Google Scholar 
Dantzer, B. et al. Density triggers maternal hormones that increase adaptive offspring growth in a wild mammal. Science 340, 1215–1217 (2013).ADS 
CAS 
PubMed 

Google Scholar 
Sarkar, A. et al. Microbial transmission in animal social networks and the social microbiome. Nat. Ecol. Evol. 4, 1020–1035 (2020).PubMed 

Google Scholar 
Kruuk, L. E. B., Merilä, J. & Sheldon, B. C. When environmental variation short-circuits natural selection. Trends Ecol. Evol. 18, 207–209 (2003).
Google Scholar 
Stinchcombe, J. R. et al. Testing for environmentally induced bias in phenotypic estimates of natural selection: Theory and practice. Am. Nat. 160, 511–523 (2002).PubMed 

Google Scholar 
Rausher, M. D. The measurement of selection on quantitative traits: Biases due to environmental covariances between traits and fitness. Evolution 46, 616–626 (1992).PubMed 

Google Scholar 
Lamontagne, J. M. & Boutin, S. Local-scale synchrony and variability in mast seed production patterns of Picea glauca. J. Ecol. https://doi.org/10.1111/j.1365-2745.2007.01266.x (2007).Article 

Google Scholar 
Fletcher, Q. E. et al. Reproductive timing and reliance on hoarded capital resources by lactating red squirrels. Oecologia https://doi.org/10.1007/s00442-013-2699-3 (2013).Article 
PubMed 

Google Scholar 
Fletcher, Q. E. et al. The functional response of a hoarding seed predator to mast seeding. Ecology 91, 2673–2683 (2010).PubMed 

Google Scholar 
Boutin, S. et al. Anticipatory reproduction and population growth in seed predators. Science 314, 1928–1930 (2006).ADS 
CAS 
PubMed 

Google Scholar 
Haines, J. A. et al. Sexually selected infanticide by male red squirrels in advance of a mast year. Ecology 99, 1242–1244 (2018).PubMed 

Google Scholar 
Dantzer, B., McAdam, A. G., Humphries, M. M., Lane, J. E. & Boutin, S. Decoupling the effects of food and density on life-history plasticity of wild animals using field experiments: Insights from the steward who sits in the shadow of its tail, the North American red squirrel. J. Anim. Ecol. 89, 2397–2414 (2020).PubMed 

Google Scholar 
Hestbeck, J. B. A Mathematical Model of Population Regulation in Cyclic Mammals. Population Biology 290–297 (Springer, 1983).
Google Scholar 
Dantzer, B., Boutin, S., Humphries, M. M. & McAdam, A. G. Behavioral responses of territorial red squirrels to natural and experimental variation in population density. Behav. Ecol. Sociobiol. 66, 865–878 (2012).
Google Scholar 
Siracusa, E. et al. Familiarity with neighbours affects intrusion risk in territorial red squirrels. Anim. Behav. 133, 11–20 (2017).
Google Scholar 
Guindre-Parker, S. et al. Individual variation in phenotypic plasticity of the stress axis. Biol. Lett. 15, 20190260 (2019).PubMed 
PubMed Central 

Google Scholar 
Laughlin, D. & Grace, J. Discoveries and novel insights in ecology using structural equation modeling. Ideas Ecol. Evol. https://doi.org/10.24908/iee.2019.12.5.c (2019).Article 

Google Scholar 
Pugesek, B. H., Tomer, A. & von Eye, A. Structural Equation Modeling: Applications in Ecological and Evolutionary Biology (Cambridge University Press, 2003).MATH 

Google Scholar 
Pearl, J. The causal foundations of structural equation modeling. (2012). https://doi.org/10.21236/ada557445Leftwich, P. T., Clarke, N. V. E., Hutchings, M. I. & Chapman, T. Gut microbiomes and reproductive isolation in Drosophila. Proc. Natl. Acad. Sci. USA. 114, 12767–12772 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Markle, J. G. M. et al. Sex differences in the gut microbiome drive hormone-dependent regulation of autoimmunity. Science 339, 1084–1088 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Reveles, K. R., Patel, S., Forney, L. & Ross, C. N. Age-related changes in the marmoset gut microbiome. Am. J. Primatol. https://doi.org/10.1002/ajp.22960 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Dantzer, B. et al. Fecal cortisol metabolite levels in free-ranging North American red squirrels: Assay validation and the effects of reproductive condition. Gen. Comp. Endocrinol. 167, 279–286 (2010).CAS 
PubMed 

Google Scholar 
Fletcher, Q. E. et al. Seasonal stage differences overwhelm environmental and individual factors as determinants of energy expenditure in free-ranging red squirrels. Funct. Ecol. https://doi.org/10.1111/j.1365-2435.2012.01975.x (2012).Article 

Google Scholar 
Lane, J. E., Boutin, S., Gunn, M. R., Slate, J. & Coltman, D. W. Female multiple mating and paternity in free-ranging North American red squirrels. Anim. Behav. 75, 1927–1937 (2008).
Google Scholar 
Ren, T. et al. Seasonal, spatial, and maternal effects on gut microbiome in wild red squirrels. Microbiome. 5, 163 (2017).PubMed 
PubMed Central 

Google Scholar 
Backhans, A., Fellström, C. & Lambertz, S. T. Occurrence of pathogenic Yersinia enterocolitica and Yersinia pseudotuberculosis in small wild rodents. Epidemiol. Infect. 139, 1230–1238 (2011).CAS 
PubMed 

Google Scholar 
Bižanov, G. & Dobrokhotova, N. D. Experimental infection of ground squirrels (Citellus pygmaeus Pallas) with Yersinia pestis during hibernation. J. Infect. 54, 198–203 (2007).PubMed 

Google Scholar 
Stothart, M. R. et al. Stress and the microbiome: Linking glucocorticoids to bacterial community dynamics in wild red squirrels. Biol. Lett. 12, 20150875 (2016).PubMed 
PubMed Central 

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

Google Scholar 
Rocca, J. D., Simonin, M., Bernhardt, E. S., Washburne, A. D. & Wright, J. P. Rare microbial taxa emerge when communities collide: Freshwater and marine microbiome responses to experimental mixing. Ecology https://doi.org/10.1002/ecy.2956 (2020).Article 
PubMed 

Google Scholar 
Shade, A. et al. Conditionally rare taxa disproportionately contribute to temporal changes in microbial diversity. MBio https://doi.org/10.1128/mbio.01371-14 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Dinan, T. G. & Cryan, J. F. The microbiome–gut–brain axis in health and disease. Gastroenterol. Clin. N. Am. 46, 77–89 (2017).
Google Scholar 
Claus, S. P. et al. Colonization-induced host–gut microbial metabolic interaction. MBio 2, e00271-e310 (2011).PubMed 
PubMed Central 

Google Scholar 
Bangsgaard Bendtsen, K. M. et al. Gut microbiota composition is correlated to grid floor induced stress and behavior in the BALB/c mouse. PLoS ONE 7, e46231 (2012).ADS 
PubMed 
PubMed Central 

Google Scholar 
Amato, K. R. et al. The gut microbiota appears to compensate for seasonal diet variation in the wild black howler monkey (Alouatta pigra). Microb. Ecol. 69, 434–443 (2015).CAS 
PubMed 

Google Scholar 
McLaren, M. R. & Callahan, B. J. Pathogen resistance may be the principal evolutionary advantage provided by the microbiome. Philos. Trans. R Soc. Lond. B Biol. Sci. 375, 20190592 (2020).PubMed 
PubMed Central 

Google Scholar 
Donohoe, D. R. et al. The microbiome and butyrate regulate energy metabolism and autophagy in the mammalian colon. Cell Metab. 13, 517–526 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rivera-Chávez, F. et al. Depletion of butyrate-producing clostridia from the gut microbiota drives an aerobic luminal expansion of salmonella. Cell Host Microbe. 19, 443–454 (2016).PubMed 
PubMed Central 

Google Scholar 
Meerburg, B. G. & Kijlstra, A. Role of rodents in transmission of Salmonella and Campylobacter. J. Sci. Food Agric. https://doi.org/10.1002/jsfa.3004 (2007).Article 

Google Scholar 
Jalal, M. S. et al. Antibiotic resistant zoonotic bacteria in Irrawaddy squirrel (Callosciurus pygerythrus). Vet. Med. Sci. 5, 260–268 (2019).CAS 
PubMed 

Google Scholar 
Petrosus, E., Silva, E. B., Lay, D. Jr. & Eicher, S. D. Effects of orally administered cortisol and norepinephrine on weanling piglet gut microbial populations and Salmonella passage. J. Anim. Sci. 96, 4543–4551 (2018).PubMed 
PubMed Central 

Google Scholar 
Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.12512 (2016).Article 

Google Scholar 
Raulo, A. et al. Social networks strongly predict the gut microbiota of wild mice. ISME J. https://doi.org/10.1038/s41396-021-00949-3 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Phillips, C. D. et al. Microbiome structural and functional interactions across host dietary niche space. Integr. Comp. Biol. 57, 743–755 (2017).CAS 
PubMed 

Google Scholar 
Galley, J. D. et al. Exposure to a social stressor disrupts the community structure of the colonic mucosa-associated microbiota. BMC Microbiol. 14, 189 (2014).PubMed 
PubMed Central 

Google Scholar 
Wu, C.-S. et al. Age-dependent remodeling of gut microbiome and host serum metabolome in mice. Aging 13, 6330–6345 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Altmann, J., Gesquiere, L., Galbany, J., Onyango, P. O. & Alberts, S. C. Life history context of reproductive aging in a wild primate model. Ann. NY Acad. Sci. https://doi.org/10.1111/j.1749-6632.2010.05531.x (2010).Article 
PubMed 

Google Scholar 
Sylvia, K. E., Jewell, C. P., Rendon, N. M., St John, E. A. & Demas, G. E. Sex-specific modulation of the gut microbiome and behavior in Siberian hamsters. Brain Behav. Immun. 60, 51–62 (2017).PubMed 

Google Scholar 
Grieneisen, L. E., Livermore, J., Alberts, S., Tung, J. & Archie, E. A. Group living and male dispersal predict the core gut microbiome in wild baboons. Integr. Comp. Biol. 57, 770–785 (2017).PubMed 
PubMed Central 

Google Scholar 
Peckett, A. J., Wright, D. C. & Riddell, M. C. The effects of glucocorticoids on adipose tissue lipid metabolism. Metabolism 60, 1500–1510 (2011).CAS 
PubMed 

Google Scholar 
Wu, T. et al. Chronic glucocorticoid treatment induced circadian clock disorder leads to lipid metabolism and gut microbiota alterations in rats. Life Sci. 192, 173–182 (2018).CAS 
PubMed 

Google Scholar 
Deaver, J. A., Eum, S. Y. & Toborek, M. Circadian disruption changes gut microbiome taxa and functional gene composition. Front. Microbiol. 9, 737 (2018).PubMed 
PubMed Central 

Google Scholar 
Schroeder, B. O. Fight them or feed them: How the intestinal mucus layer manages the gut microbiota. Gastroenterol. Rep. 7, 3–12 (2019).
Google Scholar 
Huang, E. Y. et al. Using corticosteroids to reshape the gut microbiome: Implications for inflammatory bowel diseases. Inflamm. Bowel Dis. 21, 963–972 (2015).PubMed 

Google Scholar 
Carabotti, M., Scirocco, A., Maselli, M. A. & Severi, C. The gut–brain axis: Interactions between enteric microbiota, central and enteric nervous systems. Ann. Gastroenterol. Hepatol. 28, 203–209 (2015).
Google Scholar 
de Weerth, C. Do bacteria shape our development? Crosstalk between intestinal microbiota and HPA axis. Neurosci. Biobehav. Rev. 83, 458–471 (2017).PubMed 

Google Scholar 
Luo, Y. et al. Gut microbiota regulates mouse behaviors through glucocorticoid receptor pathway genes in the hippocampus. Transl. Psychiatry. 8, 187 (2018).PubMed 
PubMed Central 

Google Scholar 
Cryan, J. F. et al. The microbiota–gut–brain axis. Physiol. Rev. 99, 1877–2013 (2019).CAS 
PubMed 

Google Scholar 
McAdam, A. G., Boutin, S., Sykes, A. K. & Humphries, M. M. Life histories of female red squirrels and their contributions to population growth and lifetime fitness. Ecoscience 14, 362–369 (2007).
Google Scholar 
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods. 7, 335–336 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 

Google Scholar 
Sheriff, M. J., Dantzer, B., Delehanty, B., Palme, R. & Boonstra, R. Measuring stress in wildlife: Techniques for quantifying glucocorticoids. Oecologia 166, 869–887 (2011).ADS 
PubMed 

Google Scholar 
Touma, C., Sachser, N., Möstl, E. & Palme, R. Effects of sex and time of day on metabolism and excretion of corticosterone in urine and feces of mice. Gen. Comp. Endocrinol. 130, 267–278 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
Van Kesteren, F. et al. Experimental increases in glucocorticoids alter function of the HPA axis in wild red squirrels without negatively impacting survival and reproduction. Physiol. Biochem. Zool. 92, 445–458 (2019).PubMed 

Google Scholar 
Paradis, E., Claude, J. & Strimmer, K. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
McMurdie, P. J. & Holmes, S. Package, “phyloseq”. Gan. 2, 7 (2013).
Google Scholar 
Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).CAS 

Google Scholar 
Zhang, X. & Yi, N. NBZIMM: Negative binomial and zero-inflated mixed models, with application to microbiome/metagenomics data analysis. BMC Bioinform. 21, 488 (2020).CAS 

Google Scholar 
Jones, S. E. & Lennon, J. T. Dormancy contributes to the maintenance of microbial diversity. Proc. Natl. Acad. Sci. USA. 107, 5881–5886 (2010).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar  More

Mogollón JM, Bouwman AF, Beusen AH, Lassaletta L, van Grinsven HJ, Westhoek H. More efficient phosphorus use can avoid cropland expansion. Nat Food. 2021;2:509–18.Article 

Google Scholar 
Goldhammer T, Brüchert V, Ferdelman TG, Zabel M. Microbial sequestration of phosphorus in anoxic upwelling sediments. Nat Geosci. 2010;3:557–61.CAS 
Article 

Google Scholar 
Oliverio AM, Bissett A, McGuire K, Saltonstall K, Turner BL, Fierer N. The role of phosphorus limitation in shaping soil bacterial communities and their metabolic capabilities. mBio. 2020;11:e01718–20.CAS 
Article 

Google Scholar 
Wu X, Rensing C, Han D, Xiao KQ, Dai Y, Tang Z, et al. Genome-resolved metagenomics reveals distinct phosphorus acquisition strategies between soil microbiomes. mSystems. 2022;7:e01107–21.PubMed Central 

Google Scholar 
Long XE, Yao H, Huang Y, Wei W, Zhu YG. Phosphate levels influence the utilisation of rice rhizodeposition carbon and the phosphate-solubilizing microbial community in a paddy soil. Soil Bio Biochem. 2018;118:103–14.CAS 
Article 

Google Scholar 
Dai Z, Liu G, Chen H, Chen C, Wang J, Ai S, et al. Long-term nutrient inputs shift soil microbial functional profiles of phosphorus cycling in diverse agroecosystems. ISME J. 2020;14:757–70.CAS 
Article 

Google Scholar 
Liang J, Liu J, Jia P, Yang T, Zeng Q, Zhang S, et al. Novel phosphate-solubilizing bacteria enhance soil phosphorus cycling following ecological restoration of land degraded by mining. ISME J. 2020;14:1600–13.CAS 
Article 

Google Scholar 
Willsky GR, Bennett RL, Malamy MH. Inorganic phosphate transport in Escherichia coli: involvement of two genes which play a role in alkaline phosphatase regulation. J Bacteriol. 1973;113:529–39.CAS 
Article 

Google Scholar 
Wanner BL. Gene regulation by phosphate in enteric bacteria. J Cell Biochem. 1993;51:47–54.CAS 
Article 

Google Scholar 
Li J, Lu J, Wang H, Fang Z, Wang X, Feng S, et al. A comprehensive synthesis unveils the mysteries of phosphate‐solubilizing microbes. Biol Rev. 2021;96:2771–93.Article 

Google Scholar 
Hessen DO, Jeyasingh PD, Neiman M, Weider LJ. Genome streamlining and the elemental costs of growth. Trends Ecol Evol. 2010;25:75–80.Article 

Google Scholar 
Li J, Mau RL, Dijkstra P, Koch BJ, Schwartz E, Liu XA, et al. Predictive genomic traits for bacterial growth in culture versus actual growth in soil. ISME J. 2019;13:2162–72.Article 

Google Scholar 
Giovannoni SJ, Cameron Thrash J, Temperton B. Implications of streamlining theory for microbial ecology. ISME J. 2014;8:1553–65.Article 

Google Scholar 
Ye L, Mei R, Liu WT, Ren H, Zhang XX. Machine learning-aided analyses of thousands of draft genomes reveal specific features of activated sludge processes. Microbiome. 2020;8:1–13.Article 

Google Scholar 
Farhat MB, Boukhris I, Chouayekh H. Mineral phosphate solubilization by Streptomyces sp. CTM396 involves the excretion of gluconic acid and is stimulated by humic acids. FEMS Microbiol Lett. 2015;362:1–8.Article 

Google Scholar 
Bücking H, Shachar-Hill Y. Phosphate uptake, transport and transfer by the arbuscular mycorrhizal fungus Glomus intraradices is stimulated by increased carbohydrate availability. New Phytol. 2005;165:899–912.Article 

Google Scholar 
Zhang L, Xu M, Liu Y, Zhang F, Hodge A, Feng G. Carbon and phosphorus exchange may enable cooperation between an arbuscular mycorrhizal fungus and a phosphate‐solubilizing bacterium. New Phytol. 2016;210:1022–32.CAS 
Article 

Google Scholar 
Spohn M, Kuzyakov Y. Phosphorus mineralization can be driven by microbial need for carbon. Soil Biol Biochem. 2013;61:69–75.CAS 
Article 

Google Scholar 
Huang Y, Dai Z, Lin J, Li D, Ye H, Dahlgren RA, et al. Labile carbon facilitated phosphorus solubilization as regulated by bacterial and fungal communities in Zea mays. Soil Biol Biochem. 2021;163:108465.CAS 
Article 

Google Scholar 
Yao Q, Li Z, Song Y, Wright SJ, Guo X, Tringe SG, et al. Community proteogenomics reveals the systemic impact of phosphorus availability on microbial functions in tropical soil. Nat Ecol Evol. 2018;2:499–509.Article 

Google Scholar  More

Geoffrey, E. Sur les Phyllostomes et les Megadermes, deux Genres de la famille des Chauve-souris. in Annales du Museum d’histoire (ed. Dufour, G.) vol. 15, 181 (d’Ocagne, 1810).Wilson, D. E. & Mittermeier, R. A. Bats. in Handbook of the Mammals of the World. Vol. 9. (eds. Wilson, D. E. & Mittermeier, R. A.) 1008 (Springer International Publishing, 2019).Hilaire, É. G. S., Pupuya, I. D. E., Del, R. & Higgins, L. B. O. Ampliación del rango de distribución sur de Desmodus rotundus. Boletín del Mus. Nac. Hist. Nat. 68, 5–12 (2019).
Google Scholar 
Kwon, M. & Gardner, A. L. Subfamily Desmodontinae. in Mammals of South America, Volume 1: Marsupials, Xenarthrans, Shrews and Bats (ed. Gardner, A. L.) 218–223 (The University of Chicago Press, 2008).Arellano-Sota, C. Vampire bat-transmitted rabies in cattle. Rev. Infect. Dis. 10, 707–709 (1988).
Google Scholar 
Fernandes, M. E. B., Da Costa, L. J. C., De Andrade, F. A. G. & Silva, L. P. Rabies in humans and non-human in the state of Pará, Brazilian Amazon. Brazilian J. Infect. Dis. 17, 251–253 (2013).
Google Scholar 
Andrade, F. A. G., Franca, E. S., Souza, V. P., Barreto, M. S. O. D. & Fernandes, M. E. B. Spatial and temporal analysis of attacks by common vampire bats (Desmodus rotundus) on humans in the rural Brazilian Amazon basin. Acta Chiropterologica 17, 393–400 (2015).
Google Scholar 
Greenhall, A. M., Joermann, G. & Schmidt, U. Desmodus rotundus. Mamm. Species 202, 1–6 (1983).
Google Scholar 
Herrera, L. G., Fleming, T. H. & Sternberg, L. S. Trophic relationships in a neotropical bat community: A preliminary study using carbon and nitrogen isotopic signatures. Trop. Ecol. 39, 23–29 (1998).
Google Scholar 
Dantas Torres, F., Valença, C. & De Andrade Filho, G. V. First record of Desmodus rotundus in urban area from the city of Olinda, Pernambuco, Northeastern Brazil: A case report. Rev. Inst. Med. Trop. Sao Paulo 47, 107–108 (2005).PubMed 

Google Scholar 
Flores-Crespo, R. & Arellano-Sota, C. Biology and control of the vampire bat. in The natural history of rabies (ed. Baer, G. M.) 461–476 (CRC Press Inc, 1991).Flores-Crespo, R. & Arellano-Sota, C. Biology and control of the vampire bat. Nat. Hist. Rabies, 2nd Ed. 10, 461–476 (2017).
Google Scholar 
Bolívar-Cimé, B., Flores-Peredo, R., García-Ortíz, S. A., Murrieta-Galindo, R. & Laborde, J. Influence of landscape structure on the abundance of Desmodus rotundus (Geoffroy 1810) in Northeastern Yucatan, Mexico. Ecosistemas y Recur. Agropecu. 6, 263 (2019).
Google Scholar 
Koopman, K. F. Systematics and distribution. in Natural History of Vampire Bats (eds. Greenhall, A. M. & Schmidt, U.) 4–28 (CRC Press, 1988).Dalquest, W. W. Natural history of the vampire bats of Eastern Mexico. Am. Midl. Nat. 53, 79–87 (1955).
Google Scholar 
Kalko, E. K. V. & Handley, C. O. Neotropical bats in the canopy: Diversity, community structure, and implications for conservation. Plant Ecol. 153, 319–333 (2001).
Google Scholar 
García-Morales, R., Badano, E. I. & Moreno, C. E. Response of neotropical bat assemblages to human land use. Conserv. Biol. 27, 1096–1106 (2013).PubMed 

Google Scholar 
Barquez, R.M., Perez, S., Miller, B. & Diaz, M. M. Desmodus rotundus. The IUCN Red List of Threatened Species 2015 e.T6510A21979045, https://doi.org/10.2305/IUCN.UK.2015-4.RLTS.T6510A21979045.en (2015).Becker, D. J. et al. Genetic diversity, infection prevalence, and possible transmission routes of Bartonella spp. in vampire bats. PLoS Negl. Trop. Dis. 12, e0006786 (2018).PubMed 
PubMed Central 

Google Scholar 
Brandão, P. E. et al. A coronavirus detected in the vampire bat Desmodus rotundus. Brazilian J. Infect. Dis. 12, 466–468 (2008).
Google Scholar 
Alves, R. S. et al. Detection of coronavirus in vampire bats (Desmodus rotundus) in southern Brazil. Transbound. Emerg. Dis. 00, 1–6 (2021).
Google Scholar 
Rocha, F. & Dias, R. A. The common vampire bat Desmodus rotundus (Chiroptera: Phyllostomidae) and the transmission of the rabies virus to livestock: A contact network approach and recommendations for surveillance and control. Prev. Vet. Med. 174, e104809 (2020).
Google Scholar 
Raoult, D. et al. Diagnosis of 22 new cases of Bartonella endocarditis. Ann. Intern. Med. 125, 646–652 (1996).CAS 
PubMed 

Google Scholar 
Raoult, D. et al. Outcome and treatment of Bartonella endocarditis. Arch. Int. Med. 163, 226–230 (2003).
Google Scholar 
Neely, B. A. et al. Surveying the vampire bat (Desmodus rotundus) serum proteome: A resource for identifying immunological proteins and detecting pathogens. J. Proteome Res. 20, 2547–2559 (2021).CAS 
PubMed 

Google Scholar 
Rupprecht, C. E., Hanlon, C. A. & Hemachudha, T. Rabies re-examined. Lancet Infect. Dis. 2, 327–343 (2002).PubMed 

Google Scholar 
World Health Organization. Rabies. WHO https://www.who.int/news-room/fact-sheets/detail/rabies (2020).Lee, D. N., Papeş, M. & Van Den Bussche, R. A. Present and potential future distribution of common vampire bats in the Americas and the associated risk to cattle. PLoS One 7, e42466 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Acha, P. N. & Malaga-Alba, A. Economic losses due to Desmodus rotundus. in Natural History of Vampire Bats (eds. Greenhall, A. M. & Schmidt, U.) 207–214 (CRC Press, 1968).Kotait, I. & Gonçalves, C. Manual Técnico MAPA – Controle da raiva dos herbívoros in Manual técnico dos herbívoros (Ministério da Agricultura, Pecuária e Abastecimento, 2009).Johnson, N., Aréchiga-Ceballos, N. & Aguilar-Setien, A. Vampire bat rabies: Ecology, epidemiology and control. Viruses 6, 1911–1928 (2014).PubMed 
PubMed Central 

Google Scholar 
Blackwood, J. C., Streicker, D. G., Altizer, S. & Rohani, P. Resolving the roles of immunity, pathogenesis, and immigration for rabies persistence in vampire bats. Proc. Natl. Acad. Sci. 110, 20837–20842 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Streicker, D. G. et al. Host-pathogen evolutionary signatures reveal dynamics and future invasions of vampire bat rabies. Proc. Natl. Acad. Sci. USA 113, 10926–10931 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Zarza, H., Martínez-Meyer, E., Suzán, G. & Ceballos, G. Geographic distribution of Desmodus rotundus in Mexico under current and future climate change scenarios: Implications for bovine paralytic rabies infection. Vet. Mex. 4, 3–16 (2017).
Google Scholar 
Hayes, M. A. & Piaggio, A. J. Assessing the potential impacts of a changing climate on the distribution of a rabies virus vector. PLoS One 13, e0192887 (2018).PubMed 
PubMed Central 

Google Scholar 
Nunez, G. B., Becker, D. J., Lawrence, R. L. & Plowright, R. K. Synergistic effects of grassland fragmentation and temperature on bovine rabies emergence. EcoHealth 17, 203–216 (2020).PubMed Central 

Google Scholar 
da Rosa, E. S. T. et al. Bat-transmitted human rabies outbreaks, Brazilian Amazon. Emerg. Infect. Dis. 12, 1197–1202 (2006).PubMed 
PubMed Central 

Google Scholar 
Rocha, S. M., de Oliveira, S. V., Heinemann, M. B. & Gonçalves, V. S. P. Epidemiological profile of wild rabies in Brazil (2002–2012). Transbound. Emerg. Dis. 64, 624–633 (2017).CAS 
PubMed 

Google Scholar 
Schneider, M. C. et al. Rabies transmitted by vampire bats to humans: An emerging zoonotic disease in Latin America? Pan Am. J. Public Health. 25, 260–269 (2009).
Google Scholar 
VERA. Vigilancia epidemiológica de la rabia en las Américas. Organ. Panam. la Salud. 34, 14–42 (2020).
Google Scholar 
World Health Organization. WHO expert consultation on rabies, second report. WHO Tech. Rep. Ser. 982, 1–139 (2013).
Google Scholar 
Gilbert, A. T. et al. Evidence of rabies virus exposure among humans in the Peruvian Amazon. Am. J. Trop. Med. Hyg. 87, 206–215 (2012).
Google Scholar 
Fahl, W. O. et al. Desmodus rotundus and Artibeus spp. bats might present distinct rabies virus lineages. Brazilian J. Infect. Dis. 16, 545–551 (2012).
Google Scholar 
Berger, F. et al. Rabies risk: Difficulties encountered during management of grouped cases of bat bites in 2 isolated villages in French Guiana. PLoS Negl. Trop. Dis. 7, e2258 (2013).PubMed 
PubMed Central 

Google Scholar 
Linhart, S. B., Flores Crespo, R. & Mitchell, G. C. Control de murciélagos vampiros por medio de un anticoagulante. Bull Pan Am Health Organ. 73, 100–109 (1972).CAS 

Google Scholar 
Streicker, D. G. et al. Ecological and anthropogenic drivers of rabies exposure in vampire bats: Implications for transmission and control. Proc. R. Soc. B Biol. Sci. 279, 3384–3392 (2012).
Google Scholar 
Henry, M., Cosson, J. F. & Pons, J. M. Modelling multi-scale spatial variation in species richness from abundance data in a complex neotropical bat assemblage. Ecol. Modell. 221, 2018–2027 (2010).
Google Scholar 
Bárcenas-Reyes, I. et al. Comportamiento epidemiológico de la rabia paralítica bovina en la región central de México, 2001-2013. Pan Am. J. Public Health. 38, 396–402 (2015).
Google Scholar 
Benavides, J. A., Valderrama, W. & Streicker, D. G. Spatial expansions and travelling waves of rabies in vampire bats. Proc. R. Soc. B 283, e20160328 (2016).
Google Scholar 
Van de Vuurst, P. et al. Desmodus rotundus Occurrence Record Database. figshare https://doi.org/10.6084/m9.figshare.15025296.v6 (2021).Wieczorek, J. et al. Darwin core: An evolving community-developed biodiversity data standard. PLoS One 7, e29715 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Robertson, T. et al. The GBIF integrated publishing toolkit: Facilitating the efficient publishing of biodiversity data on the internet. PLoS One 9, e102623 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Marcial, L. H. & Hemminger, B. M. Scientific data repositories on the web: An initial survey. J. Am. Soc. Inf. Sci. Technol. 61, 2029–2048 (2010).
Google Scholar 
GBIF.org. GBIF Occurrence Download. Global Biodiversity Information Facility. GBIF https://doi.org/10.15468/dl.my64ap (2020).Grattarola, F. et al. Biodiversidata: An open-access biodiversity database for Uruguay. Biodivers. Data J. 7, e36226 (2019).PubMed 
PubMed Central 

Google Scholar 
CRIA. speciesLink Data Download. Centro de Referência em Informação Ambiental. https://specieslink.net/search/download/20210909104533-0016416 (2021).Cambon, J. Package ‘ tidygeocoder’. CRAN 2–13 (2021).Wickham, H., Francois, R., Henry, L. & Muller, K. Package ‘ dplyr’: A grammar of data manipulation. CRAN 3–88 (2020).R Core Team. R: A language and environment for statisitical computing. https://www.R-project.org/ (2019).Zizka, A. et al. Package ‘ CoordinateCleaner’. CRAN 13-152 (2019).Wickham, H. ggplot2: Elegant graphics for data analysis. (Springer-Verlag, 2016).ESRI Inc. ArcGIS Desktop Pro, version 2.4.3. https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview (2019). More