Ecology

Bjornlund, V., Bjornlund, H. & Van Rooyen, A. F. Int. J. Water Resour. Dev. 36 (Suppl. 1), S20–S53 (2020).World Population Prospects: the 2017 Revision (United Nations Department of Economic and Social Affairs Population Division, 2017).Affokpon, A. et al. In 69th International Symposium on Crop Protection (2017).Adesiyan, S. O. & Odihirin, R. A. Nematologica 24, 132–134a (1978).Article 

Google Scholar 
Gao, Q. K. Chinese Vegetables 5, 24–25 (1992).
Google Scholar 
Pirzada, T. et al. Nat. Food https://doi.org/10.1038/s43016-023-00695-z (2023).Article 

Google Scholar 
Hague, N. G. M. Nematodes, The Unseen Enemy: A Guide to Nematode Damage (Du Pont, 1980).Zasada, I. A. et al. Annu. Rev. Phytopathol. 48, 311–328 (2010).Article 
CAS 

Google Scholar 
Ochola, J. et al. Nat. Sustain. 5, 425–433 (2022).Article 

Google Scholar 
Pirzada, T. et al. ACS Sustain. Chem. Eng. 8, 6590–6600 (2020).Article 
CAS 

Google Scholar 
Cao, J. et al. Cellulose 23, 673–687 (2016).Article 
CAS 

Google Scholar  More

Gopalaswamy, A. M. et al. Nat. Ecol. Evol. 6, 1794–1795 (2022).Article 
PubMed 

Google Scholar 
Sandom, C., Donlan, C. J., Svenning, J. C. & Hansen, D. in Key Topics In Conservation Biology 2 (eds MacDonald, D. W. & Willis, K. J.) 430–451 (2013).Ripple, W. J. et al. Science 343, 1241484 (2014).Article 
PubMed 

Google Scholar 
Jhala, Y. V., Ranjitsinh, M. K., Bipin, C. M. & Yadav, S. P. Action Plan For Introduction Of Cheetah In India (2021).Divyabhanusinh & Kazami J. Bombay Nat. Hist. Soc. 116, 22–43 (2019).
Google Scholar 
IUCN/SSG. Guidelines For Reintroductions And Other Conservation Translocations IUCN. Ecological Applications 20 (IUCN Species Survival Commission, 2013).Prost, S. et al. Mol. Ecol. 31, 4208–4223 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Buk, K. G., van der Merwe, V. C., Marnewick, K. & Funston, P. J. Conservation Of Severely Fragmented Populations: Lessons From The Transformation Of Uncoordinated Reintroductions Of Cheetahs (Acinonyx jubatus) Into A Managed Metapopulation With Self-Sustained Growth. Biodiversity And Conservation 27 (Springer Netherlands, 2018).Scientific Authority of South Africa. Gov. Gaz. Repub. South Africa 677, 1–4 (2021).Walker, E. H., Verschueren, S., Schmidt-Küntzel, A. & Marker, L. Oryx 56, 495–504 (2022).Article 

Google Scholar 
Tordiffe, A. S. W. et al. Disease Risk Analysis For Introduction Of Cheetahs (Acinonyx jubatus) To India (2022).Brugière, D., Chardonnet, B. & Scholte, P. Trop. Conserv. Sci. 8, 513–527 (2015).Article 

Google Scholar 
Jhala, Y. V. et al. Front. Ecol. Evol. 7, https://doi.org/10.3389/fevo.2019.00312 (2019).Jhala, Y. et al. People Nat. 3, 281–293 (2021).Article 

Google Scholar 
Hayward, M. W., O’Brien, J. & Kerley, G. I. H. Biol. Conserv. 139, 219–229 (2007).Article 

Google Scholar 
Ogutu, J. O., Owen-Smith, N., Piepho, H. P. & Said, M. Y. J. Zool. 285, 99–109 (2011).Article 

Google Scholar 
Houser, A. M., Somers, M. J. & Boast, L. K. J. Zool. 278, 108–115 (2009).Article 

Google Scholar  More

Ware, I. M. et al. Climate-driven reduction of genetic variation in plant phenology alters soil communities and nutrient pools. Glob. Change Biol. 25, 1514–1528 (2019).Article 
ADS 

Google Scholar 
Ware, I. M. et al. Climate-driven divergence in plant-microbiome interactions generates range-wide variation in bud break phenology. Commun. Biol. 4, 748 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Bayliss, S. L. J., Mueller, L. O., Ware, I. M., Schweitzer, J. A. & Bailey, J. K. Plant genetic variation drives geographic differences in atmosphere–plant–ecosystem feedbacks. Plant Environ. Int. 1, 166–180 (2020).Article 

Google Scholar 
Van Nuland, M. E. et al. Intraspecific trait variation across elevation predicts a widespread tree species’ climate niche and range limits. Ecol. Evol. 10, 3856–3867 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Davis, M. B. & Shaw, R. G. Range shifts and adaptive responses to quaternary climate change. Science 292, 673–679 (2001).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Hendry, A. P. Eco-Evolutionary Dynamics (Princeton University Press, 2017).Book 

Google Scholar 
Anstett, D. N., Branch, H. A. & Angert, A. L. Regional differences in rapid evolution during severe drought. Evol. Lett. 5, 130–142 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Grainger, T. N., Rudman, S. M., Schmidt, P. & Levine, J. M. Competitive history shapes rapid evolution in a seasonal climate. PNAS 118, e2015772118 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bokhorst, S., Bjerke, J. W., Street, L. E., Callaghan, T. V. & Phoenix, G. K. Impacts of multiple extreme winter warming events on sub-Arctic heathland: Phenology, reproduction, growth, and CO2 flux responses. Glob. Change Biol. 17, 2817–2830 (2011).Article 
ADS 

Google Scholar 
Anderson, J. T., Perera, N., Chowdhury, B. & Mitchell-Olds, T. Microgeographic patterns of genetic divergence and adaptation across environmental gradients in Boechera stricta (Brassicaceae). Am. Nat. 186, S60–S73 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Wooliver, R., Tittes, S. B. & Sheth, S. N. A resurrection study reveals limited evolution of thermal performance in response to recent climate change across the geographic range of the scarlet monkeyflower. Evolution 74, 1699–1710 (2020).Article 
PubMed 

Google Scholar 
McCormack, J. E., Huang, H. & Knowles, L. L. Sky Islands. in Encyclopedia of Islands (eds. Gillespie, R. G. & Clague, D. A.) 839–843 (2009).Knowles, J. F., Scott, R. L., Minor, R. L. & Barron-Gafford, G. A. Ecosystem carbon and water cycling from a sky island montane forest. Agric. For. Meteorol. 281, 107835 (2020).Article 
ADS 

Google Scholar 
Heald, W. Sky Islands (Van Nostrand, 1967).
Google Scholar 
DeBano, L. H. et al. Biodiversity and management of the Madrean Archipelago: The Sky Islands of southwestern United States and northwestern Mexico: 1994 September 19–23; Tucson, AZ. Gen Tech Rep RM-GTR-264. Fort Collins, CO: US Dep Agric For Serv, Rocky Mt For Range Exp Stn. 669 p. (1995).Pérez-Alquicira, J. et al. The role of historical factors and natural selection in the evolution of breeding systems of Oxalis alpina in the Sonoran desert ‘Sky Islands’. J. Evol. Biol. 23, 2163–2175 (2010).Article 
PubMed 

Google Scholar 
Wiens, J. J. et al. Climate change, extinction, and Sky Island biogeography in a montane lizard. Mol. Ecol. 28, 2610–2624 (2019).Article 
PubMed 

Google Scholar 
Pielou, E. C. After the Ice Age. The return of Life to Glaciated North America (The University of Chicago Press, 1991).Book 

Google Scholar 
Hosner, P. A., Nyári, Á. S. & Moyle, R. G. Water barriers and intra-island isolation contribute to diversification in the insular Aethopyga sunbirds (Aves: Nectariniidae). J. Biogeogr. 40, 1094–1106 (2013).Article 

Google Scholar 
Favé, M.-J. et al. Past climate change on Sky Islands drives novelty in a core developmental gene network and its phenotype. Bmc Evol. Biol. 15, 183 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Yanahan, A. D. & Moore, W. Impacts of 21st-century climate change on montane habitat in the Madrean Sky Island Archipelago. Divers. Distrib. 25, 1625–1638 (2019).Article 

Google Scholar 
Oline, D. K., Mitton, J. B. & Grant, M. C. Population and subspecific genetic differentiation in the Foxtail Pine (Pinus balfouriana). Evolution 54, 1813–1819 (2000).CAS 
PubMed 

Google Scholar 
Barrowclough, G. F., Groth, J. G., Mertz, L. A. & Gutiérrez, R. J. Genetic structure of Mexican spotted owl (Strix Occidentalis Lucida) populations in a fragmented landscape. Auk 123, 1090–1102 (2006).
Google Scholar 
Atwood, T. C. et al. Modeling connectivity of black bears in a desert sky island archipelago. Biol. Conserv. 144, 2851–2862 (2011).Article 

Google Scholar 
Halbritter, D. A., Storer, C. G., Kawahara, A. Y. & Daniels, J. C. Phylogeography and population genetics of pine butterflies: Sky islands increase genetic divergence. Ecol. Evol. 9, 13389–13401 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
DeChaine, E. G. & Martin, A. P. Marked genetic divergence among sky island populations of Sedum lanceolatum (Crassulaceae) in the Rocky Mountains. Am. J. Bot. 92, 477–486 (2005).Article 
CAS 
PubMed 

Google Scholar 
Baker, A. J. Islands in the sky: The impact of Pleistocene climate cycles on biodiversity. J. Biol. 7, 32 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
Robin, V. V., Sinha, A. & Ramakrishnan, U. Ancient geographical gaps and paleo-climate shape the phylogeography of an endemic bird in the sky islands of southern India. PLoS ONE 5, e13321 (2010).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Manthey, J. D. & Moyle, R. G. Isolation by environment in White-breasted Nuthatches (Sitta carolinensis) of the Madrean Archipelago sky islands: A landscape genomics approach. Mol. Ecol. 24, 3628–3638 (2015).Article 
CAS 
PubMed 

Google Scholar 
Vásquez, D. L. A., Balslev, H., Hansen, M. M., Sklenář, P. & Romoleroux, K. Low genetic variation and high differentiation across sky island populations of Lupinus alopecuroides (Fabaceae) in the northern Andes. Alpine Bot. 126, 135–142 (2016).Article 

Google Scholar 
Mairal, M. et al. Geographic barriers and Pleistocene climate change shaped patterns of genetic variation in the Eastern Afromontane biodiversity hotspot. Sci. Rep. 7, 45749 (2017).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kidane, Y. O., Steinbauer, M. J. & Beierkuhnlein, C. Dead end for endemic plant species? A biodiversity hotspot under pressure. Glob. Ecol. Conserv. 19, e00670 (2019).Article 

Google Scholar 
Williamson, J. L. et al. Ecology, not distance, explains community composition in parasites of sky-island Audubon’s Warblers. Int. J. Parasitol. 49, 437–448 (2019).Article 
PubMed 

Google Scholar 
Knowles, L. L. & Richards, C. L. Importance of genetic drift during Pleistocene divergence as revealed by analyses of genomic variation. Mol. Ecol. 14, 4023–4032 (2005).Article 
PubMed 

Google Scholar 
Woolbright, S. A., Whitham, T. G., Gehring, C. A., Allan, G. J. & Bailey, J. K. Climate relicts and their associated communities as natural ecology and evolution laboratories. Trends Ecol. Evol. 29, 406–416 (2014).Article 
PubMed 

Google Scholar 
Evans, L. M., Allan, G. J., Meneses, N., Max, T. L. & Whitham, T. G. Herbivore host-associated genetic differentiation depends on the scale of plant genetic variation examined. Evol. Ecol. 27, 65–81 (2013).Article 

Google Scholar 
Kooyers, N. J., Greenlee, A. B., Colicchio, J. M., Oh, M. & Blackman, B. K. Replicate altitudinal clines reveal that evolutionary flexibility underlies adaptation to drought stress in annual Mimulus guttatus. New Phytol. 206, 152–165 (2015).Article 
PubMed 

Google Scholar 
Price, E. A. C. & Marshall, C. Clonal plants and environmental heterogeneity—An introduction to the proceedings. Plant Ecol. 141, 3–7 (1999).Article 

Google Scholar 
Matsuo, A. et al. Female and male fitness consequences of clonal growth in a dwarf bamboo population with a high degree of clonal intermingling. Ann. Bot. Lond. 114, 1035–1041 (2014).Article 
CAS 

Google Scholar 
Barrett, S. C. H. Influences of clonality on plant sexual reproduction. PNAS 112, 8859–8866 (2015).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bittebiere, A.-K., Benot, M.-L. & Mony, C. Clonality as a key but overlooked driver of biotic interactions in plants. Persp. Plant Ecol. Evol. Syst. 43, 125510 (2020).Article 

Google Scholar 
King, D. & Roughgarden, J. Multiple switches between vegetative and reproductive growth in annual plants. Theor. Popul. Biol. 21, 194–204 (1982).Article 
MathSciNet 
MATH 

Google Scholar 
LaDeau, S. L. & Clark, J. S. Rising CO2 levels and the fecundity of forest trees. Science 292, 95–98 (2001).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Qiu, T. et al. Is there tree senescence? The fecundity evidence. PNAS 118, e2106130118 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Oddou-Muratorio, S. et al. Crown defoliation decreases reproduction and wood growth in a marginal European beech population. Ann. Bot. Lond. 128, 193–204 (2021).Article 

Google Scholar 
Knops, J. M. H., Koenig, W. D. & Carmen, W. J. Negative correlation does not imply a tradeoff between growth and reproduction in California oaks. PNAS 104, 16982–16985 (2007).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nakamura, I. et al. Phenotypic and genetic differences in a perennial herb across a natural gradient of CO2 concentration. Oecologia 165, 809–818 (2011).Article 
ADS 
PubMed 

Google Scholar 
Robinson, E. A., Ryan, G. D. & Newman, J. A. A meta-analytical review of the effects of elevated CO2 on plant–arthropod interactions highlights the importance of interacting environmental and biological variables. New Phytol. 194, 321–336 (2012).Article 
CAS 
PubMed 

Google Scholar 
Chen, X. Spatiotemporal Processes of Plant Phenology, Simulation and Prediction (Springer, 2017).Book 

Google Scholar 
Bradshaw, H. D. & Stettler, R. F. Molecular genetics of growth and development in Populus. IV. Mapping QTLs with large effects on growth, form, and phenology traits in a forest tree. Genetics 139, 963–973 (1995).Article 
CAS 
PubMed 

Google Scholar 
Rae, A. M. et al. QTL for yield in bioenergy Populus: Identifying G×E interactions from growth at three contrasting sites. Tree Genet. Genom. 4, 97–112 (2008).Article 

Google Scholar 
Rae, A. M., Street, N. R., Robinson, K. M., Harris, N. & Taylor, G. Five QTL hotspots for yield in short rotation coppice bioenergy poplar: The poplar biomass loci. Bmc Plant Biol. 9, 23 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Allwright, M. R. et al. Biomass traits and candidate genes for bioenergy revealed through association genetics in coppiced European Populus nigra (L.). Biotechnol. Biofuels 9, 195 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Badmi, R. et al. A new calmodulin-binding protein expresses in the context of secondary cell wall biosynthesis and impacts biomass properties in Populus. Front. Plant Sci. 9, 1669 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Dai, A. Drought under global warming: a review. Wiley Interdiscip. Rev. Clim. Change 2, 45–65 (2011).Article 

Google Scholar 
IPCC. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. (2021).Hughes, L., Hughes, L. & Hughes, L. Biological consequences of global warming: Is the signal already apparent?. Trends Ecol. Evol. 15, 56–61 (2000).Article 
CAS 
PubMed 

Google Scholar 
Walther, G.-R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).Article 

Google Scholar 
Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Fuller, A. et al. Physiological mechanisms in coping with climate change. Physiol. Biochem. Zool. 83, 713–720 (2010).Article 
PubMed 

Google Scholar 
Zhu, K., Woodall, C. W. & Clark, J. S. Failure to migrate: Lack of tree range expansion in response to climate change. Glob. Change Biol. 18, 1042–1052 (2012).Article 
ADS 

Google Scholar 
Zavaleta, E. et al. Ecosystem responses to community disassembly. Ann. NY. Acad. Sci. 1162, 311–333 (2009).Article 
ADS 
PubMed 

Google Scholar 
Bertel, C. et al. Natural selection drives parallel divergence in the mountain plant Heliosperma pusillum s.l. Oikos 127, 1355–1367 (2018).Article 

Google Scholar 
Knotek, A. et al. Parallel alpine differentiation in Arabidopsis arenosa. Front. Plant Sci. 11, 561526 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Tusiime, F. M. et al. Afro-alpine flagships revisited: Parallel adaptation, intermountain admixture and shallow genetic structuring in the giant senecios (Dendrosenecio). PLoS ONE 15, e0228979 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cooke, J. E. K. & Rood, S. B. Trees of the people: The growing science of poplars in Canada and worldwide. Botany 85, 1103–1110 (2007).
Google Scholar 
Evans, L. M. et al. Geographical barriers and climate influence demographic history in narrowleaf cottonwoods. Heredity 114, 387–396 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Braatne, J. H., Rood, S. B. & Heilman, P. E. Life history, ecology, and conservation of riparian cottonwoods in North America. 57–86 (1996).Schweitzer, J. A., Martinsen, G. D. & Whitham, T. G. Cottonwood hybrids gain fitness traits of both parents: A mechanism for their long-term persistence?. Am. J. Bot. 89, 981–990 (2002).Article 
PubMed 

Google Scholar 
Moore, W. et al. Introduction to the Arizona Sky Island Arthropod Project (ASAP): Systematics, biogeography, ecology, and population genetics of arthropods of the Madrean Sky Islands. Proc. RMRS 2013, 144–168 (2013).PubMed 
PubMed Central 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Van Nuland, M. E., Bailey, J. K. & Schweitzer, J. A. Divergent plant–soil feedbacks could alter future elevation ranges and ecosystem dynamics. Nat. Ecol. Evol. 1, 0150 (2017).Article 

Google Scholar 
Tuskan, G. A. et al. Characterization of microsatellites revealed by genomic sequencing of Populus trichocarpa. Can. J. For. Res. 34, 85–93 (2004).Article 
CAS 

Google Scholar 
Tuskan, G. A. et al. The genome of black cottonwood, Populus trichocarpa (Torr. & Gray). Science 313, 1596–1604 (2006).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Peakall, R. & Ssmouse, P. E. genalex 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).Article 

Google Scholar 
Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—An update. Bioinformatics 28, 2537–2539 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. Arxiv https://doi.org/10.48550/arxiv.1406.5823 (2014).Article 

Google Scholar 
Schielzeth, H. & Nakagawa, S. Nested by design: Model fitting and interpretation in a mixed model era. Methods Ecol. Evol. 4, 14–24 (2013).Article 

Google Scholar 
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).Article 
CAS 
PubMed 

Google Scholar 
Fox, J. et al. Package ‘car’: Companion to Applied Regression. R package version 3.0–10 (2020). More

Hallegraeff, G. M. Ocean climate change, phytoplankton community responses, and harmful algal blooms: A formidable predictive challenge 1. J. Phycol. 46, 220–235 (2010).Article 
CAS 

Google Scholar 
Itakura, S. & Imai, I. Economic impacts of harmful algal blooms on fisheries and aquaculture in western Japan—An overview of interannual variability and interspecies comparison. PICES Sci. Rep. 47, 17 (2014).
Google Scholar 
Haigh, N. & Esenkulova, S. Economic losses to the British Columbia salmon aquaculture industry due to harmful algal blooms, 2009–2012. PICES Sci. Rep. 47, 2 (2014).
Google Scholar 
Sharma, N. K. et al. (eds) Cyanobacteria: An Economic Perspective 245–256 (Wiley, 2014).
Google Scholar 
O’Neil, J. M., Davis, T. W., Burford, M. A. & Gobler, C. J. The rise of harmful cyanobacteria blooms: The potential roles of eutrophication and climate change. Harmful Algae 14, 313–334. https://doi.org/10.1016/j.hal.2011.10.027 (2012).Article 
CAS 

Google Scholar 
Paerl, H. W. & Huisman, J. Climate change: A catalyst for global expansion of harmful cyanobacterial blooms. Environ. Microbiol. Rep. 1, 27–37. https://doi.org/10.1111/j.1758-2229.2008.00004.x (2009).Article 
CAS 
PubMed 

Google Scholar 
Hallegraeff, G. M. et al. Perceived global increase in algal blooms is attributable to intensified monitoring and emerging bloom impacts. Commun. Earth Environ. 2, 117. https://doi.org/10.1038/s43247-021-00178-8 (2021).Article 
ADS 

Google Scholar 
Hennon, G. M. M. & Dyhrman, S. T. Progress and promise of omics for predicting the impacts of climate change on harmful algal blooms. Harmful Algae 91, 101587. https://doi.org/10.1016/j.hal.2019.03.005 (2020).Article 
CAS 
PubMed 

Google Scholar 
Kudela, R., Berdalet, E. & Urban, E. Harmful Algal Blooms: A Scientific Summary for Policy Makers (2015).Lezcano, M., Velázquez, D., Quesada, A. & El-Shehawy, R. Diversity and temporal shifts of the bacterial community associated with a toxic cyanobacterial bloom: An interplay between microcystin producers and degraders. Water Res. 125, 52–61. https://doi.org/10.1016/j.watres.2017.08.025 (2017).Article 
CAS 
PubMed 

Google Scholar 
Scherer, P. I. et al. Temporal dynamics of the microbial community composition with a focus on toxic cyanobacteria and toxin presence during harmful algal blooms in two South German Lakes. Front. Microbiol. 8, 02387. https://doi.org/10.3389/fmicb.2017.02387 (2017).Article 

Google Scholar 
Woodhouse, J. N. et al. Microbial communities reflect temporal changes in cyanobacterial composition in a shallow ephemeral freshwater lake. ISME J. 10, 1337–1351. https://doi.org/10.1038/ismej.2015.218 (2016).Article 
CAS 
PubMed 

Google Scholar 
Beaver, J. R. et al. Land use patterns, ecoregion, and microcystin relationships in U.S. lakes and reservoirs: A preliminary evaluation. Harmful Algae 36, 57–62. https://doi.org/10.1016/j.hal.2014.03.005 (2014).Article 
CAS 

Google Scholar 
Loftin, K. A. et al. Cyanotoxins in inland lakes of the United States: Occurrence and potential recreational health risks in the EPA National Lakes Assessment 2007. Harmful Algae 56, 77–90. https://doi.org/10.1016/j.hal.2016.04.001 (2016).Article 
CAS 
PubMed 

Google Scholar 
Casero, M. C., Velázquez, D., Medina-Cobo, M., Quesada, A. & Cirés, S. Unmasking the identity of toxigenic cyanobacteria driving a multi-toxin bloom by high-throughput sequencing of cyanotoxins genes and 16S rRNA metabarcoding. Sci. Total Environ. 665, 367–378. https://doi.org/10.1016/j.scitotenv.2019.02.083 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Chaffin, J. D., Sigler, V. & Bridgeman, T. B. Connecting the blooms: Tracking and establishing the origin of the record-breaking Lake Erie Microcystis bloom of 2011 using DGGE. Aquat. Microb. Ecol. 73, 29–39 (2014).Article 

Google Scholar 
Stanley, E. H. & Jones, J. B. (eds) Stream Ecosystems in a Changing Environment 321–348 (Elsevier, 2016).Book 

Google Scholar 
Giblin, S. M. & Gerrish, G. A. Environmental factors controlling phytoplankton dynamics in a large floodplain river with emphasis on cyanobacteria. River Res. Appl. 36, 1137–1150. https://doi.org/10.1002/rra.3658 (2020).Article 

Google Scholar 
Graham, J. L., Ziegler, A. C., Loving, B. L. & Loftin, K. A. Fate and Transport of Cyanobacteria and Associated Toxins and Taste-and-Odor Compounds from Upstream Reservoir Releases in the Kansas River, Kansas, September and October 2011 65 (US Geological Survey, 2012).
Google Scholar 
Knowlton, M. F. & Jones, J. R. Seston, light, nutrients and chlorophyll in the lower Missouri River, 1994–1998. J. Freshw. Ecol. 15, 283–297. https://doi.org/10.1080/02705060.2000.9663747 (2000).Article 

Google Scholar 
Otten, T. G., Crosswell, J. R., Mackey, S. & Dreher, T. W. Application of molecular tools for microbial source tracking and public health risk assessment of a Microcystis bloom traversing 300 km of the Klamath River. Harmful Algae 46, 71–81 (2015).Article 

Google Scholar 
Preece, E. P., Hardy, F. J., Moore, B. C. & Bryan, M. A review of microcystin detections in Estuarine and Marine waters: Environmental implications and human health risk. Harmful Algae 61, 31–45. https://doi.org/10.1016/j.hal.2016.11.006 (2017).Article 
CAS 

Google Scholar 
Reinl, K. L., Sterner, R. W., Lafrancois, B. M. & Brovold, S. Fluvial seeding of cyanobacterial blooms in oligotrophic Lake Superior. Harmful Algae 100, 101941. https://doi.org/10.1016/j.hal.2020.101941 (2020).Article 
CAS 
PubMed 

Google Scholar 
Bridgeman, T. B. et al. From River to Lake: Phosphorus partitioning and algal community compositional changes in Western Lake Erie. J. Great Lakes Res. 38, 90–97 (2012).Article 
CAS 

Google Scholar 
Brown, B. L. et al. Metagenomic analysis of planktonic microbial consortia from a non-tidal urban-impacted segment of James River. Stand Genomic Sci. 10, 65. https://doi.org/10.1186/s40793-015-0062-5 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Hamner, S. et al. Metagenomic profiling of microbial pathogens in the Little Bighorn River, Montana. Int. J. Environ. Res. Public Health 16, 071097. https://doi.org/10.3390/ijerph16071097 (2019).Article 
CAS 

Google Scholar 
Staley, C. et al. Application of Illumina next-generation sequencing to characterize the bacterial community of the Upper Mississippi River. J. Appl. Microbiol. 115, 1147–1158. https://doi.org/10.1111/jam.12323 (2013).Article 
CAS 
PubMed 

Google Scholar 
Winter, C., Hein, T., Kavka, G., Mach, R. L. & Farnleitner, A. H. Longitudinal changes in the bacterial community composition of the Danube River: A whole-river approach. Appl. Environ. Microbiol. 73, 421–431. https://doi.org/10.1128/aem.01849-06 (2007).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Jackson, C. R., Millar, J. J., Payne, J. T., Ochs, C. A. & Wommack, K. E. Free-living and particle-associated bacterioplankton in large rivers of the Mississippi River basin demonstrate biogeographic patterns. Appl. Environ. Microbiol. 80, 7186–7195. https://doi.org/10.1128/AEM.01844-14 (2014).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Payne, J. T., Jackson, C. R., Millar, J. J. & Ochs, C. A. Timescales of variation in diversity and production of bacterioplankton assemblages in the Lower Mississippi River. PLoS ONE 15, e0230945. https://doi.org/10.1371/journal.pone.0230945 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Payne, J. T., Millar, J. J., Jackson, C. R. & Ochs, C. A. Patterns of variation in diversity of the Mississippi river microbiome over 1,300 kilometers. PLoS ONE 12, e0174890. https://doi.org/10.1371/journal.pone.0174890 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Read, D. S. et al. Catchment-scale biogeography of riverine bacterioplankton. ISME J. 9, 516–526. https://doi.org/10.1038/ismej.2014.166 (2015).Article 
CAS 
PubMed 

Google Scholar 
Reddington, K. et al. Metagenomic analysis of planktonic riverine microbial consortia using nanopore sequencing reveals insight into river microbe taxonomy and function. GigaScience 9, 53. https://doi.org/10.1093/gigascience/giaa053 (2020).Article 
CAS 

Google Scholar 
Staley, C. et al. Core functional traits of bacterial communities in the Upper Mississippi River show limited variation in response to land cover. Front. Microbiol. 5, 414 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Staley, C. et al. Species sorting and seasonal dynamics primarily shape bacterial communities in the Upper Mississippi River. Sci. Total Environ. 505, 435–445. https://doi.org/10.1016/j.scitotenv.2014.10.012 (2015).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Van Rossum, T. et al. Year-long metagenomic study of river microbiomes across land use and water quality. Front. Microbiol. 6, 1405 (2015).PubMed 
PubMed Central 

Google Scholar 
Kim, K. H. et al. Application of metagenome analysis to characterize the molecular diversity and saxitoxin-producing potentials of a cyanobacterial community: A case study in the North Han River, Korea. Appl. Biol. Chem. 61, 153–161. https://doi.org/10.1007/s13765-017-0342-4 (2018).Article 
CAS 

Google Scholar 
Graham, J. L. et al. Cyanotoxin occurrence in large rivers of the United States. Inland Waters 10, 109–117. https://doi.org/10.1080/20442041.2019.1700749 (2020).Article 
CAS 

Google Scholar 
Zuellig, R. E., Graham, J. L., Stelzer, E. A., Loftin, K. A. & Rosen, B. H. Cyanobacteria, Cyanotoxin Synthetase Gene, and Cyanotoxin Occurrence Among Selected Large River Sites of the Conterminous United States, 2017–18 22 (US Geological Survey, 2021).
Google Scholar 
Kramer, B. J. et al. Nitrogen limitation, toxin synthesis potential, and toxicity of cyanobacterial populations in Lake Okeechobee and the St. Lucie River Estuary, Florida, during the 2016 state of emergency event. PLoS ONE 13, e0196278 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Bouma-Gregson, K. et al. Impacts of microbial assemblage and environmental conditions on the distribution of anatoxin-a producing cyanobacteria within a river network. ISME J. 13, 1618–1634. https://doi.org/10.1038/s41396-019-0374-3 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tillett, D. et al. Structural organization of microcystin biosynthesis in Microcystis aeruginosa PCC7806: An integrated peptide–polyketide synthetase system. Chem. Biol. 7, 753–764 (2000).Article 
CAS 
PubMed 

Google Scholar 
Dittmann, E., Fewer, D. P. & Neilan, B. A. Cyanobacterial toxins: Biosynthetic routes and evolutionary roots. FEMS Microbiol. Rev. 37, 23–43. https://doi.org/10.1111/j.1574-6976.2012.12000.x (2013).Article 
CAS 
PubMed 

Google Scholar 
Jungblut, A. D. & Neilan, B. A. Molecular identification and evolution of the cyclic peptide hepatotoxins, microcystin and nodularin, synthetase genes in three orders of cyanobacteria. Arch. Microbiol. 185, 107–114. https://doi.org/10.1007/s00203-005-0073-5 (2006).Article 
CAS 
PubMed 

Google Scholar 
Meriluoto, J. et al. (eds) Handbook of Cyanobacterial Monitoring and Cyanotoxin Analysis 501–525 (Wiley, 2017).Book 

Google Scholar 
Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643. https://doi.org/10.1038/ismej.2017.119 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Graham, J. L., Dubrovsky, N. M., Loftin, K. A., Rosen, B. H. & Stelzer, E. A. Cyanotoxin, Chlorophyll-a, and Cyanobacterial Toxin Genetic Data for Samples Collected at Twelve Large River Sites Throughout the United States, June Through October 2019 (U.S. Geological Survey, 2022).
Google Scholar 
Dodds, W. K. & Smith, V. H. Nitrogen, phosphorus, and eutrophication in streams. Inland Waters 6, 155–164. https://doi.org/10.5268/IW-6.2.909 (2016).Article 
CAS 

Google Scholar 
Debroas, D. et al. Overview of freshwater microbial eukaryotes diversity: A first analysis of publicly available metabarcoding data. FEMS Microbiol. Ecol. 93, 23. https://doi.org/10.1093/femsec/fix023 (2017).Article 
CAS 

Google Scholar 
Henson, M. W. et al. Nutrient dynamics and stream order influence microbial community patterns along a 2914 kilometer transect of the Mississippi River. Limnol. Oceanogr. 63, 1837–1855. https://doi.org/10.1002/lno.10811 (2018).Article 
ADS 
CAS 

Google Scholar 
Ghai, R. et al. Metagenomics of the water column in the pristine upper course of the Amazon river. PLoS ONE 6, e23785. https://doi.org/10.1371/journal.pone.0023785 (2011).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Liao, J. et al. Cyanobacteria in lakes on Yungui Plateau, China are assembled via niche processes driven by water physicochemical property, lake morphology and watershed land-use. Sci. Rep. 6, 36357. https://doi.org/10.1038/srep36357 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Monchamp, M.-E. et al. Homogenization of lake cyanobacterial communities over a century of climate change and eutrophication. Nat. Ecol. Evol. 2, 317–324. https://doi.org/10.1038/s41559-017-0407-0 (2018).Article 
PubMed 

Google Scholar 
Pessi, I. S., Maalouf, P. D. C., LaughinghouseBaurain, H. D. D. & Wilmotte, A. On the use of high-throughput sequencing for the study of cyanobacterial diversity in Antarctic aquatic mats. J. Phycol. 52, 356–368. https://doi.org/10.1111/jpy.12399 (2016).Article 
CAS 
PubMed 

Google Scholar 
Tanvir, R. U., Hu, Z., Zhang, Y. & Lu, J. Cyanobacterial community succession and associated cyanotoxin production in hypereutrophic and eutrophic freshwaters. Environ. Pollut. 290, 118056. https://doi.org/10.1016/j.envpol.2021.118056 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chételat, J., Pick, F. R. & Hamilton, P. B. Potamoplankton size structure and taxonomic composition: Influence of river size and nutrient concentrations. Limnol. Oceanogr. 51, 681–689 (2006).Article 
ADS 

Google Scholar 
Heiskary, S. & Markus, H. Establishing relationships among nutrient concentrations, phytoplankton abundance, and biochemical oxygen demand in Minnesota, USA, rivers. Lake Reserv. Manag. 17, 251–262 (2001).Article 
CAS 

Google Scholar 
Smith, V. H. Eutrophication of freshwater and coastal marine ecosystems a global problem. Environ. Sci. Pollut. Res. 10, 126–139 (2003).Article 
CAS 

Google Scholar 
Verspagen, J. M. et al. Rising CO2 levels will intensify phytoplankton blooms in eutrophic and hypertrophic lakes. PLoS ONE 9, e104325 (2014).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Zepernick, B. N. et al. Elevated pH conditions associated with Microcystis spp. blooms decrease viability of the cultured diatom Fragilaria crotonensis and natural diatoms in Lake Erie. Front. Microbiol. 12, 598736. https://doi.org/10.3389/fmicb.2021.598736 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Urban, L. et al. Freshwater monitoring by nanopore sequencing. Elife 10, 61504. https://doi.org/10.7554/eLife.61504 (2021).Article 

Google Scholar 
Lee, C. J. & Henderson, R. J. Tracking Water-Quality in U.S. Streams and Rivers: U.S. Geological Survey National Water Quality Network. https://nrtwq.usgs.gov/nwqn (2020).Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data (2010).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257. https://doi.org/10.1186/s13059-019-1891-0 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lu, J. B. F., Thielen, P. & Salzberg, S. L. Bracken: Estimating species abundance in metagenomics data. PeerJ Comput. Sci. 3, 104. https://doi.org/10.7717/peerj-cs.104 (2017).Article 

Google Scholar 
Bagley, M. et al. High-throughput environmental DNA analysis informs a biological assessment of an urban stream. Ecol. Ind. 104, 378–389. https://doi.org/10.1016/j.ecolind.2019.04.088 (2019).Article 
CAS 

Google Scholar 
Magoč, T. & Salzberg, S. L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963. https://doi.org/10.1093/bioinformatics/btr507 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857. https://doi.org/10.1038/s41587-019-0209-9 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583. https://doi.org/10.1038/nmeth.3869 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549. https://doi.org/10.1093/molbev/msy096 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nübel, U., Garcia-Pichel, F. & Muyzer, G. PCR primers to amplify 16S rRNA genes from cyanobacteria. Appl. Environ. Microbiol. 63, 3327–3332. https://doi.org/10.1128/aem.63.8.3327-3332.1997 (1997).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Neilan, B. A. et al. rRNA sequences and evolutionary relationships among toxic and nontoxic cyanobacteria of the genus Microcystis. Int. J. Syst. Bacteriol. 47, 693–697. https://doi.org/10.1099/00207713-47-3-693 (1997).Article 
CAS 
PubMed 

Google Scholar 
Team R Core. R: A Language and Environment for Statistical Computing (2013).McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Oksanen, J. et al. Vegan: Community Ecology Package. R Package Version 2.5-2 (2018).Wickham, H. ggplot2-Elegant Graphics for Data Analysis (Springer, 2016).MATH 

Google Scholar 
U.S. Geological Survey. National Water Information System Database. https://doi.org/10.5066/F7P55KJN (2022). More

Schmid-Hempel P. Parasites in social insects. Princeton University Press (1998).Lefèvre, T. et al. The ecological significance of manipulative parasites. Trends Ecol. Evol. 24, 41–48 (2009).Article 
PubMed 

Google Scholar 
Elya, C. et al. Robust manipulation of the behavior of Drosophila melanogaster by a fungal pathogen in the laboratory. Elife 7, e34414 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Herbison, R., Lagrue, C. & Poulin, R. The missing link in parasite manipulation of host behaviour. Parasites Vectors 11, 1–6 (2018).Article 

Google Scholar 
Csata, E., Billen, J., Barbu-Tudoran, L. & Markó, B. Inside Pandora’s box: development of the lethal myrmecopathogenic fungus Pandora formicae within its ant host. Fungal Ecol. 50, 101022 (2021).Article 

Google Scholar 
Trinh, T., Ouellette, R. & de Bekker, C. Getting lost: the fungal hijacking of ant foraging behaviour in space and time. Anim. Behav. 181, 165–184 (2021).Article 

Google Scholar 
Moore J. Parasites and the Behavior of Animals. Oxford University Press, Oxford (2002).Thomas, F., Fauchier, J. & Lafferty, K. D. Conflict of interest between a nematode and a trematode in an amphipod host: test of the “sabotage” hypothesis. Behav. Ecol. Sociobiol. 51, 296–301 (2002).Article 

Google Scholar 
Stroeymeyt, N. et al. Social network plasticity decreases disease transmission in a eusocial insect. Science 362, 941–945 (2018).Article 
CAS 
PubMed 

Google Scholar 
Beros, S., Foitzik, S. & Menzel, F. What are the mechanisms behind a parasite-induced decline in nestmate recognition in ants? J. Chem. Ecol. 43, 869–880 (2017).Article 
CAS 
PubMed 

Google Scholar 
Hamilton, W. D. Kinship, recognition, disease, and intelligence: constraints of social evolution. In: Ito Y., Brown J. L., Kikkawa J. (eds) Animal societies: theories and facts. Jpn Sci Soc Press, Tokyo, pp 81–102 (1987).Hunt, J. H. & Richard, F. J. Intracolony vibroacoustic communication in social insects. Insect Soc. 60, 403–417 (2013).Article 

Google Scholar 
Wyatt, T. D. Proteins and peptides as pheromone signals and chemical signatures. Anim. Behav. 97, 273–280 (2014).Article 

Google Scholar 
Leonhardt, S. D., Menzel, F., Nehring, V. & Schmitt, T. Ecology and evolution of communication in social insects. Cell 164, 1277–1287 (2016).Article 
CAS 
PubMed 

Google Scholar 
Casacci, L. P. et al. Ant pupae employ acoustics to communicate social status in their colony’s hierarchy. Curr. Biol. 23, 323–327 (2013).Article 
CAS 
PubMed 

Google Scholar 
Schönrogge, K., Barbero, F., Casacci, L. P., Settele, J. & Thomas, J. A. Acoustic communication within ant societies and its mimicry by mutualistic and socially parasitic myrmecophiles. Anim. Behav. 134, 249–256 (2017).Article 

Google Scholar 
Sheehan, M. J. & Tibbetts, E. A. Specialized face learning is associated with individual recognition in paper wasps. Science 334, 1272–1275 (2011).Article 
CAS 
PubMed 

Google Scholar 
Chittka, L. & Dyer, A. Your face looks familiar. Nature 481, 154–155 (2012).Article 
CAS 
PubMed 

Google Scholar 
Billen, J. Signal variety and communication in social insects. Proc. Neht. Entomol. Soc. Meet. 17, 9 (2006).
Google Scholar 
Blomquist G. J. Biosynthesis of cuticular hydrocarbons. In: Blomquist, G. J., Bagnères, A.-G. (eds.): Insect hydrocarbons: biology, biochemistry and chemical ecology. Cambridge University Press (2010).Hefetz, A. The evolution of hydrocarbon pheromone parsimony in ants (Hymenoptera: Formicidae) – interplay of colony odor uniformity and odor idiosyncrasy. Myrmecol. N. 10, 59–68 (2007).
Google Scholar 
Bagnères A. G., Lorenzi M. C. Chemical deception/mimicry using cuticular hydrocarbons. Insect hydrocarbons: Biology, biochemistry and chemical ecology. Chemical deception/mimicry using cuticular hydrocarbons, 282–324 (2010).van Zweden, J. S. & d’Ettorre, P. Nestmate recognition in social insects and the role of hydrocarbons. Insect Hydrocarbons: Biol. Biochem. Chem. Ecol. 11, 222–243 (2010).Article 

Google Scholar 
Esponda, F. & Gordon, D. M. Distributed nestmate recognition in ants. Proc. R. Soc. B. 282, 20142838 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Crozier, R. & 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 
Wakonigg, G., Eveleigh, L., Arnold, G. & Crailsheim, K. Cuticular hydrocarbon profiles reveal age-related changes in honey bee drones (Apis mellifera carnica). J. Apic. Res. 39, 137–141 (2000).Article 
CAS 

Google Scholar 
Cuvillier-Hot, V., Cobb, M., Malosse, C. & Peeters, C. Sex, age and ovarian activity affect cuticular hydrocarbons in Diacamma ceylonense, a queenless ant. J. Insect Physiol. 47, 485–493 (2001).Article 
CAS 
PubMed 

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

Google Scholar 
Kather, R., Drijfhout, F. P. & Martin, S. J. Task group differences in cuticular lipids in the honey bee Apis mellifera. J. Chem. Ecol. 37, 205–212 (2011).Article 
CAS 
PubMed 

Google Scholar 
Kleeberg, I., Menzel, F. & Foitzik, S. The influence of slavemaking lifestyle, caste and sex on chemical profiles in Temnothorax ants: insights into the evolution of cuticular hydrocarbons. Proc. R. Soc. B. 284, 20162249 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Sprenger, P. P. & Menzel, F. Cuticular hydrocarbons in ants (Hymenoptera: Formicidae) and other insects: how and why they differ among individuals, colonies, and species. Myrmecol. N. 30, 1–26 (2020).
Google Scholar 
Reeve, H. K. The evolution of conspecific acceptance thresholds. Am. Nat. 133, 407–435 (1989).Article 

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

Google Scholar 
Akino, T. Chemical strategies to deal with ants: a review of mimicry, camouflage, propaganda, and phytomimesis by ants (Hymenoptera: Formicidae) and other arthropods. Myrmecol. N. 11, 173–181 (2008).
Google Scholar 
Akino, T., Knapp, J. J., Thomas, J. A. & Elmes, G. W. Chemical mimicry and host specificity in the butterfly Maculinea rebeli, a social parasite of Myrmica ant colonies. Proc. Roy. Soc. B. 266, 1419–1426 (1999).Article 
CAS 

Google Scholar 
Nash, D. R., Als, T. D., Maile, R., Jones, G. R. & Boomsma, J. J. A mosaic of chemical coevolution in a large blue butterfly. Science 319, 88–90 (2008).Article 
CAS 
PubMed 

Google Scholar 
Johnson, C. A., Vander Meer, R. K. & Lavine, B. Changes in the cuticular hydrocarbon profile of the slave-maker ant queen, Polyergus breviceps Emery, after killing a Formica host queen (Hymenoptera: Formicidae). J. Chem. Ecol. 27, 1787–1804 (2001).Article 
CAS 
PubMed 

Google Scholar 
Lecuona, R., Riba, G., Cassier, P. & Clément, J. L. Alterations of insect epicuticular hydrocarbons during infection with Beauveria bassiana or B. brongniartii. J. Invertebr. Pathol. 58, 10–18 (1991).Article 
CAS 

Google Scholar 
Trabalon, M., Plateaux, L., Péru, L., Bagnères, A. G. & Hartmann, N. Modification of morphological characters and cuticular compounds in worker ants Leptothorax nylanderi induced by endoparasites Anomotaenia brevis. J. Insect Physiol. 46, 169–178 (2000).Article 
CAS 
PubMed 

Google Scholar 
Zurek, L., Watson, D. W., Krasnoff, S. B. & Schal, C. Effect of the entomopathogenic fungus, Entomophthora muscae (Zygomycetes: Entomophthoraceae), on sex pheromone and other cuticular hydrocarbons of the house fly. Musca Domestica. J. Invertebr. Pathol. 80, 171–176 (2002).Article 
CAS 
PubMed 

Google Scholar 
Nielsen, M. L. & Holman, L. Terminal investment in multiple sexual signals: immune‐challenged males produce more attractive pheromones. Func. Ecol. 26, 20–28 (2012).Article 

Google Scholar 
Beros, S., Jongepier, E., Hagemeier, F. & Foitzik, S. The parasite’s long arm: a tapeworm parasite induces behavioural changes in uninfected group members of its social host. Proc. Roy. Soc. B. 282, 20151473 (2015).Article 

Google Scholar 
Csata, E., Erős, K. & Markó, B. Effects of the ectoparasitic fungus Rickia wasmannii on its ant host Myrmica scabrinodis: changes in host mortality and behavior. Insectes Soc. 61, 247–252 (2014).Article 

Google Scholar 
Markó, B. et al. Distribution of the myrmecoparasitic fungus Rickia wasmannii (Ascomycota: Laboulbeniales) across colonies, individuals, and body parts of Myrmica scabrinodis. J. Invertebr. Pathol. 136, 74–80 (2016).Article 
PubMed 

Google Scholar 
Báthori, F., Csata, E. & Tartally, A. Rickia wasmannii increases the need for water in Myrmica scabrinodis (Ascomycota: Laboulbeniales; Hymenoptera: Formicidae). J. Invertebr. Pathol. 126, 7–82 (2015).Article 

Google Scholar 
Csata, E. et al. Lock-picks: fungal infection facilitates the intrusion of strangers into ant colonies. Sci. Rep. 7, 46323 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Csata, E., Billen, J., Bernadou, A., Heinze, J. & Markó, B. Infection-related variation in cuticle thickness in the ant Myrmica scabrinodis (Hymenoptera: Formicidae). Insectes Soc. 65, 503–506 (2018).Article 

Google Scholar 
Csősz, S., Rádai, Z., Tartally, A., Ballai, L. E. & Báthori, F. Ectoparasitic fungi Rickia wasmannii infection is associated with smaller body size in Myrmica ants. Sci. Rep. 11, 1–9 (2021).Article 

Google Scholar 
Dani, F. R., Jones, G. R., Destri, S., Spencer, S. H. & Turillazzi, S. Deciphering the recognition signature within the cuticular chemical profile of paper wasps. Anim. Behav. 62, 165–171 (2001).Article 

Google Scholar 
Lorenzi, M. C., Bagneres, A. G., Clément, J. L. & Turillazzi, S. Polistes biglumis bimaculatus epicuticular hydrocarbons and nestmate recognition (Hymenoptera Vespidae). Insectes Soc. 44, 123–138 (1997).Article 

Google Scholar 
Ruther, J., Sieben, S. & Schricker, B. Nestmate recognition in social wasps: manipulation of hydrocarbon profiles induces aggression in the European hornet. Naturwissenschaften 89, 111–114 (2002).Article 
CAS 
PubMed 

Google Scholar 
Smith, A. A., Hölldobler, B. & Liebig, J. Cuticular hydrocarbons reliably identify cheaters and allow enforcement of altruism in a social insect. Curr. Biol. 19, 78–81 (2009).Article 
CAS 
PubMed 

Google Scholar 
Ebsen, J. R., Boomsma, J. J. & Nash, D. R. Phylogeography and cryptic speciation in the Myrmica scabrinodis Nylander, 1846 species complex (Hymenoptera: Formicidae), and their conservation implications. Insect Conserv. Divers 12, 467–480 (2019).Article 

Google Scholar 
Ballinger, M. J., Moore, L. D. & Perlman, S. J. Evolution and diversity of inherited Spiroplasma symbionts in Myrmica ants. Appl. Environ. Microbiol. 84, e02299–17 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Menzel, F. et al. Crematoenones – a novel substance class exhibited by ants functions as appeasement signal. Front. Zool. 10, 1–12 (2013).Article 

Google Scholar 
Qiu, H.-L., Qin, C.-S., Fox, E. G. P., Wang, D.-S. & He, Y.-R. Differential behavioral responses of Solenopsis invicta (Hymenoptera: Formicidae) workers toward nestmate and non-nestmate corpses. J. Ins. Sci. 20, 11 (2020).Article 

Google Scholar 
Martin, S. J., Vitikainen, E., Helanterä, H. & Drijfhout, F. P. Chemical basis of nest-mate discrimination in the ant Formica exsecta. Proc. R. Soc. B. 275, 1271–1278 (2008).Article 
CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Gibbs, A. & Pomonis, J. G. Physical properties of insect cuticular hydrocarbons: the effects of chain lengths, methyl branching and unsaturation. Comp. Biochem. Physiol. 112, 243–249 (1995).Article 

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. 284, 20161727 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Breed, M. D., Leger, E. A., Pearce, A. M. & Wang, Y. J. Comb wax effects on the ontogeny of honey bee nestmate recognition. Anim. Behav. 55, 13–20 (1998).Article 
CAS 
PubMed 

Google Scholar 
Breed, M. D. & Stiller, T. M. Honey bee, Apis mellifera, nestmate discrimination: hydrocarbon effects and the evolutionary implications of comb choice. Anim. Behav. 43, 875–883 (1992).Article 

Google Scholar 
Akino, T., Yamamura, K., Wakamura, S. & Yamaoka, R. Direct behavioral evidence for hydrocarbons as nestmate recognition cues in Formica japonica (Hymenoptera: Formicidae). Appl. Entomol. Zool. 39, 381–387 (2004).Article 
CAS 

Google Scholar 
Greene, M. J. & Gordon, D. M. Structural complexity of chemical recognition cues affects the perception of group membership in the ants Linepithema humile and Aphaenogaster cockerelli. J. Exp. Biol. 210, 897–905 (2007).Article 
CAS 
PubMed 

Google Scholar 
Casacci, L. P., Barbero, F., Ślipiński, P. & Witek, M. The inquiline ant Myrmica karavajevi uses both chemical and vibroacoustic deception mechanisms to integrate into its host colonies. Biology 10, 654 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Bhatkar, A. & Whitcomb, W. Artificial diet for rearing various species of ants. Florid. Entomol. 53, 229–232 (1970).Article 

Google Scholar 
Espadaler X., Santamaria S. Ecto- and endoparasitic fungi on ants from the Holarctic region. Psyche 168478, 1–10 (2012).Csata, E. et al. Comprehensive survey of Romanian myrmecoparasitic fungi: new species, biology and distribution. North West J. Zool. 9, 23–29 (2013).
Google Scholar 
Witek, M., Barbero, F. & Markó, B. Myrmica ants host highly diverse parasitic communities: from social parasites to microbes. Insectes Soc. 61, 307–323 (2014).Article 

Google Scholar 
Tragust, S., Tartally, A., Espadaler, X. & Billen, J. Histopathology of Laboulbeniales (Ascomycota: Laboulbeniales): ectoparasitic fungi on ants (Hymenoptera: Formicidae). Myrmecol. N. 23, 81–89 (2016).
Google Scholar 
Czekes, Z. et al. The genus Myrmica Latreille, 1804 (Hymenoptera: Formicidae) in Romania: distribution of species and key for their identification. Entomol. Rom. 17, 29–50 (2012).
Google Scholar 
Buczkowski, G. & Silverman, J. Context-dependent nestmate discrimination and the effect of action thresholds on exogenous cue recognition in the Argentine ant. Anim. Behav. 69, 741–749 (2005).Article 

Google Scholar 
Diez, L., Moquet, L. & Detrain, C. Post-mortem changes in chemical profile and their influence on corpse removal in ants. J. Chem. Ecol. 39, 1424–1432 (2013).Article 
CAS 
PubMed 

Google Scholar 
Csata, E., Bernadou, A., Rákosy-Tican, E., Heinze, J. & Markó, B. The effects of fungal infection and physiological condition on the locomotory behaviour of the ant Myrmica scabrinodis. J. Insect Physiol. 98, 167–172 (2017).Article 
CAS 
PubMed 

Google Scholar 
Moroń, D., Witek, M. & Woyciechowski, M. Division of labour among workers with different life expectancy in the ant Myrmica scabrinodis. Anim. Behav. 75, 345–350 (2008).Article 

Google Scholar 
Bernadou, A., Felden, A., Moreau, M., Moretto, P. & Fourcassié, V. Ergonomics of load transport in the seed harvesting ant Messor barbarus: morphology influences transportation method and efficiency. J. Exp. Biol. 219, 2920–2927 (2016).PubMed 

Google Scholar 
Keresztes, K. K., Csata, E., Lunka-Tekla, A. & Markó, B. Friend or foe? Differential aggression towards neighbors and strangers in the ant Liometopum microcephalum (Hymenoptera: Formicidae). Sci. Entomol. 23, 351–358 (2020).Article 

Google Scholar 
Ratnasingham, S. & Hebert, P. D. BOLD: The Barcode of life data system. Mol. Ecol. Notes 7, 355–364, http://www.barcodinglife.org (2007).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (URL ) (2020).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Soft. 67, 1–48 (2015).Fox J., Weisberg S. Using car and effects Functions in Other Functions. Using Car Eff. Funct. Other Funct., 3, 1–5 (2020).Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric 312 models. Biom. J. 50, 346–363 (2008).Article 
PubMed 

Google Scholar 
Wickham H. ggplot2: elegant graphics for data analysis. (Springer Science & Business Media) (2009). More

McElroy, C. R., Constantinou, A., Jones, L. C., Summerton, L. & Clark, J. H. Towards a holistic approach to metrics for the 21st century pharmaceutical industry. Green Chem. 17, 3111–3121. https://doi.org/10.1039/C5GC00340G (2015).Article 
CAS 

Google Scholar 
Zimmerman, J. B., Anastas, P. T., Erythropel, H. C. & Leitner, W. Designing for a green chemistry future. Science 367, 397–400. https://doi.org/10.1126/science.aay3060 (2020).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Sheldon, R. A. Metrics of green chemistry and sustainability: Past, present, and future. ACS Sustain. Chem. Eng. 6, 32–48. https://doi.org/10.1021/acssuschemeng.7b03505 (2018).Article 
CAS 

Google Scholar 
Anastas, P. T. & Williamson, T. C. in Green Chemistry, Vol. 626 ACS Symposium Series Ch. 1, 1–17 (American Chemical Society, 1996). https://doi.org/10.1021/bk-1996-0626.ch001.Clark, H. J. Green chemistry: Challenges and opportunities. Green Chem. 1, 1–8. https://doi.org/10.1039/A807961G (1999).Article 
CAS 

Google Scholar 
Dekamin, M. G. & Eslami, M. Highly efficient organocatalytic synthesis of diverse and densely functionalized 2-amino-3-cyano-4 H-pyrans under mechanochemical ball milling. Green Chem. 16, 4914–4921 (2014).Article 
CAS 

Google Scholar 
Eze, A. A. et al. Wet ball milling of niobium by using ethanol, determination of the crystallite size and microstructures. Sci. Rep. 11, 1–8 (2021).Article 

Google Scholar 
Gorrasi, G. & Sorrentino, A. Mechanical milling as a technology to produce structural and functional bio-nanocomposites. Green Chem. 17, 2610–2625 (2015).Article 
CAS 

Google Scholar 
Li, L. H., Glushenkov, A. M., Hait, S. K., Hodgson, P. & Chen, Y. High-efficient production of boron nitride nanosheets via an optimized ball milling process for lubrication in oil. Sci. Rep. 4, 1–6 (2014).
Google Scholar 
Mac Naughton, G. E., Rolfe, S. A. & Siraj-Blatchford, I. E. Doing Early Childhood Research: International Perspectives on Theory and Practice (Open University Press, 2001).Evangelisti, L. et al. The borderline between reactivity and pre-reactivity of binary mixtures of gaseous carboxylic acids and alcohols. Angew. Chem. 129, 3930–3933 (2017).Article 
ADS 

Google Scholar 
Gaspa, S., Porcheddu, A. & De Luca, L. Metal-free oxidative cross esterification of alcohols via acyl chloride formation. Adv. Synth. Catal. 358, 154–158 (2016).Article 
CAS 

Google Scholar 
Fiorio, J. L., Braga, A. H., Guedes, C. L. S. B. & Rossi, L. M. Reusable heterogeneous tungstophosphoric acid-derived catalyst for green esterification of carboxylic acids. ACS Sustain. Chem. Eng. 7, 15874–15883 (2019).Article 
CAS 

Google Scholar 
Karimi, B., Mirzaei, H. M. & Mobaraki, A. Periodic mesoporous organosilica functionalized sulfonic acids as highly efficient and recyclable catalysts in biodiesel production. Catal. Sci. Technol. 2, 828–834 (2012).Article 
CAS 

Google Scholar 
Tran, T. T. V. et al. Selective production of green solvent (isoamyl acetate) from fusel oil using a sulfonic acid-functionalized KIT-6 catalyst. Mol. Catal. 484, 110724 (2020).Article 
CAS 

Google Scholar 
Afshar, S. et al. Optimization of catalytic activity of sulfated titania for efficient synthesis of isoamyl acetate by response surface methodology. Mon. Chem. Chem. Mon. 146, 1949–1957 (2015).Article 
CAS 

Google Scholar 
Chng, L. L., Yang, J. & Ying, J. Y. Efficient synthesis of amides and esters from alcohols under aerobic ambient conditions catalyzed by a Au/mesoporous Al2O3 nanocatalyst. Chemsuschem 8, 1916–1925 (2015).Article 
CAS 
PubMed 

Google Scholar 
Lozano, P., Bernal, J. M. & Navarro, A. A clean enzymatic process for producing flavour esters by direct esterification in switchable ionic liquid/solid phases. Green Chem. 14, 3026–3033 (2012).Article 
CAS 

Google Scholar 
Su, L., Hong, R., Guo, X., Wu, J. & Xia, Y. Short-chain aliphatic ester synthesis using Thermobifida fusca cutinase. Food Chem. 206, 131–136 (2016).Article 
CAS 
PubMed 

Google Scholar 
Güvenç, A., Kapucu, N., Kapucu, H., Aydoğan, Ö. & Mehmetoğlu, Ü. Enzymatic esterification of isoamyl alcohol obtained from fusel oil: Optimization by response surface methodolgy. Enzyme Microb. Technol. 40, 778–785 (2007).Article 

Google Scholar 
Torres, S., Baigorí, M. D., Swathy, S., Pandey, A. & Castro, G. R. Enzymatic synthesis of banana flavour (isoamyl acetate) by Bacillus licheniformis S-86 esterase. Food Res. Int. 42, 454–460 (2009).Article 
CAS 

Google Scholar 
Ando, H., Kurata, A. & Kishimoto, N. Antimicrobial properties and mechanism of volatile isoamyl acetate, a main flavour component of Japanese sake (Ginjo-shu). J. Appl. Microbiol. 118, 873–880 (2015).Article 
CAS 
PubMed 

Google Scholar 
Ghamgui, H., Karra-Chaâbouni, M., Bezzine, S., Miled, N. & Gargouri, Y. Production of isoamyl acetate with immobilized Staphylococcus simulans lipase in a solvent-free system. Enzyme Microb. Technol. 38, 788–794 (2006).Article 
CAS 

Google Scholar 
Romero, M., Calvo, L., Alba, C., Daneshfar, A. & Ghaziaskar, H. Enzymatic synthesis of isoamyl acetate with immobilized Candida antarctica lipase in n-hexane. Enzyme Microb. Technol. 37, 42–48 (2005).Article 
CAS 

Google Scholar 
Borges, M. E. & Díaz, L. Recent developments on heterogeneous catalysts for biodiesel production by oil esterification and transesterification reactions: A review. Renew. Sustain. Energy Rev. 16, 2839–2849 (2012).Article 
CAS 

Google Scholar 
Li, K.-T., Wang, C.-K., Wang, I. & Wang, C.-M. Esterification of lactic acid over TiO2–ZrO2 catalysts. Appl. Catal. A 392, 180–183 (2011).Article 
CAS 

Google Scholar 
Clark, J. H. & Rhodes, C. N. In Clean Synthesis Using Porous Inorganic Solid Catalysts and Supported Reagents, Vol. 4, (Royal Society of Chemistry, London, 2000). https://doi.org/10.1039/9781847550569Dekamin, M. G. et al. Sodium alginate: An efficient biopolymeric catalyst for green synthesis of 2-amino-4H-pyran derivatives. Int. J. Biol. Macromol. 87, 172–179 (2016).Article 
CAS 
PubMed 

Google Scholar 
Melfi, D. T., dos Santos, K. C., Ramos, L. P. & Corazza, M. L. Supercritical CO2 as solvent for fatty acids esterification with ethanol catalyzed by Amberlyst-15. J. Supercrit. Fluids 158, 104736 (2020).Article 
CAS 

Google Scholar 
Azudin, N. Y., Mashitah, M. & Abd Shukor, S. R. Optimization of isoamyl acetate production in a solvent-free system. J. Food Qual. 36, 441–446 (2013).Article 
CAS 

Google Scholar 
Ćorović, M. et al. Immobilization of Candida antarctica lipase B onto Purolite® MN102 and its application in solvent-free and organic media esterification. Bioprocess Biosyst. Eng. 40, 23–34 (2017).Article 
PubMed 

Google Scholar 
Liu, C. & Luo, G. Synthesis of isoamyl acetate catalyzed by ferric tri-dodecylsulfonate. Riyong Huaxue Gongye 34, 403–405 (2004).
Google Scholar 
Narwal, S. K., Saun, N. K., Dogra, P. & Gupta, R. Green synthesis of isoamyl acetate via silica immobilized novel thermophilic lipase from Bacillus aerius. Russ. J. Bioorg. Chem. 42, 69–73 (2016).Article 
CAS 

Google Scholar 
Pizzio, L., Vázquez, P., Cáceres, C. & Blanco, M. Tungstophosphoric and molybdophosphoric acids supported on zirconia as esterification catalysts. Catal. Lett. 77, 233–239 (2001).Article 
CAS 

Google Scholar 
Saha, B., Alqahtani, A. & Teo, H. T. R. Production of iso-Amyl Acetate: Heterogeneous Kinetics and Techno-feasibility Evaluation for Catalytic Distillation. Int. J. Chem. React. Eng. 3(1), https://doi.org/10.2202/1542-6580.1231 (2005).Osorio-Viana, W., Ibarra-Taquez, H. N., Dobrosz-Gomez, I. & Gómez-García, M. Á. Hybrid membrane and conventional processes comparison for isoamyl acetate production. Chem. Eng. Process. 76, 70–82 (2014).Article 
CAS 

Google Scholar 
Fang, M. et al. Synthesis of isoamyl acetate using polyoxometalate-based sulfonated ionic liquid as catalyst. Indian J. Chem. Sect. A 53A, 1485–1492 (2014).Yang, Z., Zhou, C., Zhang, W., Li, H. & Chen, M. β-MnO2 nanorods: A new and efficient catalyst for isoamyl acetate synthesis. Colloids Surf., A 356, 134–139 (2010).Article 
CAS 

Google Scholar 
Yang, Z. et al. Kinetic study and process simulation of transesterification of methyl acetate and isoamyl alcohol catalyzed by ionic liquid. Ind. Eng. Chem. Res. 54, 1204–1215 (2015).Article 
CAS 

Google Scholar 
Dohendou, M., Pakzad, K., Nezafat, Z., Nasrollahzadeh, M. & Dekamin, M. G. Progresses in chitin, chitosan, starch, cellulose, pectin, alginate, gelatin and gum based (nano)catalysts for the Heck coupling reactions: A review. Int. J. Biol. Macromol. 192, 771–819. https://doi.org/10.1016/j.ijbiomac.2021.09.162 (2021).Article 
CAS 
PubMed 

Google Scholar 
Valiey, E., Dekamin, M. G. & Alirezvani, Z. Melamine-modified chitosan materials: An efficient and recyclable bifunctional organocatalyst for green synthesis of densely functionalized bioactive dihydropyrano[2,3-c]pyrazole and benzylpyrazolyl coumarin derivatives. Int. J. Biol. Macromol. 129, 407–421. https://doi.org/10.1016/j.ijbiomac.2019.01.027 (2019).Article 
CAS 
PubMed 

Google Scholar 
Dekamin, M. G., Kazemi, E., Karimi, Z., Mohammadalipoor, M. & Naimi-Jamal, M. R. Chitosan: An efficient biomacromolecule support for synergic catalyzing of Hantzsch esters by CuSO4. Int. J. Biol. Macromol. 93, 767–774. https://doi.org/10.1016/j.ijbiomac.2016.09.012 (2016).Article 
CAS 
PubMed 

Google Scholar 
Valiey, E., Dekamin, M. G. & Bondarian, S. Sulfamic acid grafted to cross-linked chitosan by dendritic units: A bio-based, highly efficient and heterogeneous organocatalyst for green synthesis of 2,3-dihydroquinazoline derivatives. RSC Adv. 13, 320–334. https://doi.org/10.1039/D2RA07319F (2023).Article 
ADS 
CAS 

Google Scholar 
Dekamin, M. G., Azimoshan, M. & Ramezani, L. Chitosan: A highly efficient renewable and recoverable bio-polymer catalyst for the expeditious synthesis of α-amino nitriles and imines under mild conditions. Green Chem. 15, 811–820. https://doi.org/10.1039/C3GC36901C (2013).Article 
CAS 

Google Scholar 
Alirezvani, Z., Dekamin, M. G. & Valiey, E. Cu (II) and magnetite nanoparticles decorated melamine-functionalized chitosan: A synergistic multifunctional catalyst for sustainable cascade oxidation of benzyl alcohols/Knoevenagel condensation. Sci. Rep. 9, 17758 (2019).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Rostami, N., Dekamin, M., Valiey, E. & Fanimoghadam, H. Chitosan-EDTA-Cellulose network as a green, recyclable and multifunctional biopolymeric organocatalyst for the one-pot synthesis of 2-amino-4H-pyran derivatives. Sci. Rep. 12, 8642–8642 (2022).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Frindy, S., el Kadib, A., Lahcini, M., Primo, A. & García, H. Copper nanoparticles stabilized in a porous chitosan aerogel as a heterogeneous catalyst for C−S cross-coupling. ChemCatChem 7, 3307–3315 (2015).Article 
CAS 

Google Scholar 
Pettignano, A. et al. Alginic acid aerogel: A heterogeneous Brønsted acid promoter for the direct Mannich reaction. New J. Chem. 39, 4222–4226 (2015).Article 
CAS 

Google Scholar 
Schnepp, Z. Biopolymers as a flexible resource for nanochemistry. Angew. Chem. Int. Ed. 52, 1096–1108 (2013).Article 
CAS 

Google Scholar 
Khrunyk, Y., Lach, S., Petrenko, I. & Ehrlich, H. Progress in modern marine biomaterials research. Mar. Drugs 18, 589 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lee, I. Molecular self-assembly: Smart design of surface and interface via secondary molecular interactions. Langmuir 29, 2476–2489. https://doi.org/10.1021/la304123b (2013).Article 
CAS 
PubMed 

Google Scholar 
Shaheed, N., Javanshir, S., Esmkhani, M., Dekamin, M. G. & Naimi-Jamal, M. R. Synthesis of nanocellulose aerogels and Cu-BTC/nanocellulose aerogel composites for adsorption of organic dyes and heavy metal ions. Sci. Rep. 11, 18553 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Abdullah, M. A. et al. Processing Aspects and biomedical and environmental applications of sustainable nanocomposites containing nanofillers. In Sustainable Polymer Composites and Nanocomposites, (eds Inamuddin et al.) 727–757 (Springer, Cham, 2019). https://doi.org/10.1007/978-3-030-05399-4_25Dekamin, M. G. et al. Alginic acid: A highly efficient renewable and heterogeneous biopolymeric catalyst for one-pot synthesis of the Hantzsch 1,4-dihydropyridines. RSC Adv. 4, 56658–56664. https://doi.org/10.1039/C4RA11801D (2014).Article 
ADS 
CAS 

Google Scholar 
Ilkhanizadeh, S., Khalafy, J. & Dekamin, M. G. Sodium alginate: A biopolymeric catalyst for the synthesis of novel and known polysubstituted pyrano[3,2-c]chromenes. Int. J. Biol. Macromol. 140, 605–613. https://doi.org/10.1016/j.ijbiomac.2019.08.154 (2019).Article 
CAS 
PubMed 

Google Scholar 
Dekamin, M. G. et al. Alginic acid: A mild and renewable bifunctional heterogeneous biopolymeric organocatalyst for efficient and facile synthesis of polyhydroquinolines. Int. J. Biol. Macromol. 108, 1273–1280. https://doi.org/10.1016/j.ijbiomac.2017.11.050 (2018).Article 
CAS 
PubMed 

Google Scholar 
Rostami, N., Dekamin, M. G. & Valiey, E. Chitosan-EDTA-cellulose bio-based network: A recyclable multifunctional organocatalyst for green and expeditious synthesis of Hantzsch esters. Carbohydr. Polym. Technol. Appl. 5, 100279. https://doi.org/10.1016/j.carpta.2022.100279 (2023).Article 
CAS 

Google Scholar 
Bezerra, M. A., Santelli, R. E., Oliveira, E. P., Villar, L. S. & Escaleira, L. A. Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 76, 965–977. https://doi.org/10.1016/j.talanta.2008.05.019 (2008).Article 
CAS 
PubMed 

Google Scholar 
Hill, W. J. & Hunter, W. G. A review of response surface methodology: A literature survey. Technometrics 8, 571–590. https://doi.org/10.1080/00401706.1966.10490404 (1966).Article 
MathSciNet 

Google Scholar 
Hamidi, F. et al. Acid red 18 removal from aqueous solution by nanocrystalline granular ferric hydroxide (GFH); optimization by response surface methodology & genetic-algorithm. Sci. Rep. 12, 1–15 (2022).Article 

Google Scholar 
Han, X.-X. et al. Syntheses of novel halogen-free Brønsted–Lewis acidic ionic liquid catalysts and their applications for synthesis of methyl caprylate. Green Chem. 17, 499–508 (2015).Article 
CAS 

Google Scholar 
Rehman, K. et al. Operational parameters optimization for remediation of crude oil-polluted water in floating treatment wetlands using response surface methodology. Sci. Rep. 12, 1–11 (2022).Article 

Google Scholar 
Kamari, S., Ghorbani, F. & Sanati, A. M. Adsorptive removal of lead from aqueous solutions by amine–functionalized magMCM-41 as a low–cost nanocomposite prepared from rice husk: Modeling and optimization by response surface methodology. Sustain. Chem. Pharm. 13, 100153. https://doi.org/10.1016/j.scp.2019.100153 (2019).Article 

Google Scholar 
Sanati, A. M., Kamari, S. & Ghorbani, F. Application of response surface methodology for optimization of cadmium adsorption from aqueous solutions by Fe3O4@SiO2@APTMS core–shell magnetic nanohybrid. Surf. Interfaces 17, 100374. https://doi.org/10.1016/j.surfin.2019.100374 (2019).Article 
CAS 

Google Scholar 
Guner, S. G. & Dericioglu, A. Nacre-mimetic epoxy matrix composites reinforced by two-dimensional glass reinforcements. RSC Adv. 6, 33184–33196 (2016).Article 
ADS 
CAS 

Google Scholar 
Shao, Y., Zhao, H.-P. & Feng, X.-Q. Optimal characteristic nanosizes of mineral bridges in mollusk nacre. RSC Adv. 4, 32451–32456 (2014).Article 
ADS 
CAS 

Google Scholar 
Jaji, A. Z. et al. Synthesis, characterization, and cytocompatibility of potential cockle shell aragonite nanocrystals for osteoporosis therapy and hormonal delivery. Nanotechnol. Sci. Appl. 10, 23 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Çam, M. & Aaby, K. Optimization of extraction of apple pomace phenolics with water by response surface methodology. J. Agric. Food Chem. 58, 9103–9111 (2010).Article 
PubMed 

Google Scholar 
Iwuchukwu, I. J. et al. Optimization of photosynthetic hydrogen yield from platinized photosystem I complexes using response surface methodology. Int. J. Hydrog. Energy 36, 11684–11692 (2011).Article 
CAS 

Google Scholar 
Hu, C. et al. Characterization and photocatalytic activity of noble-metal-supported surface TiO2/SiO2. Appl. Catal. A 253, 389–396 (2003).Article 
CAS 

Google Scholar 
Noda, L. K., de Almeida, R. M., Probst, L. F. D. & Gonçalves, N. S. Characterization of sulfated TiO2 prepared by the sol–gel method and its catalytic activity in the n-hexane isomerization reaction. J. Mol. Catal. A Chem. 225, 39–46 (2005).Article 
CAS 

Google Scholar 
Jalali-Heravi, M., Parastar, H. & Ebrahimi-Najafabadi, H. Characterization of volatile components of Iranian saffron using factorial-based response surface modeling of ultrasonic extraction combined with gas chromatography–mass spectrometry analysis. J. Chromatogr. A 1216, 6088–6097 (2009).Article 
CAS 
PubMed 

Google Scholar 
Sendzikiene, E., Sinkuniene, D., Kazanceva, I. & Kazancev, K. Optimization of low quality rapeseed oil transesterification with butanol by applying the response surface methodology. Renew. Energy 87, 266–272 (2016).Article 
CAS 

Google Scholar 
Das, R., Sarkar, S. & Bhattacharjee, C. Photocatalytic degradation of chlorhexidine—a chemical assessment and prediction of optimal condition by response surface methodology. J. Water Process Eng. 2, 79–86 (2014).Article 

Google Scholar 
Nandiwale, K. Y., Galande, N. D. & Bokade, V. V. Process optimization by response surface methodology for transesterification of renewable ethyl acetate to butyl acetate biofuel additive over borated USY zeolite. RSC Adv. 5, 17109–17116 (2015).Article 
ADS 
CAS 

Google Scholar 
Soltani, R. D. C. & Safari, M. Periodate-assisted pulsed sonocatalysis of real textile wastewater in the presence of MgO nanoparticles: Response surface methodological optimization. Ultrason. Sonochem. 32, 181–190 (2016).Article 

Google Scholar 
Tan, K. T., Lee, K. T. & Mohamed, A. R. A glycerol-free process to produce biodiesel by supercritical methyl acetate technology: An optimization study via response surface methodology. Biores. Technol. 101, 965–969 (2010).Article 
CAS 

Google Scholar 
Nagaraju, N., Peeran, M. & Prasad, D. Synthesis of isoamyl acetate usin NaX and NaY zeolites as catalysts. React. Kinet. Catal. Lett. 61, 155–160 (1997).Article 
CAS 

Google Scholar 
Pizzio, L. R. & Blanco, M. N. Isoamyl acetate production catalyzed by H3PW12O40 on their partially substituted Cs or K salts. Appl. Catal. A 255, 265–277 (2003).Article 
CAS 

Google Scholar 
Dekamin, M. G., Karimi, Z. & Farahmand, M. Tetraethylammonium 2-(N-hydroxycarbamoyl)benzoate: A powerful bifunctional metal-free catalyst for efficient and rapid cyanosilylation of carbonyl compounds under mild conditions. Catal. Sci. Technol. 2, 1375–1381. https://doi.org/10.1039/C2CY20037F (2012).Article 
CAS 

Google Scholar 
Dekamin, M. G., Sagheb-Asl, S. & Reza Naimi-Jamal, M. An expeditious synthesis of cyanohydrin trimethylsilyl ethers using tetraethylammonium 2-(carbamoyl)benzoate as a bifunctional organocatalyst. Tetrahedron Lett. 50, 4063–4066. https://doi.org/10.1016/j.tetlet.2009.04.090 (2009).Article 
CAS 

Google Scholar 
Alirezvani, Z., Dekamin, M. G. & Valiey, E. New hydrogen-bond-enriched 1,3,5-tris(2-hydroxyethyl) isocyanurate covalently functionalized MCM-41: An efficient and recoverable hybrid catalyst for convenient synthesis of acridinedione derivatives. ACS Omega 4, 20618–20633. https://doi.org/10.1021/acsomega.9b02755 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar  More

Hobbs, R. J. (ed.) Invasive Species in a Changing World (Island press, 2000).
Google Scholar 
Marbuah, G., Gren, I. M. & McKie, B. Economics of harmful invasive species: A review. Diversity 6, 500–523. https://doi.org/10.3390/d6030500 (2014).Article 

Google Scholar 
Doherty, T. S., Glen, A. S., Nimmo, D. G., Ritchie, E. G. & Dickman, C. R. Invasive predators and global biodiversity loss. PNAS 113, 11261–11265. https://doi.org/10.1073/pnas.1602480113 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
David, P. et al. Impacts of invasive species on food webs: A review of empirical data. Adv. Ecol. Res. 56, 1–60. https://doi.org/10.1016/bs.aecr.2016.10.001 (2017).Article 

Google Scholar 
Roy, H. E. et al. Developing a list of invasive alien species likely to threaten biodiversity and ecosystems in the European Union. Glob. Change Biol. 25, 1032–1048. https://doi.org/10.1111/gcb.14527 (2019).Article 
ADS 

Google Scholar 
Walther, G. R. et al. Alien species in a warmer world: Risks and opportunities. Trends Ecol. Evol. 24, 686–693. https://doi.org/10.1016/j.tree.2009.06.008 (2009).Article 
PubMed 

Google Scholar 
Peyton, J. et al. Horizon scanning for invasive alien species with the potential to threaten biodiversity and human health on a Mediterranean island. Biol. Invasions 21, 2107–2125. https://doi.org/10.1007/s10530-019-01961-7 (2019).Article 

Google Scholar 
Meyerson, L. A. & Mooney, H. A. Invasive alien species in an era of globalization. Front. Ecol. Environ. 5, 199–208. https://doi.org/10.1890/1540-9295(2007)5[199:IASIAE]2.0.CO;2 (2007).Article 

Google Scholar 
Hulme, P. E. Trade, transport and trouble: Managing invasive species pathways in an era of globalization. J. Appl. Ecol. 46, 10–18. https://doi.org/10.1111/j.1365-2664.2008.01600.x (2009).Article 

Google Scholar 
Lodge, D. M. Biol Invasions: Lessons for ecology. Trends Ecol. Evol. 8, 133–137. https://doi.org/10.1016/0169-5347(93)90025-K (1993).Article 
CAS 
PubMed 

Google Scholar 
Keller, S. R. & Taylor, D. R. History, chance and adaptation during biological invasion: Separating stochastic phenotypic evolution from response to selection. Ecol. Lett. 11, 852–866. https://doi.org/10.1111/j.1461-0248.2008.01188.x (2008).Article 
PubMed 

Google Scholar 
Ham, D., Kim, W. G., Lee, H., Choi, D. S. & Bae, Y. J. New Korean record of the mycophagous gall midge Asynapta groverae (Diptera: Cecidomyiidae) with its outbreak situation and ecological notes. Newsl. Entomol. Soc. Korea. 11, 25–30 (2018) (in Korean).
Google Scholar 
Grover, P. Studies on gall-midges from India XXXIV. On the study of Indian Porricondylini. Cecidologia Indica 6, 1–38 (1971).
Google Scholar 
Jiang, Y. X. & Bu, W. J. A newly recorded gall midge genus (Diptera, Cecidomyiidae) with a species, Asynapta groverae Jiang et Bu, nom. Nov. from China. Acta. Zootax. Sinica. 29, 786–789 (2004).
Google Scholar 
Bae, Y. J. Research report on the outbreak of the cecidomyiids (Diptera: Cecidomyiidae) from the Well-county apartment area in Songdo, Incheon. Incheon Metropolitan Development Corporation, Incheon 171 (2009) (in Korean).Ham, D. & Bae, Y. J. Description of immature stages of Asynapta groverae (Diptera: Cecidomyiidae). Bull. Entomol. Res. 34, 103–107 (2018).
Google Scholar 
Gagné, R. J. & Jaschhof, M. A Catalog of the Cecidomyiidae (Diptera) of the World. 5th Edition, Digital, 121–124 (2021).Jones, M., Mautner, A., Luenco, S., Bismarck, A. & John, S. Engineered mycelium composite construction materials from fungal biorefineries: A critical review. Mater. Des. 187, 108397. https://doi.org/10.1016/j.matdes.2019.108397 (2020).Article 
CAS 

Google Scholar 
Ross, K. G. & Shoemaker, D. D. Estimation of the number of founders of an invasive pest insect population: The fire ant Solenopsis invicta in the USA. Proc. R. Soc. B-Biol. Sci. 275, 2231–2240. https://doi.org/10.1098/rspb.2008.0412 (2008).Article 

Google Scholar 
Brandt, M., Van Wlgenburg, E. & Tsutsui, N. D. Global-scale analyses of chemical ecology and population genetics in the invasive Argentine ant. Mol. Ecol. 18, 997–1005. https://doi.org/10.1111/j.1365-294X.2008.04056.x (2009).Article 
CAS 
PubMed 

Google Scholar 
Amouroux, P., Normand, F., Nibouche, S. & Delatte, H. Invasive mango blossom gall midge, Procontarinia mangiferae (Felt) (Diptera: Cecidomyiidae) in Reunion Island: Ecological plasticity, permanent and structured populations. Biol. Invasions 15, 1677–1693. https://doi.org/10.1007/s10530-012-0400-0 (2013).Article 

Google Scholar 
Horst, C. P. & Lau, J. A. Genetic variation in invasive species response to direct and indirect species interactions. Biol. Invasions 17, 651–659. https://doi.org/10.1007/s10530-014-0756-4 (2015).Article 

Google Scholar 
Nei, M., Maruyama, T. & Chakraborty, R. The bottleneck effect and genetic variability in populations. Evolution 29, 1–10. https://doi.org/10.2307/2407137 (1975).Article 
PubMed 

Google Scholar 
Tsutsui, N. D. & Suarez, A. V. The colony structure and population biology of invasive ants. Conserv. Biol. 17, 48–58. https://doi.org/10.1046/j.1523-1739.2003.02018.x (2003).Article 

Google Scholar 
Freeland, J. Molecular markers in ecology. In (eds Freeland, J., Pertersen, S. & Kirk, H.) Oxford 31–62 (2011).Tsutsui, N. D., Suarez, A. V., Holway, D. A. & Case, T. J. Reduced genetic variation and the success of an invasive species. PNAS 97, 5948–5953. https://doi.org/10.1073/pnas.100110397 (2000).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Davis, M. A. Invasion Biology (Oxford University Press, 2009).
Google Scholar 
Yao, Y. X. et al. Genetic variation may have promoted the successful colonization of the invasive gall midge, Obolodiplosis robiniae, in China. Front. Genet. 11, 387. https://doi.org/10.3389/fgene.2020.00387 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Johnson, R. N. & Starks, P. T. A surprising level of genetic diversity in an invasive wasp: Polistes dominulus in the northeastern United States. Ann. Entomol. Soc. Am. 97, 732–737. https://doi.org/10.1603/0013-8746(2004)097[0732:ASLOGD]2.0.CO;2 (2004).Article 

Google Scholar 
Roderick, G. K. Geographic structure of insect populations: Gene flow, phylogeography, and their uses. Annu. Rev. Entomol. 41, 325–352. https://doi.org/10.1146/annurev.en.41.010196.001545 (1996).Article 
CAS 
PubMed 

Google Scholar 
Puillandre, N. et al. Genetic bottleneck in invasive species: The potato tuber moth adds to the list. Biol. Invasions 10, 319–333. https://doi.org/10.1007/s10530-007-9132-y (2008).Article 

Google Scholar 
Zhan, A., Macisaac, H. J. & Cristescu, M. E. Invasion genetics of the Ciona intestinalis species complex: From regional endemism to global homogeneity. Mol. Ecol. 19, 4678–4694. https://doi.org/10.1111/j.1365-294X.2010.04837.x (2010).Article 
CAS 
PubMed 

Google Scholar 
Mallez, S. et al. Worldwide invasion routes of the pinewood nematode: What can we infer from population genetics analyses?. Biol. Invasions 17(4), 1199–1213. https://doi.org/10.1007/s10530-014-0788-9 (2015).Article 

Google Scholar 
Tsutsui, N. D. & Case, T. J. Population genetics and colony structure of the Argentine ant (Linepithema humile) in its native and introduced ranges. Evolution 55, 976–985. https://doi.org/10.1111/j.0014-3820.2001.tb00614.x (2001).Article 
CAS 
PubMed 

Google Scholar 
Kim, H., Hoelmer, K. A. & Lee, S. Population genetics of the soybean aphid in North America and East Asia: Test for introduction between native and introduced populations. Biol. Invasions 19, 597–614. https://doi.org/10.1007/s10530-016-1299-7 (2017).Article 

Google Scholar 
Chen, M. H. & Dorn, S. Microsatellites reveal genetic differentiation among populations in an insect species with high genetic variability in dispersal, the codling moth, Cydia pomonella (L.) (Lepidoptera: Tortricidae). Bull. Entomol. Res. 100, 75–85 (2010).Article 
CAS 
PubMed 

Google Scholar 
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. https://doi.org/10.1038/hdy.2008.125 (2009).Article 
CAS 
PubMed 

Google Scholar 
Lesieur, V. et al. The rapid spread of Leptoglossus occidentalis in Europe: A bridgehead invasion. J. Pest Sci. 92, 189–200. https://doi.org/10.1007/s10340-018-0993-x (2019).Article 

Google Scholar 
Mutitu, E. K. et al. Reconstructing early routes of invasion of the bronze bug Thaumastocoris peregrinus (Hemiptera: Thaumastocoridae): Cities as bridgeheads for global pest invasions. Biol. Invasions 22, 2325–2338. https://doi.org/10.1007/s10530-020-02258-w (2020).Article 

Google Scholar 
Peccoud, J. et al. Host range expansion of an introduced insect pest through multiple colonizations of specialized clones. Mol. Ecol. 17(21), 4608–4618. https://doi.org/10.1111/j.1365-294X.2008.03949.x (2008).Article 
CAS 
PubMed 

Google Scholar 
Eyer, P. A., Moran, M. N., Blumenfeld, A. J. & Vargo, E. L. Development of a set of microsatellite markers to investigate sexually antagonistic selection in the invasive ant Nylanderia fulva. Insects 12, 643. https://doi.org/10.3390/insects12070643 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Schauer, B., Bong, J., Popp, C., Obermaier, E. & Feldhaar, H. Dispersal limitation of saproxylic insects in a managed forest? A population genetics approach. Basic Appl. Ecol. 32, 26–38. https://doi.org/10.1016/j.baae.2018.01.005 (2018).Article 

Google Scholar 
Bereczki, J., Póliska, S., Váradi, A. & Tóth, J. P. Incipient sympatric speciation via host race formation in Phengaris arion (Lepidoptera: Lycaenidae). Org. Divers. Evol. 20, 63–76. https://doi.org/10.1007/s13127-019-00418-y (2020).Article 

Google Scholar 
Selkoe, K. A. & Toonen, R. J. Microsatellites for ecologists: A practical guide to using and evaluating microsatellite markers. Ecol. Lett. 9, 615–629. https://doi.org/10.1111/j.1461-0248.2006.00889.x (2006).Article 
PubMed 

Google Scholar 
Miah, G. et al. A review of microsatellite markers and their applications in rice breeding programs to improve blast disease resistance. Int. J. Mol. Sci. 14, 22499–22528. https://doi.org/10.3390/ijms141122499 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lloyd, C. J., Norton, A. P., Hufbauer, R. A., Bogdanowicz, S. M. & Nissen, S. J. Microsatellite isolation from the gall midge Spurgia capitigena (Diptera: Cecidomyiidae), a biological control agent of leafy spurge. Mol. Ecol. Notes 4, 605–607. https://doi.org/10.1111/j.1471-8286.2004.00751.x (2004).Article 
CAS 

Google Scholar 
Bentur, J. S. et al. Isolation and characterization of microsatellite loci in the Asian rice gall midge (Orseolia oryzae) (Diptera: Cecidomyiidae). Int. J. Mol. Sci. 12, 755–772. https://doi.org/10.3390/ijms12010755 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hinomoto, N., Higaki, T., Abe, J., Yamane, M. & Yano, E. Development and characterization of 21 polymorphic microsatellite loci in the aphidophagous gall midge, Aphidoletes aphidimyza (Diptera: Cecidomyiidae). Appl. Entomol. Zool. 47, 165–171. https://doi.org/10.1007/s13355-012-0104-z (2012).Article 
CAS 

Google Scholar 
Mezghani-Khemakhem, M. et al. Development of new polymorphic microsatellite loci for the barley stem gall midge, Mayetiola hordei (Diptera: Cecidomyiidae) from an Enriched Library. Int. J. Mol. Sci. 13, 14446–14450. https://doi.org/10.3390/ijms131114446 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kim, H. et al. Development and characterization of 12 microsatellite loci from the blueberry gall midge Dasineura oxycoccana (Diptera: Cecidomyiidae). Appl. Entomol. Zool. 50, 415–418. https://doi.org/10.1007/s13355-015-0335-x (2015).Article 

Google Scholar 
Benzécri, J. P. Construction d’une classification ascendante hiérarchique par la recherche en chaîne des voisins réciproques. Cahiers de l’analyse des données. 7, 209–218 (1982).MATH 

Google Scholar 
Simberloff, D. Invasive species. In Conservation Biology for all (eds Sodhi, N. S. & Ehrlich, P. R.) 131–152 (Oxford University Press, 2010).Chapter 

Google Scholar 
Keum, E. et al. Morphological, genetic and symptomatic identification of an invasive jujube pest in Korea, Dasineura jujubifolia Jiao & Bu (Diptera: Cecidomyiidae). J. Asia Pac. Entomol. 101935, 2002. https://doi.org/10.1016/j.aspen.2022.101935 (2022).Article 

Google Scholar 
Jaschhof, M. & Jaschhof, C. New and rarely found species of asynaptine Porricondylinae (Diptera: Cecidomyiidae) in northern Europe. Zootaxa https://doi.org/10.12651/JSR.2019.8.2.238 (2019).Article 
PubMed 

Google Scholar 
Yuxia, J. & Wenjun, B. A newly recorded gall midge genus (Diptera, cecidomyiidae) with a species, Asynapta groverae Jiang et bu. nom. Nov. from China. Dong wu fen lei xue bao = Acta Zootaxonomica Sinica 29, 786–789 (2004).
Google Scholar 
Mamaev, M. & Krivosheina, N. P. The Larvae of the Gall Miges (CRC Press, 1992).
Google Scholar 
Dorchin, N., Harris, K. M. & Stireman, J. O. III. Phylogeny of the gall midges (Diptera, Cecidomyiidae, Cecidomyiinae): Systematics, evolution of feeding modes and diversification rates. Mol. Phylogenet. Evol. 140, 106602. https://doi.org/10.1016/j.ympev.2019.106602 (2019).Article 
PubMed 

Google Scholar 
Gilpin, M. E. Minimal viable populations: Processes of species extinction. Conserv. Biol. Sci. Scarcity Divers. (1986).Frankham, R., Ballou, J. D. & Briscoe, D. A. Introduction to Conservation Genetics (Cambridge University Press, 2002).Book 

Google Scholar 
Hedrick, P. W. Genetic polymorphism in heterogeneous environments: The age of genomics. Annu. Rev. Ecol. Syst. 37, 67–93. https://doi.org/10.1146/annurev.ecolsys.37.091305.110132 (2006).Article 

Google Scholar 
Kolbe, J. J. et al. Genetic variation increases during biological invasion by a Cuban lizard. Nature 431, 177–181. https://doi.org/10.1038/nature02807 (2004).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Frankham, R. Resolving the genetic paradox in invasive species. Heredity 94, 385–385. https://doi.org/10.1038/sj.hdy.6800634 (2005).Article 
CAS 
PubMed 

Google Scholar 
Sakai, A. K. et al. The population biology of invasive species. Annu. Rev. Ecol. Syst. 32, 305–332. https://doi.org/10.1146/annurev.ecolsys.32.081501.114037 (2001).Article 

Google Scholar 
Wagner, N. P. Parthenogenesis in the larva of insects. Sci. Mem. Kasan Univ. 1, 25–111 (1862) (in Russian).
Google Scholar 
Meinert, F. Miastor metraloas: yderlige oplysning om den af Prof. Nic. Wagner nyligt beskneune insektlarva, som formerer sig ved spinedannelse. Naturhistorisk Tidsskrqt R3(3), 37–43 (1864).
Google Scholar 
Wyatt, I. J. Pupal paedogenesis in the Cecidomyiidae (Diptera). II. Proceedings of the Royal Entomological Society of London. J. Entomol. Ser. A-Gen. 38, 136–144. https://doi.org/10.1111/j.1365-3032.1963.tb00768.x (1963).Article 

Google Scholar 
Wyatt, I. J. Immature stages of Lestremiinae (Diptera: Cecidomyiidae) infesting cultivated mushrooms. Trans. R. Entomol. Soc. Lond. 116, 15–27. https://doi.org/10.1111/j.1365-2311.1964.tb00823.x (1964).Article 

Google Scholar 
Panelius, I. J. A revision of the European gall midges of the subfamily Porricondylinae (Diptera: Itonididae). Acta Zool. Fenn. 13, 1–157 (1965).
Google Scholar 
Schüpbach, P. M. & Camenzind, R. Germ cell lineage and follicle formation in paedogenetic development of Mycophila speyeri Barnes (Diptera: Cecidomyiidae). Int. J. Insect Morphol. Embryol. 12, 211–223. https://doi.org/10.1016/0020-7322(83)90018-1 (1983).Article 

Google Scholar 
Sikora, T., Jaschhof, M., Mantič, M., Kaspřák, D. & Ševčík, J. Considerable congruence, enlightening conflict: molecular analysis largely supports morphology-based hypotheses on Cecidomyiidae (Diptera) phylogeny. Zool. J. Linn. Soc. 185, 98–110. https://doi.org/10.1093/zoolinnean/zly029 (2019).Article 

Google Scholar 
Gould, S. J. Ontogeny and Phylogeny (Harvard University Press, 1985).
Google Scholar 
Went, D. F. Paedogenesis in the dipteran insect Heteropeza pygmaea: An interpretation. Int. J. Invertebr. Reprod. 1, 21–30. https://doi.org/10.1080/01651269.1979.10553296 (1979).Article 

Google Scholar 
Hodin, J. & Riddiford, L. M. Parallel alterations in the timing of ovarian ecdysone receptor and ultraspiracle expression characterize the independent evolution of larval reproduction in two species of gall midges (Diptera: Cecidomyiidae). Dev. Genes Evol. 210, 358–372. https://doi.org/10.1007/s004270000079 (2000).Article 
CAS 
PubMed 

Google Scholar 
Olfert, O., Elliott, R. H. & Hartley, S. In Ecological Impacts of Non-native Invertebrates and Fungi on Terrestrial Ecosystems (eds Langor, D. W. & Sweeney, J.) 127–133 (Springer, 2008). https://doi.org/10.1007/978-1-4020-9680-8_9.Chapter 

Google Scholar 
Miao, J. et al. Long-distance wind-borne dispersal of Sitodiplosis mosellana Géhin (Diptera: Cecidomyiidae) in Northern China. J. Insect Behav. 26, 120–129. https://doi.org/10.1007/s10905-012-9346-4 (2013).Article 

Google Scholar 
Hao, Y. N. et al. Flight performance of the orange wheat blossom midge (Diptera: Cecidomyiidae). J. Econ. Entomol. 106, 2043–2047. https://doi.org/10.1603/EC13218 (2013).Article 
PubMed 

Google Scholar 
Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).CAS 
PubMed 

Google Scholar 
Luo, R. et al. SOAPdenovo2: An empirically improved memory-efficient short-read de novo assembler. GigaScience 1, 2047-217X-1–18. https://doi.org/10.1186/2047-217X-1-18 (2012).Article 

Google Scholar 
Jiang, H., Lei, R., Ding, S. W. & Zhu, S. Skewer: A fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinform. 15, 1–12. https://doi.org/10.1186/1471-2105-15-182 (2014).Article 
CAS 

Google Scholar 
Beier, S., Thiel, T., Münch, T., Scholz, U. & Mascher, M. MISA-web: A web server for microsatellite prediction. Bioinformatics 33, 2583–2585. https://doi.org/10.1093/bioinformatics/btx198 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rozen, S. & Skaletsky, H. Primer3 on the WWW for general users and for biologist programmers. In Bioinformatics Methods and Protocols. Methods in Molecular Biology™ Vol. 132 (eds Misener, S. & Krawetz, S. A.) (Humana Press, 2000). https://doi.org/10.1385/1-59259-192-2:365.Chapter 

Google Scholar 
Excoffier, L. & Lischer, H. E. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567. https://doi.org/10.1111/j.1755-0998.2010.02847.x (2010).Article 
PubMed 

Google Scholar 
Petit, R. J., Mousadik, A. E. & Pons, O. Identifying populations for conservation on the basis of genetic markers. Conserv. Biol. 12, 844–855. https://doi.org/10.1111/j.1523-1739.1998.96489.x (1998).Article 

Google Scholar 
Teacher, A. G. F. & Griffiths, D. J. HapStar: Automated haplotype network layout and visualization. Mol. Ecol. Resour. 11, 151–153. https://doi.org/10.1111/j.1755-0998.2010.02890.x (2011).Article 
CAS 
PubMed 

Google Scholar 
Rousset, F. Genepop’007: A complete re-implementation of the genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106. https://doi.org/10.1111/j.1471-8286.2007.01931.x (2008).Article 
PubMed 

Google Scholar 
Goudet, J. FSTAT, a program to estimate and test gene diversity and fixation indices (version 2.9.3). http://www2.unil.ch/popgen/softwares/fstat.htm (2001).Van Oosterhout, C. V., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICROCHECKER v. 2.2.3. (2006).Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x (2005).Article 
CAS 
PubMed 

Google Scholar 
Belkhir, K., Borsa, P., Chikhi, L., Raufaste, N. & Bonhomme, F. GENETIX 4.05, logiciel sous Windows TM pour la génétique des populations. Montpellier, France: Laboratoire Génome, Populations, Interactions, CNRS UMR 5000, Université de Montpellier II (2004). More

Study area and general observationsFour datasets, equating to four post-whaling timeframes, were used for this study: 1997 (32 years post-whaling), 2003/2004 (38/39 years post-whaling), 2008 (43 years post-whaling) and 2014/2015 (49/50 years post-whaling). Data collection for each timeframe occurred during the annual migration of humpback whales, from breeding grounds in the Great Barrier Reef, to feeding grounds in the Antarctic Ocean. The study site was located off the coast of Peregian Beach (north of Brisbane, in Queensland, Australia), which was approximately one-third of the way along their return migration route. Here, humpback whales were still exhibiting breeding behaviours, such as singing, males joining females as escorts, and males forming competitive groups around a central female. Field work took place in September and October of each year. Generally, the number of migrating groups increased per day to peak during late September and early October. Numbers then gradually fell until the end of the migration.For this study, a group was defined as cluster of whales within approximately 100 m of each other that were diving and surfacing together (as estimated by the land-based visual observers). Groups were constantly changing membership with animals joining and splitting from the group and tend to move at different speeds, and in different directions, whilst making general progress southwards. Groups, unless joining together, were separated by at least 2 km, meaning it was relatively easy to keep a separate track of each group (see below).Acoustic recordings were made from three to five hydrophone buoys moored in 18–28 m of water and arranged in a line or T-shaped array (Fig. 6). Each hydrophone buoy consisted of a surface buoy containing a custom-built pre-amplifier (+20 dB gain) and 41B sonobuoy VHF radio transmitter. A High Tech HTI-96-MIN hydrophone with built-in +40 dB pre-amplifier was suspended approximately 1 m above each buoy’s mooring. Signals were received onshore at a base station 1.5 to 2.5 km away using a directional Yagi antenna and type 8101, four-channel sonobuoy receiver. Singing whales were located by cross-correlating the same song sound arriving at the different hydrophones to determine time-of-arrival differences. These differences, together with an accurate knowledge of the positions of the hydrophones, were then used to determine the most likely location of the singer. Singers generally move slowly and calculating an acoustic position approximately every 10 min produced a detailed track of the singer.Fig. 6: Outline of the study site including the range of visual observations and the position of the acoustic tracking array.Illustrating the study site at Peregian Beach, north of Brisbane, east coast of Australia. The map indicates the position of the land-based station (Emu Mountain) and the acoustic base station along with the position of the 5-buoy hydrophone array. The outline designates the study area. Whales moved in a southerly direction through the area daily. Whale icons illustrate acoustically tracked singing whales (circled in blue) and visually tracked presumed males (black), females (orange), and calves (small black). The 5 km social circle radius for a focal singing (blue circle) and a non-singing (black circle) male are also illustrated. The map is taken from “Google Earth” with permission to print without the need to submit a request (Brand Resource Center | Products and Services – Geo Guidelines (about.google)).Full size imageMigrating groups were tracked visually (7am to 5pm, weather permitting) from a land-based elevated survey point, Emu Mountain (73 m elevation). A theodolite (Leica TM 1100) was used in conjunction with a notebook computer running Cyclopes software (E. Kniest, Univ. Newcastle, Australia) to track the groups in real-time and note group behaviours. The field of view was approximately 20 km in a north/south direction and 10 km offshore (Fig. 6). Humpback whale groups were observed ad libitum and tracked by teams of five people. When whale groups surfaced, the observers called the sighted behaviour, compass bearing, and angle from the group to the horizon (in reticules). Each observation included group identification letter, the time, group size and composition, whether a calf was present, direction of travel, and group location, either by using a binocular reticular measurement or a theodolite measurement. Joining and splitting animals were also noted. A join was defined as one of more animals actively moving towards a group to surface within 100 m and then match the group surfacing times. Examples of this include an individual singing or non-singing whale actively moving towards, and then joining, another individual or group of whales. If more animals subsequently moved in and joined the group, this was termed an additional join to that group. These additionally joined group usually comprised of a female-calf and more than one male escort, or three or more adults, with additional joiners highly likely to be male (21,25,26, supplementary results). On rare occasions a singing whale remained in one place but was joined by another individual. This was termed an additional join given there was no evidence the singer actively moved to join this animal. However, the rarity of these occurrences meant the allocation of this behaviour to additional join, rather than join, had no influence on the results.Some of the migrating animals were biopsied during the day for post-field later sexing. Note biopsied animals were sometimes part of different studies occurring at the field site30,50 and were not necessarily the animals used in this study. However, these biopsy results were used to test assumptions made in this study regarding the sex of joining whales and whales within the observed groups (supplementary results and supplementary note). Weather was noted hourly.Statistics and reproducibilityDefining the proximate effect of male density on individual mating tacticsFor this analysis, a specific period, the 2003/2004 dataset, was chosen as it had the most instances of identified singers and non-singers. Within this timeframe, whales were migrating through the study area at sufficiently low density to avoid confusion. After 2004, it became increasingly difficult to focally follow males.First, for singing males (n = 86), their location within the study area was recorded at the start of singing using the acoustic array. Whilst singing they remained in the same location or meandered slowly within a small area. Non-singing animals that were observed to join a group (n = 31) were assumed to be male (21,25,26,30, supplementary methods and supplementary results). For these joining animals, visual observations were backtracked for 10 to 15 min until they were sighted alone. They were only included in the analysis if they could be definitively backtracked using visual (theodolite) observations, with no opportunity for confusion with other whales in area (i.e., no other whales within 2 km).For each unaccompanied focal male, the number of, and roles, of other presumed males within 5 km radius from the focal whale (Fig. 6) was used as a measure of local male density. The 5 km radius was termed social circle and was chosen as the most likely communication space for their acoustic signals51. For singing focal whales, their social circle was estimated using their location when they began to sing. For non-singing focal males, their social circle was estimated using the backtracked theodolite position to when it was first sighted alone. Next, all groups within the 5 km social circle of the focal whale, along with each group composition (singing animal, lone animal, female and calf pair, female-calf and escort number, adult-only group with the number of adults) were recorded at that timepoint. It was not logistically possible to biopsy and sex all migrating animals, therefore, to estimate the number of males within their social circle several assumptions were made. These assumptions were also tested using a biopsy study carried out in the area (supplementary methods and supplementary results). Female-calf pairs were discounted as it was assumed all adults with a calf were female. It was assumed that female-calf pairs were being escorted by males (21,25,26, supplementary methods and supplementary results). Groups of multiple adults were assumed to be comprised of a likely single female, principal male escort and secondary male escorts or challengers (21,25,26, supplementary methods and supplementary results). Lone animals not involved in any group interactions, and not singing, were given a 70% chance of being male (supplementary note). Animals within adult pairs were given a 70% chance of being male given the likelihood of having a mix of female-male pairs and male-male pairs (21,30, supplementary results and supplementary note).All analysis models were carried out in R (version 3.4.0). The first analysis aimed to determine if the likelihood of first observing the focal individual as a singing or non-singing male was significantly related to local male density, as determined by the number of males within a 5 km radius, termed social circle. Singing whales were allocated a 0 and non-singing whales were allocated a 1. A generalised linear model structure was used, assuming a binomial distribution. Likely males within their social circle were divided into non-singing and singing males (to delineate tactics) and these were included as the two covariates.$${{{{{rm{Singing}}}}}},(0),{{{{{rm{or }}}}}},{{{{{rm{Non}}}}}}{mbox{-}}{{{{{rm{singing}}}}}},(1) sim {{{{{rm{Non}}}}}}{mbox{-}}{{{{{rm{singing}}}}}},{{{{{rm{males}}}}}}, 5,{{{{{rm{km}}}}}}+{{{{{rm{Singing}}}}}},{{{{{rm{whales}}}}}}, 5,{{{{{rm{km}}}}}}$$Each focal male was an independent sample given males were migrating southwards and extremely unlikely to back-track into the study area and therefore be resampled. Significance was set at p  More