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    Temporal analysis shows relaxed genetic erosion following improved stocking practices in a subarctic transnational brown trout population

    1.Mimura, M. et al. Understanding and monitoring the consequences of human impacts on intraspecific variation. Evol. Appl. 10(2), 121–139 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Leigh, D. M., Hendry, A. P., Vázquez-Domínguez, E. & Friesen, V. L. Estimated six per cent loss of genetic variation in wild populations since the industrial revolution. Evol. Appl. 12(8), 1505–1512 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Habel, J. C., Husemann, M., Finger, A., Danley, P. D. & Zachos, F. E. The relevance of time series in molecular ecology and conservation biology. Biol. Rev. 89(2), 484–492 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Klütsch, C. F. C. et al. Genetic changes caused by restocking and hydroelectric dams in demographically bottlenecked brown trout in a transnational subarctic riverine system. Ecol. Evol. 9(10), 6068–6081 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    5.Hansen, M. M., Fraser, D. J., Meier, K. & Mensberg, K.-L.D. Sixty years of anthropogenic pressure: A spatio-temporal genetic analysis of brown trout populations subject to stocking and population declines. Mol. Ecol. 18(12), 2549–2562 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Savary, R. et al. Stocking activities for the Arctic char in Lake Geneva: Genetic effects in space and time. Ecol. Evol. 7(14), 5201–5211 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    7.Hughes, J. B., Daily, G. C. & Ehrlich, P. R. Population diversity: its extent and extinction. Science 278, 689–692 (1997).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Perrier, C., Guyomard, R., Bagliniere, J.-L., Nikolic, N. & Evanno, G. Changes in the genetic structure of Atlantic salmon populations over four decades reveal substantial impacts of stocking and potential resiliency. Ecol. Evol. 3(7), 2334–2349 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Vøllestad, L. A. & Hesthagen, T. Stocking of freshwater fish in Norway: management goals and effects. Nordic J. Freshwater Res. 75, 143–152 (2001).
    Google Scholar 
    10.Christie, M. R., Marine, M. L., French, R. A., Waples, R. S. & Blouin, M. S. Effective size of a wild salmonid population is greatly reduced by hatchery supplementation. Heredity 109, 254–260 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Araki, H., Cooper, B. & Blouin, M. S. Carry-over effect of captive breeding reduces reproductive fitness of wild-born descendants in the wild. Biol. Lett. 5, 621–624 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.O’Sullivan, R. J. et al. Captive-bred Atlantic salmon released into the wild have fewer offspring than wild-bred fish and decrease population productivity. Proc. R. Soc. B 287, 20201671 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    13.Amundsen, P.-A. et al. Invasion of vendace Coregonus albula in a subarctic watercourse. Biol. Conserv. 88(3), 405–413 (1999).Article 

    Google Scholar 
    14.Jensen, H., Bøhn, T., Amundsen, P.-A. & Aspholm, P. E. Feeding ecology of piscivorous brown trout (Salmo trutta L.) in a subarctic watercourse. Ann. Zool. Fenn. 41(1), 319–328 (2004).
    Google Scholar 
    15.Jensen, H. et al. Predation by brown trout (Salmo trutta) along a diversifying prey community gradient. Can. J. Fish. Aquat. Sci. 65, 1831–1841 (2008).Article 

    Google Scholar 
    16.Jensen, H. et al. Food consumption rates of piscivorous brown trout (Salmo trutta) foraging on contrasting coregonid prey. Fish. Manag. Ecol. 22, 295–306 (2015).Article 

    Google Scholar 
    17.Haugland, Ø. Langtidsstudie av næringsøkologi og vekst hos storørret i Pasvikvassdraget. Mastergradsoppgave i biologi (Universitetet i Tromsø, Fakultet for Biovitenskap, fiskeri og økonomi, Institutt for arktisk og marin biologi, 2014).18.Gossieaux, P., Bernatchez, L., Sirois, P. & Garant, D. Impacts of stocking and its intensity on effective population size in Brook Charr (Salvelinus fontinalis) populations. Conserv. Genet. 20(4), 729–742 (2019).Article 

    Google Scholar 
    19.Pinter, K., Epifanio, J. & Unfer, G. Release of hatchery-reared brown trout (Salmo trutta) as a threat to wild populations? A case study from Austria. Fish. Res. 219, 105296 (2019).Article 

    Google Scholar 
    20.Wringe, B. F., Purchase, C. F. & Fleming, I. A. In search of a “cultured fish phenotype”: A systematic review, meta-analysis and vote-counting analysis. Rev. Fish Biol. Fish. 26(3), 351–373 (2016).Article 

    Google Scholar 
    21.Gossieaux, P. et al. Effects of genetic origin on phenotypic divergence in Brook Trout populations stocked with domestic fish. Ecosphere 11(5), e03119 (2020).Article 

    Google Scholar 
    22.Fleming, I. A., Jonsson, B. & Gross, M. R. Phenotypic divergence of sea-ranched, farmed, and wild salmon. Can. J. Fish. Aquat. Sci. 51, 2808–2824 (1994).Article 

    Google Scholar 
    23.Heath, D. D., Heath, J. W., Bryden, C. A., Johnson, R. M. & Fox, C. W. Rapid evolution of egg size in captive salmon. Science 299, 1738–1740 (2003).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    24.Naish, K. A., Seamons, T. R., Dauer, M. B., Hauser, L. & Quinn, T. P. Relationship between effective population size, inbreeding and adult fitness-related traits in a steelhead (Oncorhynchus mykiss) population released in the wild. Mol. Ecol. 22, 1295–1309 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    25.Van Oosterhout, C., Weetman, D. & Hutchinson, W. F. Estimation and adjustment of microsatellite null alleles in nonequilibrium populations. Mol. Ecol. Notes 6(1), 255–256 (2006).Article 

    Google Scholar 
    26.Rousset, F. Genepop’007: a complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Resour. 8(6), 103–106 (2008).PubMed 
    Article 

    Google Scholar 
    27.Peakall, R. & Smouse, 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 
    28.Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28(19), 2537–2539 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Szpiech, Z. A., Jacobsson, M. & Rosenberg, N. A. ADZE: A rarefaction approach for counting alleles private to combinations of populations. Bioinformatics 24(21), 2498–2504 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Waples, R. S. & Anderson, E. C. Purging putative siblings from population genetic data sets: A cautionary view. Mol. Ecol. 26(5), 1211–1224 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155(2), 945–959 (2000).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    33.Pew, J., Muir, P. H., Wang, J. & Frasier, T. R. Related: An R package for analysing pairwise relatedness from codominant molecular markers. Mol. Ecol. Resour. 15(3), 557–561 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    34.Piry, S., Luikart, G. & Cornuet, J.-M. Bottleneck: A computer program for detecting recent reductions in the effective population size using allele frequency data. J. Heredity 90(4), 502–503 (1999).Article 

    Google Scholar 
    35.Cornuet, J. M. & Luikart, G. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144(4), 2001–2014 (1996).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Peery, M. Z. et al. Reliability of genetic bottleneck tests for detecting recent population declines. Mol. Ecol. 21(14), 3403–3418 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    37.Luikart, G. Usefulness of molecular markers for detecting population bottlenecks and monitoring genetic change. Ph. D. Thesis. (University of Montana, 1997).38.Do, C. et al. NEESTIMATOR v2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14, 209–214 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    39.Waples, R. S. & Do, C. LDNE: A program for estimating effective population size from data on linkage disequilibrium. Mol. Ecol. Resour. 8, 753–756 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40.Zhdanova, O. L. & Pudovkin, A. I. Nb_HetEx: A program to estimate the effective number of breeders. J. Hered. 99(6), 694–695 (2008).PubMed 
    Article 

    Google Scholar 
    41.Nomura, T. Estimation of effective number of breeders from molecular coancestry of single cohort sample. Evol. Appl. 1, 462–474 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.Jones, O. R. & Wang, J. COLONY: A program for parentage and sibship inference from multilocus genotype data. Mol. Ecol. Resour. 10, 551–555 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    43.Wang, J. A. comparison of single-sample estimators of effective population sizes from genetic data. Mol. Ecol. 25, 4692–4711 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Nei, M. & Chesser, R. K. Estimation of fixation indexes and gene diversities. Ann. Hum. Genet. 47(3), 253–259 (1983).CAS 
    PubMed 
    MATH 
    Article 
    PubMed Central 

    Google Scholar 
    45.Jost, L. Gst and its relatives do not measure differentiation. Mol. Ecol. 17(18), 4015–4026 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodological) 57(1), 289–300 (1995).MathSciNet 
    MATH 

    Google Scholar 
    47.R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (R Foundation for Statistical Computing, 2019).48.White, T., van der Ende, J. & Nichols, T. E. Beyond Bonferroni revisited: Concerns over inflated false positive research findings in the fields of conservation genetics, biology, and medicine. Conserv. Genet. 20, 927–937 (2019).Article 

    Google Scholar 
    49.Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies. Genetics 164(4), 1567–1587 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Hubisz, M. J., Falush, D., Stephens, M. & Pritchard, J. K. Inferring weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 9(5), 1322–1332 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    51.Miller, M. A., Pfeiffer, W. & Schwartz, T. Creating the CIPRES science gateway for inference of large phylogenetic trees. in 2010 Gateway Computing Environments Workshop (GCE) 1–8 (2010).52.Besnier, F. & Glover, K. A. ParallelStructure: A R package to distribute parallel runs of the population genetics program STRUCTURE on multi-core computers. PLoS ONE 8(7), e70651 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    53.Li, Y.-L. & Liu, J.-X. StructureSelector: A web-based software to select and visualize the optimal number of clusters using multiple methods. Mol. Ecol. Resour. 18(1), 176–177 (2018).PubMed 
    Article 

    Google Scholar 
    54.Puechmaille, S. J. The program structure does not reliably recover the correct population structure when sampling is uneven: Subsampling and new estimators alleviate the problem. Mol. Ecol. Resour. 16(3), 608–627 (2016).PubMed 
    Article 

    Google Scholar 
    55.Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. CLUMPAK: A program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15(5), 1179–1191 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    56.Anderson, E. C. & Dunham, K. K. The influence of family groups on inferences made with the program structure. Mol. Ecol. Resour. 8, 1219–1229 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    57.Dray, S. & Dufour, A. The ade4 package: Implementing the duality diagram for ecologists. J. Stat. Softw. 22(4), 1–20 (2007).Article 

    Google Scholar 
    58.Levene, H. Robust tests for equality of variances. in Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling (Olkin, I., Hotelling, H. et al. eds.). 278–292 (Stanford University Press, 1960).59.Kassambara, A. rstatix: Pipe-Friendly Framework for Basic Statistical Tests. R package version 0.4.0. https://CRAN.R-project.org/package=rstatix (2020).60.Wang, J. An estimator for pairwise relatedness using molecular markers. Genetics 160, 1203–1215 (2002).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.White, S. L., Miller, W. L., Dowell, S. A., Bartron, M. L. & Wagner, T. Limited hatchery introgression into wild brook trout (Salvelinus fontinalis) populations despite reoccurring stocking. Evol. Appl. 11(9), 1567–1581 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    62.Lehnert, S. J. et al. Multiple decades of stocking has resulted in limited hatchery introgression in wild brook trout (Salvelinus fontinalis) populations of Nova Scotia. Evol. Appl. 13(5), 1069–1089 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    63.Knudsen, C. M. et al. Comparison of life history traits between first-generation hatchery and wild upper Yakima River spring Chinook salmon. Trans. Am. Fish. Soc. 135, 1130–1144 (2006).Article 

    Google Scholar 
    64.Hansen, M. M. & Mensberg, K.-L.D. Admixture analysis of stocked brown trout populations using mapped microsatellite DNA markers: Indigenous trout persist in introgressed populations. Biol. Lett. 5, 656–659 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    65.Christie, M. R., Ford, M. J. & Blouin, M. S. On the reproductive success of early-generation hatchery fish in the wild. Evol. Appl. 7, 883–896 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    66.Fraser, D. J. et al. Population correlates of rapid captive-induced maladaptation in a wild fish. Evol. Appl. 12, 1305–1317 (2019).PubMed 
    Article 

    Google Scholar 
    67.Fischer, J. R. et al. Growth, condition, and trophic relations of stocked trout in southern Appalachian mountain streams. Trans. Am. Fish. Soc. 148, 771–784 (2019).CAS 
    Article 

    Google Scholar 
    68.Hendry, A. P. & Day, T. Population structure attributable to reproductive time: Isolation by time and adaptation by time. Mol. Ecol. 14, 901–916 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    69.Gauthey, Z. et al. Brown trout spawning habitat selection and its effects on egg survival. Ecol. Freshwater Fish 26, 133–140 (2017).Article 

    Google Scholar 
    70.Dupont, P.-P., Bourret, V. & Bernatchez, L. Interplay between ecological, behavioural and historical factors in shaping the genetic structure of sympatric walleye populations (Sander vitreus). Mol. Ecol. 16, 937–951 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    71.Sandoval-Castillo, J. et al. SWINGER: A user-friendly computer program to establish captive breeding groups that minimize relatedness without pedigree information. Mol. Ecol. Resour. 17, 278–287 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar  More

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    Spatiotemporal effects on dung beetle activities in island forests-home garden matrix in a tropical village landscape

    1.Chapin, F. S. & Díaz, S. Interactions between changing climate and biodiversity: Shaping humanity’s future. PNAS 2117, 6295–6296 (2020).Article 
    CAS 

    Google Scholar 
    2.Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    3.Scranton, K. & Amarasekare, P. Predicting phenological shifts in a changing climate. PNAS 114, 13212–13217 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    4.Madrigal-González, J. et al. Disentangling the relative role of climate change on tree growth in an extreme Mediterranean environment. Sci. Total Environ. 642, 619–628 (2018).ADS 
    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    5.Angilletta, M. J. Thermal adaptation: A Theoretical And Empirical Synthesis (Oxford University Press, 2009).Book 

    Google Scholar 
    6.Andresen, E. Effects of season and vegetation type on community organization of Dung beetles in a tropical dry forest. Biotropica 37, 291–300 (2005).Article 

    Google Scholar 
    7.Liberal, C. N., Farias, A. M. I. & Meiado, M. V. How habitat change and rainfall affect dung beetle diversity in Caatinga, a Brazilian semi-arid ecosystem. J. Insect Sci. 11, 114 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Nunes, C. A., Braga, R. F., Figueira, J. E. C., Neves, F. D. S. & Fernandes, G. W. Dung beetles along a tropical altitudinal gradient: Environmental filtering on taxonomic and functional diversity. PLoS One 11, e0157442 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    9.da Silva, P. G. & Cassenote, S. Environmental drivers of species composition and functional diversity of dung beetles along the Atlantic Forest-Pampa transition zone. Austral Ecol. 44, 786–799 (2019).Article 

    Google Scholar 
    10.Alvarado, F., Salomão, R. P., Hernandez-Rivera, Á. & de Araujo Lira, A. F. Different responses of dung beetle diversity and feeding guilds from natural and disturbed habitats across a subtropical elevational gradient. Acta Oecol. 104, 103533 (2020).Article 

    Google Scholar 
    11.Barragán, F., Moreno, C. E., Escobar, F., Halffter, G. & Navarrete, D. Negative impacts of human land use on dung beetle functional diversity. PLoS One 6, e17976 (2011).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    12.Costa, C. et al. Variegated tropical landscapes conserve diverse dung beetle communities. PeerJ 5, e3125 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    13.Gómez-Cifuentes, A., Gómez, V. C. G., Moreno, C. E. & Zurita, G. A. Tree retention in cattle ranching systems partially preserves dung beetle diversity and functional groups in the semideciduous Atlantic forest: The role of microclimate and soil conditions. Basic Appl. Ecol. 34, 64–74 (2019).Article 

    Google Scholar 
    14.Salomão, R. P. et al. Urbanization effects on dung beetle assemblages in a tropical city. Ecol. Indic. 103, 665–675 (2019).Article 

    Google Scholar 
    15.Correa, C. M., Lara, M. A., Puker, A., Noriega, J. A. & Korasaki, V. Quantifying responses of dung beetle assemblages to cattle grazing removal over a short-term in introduced Brazilian pastures. Acta Oecol. 110, 103681 (2021).Article 

    Google Scholar 
    16.Romero-Alcaraz, E. & Avila, J. M. Effect of elevation and type of habitat on the abundance and diversity of scarabaeoid dung beetles (Scarabaeoidea) assemblages in a Mediterranean area from Southern Iberian Peninsula. Zool. Stud. 39, 351–359 (2000).
    Google Scholar 
    17.Halffter, G. & Arellano, L. Response of dung beetle diversity to human-induced changes in a tropical landscape. Biotropica 34, 144–154 (2002).Article 

    Google Scholar 
    18.Rios-Diaz, C. L. et al. Sheep herding in small grasslands promotes dung beetle diversity in a mountain forest landscape. J. Insect Conserv. 25, 13–26 (2021).Article 

    Google Scholar 
    19.Krell, F. T., Krell-Westerwalbesloh, S., Weiß, I., Eggleton, P. & Linsenmair, K. E. Spatial separation of Afrotropical dung beetle guilds: A trade-off between competitive superiority and energetic constraints (Coleoptera: Scarabaeidae). Ecography 26, 210–222 (2003).Article 

    Google Scholar 
    20.Verdú, J. R., Díaz, A. & Galante, E. Thermoregulatory strategies in two closely related sympatric Scarabaeus species (Coleoptera: Scarabaeinae). Physiol. Entomol. 29, 32–38 (2004).Article 

    Google Scholar 
    21.Verdú, J. R., Arellano, L. & Numa, C. Thermoregulation in endotermic dung beetles (Coleoptera: Scarabaeidae): Effect of body size and ecophysiological constraints in flight. J. Insect Physiol. 52, 854–860 (2006).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    22.Verdú, J. R., Arellano, L., Numa, C. & Micó, E. Roles of endothermy in niche differentiation for ball-rolling dung beetles (Coleoptera: Scarabaeidae) along an altitudinal gradient. Ecol. Entomol. 32, 544–551 (2007).Article 

    Google Scholar 
    23.Verdú, J. R., Alba-Tercedor, J. & Jiménez-Manrique, M. Evidence of different thermoregulatory mechanisms between two sympatric Scarabaeus species using infrared thermography and microcomputer tomography. PLoS One 7, e33914 (2012).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    24.Giménez-Gómez, V. C., Lomáscolo, S. B., Zurita, G. A. & Ocampo, F. Daily activity patterns and thermal tolerance of three sympatric dung beetle species (Scarabaeidae: Scarabaeinae: Eucraniini) from the Monte Desert, Argentina. Neotrop. Entomol. 47, 821–827 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Gómez, V. C. G., Verdú, J. R. & Zurita, G. A. Thermal niche helps to explain the ability of dung beetles to exploit disturbed habitats. Sci. Rep. 10, 1–14 (2020).ADS 
    Article 
    CAS 

    Google Scholar 
    26.Gotcha, N., Machekano, H., Cuthbert, R. N. & Nyamukondiwa, C. Heat tolerance may determine activity time in coprophagic beetle species (Coleoptera: Scarabaeidae). Insect Sci. 28, 1076–2086 (2020).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    27.Gallego, B., Verdú, J. R. & Lobo, J. M. Comparative thermoregulation between different species of dung beetles (Coleoptera: Geotrupinae). J. Therm. Biol. 74, 84–91 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Feer, F. Effects of dung beetles (Scarabaeidae) on seeds dispersed by howler monkeys (Alouatta seniculus) in the French Guianan rainforest. J. Trop. Ecol. 15, 1–14 (1999).Article 

    Google Scholar 
    29.Feer, F. & Pincebourne, S. Diel flight activity and ecological segregation within an assemblage of tropical forest dung and carrion beetles. J. Trop. Ecol. 21, 21–30 (2005).Article 

    Google Scholar 
    30.Niino, M. et al. Diel flight activity and habitat preference of dung beetles (Coleoptera: Scarabaeidae) in Peninsular Malaysia. Raffles Bull. Zool. 62, 795–804 (2014).
    Google Scholar 
    31.da Silva, P. G., Lobo, J. M. & Hernandez, M. I. M. The role of habitat and daily activity patterns in explaining the diversity of mountain Neotropical dung beetle assemblages. Austral Ecol. 44, 300–312 (2019).Article 

    Google Scholar 
    32.Cambefort, Y. Dung beetles in tropical savannas in Africa. In Dung Beetle Ecology (eds Hanski, I. & Camberfort, Y.) 156–178 (Princeton University Press, 1991).Chapter 

    Google Scholar 
    33.Hernández, M. I. M. The night and day of dung beetles (Coleoptera, Scarabaeidae) in the Serra do Japi, Brazil: Elytra colour related to daily activity. Rev. Bras. Entomol. 46, 597–600 (2002).Article 

    Google Scholar 
    34.Krell-Westerwalbesloh, S., Krell, F. T. & Eduard Linsenmair, K. Diel separation of Afrotropical dung beetle guilds—avoiding competition and neglecting resources (Coleoptera: Scarabaeoidea). J. Nat. Hist. 38, 2225–2249 (2004).Article 

    Google Scholar 
    35.Rajesh, T. P., Prashanth Ballullaya, U., Unni, A. P., Parvathy, S. & Sinu, P. A. Interactive effects of urbanization and year on invasive and native ant diversity of sacred groves of south India. Urban Ecosyst. 23, 1335–1348 (2020).Article 

    Google Scholar 
    36.Prashanth Ballullaya, U. et al. Stakeholder motivation for the conservation of sacred groves in south India: An analysis of environmental perceptions of rural and urban neighbourhood communities. Land Use Policy 89, 104–213 (2019).Article 

    Google Scholar 
    37.Manoj, K. et al. Diversity of platygastridae in leaf litter and understory layers of tropical rainforests of the Western Ghats biodiversity hotspot, India. Environ. Entomol. 46, 685–692 (2017).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    38.Rajesh, T., Unni, A., Prashanth Ballullaya, U., Manoj, K. & Sinu, P. A. An insight into the quality of sacred groves—an island habitat—using leaf-litter ants as an indicator in a context of urbanization. J. Trop. Ecol. 37, 82–90. https://doi.org/10.1017/S0266467421000134 (2021).Article 

    Google Scholar 
    39.Asha, G., Navya, K. K., Rajesh, T. P. & Sinu, P. A. Roller dung beetles of dung piles suggest habitats are alike, but that of guarding pitfall traps suggest habitat are different. J. Trop. Ecol. https://doi.org/10.1017/S0266467421000225 (2021) (Accepted).Article 

    Google Scholar 
    40.Krell, F. T. Dung beetle sampling protocols. Denver Museum of Nature and Science Technical Report 6, 1–11 (2007).
    Google Scholar 
    41.Arrow, G. J. The Fauna of British India including Ceylon and Burma, Coleoptera: Lamellicornia (Coprinae) (Taylor and Francis, 1931).
    Google Scholar 
    42.Sabu, T. K., Vinod, K. V. & Vineesh, P. J. Guild structure, diversity and succession of dung beetles associated with Indian elephant dung in South Western Ghats forests. J. Insect Sci. 6, 6–17 (2006).Article 

    Google Scholar 
    43.Beiroz, W. et al. Dung beetle community dynamics in undisturbed tropical forests: Implications for ecological evaluations of land-use change. Insect Conserv. Divers. 10, 94–106 (2017).Article 

    Google Scholar 
    44.De Caceres, M. & Legendre, P. Associations between species and groups of sites: Indices and statistical inference. Ecology 90, 3566–3574 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    45.McGeoch, M. A., van Rensburg, B. J. & Botes, A. The verification and application of bioindicators: A case of study of dung beetles in a savanna ecosystem. J. Appl. Ecol. 39, 661–672 (2002).Article 

    Google Scholar 
    46.Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: Interpolation and extrapolation for species diversity. R package version 2.0.12 (2016).47.Oksanen, J. et al. vegan: Community Ecology Package. R package, version 2.5‐3 (2018).48.Caveney, S., Scholtz, C. H. & McIntyre, P. Patterns of daily flight activity in Onitine dung beetles (Scarabaeinae: Onitini). Oecologia 103, 444–452 (1995).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    49.Spector, S. & Ayzama, S. Rapid turnover and edge effects in dung beetle assemblages (Scarabaeidae) at a Bolivian Neotropical forest-savanna ecotone. Biotropica 35, 394–404 (2003).
    Google Scholar 
    50.Escobar, F. S. Diversity and composition of dung beetle (Scarabaeinae) assemblages in a heterogeneous Andean landscape. Trop. Zool. 17, 123–136 (2004).Article 

    Google Scholar 
    51.Lobo, J. M., Lumaret, J. P. & Jay-Robert, P. Sampling dung beetles in French Mediterranean area: Effects of abiotic factors and farm practices. Pedobiologia 42, 252–266 (1998).
    Google Scholar 
    52.Zamora, J., Verdu, J. R. & Galante, E. Species richness in Mediterranean agroecosystems: Spatial and temporal analysis for biodiversity conservation. Biol. Conserv. 134, 113–121 (2007).Article 

    Google Scholar 
    53.Calatayud, J. et al. Multidimensionality in the thermal niches of dung beetles could limit species’ responses to temperature changes. BioRxiv. https://doi.org/10.1101/2020.11.15.383612(2021) (2020).Article 

    Google Scholar 
    54.Iannuzzi, L., Salomão, R. P., Costa, F. C. & Liberal, C. N. Environmental patterns and daily activity of dung beetles (Coleoptera: Scarabaeidae) in the Atlantic Rainforest of Brazil. Entomotropica 31, 196–207 (2016).
    Google Scholar 
    55.Venugopal, K. S., Thomas, S. K. & Flemming, A. T. Diversity and community structure of dung beetles (Coleoptera: Scarabaeinae) associated with semi-urban fragmented agricultural land in the Malabar coast in southern India. JoTT 4, 2685–2692 (2012).
    Google Scholar 
    56.Price, P. W. Insect Ecology (Wiley, 1984).
    Google Scholar 
    57.Hanski, I. & Cambefort, Y. Dung Beetle Ecology (Princeton University Press, 1991).Book 

    Google Scholar 
    58.Finn, J. A. & Gittings, T. A review of competition in north temperate dung beetle communities. Ecol. Entomol. 28, 1–13 (2003).Article 

    Google Scholar 
    59.Doube, B. Dung beetles of southern Africa. In Dung Beetle Ecology (eds Hanski, I. & Cambefort, Y.) 133–155 (Princeton University Press, 1991).Chapter 

    Google Scholar 
    60.Gómez-Cifuentes, A., Munevar, A., Gimenez, V. C., Gatti, M. G. & Zurita, G. A. Influence of land use on the taxonomic and functional diversity of dung beetles (Coleoptera: Scarabaeinae) in the southern Atlantic forest of Argentina. J. Insect Conserv. 21, 147–156 (2017).Article 

    Google Scholar 
    61.Estrada, A. & Coates-Estrada, R. Howler monkeys (Alouatta palliate), dung beetles (Scarabaeidae) and seed dispersal-ecological interactions in the tropical rain-forest of Los-Tuxtlas, Mexico. J. Trop. Ecol. 7, 459–474 (1991).Article 

    Google Scholar 
    62.Slade, E. M., Mann, D. J., Villanueva, J. F. & Lewis, O. T. Experimental evidence for the effects of dung beetle functional group richness and composition on ecosystem function in a tropical forest. J. Anim. Ecol. 76, 1084–1104 (2007).Article 

    Google Scholar 
    63.Vinod, K. V. & Sabu, T. K. Species composition and community structure of dung beetles attracted to dung of gaur and elephant in the moist forests of South Western Ghats. J. Insect Sci. 7, 1–14 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    64.Chao, A., Simon-Freeman, R. & Grether, G. Patterns of Niche partitioning and alternative reproductive strategies in an east African dung beetle assemblage. J. Insect Behav. 26, 525–539 (2013).Article 

    Google Scholar 
    65.Sowig, P. Habitat selection and offspring survival rate in three paracoprid dung beetles: The influence of soil type and soil moisture. Ecography 18, 147–154 (1995).Article 

    Google Scholar 
    66.Sowig, P. Brood care in the dung beetle Onthophagus vacca (Coleoptera: Scarabaeidae): The effect of soil moisture on time budget, nest structure, and reproductive success. Ecography 19, 254–258 (1996).
    Google Scholar 
    67.Nichols, E. et al. Ecological functions and ecosystem services provided by Scarabaeinae dung beetles. Biol. Conserv. 141, 1461–1474 (2008).Article 

    Google Scholar 
    68.Latha, T. & Thomas, S. K. Edge effect on roller dung beetles (Coleoptera: Scarabaeidae: Scarabaeinae) in the moist South Western Ghats. J. Entomol. 8, 1044–1047 (2020).
    Google Scholar 
    69.Bartholomew, G. A. & Heinrich, B. Endothermy in African dung beetles during flight, ball making, and ball rolling. J. Exp. Biol. 73, 65–83 (1978).Article 

    Google Scholar 
    70.Boonrotpong, S., Sotthibandhu, S. & Pholpunthin, C. Species composition of dung beetles in the primary and secondary forests at Ton Nga Chang Wildlife Sanctuary. Sci. Asia 30, 59–65 (2004).Article 

    Google Scholar 
    71.Davis, A. J. Does reduced-impact logging help preserve biodiversity in tropical rainforest? A case study from Borneo using dung beetles (Coleoptera: Scarabaeoidea) as indicators. Environ. Entomol. 29, 467–475 (2000).Article 

    Google Scholar 
    72.Davis, A. J. et al. Dung beetles as indicators of change in the forests of northern Borneo. J. Appl. Ecol. 38, 593–616 (2001).Article 

    Google Scholar 
    73.Jayaprakash, S. B. Taxonomy guild structure and dung specificity of dung beetles in a coffee plantation belt in south Wayanad. Ph.D. thesis, University of Calicut. http://hdl.handle.net/10603/222605 (2018). More

  • in

    Tropical cyclones shape mangrove productivity gradients in the Indian subcontinent

    1.Kossin, J. P., Knapp, K. R., Olander, T. L. & Velden, C. S. Global increase in major tropical cyclone exceedance probability over the past four decades. Atmos. Planet. Sci. 117, (2020).2.Smith, T. J. et al. Cumulative impacts of hurricanes on florida mangrove ecosystems: sediment deposition, storm surges and land crabs of corcovado national park view project hydrologic response to increased water management capability at the great dismal swamp National Wildl. Wetlands https://doi.org/10.1672/08-40.1 (2009).Article 

    Google Scholar 
    3.Kumar, S., Lal, P. & Kumar, A. Turbulence of tropical cyclone ‘Fani’ in the Bay of Bengal and Indian subcontinent. Nat. Hazards 103, 1613–1622 (2020).Article 

    Google Scholar 
    4.Jayanta, B. South Bengal ravaged by Cyclone Amphan. DownToEarth (2020).5.Castañeda-Moya, E. et al. Hurricanes fertilize mangrove forests in the Gulf of Mexico (Florida Everglades, USA). Proc. Natl. Acad. Sci. U. S. A. 117, 4831–4841 (2020).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    6.Donato, D. C. et al. Mangroves among the most carbon-rich forests in the tropics. Nat. Geosci. 4, 293–297 (2011).ADS 
    CAS 
    Article 

    Google Scholar 
    7.Lovelock, C. E. Soil respiration and belowground carbon allocation in mangrove forests. Ecosystems 11, 342–354 (2008).CAS 
    Article 

    Google Scholar 
    8.Alongi, D. M. Mangrove forests: Resilience, protection from tsunamis, and responses to global climate change. Estuar. Coast. Shelf Sci. 76, 1–13 (2008).ADS 
    Article 

    Google Scholar 
    9.Lovelock, C. E., Ruess, R. W. & Feller, I. C. Co2 efflux from cleared mangrove peat. PLoS ONE 6, 1–4 (2011).Article 
    CAS 

    Google Scholar 
    10.FSI. India State of Forest Report, Ministry of Environment, Forest & Climate Change. (2019).11.Mandal, R. N. & Naskar, K. R. Diversity and classification of Indian mangroves: A review. Trop. Ecol. 49, 131–146 (2008).
    Google Scholar 
    12.Ragavan, P. et al. A review of the mangrove floristics of India. Taiwania 61, 224–242 (2016).
    Google Scholar 
    13.Blasco, F., Janodet, E. & Bellan, M. F. Natural Hazards and Mangroves in the Bay of Bengal. Source: Journal of Coastal Research (1994).14.Kathiresan, K. & Rajendran, N. Coastal mangrove forests mitigated tsunami. Estuar. Coast. Shelf Sci. 65, 601–606 (2005).ADS 
    Article 

    Google Scholar 
    15.Suresh, H.S., Mangrove area assessment in India: Implications of loss of mangroves. J. Earth Sci. Clim. Change 06, (2015).16.Kathiresan, K. & Bingham, B. L. Biology of mangroves and mangrove ecosystems. Adv. Mar. Biol. 40, 81–251 (2001).Article 

    Google Scholar 
    17.Das, S. & Vincent, J. R. Mangroves protected villages and reduced death toll during Indian super cyclone. Proc. Natl. Acad. Sci. U. S. A. 106, 7357–7360 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Rathore, L. S., Mohapatra, M. & Geetha, B. Collaborative mechanism for tropical cyclone monitoring and prediction over north Indian ocean. in Tropical Cyclone Activity over the North Indian Ocean 3–27 (Springer International Publishing, 2016). https://doi.org/10.1007/978-3-319-40576-6_119.Imbert, D. Hurricane disturbance and forest dynamics in east Caribbean mangroves. Ecosphere 9, (2018).20.Silva Pedro, M., Rammer, W. & Seidl, R. A disturbance-induced increase in tree species diversity facilitates forest productivity. Landsc. Ecol. 31, 989–1004 (2016).Article 

    Google Scholar 
    21.Matayaya, G., Wuta, M. & Nyamadzawo, G. Effects of different disturbance regimes on grass and herbaceous plant diversity and biomass in Zimbabwean dambo systems. Int. J. Biodivers. Sci. Ecosyst. Serv. Manag. 13, 181–190 (2017).Article 

    Google Scholar 
    22.Galeano, A., Urrego, L. E., Botero, V. & Bernal, G. Mangrove resilience to climate extreme events in a Colombian Caribbean Island. Wetl. Ecol. Manag. 25, 743–760 (2017).Article 

    Google Scholar 
    23.Capdeville, C. et al. Mangrove facies drives resistance and resilience of sediment microbes exposed to anthropic disturbance. Front. Microbiol. 9, 10 (2019).Article 

    Google Scholar 
    24.Banerjee, K. et al. High blue carbon stock in mangrove forests of Eastern India. Trop. Ecol. 61, 150–167 (2020).CAS 
    Article 

    Google Scholar 
    25.Murthy, T. V. R. Biophysical characterisation and site suitability analysis for Indian mangroves. (2019).26.Whelan, K. R., Smith, T. J., Anderson, G. H., & Ouellette, M. L. Hurricane Wilma’s impact on overall soil elevation and zones within the soil profile in a mangrove forest. Wetlands 29, 16–23 (2009).Article 

    Google Scholar 
    27.Smoak, J. M., Breithaupt, J. L., Smith, T. J. & Sanders, C. J. Sediment accretion and organic carbon burial relative to sea-level rise and storm events in two mangrove forests in Everglades National Park. CATENA 104, 58–66 (2013).CAS 
    Article 

    Google Scholar 
    28.Bala Krishna Prasad, M. Nutrient stoichiometry and eutrophication in Indian mangroves. Environ. Earth Sci. 67, 293–299 (2012).CAS 
    Article 

    Google Scholar 
    29.Reddy, Y. et al. Assessment of bioavailable nitrogen and phosphorus content in the sediments of Indian mangroves. Environ. Sci. Pollut. Res. https://doi.org/10.1007/s11356-021-13638-7 (2021).Article 

    Google Scholar 
    30.Bala Krishna Prasad, M., Ramanathan, A. L., Alongi, D. M. & Kannan, L. Seasonal variations and decadal trends in concentrations of dissolved inorganic nutrients in Pichavaram mangrove waters Southeast India. Bull. Mar. Sci. 79, 287–300 (2006).
    Google Scholar 
    31.Nandy Datta, P. & Ghose, M. Photosynthesis and water-use efficiency of some mangroves from Sundarbans. India. J. Plant Biol. 44, 213–219 (2001).Article 

    Google Scholar 
    32.Ball, M. C. & Critchley, C. Photosynthetic responses to irradiance by the grey mangrove, avicennia marina, grown under different light regimes. Plant Physiol. 70, 1101–1106 (1982).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    33.Cheeseman, J. M. et al. The analysis of photosynthetic performance in leaves under field conditions: A case study using Bruguiera mangroves. Photosynth. Res. 29, 11–22 (1991).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    34.Rajkumar, R., Shaijumon, C. S., Gopakumar, B. & Gopalakrishnan, D. Extreme rainfall and drought events in Tamil Nadu India. Clim. Res. 80, 175–188 (2020).Article 

    Google Scholar 
    35.Lakshmi, S., Nivethaa, E. A. K., Ibrahim, S. N. A., Ramachandran, A. & Palanivelu, K. Prediction of future extremes during the Northeast Monsoon in the coastal districts of Tamil Nadu State in India Based on ENSO. Pure Appl. Geophys. https://doi.org/10.1007/s00024-021-02768-1 (2021).Article 

    Google Scholar 
    36.Aung, T. T., Mochida, Y. & Than, M. M. Prediction of recovery pathways of cyclone-disturbed mangroves in the mega delta of Myanmar. For. Ecol. Manage. 293, 103–113 (2013).Article 

    Google Scholar 
    37.Bai, J. et al. Mangrove diversity enhances plant biomass production and carbon storage in Hainan island China. Funct. Ecol. 35, 774–786 (2021).Article 

    Google Scholar 
    38.Rasquinha, D. N. & Mishra, D. R. Impact of wood harvesting on mangrove forest structure, composition and biomass dynamics in India. Estuar. Coast. Shelf Sci. 248, 106974 (2021).Article 

    Google Scholar 
    39.Ranjan, R. K., Ramanathan, A. L., Chauhan, R. & Singh, G. Phosphorus fractionation in sediments of the Pichavaram mangrove ecosystem, south-eastern coast of India. Environ. Earth Sci. 62, 1779–1787 (2011).CAS 
    Article 

    Google Scholar 
    40.Prasad, A. M., Iverson, L. R. & Liaw, A. Newer classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems 9, 181–199 (2006).Article 

    Google Scholar 
    41.Prasad, M. B. K., Singh, G. & Ramanathan, A. L. Nutrient biogeochemistry and net ecosystem metabolism in a tropical coastal mangrove ecosystem. Indian J. Geo-Marine Sci. 45, 1499–1511 (2016).
    Google Scholar 
    42.Lovelock, C. E., Friess, D. A. & Krauss, K. W. the vulnerability of Indo-Paci & c mangrove forests to sea-level rise. (2015).43.Ward, R. D., Friess, D. A., Day, R. H. & Mackenzie, R. A. Impacts of climate change on mangrove ecosystems: a region by region overview. Ecosyst. Heal. Sustain. 2, e01211 (2016).Article 

    Google Scholar 
    44.Banerjee, K., Gatti, R. C. & Mitra, A. Climate change-induced salinity variation impacts on a stenoecious mangrove species in the Indian Sundarbans. Ambio 46, 492–499 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    45.Ranasinghe, R., Duong, T. M., Uhlenbrook, S., Roelvink, D. & Stive, M. Climate-change impact assessment for inlet-interrupted coastlines. Nat. Clim. Chang. 3, 83–87 (2013).ADS 
    Article 

    Google Scholar 
    46.Eslami-Andargoli, L., Dale, P., Sipe, N. & Chaseling, J. Mangrove expansion and rainfall patterns in Moreton Bay, Southeast Queensland Australia. Estuar. Coast. Shelf Sci. 85, 292–298 (2009).ADS 
    Article 

    Google Scholar 
    47.Gilman, E., Ellison, J. & Coleman, R. Assessment of mangrove response to projected relative sea-level rise and recent historical reconstruction of shoreline position. Environ. Monit. Assess. 124, 105–130 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    48.Field, C. D. Impact of expected climate change on mangroves. in Asia-Pacific Symposium on Mangrove Ecosystems 75–81 (Springer Netherlands, 1995). https://doi.org/10.1007/978-94-011-0289-6_1049.Duke, N., Ball, M. & Ellison, J. Factors influencing biodiversity and distributional gradients in mangroves. Glob. Ecol. Biogeogr. Lett. 7, 27–47 (1998).Article 

    Google Scholar 
    50.Smith, T. J. & Duke, N. C. Physical determinants of inter-estuary variation in mangrove species richness around the tropical coastline of Australia. J. Biogeogr. 14, 9 (1987).Article 

    Google Scholar 
    51.Van Lavieren, H., Spalding, M., Alongi, D. M., Kainuma, M., Clüsener-Godt, M., Adeel, Z. Policy brief: Securing the future of mangroves. (2012).52.Mcleod, E. et al. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO 2. Front. Ecol. Environ. 9, 552–560 (2011).Article 

    Google Scholar 
    53.Siikamäki, J., Sanchirico, J. N. & Jardine, S. L. Global economic potential for reducing carbon dioxide emissions from mangrove loss. https://doi.org/10.1073/pnas.120051910954.Barr, J. G., Fuentes, J. D., Engel, V. & Zieman, J. C. Physiological responses of red mangroves to the climate in the Florida Everglades. J. Geophys. Res. Biogeosciences. 114, 1-13 (2009).Article 
    CAS 

    Google Scholar 
    55.Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J. & Neumann, C. J. The international best track archive for climate stewardship (IBTrACS). Bull. Am. Meteorol. Soc. 91, 363–376 (2010).ADS 
    Article 

    Google Scholar 
    56.Tao, J. et al. A comparison between the MODIS product (MOD17A2) and a tide-robust empirical GPP model evaluated in a Georgia Wetland. Remote Sens. 10, 1831 (2018).ADS 
    Article 

    Google Scholar 
    57.Hutley, L. B. et al. Impacts of an extreme cyclone event on landscape-scale savanna fire, productivity and greenhouse gas emissions. Environ. Res. Lett. 8, 045023 (2013).ADS 
    Article 

    Google Scholar 
    58.Sannigrahi, S., Sen, S. & Paul, S. Estimation of Mangrove Net Primary Production and Carbon Sequestration service using Light Use Efficiency model in the Sunderban Biosphere region, India. Geophysi. Res. Abstracts 18, (2016). More

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    Whole-genome sequencing of endangered Zhoushan cattle suggests its origin and the association of MC1R with black coat colour

    Whole-genome sequencing of Zhoushan cattle and Wenling cattle populationsWe collected seven individuals of Zhoushan cattle (Fig. 1a, upper panel). We also collected nine individuals of Wenling cattle (Fig. 1a, lower panel). Wenling cattle have a prominent hump on the back, dewlap, and larger ears, suggesting that its genetic background is largely B. indicus (Fig. 1a, lower panel). We performed whole-genome sequencing of these samples. To resolve their phylogenetic positions and interrelationships within domesticated cattle, we combined our data of 16 cattle individuals with publicly-available whole-genome sequencing data of five individuals from the Angus breed, a typical B. taurus in Europe, and 33 individuals from nine breeds with genetic backgrounds similar to B. indicus3, giving a total of 54 individuals (Fig. 1b, c; Table S1). We performed read trimming and aligned the trimmed reads to the UOA_Brahman_1 assembly of the cattle genome11. This assembly represents the maternal haplotype of an F1 hybrid of Brahman cattle (dam) and Angus (sire)11. After variant calling and filtering, we identified 32,970,327 single-nucleotide polymorphisms (SNPs) and 3,331,322 small indels. Based on this genomic variant information, we conducted the population genomic analyses.Figure 1Phylogenetic analysis of Zhoushan cattle and other cattle breeds. (a) Gross appearance of Zhoushan (upper panel) and Wenling cattle (lower panel). Note that Zhoushan cattle have a dark black coat colour. The arrow indicates the curving horn of Zhoushan cattle. (b) Geographic map indicating the origins of Zhoushan (green dot) and Wenling (orange dot) cattle analysed in this study. We also examined other Chinese cattle (red dots) whose genome sequencing data were available. (c) Regional map around the Zhoushan islands. Wenling, Wannan, and Guangfeng are mainland regions close to the Zhoushan islands. (d) Neighbour-joining tree of the 54 domesticated cattle. The scale bar represents pairwise distances between different individuals. The maps were constructed by R38 and R packages of maps v3.3.0 (https://cran.r-project.org/web/packages/maps) and mapdata v2.3.0 (https://cran.r-project.org/web/packages/mapdata).Full size imageGenetic relationship between Zhoushan cattle and other domesticated cattleTo reveal the phylogenetic positions and interrelationships of Zhoushan and other domesticated cattle, we performed population genomic analyses on 54 cattle individuals. First, we calculated the pairwise evolutionary distance between individuals and generated a neighbour-joining (NJ) tree to reconstruct the phylogenetic relationships between individuals of Zhoushan and other domesticated cattle (Fig. 1d). In the NJ tree, cattle clustered consistently with their geographical location (Fig. 1d). Angus individuals formed a sister group to all other individuals, including Zhoushan cattle, Wenling cattle, and other B. indicus (Fig. 1d). The individuals of Zhoushan and Wenling cattle formed monophyletic groups and were sisters to each other (Fig. 1d). The cattle in Guangfeng formed another monophyletic group and were sisters to both Zhoushan and Wenling cattle (Fig. 1d). Cattle in Wannan, Ji’an, and Leiqiong formed a single group, sister to the cattle of Zhoushan, Wenling, and Guangfeng (Fig. 1d). Zhoushan, Wenling, Guangfeng, Wannan, and Ji’an are geographically close to each other (Fig. 1b, c). The cattle of Dianzhong and Wenshan, which are in the south part of China, were distant from them (Fig. 1d). Cattle in Pakistan and India were located near the root of the phylogenetic tree (Fig. 1d). The branch lengths of Zhoushan cattle were shorter than other B. indicus cattle, suggesting the reduced genetic diversity of Zhoushan cattle (Fig. 1d).To estimate the relatedness between Zhoushan and other domesticated cattle, we performed unsupervised clustering analysis with ADMIXTURE v1.3.0 software (https://dalexander.github.io/admixture/index.html)12. At K = 2, Angus cattle were distinct from all other cattle (Fig. 2a). At K = 3, Zhoushan and Wenling cattle were newly segregated from other cattle, suggesting that these two cattle breeds are genetically close to each other (Fig. 2a). The cattle of Guangfeng, Wannan, Ji’an, Leiqiong, and Wenshan had intermediate genetic structures between Zhoushan cattle and Dianzhong cattle (Fig. 2a). At K = 4, Zhoushan cattle and Wenling cattle were separated from each other (Fig. 2a).Figure 2Admixture and principal component analysis of Zhoushan cattle and other cattle breeds. (a) Admixture plot (K = 2, 3, 4) for the 54 cattle individuals. Each individual is shown as a vertical bar divided into K colours. (b) PCA plot showing the genetic structure of the 54 cattle individuals. The degree of explained variance is given in parentheses. Colours reflect the geographic regions of sampling in Fig. 1d. The cluster composed of cattle in Wenling, Guangfeng, Wannan, Ji’an, and Leiqiong is highlighted in the black dotted ellipse. (c) Estimate of the effective population sizes of Zhoushan (green) and Wenling (orange) cattle over the past 100 generations.Full size imageTo infer the population structure of cattle individuals analysed in this study, we conducted principal component analysis (PCA). The top three principal components accounted for 21.1% of the total variance (Fig. 2b). In the first component of PCA, Angus individuals were separated from all other cattle (Fig. 2b). Additionally, cattle of Wenling, Guangfeng, Wannan, Ji’an, and Leiqiong formed a cluster (dotted ellipse in Fig. 2b). In the second component of PCA, individuals of Zhoushan cattle were separated from all other cattle (Fig. 2b). In the third principal component, Wenling cattle individuals were separated from all other cattle (Fig. 2b).We estimated the trends of the effective population size of Zhoushan and Wenling cattle over the past 100 generations (Fig. 2c). Both populations showed decreasing trends of effective population sizes (Fig. 2c). The effective population size of Zhoushan cattle was estimated to be smaller than that of Wenling cattle, suggesting the effect of island isolation on the genetic diversity of Zhoushan cattle (Fig. 2c).Detection of candidate genes associated with dark black coat colour of Zhoushan cattleTo identify putative genes associated with the dark black coat colour of Zhoushan cattle, we searched genomic regions where the same mutations were shared between Zhoushan cattle and Angus cattle. To achieve this, we calculated the average fixation index (Fst) values in 40 kb windows with 10 kb steps (Fig. 3a). We identified four peaks of Fst at chromosomes 2, 4, 8, and 18 (Fig. 3a). Among these peaks, the highest peak of Fst was identified in the region from 51.05 to 51.35 Mbp on chromosome 18 (Fig. 3a, b). This region contains 18 genes (Fig. 3c). We searched for genes that have mutations altering the amino acid sequence and have been reported to be involved in the regulation of coat colour. Among these 18 genes, only the gene of melanocyte-stimulating hormone receptor (MC1R) is known to involved in the regulation of coat colour13,14,15. Therefore, we regarded MC1R as a strong candidate gene associated with the dark black coat colour of Zhoushan and Angus cattle (Fig. 3c). This gene is located in the region between 51,094,227 bp and 51,095,177 bp on chromosome 18. MC1R is expressed in the skin melanocyte and plays a crucial role in regulating animal coat colour formation16. Mutations of MC1R have been reported to be associated with black coat colour in some animals, such as cattle17, sheep16, pigs18, reindeer19, and geese20. In the protein-coding region of MC1R, we identified one missense mutation (c.583T  > C, p.F195L) and one synonymous mutation (c.663C  > T) (Figs. 3d, 4a). The missense mutation is located in the fifth transmembrane region of MC1R (Fig. 4b). All seven Zhoushan cattle were homozygous for the missense mutation (Figs. 3d, 4a). Four of five Angus individuals were homozygous for the missense mutation, and the remaining one was heterozygous for the missense mutation (Figs. 3d, 4a). Conversely, only 19% (8/42) and 33% (14/42) of B. indicus individuals were homozygous or heterozygous, respectively, for the missense mutation (Figs. 3d, 4a). The remaining 48% (20/42) of individuals of B. indicus were homozygous for the wild-type allele (Figs. 3d, 4a). We also found that the p.F195L mutation is also present in MC1R of Black Angus (accession number: ABX83563.1) in the NCBI Protein database (Fig. S1). Furthermore, we identified 15 upstream variants and three downstream variants in the intergenic regions between neighbouring genes (Table S2).Figure 3Genomic regions associated with dark black coat colour of Zhoushan cattle. (a) Manhattan plot for average Fst values in 40 kb windows with 10 kb steps between Zhoushan cattle plus Angus and other B. indicus. A region with an average Fst of more than 0.6 is coloured in green. The arrow indicates the highest peak. The x-axis represents chromosomal positions, and the y-axis represents the average Fst values. (b) Manhattan plot on chromosome 18 for average Fst values in 40 kb windows with 10 kb steps between Zhoushan cattle, Angus, and other B. indicus. (c) Regional plot around the MC1R gene. The genotype of each individual at each variant site is shown. The genotype homozygous for the reference allele is coloured grey. Heterozygous variants are coloured blue. The homozygous genotype for alternative alleles is coloured light blue. Note that homozygous genotypes for alternative alleles are enriched in Zhoushan and Angus cattle in this region. (d) Regional plot showing the mutations around MC1R gene.Full size imageFigure 4Secondary structure of MC1R and protein sequence alignment of MC1R orthologs. (a) Regional highlight of the c.583 T  > C mutation of MC1R. The genomic region from 51,094,590 to 51,094,598 bp on chromosome 18 is shown. Note that MC1R is located on the reverse strand. (b) Secondary structure of MC1R. MC1R is a seven-transmembrane receptor. The p.F195L mutation is located in the 5th transmembrane region and enclosed by the red circle. This figure is generated by using the Protter server application39. (c) Multiple sequence alignment of MC1R orthologs. The black rectangle highlights the 195th phenylalanine residues. The red rectangle encloses the p.F195L mutation in Zhoushan cattle. The cladogram of the species is shown to the left of the species name. The cladogram topology is derived from a previous study40.Full size imageTo characterise the missense mutation of MC1R (c.583T  > C, p.F195L) found in Zhoushan and Angus cattle, we estimated the degree of evolutionary conservation of the 195th phenylalanine of MC1R. We obtained various MC1R orthologs of vertebrates from eight eutherian mammals, two marsupial mammals, four reptiles, two birds, two amphibians, one lobe-finned fish, one polypterus fish, four teleost fish, and two cartilaginous fish (Table S3). We aligned these 26 sequences with MC1R of Zhoushan cattle and B. indicus (Fig. 4c). This analysis revealed that the 195th phenylalanine of MC1R is highly conserved among vertebrates (Fig. 4c).Furthermore, we verified whether any larger structural variants are spanning the MC1R region (chr18:51,058,185–51,148,307 bp) of Zhoushan cattle and Angus. If there are large structural variants in this region for these breeds, we should see regions where the read depth distributions are different among the groups. We assessed the integrated read depth distributions of Wenling cattle (n = 9), Zhoushan cattle (n = 7) and Angus (n = 5) (Fig. 5a). The read depth distribution was very similar among the three groups suggesting that there are not large structural variants spanning the MC1R region in these breeds (Fig. 5a). We also collected the sequence reads mapped to this region, and performed BreakDancer to detect structural variants21. However, no structural variants were detected in this region in any breeds. Moreover, we compared the reference genome sequence in MC1R region of the UOA_Brahman_1 assembly and that of the UOA_Angus_1 assembly11. The UOA_Brahman_1 assembly represents the maternal haplotype of an F1 hybrid of Brahman cattle (dam) and Angus (sire), and the UOA_Angus_1 assembly represents its paternal haplotype11. The results showed that the genome sequence in the MC1R region are highly preserved between these two assemblies (Fig. 5b).Figure 5Read depth distribution, genome alignment and admixture analysis of the MC1R region. (a) Read depth distributions in the MC1R region. The left panel shows the read depth distributions in the region from 51,058,185 to 51,148,307 bp on chromosome 18. The right panel shows the read depth distributions in the region from 51,090,618 to 51,099,796 bp on chromosome 18. For each breed, the sequencing reads were integrated. The first track represents read depth distribution in each breed, and the second track represents read alignments to the reference genome. For a given base position, if the base call in the sequencing read and the corresponding base in the reference genome are different, adenine is shown in green, thymine in red, guanine in orange, and cytosine in blue. (b) Dot plots showing the genome alignments of the MC1R regions of the UOA_Angus_1 assembly (chr18:49,477,288–49,566,766 bp) and the UOA_Brahman_1 assembly (chr18:51,058,185–51,148,307 bp). The left panel shows the genome alignment by minimap2 aligner and the right one shows the genome alignment by LASTZ aligner. The region corresponding to the MC1R gene body is highlighted in red. (c) Admixture analysis of the MC1R region. The SNPs located in the MC1R region (chr18:51,058,185–51,148,307 bp) were collected and subjected to admixture analysis. The order of the samples is the same as in Fig. 2a.Full size imageFinally, we deduced the origin of the MC1R haplotype in Zhoushan cattle. We collected the SNPs located in the MC1R region (chr18:51,058,185–51,148,307 bp) from all individuals and performed admixture analysis using these SNPs. The result showed that Zhoushan cattle and Angus shared highly similar genetic components (Fig. 5c). However, the other individuals of B. indicus showed genetic components that differed from both Zhoushan cattle and Angus (Fig. 5c). These results suggest that the MC1R haplotype in Zhoushan cattle is derived from B. taurus, even though the genome of Zhoushan cattle as a whole is that of B. indicus. More

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    Herbaceous perennial ornamental plants can support complex pollinator communities

    1.Allen-Wardell, G. et al. The potential consequences of pollinator declines on the conservation of biodiversity and stability of food crop yields. Conserv. Biol. 12, 8–17 (1998).Article 

    Google Scholar 
    2.Wagner, D. L., Grames, E. M., Forister, M. L., Berenbaum, M. R. & Stopak, D. Insectdecline in the anthropocene: Death by a thousand cuts. Proc. Natl. Acad. Sci. 118, e2023989118. https://doi.org/10.1073/pnas.2023989118 (2021).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.Harrison, T. & Winfree, R. Urban drivers of plant–pollinator interactions. Funct. Ecol. 29, 879–888 (2015).Article 

    Google Scholar 
    4.Hall, D. M. et al. The city as a refuge for insect pollinators. Conserv. Biol. 31, 24–29 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    5.McFrederick, Q. S. & LeBuhn, G. Are urban parks refuges for bumble bees Bombus spp. (Hymenoptera: Apidae)?. Biol. Conserv. 129, 372–382 (2006).Article 

    Google Scholar 
    6.Wilson, C. J. & Jamieson, M. A. The effects of urbanization on bee communities dependson floral resource availability and bee functional traits. PLoS One 14, e025852. https://doi.org/10.1371/journal.pone.0225852 (2019).CAS 
    Article 

    Google Scholar 
    7.Ives, C. D. et al. Cities are hotspots for threatened species. Glob. Ecol. Biogeogr. 25, 117–126 (2016).Article 

    Google Scholar 
    8.Tonietto, R., Fant, J., Ascher, J., Ellis, K. & Larkin, D. A comparison of bee communities of Chicago green roofs, parks and prairies. Landsc. Urban Plan. 103, 102–108 (2011).Article 

    Google Scholar 
    9.Threlfall, C. G. et al. The conservation value of urban green space habitats for Australian native bee communities. Biol. Conserv. 187, 240–248 (2015).Article 

    Google Scholar 
    10.Goddard, M. A., Dougill, A. J. & Benton, T. G. Scaling up from gardens: Biodiversity conservation in urban environments. Trends Ecol. Evol. 25, 90–98 (2010).PubMed 
    Article 

    Google Scholar 
    11.Bartomeus, I. et al. Historical changes in Northeastern US bee pollinators related to shared ecological traits. Proc. Natl. Acad. Sci. U. S. A. 110, 4656–4660 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.Willmer, P. Pollination and Floral Ecology (Princeton University Press, Princeton, 2011).Book 

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

    Google Scholar 
    14.Robertson, C. Heterotropic bees. Ecology 6, 412–436 (1925).Article 

    Google Scholar 
    15.Bascompte, J., Jordano, P., Melián, C. J. & Olesen, J. M. The nested assembly of plant-animal mutualistic networks. Proc. Natl. Acad. Sci. U. S. A. 100, 9383–9387 (2003).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

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

    Google Scholar 
    17.Tylianakis, J. M. & Coux, C. Tipping points in ecological networks. Trends Plant Sci. 19, 281–283 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    18.Geslin, B., Gauzens, B., Thébault, E. & Dajoz, I. Plant pollinator networks along agradient of urbanisation. PLoS One 8, e63421. https://doi.org/10.1371/journal.pone.0063421 (2013).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    19.Baldock, K. C. R. et al. A systems approach reveals urban pollinator hotspots and conservation opportunities. Nat. Ecol. Evol. 3, 363–373 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Kremen, C., M’Gonigle, L. K. & Ponisio, L. C. Pollinator community assembly tracks changes in floral resources as restored hedgerows mature in agricultural landscapes. Front. Ecol. Evol. 6, 170. https://doi.org/10.3389/fevo.2018.00170 (2018).Article 

    Google Scholar 
    21.Potts, S. G., Vulliamy, B., Dafni, A., Ne’eman, G. & Willmer, P. Linking bees and flowers: How do floral communities structure pollinator communities?. Ecology 84, 2628–2642 (2003).Article 

    Google Scholar 
    22.Cohen, H., Philpott, S. M., Liere, H., Lin, B. B. & Jha, S. The relationship between pollinator community and pollination services is mediated by floral abundance in urban landscapes. Urban Ecosyst. 24, 275–290 (2021).Article 

    Google Scholar 
    23.Menz, M. H. M. et al. Reconnecting plants and pollinators: Challenges in the restoration of pollination mutualisms. Trends Plant Sci. 16, 4–12 (2010).PubMed 
    Article 
    CAS 

    Google Scholar 
    24.M’Gonigle, L. K., Williams, N. M., Lonsdorf, E. & Kremen, C. A tool for selecting plants when restoring habitat for pollinators. Conserv. Lett. 10, 105–111 (2017).Article 

    Google Scholar 
    25.Köppler, M.-R. & Hitchmough, J. D. Ecology good, aut-ecology better; improving the sustainability of designed plantings. J. Landsc. Archit. 10, 82–91 (2015).Article 

    Google Scholar 
    26.Tabassum, S. et al. Using ecological knowledge for landscaping with plants in cities. Ecol. Eng. 158, 106049. https://doi.org/10.1016/j.ecoleng.2020.106049 (2020).Article 

    Google Scholar 
    27.Campbell, B., Khachatryan, H. & Rihn, A. Pollinator-friendly plants, reasons for and barriers to purchase. Am. Soc. Hortic. Sci. 27, 831–839 (2017).
    Google Scholar 
    28.Khachatryan, H. et al. Visual attention to eco-labels predicts consumer preferences for pollinator friendly plants. Sustainability 9, 1743. https://doi.org/10.3390/su9101743 (2017).Article 

    Google Scholar 
    29.Hitchmough, J. & Woudstra, J. The ecology of exotic herbaceous perennials grown in managed, native grassy vegetation in urban landscapes. Landsc. Urban Plan. 45, 107–121 (1999).Article 

    Google Scholar 
    30.Ault, J. Breeding and development of new ornamental plants from North American native taxa. Acta Hortic. 624, 37–42 (2003).Article 

    Google Scholar 
    31.Comba, L. et al. Garden flowers: Insect visits and the floral reward of horticulturally-modified variants. Ann. Bot. 83, 73–86 (1999).Article 

    Google Scholar 
    32.Garbuzov, M. & Ratnieks, F. L. W. Using the British National Collection of asters to compare the attractiveness of 228 varieties to flower-visiting insects. Environ. Entomol. 44, 638–646 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    33.Erickson, E. et al. More than meets the eye? The role of annual ornamental flowers in supporting pollinators. Environ. Entomol. 49, 178–188 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    34.Garbuzov, M. & Ratnieks, F. L. W. W. Quantifying variation among garden plants in attractiveness to bees and other flower-visiting insects. Funct. Ecol. 28, 364–374 (2014).Article 

    Google Scholar 
    35.Russo, L., DeBarros, N., Yang, S., Shea, K. & Mortensen, D. Supporting crop pollinators with floral resources: Network-based phenological matching. Ecol. Evol. 3, 3125–3140 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Thompson, J. D. How do visitation patterns vary among pollinators in relation to floral display and floral design in a generalist pollination system?. Oecologia 126, 386–394 (2001).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    37.Tuell, J. K., Fiedler, A. K., Landis, D. & Isaacs, R. Visitation by wild and managed bees (Hymenoptera: Apoidea) to eastern U.S. native plants for use in conservation programs. Environ. Entomol. 37, 707–718 (2008).PubMed 
    Article 

    Google Scholar 
    38.Fowler, J. Specialist bees of the Northeast: Host plants and habitat conservation. Northeast. Nat. 23, 305–320 (2016).Article 

    Google Scholar 
    39.Jessica J. R. Catch the buzz-pollinator diversity, distribution, and phenology in Shenandoah National Park (Natural Resource Report. NPS/SHEN/NRR—2017/1441. National Park Service, 2017).40.Savoy-Burke, G. Woodland Bee Diversity in the Mid-Atlantic. (Master’s Thesis, University of Delaware, Newark DE, 2017).41.Fisher, R. M. Evolution and host specificity: Dichotomous invasion success of Psithyrus citrinus (Hymenoptera: Apidae), a bumblebee social parasite in colonies of its two hosts. Can. J. Zool. 63, 977–981 (1985).Article 

    Google Scholar 
    42.Packer, L., Genaro, J. & Sheffield, C. S. The bee genera of Eastern Canada. Can. J. Arthropod Identif. 3, 1–32 (2007).
    Google Scholar 
    43.Richardson, L. L., McFarland, K. P., Zahendra, S. & Hardy, S. Bumble bee (Bombus) distribution and diversity in Vermont, USA: A century of change. J. Insect Conserv. 23, 45–62 (2019).Article 

    Google Scholar 
    44.Domínguez-García, V. & Muñoz, M. A. Ranking species in mutualistic networks. Sci. Rep. 5, 8182. https://doi.org/10.1038/srep08182 (2015).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Alarcón, R., Waser, N. M. & Ollerton, J. Year-to-year variation in the topology of a plant–pollinator interaction network. Oikos 117, 1796–1807 (2008).Article 

    Google Scholar 
    46.Dormann, C. F., Gruber, B. & Fruend, J. Introducing the bipartite package: Analysingecological networks. R News 8(2), 8–11 (2008).
    Google Scholar 
    47.Olesen, J. M., Bascompte, J., Dupont, Y. L. & Jordano, P. The modularity of pollination networks. Proc. Natl. Acad. Sci. 104, 19891–19896 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 
    MATH 
    Article 

    Google Scholar 
    48.Wright, G. A. & Schiestl, F. P. The evolution of floral scent: The influence of olfactory learning by insect pollinators on the honest signalling of floral rewards. Funct. Ecol. 23, 841–851 (2009).Article 

    Google Scholar 
    49.Corbet, S. et al. Native or Exotic? Double or single? Evaluating plants for pollinator-friendly gardens. Ann. Bot. 87, 219–232 (2001).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    50.Campbell, D. R., Bischoff, M., Lord, J. M. & Robertson, A. W. Flower color influences insect visitation in alpine New Zealand. Ecology 91, 2638–2649 (2010).PubMed 
    Article 

    Google Scholar 
    51.Harder, L. D. Morphology as a predictor of flower choice by bumble bees. Ecology 66, 198–210 (1985).Article 

    Google Scholar 
    52.Wilde, H. D., Gandhi, K. J. K. & Colson, G. State of the science and challenges of breeding landscape plants with ecological function. Hortic. Res. 2, 14069. https://doi.org/10.1038/hortres.2014.69 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Knauer, A. C. & Schiestl, F. P. Bees use honest floral signals as indicators of reward when visiting flowers. Ecol. Lett. 18, 135–143 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    54.Stearn, W. T. Nepeta mussinii and N. × Faassenii. J. R. Hortic. Soc. 75, 403–406 (1950).
    Google Scholar 
    55.Seitz, N., VanEngelsdorp, D. & Leonhardt, S. D. Are native and non-native pollinator friendly plants equally valuable for native wild bee communities?. Ecol. Evol. 10, 12838–12850 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    56.Kammerer, M., Tooker, J. F. & Grozinger, C. M. A long-term dataset on wild bee abundance in Mid-Atlantic United States. Sci. Data 7, 240. https://doi.org/10.1038/s41597-020-00577-0 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Vaudo, A. D., Tooker, J. F., Grozinger, C. M. & Patch, H. M. Bee nutrition and floral resource restoration. Curr. Opin. Insect Sci. 10, 133–141 (2015).PubMed 
    Article 

    Google Scholar 
    58.Salisbury, A. et al. Enhancing gardens as habitats for flower-visiting aerial insects (pollinators): Should we plant native or exotic species?. J. Appl. Ecol. 52, 1156–1164 (2015).CAS 
    Article 

    Google Scholar 
    59.Mach, B. M. & Potter, D. A. Quantifying bee assemblages and attractiveness of flowering woody landscape plants for urban pollinator conservation. PLoS One 13, e0208428. https://doi.org/10.1371/journal.pone.0208428 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    60.Sponsler, D. B., Shump, D., Richardson, R. T. & Grozinger, C. M. Characterizing the floral resources of a North American metropolis using a honey bee foraging assay. Ecosphere 11, e03102. https://doi.org/10.1002/ecs2.3102 (2020).Article 

    Google Scholar 
    61.Rollings, R. & Goulson, D. Quantifying the attractiveness of garden flowers for pollinators. J. Insect Conserv. 23, 803–817 (2019).Article 

    Google Scholar 
    62.Blaauw, B. R. & Isaacs, R. Flower plantings increase wild bee abundance and the pollination services provided to a pollination-dependent crop. J. Appl. Ecol. 51, 890–898 (2014).Article 

    Google Scholar 
    63.Vrdoljak, S. M., Samways, M. J. & Simaika, J. P. Pollinator conservation at the local scale: Flower density, diversity and community structure increase flower visiting insect activity to mixed floral stands. J. Insect Conserv. 20, 711–721 (2016).Article 

    Google Scholar 
    64.Burkle, L. A. & Alarcon, R. The future of plant–pollinator diversity: Understanding interaction networks across time, space, and global change. Am. J. Bot. 98, 528–538 (2011).PubMed 
    Article 

    Google Scholar 
    65.Roulston, T. H., Smith, S. A. & Brewster, A. L. A comparison of pan trap and intensive net sampling techniques for documenting bee (Hymenoptera: Apiformes) Fauna. J. Kansas Entomol. Soc. 80, 179–181 (2007).Article 

    Google Scholar 
    66.Baum, K. A. & Wallen, K. E. Potential bias in pan trapping as a function of floral abundance. J. Kansas Entomol. Soc. 84, 155–159 (2011).Article 

    Google Scholar 
    67.Robertson, A. W. & MacNair, M. R. The effects of floral display size on pollinator service to individual flowers of Myosotis and Mimulus. Oikos 72, 106–114 (1995).Article 

    Google Scholar 
    68.Bennett, A. B. & Lovell, S. Landscape and local site variables differentially influence pollinators and pollination services in urban agricultural sites. PLoS One 14, e0212034. https://doi.org/10.1371/journal.pone.0212034 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    69.Frankie, G. W. et al. Ecological patterns of bees and their host ornamental flowers in two Northern California cities. J. Kansas Entomol. Soc. 78, 227–246 (2005).Article 

    Google Scholar 
    70.Hamblin, A. L., Youngsteadt, E. & Frank, S. D. Wild bee abundance declines with urban warming, regardless of floral density. Urban Ecosyst. 21, 419–428 (2018).Article 

    Google Scholar 
    71.Wenzel, A., Grass, I., Belavadi, V. V. & Tscharntke, T. How urbanization is driving pollinator diversity and pollination—a systematic review. Biol. Conserv. 241, 108321. https://doi.org/10.1016/j.biocon.2019.108321 (2020).Article 

    Google Scholar 
    72.Potted herbaceous perennial plants sold. Census of Agriculture – 2014 census of horticultural specialties (USDA-NASS, 2014).73.Greenleaf, S. S., Williams, N. M., Winfree, R. & Kremen, C. Bee foraging ranges and their relationship to body size. Oecologia 153, 589–596 (2007).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    74.Herrera, C. M. Daily patterns of pollinator activity, differential pollinating effectiveness, and floral resource availability, in a summer-flowering mediterranean shrub. Oikos 58, 277–288 (1990).Article 

    Google Scholar 
    75.Tuell, J. K. & Isaacs, R. Elevated pan traps to monitor bees in flowering crop canopies. Entomol. Exp. Appl. 131, 93–98 (2009).Article 

    Google Scholar 
    76.R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria, 2020)77.Lenth, R. emmeans: Estimated marginal means, aka least-squares means. R package version 1.5.3. (2020).78.Oksanen, J. et al. vegan: Community ecology package. R package version 2.5–7. (2020).79.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, 2016).MATH 
    Book 

    Google Scholar 
    80.Lever, J. J., van Nes, E. H., Scheffer, M. & Bascompte, J. The sudden collapse of pollinator communities. Ecol. Lett. 17, 350–359 (2014).PubMed 
    Article 

    Google Scholar  More

  • in

    Validating species distribution models to illuminate coastal fireflies in the South Pacific (Coleoptera: Lampyridae)

    1.Brooks, T. M. et al. Habitat loss and extinction in the hotspots of biodiversity. Conserv. Biol. 16, 909–923 (2002).Article 

    Google Scholar 
    2.Maschinski, J. et al. Sinking ships: Conservation options for endemic taxa threatened by sea level rise. Clim. Change 107, 147–167 (2011).ADS 
    Article 

    Google Scholar 
    3.Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    4.Heaney, L. R., Balete, D. S. & Rickart, E. A. Models of oceanic island biogeography: Changing perspectives on biodiversity dynamics in archipelagoes. Front. Biogeogr. 5, 249–257 (2013).Article 

    Google Scholar 
    5.Keppel, G., Lowe, A. J. & Possingham, H. P. Changing perspectives on the biogeography of the tropical South Pacific: Influences of dispersal, vicariance and extinction. J. Biogeogr. 36, 1035–1054 (2009).Article 

    Google Scholar 
    6.Laurance, W. F. Beyond Island biogeography theory. In The Theory of Island Biogeography Revisited (eds Losos, jB. & Ricklefs, R. E.) 214–237 (Princeton University Press, 2010).
    Google Scholar 
    7.Cheesman, L. E. Biogeographical significance of Aneityum Island, New Hebrides. Nature 180, 903–904 (1957).ADS 
    Article 

    Google Scholar 
    8.Cox, B. T. M. & Burns, K. C. Convergent evolution of gigantism in the flora of an isolated archipelago. Evol. Ecol. 31, 741–752 (2017).Article 

    Google Scholar 
    9.Hamilton, A. M., Klein, E. R. & Austin, C. C. Biogeographic breaks in Vanuatu, a nascent oceanic archipelago. Pac. Sci. 64, 149–159 (2010).Article 

    Google Scholar 
    10.Coleman, P. J. Geology of the Solomon and New Hebrides islands, as part of the Melanesian re-entrant, Southwest Pacific. Pac. Sci. 24, 289–314 (1970).
    Google Scholar 
    11.Valente, L. et al. A simple dynamic model explains the diversity of island birds worldwide. Nature 579, 92–96 (2020).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Keppel, G., Buckley, Y. M. & Possingham, H. P. Drivers of lowland rain forest community assembly, species diversity and forest structure on islands in the tropical South Pacific. Ecology 98, 87–95 (2010).Article 

    Google Scholar 
    13.Cheng, L. Insects in marine environments. Marine Insects 1, 1–4 (1976).
    Google Scholar 
    14.Ballantyne, L. A. & Buck, E. Taxonomy and behavior of Luciola (Luciola) aphrogeneia, a new surf firefly from Papua New Guinea. Trans. Am. Entomol. Soc. 105, 117–137 (1979).
    Google Scholar 
    15.Doyen, J. T. Marine beetles (Coleoptera excluding Staphylinidae). In Marine Insects (ed. Cheng, L.) 497–519 (American Elsevier, 1976).16.Topp, W. & Ring, R. A. Adaptations of Coleoptera to the marine environment. II. Observations on rove beetles (Staphylinidae) from rocky shores. Can. J. Zool. 66, 2469–2474 (1988).Article 

    Google Scholar 
    17.Lloyd, J. E. Fireflies (Coleoptera: Lampyridae). In Encyclopedia of Entomology 429–1452 (Springer Dordrecht, 2008).18.McDermott, F. A. Photuris bethaniensis, a new Lampyrid firefly. Proc. U. S. Natl. Mus. 103, 35–37 (1953).Article 

    Google Scholar 
    19.Vaz, S. et al. On the intertidal firefly genus Micronaspis Green, 1948, with a new species and a phylogeny of Cratomorphini based on adult and larval traits (Coleoptera: Lampyridae). Zool. Anz. 292, 64–91 (2021).Article 

    Google Scholar 
    20.Ballantyne, L. A. & Lambkin, C. Systematics of Indo-Pacific fireflies with a redefinition of Australasian Atyphella Olliff, Madagascan Photurolociola Pic, and description of seven new genera from the Luciolinae (Coleoptera: Lampyridae). Zootaxa 1997, 1–188 (2009).Article 

    Google Scholar 
    21.Ballantyne, L. A. et al. The Luciolinae of SE Asia and the Australopacific region: A revisionary checklist (Coleoptera: Lampyridae) including description of three new genera and 13 new species. Zootaxa 4687, 1–174 (2019).Article 

    Google Scholar 
    22.Saxton, N. A., Powell, G. S., Martin, G. J. & Bybee, S. M. Two new species of coastal Atyphella Ollliff (Lampyridae: Luciolinae). Zootaxa 4722, 270–276 (2020).Article 

    Google Scholar 
    23.Gassner, P. et al. Marine Atlas. Maximizing Benefits for Vanuatu. https://grid.cld.bz/Marine-Atlas-Maximizing-Benefits-for-Vanuatu1/10/ (2019).24.Saxton, N. A., Powell, G. S., Serrano, S. J., Monson, A. K. & Bybee, S. M. Natural history and ecological niche modelling of coastal Atyphella Olliff larvae (Lampyridae: Luciolinae) in Vanuatu. J. Nat. Hist. 53, 2271–2280 (2019).Article 

    Google Scholar 
    25.Rhoden, C. M., Peterman, W. E. & Taylor, C. A. Maxent-directed field surveys identify new populations of narrowly endemic habitat specialists. PeerJ 5, e3632 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

    Google Scholar 
    27.Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: An open-source release of Maxent. Ecography 40, 887–893 (2017).Article 

    Google Scholar 
    28.Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: The importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342 (2011).Article 

    Google Scholar 
    29.Stas, M. et al. An evaluation of species distribution models to estimate tree diversity at genus level in a heterogeneous urban-rural landscape. Landsc. Urban Plan. 198, 103770 (2020).Article 

    Google Scholar 
    30.Hernandez, P. A., Graham, C. H., Master, L. L. & Albert, D. L. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29, 773–785 (2006).Article 

    Google Scholar 
    31.Hernandez, P. A. et al. Predicting species distributions in poorly-studied landscapes. Biodivers. Conserv. 17, 1353–1366 (2008).Article 

    Google Scholar 
    32.Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J. Biogeogr. 34, 102–117 (2007).Article 

    Google Scholar 
    33.Phillips, S. J. et al. Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    34.Silva, D. P., Aguiar, A. G. & Simião-Ferreira, J. Assessing the distribution and conservation status of a long-horned beetle with species distribution models. J. Insect Conserv. 20, 611–620 (2016).Article 

    Google Scholar 
    35.Cardoso, P., Erwin, T. L., Borges, P. A. & New, T. R. The seven impediments in invertebrate conservation and how to overcome them. Biol. Conserv. 144, 2647–2655 (2011).Article 

    Google Scholar 
    36.Hortal, J. et al. Seven shortfalls that beset large-scale knowledge of biodiversity. Annu. Rev. Ecol. Evol. Syst. 46, 523–549 (2015).Article 

    Google Scholar 
    37.Lomolino, M. V. Conservation biogeography. In Frontiers of Biogeography: new directions in the geography of nature (eds. Lomolino, M. V. & Heaney, L. R.) 293–296 (Sinauer Associates, Sunderland, Massachusetts, 2004).38.Whittaker, R. J. et al. Conservation biogeography: Assessment and prospect. Divers. Distrib. 11, 3–23 (2005).Article 

    Google Scholar 
    39.Cui, S., Luo, X., Li, C., Hu, H. & Jiang, Z. Predicting the potential distribution of white-lipped deer using the MaxEnt model. Biodivers. Sci. 26, 171 (2018).Article 

    Google Scholar 
    40.Moreno, R., Zamora, R., Molina, J. R., Vasquez, A. & Herrera, M. Á. Predictive modeling of microhabitats for endemic birds in South Chilean temperate forests using Maximum entropy (Maxent). Ecol. Inform. 6, 364–370 (2011).Article 

    Google Scholar 
    41.Raman, S., Shameer, T. T., Sanil, R., Usha, P. & Kumar, S. Protrusive influence of climate change on the ecological niche of endemic brown mongoose (Herpestes fuscus fuscus): A MaxEnt approach from Western Ghats, India. Model. Earth Syst. Environ. 6, 1795–1806 (2020).Article 

    Google Scholar 
    42.Abdelaal, M., Fois, M., Fenu, G. & Bacchetta, G. Using MaxEnt modeling to predict the potential distribution of the endemic plant Rosa arabica Crép, Egypt. Ecol. Inform. 50, 68–75 (2019).Article 

    Google Scholar 
    43.Kumar, S. & Stohlgren, T. J. Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. J. Ecol. Nat. 1, 094–098 (2009).
    Google Scholar 
    44.Yang, X. Q., Kushwaha, S. P. S., Saran, S., Xu, J. & Roy, P. S. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. Lesser Himalayan foothills. Ecol. Eng. 51, 83–87 (2013).CAS 
    Article 

    Google Scholar 
    45.New, T. R. Conserving narrow range endemic insects in the face of climate change: Options for some Australian butterflies. J. Insect Conserv. 12, 585–589 (2008).Article 

    Google Scholar 
    46.Booth, T. H., Nix, H. A., Busby, J. R. & Hutchinson, M. F. BIOCLIM: The first species distribution modelling package, its early applications and relevance to most current MAXENT studies. Divers. Distrib. 20, 1–9 (2014).Article 

    Google Scholar 
    47.Hijmans, R. J., Cameron, S. & Parra, J. WorldClim, Version 1.4 (University of California, 2005).
    Google Scholar 
    48.Hijmans, R. J. et al. DIVA-GIS. Version, 7.5. A Geographic Information System for the Analysis of Species Distribution Data. http://www.diva-gis.org (2012).49.Phillips, S. J., Dudik, M. & Schapire, R. E. A maximum entropy approach to species distribution modeling. In Proceedings of the 21st International Conference on Machine Learning 655–662 (2004).50.Merow, C., Smith, M. J. & Silander, J. A. Jr. A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography 36, 1058–1069 (2013).Article 

    Google Scholar 
    51.Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 31, 161–175 (2008).Article 

    Google Scholar 
    52.QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project (2020).53.Phillips, S. J. A brief tutorial on Maxent. AT&T Res. 190, 231–259 (2005).
    Google Scholar 
    54.RStudio Team RStudio: Integrated Development Environment for R. RStudio, PBC. http://www.rstudio.com/ (2020).55.Zizka, A., Antonelli, A. & Silvestro, D. Sampbias: Evaluating geographic sampling bias in biological collections. Ecography 44, 25–32 (2020).Article 

    Google Scholar 
    56.Almeida, M. C., Cortes, L. G. & De Marco Junior, P. New records and a niche model for the distribution of two Neotropical damselflies: Schistolobos boliviensis and Tuberculobasis inversa (Odonata: Coenagrionidae). Insect Conserv. Divers. 3, 252–256 (2010).Article 

    Google Scholar 
    57.De Siqueira, M. F., Durigan, G., de Marco Júnior, P. & Peterson, A. T. Something from nothing: Using landscape similarity and ecological niche modeling to find rare plant species. J. Nat. Conserv. 17, 25–32 (2009).Article 

    Google Scholar 
    58.Wisz, M. S. et al. Effects of sample size on the performance of species distribution models. Divers. Distrib. 14, 763–773 (2008).Article 

    Google Scholar 
    59.McCune, J. L. Species distribution models predict rare species occurrences despite significant effects of landscape context. J. Appl. Ecol. 53, 1871–1879 (2016).Article 

    Google Scholar 
    60.Rinnhofer, L. J. et al. Iterative species distribution modelling and ground validation in endemism research: An Alpine jumping bristletail example. Biodiversity 21, 2845–2863 (2012).
    Google Scholar 
    61.Peterman, W. E., Crawford, J. A. & Kuhns, A. R. Using species distribution and occupancy modeling to guide survey efforts and assess species status. J. Nat. Conserv. 21, 114–121 (2013).Article 

    Google Scholar 
    62.Searcy, C. A. & Shaffer, H. B. Field validation supports novel niche modeling strategies in a cryptic endangered amphibian. Ecography 37, 983–992 (2014).Article 

    Google Scholar 
    63.Virzi, T., Lockwood, J. L., Lathrop, R. G., Grodsky, S. M. & Drake, D. Predicting American Oystercatcher (Haematopus palliatus) breeding distribution in an urbanized coastal ecosystem using maximum entropy modeling. Waterbirds 40, 104–122 (2017).Article 

    Google Scholar 
    64.Greaves, G. J., Mathieu, R. & Seddon, P. J. Predictive modelling and ground validation of the spatial distribution of the New Zealand long-tailed bat (Chalinolobus tuberculatus). Biol. Conserv. 132, 211–221 (2006).Article 

    Google Scholar 
    65.Raxworthy, C. J. et al. Predicting distributions of known and unknown reptile species in Madagascar. Nature 426, 837–841 (2003).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Thorn, J. S., Nijman, V., Smith, D. & Nekaris, K. A. I. Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates: Nycticebus). Divers. Distrib. 15, 289–298 (2009).Article 

    Google Scholar 
    67.Faith, D. et al. Bridging the biodiversity data gaps: Recommendations to meet users’ data needs. Biodivers. Inform. 8, 41–58 (2013).Article 

    Google Scholar 
    68.Pyke, G. H. & Ehrlich, P. R. Biological collections and ecological/environmental research: A review, some observations and a look to the future. Biology 85, 247–266 (2010).
    Google Scholar 
    69.Boakes, E. H. et al. Distorted views of biodiversity: Spatial and temporal bias in species occurrence data. PLoS Biol. 8, e1000385 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    70.Kramer-Schadt, S. et al. The importance of correcting for sampling bias in MaxEnt species distribution models. Divers. Distrib. 19, 1366–1379 (2013).Article 

    Google Scholar 
    71.Fourcade, Y., Engler, J. O., Rödder, D. & Secondi, J. Mapping species distributions with MAXENT using a geographically biased sample of presence data: A performance assessment of methods for correcting sampling bias. PLoS ONE 9, e97122 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    72.National Integrated Coastal Management Framework. National Integrated Coastal Management Framework and Implementation Strategy for Vanuatu. https://extwprlegs1.fao.org/docs/pdf/van171039.pdf (2010).73.Department of Environmental and Protection and Conservation. Coastal Development. https://environment.gov.vu/images/EIA/EIA_G%20Coastal%20development.pdf (2017). More

  • in

    Increased ranking change in wheat breeding under climate change

    1.Reynolds, M. P. et al. Improving global integration of crop research. Science 357, 359–360 (2017).CAS 
    Article 

    Google Scholar 
    2.Braun, H., Atlin, G. & Payne, T. Multi-location testing as a tool to identify plant response to global climate change. in Climate Change and Crop Production (ed. Reynolds, M. P.) 115–138 (CABI, 2010).3.Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C. & Foley, J. A. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3, 1293 (2012).Article 

    Google Scholar 
    4.Liu, B. et al. Similar estimates of temperature impacts on global wheat yield by three independent methods. Nat. Clim. Change 6, 1130–1136 (2016).Article 

    Google Scholar 
    5.Lobell, D. B. & Field, C. B. Global scale climate–crop yield relationships and the impacts of recent warming. Environ. Res. Lett. 2, 14–21 (2007).Article 

    Google Scholar 
    6.Asseng, S. et al. Rising temperatures reduce global wheat production. Nat. Clim. Change 5, 143–147 (2015).Article 

    Google Scholar 
    7.Crespo-Herrera, L. A. et al. Genetic yield gains in CIMMYT’s international elite spring wheat yield trials by modeling the genotype × environment interaction. Crop Sci. 57, 789–801 (2017).Article 

    Google Scholar 
    8.Tester, M. & Langridge, P. Breeding technologies to increase crop production in a changing world. Science 327, 818–822 (2010).CAS 
    Article 

    Google Scholar 
    9.Rosegrant, M. W. & Cline, S. A. Global food security: challenges and polices. Science 302, 1917–1919 (2003).CAS 
    Article 

    Google Scholar 
    10.Li, Y., Suontama, M., Burdon, R. D. & Dungey, H. S. Genotype by environment interactions in forest tree breeding: review of methodology and perspective on research and application. Tree Genet. Genomes 13, 60 (2017).Article 

    Google Scholar 
    11.Mishra, R. M. et al. Crossover interactions for grain yield in multienvironmental trials of winter wheat. Crop Sci. 46, 1291–1298 (2006).Article 

    Google Scholar 
    12.Allard, R. W. & Bradshaw, A. D. Implications of genotype–environmental interactions in applied plant breeding. Crop Sci. 4, 503–508 (1964).Article 

    Google Scholar 
    13.Reynolds, M. P., Hays, D. & Chapman, S. Breeding for adaptation to heat and drought stress. in Climate Change and Crop Production (ed. Reynolds, M. P.) 71–91 (CABI, 2010).14.Leon, N., Jannink, J., Edwards, J. W. & Kaeppler, S. M. Introduction to a special issue on genotype by environment interaction. Crop Sci. 56, 2081–2089 (2016).Article 

    Google Scholar 
    15.Reynolds, M. & Langridge, P. Physiological breeding. Curr. Opin. Plant Biol. 31, 162–171 (2016).Article 

    Google Scholar 
    16.Gourdji, S. M., Mathews, K. L., Reynolds, M., Crossa, J. & Lobell, D. B. An assessment of wheat yield sensitivity and breeding gains in hot environments. Proc. R. Soc. B. 2018, 20122190 (2012).
    Google Scholar 
    17.Pingali, P. L. Green revolution: impacts, limits, and the path ahead. Proc. Natl Acad. Sci. USA 109, 12302–12308 (2012).CAS 
    Article 

    Google Scholar 
    18.Sharma, R. C. et al. Genetic gains for grain yield in CIMMYT spring bred wheat across international environment. Crop Sci. 52, 1522–1533 (2012).Article 

    Google Scholar 
    19.Boehm Jr, J. D., Ibba, M., Kiszonas, A. & Morris, C. F. End-use quality of CIMMYT-derived soft kernel durum wheat germplasm. II. Dough strength and pan bread quality. Crop Sci. 57, 1485–1498 (2017).Article 

    Google Scholar 
    20.Lillemo, M., van Ginkel, M., Trethowan, R. M., Hernandez, E. & Crossa, J. Differential adaptation of CIMMYT bread wheat to global high temperature environments. Crop Sci. 45, 2443–2453 (2005).Article 

    Google Scholar 
    21.Manes, Y. et al. Genetic yield gains of the CIMMYT international semi-arid wheat yield trials from 1994 to 2010. Crop Sci. 52, 1543–1552 (2012).Article 

    Google Scholar 
    22.You, L. et al. Spatial Production Allocation Model (SPAM) 2005 V3.2 International Food Policy Research Institute (IFPRI), International Institute fo Applied Systems Analysis (IIASA) (2017).23.Finlay, K. W. & Wilkinson, G. N. The analysis of adaptation in a plant-breeding programme. Aus. J. Agric. Res. 14, 742–754 (1963).Article 

    Google Scholar 
    24.De los Campos et al. A data-driven simulation platform to predict cultivars’ performance under uncertain weather conditions. Nat. Commun. 11, 4876 (2020).Article 

    Google Scholar 
    25.Lantican, M. A. et al. Impacts of International Wheat Improvement Research 1994–2014 (CIMMYT, 2016).26.Dreccer, M. F., Bonnett, D. & Lafarge, T. Plant breeding under a changing climate. in Encyclopedia of Sustainability Science and Technology (ed. Meyers, R. A.) 8013–8024 (Springer, 2012).27.Laiding, F., Drobek, T. & Meyer, U. Genotypic and environmental variability of yield for cultivars from 30 different crops in German official variety trials. Plant Breed. 127, 541–547 (2008).Article 

    Google Scholar 
    28.Allard, R. W. Principles of Plant Breeding 2nd edn (John Wiley & Sons, 1999).29.Kusmec, A., Srinivasan, S., Nettleton, D. & Schnable, P. S. Distinct genetic architectures for phenotype means and plasticities in Zea mays. Nat. Plants 3, 715–723 (2017).CAS 
    Article 

    Google Scholar 
    30.Gauch, H. G. Statistical Analysis of Regional Yield Trials: AMMI Analysis of Factorial Designs (Elsevier, 1992). More

  • in

    The declining tropical carbon sink

    1.Lapola, D. M. et al. Proc. Natl Acad. Sci. USA 115, 11671–11679 (2018).CAS 
    Article 

    Google Scholar 
    2.Phillips, O. L., Brienen, R. J. W. & the RAINFOR collaboration. Carbon Balance Manag. 12, 1 (2017)..3.Hubau, W. et al. Nature 579, 80–87 (2020).CAS 
    Article 

    Google Scholar 
    4.Fleischer, K. et al. Nat. Geosci. 12, 736–741 (2019).CAS 
    Article 

    Google Scholar 
    5.Huntingford, C. et al. Nat. Geosci. 6, 268–273 (2013).CAS 
    Article 

    Google Scholar 
    6.Koch, A., Hubau, W. & Lewis, S. L. Earth’s Future 9, e2020EF001874 (2021).CAS 
    Article 

    Google Scholar 
    7.Eyring, V. et al. Geosci. Model Dev. 9, 1937–1958 (2016).Article 

    Google Scholar 
    8.Climate Change and Land: an IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems. (IPCC, 2019).9.Friend, A. D. et al. Proc. Natl Acad. Sci. USA 111, 3280–3285 (2014).CAS 
    Article 

    Google Scholar 
    10.Pugh, T. A. M. et al. Biogeosciences https://doi.org/10.5194/bg-17-3961-2020 (2020).Article 

    Google Scholar 
    11.Hartmann, H., Adams, H. D., Anderegg, W. R. L., Jansen, S. & Zeppel, M. J. B. New Phytol. 205, 965–969 (2015).Article 

    Google Scholar 
    12.Scheiter, S., Langan, L. & Higgins, S. I. New Phytol. 198, 957–969 (2013).Article 

    Google Scholar  More