Ecology

Acuna R, Padilla BE, Florez-Ramos CP, Rubio JD, Herrera JC, Benavides P et al. (2012) Adaptive horizontal transfer of a bacterial gene to an invasive insect pest of coffee. Proc Natl Acad Sci USA 109(11):4197–4202CAS 
PubMed 
Article 

Google Scholar 
Aoyama K, Watabe M, Nakaki T (2008) Regulation of neuronal glutathione synthesis. J Pharm Sci 108(3):227–238CAS 
Article 

Google Scholar 
Arias M, Meichanetzoglou A, Elias M, Rosser N, de-Silva DL, Nay B et al. (2016) Variation in cyanogenic compounds concentration within a Heliconius butterfly community: does mimicry explain everything? BMC Evol Biol 16(1):272PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Baloch MN, Fan JY, Haseeb M, Zhang RZ (2020) Mapping potential distribution of Spodoptera frugiperda (Lepidoptera: Noctuidae) in central Asia. Insects 11(3):172PubMed Central 
Article 
PubMed 

Google Scholar 
Barbehenn RV, Kochmanski J, Menachem B, Poirier LM (2013a) Allocation of cysteine for glutathione production in caterpillars with different antioxidant defense strategies: a comparison of Lymantria dispar and Malacosoma disstria. Arch Insect Biochem 84(2):90–103CAS 

Google Scholar 
Barbehenn RV, Niewiadomski J, Kochmanski J (2013b) Importance of protein quality versus quantity in alternative host plants for a leaf-feeding insect. Oecologia 173(1):1–12PubMed 
Article 

Google Scholar 
Bogicevic B, Berthoud H, Portmann R, Meile L, Irmler S (2012) CysK from Lactobacillus casei encodes a protein with O-acetylserine sulfhydrylase and cysteine desulfurization activity. Appl Microbiol Biot 94(5):1209–1220CAS 
Article 

Google Scholar 
Bonner ER, Cahoon RE, Knapke SM, Jez JM (2005) Molecular basis of cysteine biosynthesis in plants: structural and functional analysis of O-acetylserine sulfhydrylase from Arabidopsis thaliana. J Biol Chem 280(46):38803–38813CAS 
PubMed 
Article 

Google Scholar 
Boto L (2014) Horizontal gene transfer in the acquisition of novel traits by metazoans. Proc Biol Sci 281(1777):20131834
Google Scholar 
Brown ES, Dewhurst CF (2009) The genus Spodoptera (Lepidoptera, Noctuidae) in Africa and the Near East. Bull Entomological Res 65(2):221–262Article 

Google Scholar 
Budde MW, Roth MB (2011) The response of Caenorhabditis elegans to hydrogen sulfide and hydrogen cyanide. Genetics 189(2):521–532CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Burkhard P, Rao GS, Hohenester E, Schnackerz KD, Cook PF, Jansonius JN (1998) Three-dimensional structure of O-acetylserine sulfhydrylase from Salmonella typhimurium. J Mol Biol 283(1):121–133CAS 
PubMed 
Article 

Google Scholar 
Dai X, Li R, Li X, Liang Y, Gao Y, Xu Y et al. (2019) Gene duplication and subsequent functional diversification of sucrose hydrolase in Papilio xuthus. Insect Mol Biol 28(6):862–872CAS 
PubMed 
Article 

Google Scholar 
Daimon T, Katsuma S, Iwanaga M, Kang WK, Shimada T (2005) The BmChi-h gene, a bacterial-type chitinase gene of Bombyx mori, encodes a functional exochitinase that plays a role in the chitin degradation during the molting process. Insect Biochem Mol Biol 35(10):1112–1123CAS 
PubMed 
Article 

Google Scholar 
Daimon T, Taguchi T, Meng Y, Katsuma S, Mita K, Shimada T (2008) Beta-fructofuranosidase genes of the silkworm, Bombyx mori – Insights into enzymatic adaptation of B. mori to toxic alkaloids in mulberry latex. J Biol Chem 283(22):15271–15279CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Danchin EG, Rosso MN, Vieira P, de Almeida-Engler J, Coutinho PM, Henrissat B et al. (2010) Multiple lateral gene transfers and duplications have promoted plant parasitism ability in nematodes. Proc Natl Acad Sci USA 107(41):17651–17656CAS 
PubMed 
Article 

Google Scholar 
Darriba D, Taboada GL, Doallo R, Posada D (2011) ProtTest 3: fast selection of best-fit models of protein evolution. Bioinformatics 27(8):1164–1165CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Doxey AC, Yaish MWF, Moffatt BA, Griffith M, McConkey BJ (2007) Functional divergence in the Arabidopsis beta-1,3-glucanase gene family inferred by phylogenetic reconstruction of expression states. Mol Biol Evol 24(4):1045–1055CAS 
PubMed 
Article 

Google Scholar 
Eddy SR (2011) Accelerated profile HMM searches. PLoS Comput Biol 7(10):e1002195CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Edgar RC (2004) MUSCLE: a multiple sequence alignment method with reduced time and space complexity. Bmc Bioinforma 5:1–19Article 
CAS 

Google Scholar 
Fan X, Qiu H, Han W, Wang Y, Xu D, Zhang X et al. (2020) Phytoplankton pangenome reveals extensive prokaryotic horizontal gene transfer of diverse functions. Sci Adv 6(18):eaba0111CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Farre D, Alba MM (2010) Heterogeneous patterns of gene-expression diversification in mammalian gene duplicates. Mol Biol Evol 27(2):325–335CAS 
PubMed 
Article 

Google Scholar 
Feldman-Salit A, Wirtz M, Hell R, Wade RC (2009) A mechanistic model of the cysteine synthase complex. J Mol Biol 386(1):37–59CAS 
PubMed 
Article 

Google Scholar 
Force A, Lynch M, Pickett FB, Amores A, Yan YL, Postlethwait J (1999) Preservation of duplicate genes by complementary, degenerative mutations. Genetics 151(4):1531–1545CAS 
PubMed 
PubMed Central 

Google Scholar 
Gaitonde MK (1967) A spectrophotometric method for direct determination of cysteine in presence of other naturally occurring amino acids. Biochem J 104(2):627–633CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gan Q, Zhang XW, Zhang DB, Shi L, Zhou Y, Sun TT et al. (2018) BmSUC1 is essential for glycometabolism modulation in the silkworm, Bombyx mori. BBA-Gene Regul Mech 1861(6):543–553CAS 

Google Scholar 
Ganko EW, Meyers BC, Vision TJ (2007) Divergence in expression between duplicated genes in Arabidopsis. Mol Biol Evol 24(10):2298–2309CAS 
PubMed 
Article 

Google Scholar 
Gao Y, Liu YC, Jia SZ, Liang YT, Tang Y, Xu YS et al. (2020) Imaginal disc growth factor maintains cuticle structure and controls melanization in the spot pattern formation of Bombyx mori. PLoS Genet 16(9):e1008980CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Goldsmith MR, Shimada T, Abe H (2005) The genetics and genomics of the silkworm, Bombyx mori. Annu Rev Entomol 50:71–100CAS 
PubMed 
Article 

Google Scholar 
Gu X, Zhang ZQ, Huang W (2005) Rapid evolution of expression and regulatory divergences after yeast gene duplication. Proc Natl Acad Sci USA 102(3):707–712CAS 
PubMed 
Article 

Google Scholar 
Gu ZL, Nicolae D, Lu HHS, Li WH (2002) Rapid divergence in expression between duplicate genes inferred from microarray data. Trends Genet 18(12):609–613CAS 
PubMed 
Article 

Google Scholar 
Helmkampf M, Cash E, Gadau J (2015) Evolution of the insect desaturase gene family with an emphasis on social Hymenoptera. Mol Biol Evol 32(2):456–471PubMed 
Article 

Google Scholar 
Hendrickson HR, Conn EE (1969) Cyanide metabolism in higher plants. IV. Purification and properties of the beta-cyanolanine synthase of blue lupine. J Biol Chem 244(10):2632–2640CAS 
PubMed 
Article 

Google Scholar 
Herfurth AM, van Ohlen M, Wittstock U (2017) Beta-cyanoalanine synthases and their possible role in Pierid host plant adaptation. Insects 8(2):62PubMed Central 
Article 
PubMed 

Google Scholar 
Husnik F, McCutcheon JP (2018) Functional horizontal gene transfer from bacteria to eukaryotes. Nat Rev Microbiol 16(2):67–79CAS 
PubMed 
Article 

Google Scholar 
Jeschke V, Gershenzon J, Vassao DG (2016) A mode of action of glucosinolate-derived isothiocyanates: detoxification depletes glutathione and cysteine levels with ramifications on protein metabolism in Spodoptera littoralis. Insect Biochem Molec 71:37–48CAS 
Article 

Google Scholar 
Jiggins FM, Hurst GD (2011) Microbiology. Rapid insect evolution by symbiont transfer. Science 332(6026):185–186CAS 
PubMed 
Article 

Google Scholar 
Koonin EV, Makarova KS, Aravind L (2001) Horizontal gene transfer in prokaryotes: quantification and classification. Annu Rev Microbiol 55:709–742CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kumar S, Stecher G, Tamura K (2016) MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol 33(7):1870–1874CAS 
Article 

Google Scholar 
Lai KW, Yau CP, Tse YC, Jiang LW, Yip WK (2009) Heterologous expression analyses of rice OsCAS in Arabidopsis and in yeast provide evidence for its roles in cyanide detoxification rather than in cysteine synthesis in vivo. J Exp Bot 60(3):993–1008CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lee BS, Huang JS, Jayathilaka LP, Lee J, Gupta S (2016) Antibody production with synthetic peptides. Methods Mol Biol 1474:25–47CAS 
PubMed 
Article 

Google Scholar 
Lee HL, Irish VF (2011) Gene duplication and loss in a MADS box gene transcription factor circuit. Mol Biol Evol 28(12):3367–3380CAS 
PubMed 
Article 

Google Scholar 
Leite DJ, Baudouin-Gonzalez L, Iwasaki-Yokozawa S, Lozano-Fernandez J, Turetzek N, Akiyama-Oda Y et al. (2018) Homeobox gene duplication and divergence in arachnids. Mol Biol Evol 35(9):2240–2253CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li ZW, Shen YH, Xiang ZH, Zhang Z (2011) Pathogen-origin horizontally transferred genes contribute to the evolution of lepidopteran insects. Bmc Evolut Biol 11(1):356CAS 
Article 

Google Scholar 
Liu HJ, Tang ZX, Han XM, Yang ZL, Zhang FM, Yang HL et al. (2015) Divergence in enzymatic activities in the soybean GST supergene family provides new insight into the evolutionary dynamics of whole-genome duplicates. Mol Biol Evol 32(11):2844–2859CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lunn JE, Droux M, Martin J, Douce R (1990) Localization of atp sulfurylase and O-acetylserine(Thiol)lyase in spinach leaves. Plant Physiol 94(3):1345–1352CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lynch M, Force A (2000) The probability of duplicate gene preservation by subfunctionalization. Genetics 154(1):459–473CAS 
PubMed 
PubMed Central 

Google Scholar 
Nikoh N, McCutcheon JP, Kudo T, Miyagishima S, Moran NA, Nakabachi A (2010) Bacterial genes in the aphid genome: absence of functional gene transfer from Buchnera to its host. Plos Genet 6(2):e1000827PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Novakova E, Moran NA (2012) Diversification of genes for carotenoid biosynthesis in aphids following an ancient transfer from a fungus. Mol Biol Evol 29(1):313–323CAS 
PubMed 
Article 

Google Scholar 
Ochman H, Lawrence JG, Groisman EA (2000) Lateral gene transfer and the nature of bacterial innovation. Nature 405(6784):299–304CAS 
PubMed 
Article 

Google Scholar 
Pallen MJ, Wren BW (2007) Bacterial pathogenomics. Nature 449(7164):835–842CAS 
PubMed 
Article 

Google Scholar 
Polz MF, Alm EJ, Hanage WP (2013) Horizontal gene transfer and the evolution of bacterial and archaeal population structure. Trends Genet 29(3):170–175CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rane RV, Walsh TK, Pearce SL, Jermiin LS, Gordon KH, Richards S et al. (2016) Are feeding preferences and insecticide resistance associated with the size of detoxifying enzyme families in insect herbivores? Curr Opin Insect Sci 13:70–76PubMed 
Article 

Google Scholar 
Schramm K, Vassao DG, Reichelt M, Gershenzon J, Wittstock U (2012) Metabolism of glucosinolate-derived isothiocyanates to glutathione conjugates in generalist lepidopteran herbivores. Insect Biochem Molec 42(3):174–182CAS 
Article 

Google Scholar 
Stauber EJ, Kuczka P, van Ohlen M, Vogt B, Janowitz T, Piotrowski M et al. (2012) Turning the ‘mustard oil bomb’ into a ‘cyanide bomb’: aromatic glucosinolate metabolism in a specialist insect herbivore. Plos One 7(4):e35545CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sun BF, Xiao JH, He SM, Liu L, Murphy RW, Huang DW (2013) Multiple ancient horizontal gene transfers and duplications in lepidopteran species. Insect Mol Biol 22(1):72–87CAS 
PubMed 
Article 

Google Scholar 
Suzuki K, Moriguchi K, Yamamoto S (2015) Horizontal DNA transfer from bacteria to eukaryotes and a lesson from experimental transfers. Res Microbiol 166(10):753–763CAS 
PubMed 
Article 

Google Scholar 
Van Ohlen M, Herfurth AM, Kerbstadt H, Wittstock U (2016) Cyanide detoxification in an insect herbivore: molecular identification of beta-cyanoalanine synthases from Pieris rapae. Insect Biochem Molec 70:99–110CAS 
Article 

Google Scholar 
Wada M, Awano N, Yamazawa H, Takagi H, Nakamori S (2004) Purification and characterization of O-acetylserine sulfhydrylase of Corynebacterium glutamicum. Biosci Biotech Bioch 68(7):1581–1583CAS 
Article 

Google Scholar 
Wadleigh RW, Yu SJ (1988) Detoxification of isothiocyanate allelochemicals by glutathione transferase in three lepidopterous species. J Chem Ecol 14(4):1279–1288CAS 
PubMed 
Article 

Google Scholar 
Wagner A (2002) Asymmetric functional divergence of duplicate genes in yeast. Mol Biol Evol 19(10):1760–1768CAS 
PubMed 
Article 

Google Scholar 
Wybouw N, Dermauw W, Tirry L, Stevens C, Grbic M, Feyereisen R et al. (2014) A gene horizontally transferred from bacteria protects arthropods from host plant cyanide poisoning. Elife 3:e02365PubMed 
PubMed Central 
Article 

Google Scholar 
Wybouw N, Pauchet Y, Heckel DG, Van Leeuwen T (2016) Horizontal gene transfer contributes to the evolution of arthropod herbivory. Genome Biol Evol 8(6):1785–1801CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yamaguchi Y, Nakamura T, Kusano T, Sano H (2000) Three arabidopsis genes encoding proteins with differential activities for cysteine synthase and beta-cyanoalanine synthase. Plant Cell Physiol 41(4):465–476CAS 
PubMed 
Article 

Google Scholar 
Yi H, Juergens M, Jez JM (2012) Structure of soybean beta-cyanoalanine synthase and the molecular basis for cyanide detoxification in plants. Plant Cell 24(6):2696–2706CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhou YY, Li XT, Katsuma S, Xu YS, Shi LG, Shimada T et al. (2019) Duplication and diversification of trehalase confers evolutionary advantages on lepidopteran insects. Mol Ecol 28(24):5282–5298CAS 
PubMed 
Article 

Google Scholar 
Zhu B, Lou MM, Xie GL, Zhang GQ, Zhou XP, Li B et al. (2011) Horizontal gene transfer in silkworm, Bombyx mori. Bmc Genomics 12:248PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Ceballos, G. et al. Accelerated modern human–induced species losses: entering the sixth mass extinction. Sci. Adv. 1, e1400253 (2015).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
2.Baillie, J. E. ., Hilton-Taylor, C. & Stuart, S. N. 2004 IUCN Red List of Threatened Species. A Global Species Assessment. (2004).3.Hughes, A. R. & Stachowicz, J. J. Genetic diversity enhances the resistance of a seagrass ecosystem to disturbance. Proc. Natl. Acad. Sci. 101, 8998–9002 (2004).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Cowen, R. K. Scaling of connectivity in marine populations. Science 311, 522–527 (2006).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Ronce, O. How does it feel to be like a rolling stone? Ten questions about dispersal evolution. Annu. Rev. Ecol. Evol. Syst. 38, 231–253 (2007).Article 

Google Scholar 
6.Cowen, R. K. & Sponaugle, S. Larval dispersal and marine population connectivity. Ann. Rev. Mar. Sci. 1, 443–466 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
7.White, J. W. et al. Connectivity, dispersal, and recruitment. Oceanography 32, 50–59 (2019).Article 

Google Scholar 
8.Munday, P. L. et al. Climate change and coral reef connectivity. Coral Reefs 28, 379–395 (2009).ADS 
Article 

Google Scholar 
9.Saunders, M. I. et al. Human impacts on connectivity in marine and freshwater ecosystems assessed using graph theory: A review. Mar. Freshw. Res. 67, 277 (2016).Article 

Google Scholar 
10.Peyran, C., Morage, T., Nebot-Colomer, E., Iwankow, G. & Planes, S. Unexpected residual habitats raise hope for the survival of the over the edge of extinction fan mussel, Pinna nobilis, along the Occitan coast (north-western Mediterranean Sea) (2020).11.De Gaulejac, B. Mise en évidence de l’hermaphrodisme successif à maturation asynchrone de Pinna nobilis. Biol. Pathol. Anim. 1, 99–103 (1995).
Google Scholar 
12.Butler, A., Vicente, N. & de Gaulejac, B. Ecology of the pterioid bivalves Pinna bicolor Gmelin and Pinna nobilis L. Mar. Life 3, 37–45 (1993).
Google Scholar 
13.Trigos, S., Vicente, N., Prado, P. & Espinós, F. J. Adult spawning and early larval development of the endangered bivalve Pinna nobilis. Aquaculture 483, 102–110 (2018).Article 

Google Scholar 
14.Öndes, F., Kaiser, M. J. & Güçlüsoy, H. Human impacts on the endangered fan mussel, Pinna nobilis. Aquat. Conserv. Mar. Freshw. Ecosyst. 30, 31–41 (2020).Article 

Google Scholar 
15.IOPR. Premier séminaire international sur la grande nacre de Méditerranée : Pinna nobilis. Mém. Inst. Océanogr. Paul Ricard 134 (2003).16.Katsares, V., Tsiora, A., Galinou-Mitsoudi, S. & Imsiridou, A. Genetic structure of the endangered species Pinna nobilis (Mollusca: Bivalvia) inferred from mtDNA sequences. Biologia 63, 412–417 (2008).CAS 
Article 

Google Scholar 
17.Rabaoui, L. et al. Genetic variation among populations of the endangered fan mussel Pinna nobilis (Mollusca: Bivalvia) along the Tunisian coastline. Hydrobiologia 678, 99–111 (2011).CAS 
Article 

Google Scholar 
18.Sanna, D. et al. Mitochondrial DNA reveals genetic structuring of Pinna nobilis across the mediterranean sea. PLoS ONE 8, e67372 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.González-Wangüemert, M. et al. Gene pool and connectivity patterns of Pinna nobilis in the Balearic Islands (Spain, Western Mediterranean Sea): Implications for its conservation through restocking. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 175–188 (2019).Article 

Google Scholar 
20.Wesselmann, M. et al. Genetic and oceanographic tools reveal high population connectivity and diversity in the endangered pen shell Pinna nobilis. Sci. Rep. 8, 4770 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
21.Sanna, D. et al. New mitochondrial and nuclear primers for the Mediterranean marine bivalve Pinna nobilis. Mediterr. Mar. Sci. 15, 416 (2014).Article 

Google Scholar 
22.Catanese, G. et al. Haplosporidium pinnae sp. nov., a haplosporidan parasite associated with mass mortalities of the fan mussel, Pinna nobilis, in the Western Mediterranean Sea. J. Invertebr. Pathol. 157, 9–24 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Scarpa, F. et al. Multiple non-species-specific pathogens possibly triggered the mass mortality in Pinna nobilis. Life 10, 238 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
24.Grau, A. et al. Wide-geographic and long-term analysis of the role of pathogens in the decline of Pinna nobilis to critically endangered species. (2021).25.Vázquez-Luis, M. et al. Pinna nobilis: A mass mortality event in Western Mediterranean Sea. Front. Mar. Sci. 4, 1–6 (2017).Article 

Google Scholar 
26.Cabanellas-Reboredo, M. et al. Tracking a mass mortality outbreak of pen shell Pinna nobilis populations: A collaborative effort of scientists and citizens. Sci. Rep. 9, 13355 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.García-March, J. R. et al. Can we save a marine species affected by a highly infective, highly lethal, waterborne disease from extinction?. Biol. Conserv. 243, 108498 (2020).Article 

Google Scholar 
28.Kersting, D. et al. Pinna nobilis. The IUCN Red List of Threatened Species 2019. (2019). https://doi.org/10.2305/IUCN.UK.2019-3.RLTS.T160075998A160081499.en29.Ifremer. Réseau de Suivi Lagunaire du Languedoc-Roussillon. (2014).30.García-March, J. R., García-Carrascosa, A. M. & Pena, Á. L. In situ measurement of Pinna nobilis shells for age and growth studies: A new device. Mar. Ecol. 23, 207–217 (2002).ADS 
Article 

Google Scholar 
31.De Gaulejac, B. Etude écophysiologique du mollusque bivalve méditerranéen Pinna nobilis L. reproduction; croissance; respiration. (1993).32.Peyran, C., Planes, S., Tolou, N., Iwankow, G. & Boissin, E. Development of 26 highly polymorphic microsatellite markers for the highly endangered fan mussel Pinna nobilis and cross-species amplification. Mol. Biol. Rep. https://doi.org/10.1007/s11033-020-05338-1 (2020).Article 
PubMed 

Google Scholar 
33.González-Wangüemert, M. et al. Highly polymorphic microsatellite markers for the Mediterranean endemic fan mussel Pinna nobilis. Mediterr. Mar. Sci. 16, 31 (2014).Article 

Google Scholar 
34.Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. micro-checker: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).Article 
CAS 

Google Scholar 
35.Peakall, R. & Smouse, P. E. GenAlEx 65: Genetic analysis in Excel. Population genetic software for teaching and research: An update. Bioinformatics 28, 2537–2539 (2012).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
37.Rousset, F. genepop’007: A complete re-implementation of the genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).PubMed 
Article 

Google Scholar 
38.Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358 (1984).CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Belkhir, K., Borsa, P., Chikhi, L., Raufaste, N. & Bonhomme, F. GENETIX 4.05, Population genetics software for Windows TM. Université de Montpellier II (2004).40.Robertson, A. & Hill, W. G. Deviations from Hardy–Weinberg proportions: Sampling variances and use in estimation of inbreeding coefficients. Genetics 107, 703–718 (1984).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Raufaste, N. & Bonhomme, F. Properties of bias and variance of two multiallelic estimators of FST. Theor. Popul. Biol. 57, 285–296 (2000).CAS 
PubMed 
MATH 
Article 
PubMed Central 

Google Scholar 
42.Rice, W. R. Analyzing tables of statistical tests. Evolution 43, 223–225 (1989).PubMed 
Article 
PubMed Central 

Google Scholar 
43.R Core Team. R: A Language and Environment for Statistical Computing. (2018).44.Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 35: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
45.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of Population Structure Using Multilocus Genotype Data. (2000).46.Earl, D. A. & VonHoldt, B. M. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).Article 

Google Scholar 
47.Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software structure: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.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, 608–627 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
49.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, 176–177 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
50.Paradis, E. & Schliep, K. ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Queller, D. C. & Goodnight, K. F. Estimating relatedness using genetic markers. Evolution 43, 258 (1989).PubMed 
Article 
PubMed Central 

Google Scholar 
52.Kraemer, P. & Gerlach, G. Demerelate: Calculating interindividual relatedness for kinship analysis based on codominant diploid genetic markers using R. Mol. Ecol. Resour. 17, 1371–1377 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Hare, M. P., Karl, S. A. & Avise, J. C. Anonymous nuclear DNA markers in the American oyster and their implications for the heterozygote deficiency phenomenon in marine bivalves. Mol. Biol. Evol. 13, 334–345 (1996).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Giantsis, I. A., Mucci, N., Randi, E., Abatzopoulos, T. J. & Apostolidis, A. P. Microsatellite variation of mussels (Mytilus galloprovincialis) in central and eastern Mediterranean: Genetic panmixia in the Aegean and the Ionian Seas. J. Mar. Biol. Assoc. UK 94, 797–809 (2014).Article 

Google Scholar 
55.Tarnowska, K., Chenuil, A., Nikula, R., Féral, J. & Wolowicz, M. Complex genetic population structure of the bivalve Cerastoderma glaucum in a highly fragmented lagoon habitat. Mar. Ecol. Prog. Ser. 406, 173–184 (2010).ADS 
Article 

Google Scholar 
56.Šegvić-Bubić, T. et al. Translocation and aquaculture impact on genetic diversity and composition of wild self-sustainable Ostrea edulis populations in the Adriatic sea. Front. Mar. Sci. 7, 1–13 (2020).Article 

Google Scholar 
57.Dupont, L., Ellien, C. & Viard, F. Limits to gene flow in the slipper limpet Crepidula fornicata as revealed by microsatellite data and a larval dispersal model. Mar. Ecol. Prog. Ser. 349, 125–138 (2007).ADS 
Article 

Google Scholar 
58.Ellegren, H. & Ellegren, N. Determinants of genetic diversity. Nat. Publ. Gr. 17, 422–433 (2016).CAS 

Google Scholar 
59.Mendo, T., Moltschaniwskyj, N., Lyle, J. M., Tracey, S. R. & Semmens, J. M. Role of density in aggregation patterns and synchronization of spawning in the hermaphroditic scallop Pecten fumatus. Mar. Biol. 161, 2857–2868 (2014).Article 

Google Scholar 
60.Žuljević, A., Despalatović, M., Cvitković, I., Morton, B. & Antolić, B. Mass spawning by the date mussel Lithophaga lithophaga. Sci. Rep. 8, 10781 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
61.Lamare, M. D. & Stewart, B. G. Mass spawning by the sea urchin Evechinus chloroticus (Echinodermata: Echinoidea) in a New Zealand fiord. Mar. Biol. 132, 135–140 (1998).Article 

Google Scholar 
62.Soong, K., Chang, D. & Chao, S. Presence of spawn-inducing pheromones in two brittle stars (Echinodermata: Ophiuroidea). Mar. Ecol. Prog. Ser. 292, 195–201 (2005).ADS 
Article 

Google Scholar 
63.Watson, G., Bentley, M., Gaudron, S. & Hardege, J. The role of chemical signals in the spawning induction of polychaete worms and other marine invertebrates. J. Exp. Mar. Biol. Ecol. 294, 169–187 (2003).CAS 
Article 

Google Scholar 
64.Gaulejac, B. D., Henry, M. & Vicente, N. An ultrastructural study of gametogenesis of the marine bivalve Pinna nobilis (Linnaeus 1758) II, Spermatogenesis. J. Molluscan Stud. 61, 393–403 (1995).Article 

Google Scholar 
65.Cabanellas-Reboredo, M. et al. Recruitment of Pinna nobilis (Mollusca: Bivalvia) on artificial structures. Mar. Biodivers. Rec. 2, e126 (2009).Article 

Google Scholar 
66.Prado, P. et al. Breeding, planktonic and settlement factors shape recruitment patterns of one of the last remaining major population of Pinna nobilis within Spanish waters. Hydrobiologia 847, 771–786 (2020).Article 

Google Scholar 
67.Deudero, S. et al. Reproductive investment of the pen shell Pinna nobilis Linnaeus, 1758 in Cabrera National Park (Spain). Mediterr. Mar. Sci. 18, 271 (2017).Article 

Google Scholar 
68.Costantini, F., Rugiu, L., Cerrano, C. & Abbiati, M. Living upside down: Patterns of red coral settlement in a cave. Mediterr. Mar. Sci. https://doi.org/10.7717/peerj.4649 (2018).Article 

Google Scholar 
69.Cárdenas, L., Castilla, J. C. & Viard, F. Hierarchical analysis of the population genetic structure in Concholepas concholepas, a marine mollusk with a long-lived dispersive larva. Mar. Ecol. 37, 359–369 (2016).ADS 
Article 

Google Scholar 
70.Morvezen, R. et al. Genetic structure of a commercially exploited bivalve, the great scallop Pecten maximus, along the European coasts. Conserv. Genet. 17, 57–67 (2016).Article 

Google Scholar 
71.Borsa, P., Jarne, P., Belkhir, K. & Bonhomme, F. Genetic structure of the palourde 103. Genet. Evol. Aquat. Org. 103, 1–12 (1994).
Google Scholar 
72.Skalamera, J., Renaud, F., Raymond, M. & de Meeûs, T. No evidence for genetic differentiation of the mussel Mytilus galloprovincialis between lagoons and the seaside. Mar. Ecol. Prog. Ser. 178, 251–258 (1999).ADS 
Article 

Google Scholar 
73.Boissin, E., Hoareau, T. B. & Berrebi, P. Effects of current and historic habitat fragmentation on the genetic structure of the sand goby Pomatoschistus minutus (Osteichthys, Gobiidae). Biol. J. Linn. Soc. 102, 175–198 (2011).Article 

Google Scholar 
74.Pérez-Ruzafa, A. et al. Connectivity between coastal lagoons and sea: Asymmetrical effects on assemblages’ and populations’ structure. Estuar. Coast. Shelf Sci. 216, 171–186 (2019).ADS 
Article 

Google Scholar 
75.Frankham, R. Quantitative genetics in conservation biology. Genet. Res. 74, 237–244 (1999).CAS 
PubMed 
Article 

Google Scholar  More

Sayh QatanahSince 1978 the town of Sayh Qatanah has experienced a strong physical expansion, initially driven by the building of secondary houses by families from the oases below. This was increasingly followed by population transfer, family growth, tourism facilities, and general expansion of urban infrastructure. The number of developed plots within the town area rose from 276 in 2009 to 534 in 2018 (+ 90%). During the same period the total plot area increased from 41.6 ha to 73.5 ha (+ 77%). This lead to an increase in the urban area from 206 ha in 2009 by 24 ha in 2014 (+ 13.6%3) to 252 ha in 2018 (+ 8%). At the current rate of growth, the planned urban space of 268 ha will be reached by 2023, likely followed by densification of the built-up area (Fig. 3). To the east of the city centre a new settlement of 8.6 ha has been established, which, in addition to the typical residential buildings and home gardens, contains a new mosque and an olive grove of 0.7 ha.In 2018 the town’s 56.3 ha non-governmental land comprised 19.3 ha private green spaces, 15.2 ha private buildings, and 4.0 ha public green areas. The total irrigated area thus amounted to 23.3 ha (Fig. 4). The size of individual homegardens ranged between 7 and 3590 m2 with an average of 368 m2. Some homegardens were partly outside the property wall and contained fruit trees and annual crops. In total 33 perennial and annual plant species of 16 families were identified (Table 1). Abundance was highest for pomegranate, olive, rose bushes, and vine, but also peach, apricot, pear, and fig trees were encountered. Garlic was cultivated in 14 of the 25 homegardens studied, followed by onion, maize, and some fodder barley.Figure 4Map of Sayh Qatanah (2000 m a.s.l., Al Jabal Al Akhdar, northern Oman) with all buildings and irrigated areas (gardens) in April 2018.Full size imageTable 1 Species occurrence in the homegardens of the town Sayh Qatanah (Al Jabal Al Akhdar, northern Oman) in 25 randomly selected households.Full size tableOur surveys indicated that besides some private cisterns of unknown capacity for rainwater collection, most residents of Sayh Qatanah used tap water from local borewells for irrigation whereby little attention was given to crop-specific water needs. Average monthly water consumption varied from 43 to 213 l m−2 (mean 97 l m−2 ± 49 SD). This translated to a total irrigation water use in the 19.3 ha private homegardens of 224,652 m3 in 2018. Including the public green areas, the annual estimated water consumption of all green areas of the town amounted to 272,054 m3.Terrace gardensExcluding the newly created terrace areas southwest of Ash Sharayjah and the information-free plots of Al ‘Ayn, by 2018 the actively used area of all five oasis systems had declined from 20.3 ha in 2007 to 19.9 ha (− 2.0%). Fallow land increased by 3.5%, while the use of non-perennial crops decreased by 1.9%. The share of perennial crops without underplanting decreased by 5.1%. In contrast, the share of land under agroforestry increased by 2.1% (Table 2). The 2018 plant census yielded an NS of 13,739 with 25 different perennial species from 12 families. The 2007 count resulted in 1150 individuals less, with 24 different species from 14 plant families.Table 2 Landuse of terraces in the oases of Wadi Muyadin, Al Jabal Al Akhdar, northern Oman, in 2007 and 2018. Data of 2007 are from Luedeling and Buerkert11.Full size tableIn 2007 DN was highest for pomegranate (51%), rose (21%), date (9%), true lime (5%), peach (4%), and banana (3%). By 2018, DN increased for pomegranate (52%) and rose (28%), but decreased for date (7%), banana (2%), lime (1%), and peach (1%). The establishment of drip-irrigated olive yielded a DN of 4% in 2018, while this crop was non-existent in 2007. Over the past decade olive has thus become the third most common crop species in the study region.In 2018 the information-free plots of Al ‘Ayn had a similar composition than the other ones, with the three most common species being pomegranate (51%), rose (27%), and olive (6%). Also the newly established Ash Sharayjah terraces were dominated by pomegranate (38%), rose (28%), and olive (23%).From 2007 to 2018 the NA of most species declined. Sapodilla, pigeon pea, almond, prickly pear (Opuntia vulgaris Mill.) and lemon were no longer recorded in the oases. Instead, prickly pear was identified on the newly created terrace areas of Ash Sharayjah and a young almond tree was spotted in Al ‘Ayn. In addition, a sorb tree (Sorbus domestica L.) was discovered in Al ‘Ayn. The stand of pome fruits such as apple and pear decreased by 89% and 86%, respectively, and stone fruits recorded a similar decline. The NA of apricots decreased by 88%, while the decline of peaches was 71% and of plums 64%. Bitter orange, true lime, orange, and Palestinian lime were decimated by 91%, 71%, 63%, and 22%, respectively, while date and banana stocks decreased by 14% and 16%. In contrast, the NA of pomegranate increased by 11% and of rose by 50%.Al ‘AqrAt a constant total terraced area of 1.7 ha the actively used land declined by 3.4% (Fig. 5). Thereof the proportion of agroforestry systems increased by 3.8%, woody plant alone areas declined by 4.8% and annual crops by 3.0%, and fallows increased by 0.8%. Pomegranate and rose were the dominant species in both years (Fig. 5). While the DN of pomegranate decreased from 63.6 to 58.0%, that of rose increased from 22.8 to 39.4%. Whereas the DN of peach fell from 4.5 to 1.6% and bitter orange, orange, lemon, pear and plum completely disappeared, barley, maize, eggplant, and Rhodes grass (Chloris gayana Kunth) continued to be cultivated on the terrace areas.Figure 5Landuse map of the oasis Al ‘Aqr (1,950 m a.s.l.) in Wadi Muaydin (Al Jabal Al Akhdar, Oman) in 2007 and 2018.Full size imageAl ‘AynAlso Al ‘Ayn’s total terraced area of 1.9 ha remained constant. For the 2007 investigation period, information on landuse of ~ 0.3 ha was missing. This was taken into account in the data on relative landuse changes by not considering information-free plots from 2007 which in 2018 contained 20.5% agroforestry systems, 52.0% woody plants only, 1.4% annual crops, and 26.1% fallow land (Fig. 6, Appendix 1).Figure 6Landuse map of the oasis Al ‘Ayn (1900 m a.s.l.) in Wadi Muaydin (Al Jabal Al Akhdar, Oman) in 2007 and 2018.Full size imageDuring the decadal study period the active cultivation area of Al ‘Ayn declined by 0.2%. Areas with agroforestry systems were expanded by 9.3%, while the use of woody plants only recorded a decline of 16.4%, fallow land increased by 25.1%, and the annual cropping area declined by 18.1% (Fig. 6).Between 2007 and 2018, the DN of rose increased from 54.6 to 61.8% and of pomegranate from 28.2 to 30.5%. The DN of peach decreased from 4.3 to 2.1%, and of papaya, lime, and apricot to less than 2.0%. In contrast to 2007, no records of apple and lemon were obtained in 2018. However, barley, garlic, onion, sweet potato, sorghum, and oats continued to be cultivated.Ash SharayjahIn 2007 Ash Sharayjah’s total area was 15.2 ha to which, by 2018, 1.7 ha of newly developed farmland were added and included in our digital mapping (Fig. 7, Appendix 2). For the determination of relative area changes, however, these newly established terraces areas were not taken into account. During the transformation decade the agriculturally used area of Ash Sharayjah decreased by 4.5%. The total area with agroforestry systems increased by 0.2%, woody plants only declined by 4.5%, areas with annual crops decreased by 2.1% and fallow fields expanded by 3.1% (Fig. 7).Figure 7Landuse map of the oasis Ash Sharayjah (1900 m a.s.l.) in Wadi Muaydin (Al Jabal Al Akhdar, Oman) in 2007 and 2018.Full size imageUntil 2018 the DN of roses increased from 21.4 to 26.7%, while if fell for pomegranate from 63.9 to 61.7%, for true lime from 5.6 to 0.9%, and for apricot and peach it declined to  More

1.Kasischke, E. S. & Stocks, B. J. Fire, Climate Change, and Carbon Cycling in the Boreal Forest (Springer-Verlag, 2000).
Google Scholar 
2.Kurz, W. A. & Apps, M. J. A 70-year retrospective analysis of carbon fluxes in the Canadian forest sector. Ecol. Appl. 9, 526–547. https://doi.org/10.1890/1051-0761(1999)009[0526:AYRAOC]2.0.CO;2 (1999).Article 

Google Scholar 
3.Amiro, B. D. et al. Carbon, energy and water fluxes at mature and disturbed forest sites, Saskatchewan, Canada. Agric. For. Meteorol. 136, 237–251. https://doi.org/10.1016/j.agrformet.2004.11.012 (2006).ADS 
Article 

Google Scholar 
4.Li, F., Lawrence, D. M. & Bond-Lamberty, B. Impact of fire on global land surface air temperature and energy budget for the 20th century due to changes within ecosystems. Environ. Res. Lett. 12, 044014. https://doi.org/10.1088/1748-9326/aa6685 (2017).ADS 
Article 

Google Scholar 
5.Gillett, N. P., Weaver, A. J., Zwiers, F. W. & Flannigan, M. D. Detecting the effect of climate change on Canadian forest fires. Geophys. Res. Lett. https://doi.org/10.1029/2004GL020876 (2004).Article 

Google Scholar 
6.Kasischke, E. S. & Turetsky, M. R. Recent changes in the fire regime across the North American boreal region—Spatial and temporal patterns of burning across Canada and Alaska. Geophys. Res. Lett. https://doi.org/10.1029/2006GL025677 (2006).Article 

Google Scholar 
7.de Groot, W. J., Flannigan, M. D. & Cantin, A. S. Climate change impacts on future boreal fire regimes. For. Ecol. Manage. 294, 35–44. https://doi.org/10.1016/j.foreco.2012.09.027 (2013).Article 

Google Scholar 
8.Rogers, B. M., Soja, A. J., Goulden, M. L. & Randerson, J. T. Influence of tree species on continental differences in boreal fires and climate feedbacks. Nat. Geosci. 8, 228. https://doi.org/10.1038/ngeo2352 (2015).ADS 
CAS 
Article 

Google Scholar 
9.Montes-Helu, M. C. et al. Persistent effects of fire-induced vegetation change on energy partitioning and evapotranspiration in ponderosa pine forests. Agric. For. Meteorol. 149, 491–500. https://doi.org/10.1016/j.agrformet.2008.09.011 (2009).ADS 
Article 

Google Scholar 
10.Denslow, J. S. Patterns of plant species diversity during succession under different disturbance regimes. Oecologia 46, 18–21. https://doi.org/10.1007/bf00346960 (1980).ADS 
Article 
PubMed 

Google Scholar 
11.Bond-Lamberty, B., Peckham, S. D., Ahl, D. E. & Gower, S. T. Fire as the dominant driver of central Canadian boreal forest carbon balance. Nature 450, 89. https://doi.org/10.1038/nature06272 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
12.Gewehr, S., Drobyshev, I., Berninger, F. & Bergeron, Y. Soil characteristics mediate the distribution and response of boreal trees to climatic variability. Can. J. For. Res. 44, 487–498. https://doi.org/10.1139/cjfr-2013-0481 (2014).Article 

Google Scholar 
13.Sullivan, B. W. et al. Wildfire reduces carbon dioxide efflux and increases methane uptake in ponderosa pine forest soils of the southwestern USA. Biogeochemistry 104, 251–265. https://doi.org/10.1007/s10533-010-9499-1 (2011).CAS 
Article 

Google Scholar 
14.Post, W. M., Emanuel, W. R., Zinke, P. J. & Stangenberger, A. G. Soil carbon pools and world life zones. Nature 298, 156–159. https://doi.org/10.1038/298156a0 (1982).ADS 
CAS 
Article 

Google Scholar 
15.Tarnocai, C. et al. Soil organic carbon pools in the northern circumpolar permafrost region. Glob. Biogeochem. Cycles. https://doi.org/10.1029/2008gb003327 (2009).Article 

Google Scholar 
16.Walker, X. J. et al. Cross-scale controls on carbon emissions from boreal forest megafires. Glob. Change Biol. 24, 4251–4265. https://doi.org/10.1111/gcb.14287 (2018).ADS 
Article 

Google Scholar 
17.Kulmala, L. et al. Changes in biogeochemistry and carbon fluxes in a boreal forest after the clear-cutting and partial burning of slash. Agric. For. Meteorol. 188, 33–44. https://doi.org/10.1016/j.agrformet.2013.12.003 (2014).ADS 
Article 

Google Scholar 
18.Yoshikawa, K., Bolton, W. R., Romanovsky, V. E., Fukuda, M. & Hinzman, L. D. Impacts of wildfire on the permafrost in the boreal forests of Interior Alaska. J. Geophys. Res. Atmos. 107, 4–14. https://doi.org/10.1029/2001jd000438 (2002).Article 

Google Scholar 
19.Tsuyuzaki, S., Kushida, K. & Kodama, Y. Recovery of surface albedo and plant cover after wildfire in a Picea mariana forest in interior Alaska. Clim. Change 93, 517. https://doi.org/10.1007/s10584-008-9505-y (2008).ADS 
Article 

Google Scholar 
20.Hamman, S. T., Burke, I. C. & Stromberger, M. E. Relationships between microbial community structure and soil environmental conditions in a recently burned system. Soil Biol. Biochem. 39, 1703–1711. https://doi.org/10.1016/j.soilbio.2007.01.018 (2007).CAS 
Article 

Google Scholar 
21.Atchley, A. L., Kinoshita, A. M., Lopez, S. R., Trader, L. & Middleton, R. Simulating surface and subsurface water balance changes due to burn severity. Vadose Zone J. https://doi.org/10.2136/vzj2018.05.0099 (2018).Article 

Google Scholar 
22.Taş, N. et al. Impact of fire on active layer and permafrost microbial communities and metagenomes in an upland Alaskan boreal forest. ISME J. 8, 1904–1919. https://doi.org/10.1038/ismej.2014.36 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
23.Ribeiro-Kumara, C., Köster, E., Aaltonen, H. & Köster, K. How do forest fires affect soil greenhouse gas emissions in upland boreal forests? A review. Environ. Res. 184, 109328. https://doi.org/10.1016/j.envres.2020.109328 (2020).CAS 
Article 
PubMed 

Google Scholar 
24.Köster, K., Berninger, F., Lindén, A., Köster, E. & Pumpanen, J. Recovery in fungal biomass is related to decrease in soil organic matter turnover time in a boreal fire chronosequence. Geoderma 235–236, 74–82. https://doi.org/10.1016/j.geoderma.2014.07.001 (2014).ADS 
CAS 
Article 

Google Scholar 
25.Conard, S. G. & Ivanova, G. A. Wildfire in Russian boreal forests—Potential impacts of fire regime characteristics on emissions and global carbon balance estimates. Environ. Pollut. 98, 305–313. https://doi.org/10.1016/S0269-7491(97)00140-1 (1997).CAS 
Article 

Google Scholar 
26.Balshi, M. S. et al. The role of historical fire disturbance in the carbon dynamics of the pan-boreal region: A process-based analysis. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2006JG000380 (2007).Article 

Google Scholar 
27.French, N. H. F., Kasischke, E. S. & Williams, D. G. Variability in the emission of carbon-based trace gases from wildfire in the Alaskan boreal forest. J. Geophys. Res. Atmos. 107, 7–11. https://doi.org/10.1029/2001JD000480 (2002).CAS 
Article 

Google Scholar 
28.Kajii, Y. et al. Boreal forest fires in Siberia in 1998: Estimation of area burned and emissions of pollutants by advanced very high resolution radiometer satellite data. J. Geophys. Res. Atmos. 107, 4–8. https://doi.org/10.1029/2001JD001078 (2002).CAS 
Article 

Google Scholar 
29.Amiro, B. D. et al. Direct carbon emissions from Canadian forest fires, 1959–1999. Can. J. For. Res. 31, 512–525. https://doi.org/10.1139/x00-197 (2001).CAS 
Article 

Google Scholar 
30.Kasischke, E. S. et al. Influences of boreal fire emissions on Northern Hemisphere atmospheric carbon and carbon monoxide. Glob. Biogeochem. Cycles. https://doi.org/10.1029/2004GB002300 (2005).Article 

Google Scholar 
31.Seiler, W. & Crutzen, P. J. Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning. Clim. Change 2, 207–247. https://doi.org/10.1007/BF00137988 (1980).ADS 
CAS 
Article 

Google Scholar 
32.Mouillot, F., Narasimha, A., Balkanski, Y., Lamarque, J.-F. & Field, C. B. Global carbon emissions from biomass burning in the 20th century. Geophys. Res. Lett. https://doi.org/10.1029/2005GL024707 (2006).Article 

Google Scholar 
33.Cansler, C. A. & McKenzie, D. Climate, fire size, and biophysical setting control fire severity and spatial pattern in the northern Cascade Range, USA. Ecol. Appl. 24, 1037–1056 (2014).Article 

Google Scholar 
34.Zhuang, Q. et al. Modeling soil thermal and carbon dynamics of a fire chronosequence in interior Alaska. J. Geophys. Res. Atmos. 107, 3–26. https://doi.org/10.1029/2001jd001244 (2002).Article 

Google Scholar 
35.Zackrisson, O. Influence of forest fires on the north Swedish boreal forest. Oikos 29, 22–32. https://doi.org/10.2307/3543289 (1977).Article 

Google Scholar 
36.Allen, J. L. & Sorbel, B. Assessing the differenced normalized burn ratio’s ability to map burn severity in the boreal forest and tundra ecosystems of Alaska’s national parks. Int. J. Wildl. Fire. https://doi.org/10.1071/WF08034 (2008).Article 

Google Scholar 
37.French, N. H. F. et al. Using landsat data to assess fire and burn severity in the North American boreal forest region: An overview and summary of results. Int. J. Wildl. Fire 17, 443–462. https://doi.org/10.1071/WF08007 (2008).Article 

Google Scholar 
38.Hoy, E., French, N., Turetsky, M., Trigg, S. & Kasischke, E. Evaluating the potential of Landsat TM/ETM+ imagery for assessing fire severity in Alaskan black spruce forests. Int. J. Wildl. Fire 17, 500–514. https://doi.org/10.1071/WF08107 (2008).Article 

Google Scholar 
39.Soverel, N. O., Perrakis, D. D. B. & Coops, N. C. Estimating burn severity from Landsat dNBR and RdNBR indices across western Canada. Remote Sens. Environ. 114, 1896–1909. https://doi.org/10.1016/j.rse.2010.03.013 (2010).ADS 
Article 

Google Scholar 
40.Boby, L. A., Schuur, E. A. G., Mack, M. C., Verbyla, D. & Johnstone, J. F. Quantifying fire severity, carbon, and nitrogen emissions in Alaska’s boreal forest. Ecol. Appl. 20, 1633–1647. https://doi.org/10.1890/08-2295.1 (2010).Article 
PubMed 

Google Scholar 
41.Rogers, B. M. et al. Quantifying fire-wide carbon emissions in interior Alaska using field measurements and Landsat imagery. J. Geophys. Res. Biogeosci. 119, 1608–1629. https://doi.org/10.1002/2014jg002657 (2014).CAS 
Article 

Google Scholar 
42.Kasischke, E. S. & Hoy, E. E. Controls on carbon consumption during Alaskan wildland fires. Glob. Change Biol. 18, 685–699. https://doi.org/10.1111/j.1365-2486.2011.02573.x (2012).ADS 
Article 

Google Scholar 
43.Tan, Z., Tieszen, L. L., Zhu, Z., Liu, S. & Howard, S. M. An estimate of carbon emissions from 2004 wildfires across Alaskan Yukon River Basin. Carbon Balance Manage. 2, 12. https://doi.org/10.1186/1750-0680-2-12 (2007).CAS 
Article 

Google Scholar 
44.Sedano, F. & Randerson, J. T. Multi-scale influence of vapor pressure deficit on fire ignition and spread in boreal forest ecosystems. Biogeosciences 11, 3739–3755. https://doi.org/10.5194/bg-11-3739-2014 (2014).ADS 
Article 

Google Scholar 
45.Veraverbeke, S., Rogers, B. M. & Randerson, J. T. Daily burned area and carbon emissions from boreal fires in Alaska. Biogeosciences 12, 3579–3601. https://doi.org/10.5194/bg-12-3579-2015 (2015).ADS 
CAS 
Article 

Google Scholar 
46.Boucher, J., Beaudoin, A., Hébert, C., Guindon, L. & Bauce, É. Assessing the potential of the differenced Normalized Burn Ratio (dNBR) for estimating burn severity in eastern Canadian boreal forests. Int. J. Wildl. Fire 26, 32–45. https://doi.org/10.1071/WF15122 (2017).Article 

Google Scholar 
47.Moody, J. A. et al. Relations between soil hydraulic properties and burn severity. Int. J. Wildl. Fire 25, 279–293. https://doi.org/10.1071/WF14062 (2016).Article 

Google Scholar 
48.Ebel, B. A., Romero, O. C. & Martin, D. A. Thresholds and relations for soil-hydraulic and soil-physical properties as a function of burn severity 4 years after the 2011 Las Conchas Fire, New Mexico, USA. Hydrol. Process. 32, 2263–2278. https://doi.org/10.1002/hyp.13167 (2018).ADS 
Article 

Google Scholar 
49.Stinson, G. et al. An inventory-based analysis of Canada’s managed forest carbon dynamics, 1990 to 2008. Glob. Change Biol. 17, 2227–2244. https://doi.org/10.1111/j.1365-2486.2010.02369.x (2011).ADS 
Article 

Google Scholar 
50.Goodale, C. L. et al. Forest carbon sinks in the northern hemisphere. Ecol. Appl. 12, 891–899. https://doi.org/10.1890/1051-0761(2002)012[0891:FCSITN]2.0.CO;2 (2002).Article 

Google Scholar 
51.Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob. Biogeochem. Cycles. https://doi.org/10.1029/2003GB002199 (2005).Article 

Google Scholar 
52.Thurner, M. et al. Carbon stock and density of northern boreal and temperate forests. Glob. Ecol. Biogeogr. 23, 297–310. https://doi.org/10.1111/geb.12125 (2014).Article 

Google Scholar 
53.Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988. https://doi.org/10.1126/science.1201609 (2011).ADS 
CAS 
Article 

Google Scholar 
54.Dieleman, C. M. et al. Wildfire combustion and carbon stocks in the southern Canadian boreal forest: Implications for a warming world. Glob. Change Biol. 26, 6062–6079. https://doi.org/10.1111/gcb.15158 (2020).ADS 
Article 

Google Scholar 
55.French, N. H. F., Goovaerts, P. & Kasischke, E. S. Uncertainty in estimating carbon emissions from boreal forest fires. J. Geophys. Res. Atmos. https://doi.org/10.1029/2003JD003635 (2004).Article 

Google Scholar 
56.Chen, G., Hayes, D. J. & David McGuire, A. Contributions of wildland fire to terrestrial ecosystem carbon dynamics in North America from 1990 to 2012. Glob. Biogeochem. Cycles 31, 878. https://doi.org/10.1002/2016gb005548 (2017).ADS 
CAS 
Article 

Google Scholar 
57.Goetz, S. J. et al. Observations and assessment of forest carbon dynamics following disturbance in North America. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2011JG001733 (2012).Article 

Google Scholar 
58.Wiedinmyer, C. & Neff, J. C. Estimates of CO2 from fires in the United States: Implications for carbon management. Carbon Balance Manage. 2, 10–10. https://doi.org/10.1186/1750-0680-2-10 (2007).CAS 
Article 

Google Scholar 
59.Kurz, W. A. et al. Carbon in Canada’s boreal forest—A synthesis. Environ. Rev. 21, 260 (2013).CAS 
Article 

Google Scholar 
60.van der Werf, G. R. et al. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos. Chem. Phys. 10, 11707–11735. https://doi.org/10.5194/acp-10-11707-2010 (2010).ADS 
CAS 
Article 

Google Scholar 
61.van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720. https://doi.org/10.5194/essd-9-697-2017 (2017).ADS 
Article 

Google Scholar 
62.Hicke, J. A. et al. Postfire response of North American boreal forest net primary productivity analyzed with satellite observations. Glob. Change Biol. 9, 1145–1157. https://doi.org/10.1046/j.1365-2486.2003.00658.x (2003).ADS 
Article 

Google Scholar 
63.Sparks, A. M. et al. Fire intensity impacts on post-fire temperate coniferous forest net primary productivity. Biogeosciences 15, 1173–1183. https://doi.org/10.5194/bg-15-1173-2018 (2018).ADS 
Article 

Google Scholar 
64.Amiro, B. D., Chen, J. M. & Liu, J. Net primary productivity following forest fire for Canadian ecoregions. Can. J. For. Res. 30, 939–947. https://doi.org/10.1139/x00-025 (2000).Article 

Google Scholar 
65.Turner, M. G., Smithwick, E. A. H., Metzger, K. L., Tinker, D. B. & Romme, W. H. Inorganic nitrogen availability after severe stand-replacing fire in the Greater Yellowstone ecosystem. Proc. Natl. Acad. Sci. 104, 4782. https://doi.org/10.1073/pnas.0700180104 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
66.Gower, S. T., McMurtrie, R. E. & Murty, D. Aboveground net primary production decline with stand age: Potential causes. Trends Ecol. Evol. 11, 378–382. https://doi.org/10.1016/0169-5347(96)10042-2 (1996).CAS 
Article 
PubMed 

Google Scholar 
67.Pare, D. & Bergeron, Y. Above-ground biomass accumulation along a 230-year chronosequence in the southern portion of the Canadian boreal forest. J. Ecol. 83, 1001–1007. https://doi.org/10.2307/2261181 (1995).Article 

Google Scholar 
68.Ice, G., Neary, D. & Adams, P. Effects of wildfire on soils and watershed processes. J. For. 102, 16–20 (2004).
Google Scholar 
69.Aaltonen, H. et al. Temperature sensitivity of soil organic matter decomposition after forest fire in Canadian permafrost region. J. Environ. Manage. 241, 637–644. https://doi.org/10.1016/j.jenvman.2019.02.130 (2019).CAS 
Article 
PubMed 

Google Scholar 
70.Dooley, S. R. & Treseder, K. K. The effect of fire on microbial biomass: A meta-analysis of field studies. Biogeochemistry 109, 49–61. https://doi.org/10.1007/s10533-011-9633-8 (2012).Article 

Google Scholar 
71.Köster, E. et al. Carbon dioxide, methane and nitrous oxide fluxes from a fire chronosequence in subarctic boreal forests of Canada. Sci. Total Environ. 601–602, 895–905. https://doi.org/10.1016/j.scitotenv.2017.05.246 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
72.Auclair, A. N. D. & Carter, T. B. Forest wildfires as a recent source of CO2 at northern latitudes. Can. J. For. Res. 23, 1528–1536. https://doi.org/10.1139/x93-193 (1993).CAS 
Article 

Google Scholar 
73.Hayes, D. J. et al. Is the northern high-latitude land-based CO2 sink weakening?. Glob. Biogeochem. Cycles. https://doi.org/10.1029/2010GB003813 (2011).Article 

Google Scholar 
74.Zhuang, Q. et al. CO2 and CH4 exchanges between land ecosystems and the atmosphere in northern high latitudes over the 21st century. Geophys. Res. Lett. https://doi.org/10.1029/2006GL026972 (2006).Article 

Google Scholar 
75.Osterkamp, T. E. et al. Observations of Thermokarst and Its Impact on Boreal Forests in Alaska, USA. Arctic Antarct. Alpine Res. 32, 303–315. https://doi.org/10.1080/15230430.2000.12003368 (2000).Article 

Google Scholar 
76.Jorgenson, M. T. et al. Reorganization of vegetation, hydrology and soil carbon after permafrost degradation across heterogeneous boreal landscapes. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/8/3/035017 (2013).Article 

Google Scholar 
77.Beck, P. S. A. et al. The impacts and implications of an intensifying fire regime on Alaskan boreal forest composition and albedo. Glob. Change Biol. 17, 2853–2866. https://doi.org/10.1111/j.1365-2486.2011.02412.x (2011).ADS 
Article 

Google Scholar 
78.Terrier, A., Girardin, M., Perie, C., Legendre, P. & Bergeron, Y. Potential changes in forest composition could reduce impacts of climate change on boreal wildfires. Ecol. Appl. 23, 21–35. https://doi.org/10.2307/23440814 (2013).Article 
PubMed 

Google Scholar 
79.Miller, J. D. & Thode, A. E. Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sens. Environ. 109, 66–80. https://doi.org/10.1016/j.rse.2006.12.006 (2007).ADS 
Article 

Google Scholar 
80.Key, C. H. & Benson, N. C. Landscape Assessment (LA). U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. LA 1–55 (2006).81.Epting, J., Verbyla, D. & Sorbel, B. Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+. Remote Sens. Environ. 96, 328–339. https://doi.org/10.1016/j.rse.2005.03.002 (2005).ADS 
Article 

Google Scholar 
82.Mitchell, T., Carter, T., Jones, P. & Hulme, M. A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: The observed record (1901–2000) and 16 scenarios (2001–2100). Tyndall Centre Work. Pap. 55, 25 (2004).
Google Scholar 
83.FAO-Unesco. Soil Map of the World Vol. 1 (Food and Agriculture Organization of the United Nations and the United Nations Educational, Scientific and Cultural Organization, 1974).
Google Scholar 
84.Melillo, J. M. et al. Global climate change and terrestrial net primary production. Nature 363, 234–240. https://doi.org/10.1038/363234a0 (1993).ADS 
CAS 
Article 

Google Scholar 
85.Genet, H. et al. The role of driving factors in historical and projected carbon dynamics of upland ecosystems in Alaska. Ecol. Appl. 28, 5–27. https://doi.org/10.1002/eap.1641 (2018).Article 
PubMed 

Google Scholar 
86.Turetsky, M. R. et al. Recent acceleration of biomass burning and carbon losses in Alaskan forests and peatlands. Nat. Geosci. 4, 27–31. https://doi.org/10.1038/ngeo1027 (2011).ADS 
CAS 
Article 

Google Scholar  More

1.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 
Article 

Google Scholar 
2.Lionello, P. et al. In Mediterranean Climate Variability Vol. 4 (eds Lionello, P. et al.) 1–26 (Elsevier, 2006).3.Molina, M., Sánchez, E. & Gutiérrez, C. Future heat waves over the Mediterranean from an euro-coRDeX regional climate model ensemble. Sci. Rep. 10, 1–10 (2020).Article 
CAS 

Google Scholar 
4.Bucchignani, E., Mercogliano, P., Panitz, H.-J. & Montesarchio, M. Climate change projections for the Middle East-North Africa domain with COSMO-CLM at different spatial resolutions. Adv. Clim. Change 9, 66–80 (2018).Article 

Google Scholar 
5.García, N., Cuttelod, A. & Malak, D. A. The Status and Distribution of Freshwater Biodiversity in Northern Africa (IUCN, 2010).6.Di Castri, F. & Mooney, H. A. Mediterranean Type Ecosystems: Origin and Structure Vol. 7 (Springer Science & Business Media, 2012).7.Stoks, R., Geerts, A. N. & De Meester, L. Evolutionary and plastic responses of freshwater invertebrates to climate change: Realized patterns and future potential. Evol. Appl. 7, 42–55 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
8.Wellborn, G. A., Skelly, D. K. & Werner, E. E. Mechanisms creating community structure across a freshwater habitat gradient. Annu. Rev. Ecol. Evol. Syst. 27, 337–363 (1996).Article 

Google Scholar 
9.Arribas, P. et al. Dispersal ability rather than ecological tolerance drives differences in range size between lentic and lotic water beetles (Coleoptera: Hydrophilidae). J. Biogeogr. 39, 984–994 (2012).Article 

Google Scholar 
10.Hof, C., Brändle, M. & Brandl, R. Lentic odonates have larger and more northern ranges than lotic species. J. Biogeogr. 33, 63–70 (2006).Article 

Google Scholar 
11.Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R. & Cushing, C. E. The river continuum concept. Can. J. Fish. Aquat. Sci. 37, 130–137 (1980).Article 

Google Scholar 
12.Ibàñez, C., Prat, N. & Canicio, A. Changes in the hydrology and sediment transport produced by large dams on the lower Ebro river and its estuary. Regul. Rivers Res. Manag. 12, 51–62 (1996).Article 

Google Scholar 
13.Kondolf, G., Rubin, Z. & Minear, J. Dams on the Mekong: Cumulative sediment starvation. Water Resour. Res. 50, 5158–5169 (2014).ADS 
Article 

Google Scholar 
14.Pringle, C. M., Freeman, M. C. & Freeman, B. J. Regional effects of hydrologic alterations on riverine macrobiota in the new world: Tropical-temperate comparisons. Bioscience 50, 807–823 (2000).Article 

Google Scholar 
15.Liu, X. et al. Effects of dams and their environmental impacts on the genetic diversity and connectivity of freshwater mussel populations in Poyang Lake Basin, China. Freshw. Biol. 65, 264–277 (2020).Article 

Google Scholar 
16.Barbarossa, V. et al. Impacts of current and future large dams on the geographic range connectivity of freshwater fish worldwide. Proc. Natl. Acad. Sci. U.S.A. 117, 3648–3655 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.López-Moreno, J. I. et al. Dam effects on droughts magnitude and duration in a transboundary basin: The Lower River Tagus, Spain and Portugal. Water Resour. Res. 45, W02405 (2009).ADS 
Article 

Google Scholar 
18.McMahon, T. & Finlayson, B. Droughts and anti-droughts: The low flow hydrology of Australian rivers. Freshw. Biol. 48, 1147–1160 (2003).Article 

Google Scholar 
19.Aguiar, F. C. & Ferreira, M. T. Human-disturbed landscapes: effects on composition and integrity of riparian woody vegetation in the Tagus River basin, Portugal. Environ. Conserv. 32, 30–41 (2005).Article 

Google Scholar 
20.Costa, M. J., Vasconcelos, R., Costa, J. & Cabral, H. River flow influence on the fish community of the Tagus estuary (Portugal). Hydrobiologia 587, 113–123 (2007).Article 

Google Scholar 
21.Dallas, H. F. The influence of biotope availability on macroinvertebrate assemblages in South African rivers: Implications for aquatic bioassessment. Freshw. Biol. 52, 370–380 (2007).Article 

Google Scholar 
22.Demars, B. O., Kemp, J. L., Friberg, N., Usseglio-Polatera, P. & Harper, D. M. Linking biotopes to invertebrates in rivers: Biological traits, taxonomic composition and diversity. Ecol. Indic. 23, 301–311 (2012).Article 

Google Scholar 
23.Wallace, J. B. Recovery of lotic macroinvertebrate communities from disturbance. Environ. Manag. 14, 605–620 (1990).ADS 
Article 

Google Scholar 
24.Boulton, A. J. Parallels and contrasts in the effects of drought on stream macroinvertebrate assemblages. Freshw. Biol. 48, 1173–1185 (2003).Article 

Google Scholar 
25.Desrosiers, M. et al. Assessing anthropogenic pressure in the St. Lawrence River using traits of benthic macroinvertebrates. Sci. Total Environ. 649, 233–246 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Durance, I. & Ormerod, S. J. Climate change effects on upland stream macroinvertebrates over a 25-year period. Glob. Change Biol. 13, 942–957 (2007).ADS 
Article 

Google Scholar 
27.Santos, R. et al. Impacts of climate change and land-use scenarios on Margaritifera margaritifera, an environmental indicator and endangered species. Sci. Total Environ. 511, 477–488 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Junior, R. F. V. et al. Impacts of land use conflicts on riverine ecosystems. Land Use Policy 43, 48–62 (2015).Article 

Google Scholar 
29.Fonseca, A., Fernandes, L. S., Fontainhas-Fernandes, A., Monteiro, S. & Pacheco, F. The impact of freshwater metal concentrations on the severity of histopathological changes in fish gills: A statistical perspective. Sci. Total Environ. 599, 217–226 (2017).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
30.Ferreira, A., Fernandes, L. S., Cortes, R. & Pacheco, F. Assessing anthropogenic impacts on riverine ecosystems using nested partial least squares regression. Sci. Total Environ. 583, 466–477 (2017).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
31.Fernandes, L. S., Fernandes, A., Ferreira, A., Cortes, R. & Pacheco, F. A partial least squares—Path modeling analysis for the understanding of biodiversity loss in rural and urban watersheds in Portugal. Sci. Total Environ. 626, 1069–1085 (2018).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
32.Intergovernmental Panel on Climate Change. Climate Change 2014–Impacts, Adaptation and Vulnerability: Regional Aspects (Cambridge University Press, 2014).33.Khelifa, R. Flight period, apparent sex ratio and habitat preferences of the Maghribian endemic Calopteryx exul Selys, 1853 (Odonata: Zygoptera). Revue d’Ecologie (La Terre et La Vie) 68, 37–45 (2013).
Google Scholar 
34.Khelifa, R. & Mellal, M. K. Host-plant-based restoration as a potential tool to improve conservation status of odonate specialists. Insect Conserv. Divers. 10(2), 151–160. https://doi.org/10.1111/icad.12212 (2017).Article 

Google Scholar 
35.Khelifa, R. et al. A hotspot for threatened Mediterranean odonates in the Seybouse River (Northeast Algeria): Are IUCN population sizes drastically underestimated?. Int. J. Odonatol. 19, 1–11. https://doi.org/10.1080/13887890.2015.1133331 (2016).Article 

Google Scholar 
36.Boudot, J.-P. Calopteryx exul. The IUCN Red List of Threatened Species 2018 e.T60287A72725790. https://doi.org/10.2305/IUCN.UK.2018-2301.RLTS.T60287A72725790.en. Downloaded on 72725729 January 72722021. (2018).37.Martin, R. Contribution à l’étude des Neuroptères de l’Afrique. II. Les odonates du département de Constantine. Ann. Soc. Entomol. Fr. 79, 95–104 (1910).
Google Scholar 
38.Chelli, A., Zebsa, R. & Khelifa, R. Discovery of a new population of the endangered Calopteryx exul in central North Algeria (Odonata: Calopterygidae). Not. Odonatol. 9, 150–154 (2019).
Google Scholar 
39.Feyen, L. & Dankers, R. Impact of global warming on streamflow drought in Europe. J. Geophys. Res. Atmos. 114, D17116 (2009).ADS 
Article 

Google Scholar 
40.Schneider, C., Laizé, C., Acreman, M. & Florke, M. How will climate change modify river flow regimes in Europe?. Hydrol. Earth Syst. Sci. 17, 325–339 (2013).ADS 
Article 

Google Scholar 
41.Dudgeon, D. et al. Freshwater biodiversity: Importance, threats, status and conservation challenges. Biol. Rev. 81, 163–182 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
42.Strayer, D. L. & Dudgeon, D. Freshwater biodiversity conservation: Recent progress and future challenges. J. North Am. Benthol. Soc. 29, 344–358 (2010).Article 

Google Scholar 
43.Van Vliet, M. & Zwolsman, J. Impact of summer droughts on the water quality of the Meuse river. J. Hydrol. 353, 1–17 (2008).ADS 
Article 

Google Scholar 
44.Caruso, B. Temporal and spatial patterns of extreme low flows and effects on stream ecosystems in Otago, New Zealand. J. Hydrol. 257, 115–133 (2002).ADS 
CAS 
Article 

Google Scholar 
45.Stanley, E. H., Fisher, S. G. & Grimm, N. B. Ecosystem expansion and contraction in streams. Bioscience 47, 427–435 (1997).Article 

Google Scholar 
46.Truchy, A. et al. Habitat patchiness, ecological connectivity and the uneven recovery of boreal stream ecosystems from an experimental drought. Glob. Change Biol. 26, 3455–3472 (2020).ADS 
Article 

Google Scholar 
47.Boulton, A. J. & Lake, P. S. Effects of drought on stream insects and its ecological consequences. Aquatic insects: Challenges to populations 81–102 (CABI, 2008).48.Andersen, C. B., Lewis, G. P. & Sargent, K. A. Influence of wastewater-treatment effluent on concentrations and fluxes of solutes in the Bush River, South Carolina, during extreme drought conditions. Environ. Geosci. 11, 28–41 (2004).Article 

Google Scholar 
49.Wada, Y., Van Beek, L. P., Wanders, N. & Bierkens, M. F. Human water consumption intensifies hydrological drought worldwide. Environ. Res. Lett 8, 034036 (2013).ADS 
Article 

Google Scholar 
50.Aldous, A., Fitzsimons, J., Richter, B. & Bach, L. Droughts, floods and freshwater ecosystems: Evaluating climate change impacts and developing adaptation strategies. Mar. Freshw. Res. 62, 223–231 (2011).CAS 
Article 

Google Scholar 
51.Conley, D. J. et al. Controlling eutrophication: Nitrogen and phosphorus. Science 123, 1014–1015 (2009).Article 

Google Scholar 
52.Park, T.-J. et al. Development of water quality criteria of ammonia for protecting aquatic life in freshwater using species sensitivity distribution method. Sci. Total Environ. 634, 934–940 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Reggam, A., Bouchelaghem, E.-H., Hanane, S. & Houhamdi, M. Effects of anthropogenic activities on the quality of surface water of Seybouse River (northeast of the Algeria). Arab. J. Geosci. 10, 219 (2017).Article 
CAS 

Google Scholar 
54.Khelifa, R. et al. Long-range movements of an endangered endemic damselfly Calopteryx exul Selys, 1853 (Calopterygidae: Odonata). Afr. J. Ecol. 52, 375–377 (2014).
Google Scholar 
55.Khelifa, R. Partial bivoltinism and emergence patterns in the North African endemic damselfly Calopteryx exul: Conservation implications. Afr. J. Ecol. 55, 145–151 (2017).Article 

Google Scholar 
56.Adams, H. D. et al. Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-chang-type drought. Proc. Natl. Acad. Sci. U.S.A. 106, 7063–7066 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Scrimgeour, G. J. & Winterbourn, M. J. Effects of floods on epilithon and benthic macroinvertebrate populations in an unstable New Zealand river. Hydrobiologia 171, 33–44 (1989).Article 

Google Scholar 
58.Giller, P., Sangpradub, N. & Twomey, H. Catastrophic flooding and macroinvertebrate community structure. Verh. Int. Ver. Theor. Angew. Limnol. 24, 1724–1729 (1991).
Google Scholar 
59.Siva-Jothy, M. T., Gibbons, D. W. & Pain, D. Female oviposition-site preference and egg hatching success in the damselfly Calopteryx splendens xanthostoma. Behav. Ecol. Sociobiol. 37, 39–44 (1995).Article 

Google Scholar 
60.Stettmer, C. Colonisation and dispersal patterns of banded (Calopteryxsplendens) and beautiful demoiselles (C. virgo) (Odonata: Calopterygidae) in south-east German streams. Eur. J. Entomol. 93, 579–593 (1996).
Google Scholar 
61.Chaput-Bardy, A., Grégoire, A., Baguette, M., Pagano, A. & Secondi, J. Condition and phenotype-dependent dispersal in a damselfly, Calopteryx splendens. PLoS ONE 5, e10694 (2010).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
62.Ward, L. & Mill, P. Long range movements by individuals as a vehicle for range expansion in Calopteryx splendens (Odonata: Zygoptera). Eur. J. Entomol. 104, 195 (2007).Article 

Google Scholar 
63.Mellal, M. K., Bensouilah, M., Houhamd, M. & Khelifa, R. Reproductive habitat provisioning promotes survival and reproduction of the endangered endemic damselfly Calopteryx exul. J. Insect Conserv. 22, 563–570 (2018).Article 

Google Scholar 
64.Cordero-Rivera, A. & Stoks, R. In Dragonflies and Damselflies: Model Organisms for Ecological and Evolutionary Research (ed. Córdoba-Aguilar, A.) 7–20 (Oxford University Press, 2008).65.Iglesias, A., Garrote, L., Flores, F. & Moneo, M. Challenges to manage the risk of water scarcity and climate change in the Mediterranean. Water Resour. Manag. 21, 775–788 (2007).Article 

Google Scholar 
66.Barnett, T. P. et al. Human-induced changes in the hydrology of the western United States. Science 319, 1080–1083 (2008).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Samways, M. J. et al. Value of artificial ponds for aquatic insects in drought-prone southern Africa: A review. Biodivers. Conserv. 29, 3131–3150 (2020).Article 

Google Scholar 
68.Deacon, C., Samways, M. J. & Pryke, J. S. Aquatic insects decline in abundance and occupy low-quality artificial habitats to survive hydrological droughts. Freshw. Biol. 64, 1643–1654 (2019).Article 

Google Scholar 
69.Briggs, A. J., Pryke, J. S., Samways, M. J. & Conlong, D. E. Complementarity among dragonflies across a pondscape in a rural landscape mosaic. Insect Conserv. Divers. 12, 241–250 (2019).Article 

Google Scholar 
70.Geist, J. Integrative freshwater ecology and biodiversity conservation. Ecol. Indic. 11, 1507–1516 (2011).Article 

Google Scholar 
71.Brooks, A. J., Chessman, B. C. & Haeusler, T. Macroinvertebrate traits distinguish unregulated rivers subject to water abstraction. J. North Am. Benthol. Soc. 30, 419–435 (2011).Article 

Google Scholar 
72.Garibaldi, L. A. et al. Working landscapes need at least 20% native habitat. Conserv. Lett. https://doi.org/10.1111/conl.12773 (2020).Article 

Google Scholar 
73.Vincent, A. & Fleury, P. Development of organic farming for the protection of water quality: Local projects in France and their policy implications. Land Use Policy 43, 197–206 (2015).Article 

Google Scholar 
74.Bengtsson, J., Ahnström, J. & Weibull, A. C. The effects of organic agriculture on biodiversity and abundance: A meta-analysis. J. Appl. Ecol. 42, 261–269 (2005).Article 

Google Scholar 
75.Lichtenberg, E. M. et al. A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes. Glob. Change Biol. 23, 4946–4957 (2017).ADS 
Article 

Google Scholar 
76.ABHCSM. A.G.I.R.E (Agence nationale de la gestion intégrée des ressources en eau) (2016). Rapport sur l’analyse de l’année hydrologique (2015–2016) du barrage Hammam Debagh. Agence de bassin hydrographique Constantinois-Seybouse-Mellegue (2016).77.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
78.Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3. 10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

Google Scholar 
79.Wildlife Conservation Society—WCS and Center for International Earth Science Information Network—CIESIN—Columbia University (NASA Socioeconomic Data and Applications Center (SEDAC), 2005).80.Vicente-Serrano, S. M. & Staff. The Climate Data Guide: Standardized Precipitation Evapotranspiration Index (SPEI). Retreived from https://climatedataguide.ucar.edu/climate-data/standardized-precipitation-evapotranspiration-index-spei (2015).81.D’Orangeville, L. et al. Drought timing and local climate determine the sensitivity of eastern temperate forests to drought. Glob. Change Biol. 24, 2339–2351 (2018).ADS 
Article 

Google Scholar 
82.Khelifa, R. Females ‘assist’ sneaker males to dupe dominant males in a rare endemic damselfly: Sexual conflict at its finest. Ecology 100, e02811 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
83.R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).84.Laake, J. RMark: An R Interface for Analysis of Capture–Recapture Data with MARK, AFSC Processed Rep 2013-01 (Alaska Fish. Sci. Cent., NOAA, National Marine Fisheries Service, 2013).85.Burnham, K. P. Design and Analysis Methods for Fish Survival Experiments Based on Release-Recapture Vol. 5 (America Fisheries Society Monograph, 1987).86.Amstrup, S. C., McDonald, T. L. & Manly, B. F. Handbook of Capture–Recapture Analysis (Princeton University Press, 2010). More

1.Goergen, G., Kumar, P. L., Sankung, S. B., Togola, A. & Tamò, M. First report of outbreaks of the fall armyworm Spodoptera frugiperda (J E Smith) (Lepidoptera, Noctuidae), a new alien invasive pest in West and Central Africa. PLoS ONE 11, e0165632 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
2.Nagoshi, R. N. et al. Comparative molecular analyses of invasive fall armyworm in Togo reveal strong similarities to populations from the eastern United States and the Greater Antilles. PLoS ONE 12, e0181982 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
3.Nagoshi, R. N., Goergen, G., Plessis, H. D., van den Berg, J. & Meagher, R. Genetic comparisons of fall armyworm populations from 11 countries spanning sub-Saharan Africa provide insights into strain composition and migratory behaviors. Sci. Rep. 9, 1–11 (2019).CAS 
Article 

Google Scholar 
4.Ganiger, P. C. et al. Occurrence of the new invasive pest, fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), in the maize fields of Karnataka, India. Curr. Sci. 115, 621 (2018).CAS 
Article 

Google Scholar 
5.Deshmukh, S. et al. First report of the fall armyworm, Spodoptera frugiperda (J E Smith) (Lepidoptera: Noctuidae), an alien invasive pest on maize in India. Pest Manag. Hortic. Ecosyst. 24, 23–29 (2018).
Google Scholar 
6.Shylesha, A. N. et al. Studies on new invasive pest Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae) and its natural enemies. J. Biol. Control 32, 145–151 (2018).Article 

Google Scholar 
7.Swamy, H. M. M. et al. Prevalence of “R” strain and molecular diversity of fall army worm Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) in India. Indian J. Entomol. 80, 544 (2018).Article 

Google Scholar 
8.Chormule, A. et al. First report of the fall armyworm, Spodoptera frugiperda (J. E. Smith) (Lepidoptera, Noctuidae) on sugarcane and other crops from Maharashtra, India. J. Entomol. Zool. Stud. 7, 114–117 (2019).
Google Scholar 
9.Visalakshi, M. et al. Report of the invasive fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) and its natural enemies on maize and other crops from Andhra Pradesh, India. J. Entomol. Zool. Stud. 7, 1348–1352 (2019).MathSciNet 

Google Scholar 
10.Srikanth, J. et al. First report of occurrence of fall armyworm Spodoptera frugiperda in sugarcane from Tamil Nadu, India. J. Sugarcane Res. 8, 195–202 (2019).
Google Scholar 
11.Babu, S. R. et al. Report of an exotic invasive pest the fall armyworm, Spodoptera frugiperda (J.E. Smith) on maize in Southern Rajasthan. J. Entomol. Zool. Stud. 7, 1296–1300 (2019).
Google Scholar 
12.Pashley, D. P. Host-associated genetic differentiation in fall armyworm (Lepidoptera: Noctuidae): a sibling species complex?. Ann. Entomol. Soc. Am. 79, 898–904 (1986).Article 

Google Scholar 
13.Pashley, D. P., Sparks, T. C., Quisenberry, S. S., Jamjanya, T. & Dowd, P. F. Two fall armyworm strains feed on corn, rice and Bermuda-grass. La. Agric. 30, 8–9 (1987).
Google Scholar 
14.Pashley, D. P. & Martin, J. A. Reproductive incompatibility between host strains of the fall armyworm (Lepidoptera: Noctuidae). Ann. Entomol. Soc. Am. 80, 731–733 (1987).Article 

Google Scholar 
15.Lima, E. R. & McNeil, J. N. Female sex pheromones in the host races and hybrids of the fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae). Chemoecology 19, 29–36 (2009).CAS 
Article 

Google Scholar 
16.Levy, H. C., Garcia-Maruniak, A. & Maruniak, J. E. Strain identification of Spodoptera frugiperda (Lepidoptera: Noctuidae) insects and cell line: PCR-RFLP of cytochrome oxidase C subunit-I gene. Fla. Entomol. 85, 186–190 (2002).CAS 
Article 

Google Scholar 
17.Nagoshi, R. N. The fall armyworm triose phosphate isomerase (Tpi) gene as a marker of strain identity and interstrain mating. Ann. Entomol. Soc. Am. 103, 283–292 (2010).CAS 
Article 

Google Scholar 
18.Nagoshi, R. N. et al. Genetic characterization of fall armyworm infesting South Africa and India indicate recent introduction from a common source population. PLoS ONE 14, e0217755 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Nagoshi, R. N. et al. Southeastern Asia fall armyworms are closely related to populations in Africa and India, consistent with common origin and recent migration. Sci. Rep. 10, 1–10 (2020).Article 
CAS 

Google Scholar 
20.Zhang, L. et al. High-depth resequencing reveals hybrid population and insecticide resistance characteristics of fall armyworm (Spodoptera frugiperda) invading China; https://doi.org/10.1101/813154 (2019).21.Yainna, S. et al. Genomic balancing selection is key to the invasive success of the fall armyworm; https://doi.org/10.22541/au.160363803.32074105/v1 (2020).22.Tay, W. T. et al. Global FAW population genomic signature supports complex introduction events across the Old World. bioRxiv; https://doi.org/10.1101/2020.06.12.147660 (2020).23.South, A. rworldmap: a new R package for mapping global data. R J. 3(1), 35–43 (2011).MathSciNet 
Article 

Google Scholar 
24.Wickham, et al. Welcome to the tidyverse. J. Open Source Softw. 4(43), 1686 (2019).ADS 
Article 

Google Scholar 
25.Nagoshi, R. N. et al. Using haplotypes to monitor the migration of fall armyworm (Lepidoptera: Noctuidae) corn-strain populations from Texas and Florida. J. Econ. Entomol. 101, 742–749 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
26.Pedersen, T. L. patchwork: the composer of plots; https://CRAN.R-project.org/package=patchwork (2020).27.Yan, L. ggvenn: draw Venn diagram by ‘ggplot2’; https://CRAN.R-project.org/package=ggvenn (2020).28.Marchese, C. Biodiversity hotspots: a shortcut for a more complicated concept. Glob. Ecol. Conserv. 3, 297–309 (2015).Article 

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

Google Scholar 
30.Behere, G. T., Tay, W. T., Russell, D. A., Kranthi, K. R. & Batterham, P. Population genetic structure of the cotton bollworm Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) in India as inferred from EPIC-PCR DNA markers. PLoS ONE 8, e53448 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Chowda-Reddy, R. et al. Bemisia tabaci phylogenetic groups in India and the relative transmission efficacy of tomato leaf curl Bangalore virus by an indigenous and an exotic population. J. Integr. Agric. 11, 235–248 (2012).Article 

Google Scholar 
32.Naik, V. C. B. et al. Evidence for population expansion of cotton pink bollworm Pectinophora gossypiella (Saunders) (Lepidoptera: Gelechiidae) in India. Sci. Rep. 10, 1–11 (2020).Article 
CAS 

Google Scholar 
33.Ciborowski, K. L. et al. Rare and fleeting: an example of interspecific recombination in animal mitochondrial DNA. Biol. Lett. 3, 554–557 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Andolfatto, P., Scriber, J. M. & Charlesworth, B. No association between mitochondrial DNA haplotypes and a female-limited mimicry phenotype in Papilio glaucus. Evolution 57, 305 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Gantenbein, B., Fet, V., Gantenbein-Ritter, I. A. & Balloux, F. Evidence for recombination in scorpion mitochondrial DNA (Scorpiones: Buthidae). Proc. R. Soc. B Biol. Sci. 272, 697–704 (2005).CAS 
Article 

Google Scholar 
36.Hebert, P. D. N., Cywinska, A., Ball, S. L. & Dewaard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B Biol. Sci. 270, 313–321 (2003).CAS 
Article 

Google Scholar 
37.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34, 3299–3302 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2020).
Google Scholar 
40.Paradis, E. pegas: an R package for population genetics with an integrated-modular approach. Bioinformatics 26, 419–420 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Templeton, A. R., Crandall, K. A. & Sing, C. F. A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping and DNA sequence data. III. Cladogram estimation. Genetics 132, 619–633 (1992).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281 (2014).PubMed 
PubMed Central 
Article 

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

Google Scholar 
45.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 
46.Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinform. Oxf. Engl. 24, 1403–1405 (2008).CAS 
Article 

Google Scholar  More

Solving the problem of engineering water shortage is key to ensure water resource security in the karst area It can be seen from the subsystems of the indices sorted by the absolute MIV that the engineering water shortage subsystem had the greatest impact on water resource security in the karst area, which is the main reason to promote its transformation.The water shortage in karst areas is caused by poor natural conditions and inadequate engineering conditions, that is, “engineering water shortage”. It is a serious problem in the Guizhou karst area. The main reasons are as follows. First, the karst hydrogeological and geomorphic conditions, with high mountains and deep rivers, make Guizhou a water shortage area. Second, the karst area is rich in water resources, but it is difficult to develop and utilize these resources. Inter annual variations of rainfall are not significant, but there are large differences within the year, which can easily lead to seasonal drought. Third, the layout of water conservancy projects such as water retention, water storage, and water transfer is unreasonable or insufficient, resulting in conditions of shortage of irrigation and the inadequacy of drinking water for people and livestock. Therefore, the Guizhou karst area has become an area of water shortage, especially engineering water shortage. This is the main bottleneck restricting the coordinated development of the region’s social economy and ecology.Water conservancy projects can determine the diversion and allocation of water resources across time and district to achieve reasonable allocation, efficient utilization, and protection. This indicates the need for higher requirements for engineering water storage and improving water resource utilization efficiency. Therefore, the construction of water conservancy projects is key to ensure future water resource security.The modes of development and utilization of water resources are also significant in the karst area In the past 15 years, Guizhou Province has attached great importance to the development and utilization of water resources. The subsystems of water resource carrying capacity and vulnerability in the Guizhou karst area have risen steadily, which has improved water resource security. However, the development and utilization of water resources will cause changes in the quantity and structure of water usage. This has both optimization and constraints on regional development. Therefore, the geological, hydrological, and hydrogeological characteristics of the karst area must be investigated. The development and utilization of water resources in the karst area should involve appropriate technologies or methods in accordance with these different hydrogeological structures. Geology, geomorphology, rainwater, distributions of farmland and residences, and hydrogeological structures in the karst area are the major factors to consider for solving water shortages in this area35. Rain collection, underground reservoirs, a decentralized water supply and runoff gathering are significant modes of development in the karst area.The situation of water resource security in karst area of Guizhou is gradually getting better This is achieved through water conservation projects and technological measures for water resource exploitation, utilization, projection, and reasonable allocation and control. Meanwhile, Guizhou achieves the security of regional water resource utilization and development through adjusting the regional economic pattern, water resource utilization technology, and so forth.From 2001 to 2006, the status of water resource security was serious, and there was a moderate warning level. At that time, the industrialization of Guizhou province was developing rapidly, and the construction of water conservancy and other infrastructure was also advancing rapidly. Increased attention was given to soil erosion, desertification, water resource pollution, and other problems. Despite high water consumption, the water environment was gradually improving. However, rapid economic and social development has exceeded the carrying capacity of the water resources during this period. Some problems persist in the study area, such as inadequacy of urban sewage treatment facilities, outdated water conservancy facilities, and insufficient prevention of environmental pollution. Urban water pollution treatment facilities and garbage treatment facilities are seriously outdated and cannot meet the requirements of urban development and water environmental protection. These problems have led to a low starting point for water resource security utilization in Guizhou Province. Although the situation has been improved and alleviated year by year, it is still in a moderate warning level, and the water resource security situation is still severe.After reaching the critical safety level in 2007, the water resource security of Guizhou Province declined slightly in 2009 and 2013, although a critical safety level was maintained; the safety level further deteriorated to a moderate warning level in 2011. This deterioration occurred because Guizhou suffered its worst drought in a century from 2009 to 2011, and another drought in 2013. According to the information provided by single indices, the treatment rate of urban waste water, proportion of water supply for water lifting and diversion projects, qualifying rate of water environment function zones, qualifying rate of industrial waste water, degree of development and utilization of groundwater, and density of large and medium-sized reservoirs all showed increasing trends year by year or showed relatively high levels. In contrast, the indices of irrigation water consumption per unit area, above moderate rocky desertification area ratio, water consumption per ten thousand yuan GDP, and water consumption per ten thousand yuan industrial output decreased year by year. All of these indices played a driving role in water utilization and water resource security in the study area. Although the once-in-a-century drought reduced the amount of water, Guizhou Province improved the utilization rate of water resources in the dry years, which alleviated the impact of the reduction of water resources to a certain extent, and allowed the water resource security in the study area to barely maintain the critical safety level. This finding is consistent with previous research conclusions: the engineering water shortage subsystem had largest effect on water resource security in the karst area, whereas the water quantity subsystem had the least influence.It can be inferred that the requirements for ensuring water resource security in the karst area are a good economic development model, environmental protection, pollution control, and improvement of basic water conservancy facilities. These measures can be conducive to actively coping with the impact of abnormal climate changes on the utilization of water resources. More

1.Weiner, J. Physiological limits to sustainable energy budgets in birds and mammals: ecological implications. Trends Ecol. Evol. 7, 384–388 (1992).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Mcnab, B. K. Food habits, energetics, and the population biology of mammals. Am. Nat. 116, 106–124 (1980).Article 

Google Scholar 
3.Hovey, F. W. & Harestad, A. S. Estimating effects of snow on shrub availability for black-tailed deer in southwestern British Columbia. Wildl. Soc. Bull. 20, 308–313 (1992).
Google Scholar 
4.Post, E. & Stenseth, N. Climatic variability, plant phenology, and northern ungulates. Ecology 80, 1322–1339 (1999).Article 

Google Scholar 
5.Moen, A. N. Seasonal changes in heart rates, activity, metabolism, and forage intake of white-tailed deer. J. Wildl. Manag. 42, 715–738 (1978).Article 

Google Scholar 
6.Holand, Ø., Mysterud, A., Wannag, A. & Linnell, J. D. C. Roe deer in northern environments: physiology and behaviour. In The European Roe Deer: Biology of Success (eds Andersen, R. et al.) 117–137 (Scandinavian University Press, 1998).
Google Scholar 
7.Foromozov, A. N. Snow Cover as an Integral Factor of the Environment and Its Importance in the Ecology of Mammals and Birds (The University of Alberta, 1963).
Google Scholar 
8.Cagnacci, F. et al. Partial migration in roe deer: migratory and resident tactics are end points of a behavioural gradient determined by ecological factors. Oikos 120, 1790–1802 (2011).Article 

Google Scholar 
9.Dussault, C., Courtois, R., Ouellet, J.-P. & Girard, I. Space use of moose in relation to food availability. Can. J. Zool. 83, 1431–1437 (2005).Article 

Google Scholar 
10.Mysterud, A. & Sæther, B.-E. Climate change and implications for the future distribution and management of ungulates in Europe. In Ungulate Management in Europe: Problems and Practices (eds Putman, R. et al.) 349–375 (Cambridge University Press, 2011).
Google Scholar 
11.Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).Article 

Google Scholar 
12.Scherrer, S. C., Wüthrich, C., Croci-Maspoli, M., Weingartner, R. & Appenzeller, C. Snow variability in the Swiss Alps 1864–2009. Int. J. Climatol. 33, 3162–3173 (2013).Article 

Google Scholar 
13.Milner, J. M., van Beest, F. M., Schmidt, K. T., Brook, R. K. & Storaas, T. To feed or not to feed? Evidence of the intended and unintended effects of feeding wild ungulates. J. Wildl. Manag. 78, 1322–1334 (2014).Article 

Google Scholar 
14.Ossi, F. et al. Plastic response by a small cervid to supplemental feeding in winter across a wide environmental gradient. Ecosphere 8, e01629 (2017).Article 

Google Scholar 
15.Putman, R. & Staines, B. W. Supplementary winter feeding of wild red deer Cervus elaphus in Europe and North America: justifications, feeding practice and effectiveness. Mamm. Rev. 34, 285–306 (2004).Article 

Google Scholar 
16.Cagnacci, F., Boitani, L., Powell, R. A. & Boyce, M. S. Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges. Philos. Trans. R. Soc. B Biol. Sci. 365, 2157–2162 (2010).Article 

Google Scholar 
17.Peters, W. et al. Migration in geographic and ecological space by a large herbivore. Ecol. Monogr. 87, 297–320 (2017).Article 

Google Scholar 
18.Morellet, N. et al. Seasonality, weather and climate affect home range size in roe deer across a wide latitudinal gradient within Europe. J. Anim. Ecol. 82, 1326–1339 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Johnson, D. H. The comparison of usage and availability measurements for evaluating resource preference. Ecology 61, 65–71 (1980).Article 

Google Scholar 
20.Ossi, F., Gaillard, J. M., Hebblewhite, M. & Cagnacci, F. Snow sinking depth and forest canopy drive winter resource selection more than supplemental feeding in an alpine population of roe deer. Eur. J. Wildl. Res. 61, 111–124 (2015).Article 

Google Scholar 
21.Mysterud, A. & Østbye, E. Bed-site selection by European roe deer (Capreolus capreolus) in southern Norway during winter. Can. J. Zool. 73, 924–932 (1995).Article 

Google Scholar 
22.Ramanzin, M., Sturaro, E. & Zanon, D. Seasonal migration and home range of roe deer (Capreolus capreolus) in the Italian eastern Alps. Can. J. Zool. 85, 280–289 (2007).Article 

Google Scholar 
23.Endrizzi, S., Gruber, S., Dall’Amico, M. & Rigon, R. GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects. Geosci. Model. Dev. 7, 2831–2857 (2014).Article 
ADS 

Google Scholar 
24.Cohen, J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20, 37–46 (1960).Article 

Google Scholar 
25.Thomson, A. M. et al. RCP 4.5: a pathway for stabilization of radiative forcing by 2100. Clim. Change 109, 77–94 (2011).CAS 
Article 
ADS 

Google Scholar 
26.Riahi, K. et al. RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Clim. Change 109, 33–57 (2011).CAS 
Article 
ADS 

Google Scholar 
27.Thomas, C. D. Climate, climate change and range boundaries. Divers. Distrib. 16, 488–495 (2010).Article 

Google Scholar 
28.Penteriani, V. et al. Evolutionary and ecological traps for brown bears Ursus arctos in human-modified landscapes. Mamm. Rev. 48, 180–193 (2018).Article 

Google Scholar 
29.Sorensen, A., van Beest, F. M. & Brook, R. K. Impacts of wildlife baiting and supplemental feeding on infectious disease transmission risk: a synthesis of knowledge. Prev. Vet. Med. 113, 356–363 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Mysterud, A., Viljugrein, H., Solberg, E. J. & Rolandsen, C. M. Legal regulation of supplementary cervid feeding facing chronic wasting disease. J. Wildl. Manag. 83, 1667–1675 (2019).Article 

Google Scholar 
31.Ceacero, F. et al. Benefits for dominant red deer hinds under a competitive feeding system: food access behavior, diet and nutrient selection. PLoS ONE 7, e32780 (2012).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
32.Beever, E. A. et al. Behavioral flexibility as a mechanism for coping with climate change. Front. Ecol. Environ. 15, 299–308 (2017).Article 

Google Scholar 
33.Loe, L. E. et al. Behavioral buffering of extreme weather events in a high-Arctic herbivore. Ecosphere 7, e01374 (2016).Article 

Google Scholar 
34.Sih, A., Ferrari, M. C. O. & Harris, D. J. Evolution and behavioural responses to human-induced rapid environmental change. Evol. Appl. 4, 367–387 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
35.Radchuk, V. et al. Adaptive responses of animals to climate change are most likely insufficient. Nat. Commun. 10, 3109 (2019).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
36.Mysterud, A. Still walking on the wild side? Management actions as steps towards ‘semi-domestication’ of hunted ungulates. J. Appl. Ecol. 47, 920–925 (2010).Article 

Google Scholar 
37.Felton, A. M. et al. Interactions between ungulates, forests, and supplementary feeding: the role of nutritional balancing in determining outcomes. Mamm. Res. 62, 1–7 (2017).Article 

Google Scholar 
38.Ricci, S. et al. Impact of supplemental winter feeding on ruminal microbiota of roe deer Capreolus capreolus. Wildl. Biol. 2019, wlb.00572 (2019).Article 

Google Scholar 
39.Lone, K. et al. Living and dying in a multi-predator landscape of fear: roe deer are squeezed by contrasting pattern of predation risk imposed by lynx and humans. Oikos 123, 641–651 (2014).Article 

Google Scholar 
40.Chapron, G. et al. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science (80-) 346, 1517–1519 (2014).CAS 
Article 
ADS 

Google Scholar 
41.Milanesi, P., Breiner, F. T., Puopolo, F. & Holderegger, R. European human-dominated landscapes provide ample space for the recolonization of large carnivore populations under future land change scenarios. Ecography (Cop.) 40, 1359–1368 (2017).Article 

Google Scholar 
42.Pascual-Rico, R. et al. Is diversionary feeding a useful tool to avoid human-ungulate conflicts? A case study with the aoudad. Eur. J. Wildl. Res. 64, 1–7 (2018).Article 

Google Scholar 
43.van Beest, F. M., Loe, L. E., Mysterud, A. & Milner, J. M. Comparative space use and habitat selection of moose around feeding stations. J. Wildl. Manag. 74, 219–227 (2010).Article 

Google Scholar 
44.Jerina, K. Roads and supplemental feeding affect home-range size of Slovenian red deer more than natural factors. J. Mamm. 93, 1139–1148 (2012).Article 

Google Scholar 
45.Ranc, N. et al. Preference and familiarity mediate spatial responses of a large herbivore to experimental manipulation of resource availability. Scientific Reports 10, 11946 (2020). 46.Brown, R. D. & Robinson, D. A. Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty. Cryosphere 5, 219–229 (2011).Article 
ADS 

Google Scholar 
47.Schloss, C. A., Nuñez, T. A. & Lawler, J. J. Dispersal will limit ability of mammals to track climate change in the Western Hemisphere. Proc. Natl. Acad. Sci. U. S. A. 109, 8606–8611 (2012).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
48.Gurarie, E. et al. A framework for modelling range shifts and migrations: asking when, whither, whether and will it return. J. Anim. Ecol. 86, 943–959 (2017).PubMed 
Article 

Google Scholar 
49.Rivrud, I. M. et al. Leave before it’s too late: anthropogenic and environmental triggers of autumn migration in a hunted ungulate population. Ecology 97, 1058–1065 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
50.Courtois, R., Dussault, C., Potvin, F. & Daigle, G. Habitat selection by moose (Alces alces) in clear-cut landscapes. Alces 38, 177–192 (2002).
Google Scholar 
51.Gilbert, S. L., Hundertmark, K. J., Person, D. K., Lindberg, M. S. & Boyce, M. S. Behavioral plasticity in a variable environment: snow depth and habitat interactions drive deer movement in winter. J. Mamm. 98, 246–259 (2017).Article 

Google Scholar 
52.Chevin, L. M., Lande, R. & Mace, G. M. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLoS Biol. 8, e1000357 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
53.Bauer, S. & Hoye, B. J. Migratory animals couple biodiversity and ecosystem functioning worldwide. Science (80-) 344, 1242552 (2014).CAS 
Article 

Google Scholar 
54.Mason, T. H. E., Stephens, P. A., Apollonio, M. & Willis, S. G. Predicting potential responses to future climate in an alpine ungulate: Interspecific interactions exceed climate effects. Glob. Change Biol. 20, 3872–3882 (2014).Article 
ADS 

Google Scholar 
55.Carnevali, L., Pedrotti, L., Riga, F. & Toso, S. Banca dati ungulati: Status, distribuzione, consistenza, gestione e prelievo venatorio delle popolazioni di ungulati in Italia. Rapporto 2001–2005 Vol. 117 (Biologia e Conservazione della Fauna, 2009).
Google Scholar 
56.Provincia Autonoma di Trento. Analisi delle consistenze e dei prelievi di ungulati, tetraonidi e coturnice. Stagione Venatoria 2018 (Provincia Autonoma di Trento, 2018).
Google Scholar 
57.Rockel, B., Will, A. & Hense, A. The regional climate model COSMO-CLM (CCLM). Meteorol. Z. 17, 347–348 (2008).Article 

Google Scholar 
58.Boyce, M. S. & McDonald, L. L. Relating populations to habitats using resource selection functions. Trends Ecol. Evol. 14, 268–272 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
59.Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Modell. 157, 281–300 (2002).Article 

Google Scholar 
60.Benoit, T. & Achraf, E. suncalc: compute sun position, sunlight phases, moon position and lunar phase. R package version 0.5.0. https://cran.r-project.org/package=suncalc (2019).61.DeCesare, N. J. et al. Transcending scale dependece in identifying habitat with resource selection functions. Ecol. Appl. 22, 1068–1083 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
62.Kendall, M. A new measure of rank correlation. Biometrika 30, 81–89 (1938).MATH 
Article 

Google Scholar 
63.Cohen, J. Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psychol. Bull. 70, 213–220 (1968).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Gamer, M., Lemon, J., Fellows, I. & Singh, P. irr: various coefficients of interrater reliability and agreement. R package version 0.84.1. https://cran.r-project.org/package=irr (2019).65.Lele, S. R., Keim, J. L. & Solymos, P. ResourceSelection: resource selection (probability) functions for use-availability data. R package version 0.3-5. https://cran.r-project.org/package=ResourceSelection (2019).66.Bivand, R., Keitt, T. & Rowlingson, B. rgdal: bindings for the ‘Geospatial’ Data Abstraction Library. R package version 1.4-8. https://cran.r-project.org/package=rgdal (2019).67.McLeod, A. I. Kendall: Kendall rank correlation and Mann-Kendall trend test. R package version 2.2. https://cran.r-project.org/package=Kendall (2011).68.Bright Ross, J. G., Peters, W., Ossi, F., Moorcroft P. R., Cordano, E., Eccel, E., Bianchini, F., Ramanzin, M., and Cagnacci, F. Datasets for “Climate change and anthropogenic food manipulation interact in shifting the distribution of a large herbivore at its altitudinal range limit.” https://doi.org/10.5281/zenodo.4637674 (2021). More