Ecology

Insect rearingA CLas negative colony of ACP was initially collected from CLas-free Murraya exotica L. growing in the ornamental landscape of South China Agricultural University (SCAU, Guangzhou, China) in May 2014. Then it was reared on potted M. exotica in a greenhouse at SCAU. M. exotica plants were pruned regularly to promote the growth flushes necessary to stimulate ACP oviposition. The ACP populations were periodically (at least once a month) tested to ensure the colony was CLas-free using nested quantitative PCR detection according to the method described by Coy et al.30.The parasitoid T. radiata used in the current study was initially collected from ACP hosts on M. exotica plants in the above-mentioned location during June 2015. Its population was maintained in rearing cages (60 × 60 × 60 cm) using a CLas-free ACP-M. exotica rearing system under laboratory conditions (26 ± 1 °C, RH 80 ± 10% with L:D = 14:10 photoperiods in insect incubators).Host plantsCLas-free and CLas-infected plants of Citrus reticulata Blanco cv. Shatangju were used in the current study. Both plant types were obtained from The Citrus Research Institute of Zhaoqing University (Guangdong, China). The CLas-infected plants were inoculated by shoot grafting. All plants were approximately 4-year old and 1.2−1.5 m in height, separated in nylon net greenhouses (70 mesh per inch2) at two different locations about 2.2 km apart in SCAU. Again, nested qPCR detection was performed periodically (at least once a month) to test for the presence or absence of CLas in the citrus plants according to the method described by Coy et al.30.Acquisition and persistence of CLas in Tamarixia radiata
When new shoots of CLas-infected C. reticulata plants were grown to 5–8 cm, 20 pairs of 1 week-old ACP adults were introduced into one nylon bag covering one fresh shoot to lay eggs for 48 h. When the progeny of ACP developed through to 4th or 5th instar nymph (CLas-donor ACP), which are the stages preferred by T. radiata parasitoids, 150 of the ACP nymphs were randomly selected and the remaining ones were removed. Following this, 10 pairs of 3-day old T. radiata adults, randomly selected from the population that has been tested to be CLas-free, were introduced into the nylon bag in order to parasitize the 4th or 5th instar ACP nymphs for 48 h before being recaptured. Then the potentially parasitized ACP nymphs together with the citrus plants were cultured in a plant growth chamber (Jiangnan Instrument Company, RXZ-500D, at 26 ± 1 °C, 60 ± 2% RH and 14:10 h L:D photoperiod of 3,000 lx illumination).When the progeny of T. radiata (considered F0 generation) developed to 3-day egg, 1st to 4th instar larvae, pupae, and adult stages respectively, they were identified and collected with the assistance of a stereomicroscope. DNA of each stage sample was extracted using the TIANamp Genomic DNA Kit (TIANGEN, Beijing, China) for CLas qPCR detection and titer quantification. Thirty eggs, 20 individuals of 1st or 2nd instar, 10 individuals of 3rd or 4th instar larvae or pupa, as well as three individuals of female or male adults were subsequently ground together to represent each life stage in qPCR, and each stage qPCR detection was repeated three times.The primers used for CLas qPCR detection were LJ900 primers, (F5′-GCCGTTTTAAC ACAAAAGATGAATATC-3′, R5′-ATAAATCAATTTGTTCTAGTTTAC GAC-3′), and 18S rRNA gene of T. radiata (F5′-AAACGGCTACCACATCCA-3′, R5′-ACCAGACT TGCCCTC CA-3′)31 was used as an internal control for DNA normalization and quantification. In order to normalize the qPCR values, each qPCR reaction was performed in three independent runs using SYBR Premix Ex Taq (Takara, Dalian, China) in Bio-Rad CFX Connect™ Real-Time PCR Detection System, with a protocol of initial denaturation at 95 °C for 3 min, followed by 40 cycles at 95 °C for 10 s, 60 °C for 20 s and 72 °C for 30 s.To monitor the CLas persistence in T. radiata, newly emerged female adults of T. radiata (considered F1 generation) were collected from the above experiment and fed with 20% honey water. After 1, 5, 10, and 15 days, 10 parasitoids were recaptured, subsequently ground for DNA extraction and CLas titer detection and quantification using qPCR. The protocol of DNA extraction and qPCR reaction was the same as above, and qPCR quantification was repeated three times for each treatment.Localization patterns of CLas in Tamarixia radiata
Localization patterns of CLas in different instars of T. radiataFluorescent in situ hybridization (FISH) was used to visualize the distribution of CLas in T. radiata exposed to CLas positive ACP, following the method of Gottlieb et al.32 with a slight modification. Eggs and different larval instars of T. radiata were collected and fixed in Carnoy’s solution (chloroform-ethanol-glacial acetic acid [6:3:1,vol/vol] formamide) overnight at 4 °C. After fixation, the samples were washed three times in 50% ethanol with 1× phosphate buffered saline (PBS) for 5 min. Then the samples were decolorized in 6% H2O2 in ethanol for 12 h, after which they were hybridized overnight in 1 ml hybridization buffer (20 mM Tris-HCl pH 8.0, 0.9 M NaCl, 0.01% sodium dodecyl sulfate, 30% formamide) containing 10 pmol of fluorescent probes/ml in a 37 °C water bath under dark conditions. The CLas probe used for FISH was 5′-Cy3-GCCTCGCGACTTCGCAACCCAT-3′. Finally, the stained T. radiata samples were washed three times in a washing buffer (0.3 M NaCl, 0.03 M sodium citrate, 0.01% sodium dodecyl sulfate, 10 min per time). After the samples were whole mounted and stained, the slides were observed and photographed using a Nikon eclipse Ti-U inverted microscope. For each stage sample, approximately 20 individuals were examined to confirm the results.Localization patterns of CLas in different organs of T. radiataDifferent organs (gut, fat body, ovary, poison sac, salivary glands, spermatheca, and chest muscle) were dissected from newly emerged adults of T. radiata in 1× phosphate buffered saline (PBS) under a stereomicroscope using a depression microscope slide and a fine anatomical needle. After a sufficient number of each tissue sample was collected (20 or more), the tissues were washed three times with 1 × PBS, followed by the fixation, decolorization, and hybridization procedures as outlined above, except that this time of decolorization was 2 h. After hybridization, nuclei in the different organs were counterstained with DAPI (0.1 mg/ml in 1 × PBS) for 10 min, then the samples were transferred to slides, mounted whole in hybridization buffer, and viewed using confocal microscopy (Nikon, Japan).Maternal transmission of CLas between Tamarixia generationsFive groups of experiments were used to clarify whether CLas can be transmitted vertically between different T. radiata generations. In the first group, 60 pairs of newly emerged T. radiata adults from the CLas-infected ACP colony (potential CLas-acquired parasitoid adults, F0 generation) were introduced into 60 nylon bags (one female per cage). Each bag covered one fresh citrus plant shoot with one marked CLas-free 4th instar nymph of ACP, the parasitoid females were given 24 h to oviposit, then transferred to another four groups successively to oviposit with intervals of 24 h before they were recaptured for CLas-PCR detection (58/60 and 56/60 T. radiata females and males respectively were CLas-infected). Only the progeny (F1 generation) in which parasitoid parents were both CLas-infected continued to be investigated.When the F1 progeny of CLas-infected parasitoid females developed to egg, larval, pupal, and adult stages respectively, they were collected and divided into two groups; in one group samples were used for the qPCR detection of the CLas titer, and the other group was used for the FISH visualization of CLas. The qPCR and FISH analysis protocols of CLas as well as the number of tested individuals were the same as previously outlined. Each stage was repeated three times.
CLas detection in T. radiata-inoculated ACPQuantitative PCR detection of CLasApproximately 60 newly emerged parasitoid adult females from CLas-infected ACP hosts (potential CLas-acquired parasitoid adults) were collected using an aspirator. They were first starved for 5 h, then released into finger tubes (diameter 6 mm × length 30 mm); one female per tube containing one 4th instar nymph of CLas-free ACP (this was treated as one experimental replicate). The probing behavior of the parasitoids was observed under a stereomicroscope, after which the parasitoids were recaptured for CLas PCR detection (similar to the above experiment, approximately 95% were CLas-infected). Only those 4th instar ACP nymphs, probed for egg-laying by a CLas-infected parasitoid but survived from the probing (the averaged proportion of such samples was 5.36 ± 0.47% and were 100% CLas infected), were transferred onto fresh CLas-free M. exotica shoots to complete their development (hereafter referred as “T. radiata-inoculated ACP”). The experiment was repeated in 32 parallel replicates (Supplementary Table 1), in which 103 T. radiata-inoculated ACP nymphs were finally obtained.Following the above, thirty T. radiata-inoculated ACP nymphs were collected when they developed into 5th instar nymphs (the stage when infection proliferation might have just begun since the infection was introduced at the 4th instar). In addition, thirty 8-day old adults that developed from the T. radiata-inoculated ACP nymphs were also collected. This was because the results in Wu et al.28 revealed that the proportion of CLas-infected ACP individuals exceeds 90% at the 12th day after infection acquisition, while ACP takes 4 days to develop into an adult from 5th instar stage. Their alimentary canals and salivary glands were dissected under a stereomicroscope using the methods of Ammar et al.33, and hemolymphs were collected with a 10 μl pipette tip using the method of Killiny et al.34. The DNA of the alimentary canals, salivary glands and hemolymphs were extracted using TIANamp Micro DNA Kit (Tiangen, Beijing, China), and the relative titers of CLas in each tissue of ACP nymphs and adults were detected by qPCR with of LJ900. The β-actin gene of ACP (F 5′-CCCTGGACTTTGAACAGGAA-3′; R 5′-CTCGTGGATACCGCAAGATT-3′) was selected as an internal control for data normalization and quantification35. For each sample, qPCR detection was repeated three times.FISH visualization of CLasThe alimentary canals and salivary glands of 5th instar nymphs and 8-day old adults of T. radiata-inoculated ACP were dissected as described above, and the distribution of CLas was visualized by FISH and confocal microscopy. The alimentary canals and salivary glands of CLas-infected ACP nymphs and adults (collected from CLas-infected citrus plants) were used as a positive control, and five to ten samples were detected by FISH for each tissue.
CLas transmission from T. radiata-inoculated ACP to citrus plantsAccording to the above experimental results, if the CLas could be detected in the salivary glands of the 8-day old ACP adults (T. radiata-inoculated ACP), 30 more of these adults were randomly selected to inoculate on fresh shoots of CLas-free citrus. ACP adults that acquired CLas from plants and CLas-free ACP adults were used as positive and negative controls respectively.After 20, 30, 40, and 50 days of feeding samples of the citrus leaves fed on by T. radiata-inoculated ACP (named as CLas-recipient citrus leaves), fed on by ACP that acquired CLas from plants (positive control), and fed on by CLas-free ACP (negative control) were cut (1 cm2). Their DNAs were extracted using DNAsecure Plant Kit (Tiangen, Beijing, China). The infections of CLas in these plants were detected by nested PCR based on the methods of Jagoueix et al.36 and Deng et al.37. The experiment was repeated in six plants for each of 20, 30, 40, and 50 days feeding duration, and the infection rates of CLas were calculated.Localization of CLas in citrus plants fed on by T. radiata-inoculated ACPIn order to further confirm the infection of CLas in the recipient citrus leaves, FISH was used to visualize the localization of CLas. According to the above experimental results, after being fed on for 50 days by the T. radiata-inoculated ACP adults, citrus leaf sections containing the midrib were cross-sliced in 30 µ sections using a cryostat (CM1950, Leica, Germany). The leaf samples were prepared for FISH vitalization according to the protocol described by Gottlieb et al.32. Citrus leaves from the plant that had been fed on by ACP adults that acquired CLas from plants and CLas-free ACP adults were used as positive and negative controls, respectively. Five to 10 leaf samples were detected by FISH for each treatment.Phylogenetic analysis of CLas bacteria in different ACP populations and citrus plantsTo assess the identity of the CLas bacteria in CLas donor ACP, CLas vectored parasitoids, T. radiata-inoculated ACP and recipient citrus leaves, the outer membrane protein gene (omp) of CLas was PCR amplified with the primers HP1asinv (5′-GATGATAGG TGCATAAAAGTACAGAAG-3′) and Lp1c (5′-AATACCCTTATGGGATACAAAAA-3′) following the procedure described in Bastianel et al.38. Then the PCR products were sent for sequencing after visualizing the expected bands on 1% agarose gels.All the DNA sequences of CLas omp gene were edited and aligned manually using Clustal X1.8339 in Mega 640. The best model and partitioning scheme were chosen using the Bayesian information criterion in PartitionFinder v.1.0.141. Phylogenetic analysis was undertaken using a maximum likelihood (ML) method with 1000 non-parametric bootstrap replications in RAxML42. Escherichia coli was used as an outgroup.Statistics and reproducibilityTaking 18S rRNA gene of T. radiata and the β-actin gene of ACP as housekeeping genes, the relative titers of CLas in different stages and different tissues of T. radiata and ACP were calculated using the method of 2[−ΔΔct 43. For the parallel experiments that had more than three replicates the differences were compared using analysis of variance (ANOVA) with SPSS 18.0 at a significance level α = 0.05; while for CLas titer, two-sample comparison between genders of Tamarixia adults analysis was performed using paired t-test. Fluorescent pictures were processed using Photoshop CS5 software.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Insect extractsThai edible insects (Fig. 1) were extracted and yield of each extract is shown in Fig. 2. Hexane extracts of most insects, except for P. succincta, provided the highest yield, followed by ethanolic extracts, and aqueous extracts, respectively. The reason might be due to a high amount of fat content of insects. Since these fat components are hydrophobic, they could be extracted well using nonpolar solvent, e.g. hexane. Semi-polar solvent like ethanol could also be used to extract hydrophobic compounds but with less extraction efficacy5. Several previous studies reported that fat was abundant in biomass of insects, ranging from 4.2 to 77.2%, which was accounted for about 26.8% on average dried insects6,7.Figure 1External appearances of Thai edible insects, including (a) rice grasshopper (Euconocephalus sp.), (b) bamboo caterpillar (O. fuscidentalis), (c) house cricket (A. domesticus), (d) silkworm pupae (B. mori), (e) Bombay locust (P. succincta), and (f) giant water bug (L. indicus).Full size imageFigure 2Yields of insect extracts, including B. mori (BM), O. fuscidentalis (OF), Euconocephalus sp. (EU), P. succincta (PS), A. domesticus (AD), and L. indicus (LI). The data are expressed as mean ± SD (n = 3). The Greek alphabet letters (α, β, γ, and δ) indicate significant differences among hexane extracts, the capital letters (A, B, C, and D) indicate significant differences among ethanolic extracts, and the small case letters (a, b, and c) indicate significant differences among aqueous extracts. The data were analyzed using One-Way ANOVA followed by post hoc Tukey test (p  More

1.Weinert, L. A., Araujo-Jnr, E. V., Ahmed, M. Z. & Welch, J. J. The incidence of bacterial endosymbionts in terrestrial arthropods. Proc. R. Soc. B: Biol. Sci. 282, 20150249 (2015).
Google Scholar 
2.Werren, J. H. Biology of Wolbachia. Annu Rev. Entomol. 42, 587–609 (1997).CAS 
PubMed 

Google Scholar 
3.Turelli, M. & Hoffmann, A. A. Rapid spread of an inherited incompatibility factor in California Drosophila. Nature 353, 440–442 (1991).CAS 
PubMed 

Google Scholar 
4.Werren, J. H., Baldo, L. & Clark, M. E. Wolbachia: master manipulators of invertebrate biology. Nat. Rev. Microbiol. 6, 741–751 (2008).CAS 
PubMed 

Google Scholar 
5.Teixeira, L., Ferreira, A. & Ashburner, M. The bacterial symbiont Wolbachia induces resistance to RNA viral infections in Drosophila melanogaster. Plos Biol. 6, e2 (2008).PubMed 

Google Scholar 
6.Hedges, L. M., Brownlie, J. C., O’Neill, S. L. & Johnson, K. N. Wolbachia and virus protection in insects. Science 322, 702 (2008).CAS 
PubMed 

Google Scholar 
7.Rocha, M. N. et al. Pluripotency of Wolbachia against Arboviruses: the case of yellow fever. Gates Open Res. 3, 161 (2019).PubMed 
PubMed Central 

Google Scholar 
8.Moreira, L. A. et al. A Wolbachia symbiont in Aedes aegypti limits infection with dengue, Chikungunya, and Plasmodium. Cell 139, 1268–1278 (2009).PubMed 

Google Scholar 
9.Dutra, H. L. et al. Wolbachia blocks currently circulating zika virus isolates in Brazilian Aedes aegypti mosquitoes. Cell Host Microbe 19, 771–774 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Aliota, M. T. et al. The wMel strain of Wolbachia reduces transmission of chikungunya virus in Aedes aegypti. PLoS Negl. Trop. Dis. 10, e0004677 (2016).PubMed 
PubMed Central 

Google Scholar 
11.Schmidt, T. L. et al. Local introduction and heterogeneous spatial spread of dengue-suppressing Wolbachia through an urban population of Aedes aegypti. PLoS Biol. 15, e2001894 (2017).PubMed 
PubMed Central 

Google Scholar 
12.Ryan, P. A. et al. Establishment of wMel Wolbachia in Aedes aegypti mosquitoes and reduction of local dengue transmission in Cairns and surrounding locations in northern Queensland, Australia. Gates Open Res. 3, 1547 (2020).PubMed 
PubMed Central 

Google Scholar 
13.Indriani, C. et al. Reduced dengue incidence following deployments of Wolbachia-infected Aedes aegypti in Yogyakarta, Indonesia: a quasi-experimental trial using controlled interrupted time series analysis. Gates Open Res. 4, 50 (2020).PubMed 
PubMed Central 

Google Scholar 
14.Zug, R. & Hammerstein, P. Bad guys turned nice? A critical assessment of Wolbachia mutualisms in arthropod hosts. Biol. Rev. Camb. Philos. Soc. 90, 89–111 (2015).PubMed 

Google Scholar 
15.Shi, M. et al. No detectable effect of Wolbachia wMel on the prevalence and abundance of the RNA virome of Drosophila melanogaster. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2018.1165 (2018).16.Webster, C. L. et al. The discovery, distribution, and evolution of viruses associated with Drosophila melanogaster. PLoS Biol. 13, e1002210 (2015).PubMed 
PubMed Central 

Google Scholar 
17.Pimentel, A. C., Cesar, C. S., Martins, M. & Cogni, R. The antiviral effects of the symbiont bacteria Wolbachia in insects. Front Immunol. 11, 626329 (2021).PubMed 
PubMed Central 

Google Scholar 
18.Kriesner, P., Hoffmann, A. A., Lee, S. F., Turelli, M. & Weeks, A. R. Rapid sequential spread of two Wolbachia variants in Drosophila simulans. PLoS Pathog. 9, e1003607 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Weeks, A. R., Turelli, M., Harcombe, W. R., Reynolds, K. T. & Hoffmann, A. A. From parasite to mutualist: rapid evolution of Wolbachia in natural populations of Drosophila. PLoS Biol. 5, e114 (2007).PubMed 
PubMed Central 

Google Scholar 
20.Hoffmann, A. A. & Turelli, M. Unidirectional incompatibility in Drosophila simulans: inheritance, geographic variation and fitness effects. Genetics 119, 435–444 (1988).CAS 
PubMed 
PubMed Central 

Google Scholar 
21.Cross, S. T. et al. Partitiviruses infecting Drosophila melanogaster and Aedes aegypti exhibit efficient biparental vertical transmission. J. Virol. https://doi.org/10.1128/jvi.01070-20 (2020).22.Webster, C. L., Longdon, B., Lewis, S. H. & Obbard, D. J. Twenty-five new viruses associated with the Drosophilidae (Diptera). Evolut. Bioinforma. online 12, 13–25 (2016).CAS 

Google Scholar 
23.Jousset, F. X. & Plus, N. Study of the vertical transmission and horizontal transmission of “Drosophila melanogaster” and “Drosophila immigrans” picornavirus (author’s transl). Ann. Microbiol. 126, 231–249 (1975).CAS 

Google Scholar 
24.Jousset, F. X., Plus, N., Croizier, G. & Thomas, M. Existence in Drosophila of 2 groups of picornavirus with different biological and serological properties. C. R. Acad. Hebd. Seances Acad. Sci. D. 275, 3043–3046 (1972).CAS 
PubMed 

Google Scholar 
25.Kapun, M. et al. Genomic Analysis of European Drosophila melanogaster populations reveals longitudinal structure, continent-wide selection, and previously unknown DNA viruses. Mol. Biol. Evol. 37, 2661–2678 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Medd, N. C. et al. The virome of Drosophila suzukii, an invasive pest of soft fruit. Virus Evol. 4, vey009 (2018).PubMed 
PubMed Central 

Google Scholar 
27.Longdon, B. et al. The evolution, diversity, and host associations of rhabdoviruses. Virus Evol. 1, vev014 (2015).PubMed 
PubMed Central 

Google Scholar 
28.Schoonvaere, K., Smagghe, G., Francis, F. & de Graaf, D. C. Study of the metatranscriptome of eight social and solitary wild bee species reveals novel viruses and bee parasites. Front. Microbiol. 9, 177 (2018).PubMed 
PubMed Central 

Google Scholar 
29.Pettersson, J. H., Shi, M., Eden, J. S., Holmes, E. C. & Hesson, J. C. Meta-transcriptomic comparison of the RNA viromes of the mosquito vectors Culex pipiens and Culex torrentium in Northern Europe. Viruses https://doi.org/10.3390/v11111033 (2019).30.Mahar, J. E., Shi, M., Hall, R. N., Strive, T. & Holmes, E. C. Comparative analysis of RNA virome composition in rabbits and associated ectoparasites. J. Virol. https://doi.org/10.1128/jvi.02119-19 (2020).31.Martinez, J. et al. Symbionts commonly provide broad spectrum resistance to viruses in insects: a comparative analysis of Wolbachia strains. PLoS Pathogens https://doi.org/10.1371/journal.ppat.1004369 (2014).32.Cross, S. T. et al. Galbut virus infection minimally influences Drosophila melanogaster fitness traits in a strain and sex-dependent manner. Preprint at bioRxiv https://doi.org/10.1101/2021.05.18.444759 (2021).33.Yampolsky, L. Y., Webb, C. T., Shabalina, S. A. & Kondrashov, A. S. Rapid accumulation of a vertically transmitted parasite triggered by relaxation of natural selection among hosts. Evolut. Ecol. Res. 1, 581–589 (1999).
Google Scholar 
34.Wilfert, L. & Jiggins, F. M. The dynamics of reciprocal selective sweeps of host resistance and a parasite counter-adaptation in Drosophila. Evolution 67, 761–773 (2013).CAS 
PubMed 

Google Scholar 
35.Chrostek, E., Martins, N., Marialva, M. S. & Teixeira, L. Wolbachia conferred antiviral protection is determined by developmental temperature. mBio 12, e0292320 (2021).PubMed 

Google Scholar 
36.Ortiz-Baez, A. S., Shi, M., Hoffmann, A. A. & Holmes, E. C. RNA virome diversity and Wolbachia infection in individual Drosophila simulans flies. J. Gen. Virol. 102, 001639 (2021).
Google Scholar 
37.Haine, E. R. Symbiont-mediated protection. Proc. Biol. Sci. 275, 353–361 (2008).PubMed 

Google Scholar 
38.Martinez, J. et al. Addicted? Reduced host resistance in populations with defensive symbionts. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2016.0778 (2016).39.Cogni, R. et al. Variation in Drosophila melanogaster central metabolic genes appears driven by natural selection both within and between populations. P R. Soc. B-Biol. Sci. 282, 20142688 (2015).CAS 

Google Scholar 
40.Cogni, R. et al. On the long-term stability of clines in some metabolic genes in Drosophila melanogaster. Sci. Rep. https://doi.org/10.1038/srep42766 (2017).41.Longdon, B. et al. The causes and consequences of changes in virulence following pathogen host shifts. PLoS Pathogens https://doi.org/10.1371/journal.ppat.1004728 (2015).42.Longdon, B., Hadfield, J. D., Webster, C. L., Obbard, D. J. & Jiggins, F. M. Host phylogeny determines viral persistence and replication in novel hosts. PLoS Pathog. 7, e1002260 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
43.Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb. Protoc. 2010, pdb.prot5448 (2010).PubMed 

Google Scholar 
44.Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
PubMed 

Google Scholar 
46.Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).CAS 

Google Scholar 
49.Notredame, C., Higgins, D. G. & Heringa, J. T-Coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 302, 205–217 (2000).CAS 
PubMed 

Google Scholar 
50.Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Coyle, M. C., Elya, C. N., Bronski, M. & Eisen, M. B. Entomophthovirus: an insect-derived iflavirus that infects a behavior manipulating fungal pathogen of dipterans. Preprint at bioRxiv https://doi.org/10.1101/371526 (2018).52.Longdon, B. et al. Vertically transmitted rhabdoviruses are found across three insect families and have dynamic interactions with their hosts. P Roy Soc B-Biol Sci https://doi.org/10.1098/rspb.2016.2381 (2017).53.Untergasser, A. et al. Primer3-new capabilities and interfaces. Nucleic Acids Res. 40, e115 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Ye, J. et al. Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction. BMC Bioinforma. 13, 134 (2012).CAS 

Google Scholar 
55.Lefever, S., Pattyn, F., Hellemans, J. & Vandesompele, J. Single-nucleotide polymorphisms and other mismatches reduce performance of quantitative PCR assays. Clin. Chem. 59, 1470–1480 (2013).CAS 
PubMed 

Google Scholar 
56.Hadfield, J. D. MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package. 2010 33, 22, (2010).57.Cogni, R., Ding, S. D., Pimentel, A. C., Day, J. P. & Jiggins, F. M. https://doi.org/10.5281/zenodo.5525967 (Zenodo 2021). More

1.Schmidt MWI, Torn MS, Abiven S, Dittmar T, Guggenberger G, Janssens IA, et al. Persistence of soil organic matter as an ecosystem property. Nature. 2011;478:49–56.CAS 
PubMed 

Google Scholar 
2.Bond-Lamberty B, Bailey VL, Chen M, Gough CM, Vargas R. Globally rising soil heterotrophic respiration over recent decades. Nature. 2018;560:80–3.CAS 
PubMed 

Google Scholar 
3.Bradford MA. Thermal adaptation of decomposer communities in warming soils. Front Microbiol. 2013;4:1–16.
Google Scholar 
4.Cavicchioli R, Ripple WJ, Timmis KN, Azam F, Bakken LR, Baylis M, et al. Scientists’ warning to humanity: microorganisms and climate change. Nat Rev Microbiol. 2019;17:569–86.CAS 
PubMed 
PubMed Central 

Google Scholar 
5.Jansson JK, Hofmockel KS. Soil microbiomes and climate change. Nat Rev Microbiol. 2020;18:35–46.CAS 
PubMed 

Google Scholar 
6.Liu L, Greaver TL. A global perspective on belowground carbon dynamics under nitrogen enrichment. Ecol Lett. 2010;13:819–28.PubMed 

Google Scholar 
7.Knicker H. Soil organic N – An under-rated player for C sequestration in soils? Soil Biol Biochem. 2011;43:1118–29.CAS 

Google Scholar 
8.Soong JL, Fuchslueger L, Marañon-Jimenez S, Torn MS, Janssens IA, Peñuelas J, et al. Microbial carbon limitation: The need for integrating microorganisms into our understanding of ecosystem carbon cycling. Glob Chang Biol. 2020;26:1953–61.
Google Scholar 
9.Mooshammer M, Wanek W, Hämmerle I, Fuchslueger L, Hofhansl F, Knoltsch A, et al. Adjustment of microbial nitrogen use efficiency to carbon:nitrogen imbalances regulates soil nitrogen cycling. Nat Commun. 2014;5:1–7.
Google Scholar 
10.Geisseler D, Horwath WR, Joergensen RG, Ludwig B. Pathways of nitrogen utilization by soil microorganisms – a review. Soil Biol Biochem. 2010;42:2058–67.CAS 

Google Scholar 
11.Wang X, Wang C, Cotrufo MF, Sun L, Jiang P, Liu Z, et al. Elevated temperature increases the accumulation of microbial necromass nitrogen in soil via increasing microbial turnover. Glob Chang Biol. 2020;26:5277–89.PubMed 

Google Scholar 
12.Simpson AJ, Simpson MJ, Smith E, Kelleher BP. Microbially derived inputs to soil organic matter: Are current estimates too low? Environ Sci Technol. 2007;41:8070–6.CAS 
PubMed 

Google Scholar 
13.Kuypers MMM, Marchant HK, Kartal B. The microbial nitrogen-cycling network. Nat Rev Microbiol. 2018;16:263–76.CAS 
PubMed 

Google Scholar 
14.Walker TWN, Kaiser C, Strasser F, Herbold CW, Leblans NIW, Woebken D, et al. Microbial temperature sensitivity and biomass change explain soil carbon loss with warming. Nat Climate Change. 2018;8:885–9.CAS 

Google Scholar 
15.Marañón-Jiménez S, Peñuelas J, Richter A, Sigurdsson BD, Fuchslueger L, Leblans NIW, et al. Coupled carbon and nitrogen losses in response to seven years of chronic warming in subarctic soils. Soil Biol Biochem. 2019;134:152–61.
Google Scholar 
16.Nguyen TTH, Myrold DD, Mueller RS. Distributions of extracellular peptidases across prokaryotic genomes reflect phylogeny and habitat. Front Microbiol. 2019;10:1–14.
Google Scholar 
17.Zimmerman AE, Martiny AC, Allison SD. Microdiversity of extracellular enzyme genes among sequenced prokaryotic genomes. ISME J. 2013;7:1187–99.CAS 
PubMed 
PubMed Central 

Google Scholar 
18.Beier S, Bertilsson S. Bacterial chitin degradation-mechanisms and ecophysiological strategies. Front Microbiol. 2013;4:1–12.
Google Scholar 
19.Kielak AM, Cretoiu MS, Semenov AV, Sørensen SJ, Van, Elsas JD. Bacterial chitinolytic communities respond to chitin and pH alteration in soil. Appl Environ Microbiol. 2013;79:263–72.CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Weintraub MN, Schimel JP. Seasonal protein dynamics in Alaskan arctic tundra soils. Soil Biol Biochem. 2005;37:1469–75.CAS 

Google Scholar 
21.Boer VM, De Winde JH, Pronk JT, Piper MDW. The genome-wide transcriptional responses of Saccharomyces cerevisiae grown on glucose in aerobic chemostat cultures limited for carbon, nitrogen, phosphorus, or sulfur. J Biol Chem. 2003;278:3265–74.CAS 
PubMed 

Google Scholar 
22.Kolkman A, Daran-Lapujade P, Fullaondo A, Olsthoorn MMA, Pronk JT, Slijper M, et al. Proteome analysis of yeast response to various nutrient limitations. Mol Syst Biol. 2006;2:1–16.
Google Scholar 
23.Silberbach M, Hüser A, Kalinowski J, Pühler A, Walter B, Krämer R, et al. DNA microarray analysis of the nitrogen starvation response of Corynebacterium glutamicum. J Biotechnol. 2005;119:357–67.CAS 
PubMed 

Google Scholar 
24.Merrick MJ, Edwards RA. Nitrogen control in bacteria. Microbiol Rev. 1995;59:604–22.CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Daebeler A, Abell GCJ, Bodelier PLE, Bodrossy L, Frampton DMF, Hefting MM, et al. Archaeal dominated ammonia-oxidizing communities in Icelandic grassland soils are moderately affected by long-term N fertilization and geothermal heating. Front Microbiol. 2012;3:1–14.
Google Scholar 
26.Yeager CM, Kornosky JL, Housman DC, Grote EE, Belnap J, Kuske CR. Diazotrophic community structure and function in two successional stages of biological soil crusts from the colorado plateau and Chihuahuan Desert. Appl Environ Microbiol. 2004;70:973–83.CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Malik AA, Swenson T, Weihe C, Morrison EW, Martiny JBH, Brodie EL, et al. Drought and plant litter chemistry alter microbial gene expression and metabolite production. ISME J. 2020;14:2236–47.CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Tveit A, Schwacke R, Svenning MM, Urich T. Organic carbon transformations in high-Arctic peat soils: Key functions and microorganisms. ISME J. 2013;7:299–311.CAS 
PubMed 

Google Scholar 
29.Geisen S, Tveit AT, Clark IM, Richter A, Svenning MM, Bonkowski M, et al. Metatranscriptomic census of active protists in soils. ISME J. 2015;9:2178–90.CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Urich T, Lanzén A, Qi J, Huson DH, Schleper C, Schuster SC. Simultaneous assessment of soil microbial community structure and function through analysis of the meta-transcriptome. PLoS ONE. 2008;3:1–13.
Google Scholar 
31.Kallenbach CM, Frey SD, Grandy AS. Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls. Nat Commun. 2016;7:1–10.
Google Scholar 
32.Walker TWN, Janssens IA, Weedon JT, Sigurdsson BD, Richter A, Peñuelas J, et al. A systemic overreaction to years versus decades of warming in a subarctic grassland ecosystem. Nat Ecol Evol. 2020;4:101–8.PubMed 

Google Scholar 
33.Sigurdsson BD, Wallander H, Gunnarsdóttir GE, Richter A, Sigurðsson P, Leblans NIW, et al. Geothermal ecosystems as natural climate change experiments: the ForHot research site in Iceland as a case study. Icelandic Agric Sci. 2016;29:53–71.
Google Scholar 
34.Söllinger A, Séneca J, Dahl MB, Prommer J, Verbruggen E, Sigurdsson BD, et al. Downregulation of the microbial protein biosynthesis machinery in response to weeks, years and decades of soil warming. 2021 Research Square preprint. https://doi.org/10.21203/rs.3.rs-132190/v235.Leblans N. Natural gradients in temperature and nitrogen: Iceland represents a unique environment to clarify long-term global change effects on carbon dynamics. Joint doctoral dissertation. Antwerp University and Agricultural University of Iceland, Reykjavik, Iceland; 2016:1–229.36.Angel R, Claus P, Conrad R. Methanogenic archaea are globally ubiquitous in aerated soils and become active under wet anoxic conditions. ISME J. 2012;6:847–62.CAS 
PubMed 

Google Scholar 
37.Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:5–11.
Google Scholar 
38.Gillespie CS. Fitting heavy tailed distributions: the poweRlaw Package. J Stat Softw. 2015;64:1–16.
Google Scholar 
39.El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, et al. The Pfam protein families database in 2019. Nucleic Acids Res. 2018;47:427–32.
Google Scholar 
40.Eddy SR. Accelerated profile HMM searches. PLOS Comput Biol. 2011;7:e1002195.CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Petersen TN, Brunak S, von Heijne G, Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods. 2011;8:785–6.CAS 
PubMed 

Google Scholar 
42.Bendtsen JD, Kiemer L, Fausbøll A, Brunak S. Non-classical protein secretion in bacteria. BMC Microbiol. 2005;5:1–13.
Google Scholar 
43.Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics. 2010;26:1608–15.CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Orsi WD. MetaProt: an integrated database of predicted proteins for improved annotation of metaomic datasets. Open Data LMU. 2020. https://doi.org/10.5282/ubm/data.18345.Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2013;42:490–5.
Google Scholar 
46.Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2014;12:59–60.PubMed 

Google Scholar 
47.Oksanen AJ, Blanchet FG, Kindt R, Legen- P, Minchin PR, Hara RBO, et al. vegan: Community Ecology Package. 2019. https://cran.r-project.org/package=vegan48.Lê S, Josse J, Husson F. FactoMineR: an R package for multivariate analysis. J Stat Softw. 2008;25:1–18.
Google Scholar 
49.Kolde R. pheatmap: pretty heatmaps. 2019. https://cran.r-project.org/package=pheatmap50.Noll L, Zhang S, Zheng Q, Hu Y, Wanek W. Wide-spread limitation of soil organic nitrogen transformations by substrate availability and not by extracellular enzyme content. Soil Biol Biochem. 2019;133:37–49.CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Schimel JP, Bennett J. Nitrogen mineralization: challenges of a changing paradigm. Ecology. 2004;85:591–602.
Google Scholar 
52.Wild B, Ambus P, Reinsch S, Richter A. Resistance of soil protein depolymerization rates to eight years of elevated CO2, warming, and summer drought in a temperate heathland. Biogeochemistry. 2018;140:255–67.CAS 

Google Scholar 
53.Wanek W, Mooshammer M, Blöchl A, Hanreich A, Richter A. Determination of gross rates of amino acid production and immobilization in decomposing leaf litter by a novel 15N isotope pool dilution technique. Soil Biol Biochem. 2010;42:1293–302.CAS 

Google Scholar 
54.Liang C, Schimel JP, Jastrow JD. The importance of anabolism in microbial control over soil carbon storage. Nat Microbiol. 2017;2:1–6.
Google Scholar 
55.Vranova V, Rejsek K, Formanek P. Proteolytic activity in soil: a review. Appl Soil Ecol. 2013;70:23–32.
Google Scholar 
56.Schimel JP, Weintraub MN. The implications of exoenzyme activity on microbial carbon and nitrogen limitation in soil: a theoretical model. Soil Biol Biochem. 2003;35:549–63.CAS 

Google Scholar 
57.Rawlings ND, Waller M, Barrett AJ, Bateman A. MEROPS: The database of proteolytic enzymes, their substrates and inhibitors. Nucleic Acids Res. 2014;42:503–9.
Google Scholar 
58.Vollmer W, Joris B, Charlier P, Foster S. Bacterial peptidoglycan (murein) hydrolases. FEMS Microbiol Rev. 2008;32:259–86.CAS 
PubMed 

Google Scholar 
59.Vermassen A, Leroy S, Talon R, Provot C, Popowska M, Desvaux M. Cell wall hydrolases in bacteria: Insight on the diversity of cell wall amidases, glycosidases and peptidases toward peptidoglycan. Front Microbiol. 2019;10:1–27.
Google Scholar 
60.Donhauser J, Qi W, Bergk-Pinto B, Frey B. High temperatures enhance the microbial genetic potential to recycle C and N from necromass in high-mountain soils. Glob Chang Biol. 2020;27:1365–86.61.Vollmer W, Blanot D, De Pedro MA. Peptidoglycan structure and architecture. FEMS Microbiology Reviews. 2008;32:149–67.CAS 
PubMed 

Google Scholar 
62.Semchenko M, Leff JW, Lozano YM, Saar S, Davison J, Wilkinson A, et al. Fungal diversity regulates plant-soil feedbacks in temperate grassland. Science Adv. 2018;4.63.Saary P, Mitchell AL, Finn RD. Estimating the quality of eukaryotic genomes recovered from metagenomic analysis. Genome Biol. 2020;21:244.PubMed 
PubMed Central 

Google Scholar  More

Case studyThe Zayandeh-Rud basin (Fig. 1), a arid region of Iran, was selected to evaluate the SPIs. The Zayandeh-Rud basin is located in the central part of Iran. It has an area of 26,972 km2 area, where there are multiple water stakeholders such as agriculture, industry, urban and the environment sectors, with agriculture being the main user of the basin. Water resources in the basin are divided into surface water and groundwater. Approximately 100,000 ha among 113,000 ha of the agricultural area is irrigated by Zayandeh-Rud dam, and 3100 mm3 of water resources are used in the agricultural sector. The main surface water source in the basin, Zayandeh-Rud River originates in the Zagros Mountains and is about 350 km long in a west to east direction passing by the city of Isfahan. The Zayandeh-Rud River is an important water source for the agricultural, industrial, health, and urban sectors in Central Iran and the Chaharmahal-Bakhtiari and Isfahan provinces.Figure 1The location of the Zayandeh-Rud basin in Iran.Full size imageMulti-criteria decision makingMulti-criteria decision making includes two categories of multi-objective decision making and multi-criteria decision making, which are implemented to select the best decision among several alternatives or to evaluate decisions. This work applies decision making as a multi-criteria decision to achieve a goal. Each decision includes objectives, alternatives, and criteria. A problem’s goal is first defined. Alternatives are different options for wastewater management in this instance that are assigned weights based on their contribution to achieving the goal. Criteria are also factors that are measured by the purpose of the alternatives23. The AHP method helps achieve a defined goal after completing the steps outlined below.The AHP methodThe Analytical Hierarchy Process (AHP), developed by Saaty24, is a multi-criteria decision-making method for solving complex problems. It combines objective and quantitative evaluation in an integrated manner based on multi-level comparisons, and helps organize the essential aspects of a problem into a hierarchical format. It regularly organizes tangible and intangible factors and offers a structured and a relatively simple solution to decision problems. The AHP method ranks alternatives propose to tackle a decision-making problem. The ranking is based through a sequence of pairwise comparisons of evaluation criteria and sub-criteria.The AHP structureIn a hierarchical structure the communication flow is top-down. First, indicators and evaluation criteria are defined from experts who are asked for their expert opinions. The criteria serve the purpose of determining the relative worth of alternatives entertained to solve a multi-criteria decision-making problem. Thereafter, the problem is divided into criteria and sub-criteria for the evaluation of alternatives. Figure 2 depicts a generic AHP structure depicting a goal to be met with (n) = 4 evaluation criteria, and (m=3) alternatives to cope with a problem (in our case SIPs).Figure 2Goal, criteria, and alternatives in a generic hierarchical structure.Full size imageThe pairwise comparison matrixThe pairwise comparison matrix ((A)), called the Saaty Hierarchy Matrix, measures the importance of each criterion (or sub-criterion) relative to other criteria based on a numeric scale ranging from 1 to 9. Criteria that are extremely preferred, very strongly preferred, strongly preferred, moderately preferred, and equally preferred are assigned the values 9, 7, 5, 3, and 1, respectively, in the scale of preference; intermediate values are assigned to adjacent scales of preference. Thus, the values 8, 6, 4, and 2 are assigned respectively to the adjacent scales (9,7), (7,5), (5,3), and (3,1)24. These numerical assignment of values is made based on the opinion of experts25. The pairwise comparison matrix ((A)), therefore, represents a set of relative weights assigned to the criteria23. The general form of a pairwise comparison matrix when there are (n) evaluation criteria is written in Eq. (1):$$A=left[{a}_{ij}right]=left[begin{array}{cccc}{1=w}_{1}/{w}_{1}& {w}_{1}/{w}_{2}& dots & {w}_{1}/{w}_{n}\ {w}_{2}/{w}_{1}& 1={w}_{2}/{w}_{2}& dots & {w}_{2}/{w}_{n}\ .& .& dots & .\ .& .& dots & .\ .& .& dots & .\ {w}_{n}/{w}_{1}& {w}_{n}/{w}_{2}& …& 1={w}_{n}/{w}_{n}end{array}right]$$
(1)

where ({w}_{i}/{w}_{j}) denotes the weight assigned to the (i)-th criterion relative to the (j)-th criterion24. Clearly, ({a}_{ji}=1/{a}_{ij}), with ({a}_{ji}={a}_{ij}=1) when (i=j).The ratio matrixThe ratio matrix ((R)) has elements ({r}_{ij}) is calculated by Eq. (2):$$R=left[{r}_{ij}right]=left[begin{array}{cccc}1& {a}_{12}& dots & {a}_{1n}\ 1/{a}_{12}& 1& dots & {a}_{2n}\ .& .& .& .\ .& .& .& .\ .& .& .& .\ 1/{a}_{1n}& 1/{a}_{2n}& dots & 1end{array}right]$$
(2)

clearly, ({r}_{ij}={a}_{ij}) when (jge i), and ({r}_{ij}=1/{a}_{ji}) when (j More

Study site and systemData were collected at Cape Crozier (77°27′S, 169°12′E), Ross Island, one of the largest Adélie penguin breeding colonies (~ 275 000 pairs at the time of the study32), during austral summer 2018–2019. Individuals arrive at Cape Crozier in late October/early November, lay (usually two) eggs in mid-November, and feed their chicks between mid-December and early February. They are one of the few penguin species that can fledge two chicks. During the brood/guard stage, one parent remains with the chick(s) while the other forages at sea. Nest reliefs at Crozier occur every 1–2 days during early chick-rearing and chicks are fed relatively small meals (0.43–0.58 kg) by the attending parent33. After about two weeks, chick demands are too great for adequate provisioning by one parent, so chicks are left on their own (“crèche” stage) while both parents forage simultaneously. Our study period included most of chick-rearing, i.e., all of the guard stage and half the crèche stage, from December 21, 2018 to January 15, 2019.Since 1997, every austral summer, the same subcolony of ~ 200 pairs (152 pairs in the year of study) was surrounded by a plastic fence, leaving only one opening as an access point, where the weighbridge was located30. The weighbridge consisted of an electronic scale, direction indicator, and radio frequency identification (RFID) reader34,35. In 2018–2019, it was installed on November 16 and removed on January 20. A subset of adult individuals were implanted with unique RFID tags beginning in 1997, with a few more added each year30,36. RFID code, date and time, direction, and weight were recorded automatically as the RFID-implanted birds crossed the weighbridge. Adults were captured on the nest during incubation, when they can be approached slowly and gently lifted off their nest. A warm hat was placed over the eggs or small chicks to avoid chilling, while the RFID tag was injected into the bird.All penguin survey, capture and handling methods used for data collection were performed following all relevant guidelines and regulations under the approval and oversight of the Institutional Animal Care and Use Committees of Oregon State University and Point Blue Conservation Science. Additionally, all work was approved and conducted under Antarctic Conservation Act permits issued by the US National Science Foundation and the U.S. Antarctic Program. The study is reported in accordance with ARRIVE guidelines.Diving parametersBetween November 2 and December 7, 2018, we equipped 32 RFID-implanted birds with geolocating dive recorders (“LUL” tags, 22 × 21 × 15 mm, weight = 4 g, from Atesys, Strasbourg, France, hereafter referred to as GDRs) that recorded light every minute, temperature (with a precision of ± 0.5 °C) every 30 s and pressure (with a precision of ± 0.3 m) every second for 12–15 months. Adults were captured using a hand net (2 m long handle) or on the nest during incubation (see above). The GDRs were encapsulated in flexible heat-shrink tubing shaped into a leg strap and attached to the tibio-fibula of each bird in the field using a polyester-coated stainless-steel zip tie to secure the ends of the strap together such that the tag could rotate freely around the leg but not slip over the tarsus joint. Tags were left in place for one year, with 21 recovered at the beginning of the 2019–2020 breeding season. Pressure data were processed in R (v. 3.6.0) with several processes modified from the diveMove package (v. 1.4.5)37. To correct for instrument drift, pressure data were zero offset corrected using the calibrateDepth function38. We used a depth threshold of 3 m to qualify as a dive. Following methods described in previous studies27,39,40, we computed a number of statistics about each dive including dive duration, maximum dive depth, post-dive interval duration, bottom time, the number of undulations (changes of any amplitude in underwater swimming duration from either ascent to descent, or descent to ascent—used for the purposes of categorizing dives) and the number of undulations  > 1 m (changes in underwater swimming direction from ascent to descent  > 1m39). The two undulation metrics are highly correlated (Pearson’s r = 0.92 in our data set). Bottom time was defined as the time spent at  > 60% of maximum depth of dive with  60 h (trip duration during chick-rearing takes 1–2 days on average36,39 but their frequency distribution showed a tail from 60 to 100 h in our data).Figure 2Conceptual visualization of the study design. (a) chick-rearing Adélie penguins breeding in a semi-enclosed subcolony are implanted with a RFID tag and equipped with a leg-mounted time-depth recorder (GDR). (b) Bird ID, departure mass and direction of travel are recorded by the weighbridge as penguins leave the colony to forage at sea. (c) During the foraging trip, the GDR tag records depth every second, enabling the calculation of several dive behavior metrics. (d) Bird ID, return mass and direction of travel are recorded by the weighbridge as penguins return to the colony to feed their chicks.Full size imageBody mass estimationFor each foraging trip, we calculated meal size and body mass change (see Supplementary Information for more details on the weight calculation). Meal size (in kg) is the difference between an individual’s out-mass (departing) and its most recent in-mass (returning from sea), i.e. this is a measure of how much food a parent left in the colony and includes both the food delivered to chicks and the food digested by the parent while attending the nest39. Body mass change (in kg) of individual birds over each foraging trip was calculated as the return mass (post-foraging trip at sea) minus the departure mass (pre-foraging trip at sea). Hence, body mass change measures the amount of food that was collected during the trip at sea (i.e. foraging success43), minus what could have been digested before returning to the colony at the end of this trip (Fig. 2). We further filtered trips based on these two variables, keeping only trips where meal size was  > 0 and  − 0.8 and  1 m per hour, as previous work indicated that undulations in the dive profile represent feeding and/or prey capture16,24,25, (2) dive (underwater) time per hour, (3) dive time per hour during foraging dives only, (4) bottom time per hour, (5) number of foraging dives per hour, (6) Attempts of Catch per Unit Effort (ACPUE, calculated as the number of undulations per trip divided by total bottom duration23,49). We also considered two variables calculated at the scale of dive bouts: (7) mean bout duration, thought to reflect the time spent within a prey patch50,51, (8) number of dives per bout, as an index of the size of the prey patch51,52,53. Dive bouts were defined as successive diving events interrupted by relatively longer surfacing periods. To separate post-dive intervals from inter-bout duration, we used a maximum likelihood approach54 using the diveMove package37 in R, which allowed us to determine a bout-ending-criterion (BEC). In this study, BEC = 47.6 s.Statistical analysesWe first calculated a Pearson correlation matrix using the corrplot package in R and removed highly correlated (r  > 0.7) behavioral covariates, keeping those that were the most correlated with body mass change. To test the hypothesis that some behavioral dive variables can be used to predict the amount of food collected while foraging at sea, we evaluated linear mixed models including body mass change as the dependent variable, each of the selected behavioral variables as independent variables and bird ID as a random effect, as well as a null model (intercept only) using the nlme package55 in R. Once we had determined the most competitive models, and as Adélie penguin’s foraging success can vary according to sex29,36 and chick needs39, and also be influenced by the trip duration56, we added sex, study day (day in the season as a Julian date with Dec 20 = 0) and trip duration (in hours) to the top intrinsic model(s) including potential interactions with the selected behavioral variable(s). A null model was also included in this second model set. Residuals were examined to verify normality, homogeneity of variances, and independence. To evaluate these models and determine the strength of evidence supporting specific effects, we used an information theoretic approach57. Models were ranked using the small-sample-size corrected version of Akaike Information Criterion (AICc), with the best model having the lowest AICc value. We calculated ΔAICc as the difference in AICc between each candidate model and the model with the lowest AICc value, and considered all models within 2 ΔAICc as competitive models57. We determined the strength of evidence supporting specific effects by examining the unstandardized effect sizes (slope coefficients and differences in means) and the associated 95% confidence intervals (CI). If the 95% CI for a parameter in a competitive model (ΔAICc  More

1.Jenkins, P. T. Free trade and exotic species introductions. Conserv. Biol. 10, 300–302 (1996).Article 

Google Scholar 
2.Sharov, A. A. Bioeconomics of managing the spread of exotic pest species with barrier zones. Risk Anal. 24, 879–892 (2004).Article 

Google Scholar 
3.Lodge, D. M. et al. Biological invasions: Recommendations for U.S. policy and management. Ecol. Appl. 16, 2035–2054 (2006).Article 

Google Scholar 
4.Yemshanov, D. et al. Optimizing surveillance strategies for early detection of invasive alien species. Ecol. Econ. 162, 87–99 (2019).Article 

Google Scholar 
5.Hauser, C. E. & Mccarthy, M. A. Streamlining “search and destroy”: Cost-effective surveillance for invasive species management. Ecol. Lett. 12, 683–692 (2009).Article 

Google Scholar 
6.Gottwald, T. R., da Graça, J. V. & Bassanezi, R. B. Citrus Huanglongbing: The pathogen and its impact. Plant Health Prog. https://doi.org/10.1094/PHP-2007-0906-01-RV (2007).Article 

Google Scholar 
7.Anderson, D. P. et al. Bio-economic optimisation of surveillance to confirm broadscale eradications of invasive pests and diseases. Biol. Invasions 19, 2869–2884 (2017).Article 

Google Scholar 
8.Russell, J. C., Binnie, H. R., Oh, J., Anderson, D. P. & Samaniego-Herrera, A. Optimizing confirmation of invasive species eradication with rapid eradication assessment. J. Appl. Ecol. 54, 160–169 (2017).Article 

Google Scholar 
9.Moffitt, L. J., Stranlund, J. K. & Osteen, C. D. Robust detection protocols for uncertain introductions of invasive species. J. Environ. Manag. 89, 293–299 (2008).Article 

Google Scholar 
10.Knight, F. H. Risk, Uncertainty, and Profit (Houghton Mifflin Company, 1921).11.Ben-Haim, Y. Uncertainty, probability and information-gaps. Reliab. Eng. Syst. Saf. 85, 249–266 (2004).Article 

Google Scholar 
12.Johnson, D. R. & Geldner, N. B. Contemporary decision methods for agricultural, environmental, and resource management and policy. Annu. Rev. Resour. Econ. 11, 19–41 (2019).Article 

Google Scholar 
13.Baker, C. M. & Bode, M. Recent advances of quantitative modeling to support invasive species eradication on islands. Conserv. Sci. Pract. 3, e246. https://doi.org/10.1111/csp2.246 (2021).Article 

Google Scholar 
14.Bertsimas, D. & Sim, M. The price of robustness. Oper. Res. 52, 35–53 (2004).MathSciNet 
Article 

Google Scholar 
15.Ben-Haim, Y. & Demertzis, M. Decision making in times of knightian uncertainty: An info-gap perspective. Economics 10, 1–29 (2016).Article 

Google Scholar 
16.Ben-Haim, Y. Management of invasive species: Info-gap perspectives. in Invasive Species: Risk Assessment and Management (eds. Robinson, A., Walshe, T., Burgman, M. A., Nunn, M.) 266–286 (Cambridge University Press, 2017).17.Davidovitch, L. et al. Info-gap theory and robust design of surveillance for invasive species: The case study of Barrow Island. J. Environ. Manag. 90, 2785–2793 (2009).Article 

Google Scholar 
18.Rout, T. M., Thompson, C. J. & McCarthy, M. A. Robust decisions for declaring eradication of invasive species. J. Appl. Ecol. 46, 782–786 (2009).Article 

Google Scholar 
19.Foxcroft, L. C. Developing thresholds of potential concern for invasive alien species: Hypotheses and concepts. Koedoe. https://doi.org/10.4102/koedoe.v51i1.157 (2009).Article 

Google Scholar 
20.Pitt, J. P. W. Modelling the Spread of Invasive Species Across Heterogeneous Landscapes. (Lincoln University, 2008).21.Mehta, S. V., Haight, R. G., Homans, F. R., Polasky, S. & Venette, R. C. Optimal detection and control strategies for invasive species management. Ecol. Econ. 61, 237–245 (2007).Article 

Google Scholar 
22.Mcdonald-madden, E., Peter, W. J. B. & Possingham, H. P. Making robust decisions for conservation with restricted money and knowledge. J. Appl. Ecol. 45, 1630–1638 (2008).Article 

Google Scholar 
23.Rout, T. M., Moore, J. L. & Mccarthy, M. A. Prevent, search or destroy? A partially observable model for invasive species management. J. Appl. Ecol. 51, 804–813 (2014).Article 

Google Scholar 
24.Yemshanov, D. et al. Robust surveillance and control of invasive species using a scenario optimization approach. Ecol. Econ. 133, 86–98 (2017).Article 

Google Scholar 
25.Rödder, D., Solé, M. & Böhme, W. Predicting the potential distributions of two alien invasive Housegeckos (Gekkonidae: Hemidactylus frenatus, Hemidactylus mabouia). North-West. J. Zool. 4, 236–246 (2008).
Google Scholar 
26.Hoskin, C. J. The invasion and potential impact of the Asian House Gecko (Hemidactylus frenatus) in Australia. Austral. Ecol. 36, 240–251 (2011).Article 

Google Scholar 
27.García-Díaz, P., Ross, J. V., Vall-llosera, M. & Cassey, P. Low detectability of alien reptiles can lead to biosecurity management failure: A case study from Christmas Island (Australia). NeoBiota 45, 75–92 (2019).Article 

Google Scholar 
28.Scott, J. K. et al. Zero-tolerance biosecurity protects high-conservation-value island nature reserve. Sci. Rep. 7, 772–779 (2017).ADS 
Article 

Google Scholar 
29.Commonwealth Government of Australia. Approval—Gorgon Gas Development (EPBC Reference: 2008/4178). (2009).30.Jarrad, F. C. et al. Improved design method for biosecurity surveillance and early detection of non-indigenous rats. N. Z. J. Ecol. 35, 132–144 (2011).
Google Scholar 
31.Metlay, D. From tin roof to torn wet blanket: Predicting and observing ground water movement at a proposed nuclear waste site. in Prediction: Science, Decision Making, and the Future of Nature (eds. Sarewitz, D. R., Byerly, R., Pielke, R. A.). 276–319. (Island Press, 2000).32.Wintle, B. & Burgman, M. Expert Elicitation for Barrow Island Surveillance System Revision, Project Report. (2015).33.Vanderduys, E. & Kutt, A. Is the Asian house gecko, Hemidactylus frenatus, really a threat to Australia’s biodiversity?. Aust. J. Zool. 60, 361–367 (2013).Article 

Google Scholar 
34.McGinnis, S. M. & Stebbins, R. C. A Field Guide to Western Reptiles and Amphibians. 4th edn. (Houghton Mifflin Harcourt, 2018).35.Whittle, P., Jarrad, F., Edwards, K. & Mengersen, K. Design of the quarantine surveillance for non-indigenous species of invertebrates on Barrow Island. Rec. West. Aust. Mus. Suppl. 83, 113–130 (2013).Article 

Google Scholar 
36.Ben-Haim, Y. Info-gap Decision Theory: Decisions Under Severe Uncertainty. 2nd edn. (Academic Press, 2006).37.MathWorks. MATLAB R2018b. (MathWorks, 2018).38.Bogich, T. L., Liebhold, A. M. & Shea, K. To sample or eradicate? A cost minimization model for monitoring and managing an invasive species. J. Appl. Ecol. 45, 1134–1142 (2008).Article 

Google Scholar 
39.Epanchin-Niell, R. S., Haight, R. G., Berec, L., Kean, J. M. & Liebhold, A. M. Optimal surveillance and eradication of invasive species in heterogeneous landscapes. Ecol. Lett. 15, 803–812 (2012).Article 

Google Scholar 
40.Trebitz, A. S. et al. Early detection monitoring for aquatic non-indigenous species: Optimizing surveillance, incorporating advanced technologies, and identifying research needs. J. Environ. Manag. 202, 299–310 (2017).CAS 
Article 

Google Scholar 
41.Molina, R., Horton, T., Trappe, J. & Marcot, B. Addressing uncertainty: How to conserve and manage rare or little-known fungi. Fungal Ecol. 4, 134–146 (2011).Article 

Google Scholar  More

1.Paillard, D. The timing of Pleistocene glaciations from a simple multiple-state climate model. Nature 391, 378–381 (1998).ADS 

Google Scholar 
2.Hewitt, G. M. Genetic consequences of climatic oscillations in the Quaternary. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 359, 183–195 (2004).CAS 

Google Scholar 
3.Hewitt, G. The genetic legacy of the quaternary ice ages. Nature 405, 907–913 (2000).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Taberlet, P., Fumagalli, L., Wust-Saucy, A.-G. & Cosson, J.-F. Comparative phylogeography and postglacial colonization routes in Europe. Mol. Ecol. 7, 453–464 (1998).CAS 
PubMed 

Google Scholar 
5.Incagnone, G., Marrone, F., Barone, R., Robba, L. & Naselli-Flores, L. How do freshwater organisms cross the ‘dry ocean’? A review on passive dispersal and colonization processes with a special focus on temporary ponds. Hydrobiologia 750, 103–123 (2015).
Google Scholar 
6.Schmitt, T. & Varga, Z. Extra-Mediterranean refugia: The rule and not the exception?. Front Zool 9, 22 (2012).PubMed 
PubMed Central 

Google Scholar 
7.Hewitt, G. M. Speciation, hybrid zones and phylogeography—Or seeing genes in space and time. Mol. Ecol. 10, 537–549 (2001).CAS 
PubMed 

Google Scholar 
8.Habel, J. C., Drees, C., Schmitt, T. & Assmann, T. Review refugial areas and postglacial colonizations in the Western Palearctic. In Relict Species (eds Habel, J. C. & Assmann, T.) 189–197 (Springer, 2010).
Google Scholar 
9.Hewitt, G. Some genetic consequences of ice ages, and their role in divergence and speciation. Biol. J. Lin. Soc. 58, 247–276 (1996).
Google Scholar 
10.Marrone, F., Lo Brutto, S. & Arculeo, M. Molecular evidence for the presence of cryptic evolutionary lineages in the freshwater copepod genus Hemidiaptomus G.O. Sars, 1903 (Calanoida, Diaptomidae). Hydrobiologia 644, 115–125 (2010).CAS 

Google Scholar 
11.Husemann, M., Schmitt, T., Zachos, F. E., Ulrich, W. & Habel, J. C. Palaearctic biogeography revisited: Evidence for the existence of a North African refugium for Western Palaearctic biota. J. Biogeogr. 41, 81–94 (2014).
Google Scholar 
12.García-Vázquez, D., Bilton, D. T., Foster, G. N. & Ribera, I. Pleistocene range shifts, refugia and the origin of widespread species in western Palaearctic water beetles. Mol. Phylogenet. Evol. 114, 122–136 (2017).PubMed 

Google Scholar 
13.Perktas, U., Barrowclough, G. F. & Groth, J. G. Phylogeography and species limits in the green woodpecker complex (Aves: Picidae): Multiple Pleistocene refugia and range expansion across Europe and the Near East. Biol. J. Lin. Soc. 104, 710–723 (2011).
Google Scholar 
14.Stewart, J. R. & Lister, A. M. Cryptic northern refugia and the origins of the modern biota. Trends Ecol. Evol. 16, 608–613 (2001).
Google Scholar 
15.Stewart, J. R., Lister, A. M., Barnes, I. & Dalén, L. Refugia revisited: Individualistic responses of species in space and time. Proc. R. Soc. B Biol. Sci. 277, 661–671 (2010).
Google Scholar 
16.Sworobowicz, L., Mamos, T., Grabowski, M. & Wysocka, A. Lasting through the ice age: The role of the proglacial refugia in the maintenance of genetic diversity, population growth, and high dispersal rate in a widespread freshwater crustacean. Freshw. Biol. 65, 1028–1046 (2020).CAS 

Google Scholar 
17.Provan, J. & Bennett, K. D. Phylogeographic insights into cryptic glacial refugia. Trends Ecol. Evol. 23, 564–571 (2008).PubMed 

Google Scholar 
18.Antal, L. et al. Phylogenetic evidence for a new species of Barbus in the Danube River basin. Mol. Phylogenet. Evol. 96, 187–194 (2016).CAS 
PubMed 

Google Scholar 
19.Copilaş-Ciocianu, D., Fišer, C., Borza, P. & Petrusek, A. Is subterranean lifestyle reversible? Independent and recent large-scale dispersal into surface waters by two species of the groundwater amphipod genus Niphargus. Mol. Phylogenet. Evol. 119, 37–49 (2018).PubMed 

Google Scholar 
20.Říčanová, Š et al. Multilocus phylogeography of the European ground squirrel: Cryptic interglacial refugia of continental climate in Europe. Mol. Ecol. 22, 4256–4269 (2013).PubMed 

Google Scholar 
21.Vörös, J., Mikulíček, P., Major, Á., Recuero, E. & Arntzen, J. W. Phylogeographic analysis reveals northerly refugia for the riverine amphibian Triturus dobrogicus (Caudata: Salamandridae). Biol. J. Linn. Soc. 119, 974–991 (2016).
Google Scholar 
22.Wielstra, B. et al. Tracing glacial refugia of Triturus newts based on mitochondrial DNA phylogeography and species distribution modeling. Front. Zool. 10, 13 (2013).PubMed 
PubMed Central 

Google Scholar 
23.Hutchison, D. W. & Templeton, A. R. Correlation of pairwise genetic and geographic distance measures: Inferring the relative influences of gene flow and drift on the distribution of genetic variability. Evolution 53, 1898–1914 (1999).PubMed 

Google Scholar 
24.Schmitt, T. Molecular biogeography of Europe: Pleistocene cycles and postglacial trends. Front. Zool. 4, 11 (2007).PubMed 
PubMed Central 

Google Scholar 
25.Ewart, K. M. et al. Phylogeography of the iconic Australian red-tailed black-cockatoo (Calyptorhynchus banksii) and implications for its conservation. Heredity 125, 85–100 (2020).PubMed 
PubMed Central 

Google Scholar 
26.Hutama, A. et al. Identifying spatially concordant evolutionary significant units across multiple species through DNA barcodes: Application to the conservation genetics of the freshwater fishes of Java and Bali. Glob. Ecol. Conserv. 12, 170–187 (2017).
Google Scholar 
27.Médail, F. & Baumel, A. Using phylogeography to define conservation priorities: The case of narrow endemic plants in the Mediterranean Basin hotspot. Biol. Cons. 224, 258–266 (2018).
Google Scholar 
28.Previšić, A., Walton, C., Kučinić, M., Mitrikeski, P. T. & Kerovec, M. Pleistocene divergence of Dinaric Drusus endemics (Trichoptera, Limnephilidae) in multiple microrefugia within the Balkan Peninsula. Mol. Ecol. 18, 634–647 (2009).PubMed 

Google Scholar 
29.Brendonck, L. & Riddoch, B. J. Wind-borne short-range egg dispersal in anostracans (Crustacea: Branchiopoda). Biol. J. Linn. Soc. 67, 87–95 (1999).
Google Scholar 
30.Horváth, Z., Vad, C. F. & Ptacnik, R. Wind dispersal results in a gradient of dispersal limitation and environmental match among discrete aquatic habitats. Ecography 39, 726–732 (2016).PubMed 

Google Scholar 
31.Brochet, A. L. et al. Field evidence of dispersal of branchiopods, ostracods and bryozoans by teal (Anas crecca) in the Camargue (southern France). Hydrobiologia 637, 255 (2009).
Google Scholar 
32.Figuerola, J. & Green, A. J. Dispersal of aquatic organisms by waterbirds: A review of past research and priorities for future studies. Freshw. Biol. 47, 483–494 (2002).
Google Scholar 
33.Vanschoenwinkel, B. et al. Dispersal of freshwater invertebrates by large terrestrial mammals: A case study with wild boar (Sus scrofa) in Mediterranean wetlands. Freshw. Biol. 53, 2264–2273 (2008).
Google Scholar 
34.Brendonck, L., Rogers, D. C., Olesen, J., Weeks, S. & Hoeh, W. R. Global diversity of large branchiopods (Crustacea : Branchiopoda) in freshwater. Hydrobiologia 595, 167–176 (2008).
Google Scholar 
35.Dumont, H. J. & Negrea, S. V. Introduction to the Class Branchiopoda. (Backhuys Publishers, 2002).36.Belk, D. Global status and trends in ephemeral pool invertebrate conservation: Implications for Californian fairy shrimp. In Ecology, Conservation, and Management of Vernal Pool Ecosystems—Proceedings from a 1996 conference 147–150 (California Native Plant Society, 1998).37.Jocque, M., Vanschoenwinkel, B. & Brendonck, L. Anostracan monopolisation of early successional phases in temporary waters?. Fundam. Appl. Limnol. 176, 127–132 (2010).
Google Scholar 
38.Lukić, D., Horváth, Z., Vad, C. F. & Ptacnik, R. Food spectrum of Branchinecta orientalis—Are anostracans omnivorous top consumers of plankton in temporary waters?. J. Plankton Res. 40, 436–445 (2018).
Google Scholar 
39.Lukić, D., Ptacnik, R., Vad, C. F., Pόda, C. & Horváth, Z. Environmental constraint of intraguild predation: Inorganic turbidity modulates omnivory in fairy shrimps. Freshw. Biol. 65, 226–239 (2020).
Google Scholar 
40.Waterkeyn, A., Grillas, P., Anton-Pardo, M., Vanschoenwinkel, B. & Brendonck, L. Can large branchiopods shape microcrustacean communities in Mediterranean temporary wetlands?. Mar. Freshw. Res. 62, 46–53 (2011).CAS 

Google Scholar 
41.Brendonck, L. & De Meester, L. Egg banks in freshwater zooplankton: Evolutionary and ecological archives in the sediment. Hydrobiologia 491, 65–84 (2003).
Google Scholar 
42.Hairston, N. G., Brunt, R. A. V., Kearns, C. M. & Engstrom, D. R. Age and survivorship of diapausing eggs in a sediment egg bank. Ecology 76, 1706–1711 (1995).
Google Scholar 
43.Lukić, D. et al. High genetic variation and phylogeographic relations among Palearctic fairy shrimp populations reflect persistence in multiple southern refugia during Pleistocene ice ages and postglacial colonisation. Freshw. Biol. 64, 1896–1907 (2019).
Google Scholar 
44.Marrone, F., Alfonso, G., Naselli-Flores, L. & Stoch, F. Diversity patterns and biogeography of Diaptomidae (Copepoda, Calanoida) in the Western Palearctic. Hydrobiologia 800, 45–60 (2017).CAS 

Google Scholar 
45.Vanschoenwinkel, B. et al. Toward a global phylogeny of the “living fossil’’ crustacean order of the Notostraca. PLos ONE 7, e34998 (2012).46.Boileau, M. & Hebert, P. Genetic consequences of passive dispersal in pond-dwelling Copepods. Evolution 45, 721–733 (1991).PubMed 

Google Scholar 
47.Deng, Z., Chen, Y., Ma, X., Hu, W. & Yin, M. Dancing on the top: Phylogeography and genetic diversity of high-altitude freshwater fairy shrimps (Branchiopoda, Anostraca) with a focus on the Tibetan Plateau. Hydrobiologia 848, 2611–2626 (2021).CAS 

Google Scholar 
48.Ketmaier, V. et al. Mitochondrial DNA regionalism and historical demography in the extant populations of Chirocephalus kerkyrensis (Branchiopoda: Anostraca). PLoS ONE 7, e30082 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
49.Korn, M. et al. Phylogeny, molecular ecology and taxonomy of southern Iberian lineages of Triops mauritanicus (Crustacea: Notostraca). Org. Divers. Evol. 10, 409–440 (2010).
Google Scholar 
50.Stoch, F., Korn, M., Turki, S., Naselli-Flores, L. & Marrone, F. The role of spatial environmental factors as determinants of large branchiopod distribution in Tunisian temporary ponds. Hydrobiologia 782, 37–51 (2016).
Google Scholar 
51.Lindholm, M., d’Auriac, M. A., Thaulow, J. & Hobaek, A. Dancing around the pole: Holarctic phylogeography of the Arctic fairy shrimp Branchinecta paludosa (Anostraca, Branchiopoda). Hydrobiologia 772, 189–205 (2016).CAS 

Google Scholar 
52.Vörös, J., Alcobendas, M., Martínez-Solano, I. & García-París, M. Evolution of Bombina bombina and Bombina variegata (Anura: Discoglossidae) in the Carpathian Basin: A history of repeated mt-DNA introgression across species. Mol. Phylogenet. Evol. 38, 705–718 (2006).PubMed 

Google Scholar 
53.Zharov, A. A. et al. Pleistocene branchiopods (Cladocera, Anostraca) from Transbaikalian Siberia demonstrate morphological and ecological stasis. Water 12, 3063 (2020).
Google Scholar 
54.Velonà, A., Luchetti, A., Scanabissi, F. & Mantovani, B. Genetic variability and reproductive modalities in European populations of Triops cancriformis (Crustacea, Branchiopoda, Notostraca). Ital. J. Zool. 76, 366–375 (2009).
Google Scholar 
55.Vanschoenwinkel, B., Gielen, S., Vandewaerde, H., Seaman, M. & Brendonck, L. Relative importance of different dispersal vectors for small aquatic invertebrates in a rock pool metacommunity. Ecography 31, 567–577 (2008).
Google Scholar 
56.Hulsmans, A., Moreau, K., Meester, L. D., Riddoch, B. J. & Brendonck, L. Direct and indirect measures of dispersal in the fairy shrimp Branchipodopsis wolfi indicate a small scale isolation-by-distance pattern. Limnol. Oceanogr. 52, 676–684 (2007).ADS 

Google Scholar 
57.Vanschoenwinkel, B., Vries, C. D., Seaman, M. & Brendonck, L. The role of metacommunity processes in shaping invertebrate rock pool communities along a dispersal gradient. Oikos 116, 1255–1266 (2007).
Google Scholar 
58.Sánchez, M. I., Green, A. J., Amat, F. & Castellanos, E. M. Transport of brine shrimps via the digestive system of migratory waders: Dispersal probabilities depend on diet and season. Mar. Biol. 151, 1407–1415 (2007).
Google Scholar 
59.Horváth, Z. et al. Eastern spread of the invasive Artemia franciscana in the Mediterranean Basin, with the first record from the Balkan Peninsula. Hydrobiologia 822, 229–235 (2018).
Google Scholar 
60.Muñoz, J., Amat, F., Green, A. J., Figuerola, J. & Gómez, A. Bird migratory flyways influence the phylogeography of the invasive brine shrimp Artemia franciscana in its native American range. PeerJ 1, e200 (2013).PubMed 
PubMed Central 

Google Scholar 
61.Muñoz, J. et al. Phylogeography and local endemism of the native Mediterranean brine shrimp Artemia salina (Branchiopoda: Anostraca). Mol. Ecol. 17, 3160–3177 (2008).PubMed 

Google Scholar 
62.Sánchez, M. I., Hortas, F., Figuerola, J. & Green, A. J. Comparing the potential for dispersal via waterbirds of a native and an invasive brine shrimp. Freshw. Biol. 57, 1896–1903 (2012).
Google Scholar 
63.Viana, D. S., Santamaría, L., Michot, T. C. & Figuerola, J. Migratory strategies of waterbirds shape the continental-scale dispersal of aquatic organisms. Ecography 36, 430–438 (2013).
Google Scholar 
64.Green, A. J. et al. Dispersal of invasive and native brine shrimps Artemia (Anostraca) via waterbirds. Limnol. Oceanogr. 50, 737–742 (2005).ADS 

Google Scholar 
65.Kappas, I. et al. Molecular and morphological data suggest weak phylogeographic structure in the fairy shrimp Streptocephalus torvicornis (Branchiopoda, Anostraca). Hydrobiologia 801, 21–32 (2017).CAS 

Google Scholar 
66.Rogers, D. C. Larger hatching fractions in avian dispersed anostracan eggs (Branchiopoda). J. Crustac. Biol. 34, 135–143 (2014).
Google Scholar 
67.Angeler, D. G., Viedma, O., Sánchez-Carrillo, S. & Alvarez-Cobelas, M. Conservation issues of temporary wetland Branchiopoda (Anostraca, Notostraca: Crustacea) in a semiarid agricultural landscape: What spatial scales are relevant?. Biol. Cons. 141, 1224–1234 (2008).
Google Scholar 
68.Horváth, Z., Vad, C. F., Vörös, L. & Boros, E. Distribution and conservation status of fairy shrimps (Crustacea: Anostraca) in the astatic soda pans of the Carpathian basin: the role of local and spatial factors. J. Limnol. 72, 103–116 (2013).
Google Scholar 
69.Svensson, L., Mullarney, K. & Zetterström, D. Collins Bird Guide 2nd edn. (HarperCollins Publishers Ltd., 2009).
Google Scholar 
70.Horváth, Z., Vad, C. F., Vörös, L. & Boros, E. The keystone role of anostracans and copepods in European soda pans during the spring migration of waterbirds. Freshw. Biol. 58, 430–440 (2013).
Google Scholar 
71.Gill, J. L. Ecological impacts of the late Quaternary megaherbivore extinctions. New Phytol. 201, 1163–1169 (2014).PubMed 

Google Scholar 
72.Neretina, A. N. et al. Crustacean remains from the Yuka mammoth raise questions about non-analogue freshwater communities in the Beringian region during the Pleistocene. Sci. Rep. 10, 859 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Chang, D. et al. The evolutionary and phylogeographic history of woolly mammoths: A comprehensive mitogenomic analysis. Sci. Rep. 7, 44585 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
74.Lister, A. M., Sher, A. V., van Essen, H. & Wei, G. The pattern and process of mammoth evolution in Eurasia. Quatern. Int. 126–128, 49–64 (2005).
Google Scholar 
75.Vanschoenwinkel, B. et al. Passive external transport of freshwater invertebrates by elephant and other mud-wallowing mammals in an African savannah habitat. Freshw. Biol. 56, 1606–1619 (2011).
Google Scholar 
76.Waterkeyn, A., Pineau, O., Grillas, P. & Brendonck, L. Invertebrate dispersal by aquatic mammals: A case study with nutria Myocastor coypus (Rodentia, Mammalia) in Southern France. Hydrobiologia 654, 267–271 (2010).
Google Scholar 
77.Belk, D. & Brtek, J. Checklist of the Anostraca. Hydrobiologia 298, 315–353 (1995).
Google Scholar 
78.Marrone, F., Korn, M., Stoch, F., Naselli Flores, L. & Turki, S. Updated checklist and distribution of large branchiopods (Branchiopoda: Anostraca, Notostraca, Spinicaudata) in Tunisia. Biogeogr. J. Integr. Biogeogr. 31, 27–53 (2016).79.Mura, G. & Brtek, J. Revised key to families and genera of the Anostraca with notes on their geographical distribution. Crustaceana 73, 1037–1088 (2000).
Google Scholar 
80.Atashbar, B., Agh, N., Van Stappen, G., Mertens, J. & Beladjal, L. Combined effect of temperature and salinity on hatching characteristics of three fairy shrimp species (Crustacea: Anostraca). J. Limnol. 73, 574–583 (2014).
Google Scholar 
81.Eder, E., Hödl, W. & Gottwald, R. Distribution and phenology of large branchiopods in Austria. Hydrobiologia 359, 13–22 (1997).
Google Scholar 
82.Šćiban, M., Marković, A., Lukić, D. & Miličić, D. Autumn populations of Branchinecta orientalis G. O. Sars, 1903 and Chirocephalus diaphanus Prevost, 1803 (Crustacea, Branchiopoda) in the Central European Lowlands (Pannonian Plain, Serbia). North-West. J. Zool. 10, 435–437 (2014).
Google Scholar 
83.Alonso, M. A survey of the Spanish Euphyllopoda. Miscelania Zool. 9, 179–208 (1985).
Google Scholar 
84.Petkovski, S. On the presence of the genus Branchinecta Verrill, 1869 (Crustacea, Anostraca) in Yugoslavia. Hydrobiologia 226, 17–27 (1991).
Google Scholar 
85.Dimentman, C. The rainpool ecosystems of Israel: Geographical distribution of freshwater Anostraca (Crustacea). Israel J. Ecol. Evol. 30, 1–15 (1981).
Google Scholar 
86.Eid, E. K. New records of large branchiopods from northern Jordan (Crustacea: Branchiopoda). Zool. Middle East 46, 116–117 (2009).
Google Scholar 
87.Mura, G., Ozkutuk, S. R., Aygen, C. & Cottarelli, V. New data on the taxonomy and distribution of anostracan fauna from Turkey. J. Biol. Res. 15, 17–23 (2011).
Google Scholar 
88.Rogers, D. C., Quinney, D. L., Weaver, J. & Olesen, J. A new giant species of predatory fairy shrimp from Idaho, USA (Branchiopoda: Anostraca). J. Crustac. Biol. 26, 1–12 (2006).
Google Scholar 
89.Rodríguez-Flores, P. C., Jiménez-Ruiz, Y., Forró, L., Vörös, J. & García-París, M. Non-congruent geographic patterns of genetic divergence across European species of Branchinecta (Anostraca: Branchinectidae). Hydrobiologia 801, 47–57 (2017).
Google Scholar 
90.Atashbar, B., Agh, N., Van Stappen, G. & Beladjal, L. Diversity and distribution patterns of large branchiopods (Crustacea: Branchiopoda) in temporary pools (Iran). J. Arid. Environ. 111, 27–34 (2014).ADS 

Google Scholar 
91.Belk, D. & Esparza, C. E. Anostraca of the Indian Subcontinent. Hydrobiologia 298, 287–293 (1995).
Google Scholar 
92.Brtek, J. & Thiéry, A. The geographic distribution of the European Branchiopods (Anostraca, Notostraca, Spinicaudata, Laevicaudata). Hydrobiologia 298, 263–280 (1995).
Google Scholar 
93.Horn, W. & Paul, M. Occurrence and distribution of the Eurasian Branchinecta orientalis (Anostraca) in Central Asia (Northwest Mongolia, Uvs Nuur Basin) and in other holarctic areas. Lauterbornia 49, 81–91 (2004).
Google Scholar 
94.Marrone, F., Alonso, M., Pieri, V., Augugliaro, C. & Stoch, F. The crustacean fauna of Bayan Onjuul area (Tov Province, Mongolia) (Crustacea: Branchiopoda, Copepoda, Ostracoda). North West. J. Zool. 11, 288–295 (2015).
Google Scholar 
95.Mura, G. & Takami, G. A. A contribution to the knowledge of the anostracan fauna of Iran. Hydrobiologia 441, 117–121 (2000).
Google Scholar 
96.Naganawa, H. et al. Does the dispersal of fairy shrimps (Branchiopoda, Anostraca) reflect the shifting geographical distribution of freshwaters since the late Mesozoic?. Limnology https://doi.org/10.1007/s10201-019-00589-9 (2019).Article 

Google Scholar 
97.Padhye, S. M., Kulkarni, M. R. & Dumont, H. J. Diversity and zoogeography of the fairy shrimps (Branchiopoda: Anostraca) on the Indian subcontinent. Hydrobiologia 801, 117–128 (2017).
Google Scholar 
98.Petkovski, S. Taksonomsko-morfološka i zoogeografsko-ekološka studija Anostraca (Crustacea: Branchiopoda) jugoslovenskih zemalja. (Prirodno-matematički fakultet, Novi Sad, 1993).99.Pretus, J. L. A commented check-list of the Balearic Branchiopoda (Crustacea). Limnetica 6, 157–164 (1990).
Google Scholar 
100.van den Broeck, M., Waterkeyn, A., Rhazi, L. & Brendonck, L. Distribution, coexistence, and decline of Moroccan large branchiopods. J. Crustacean Biol. 35, 355–365 (2015).
Google Scholar 
101.Hijmans, R. J., Philips, S., Leathwick, J. & Elith, J. Package ‘dismo’. 9, 1–68 (2017).102.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/. (2014).103.Hijmans, R. J., Cameron, S. E. & Parra, J. L. Climate Date from Worldclim (2004).104.Alfonso, G. & Marrone, F. Branchiopoda Anostraca, Notostraca, Spinicaudata. In Checklist of the Italian fauna (in press).105.Defaye, D., Rabet, N. & Thiéry, A. Atlas et bibliographie des crustaces branchiopodes (Anostraca, Notostraca, Spinicaudata) de France metropolitaine. Collection patrimoines naturels (1998).106.Song, H., Buhay, J. E., Whiting, M. F. & Crandall, K. A. Many species in one: DNA barcoding overestimates the number of species when nuclear mitochondrial pseudogenes are coamplified. PNAS 105, 13486–13491 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
107.Hall, T. A. BioEdit: A user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucl. Acids Symp. Ser. 41, 95–98 (1999).CAS 

Google Scholar 
108.Aguilar, A. et al. High intraspecific genetic divergence in the versatile fairy shrimp Branchinecta lindahli with a comment on cryptic species in the genus Branchinecta (Crustacea: Anostraca). Hydrobiologia 801, 59–69 (2017).
Google Scholar 
109.Jeffery, N. W., Elías-Gutiérrez, M. & Adamowicz, S. J. Species diversity and phylogeographical affinities of the Branchiopoda (Crustacea) of Churchill, Manitoba, Canada. PLoS ONE 6, e18364 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
110.Lanfear, R., Frandsen, P. B., Wright, A. M., Senfeld, T. & Calcott, B. PartitionFinder 2: New methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. Evol. 34, 772–773 (2017).CAS 
PubMed 

Google Scholar 
111.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 

Google Scholar 
112.Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).113.Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755 (2001).CAS 
PubMed 

Google Scholar 
114.Ronquist, F. & Huelsenbeck, J. P. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
115.Ronquist, F. et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).116.Bandelt, H. J., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
117.Leigh, J. W. & Bryant, D. popart: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).
Google Scholar 
118.Xia, X. & Kumar, S. DAMBE7: New and improved tools for data analysis in molecular biology and evolution. Mol. Biol. Evol. 35, 1550–1552 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
119.Xia, X. & Lemey, P. Assessing substitution saturation with DAMBE. In The phylogenetic Handbook 615–630 (Cambridge University Press, 2009).120.Xia, X., Xie, Z., Salemi, M., Chen, L. & Wang, Y. An index of substitution saturation and its application. Mol. Phylogenet. Evol. 26, 1–7 (2003).CAS 
PubMed 

Google Scholar 
121.Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).ADS 
CAS 
PubMed 

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

Google Scholar 
123.Nychka, D. et al. fields: Tools for Spatial Data (2020).124.Oksanen, J. et al. vegan: Community ecology package. – R package ver. 2.0-4. http://CRAN.R-project.org/package=vegan. (2012). More