Ecology

Fu, C. H. et al. Relationships among fisheries exploitation, environmental conditions, and ecological indicators across a series of marine ecosystems. J. Mar. Syst. 148, 101–111 (2015).Article 

Google Scholar 
Too, C. C., Ong, K. S., Yule, C. M. & Keller, A. Putative roles of bacteria in the carbon and nitrogen cycles in a tropical peat swamp fores. Basic Appl. Ecol. 52, 109–123 (2020).Article 

Google Scholar 
Hou, R. J. et al. Effects of biochar and straw on greenhouse gas emission and its response mechanism in seasonally frozen farmland ecosystems. Catena 194, 104735 (2020).CAS 
Article 

Google Scholar 
Wang, X., Lu, P., Yang, P. L. & Ren, S. M. Effects of fertilizer and biochar applications on the relationship among soil moisture, temperature, and N2O emissions in farmland. PeerJ 9, e11674–e11674 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Tang, Z. M., Liu, X. R., Zhang, Q. W. & Li, G. C. Effects of biochar and straw on soil N2O emission from a wheat maize rotation system. Huan Jing Ke Xue 42(3), 1569–1580 (2021).PubMed 

Google Scholar 
Kong, Q., Wang, Z. B., Niu, P. F. & Miao, M. S. Greenhouse gas emission and microbial community dynamics during simultaneous nitrification and denitrification process. Biores. Technol. 210, 94–100 (2016).CAS 
Article 

Google Scholar 
Han, Z. M. et al. Spatial-temporal dynamics of agricultural drought in the Loess Plateau under a changing environment: Characteristics and potential influencing factors. Agric. Water Manag. 244, 106540 (2021).Article 

Google Scholar 
Clemens, S. et al. Nitrification inhibitors can increase post-harvest nitrous oxide emissions in an intensive vegetable production system. Sci. Rep. 7(1), 1–9 (2017).Article 
CAS 

Google Scholar 
Zhang, D. J. et al. Effects of tillage and fertility on soil nitrogen balance and greenhouse gas emissions of wheat-maize rotation system in Central Henan Province, China. J. Appl. Ecol. 32(5), 1753–1760 (2021).
Google Scholar 
Liu, X. C. et al. Response of soil N2O emissions to precipitation pulses under different nitrogen availabilities in a semiarid temperate steppe of Inner Mongolia, China. J. Arid Land 6(04), 410–422 (2014).Article 

Google Scholar 
Hu, Q. Y. et al. Combined effects of straw returning and chemical n fertilization on greenhouse gas emissions and yield from paddy fields in northwest Hubei Province, China. J. Soil Sci. Plant Nutr. 20(2), 392–406 (2019).Article 
CAS 

Google Scholar 
Sun, Z. C. et al. Effects of straw returning and feeding on greenhouse gas emissions from integrated rice-crayfish farming in Jianghan Plain, China. Environ. Sci. Pollut. Res. 26(12), 11710–11718 (2019).CAS 
Article 

Google Scholar 
Mei, K. et al. Stimulation of N2O emission by conservation tillage management in agricultural lands: A meta-analysis. Soil Tillage Res. 182, 86–93 (2018).Article 

Google Scholar 
Wang, H. Y., Wu, J. Q., Li, G. & Yan, L. J. Changes in soil carbon fractions and enzyme activities under different vegetation types of the northern Loess Plateau. Ecol. Evol. 10(21), 12211–12223 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Sadiq, M., Li, G., Rahim, N. & Tahir, M. M. Sustainable conservation tillage technique for improving soil health by enhancing soil physicochemical quality indicators under wheat mono-cropping system conditions. Sustainability 13(15), 8177–8177 (2021).CAS 
Article 

Google Scholar 
Nie, Z. G. et al. Evaluating the effects of different sowing dates and tillage methods on dry-land wheat grain dry matter accumulation based on the APSIM model. J. Appl. Ecol. 32(3), 913–920 (2021).
Google Scholar 
Alhassan, A. M., Yang, C. J., Ma, W. W. & Li, G. Influence of conservation tillage on Greenhouse gas fluxes and crop productivity in spring-wheat agroecosystems on the Loess Plateau of China. PeerJ 9, e11064–e11064 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Mou, L. M. et al. Breeding report of a new dryland spring wheat variety Dingxi 42. Gansu Agric. Sci. Technol. 01, 1–3 (2015).ADS 

Google Scholar 
Ma, W. W., Li, G., Wu, J. H., Xu, G. R. & Wu, J. Q. Respiration and CH4 fluxes in Tibetan peatlands are influenced by vegetation degradation. CATENA 195, 104789 (2020).CAS 
Article 

Google Scholar 
Wu, J. Q. et al. Vegetation degradation impacts soil nutrients and enzyme activities in wet meadow on the Qinghai-Tibet Plateau. Sci. Rep. 10(1), 21271–21271 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Défossez, P. et al. Impact of soil water content on the overturning resistance of young Pinus Pinaster in sandy soil. For. Ecol. Manag. 480, 118614 (2021).Article 

Google Scholar 
Mao, J., Nierop, K. G., Rietkerk, M., Damsté, J. S. S. & Te Dekker, S. C. infuence of vegetation on soil water repellency-markers and soil hydrophobicity. Sci. Total Environ. 566, 608–620 (2016).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Lu, Y., Si, B., Li, H. & Biswas, A. Elucidating controls of the variability of deep soil bulk density. Geoderma 348, 146–157 (2019).ADS 
Article 

Google Scholar 
Huang, T. T., Yang, N., Lu, C., Qin, X. L. & Siddique, K. Soil organic carbon, total nitrogen, available nutrients, and yield under different straw returning methods. Soil Tillage Res. 214, 105171 (2021).Article 

Google Scholar 
Yang, J. M., Zhang, Z. Q. & Cao, G. J. Soil nitrate and nitrite content determined by Skalar SAN++. Soil Fertil. Sci. China 02, 101–105 (2014).
Google Scholar 
Chen, N. et al. Effect of biodegradable film mulching on crop yield, soil microbial and enzymatic activities, and optimal levels of irrigation and nitrogen fertilizer for the Zea mays crops in arid region. Sci. Total Environ. 776, 145970–145970 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Akhtar, K. et al. Straw mulching with inorganic nitrogen fertilizer reduces soil CO2 and N2O emissions and improves wheat yield. Sci. Tot. Environ. 741, 140488 (2020).CAS 
Article 

Google Scholar 
Ma, E. et al. Effects of rice straw returning methods on N2O emission during wheat-growing season. Nutr. Cycl. Agroecosyst. 88(3), 463–469 (2009).Article 
CAS 

Google Scholar 
Yeboah, S. et al. Greenhouse gas emissions in a spring wheat–field pea sequence under different tillage practices in semi-arid Northwest China. Nutr. Cycl. Agroecosyst. 106(1), 77–91 (2016).CAS 
Article 

Google Scholar 
Zahid, A., Ali, S., Ahmed, M. & Iqbal, N. Improvement of soil health through residue management and conservation tillage in rice-wheat cropping system of Punjab, Pakistan. Agronomy 10(12), 1844–1844 (2020).CAS 
Article 

Google Scholar 
Dharmendra, S. et al. Effect of reversal of conservation tillage on soil nutrient availability and crop nutrient uptake in soybean in the vertisols of central India. Sustainability. 12(16), 6608 (2020).Article 
CAS 

Google Scholar 
Orzech, K., Wanic, M. & Załuski, D. The effects of soil compaction and different tillage systems on the bulk density and moisture content of soil and the yields of winter oilseed rape and cereals. Agriculture 11(7), 666–666 (2021).CAS 
Article 

Google Scholar 
Fan, B. Q. & Liu, Q. L. Effect of conservation tillage and straw application on the soil microorganism and P-dissolving characteristics. Chin. J. Eco-Agric. 03, 130–132 (2005).
Google Scholar 
Liu, X. et al. Dynamic contribution of microbial residues to soil organic matter accumulation influenced by maize straw mulching. Geoderma 333, 35–42 (2019).ADS 
CAS 
Article 

Google Scholar 
Wang, W. Y. et al. Conservation tillage enhances crop productivity and decreases soil nitrogen losses in a rainfed agroecosystem of the Loess Plateau, China. J. Clean. Prod. 274, 122854 (2020).CAS 
Article 

Google Scholar 
Zhang, Y., Xie, D. T., Ni, J. P. & Zeng, X. B. Conservation tillage practices reduce nitrogen losses in the sloping upland of the Three Gorges Reservoir area: No-till is better than mulch-till. Agric. Ecosyst. Environ. 300, 107003 (2020).CAS 
Article 

Google Scholar 
Andrea, F. et al. May conservation tillage enhance soil C and N accumulation without decreasing yield in intensive irrigated croplands? Results from an eight-year maize monoculture. Agric. Ecosyst. Environ. 296, 106926 (2020).Article 
CAS 

Google Scholar 
Wu, J. et al. Effects of different tillage and straw retention practices on soil aggregates and carbon and nitrogen sequestration in soils of the northwestern China. J. Arid. Land 11(04), 567–578 (2019).Article 

Google Scholar 
Niu, Y. N., Shen, Y. Y., Nan, Z. B., Yang, J. & Yang, Z. W. College of Pastoral Agriculture Science & Technology, Lanzhou University, China. Influence of different cultivation managements on organic carbon and nitrate nitrogen of top soil in the Loess Plateau, northwestern China. Proceedings of the XXI International Grassland Congress and the VIII International Rangeland Congress (volume II) (2008).Wang, Q., Li, F. R., Zhang, E. H., Li, G. & Vance, M. The effects of irrigation and nitrogen application rates on yield of spring wheat (longfu-920), and water use efficiency and nitrate nitrogen accumulation in soil. Aust. J. Crop Sci. 6(4), 662–672 (2012).
Google Scholar 
Pisani, O. et al. Soil nitrogen dynamics and leaching under conservation tillage in the Atlantic Coastal Plain, Georgia, United States. J. Soil Water Conserv. 72(5), 519–529 (2017).Article 

Google Scholar 
Cao, W. C. et al. Key production processes and influencing factors of nitrous oxide emissions from agricultural soils. J. Nutr. Fertil. 25(10), 1781–1798 (2019).
Google Scholar 
Liu, B., Huang, G. B., Gao, Y. Q., Li, Q. P. & Huang, T. Effects of no-tillage on daily dynamics of CO2 and N2O emission from spring wheat field during mature stage. J. Gansu Agric. Univ. 45(01), 82–87 (2010).
Google Scholar 
Akhtar, K. et al. Straw mulching with inorganic nitrogen fertilizer reduces soil CO2 and N2O emissions and improves wheat yield. Sci. Total Environ. 741, 140488 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Sina, B., Youngsun, K., Janine, K. & Gerhard, G. Plastic mulching in agriculture: Friend or foe of N2O emissions. Agric. Ecosyst. Environ. 167, 43–51 (2013).Article 
CAS 

Google Scholar 
Seiichi, N., Michio, K., Masako, T., Seiichiro, Y. & Naoto, K. Nitrous oxide evolved from soil covered with plastic mulch film in horticultural field. Biol. Fertil. Soils 48(7), 787–795 (2012).Article 
CAS 

Google Scholar 
Wang, J., Cai, L. Q., Zhang, R. Z., Wang, Y. L. & Dong, W. J. Effects of Tillage Measures on soil greenhouse gas (CO2, CH4, N2O) flux in temperate semi-arid area. Chin. J. Eco-Agric. 19(06), 1295–1300 (2011).CAS 
Article 

Google Scholar 
Chen, G. H. et al. Can conservation tillage reduce N2O emissions on cropland transitioning to organic vegetable production?. Sci. Total Environ. 618, 927–940 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Narendra, K. L. & Rattan, L. Soil aggregation and greenhouse gas flux after 15 years of wheat straw and fertilizer management in a no-till system. Soil Tillage Res. 126, 78–89 (2013).Article 

Google Scholar 
Liang, W., Shi, Y., Zhang, H., Yue, J. & Huang, G. H. Greenhouse gas emissions from Northeast china rice fields in fallow season. Pedosphere 17(5), 630–638 (2007).CAS 
Article 

Google Scholar 
Bremner, J. M., Robbins, S. G. & Blackmer, A. M. Seasonal variability in emission of nitrous oxide from soil. Geophys. Res. Lett. 7(9), 641–644 (1980).ADS 
CAS 
Article 

Google Scholar 
Maag, M. & Vinther, F. P. Nitrous oxide emission by nitrification and denitrification in the different soil types and at different soil moisture contents and temperature. Appl. Soil. Ecol. 4(1), 5–14 (1996).Article 

Google Scholar 
Castaldi, S. Responses of nitrous oxide, dinitrogen and carbon dioxide production and oxygen consumption to temperature in forest and agricultural light-textured soils determined by model experiment. Biol. Fertil. Soils 32(1), 67–72 (2000).MathSciNet 
CAS 
Article 

Google Scholar 
Braker, G., Schwarz, J. & Conrad, R. Influence of temperature on the composition and activity of denitrifying soil communities. FEMS Microbiol. Ecol. 73(1), 134–148 (2010).CAS 
PubMed 

Google Scholar 
Hu, H. W., Chen, D. & He, J. Z. Microbial regulation of terrestrial nitrous oxide formation: Understanding the biological pathways for prediction of emission rates. Narnia 39(5), 729–749 (2015).CAS 

Google Scholar 
Pokharel, P. & Chang, S. X. Biochar decreases the efficacy of the nitrification inhibitor nitrapyrin in mitigating nitrous oxide emissions at different soil moisture levels. J. Environ. Manage. 295, 113080–113080 (2021).CAS 
PubMed 
Article 

Google Scholar 
Shu, X. X. et al. Response of soil N2O emission and nitrogen utilization to organic matter in the wheat and maize rotation system. Sci. Rep. 11(1), 4396–4396 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bergaust, L., Mao, Y. J., Bakken, L. R. & Frostegård, A. Denitrification response patterns during the transition to anoxic respiration and posttranscriptional effects of suboptimal pH on nitrous [corrected] oxide reductase in Paracoccus denitrificans. Appl. Environ. Microbiol. 76(19), 6387–6396 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Outcome of DNA sequencing, assembly, and validationIn this study, initially total DNA was isolated from the finely chopped, full-grown pupa of Blepharipa sp. The NanoDrop spectrophotometer (1294 ng/μl) and the Qubit fluorometer (732.8 ng/μl) both found that the concentration of total DNA in the sample at an optimum level for mitochondrial DNA enrichment. The Tape Station profile showed that the size of the fragments of the mitogenomic library were in the range of 250 to 550 bp. The complete insert size distribution ranged from 130 to 430 bp, with the combined adapter size being ~ 120 bp with mitogenome fragments. The appropriate distribution of fragments and their concentrations (~ 27.1 ng/μl) were also found to be suitable for sequencing. Sequencing through Illumina NextSeq500 yielded 4,402,752 raw reads, of which around 3,663,404 high-quality reads were retained after post-quality filtering. The final scaffolding and assembly of contigs generated a 15,080 bp single scaffold MtDNA in Blepharipa sp. (N50 = 15,080).The sequencing outcome was validated by performing PCR amplification of one of the protein-coding genes, in this case, nad6. Where PCR amplification resulted in a single band of expected amplicon size (shown in Supplementary Method Online). Sanger sequencing and subsequent alignment of these amplicons showed almost 92% sequence similarity to our assembled Blepharipa sp. nad6 gene (see Supplementary Method Online). This provided strong evidence that our mitogenome assembly is reliable and can be used for general applications of mitochondrial genes, e.g., as a biomarker. The second mitogenomic region, the control region (CR) was suggested by the reviewer. We have discussed that CRs constitute repetitive A + T regions (“AT richness of Control Region and role of sequencing method” and “Impact of repeats on different sequencing technologies and assembly method” section). One or more repetitive regions within the CR identified in certain species (e.g. fish, human) have shown undesirable effects on PCR amplification and sequencing125,126. Many organisms have segmental duplications in CR induced by the appearance of pseudogenes that PCR can co-amplify127,128,129,130,131. Due to these associated problems, researchers generally rely on protein or ribosomal RNA genes for phylogenetics instead of CRs132,133,134. In this case, we also faced problems validating the CR. The PCR and gel electrophoresis using external PCR primers did not show a desirable single band as seen for nad6. As an alternative strategy, we used two pairs of primers, CR int_fwd and CR int_rev, internal primers, with CR15fwd and CR08rev primers, to perform a two-way sequencing of each amplicon, which generated multiple bands (see Supplementary Method Online, Figs. S1, S2). The most prominent bands were subjected to sequencing and yielded two mixed sequences, the best of which exhibited nearly 54% sequence resemblance with the Blepharipa sp. control region (see Method in Supplementary Note). Further mapping of the Illumina reads with the assembly revealed that the depth of coverage across the CR was not as deep as that of protein-coding genes such as cox2, and it was also not inflated only over a repeated section of the CR. The depth over 1–112 varied from 5 to 20×, and that for the 15,025–15,080 bp was around 30×. We did observe that our reads didn’t cover a 10 bp stretch of CR around 15,030–15,040 bp (see Method in Supplementary Note and Figs. S3–S6). We believe that our sequencing and assembly experiment was able to cover the majority of CR successfully with reasonable coverage barring that 10 bp stretch. Our results corroborate with the difficulties of CR sequencing seen with other species, and while this doesn’t reflect on the quality of our whole mitogenome assembly, researchers using mitogenomic CR regions for any kind of phylogenetic inference should proceed with caution.Size and organization of mitogenome
Blepharipa sp. mitogenome organization and structureThe newly sequenced mitochondrial genome of Blepharipa sp. is closed circular and has a size of 15,080 bp, which falls within the typical insect mitogenome size (14 to 20 kb)135,136,137. Similar to other sequenced bilaterian mitogenomes, the Blepharipa sp. mitogenome has conventional gene content, a total of 37 genes (viz. 13 PCGs, 22 tRNAs, 2 rRNAs) and an AT-rich control region (CR) (Fig. 2A)138,139,140,141. Among these, 23 genes are present on the major strand (J strand or +ve strand), while the remaining 14 genes are present in the minor strand (N strand or –ve strand). The intron-less 13 PCGs are also separately encoded by these two strands, 9 PCGs (nad2, cox1, cox2, atp8, atp6, cox3, nad3, nad6, cytb) from the J strand and 4 PCGs (nad5, nad4, nad4l, nad1) from N strand covering 6899 bp and 4300 bp respectively constituting around 74.31% of the entire mitogenome (Fig. 2). The largest PCG present in this organism is nad5 (1716 bp), and the smallest one is the atp8 (165 bp). Excluding stop codons, the J strand has 2237 codons, and the N strand has 1430 codons. Apart from cox1 (TCG) and nad1 (TTG), 11 PCGs follow the canonical “ATN” start codon. Ten PCGs of this mitogenome have “TAA or TAG” as their stop codon except for cox1, cox2, and nad4, where they end with an incomplete stop codon, a single T (Fig. 2)142. A total of 22 tRNAs are interspersed all over the entire mitogenome, ranging from 63 bp (trnT) to 72 bp (trnV) in size. The J and N strands have 14 tRNAs and 8 tRNAs, respectively, with 928 bp and 528 bp of nucleotides. Typical clover-leaf shaped secondary structures of tRNAs have been observed with a few exceptions where trnC, trnF, trnP, and trnN lack a stable TΨC loop see Supplementary Fig. S7 online). Two N-strand rRNAs with nucleotides of 1360 bp and 783 bp are transcribed individually for rrnL and rrnS (Fig. 2B).Figure 2Complete mitochondrial genome structure of Blepharipa sp.; (A) Circular Map (B) Annotation and genome organization of mitogenome. tRNAs are represented as trn followed by the IUPAC-IUB single letter amino acid codes e.g., trnI denote tRNA-Ile.Full size imageThis mitogenome has 10 gene boundaries where genes overlap with adjacent genes, varying from 1 to 8 bp in length, for a total of 35 bp. The longest overlapping sequence of 8 bp is present over the trnW and trnC genes. Likewise, the total length of all intergenic spacer sequences (excluding the control region) is 139 bp, present at 15 gene boundaries. The length of each intergenic spacer varies between 1 and 40 bp, and the longest one is located between the trnE and trnF genes. In this organism, eleven pairs of genes are located discreetly but adjacent to each other and any PCG adjacent to tRNA, ending with an incomplete stop codon (cox1-trnL2, cox2-trnK). The control region’s length of this dipteran fly is 168 bp, and the nature of this region is highly biased towards A + T content (Fig. 2).Size comparison of Oestroidea mitogenome and their genesTo better understand the mitogenome of Blepharipa sp., it has been compared with the flies of the Oestroidea superfamily (blowflies, bot flies, flesh flies, uzi flies, and relatives). Various features have been taken into account for this comparison: mitogenome size, gene sizes, gene content, and how genes are placed in each mitogenome.The mitogenome of eukaryotic organisms shows that there are significant size differences across mammals, fungi, and plants. The typical size of an animal mitogenome is near about 16 kb, a fungal mitogenome is 19–176 kb, and a plant mitogenome is far larger, with a size range of 200 to 2500 kb143. We have shown that the Blepharipa sp. whole mitogenome size (15,080 bp) is 416 bp smaller than the average Oestroidea flies mitogenome. As for the Oestroidea superfamily, D. hominis (human bot fly), an Oestridae fly has the longest mitogenome of all (16,360 bp), and A. grahami, a Calliphoridae fly, has the shortest mitogenome of all (14,903 bp). Tachinid flies have a smaller average mitogenome size (~ 15,076 bp) than the other flies in this superfamily, and the Oestridae flies have a relatively larger mitogenome (~ 16,031 bp). We observed that the size of the total PCGs, tRNAs, and rRNAs are well-maintained across this superfamily, with an average length of 11,145 bp, 1482 bp, and 2113 bp, respectively (Fig. 1A, green, yellow, and blue line, Table 1).The difference in mitogenome size in insects can be attributed to variations in the length of non-coding regions, especially the control region that differs in length as well as the pattern of sequences (Fig. 1B)104,144. In addition, based on mtDNA sequence similarity among all the Oestroidea flies, Blepharipa sp. has high similitude with the Tachinid Fly E. flavipalpis (87.83%), followed by the two hairy maggot blowflies, Chrysomya albiceps (85.51%) and C. rufifacies (85.44%). Another well-studied uzi fly, E. sorbilans has an 84.82% sequence similarity with Blepharipa sp., while Gasterophilus horse botfly has the lowest sequence similarity (~ 77%) with Blepharipa sp. (Supplementary Data 3A).Gene content and arrangementWe found that the Oestroidea mitogenome represents the reserved gene arrangement of Ecdysozoan, for which it can be easily distinguishable from other bilaterians (Lophotrochozoa and Deuterostomia)140. The mitogenome of Blepharipa sp. and other Oestroidea have three core tRNA clusters, including (1) trnI-trnQ-trnM, (2) trnW-trnC-trnY and (3) trnA-trnR-trnN-trnS1-trnE-trnF, as depicted in Figs. 1C and 2. A comparative study revealed that the Oestroidea superfamily has 4 different kinds of mitogenome arrangements (Fig. 1C). The majority of the Oestroidea flies (25 out of 36) in this study have ancestral (A) dipteran type mitogenome sequences (Table 1)145. However, there are some minor inconsistencies exist in the Calliphoridae family (blowflies), such as the insertion of extra tRNAs (trnI in the genus Chrysomya and trnV in D. hominis) or the translocation of tRNA (trnS1 in C. chinghaiensis) (Fig. 1C)21,24. Barring this, all organisms, including Blepharipa sp., follow a standard dipteran gene arrangement and have 37 genes in their respective mitogenomes (insertion of tRNA into the genus Chrysomya and D. hominis raises gene count) (Fig. 1C (i)(ii), Table 1). In the case of dipterans other than the Oestroidea superfamily, species like gall midge (Cecidomyiidae), mosquitos (Culicidae), and crane flies (Tipulidae) exhibit various rearrangements in mitochondrial tRNAs, such as the absence, inversion, translocation, and extreme truncation of certain genes (Supplementary Data 1A)146,147.Non-coding regionsControl region (CR) of Blepharipa sp. and comparison with OestroideaThis region in the metazoan mitogenome is a single sizeable non-coding sequence containing essential regulatory elements for transcription and replication initiation; it is therefore named the control region148,149. Similar to other Diptera, the CR of Blepharipa sp. is also flanked by rrnS and the trnI-trnQ-trnM gene cluster (Fig. 2). Sequence similarity with other Oestroidea superfamily species indicates that this segment is variable due to the lack of coding constraints150. The CR sequence of Blepharipa sp. 75.49% similar to another tachinid fly Elodia flavipalpis, followed by Chrysomya bezziana (71.15%) (Supplementary Data 3B). Despite its overall high variation in nucleotides, this region harbors multiple different types of repeats (e.g., tandem repeats, inverted repeats)42,151 and conserved structures namely Poly-T stretch (15 bp), [TA(A)]n-like, G(A)nT-like stretches, and poly A tail (15 bp)152,153,154(Fig. 3A). Another conserved motif, “ATTGTAAATT” we found in the CR of Blepharipa sp. and E. flavipalpis (Fig. 3A). Such conserved structures are thought to play role in the regulatory process of transcription or replication. After binding with RNA polymerase,  they keep the initiating mode of transcription or replication by preventing the transition to elongation mode without affecting its open-complex structure155,156.Figure 3Conserved non-coding regions; (A) AT rich control region Alignment of Blepharipa sp. with other two Tachinidae species. (B) Three alignments of the common overlap region between trnW-trnC, atp8-atp6 and nad4-nad4l. (C) Three alignment of the consensus gap region between trnS2-nad1 (TACTAAAHHHHAWWMH), trnE-trnF (ACTAAHWWWAATTMHHWA), nad5-trnH (WGAYADATWYTTCAY) genes of all 36 Oestroidea mitogenome (where, W = A/T, H = A/T/C, Y = T/C, D = G/T/A, M = A/C).Full size imageThe CR is also known as the AT-rich region for having the maximum proportion of A/T nucleotides (91.4% for Blepharipa sp.) than other regions of the entire mitogenome. We observed that the Tachinidae family has higher A + T content than other groups, with the highest levels in the Mulberry uzi fly, E. sorbillans (98.10%), and AT poor CR regions identified in G. intestinalis (80.80%) and G. pecorum (80.82%) (Oestridae)42 (Supplementary Data 2A). In this study, the CR of thirteen species have above 90% A + T content, and the top 3 are the tachinid flies, led by A. grahami, D. hominis and Blepharipa sp. consecutively. The CR is prone to high mutation, yet the substitution rate is low due to high A + T content and directional mutation pressure144,154. This part of the mitogenome differs significantly in length among insects, ranging from 70 bp to 13 kb, and it accounts for most of the variation in mitogenome size153. We noted that the CR size of 36 Oestroidea flies ranges from 89 to 1750 bp, of which 16, 12, and 8 species can be categorized as large ( > 800 bp), medium (200–800 bp), and small ( 5 to  0.025 to  0.005 to  More

Emberts, Z., Escalante, I. & Bateman, P. W. The ecology and evolution of autotomy. Biol. Rev. 94, 1881–1896. https://doi.org/10.1111/brv.12539 (2019).Article 
PubMed 

Google Scholar 
Dunoyer, L. A., Seifert, A. W. & Van Cleve, J. Evolutionary bedfellows: Reconstructing the ancestral state of autotomy and regeneration. J. Exp. Zool. Part B Mol. Dev. Evol. 336, 94–115. https://doi.org/10.1002/jez.b.22974 (2021).Article 

Google Scholar 
Dial, B. E. & Fitzpatrick, L. C. Lizard tail autotomy: function and energetics of postautotomy tail movement in Scincella lateralis. Science https://doi.org/10.1126/science.219.4583.391 (1983).Article 
PubMed 

Google Scholar 
Arnold, E. Caudal autotomy as a defense. Biol. Reptil. 16, 235–273 (1988).
Google Scholar 
Bateman, P. W. & Fleming, P. A. To cut a long tail short: A review of lizard caudal autotomy studies carried out over the last 20 years. J. Zool. (Lond.) 277, 1–14 (2009).Article 

Google Scholar 
Woodland, W. Memoirs: Some observations on caudal autotomy and regeneration in the gecko (Hemidactylus flaviviridis, Rüppel), with notes on the tails of Sphenodon and Pygopus. J. Cell Sci. 2, 63–100 (1920).Article 

Google Scholar 
Alibardi, L. Morphological and Cellular Aspects of Tail and Limb Regeneration in Lizards: A Model System with Implications for Tissue Regeneration in Mammals (Springer, 2010).Book 

Google Scholar 
Maginnis, T. L. The costs of autotomy and regeneration in animals: A review and framework for future research. Behav. Ecol. 17, 857–872. https://doi.org/10.1093/beheco/arl010 (2006).Article 

Google Scholar 
Dial, B. E. & Fitzpatrick, L. C. The energetic costs of tail autotomy to reproduction in the lizard Coleonyx brevis (Sauria: Gekkonidae). Oecologia 51, 310–317. https://doi.org/10.1007/bf00540899 (1981).ADS 
Article 
PubMed 

Google Scholar 
Vitt, L. J., Congdon, J. D. & Dickson, N. A. Adaptive strategies and energetics of tail autotomy in Lizards. Ecology 58, 326–337. https://doi.org/10.2307/1935607 (1977).Article 

Google Scholar 
Clause, A. R. & Capaldi, E. A. Caudal autotomy and regeneration in lizards. J. Exp. Zool. 305, 965–973 (2006).Article 

Google Scholar 
Barr, J. I., Boisvert, C. A. & Bateman, P. W. At what cost? Trade-offs and influences on energetic investment in tail regeneration in lizards following autotomy. J. Dev. Biol. 9, 53 (2021).Article 

Google Scholar 
Etheridge, R. Lizard caudal vertebrae. Copeia, 699–721 (1967).Arnold, E. Evolutionary aspects of tail shedding in lizards and their relatives. J. Nat. Hist. 18, 127–169 (1984).Article 

Google Scholar 
Zani, P. A. Patterns of caudal-autotomy evolution in lizards. J. Zool. (Lond.) 240, 201–220 (1996).Article 

Google Scholar 
Russell, A. & Bauer, A. The m. caudifemoralis longus and its relationship to caudal autotomy and locomotion in lizards (Reptilia: Sauria). J. Zool. (Lond.) 227, 127–143. https://doi.org/10.1111/j.1469-7998.1992.tb04349.x (1992).Article 

Google Scholar 
Arnold, E. Investigating the evolutionary effects of one feature on another: Does muscle spread suppress caudal autotomy in lizards?. J. Zool. (Lond.) 232, 505–523. https://doi.org/10.1111/j.1469-7998.1994.tb01591.x (1994).Article 

Google Scholar 
Bellairs, A. & Bryant, S. Autotomy and regeneration in reptiles. Biol. Reptil. 15, 301–410 (1985).
Google Scholar 
Hoffstetter, R. & Gasc, J. P. Vertebrae and ribs of modern reptiles. Biol. Reptil. 1, 201–310 (1969).
Google Scholar 
Cooper, W. E. Jr. & Frederick, W. G. Predator lethality, optimal escape behavior, and autotomy. Behav. Ecol. 21, 91–96. https://doi.org/10.1093/beheco/arp151 (2009).Article 

Google Scholar 
Fleming, P. A., Valentine, L. E. & Bateman, P. W. Telling tails: Selective pressures acting on investment in lizard tails. Physiol. Biochem. Zool. 86, 645–658 (2013).Article 

Google Scholar 
Bateman, P. W., Fleming, P. A. & Rolek, B. Bite me: Blue tails as a ‘risky-decoy’defense tactic for lizards. Curr. Zool. 60, 333–337 (2014).Article 

Google Scholar 
Hawlena, D., Boochnik, R., Abramsky, Z. & Bouskila, A. Blue tail and striped body: Why do lizards change their infant costume when growing up?. Behav. Ecol. 17, 889–896. https://doi.org/10.1093/beheco/arl023 (2006).Article 

Google Scholar 
Barr, J. I., Somaweera, R., Godfrey, S. S. & Bateman, P. W. Increased tail length in the King’s skink, Egernia kingii (Reptilia: Scincidae): An anti-predation tactic for juveniles?. Biol. J. Linn. Soc. 126, 268–275 (2019).Article 

Google Scholar 
Pafilis, P. & Valakos, E. D. Loss of caudal autotomy during ontogeny of Balkan Green Lizard, Lacerta trilineata. J. Nat. Hist. 42, 409–419 (2008).Article 

Google Scholar 
Masters, C. & Shine, R. Sociality in lizards: family structure in free-living King’s Skinks Egernia kingii from southwestern Australia. Aust. Zool. 32, 377–380 (2003).Article 

Google Scholar 
Cury de Barros, F., Eduardo de Carvalho, J., Abe, A. S. & Kohlsdorf, T. Fight versus flight: The interaction of temperature and body size determines antipredator behaviour in tegu lizards. Anim. Behav. 79, 83–88. https://doi.org/10.1016/j.anbehav.2009.10.006 (2010).Article 

Google Scholar 
Storr, G. The genus Egernia (Lacertilia, Scincidae) in Western Australia. Rec. West. Aust. Mus. 6, 147–187 (1978).
Google Scholar 
Cogger, H. G. Reptiles and Amphibians of Australia. 7th edn, (CSIRO Publishing, 2014).Arena, P. C. & Wooller, R. D. The reproduction and diet of Egernia kingii (Reptilia : Scincidae) on Penguin Island, Western Australia. Aust. J. Zool. 51, 495–504. https://doi.org/10.1071/ZO02040 (2003).Article 

Google Scholar 
Dilly, M. L. Factors Affecting the Distribution and Variation in Abundance of the King’s Skink (Egernia kingii) (Gray) in Western Australia, Murdoch University (2000).Pearson, D., Shine, R. & How, R. Sex-specific niche partitioning and sexual size dimorphism in Australian pythons (Morelia spilota imbricata). Biol. J. Linn. Soc. 77, 113–125 (2002).Article 

Google Scholar 
Chapple, D. G. Ecology, life-history, and behaviour in the Australian scincid genus Egernia, with comments on the evolution of complex sociality in lizards. Herpetol. Monogr. 17, 145–180. https://doi.org/10.1655/0733-1347(2003)017[0145:ELABIT]2.0.CO;2 (2003).Article 

Google Scholar 
Itescu, Y., Schwarz, R., Meiri, S., Pafilis, P. & Clegg, S. Intraspecific competition, not predation, drives lizard tail loss on islands. J. Anim. Ecol. 86, 66–74. https://doi.org/10.1111/1365-2656.12591 (2017).Article 
PubMed 

Google Scholar 
Siliceo-Cantero, H., Zúñiga-Vega, J., Renton, K. & Garcia, A. Assessing the relative importance of intraspecific and interspecific interactions on the ecology of Anolis nebulosus lizards from an island vs. a mainland population. Herpetol. Conserv. Biol. 12, 673–682 (2017).
Google Scholar 
Langkilde, T. & Shine, R. Interspecific conflict in lizards: Social dominance depends upon an individual’s species not its body size. Austral Ecol. 32, 869–877 (2007).Article 

Google Scholar 
Pafilis, P., Pérez-Mellado, V. & Valakos, E. Postautotomy tail activity in the Balearic lizard, Podarcis lilfordi. Naturwissenschaften 95, 217–221 (2008).ADS 
CAS 
Article 

Google Scholar 
Browne, C. King’s Skinks (Egernia kingii) Abundance and Juvenile Survival Unaffected by Temporal Change or Presence of Invasive BLACK Rats (Rattus rattus) on Penguin Island, Western Australia, The University of Western Australia (2014).Langton, J. Population Biology of the King’s Skink (Egernia kingii) (Gray) on Penguin Island, Western Australia, Murdoch University (2000).Arena, P. Aspects of the Biology of the King’s Skink Egernia kingii (Gray), Murdoch University (1986).Pafilis, P., Meiri, S., Foufopoulos, J. & Valakos, E. Intraspecific competition and high food availability are associated with insular gigantism in a lizard. Naturwissenschaften 96, 1107–1113. https://doi.org/10.1007/s00114-009-0564-3 (2009).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Martín, J. & Salvador, A. Tail loss reduces mating success in the Iberian rock-lizard, Lacerta monticola. Behav. Ecol. Sociobiol. 32, 185–189 (1993).Article 

Google Scholar 
Salvador, A., Martin, J. & López, P. Tail loss reduces home range size and access to females in male lizards, Psammodromus algirus. Behav. Ecol. 6, 382–387. https://doi.org/10.1093/beheco/6.4.382 (1995).Article 

Google Scholar 
Smyth, M. Changes in the fat scores of the skinks Morethia boulengeri and Hemiergis peronii (Lacertilia). Aust. J. Zool. 22, 135–145. https://doi.org/10.1071/ZO9740135 (1974).Article 

Google Scholar 
Wilson, R. S. & Booth, D. Effect of tail loss on reproductive output and its ecological significance in the skink Eulamprus quoyii. J. Herpetol. 32, 128–131 (1998).Article 

Google Scholar 
Fox, S. F. & McCoy, J. K. The effects of tail loss on survival, growth, reproduction, and sex ratio of offspring in the lizard Uta stansburiana in the field. Oecologia 122, 327–334. https://doi.org/10.1007/s004420050038 (2000).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Dial, B. E. & Fitzpatrick, L. C. Predator escape success in tailed versus tailless Scinella lateralis (Sauria: Scincidae). Anim. Behav. 32, 301–302 (1984).Article 

Google Scholar 
Downes, S. & Shine, R. Why does tail loss increase a lizard’s later vulnerability to snake predators?. Ecology 82, 1293–1303 (2001).Article 

Google Scholar 
Bernardo, J. & Agosta, S. J. Evolutionary implications of hierarchical impacts of nonlethal injury on reproduction, including maternal effects. Biol. J. Linn. Soc. 86, 309–331 (2005).Article 

Google Scholar 
Stankowich, T. & Blumstein, D. T. Fear in animals: A meta-analysis and review of risk assessment. Proc. R. Soc. Biol. Sci. Ser. B 272, 2627–2634. https://doi.org/10.1098/rspb.2005.3251 (2005).Article 

Google Scholar 
Steindler, L. A., Blumstein, D. T., West, R., Moseby, K. E. & Letnic, M. Exposure to a novel predator induces visual predator recognition by naïve prey. Behav. Ecol. Sociobiol. 74, 102. https://doi.org/10.1007/s00265-020-02884-3 (2020).Article 

Google Scholar 
Blumstein, D. T. Moving to suburbia: Ontogenetic and evolutionary consequences of life on predator-free islands. J. Biogeogr. 29, 685–692. https://doi.org/10.1046/j.1365-2699.2002.00717.x (2002).Article 

Google Scholar 
Sih, A. et al. Predator–prey naïveté, antipredator behavior, and the ecology of predator invasions. Oikos 119, 610–621 (2010).Article 

Google Scholar 
Cooper, J. W. E.; Blumstein, D. T. Escaping From Predators: An Integrative View of Escape Decisions. (Cambridge University Press, 2015).Cox, J. G. & Lima, S. L. Naiveté and an aquatic–terrestrial dichotomy in the effects of introduced predators. Trends Ecol. Evol. 21, 674–680 (2006).Article 

Google Scholar 
Blumstein, D. T. & Daniel, J. C. The loss of anti-predator behaviour following isolation on islands. Proc. R. Soc. Biol. Sci. Ser. B 272, 1663–1668 (2005).Article 

Google Scholar 
Blumstein, D. T., Daniel, J. C. & Springett, B. P. A test of the multi-predator hypothesis: Rapid loss of antipredator behavior after 130 years of isolation. Ethology 110, 919–934 (2004).Article 

Google Scholar 
Jolly, C. J., Webb, J. K. & Phillips, B. L. The perils of paradise: An endangered species conserved on an island loses antipredator behaviours within 13 generations. Biol. Lett. 14, 20180222 (2018).Article 

Google Scholar 
Cooper, W. E., Pérez-Mellado, V. & Vitt, L. J. Ease and effectiveness of costly autotomy vary with predation intensity among lizard populations. J. Zool. 262, 243–255 (2004).Article 

Google Scholar 
Elwood, C., Pelsinski, J. & Bateman, B. Anolis sagrei (Brown Anole). Voluntary autotomy. Herpetol. Rev. 43, 642–642 (2012).
Google Scholar 
Slotopolsky, B. Beiträge zur Kenntnis der Verstümmelungs-und Regenerationsvorgänge am Lacertilierschwanze. Zool. Jahrb. Abt. Anat. Ontog. Tiere 43, 39–48 (1922).
Google Scholar  More

At least 10,000 virus species have the capacity to infect humans, but at present, the vast majority are circulating silently in wild mammals1,2. However, climate and land use change will produce novel opportunities for viral sharing among previously geographically-isolated species of wildlife3,4. In some cases, this will facilitate zoonotic spillover—a mechanistic link between global environmental change and disease emergence. Here, we simulate potential hotspots of future viral sharing, using a phylogeographic model of the mammal-virus network, and projections of geographic range shifts for 3,139 mammal species under climate change and land use scenarios for the year 2070. We predict that species will aggregate in new combinations at high elevations, in biodiversity hotspots, and in areas of high human population density in Asia and Africa, driving the novel cross-species transmission of their viruses an estimated 4,000 times. Because of their unique dispersal capacity, bats account for the majority of novel viral sharing, and are likely to share viruses along evolutionary pathways that will facilitate future emergence in humans. Surprisingly, we find that this ecological transition may already be underway, and holding warming under 2 °C within the century will not reduce future viral sharing. Our findings highlight an urgent need to pair viral surveillance and discovery efforts with biodiversity surveys tracking species’ range shifts, especially in tropical regions that harbor the most zoonoses and are experiencing rapid warming. More

The moon increases mate finding in mothsTo investigate the impact of natural and artificial light sources on mate finding, we analyzed flight behavior in male moths, which were reliably attracted by caged virgin females (see Materials and Methods for details). Since we used these females specifically to exploit their attraction effect, we refer to them as ‘traps’ in the following. To establish a choice scenario (see below), males were released equidistantly from the traps, which were located north and south of the core release site in central Germany. Besides the stars, the moon creates the natural light environment that moths might use for visual orientation. We therefore first tested if the moon affects mate finding. We found that the percentage of males arriving within the experimental time (8 min from release, 58.6% of flights) at a trap increased significantly with the appearance of the moon (logistic regression: z = −2.06, p = 0.04, n = 58) and did not depend on the presence of clouds in front of the moon (z = −0.83, p = 0.406, n = 58). A few males reached the females later during the experimental night (13.8% of flights) and were released again on the next day. Some males never reached a trap and could therefore not be tested again in the next days (27.6% of flights). Furthermore, the time that successful males needed to reach a trap was significantly influenced by the height of the moon above or below the horizon (Fig.1; Cox PH survival model, z = 2.46, p = 0.014, n = 34): the higher the moon was above the horizon, the faster males were able to locate and reach the females. The presence of clouds in front of the moon did not play a significant role in this context either (z = −0.65, p = 0.519, n = 34), leading to the conclusion that the moon was equally well perceived if covered partly by clouds and used for effective orientation towards the females. Although the lunar phase changed during the period of the experiment from full moon to new moon, flight duration was not significantly affected by the percentage of the lit moon disk (z = 0.44, p = 0.66, n = 34). Thus, the properties of the moon that affected the flight duration of males were independent of the lunar phase.Fig. 1: Expected flight duration of a moth.Flight duration (black line) was calculated as the median flight duration predicted by the Cox PH model (p = 0.014, n = 34) for arrivals within 8 minutes after release and averaged over all individuals. Circles represent the actual measured values. Dashed lines indicate the confidence interval of the predicted duration at α = 5% level estimated by bootstrapping (5000 replicates).Full size imageIt is important to emphasize that the results were not significantly affected by traits on the individual level like body size or origin of the animal (see Supplementary Results and Discussion for details). Furthermore, a possible learning effect of animals that were released more than once was not detectable since flight duration did not decrease depending on ‘experience’ but only with the elevation of the moon (Fig. S1). Thus, the moon as an easily perceivable orientation cue increased mate finding in general but also depended on its elevation. Despite two exceptions of long flight durations at moon elevations > 20° that go back to the same animal probably for individual reasons (Fig. S1), the variance in flight duration was highest at low moon elevations (Fig. 1). This relatively high variance at low moon elevations emphasizes the question if artificial lights affected mate finding, particularly whenever the moon as a natural light cue was not yet prominent.Linking flight behavior to the light environmentWe used a calibrated digital all-sky camera to track changes in the natural and artificial components of the night sky brightness24 (Fig. 2 a–c). A similar camera system was recently used to study dung beetle behavior21. Although the impact of light pollution on the site was not strong, the night sky was also not completely pristine. Luminance (LVv) values were about 0.34 mcd/m² at zenith and 1.6 mcd/m² near the horizon under clear sky conditions when the moon was not visible. A natural (unpolluted) sky brightness is 0.25 mcd/m² at zenith and can be used as the reference value “Natural Sky Unit” (NSU) for easy comparison (see also Materials and Methods). The analysis of specific sky sectors revealed that the moon was the strongest factor determining the ambient brightness, brightening every sector of the sky as soon as it appeared above the horizon (Fig. 2d). During observation times, the course of the moon mainly progressed through the eastern part of the sky, affecting particularly the LvV values in the corresponding sectors (Fig. 2d). Furthermore, light conditions never corresponded to a non-light polluted sky, as NSU values were always greater than one. Most sectors in the south, west and north (sectors seven to 12 and one) were hardly subjected to fluctuations. Nevertheless, it is recognizable that the moon made a decisive contribution to the light environment in all directions since images with the moon above the horizon were always brighter than those with the moon below the horizon (Fig. 2d).Fig. 2: Quantification of the light environment with all-sky imagery and its impact on flight behavior of moths.a Raw RGB all-sky image with clear sky and a visible moon 26° above the horizon at 119° azimuth angle, South-east (24 July 2019, 03:23). b Same image as in a with processed luminance values. c Processed all-sky image in luminance with clear sky, a visible milky way (green patches in a ‘ribbon-shape’ across the (blue) night sky), skyglow near the horizon, and a non-visible moon 0° above the horizon at 87° azimuth angle, East (24 July 2019, 0:25). The colors of the processed image correspond to the legend in b. The black lines mark the sky segments used to quantify the light environment. The outer ring covers 5° above the horizon (85°−90° zenith angle), the inner ring 20° above the outer ring (65°−85° zenith angle). Furthermore, the sky was divided into 12 sectors of 30° width along the azimuth direction (extension by dashed line), starting with the sector marked with the small circle (counting clockwise). d Luminance in natural sky units (NSU) for each full sector of 30°. The moon icons indicate sectors in which the moon was visible, regardless of its phase. The size of each symbol encodes the rank of the frequency (n = 33). e Trap choice of arrived males depending on the position of the moon at the moment of release on the north-south axis (north = 0°). The y-axis displays choice of the southern trap at 0.0 and of the northern trap at 1.0. p = 0.022, n = 42. f Male moth affinity to northern trap in response to the direction of maximum luminance measured in the outer ring of 5°. Each circle indicates an observed arrival, p = 0.753, n = 41. g Male moth affinity to northern trap as in f but with luminance measured in the inner ring of 20°, p = 0.065, n = 41. e–g The line represents the prediction of the logistic model, providing a probability value for arriving at the northern trap (north prone = 1; south prone = 0). Dashed lines indicate the confidence interval of the prediction at α = 5% level estimated by bootstrapping (5000 replicates).Full size imageDue to the design of the experiment with one trap located in the north and the other in the south of a central release site, we were able to investigate the choice behavior of males, especially in respect of the possible influence of the cardinal position of the moon as it was almost exclusively visible in the southern hemisphere of the sky (Fig. 2d). Although the moon continued to move south during the night, the moon’s cardinal position never overlapped with the exact direction of the southern trap. The only parameter that had a significant effect on choice behavior was indeed the cardinal position of the moon (Fig. 2e, logistic regression, z = −2.3, p = 0.022, n = 42). The more southern the moon’s position was, the more likely males flew to the southern trap. However, while some clouds in front of the moon had no significant effect on choice behavior (z = 0, p = 1, n = 42), moon above the horizon showed a tendency to affect males (z = −1.82, p = 0.069, n = 42). The results indicate that despite the general increase of ambient brightness by the moon, it is its position that mainly influenced the flight direction of males. Thus, moths preferred a flight direction with the prominent compass cue ahead to steer their flight towards the females but it is important to emphasize that moon and trap had an angular difference of at least 23° (80.8° to the moon’s mean cardinal direction). Therefore, males that chose to fly towards the southern trap did not fly directly towards the direction of the moon.As the moon represents a natural distant light source, we tested whether distant artificial light sources or skyglow might elicit a comparable effect on the behavior of male moths and if such light sources might mask the moon. To evaluate the light environment with regards to these aspects, we defined sky segments of particular interest that occurred due to the location of the experimental field (Fig. 2c). For each arrival at a trap, the brightest sector of the environment was determined and placed on a north-south axis of maximum 180 degrees (Fig. 2f, g). If we look at the brightest sector of the environment and distinguish between the area close to the horizon, i.e. “outer ring” (Fig. 2f) and the one above, i.e. “inner ring” (Fig. 2g), we can observe differences in trap choice. The line indicates the logistic regression model and provides the probability of arriving at the northern trap. For the Lv in the area close to the horizon no effect of maximum Lv on trap choice could be found (logistic regression, z = 0.31, p = 0.753, n = 41). For the segment further above the horizon the probability of flying to the southern trap increased with maximum Lv but the results are marginally not significant (z = −1.85, p = 0.065, n = 41). Our results for trap selection indicate that distant artificial lights of the surroundings did not attract males and support the hypothesis that the moon, once it appears above the horizon and stands out from the general light (pollution) near the horizon (above five degrees), is used as an effective visual cue with moths rather flying towards than away from.Digital cameras are suitable to measure the dynamics of night-time lighting conditions25,26, and allow researchers to track changes in artificial lighting conditions and brightness of the sky simultaneously27. However, it is not straightforward to distinguish between ALAN and natural light sources like the moon with luminance images when the moon is close to the horizon and thus in the section of the sky where most light pollution occurred. Yet, once the moon rose higher than 5° and thus stood out distinctly from the light-polluted horizon, it could be clearly identified on the images (Fig. 2b). In this context, it is particularly remarkable that the speed at which the females were reached increased reliably only above a similar threshold (Fig. 1), with the only exceptions of two flights with long durations at a moon elevation greater than 20° (Fig. 1); both flights originated from the same individual (Fig. S1). Thus, the high variance of flight durations at low moon elevations (Fig. 1) supports our hypothesis that the moon, as an orientation cue, can be masked by artificial light for the animals as well. Yet, this hypothesis needs to be explicitly tested in future experiments. In general, the possible consequences of light pollution are still uncertain28, especially because the amount of artificial light emitted during the night continues to increase exponentially worldwide18. But regardless of this, the moon is the decisive orientation cue as soon as it is visibly silhouetted against the horizon despite possible diffuse light pollution.Another interesting next research project would be to investigate the relevance of polarized light, as this could provide an explanation for the occasional fast flights at times of low lunar elevations (cf. Figure 1). Furthermore, it might explain why flight duration was not significantly affected by clouds in front of the moon since the polarization pattern extends over the whole sky and is therefore not shielded completely by scattered clouds29. For dung beetles it has been already shown that they are capable of using the polarization signal for navigation16,30,31 and it has been proposed that moths might be capable of utilizing the same signal32. At the same time, it has already been demonstrated that urban skyglow can diminish the lunar polarization signal33, making a detailed investigation of the interplay between these two factors and the significance for moth orientation particularly exciting to understand underlying mechanisms.Our results confirm that moths use the moon as an orientation cue, supporting the notion of Vickers & Baker34 that pheromones alone are not sufficient for successful (and fast) orientation. Since flight duration decreased as a function of lunar elevation, we conclude that the moon contributes to mating success, especially when it can be easily perceived. Since nocturnal landscapes around the world have been drastically restructured in terms of light intensity and light spectrum due to the rapid spread and increase of electrical lighting18, a deeper understanding of orientation mechanisms even in the absence of the moon as an easily perceivable cue could provide a valuable contribution to counteract insect decline. More

Dennell, R. Dispersal and colonisation, long and short chronologies: how continuous is the Early Pleistocene record for hominids outside East Africa?. J. Hum. Evol. 45, 421–440. https://doi.org/10.1016/j.jhevol.2003.09.006 (2003).Article 
PubMed 

Google Scholar 
Moncel, M.-H. et al. Early Levallois core technology between Marine Isotope Stage 12 and 9 in Western Europe. J. Hum. Evol. 139, 102735. https://doi.org/10.1016/j.jhevol.2019.102735 (2020).Article 
PubMed 

Google Scholar 
Moncel, M.-H. et al. Linking environmental changes with human occupations between 900 and 400 ka in Western Europe. Quatern. Int. 480, 78–94. https://doi.org/10.1016/j.quaint.2016.09.065 (2018).Article 

Google Scholar 
Meyer, M. et al. Nuclear DNA sequences from the Middle Pleistocene Sima de los Huesos hominins. Nature 531, 504–507. https://doi.org/10.1038/nature17405 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Meyer, M. et al. A mitochondrial genome sequence of a hominin from Sima de los Huesos. Nature 505, 403–406. https://doi.org/10.1038/nature12788 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Rightmire, G. P. Homo in the Middle Pleistocene: Hypodigms, variation, and species recognition. Evolut. Anthropol. Issues News Rev. 17, 8–21. https://doi.org/10.1002/evan.20160 (2008).Article 

Google Scholar 
Stringer, C. B. The Status of Homo heidelbergensis (Schoetensack 1908). Evol. Anthropol. 21, 101–107 (2012).Article 

Google Scholar 
Dennell, R. W., Martinón-Torres, M. & Bermúdez de Castro, J. M. Hominin variability, climatic instability and population demography in Middle Pleistocene Europe. Quat. Sci. Rev. 30, 1511–1524 (2011).ADS 
Article 

Google Scholar 
Galway-Witham, J., Cole, J. & Stringer, C. Aspects of human physical and behavioural evolution during the last 1 million years. J. Quat. Sci. 34, 355–378. https://doi.org/10.1002/jqs.3137 (2019).Article 

Google Scholar 
Powell, A., Shennan, S. & Thomas, M. G. Late Pleistocene Demography and the Appearance of Modern Human Behavior. Science 324, 1298–1301. https://doi.org/10.1126/science.1170165 (2009).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Vaesen, K., Collard, M., Cosgrove, R. & Roebroeks, W. Population size does not explain past changes in cultural complexity. Proc. Natl. Acad. Sci. 113, E2241–E2247. https://doi.org/10.1073/pnas.1520288113 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Henrich, J. Demography and cultural evolution: how adaptive cultural processes can produce maladaptive losses: the Tasmanian case. Am. Antiq. 69, 197–214. https://doi.org/10.2307/4128416 (2004).Article 

Google Scholar 
Cavalli-Sforza, L., Barrai, I. & Edwards, A. W. F. Analysis of human evolution under random genetic drift. Symp. Quant. Biol. 29, 9–20. https://doi.org/10.1101/SQB.1964.029.01.006 (1964).Article 

Google Scholar 
Boaz, N. T. Early hominid population densities: new estimates. Science 206, 592–595. https://doi.org/10.1126/science.206.4418.592 (1979).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Ashton, N. & Davis, R. Cultural mosaics, social structure, and identity: the Acheulean threshold in Europe. J. Hum. Evol. 156, 103011. https://doi.org/10.1016/j.jhevol.2021.103011 (2021).Article 
PubMed 

Google Scholar 
Hayden, B. Neandertal social structure?. Oxf. J. Archaeol. 31, 1–26. https://doi.org/10.1111/j.1468-0092.2011.00376.x (2012).Article 

Google Scholar 
Bocquet-Appel, J.-P., Demars, P.-Y., Noiret, L. & Dobrowsky, D. Estimates of Upper Paleolithic meta-population size in Europe from archaeological data. J. Archaeol. Sci. 32, 1656–1668 (2005).Article 

Google Scholar 
Maier, A. et al. Demographic estimates of hunter–gatherers during the Last Glacial Maximum in Europe against the background of palaeoenvironmental data. Quatern. Int. 425, 49–61. https://doi.org/10.1016/j.quaint.2016.04.009 (2016).Article 

Google Scholar 
Gautney, J. R. & Holliday, T. W. New estimations of habitable land area and human population size at the Last Glacial Maximum. J. Archaeol. Sci. 58, 103–112. https://doi.org/10.1016/j.jas.2015.03.028 (2015).Article 

Google Scholar 
Rodríguez-Gómez, G., Rodríguez, J., Martín-González, J. A., Goikoetxea, I. & Mateos, A. Modeling trophic resource availability for the first human settlers of Europe: the case of Atapuerca TD6. J. Hum. Evol. 64, 645–657. https://doi.org/10.1016/j.jhevol.2013.02.007 (2013).Article 
PubMed 

Google Scholar 
Tallavaara, M., Luoto, M., Korhonen, N., Järvinen, H. & Seppä, H. Human population dynamics in Europe over the Last Glacial Maximum. Proc. Natl. Acad. Sci. 112, 8232–8237. https://doi.org/10.1073/pnas.1503784112 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sánchez-Quinto, F. & Lalueza-Fox, C. Almost 20 years of Neanderthal palaeogenetics: adaptation, admixture, diversity, demography and extinction. Philosophical Trans. Royal Soc. B Biol. Sci. 370, 20130374. https://doi.org/10.1098/rstb.2013.0374 (2015).CAS 
Article 

Google Scholar 
Rodríguez, J., Willmes, C. & Mateos, A. Shivering in the Pleistocene. Human adaptations to cold exposure in Western Europe from MIS 14 to MIS 11. J. Hum. Evol. https://doi.org/10.1016/j.jhevol.2021.102966 (2021).Article 
PubMed 

Google Scholar 
Railsback, L. B., Gibbard, P. L., Head, M. J., Voarintsoa, N. R. G. & Toucanne, S. An optimized scheme of lettered marine isotope substages for the last 1.0 million years, and the climatostratigraphic nature of isotope stages and substages. Quatern. Sci. Rev. 111, 94–106. https://doi.org/10.1016/j.quascirev.2015.01.012 (2015).Article 

Google Scholar 
MacDonald, K., Martinón-Torres, M., Dennell, R. W. & Bermúdez de Castro, J. M. Discontinuity in the record for hominin occupation in south-western Europe: implications for occupation of the middle latitudes of Europe. Quatern. Int 271, 84–97. https://doi.org/10.1016/j.quaint.2011.10.009 (2012).Article 

Google Scholar 
Gamisch, A. Oscillayers: A dataset for the study of climatic oscillations over Plio-Pleistocene time-scales at high spatial-temporal resolution. Glob. Ecol. Biogeogr. 28, 1552–1156. https://doi.org/10.1111/geb.12979 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Gamisch, A. Oscillayers: A dataset for the study of climatic oscillations over Plio-Pleistocene time-scales at high spatial-temporal resolution. https://doi.org/10.5061/dryad.27f8s90 (Dryad, 2019).Banks, W. E. et al. An ecological niche shift for Neanderthal populations in Western Europe 70,000 years ago. Sci. Rep. 11, 5346. https://doi.org/10.1038/s41598-021-84805-6 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Banks, W. E., d’Errico, F. & Zilhão, J. Human–climate interaction during the Early Upper Paleolithic: testing the hypothesis of an adaptive shift between the Proto-Aurignacian and the Early Aurignacian. J. Hum. Evol. 64, 39–55. https://doi.org/10.1016/j.jhevol.2012.10.001 (2013).Article 
PubMed 

Google Scholar 
Soberón, J. & Nakamura, M. Niches and distributional areas: Concepts, methods, and assumptions. Proc. Natl. Acad. Sci. 106, 19644–19650. https://doi.org/10.1073/pnas.0901637106 (2009).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Tallavaara, M., Eronen, J. T. & Luoto, M. Productivity, biodiversity, and pathogens influence the global hunter-gatherer population density. Proc. Natl. Acad. Sci. 115, 1232–1237. https://doi.org/10.1073/pnas.1715638115 (2018).CAS 
Article 
PubMed 

Google Scholar 
Binford, L. R. Constructing frames of reference: an analytical method for archaeological theory building using ethnographic and environmental data set (University of California Press, Berkeley, 2001).
Google Scholar 
Hamilton, M. J., Milne, B. T., Walker, R. S. & Brown, J. H. Nonlinear scaling of space use in human hunter–gatherers. Proc. Natl. Acad. Sci. 104, 4765. https://doi.org/10.1073/pnas.0611197104 (2007).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Coe, M. J., Cumming, D. H. & Phillipson, J. Biomass and production of large African herbivores in relation to rainfall and primary production. Oecologia 22, 341–354 (1976).ADS 
CAS 
Article 

Google Scholar 
Hatton, I. A. et al. The predator-prey power law: biomass scaling across terrestrial and aquatic biomes. Science https://doi.org/10.1126/science.aac6284 (2015).Article 
PubMed 

Google Scholar 
Carbone, C. & Gittleman, J. L. A common rule for the scaling of carnivore density. Science 295, 2273–2275 (2002).ADS 
CAS 
Article 

Google Scholar 
Braun, D. R. et al. Early hominin diet included diverse terrestrial and aquatic animals 1.95 Ma in East Turkana, Kenya. Proc Natl Acad Sci 107, 10002–10007. https://doi.org/10.1073/pnas.1002181107 (2010).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Marlowe, F. W. Hunter-gatherers and human evolution. Evolut. Anthropol. Issues News Rev. 14, 54–67. https://doi.org/10.1002/evan.20046 (2005).Article 

Google Scholar 
Steele, T. A unique hominin menu dated to 1.95 million years ago. Proc. Natl Acad Sci United States of Am 107, 10771–10772. https://doi.org/10.1073/pnas.1005992107 (2010).ADS 
CAS 
Article 

Google Scholar 
Conard, N. J. et al. Excavations at Schöningen and paradigm shifts in human evolution. J. Hum. Evol. 89, 1–17. https://doi.org/10.1016/j.jhevol.2015.10.003 (2015).Article 
PubMed 

Google Scholar 
Kelly, R. L. The lifeways of hunter-gatherers: the foraging spectrum 2nd edn. (Cambridge Univ Press, Cambridge, 2013).Book 

Google Scholar 
Muscarella, R. et al. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol. Evol. 5, 1198–1205. https://doi.org/10.1111/2041-210X.12261 (2014).Article 

Google Scholar 
Radosavljevic, A. & Anderson, R. P. Making better Maxent models of species distributions: complexity, overfitting and evaluation. J. Biogeogr. 41, 629–643. https://doi.org/10.1111/jbi.12227 (2014).Article 

Google Scholar 
Morales, N. S., Fernández, I. & Baca-González, V. MaxEnt’s parameter configuration and small samples: are we paying attention to recommendations? A systematic review. PeerJ 5, e3093. https://doi.org/10.7717/peerj.3093 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Anderson, R. P. & Gonzalez, I. Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. Ecol. Model. 222, 2796–2811. https://doi.org/10.1016/j.ecolmodel.2011.04.011 (2011).Article 

Google Scholar 
Lisiecki, L. & Raymo, M. A Pliocene-Pleistocene stack of 57 globally distributed benthic 18O records. Paleoceanography https://doi.org/10.1029/2004PA001071 (2005).Article 

Google Scholar 
Carrión, J. S., Rose, J. & Stringer, C. B. Early human evolution in the western Palaearctic: ecological scenarios. Quat. Sci. Rev. 30, 1281–1295 (2011).ADS 
Article 

Google Scholar 
Davis, R. & Ashton, N. Landscapes, environments and societies: the development of culture in Lower Palaeolithic Europe. J. Anthropol. Archaeol. 56, 101107. https://doi.org/10.1016/j.jaa.2019.101107 (2019).Article 

Google Scholar 
Davis, R., Ashton, N., Hatch, M., Hoare, P. G. & Lewis, S. G. Palaeolithic archaeology of the Bytham River: human occupation of Britain during the early Middle Pleistocene and its European context. J. Quat. Sci. 36, 526–546. https://doi.org/10.1002/jqs.3305 (2021).Article 

Google Scholar 
Soberón, J. & Peterson, A. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers. Inform. https://doi.org/10.17161/bi.v2i0.4 (2005).Article 

Google Scholar 
Kahlke, R.-D. et al. Western Palaearctic palaeoenvironmental conditions during the Early and early Middle Pleistocene inferred from large mammal communities, and implications for hominin dispersal in Europe. Quat. Sci. Rev. 11–12, 1368–1395 (2011).ADS 
Article 

Google Scholar 
Hosfield, R. The earliest Europeans a year in the life (Oxbow Books, Oxford, 2020).Book 

Google Scholar 
Dunbar, R. I. M. Neocortex size as a constraint on group size in primates. J. Hum. Evol. 22, 469–493. https://doi.org/10.1016/0047-2484(92)90081-J (1992).Article 

Google Scholar 
Bird, D. W., Bird, R. B., Codding, B. F. & Zeanah, D. W. Variability in the organization and size of hunter-gatherer groups: foragers do not live in small-scale societies. J. Hum. Evol. 131, 96–108. https://doi.org/10.1016/j.jhevol.2019.03.005 (2019).Article 
PubMed 

Google Scholar 
Arsuaga, J. L. et al. Neandertal roots: Cranial and chronological evidence from Sima de los Huesos. Science 344, 1358–1363. https://doi.org/10.1126/science.1253958 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Traill, L. W., Bradshaw, R. H. W. & Brook, B. W. Minimum viable population size: a meta-analysis of 30 years of published estimates. Biol. Cons. 139, 159–166 (2007).Article 

Google Scholar 
Booth, T. H., Nix, H. A., Busby, J. R. & Hutchinson, M. F. BIOCLIM: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies. Divers. Distrib. 20, 1–9. https://doi.org/10.1111/ddi.12144 (2014).Article 

Google Scholar 
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978. https://doi.org/10.1002/joc.1276 (2005).Article 

Google Scholar 
Lieth, H. F. H. Primary production: terrestrial ecosystems. Hum. Ecol. 1, 303–332 (1973).Article 

Google Scholar 
Guisan, A. & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecol. Model. 135, 147–186. https://doi.org/10.1016/S0304-3800(00)00354-9 (2000).Article 

Google Scholar 
Merow, C., Smith, M. J. & Silander, J. A. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36, 1058–1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x (2013).Article 

Google Scholar 
Braunisch, V. et al. Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions under climate change. Ecography 36, 971–983. https://doi.org/10.1111/j.1600-0587.2013.00138.x (2013).Article 

Google Scholar 
De Marco, P. J. & Nóbrega, C. C. Evaluating collinearity effects on species distribution models: an approach based on virtual species simulation. PLoS ONE 13, e0202403. https://doi.org/10.1371/journal.pone.0202403 (2018).CAS 
Article 

Google Scholar 
Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x (2013).Article 

Google Scholar 
Fourcade, Y., Besnard, A. G. & Secondi, J. Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics. Glob. Ecol. Biogeogr. 27, 245–256. https://doi.org/10.1111/geb.12684 (2018).Article 

Google Scholar 
Harell Jr., F. E. & with contributions from Charles Dupont and many others. Hmisc: Harrell Miscellaneous (2021).Barbosa, A. M. fuzzySim: applying fuzzy logic to binary similarity indices in ecology. Methods Ecol. Evol. 6, 853–858. https://doi.org/10.1111/2041-210X.12372 (2015).Article 

Google Scholar 
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14. https://doi.org/10.1111/j.2041-210X.2009.00001.x (2010).Article 

Google Scholar 
James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning with Appllication in R. 1 edn, (Springer, 2013).Amante, C. & Eakins, B. ETOPO1 1 Arc-Minute Global Relief Model: procedures, data sources and analysis. https://doi.org/10.7289/V5C8276M (2009).Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x (2006).Article 

Google Scholar 
Tsoar, A., Allouche, O., Steinitz, O., Rotem, D. & Kadmon, R. A comparative evaluation of presence-only methods for modelling species distribution. Divers. Distrib. 13, 397–405. https://doi.org/10.1111/j.1472-4642.2007.00346.x (2007).Article 

Google Scholar 
Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x (2008).Article 

Google Scholar 
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: an open-source release of Maxent. Ecography 40, 887–893. https://doi.org/10.1111/ecog.03049 (2017).Article 

Google Scholar 
755026R: A Language and Environment for STATISTICAL Computing (R Foundation for Statistical Computing, Vienna, Austria, 2021).Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342. https://doi.org/10.1890/10-1171.1 (2011).Article 
PubMed 

Google Scholar 
Kelt, D. & Vuren, D. The ecology and macroecology of mammalian home range area. Am. Nat. 157, 637–645. https://doi.org/10.1086/320621 (2001).CAS 
Article 
PubMed 

Google Scholar 
Rodríguez, J., Sommer, C., Willmes, C. & Mateos, A. Data and code for “Sustainable Human Population Density in Western Europe between 560.000 and 360.000 years ago” https://doi.org/10.5281/zenodo.6045917 (2022). More

Cumming, G. S. et al. Implications of agricultural transitions and urbanization for ecosystem services. Nature 515, 50–57 (2014).CAS 
Article 

Google Scholar 
Cumming, G. S. & Von Cramon-Taubadel, S. Linking economic growth pathways and environmental sustainability by understanding development as alternate social-ecological regimes. Proc. Natl. Acad. Sci.115, 9533–9538 (2018).CAS 
Article 

Google Scholar 
Costanza, R. et al. The value of the world’s ecosystem services and natural capital. Nature 387, 253–260 (1997).CAS 
Article 

Google Scholar 
de Groot, R. S., Alkemade, R., Braat, L., Hein, L. & Willemen, L. Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecol. Complex. 7, 260–272 (2010).Article 

Google Scholar 
Clapp, J. Financialization, distance and global food politics. J. Peasant Stud. 41, 797–814 (2014).Article 

Google Scholar 
Crona, B. I. et al. Masked, diluted and drowned out: how global seafood trade weakens signals from marine ecosystems. Fish Fish. 17, 1175–1182 (2016).Article 

Google Scholar 
United Nations Environment Programme International Resource Panel. Decoupling Natural Resource Use and Environmental Impacts from Economic Growth (2011).Srinivasana, U. T. et al. The debt of nations and the distribution of ecological impacts from human activities. Proc. Natl. Acad. Sci. 105, 1768–1773 (2008).Article 

Google Scholar 
Rist, L. et al. Applying resilience thinking to production ecosystems. Ecosphere 5, 1–11 (2014).Article 

Google Scholar 
Dermody, B. J. et al. A virtual water network of the Roman world. Hydrol. Earth Syst. Sci. 18, 5025–5040 (2014).Article 

Google Scholar 
Maskell, L. C. et al. Exploring the ecological constraints to multiple ecosystem service delivery and biodiversity. J. Appl. Ecol. 50, 561–571 (2013).Article 

Google Scholar 
Potschin, M. B. & Haines-Young, R. H. Ecosystem services: Exploring a geographical perspective. Prog. Phys. Geogr. 35, 575–594 (2011).Article 

Google Scholar 
Peng, J. et al. Ecosystem services response to urbanization in metropolitan areas: Thresholds identification. Sci. Total Environ. 607–608, 706–714 (2017).Article 
CAS 

Google Scholar 
Millennium Ecosystem Assessment. Ecosystems and human well-being: Biodiversity synthesis (2005). https://doi.org/10.1057/9780230625600Díaz, S. et al. Assessing nature’s contributions to people: Recognizing culture, and diverse sources of knowledge, can improve assessments. Science 359, 270–272 (2018).Article 

Google Scholar 
Wallace, K. J. Classification of ecosystem services: Problems and solutions. Biol. Conserv. 139, 235–246 (2007).Article 

Google Scholar 
Lee, H. & Lautenbach, S. A quantitative review of relationships between ecosystem services. Ecol. Indic. 66, 340–351 (2016).Article 

Google Scholar 
Bennett, E. M., Peterson, G. D. & Gordon, L. J. Understanding relationships among multiple ecosystem services. Ecol. Lett. 12, 1394–1404 (2009).Article 

Google Scholar 
Saidi, N. & Spray, C. Ecosystem services bundles: Challenges and opportunities for implementation and further research. Environ. Res. Lett. 13, 113001 (2018).Cord, A. F. et al. Towards systematic analyses of ecosystem service trade-offs and synergies: Main concepts, methods and the road ahead. Ecosyst. Serv. 28, 264–272 (2017).Article 

Google Scholar 
Mitsch, W. J. & Gosselink, J. G. The value of wetlands: importance of scale and landscape setting. Ecol. Econ. 35, 25–33 (2000).Article 

Google Scholar 
Raudsepp-Hearne, C., Peterson, G. D. & Bennett, E. M. Ecosystem service bundles for analyzing tradeoffs in diverse landscapes. Proc. Natl. Acad. Sci. 107, 5242–5247 (2010).CAS 
Article 

Google Scholar 
Hamann, M., Biggs, R. & Reyers, B. Mapping social-ecological systems: Identifying ‘green-loop’ and ‘red-loop’ dynamics based on characteristic bundles of ecosystem service use. Glob. Environ. Change 34, 218–226 (2015).Article 

Google Scholar 
Macklin, M. G. & Lewin, J. The rivers of civilization. Quat. Sci. Rev. 114, 228–244 (2015).Article 

Google Scholar 
Barbier, E. B. et al. The value of estuarine and coastal ecosystem services. Ecol. Monogr. 81, 169–193 (2011).Article 

Google Scholar 
Stanley, D. J. & Warne, A. G. Sea level and initiation of Predynastic culture in the Nile delta. Nature 363, 435–438 (1993).Article 

Google Scholar 
Costanza, R. et al. Changes in the global value of ecosystem services. Glob. Environ. Change 26, 152–158 (2014).Article 

Google Scholar 
Edmonds, D. A., Caldwell, R. L., Brondizio, E. S. & Siani, S. M. O. Coastal flooding will disproportionately impact people on river deltas. Nat. Commun. 11, 1–8 (2020).Article 
CAS 

Google Scholar 
Renaud, F. G. et al. Tipping from the Holocene to the Anthropocene: How threatened are major world deltas? Curr. Opin. Environ. Sustain. 5, 644–654 (2013).Article 

Google Scholar 
Santos, M. J. & Dekker, S. C. Locked‑in and living delta pathways in the Anthropocene. Sci. Rep. 10, 19598 (2020).Tessler, Z. D. et al. Profiling risk and sustainability in coastal deltas of the world. Science 349, 638–643 (2015).CAS 
Article 

Google Scholar 
Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch-Mordo, S. & Kiesecker, J. Managing the middle: A shift in conservation priorities based on the global human modification gradient. Glob. Change Biol. 25, 811–826 (2019).Article 

Google Scholar 
Seto, K. C. Exploring the dynamics of migration to mega-delta cities in Asia and Africa: Contemporary drivers and future scenarios. Glob. Environ. Change 21, S94–S107 (2011).Article 

Google Scholar 
Carpenter, S. R., Stanley, E. H. & Vander Zanden, M. J. State of the World’s Freshwater Ecosystems: Physical, Chemical, and Biological Changes. Annu. Rev. Environ. Resour. 36, 75–99 (2011).Article 

Google Scholar 
Dugan, P. J. et al. Fish migration, dams, and loss of ecosystem services in the mekong basin. Ambio 39, 344–348 (2010).Article 

Google Scholar 
Notebaert, B., Broothaerts, N. & Verstraeten, G. Evidence of anthropogenic tipping points in fluvial dynamics in Europe. Glob. Planet. Change 164, 27–38 (2018).Article 

Google Scholar 
Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010).Article 
CAS 

Google Scholar 
Haberl, H. et al. Quantifying and mapping the human appropriation of net primary production in earth’s terrestrial ecosystems. Proc. Natl. Acad. Sci. 104, 12942–12947 (2007).CAS 
Article 

Google Scholar 
Minderhoud, P. S. J. et al. The relation between land use and subsidence in the Vietnamese Mekong delta. Sci. Total Environ. 634, 715–726 (2018).CAS 
Article 

Google Scholar 
Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).Article 

Google Scholar 
FAO. AQUASTAT Database. (2022). Available at: https://www.fao.org/aquastat/statistics/query/index.html. (Accessed: 14th February 2022)Chau, N. D. G., Sebesvari, Z., Amelung, W. & Renaud, F. G. Pesticide pollution of multiple drinking water sources in the Mekong Delta, Vietnam: evidence from two provinces. Environ. Sci. Pollut. Res. 22, 9042–9058 (2015).CAS 
Article 

Google Scholar 
Phien-wej, N., Giao, P. H. & Nutalaya, P. Land subsidence in Bangkok, Thailand. Eng. Geol. 82, 187–201 (2006).Article 

Google Scholar 
Käkönen, M. Mekong Delta at the crossroads: more control or adaptation? Ambio 37, 205–212 (2008).Article 

Google Scholar 
Smajgl, A. et al. Responding to rising sea levels in the Mekong Delta. Nat. Clim. Change 5, 167–174 (2015).Article 

Google Scholar 
Schneider, P. & Asch, F. Rice production and food security in Asian Mega deltas—A review on characteristics, vulnerabilities and agricultural adaptation options to cope with climate change. J. Agron. Crop Sci. 206, 491–503 (2020).Article 

Google Scholar 
Gibson, L. et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478, 378–381 (2011).CAS 
Article 

Google Scholar 
Davis, M., Faurby, S. & Svenning, J. C. Mammal diversity will take millions of years to recover from the current biodiversity crisis. Proc. Natl. Acad. Sci. 115, 11262–11267 (2018).CAS 
Article 

Google Scholar 
Arowolo, A. O., Deng, X., Olatunji, O. A. & Obayelu, A. E. Assessing changes in the value of ecosystem services in response to land-use/land-cover dynamics in Nigeria. Sci. Total Environ. 636, 597–609 (2018).CAS 
Article 

Google Scholar 
Lang, Y. & Song, W. Quantifying and mapping the responses of selected ecosystem services to projected land use changes. Ecol. Indic. 102, 186–198 (2019).Article 

Google Scholar 
Tilman, D., Reich, P. B. & Isbell, F. Biodiversity impacts ecosystem productivity as much as resources, disturbance, or herbivory. Proc. Natl. Acad. Sci. 109, 10394–10397 (2012).CAS 
Article 

Google Scholar 
Liang, J. et al. Positive biodiversity-productivity relationship predominant in global forests. Science 354, aaf8957 (2016).Diaz, R. J. & Rosenberg, R. Spreading dead zones and consequences for marine ecosystems. Science 321, 926–929 (2008).CAS 
Article 

Google Scholar 
Dalin, C., Konar, M., Hanasaki, N., Rinaldo, A. & Rodriguez-Iturbe, I. Evolution of the global virtual water trade network. Proc. Natl. Acad. Sci. 109, 5989–5994 (2012).CAS 
Article 

Google Scholar 
Van Asselen, S., Verburg, P. H., Vermaat, J. E. & Janse, J. H. Drivers of wetland conversion: A global meta-analysis. PLoS One 8, e81292 (2013).Davidson, N. C., Fluet-Chouinard, E. & Finlayson, C. M. Global extent and distribution of wetlands: trends and issues. Mar. Freshw. Res. 69, 620–627 (2018).Article 

Google Scholar 
Gordon, L. J., Finlayson, C. M. & Falkenmark, M. Managing water in agriculture for food production and other ecosystem services. Agric. Water Manag. 97, 512–519 (2010).Article 

Google Scholar 
Syvitski, J. P. M. & Kettner, A. J. Sediment flux and the anthropocene. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 369, 957–975 (2011).Article 

Google Scholar 
Nienhuis, J. H. et al. Global-scale human impact on delta morphology has led to net land area gain. Nature 577, 514–518 (2020).CAS 
Article 

Google Scholar 
Cinner, J. E. et al. Bright spots among the world’s coral reefs. Nature 535, 416–419 (2016).CAS 
Article 

Google Scholar 
Stott, I., Soga, M., Inger, R. & Gaston, K. J. Land sparing is crucial for urban ecosystem services. Front. Ecol. Environ. 13, 387–393 (2015).Article 

Google Scholar 
Caldwell, R. L. et al. A global delta dataset and the environmental variables that predict delta formation. Earth Surf. Dyn. Discuss. 7, 773–787 (2019).Article 

Google Scholar 
Lehner, B., Verdin, K. & Jarvis, A. New global hydrography derived from spaceborne elevation data. Eos (Washington DC) 89, 93–94 (2008).USGS. HYDRO1k Elevation Derivative Database. https://doi.org/10.5066/F77P8WN0 (2000).CIESIN – Center for International Earth Science Information Network Columbia University. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC) https://doi.org/10.7927/H4JW8BX5 (2018).Venter, O. et al. Last of the Wild Project, Version 3 (LWP-3): 2009 Human Footprint, 2018 Release. NASA Socioeconomic Data and Applications Center https://doi.org/10.7927/H46T0JQ4 (2018).Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 1–11 (2016).Article 
CAS 

Google Scholar 
Zeileis, A., Leisch, F., Hornik, K. & Kleiber, C. strucchange: An R package for testing for structural change in linear regression models. J. Stat. Softw. 7, 1–38 (2002).Article 

Google Scholar 
Monti, S., Tamayo, P., Mesirov, J. & Golub, T. Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data. Mach. Learn. 52, 91–118 (2003).Article 

Google Scholar 
Reader, M. O. et al. Zenodo. https://doi.org/10.5281/zenodo.6346472 (2022).QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. (2019).R Core Team. R: A language and environment for statistical computing. (2020). More

Melillo, J. M. et al. Soil warming and carbon-cycle feedbacks to the climate system. Science 298, 2173–2176 (2002).CAS 
PubMed 
Article 

Google Scholar 
Stockmann, U. et al. The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agric. Ecosyst. Environ. 164, 80–99 (2013).CAS 
Article 

Google Scholar 
Sokol, N. W., Sanderman, J. & Bradford, M. A. Pathways of mineral-associated soil organic matter formation: Integrating the role of plant carbon source, chemistry, and point of entry. Glob. Chang. Biol. 25, 12–24 (2019).PubMed 
Article 

Google Scholar 
Krull, E. S., Baldock, J. A. & Skjemstad, J. O. Importance of mechanisms and processes of the stabilisation of soil organic matter for modelling carbon turnover. Funct. Plant Biol. 30, 207–222 (2003).PubMed 
Article 

Google Scholar 
Langley, J. A. & Hungate, B. A. Mycorrhizal controls on belowground litter quality. Ecology 84, 2302–2312 (2003).Article 

Google Scholar 
Strickland, M. S., Osburn, E., Lauber, C., Fierer, N. & Bradford, M. A. Litter quality is in the eye of the beholder: Initial decomposition rates as a function of inoculum characteristics. Funct. Ecol. 23, 627–636 (2009).Article 

Google Scholar 
Cou ̂teaux, M. M., Bottner, P. & Berg, B. Litter decomposition, climate and litter quality. Trends Ecol. Evol. 10, 63–66 (1995).Article 

Google Scholar 
Prescott, C. E. Litter decomposition: What controls it and how can we alter it to sequester more carbon in forest soils? Biogeochemistry 101, 133–149 (2010).CAS 
Article 

Google Scholar 
Frey, S. D., Lee, J., Melillo, J. M. & Six, J. The temperature response of soil microbial efficiency and its feedback to climate. Nat. Clim. Chang. 3, 395–398 (2013).CAS 
Article 

Google Scholar 
Fernandez, C. W., Heckman, K., Kolka, R. & Kennedy, P. G. Melanin mitigates the accelerated decay of mycorrhizal necromass with peatland warming. Ecol. Lett. 22, 498–505 (2019).PubMed 
Article 

Google Scholar 
Brovkin, V. et al. Plant-driven variation in decomposition rates improves projections of global litter stock distribution. Biogeosciences 9, 565–576 (2012).CAS 
Article 

Google Scholar 
Aponte, C., García, L. V., & Marañón, T. Tree species effect on litter decomposition and nutrient release in mediterranean oak forests changes over time. Ecosystems 15, 1204–1218 (2012).CAS 
Article 

Google Scholar 
Hättenschwiler, S. & Jørgensen, H. B. Carbon quality rather than stoichiometry controls litter decomposition in a tropical rain forest. J. Ecol. 98, 754–763 (2010).Article 
CAS 

Google Scholar 
van der Heijden, M. G., Martin, F. M., Selosse, M.-A. & Sanders, I. R. Mycorrhizal ecology and evolution: the past, the present, and the future. N. Phytol. 205, 1406–1423 (2015).Article 
CAS 

Google Scholar 
Lin, G., McCormack, M. L., Ma, C. & Guo, D. Similar below-ground carbon cycling dynamics but contrasting modes of nitrogen cycling between arbuscular mycorrhizal and ectomycorrhizal forests. N. Phytol. 213, 1440–1451 (2017).CAS 
Article 

Google Scholar 
Högberg, M. N. & Högberg, P. Extramatrical ectomycorrhizal mycelium contributes one‐third of microbial biomass and produces, together with associated roots, half the dissolved organic carbon in a forest soil. N. Phytol. 154, 791–795 (2002).Article 

Google Scholar 
Leake, J. et al. Networks of power and influence: the role of mycorrhizal mycelium in controlling plant communities and agroecosystem functioning. Can. J. Bot. 82, 1016–1045 (2004).Article 

Google Scholar 
Bååth, E., Nilsson, L. O., Göransson, H. & Wallander, H. Can the extent of degradation of soil fungal mycelium during soil incubation be used to estimate ectomycorrhizal biomass in soil? Soil Biol. Biochem. 36, 2105–2109 (2004).Article 
CAS 

Google Scholar 
Kaiser, C. et al. Exploring the transfer of recent plant photosynthates to soil microbes: Mycorrhizal pathway vs direct root exudation. N. Phytol. 205, 1537–1551 (2015).CAS 
Article 

Google Scholar 
Konvalinková, T., Püschel, D., Řezáčová, V., Gryndlerová, H. & Jansa, J. Carbon flow from plant to arbuscular mycorrhizal fungi is reduced under phosphorus fertilization. Plant Soil 419, 319–333 (2017).Article 
CAS 

Google Scholar 
Ouimette, A. P. et al. Accounting for carbon flux to mycorrhizal fungi may resolve discrepancies in forest carbon budgets. Ecosystems 23, 715–729 (2019).Article 
CAS 

Google Scholar 
Wallander, H., Nilsson, L. O., Hagerberg, D. & Bååth, E. Estimation of the biomass and seasonal growth of external mycelium of ectomycorrhizal fungi in the field. N. Phytol. 151, 753–760 (2001).CAS 
Article 

Google Scholar 
Allen, M. F. & Kitajima, K. Net primary production of ectomycorrhizas in a California forest. Fungal Ecol. 10, 81–90 (2014).Article 

Google Scholar 
Godbold, D. L. et al. Mycorrhizal hyphal turnover as a dominant process for carbon input into soil organic matter. Plant Soil 281, 15–24 (2006).CAS 
Article 

Google Scholar 
Frey, S. D. Mycorrhizal fungi as mediators of soil organic matter dynamics. Annu. Rev. Ecol. Evol. Syst. 50, 237–259 (2019).Article 

Google Scholar 
Brundrett, M. C. & Tedersoo, L. Evolutionary history of mycorrhizal symbioses and global host plant diversity. N. Phytol. 220, 1108–1115 (2018).Article 

Google Scholar 
Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 5077 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Phillips, R. P., Brzostek, E. & Midgley, M. G. The mycorrhizal‐associated nutrient economy: a new framework for predicting carbon–nutrient couplings in temperate forests. N. Phytol. 199, 41–51 (2013).CAS 
Article 

Google Scholar 
Miyauchi, S. et al. Large-scale genome sequencing of mycorrhizal fungi provides insights into the early evolution of symbiotic traits. Nat. Commun. 11, 5125 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Harley, J. L. Fungi in ecosystems. J. Ecol. 59, 653 (1971).Article 

Google Scholar 
Fernandez, C. W., Langley, J. A., Chapman, S., McCormack, M. L. & Koide, R. T. The decomposition of ectomycorrhizal fungal necromass. Soil Biol. Biochem. 93, 38–49 (2016).CAS 
Article 

Google Scholar 
Fernandez, C. W. & Koide, R. T. Initial melanin and nitrogen concentrations control the decomposition of ectomycorrhizal fungal litter. Soil Biol. Biochem. 77, 150–157 (2014).CAS 
Article 

Google Scholar 
Trofymow, J. A. The Canadian Institute Decomposition Experiment (CIDET): project and site establishment report / J.A. Trofymow and the CIDET Working Group. (1998).Gholz, H. L., Wedin, D. A., Smitherman, S. M., Harmon, M. E. & Parton, W. J. Long-term dynamics of pine and hardwood litter in contrasting environments: Toward a global model of decomposition. Glob. Chang. Biol. 6, 751–765 (2000).Article 

Google Scholar 
Kögel-Knabner, I. The macromolecular organic composition of plant and microbial residues as inputs to soil organic matter. Soil Biol. Biochem 34, 139–162 (2002).Article 

Google Scholar 
Zeglin, L. H. & Myrold, D. D. Fate of decomposed fungal cell wall material in organic horizons of old-growth douglas-fir forest soils. Soil Sci. Soc. Am. J. 77, 489–500 (2013).CAS 
Article 

Google Scholar 
Kleber, M. et al. Mineral-organic associations: formation, properties, and relevance in soil environments. in. Adv. Agron. 130, 1–140 (2015).Article 

Google Scholar 
Fortin, J. A. et al. Arbuscular mycorrhiza on root-organ cultures. Can. J. Bot. 80, 1–20 (2002).CAS 
Article 

Google Scholar 
Declerck, S., Séguin, S. & Dalpé, Y. The monoxenic culture of Arbuscular Mycorrhizal fungi as a tool for germplasm collections. in In Vitro Culture of Mycorrhizas 17–30 (Springer-Verlag, 2005).Lalaymia, I. & Declerck, S. The Mycorrhizal Donor Plant (MDP) in vitro culture system for the efficient colonization of whole plants. 2146, (Springer US, 2020).Crous, P. W., Verkley, G. J. M., Groenewald, J. Z. & Houbraken, J. Westerdijk Laboratory Manual Series 1: Fungal Biodiversity. (2019).Tuomi, M. et al. Leaf litter decomposition-Estimates of global variability based on Yasso07 model. Ecol. Modell. 220, 3362–3371 (2009).CAS 
Article 

Google Scholar 
Clemmensen, K. E. et al. Carbon sequestration is related to mycorrhizal fungal community shifts during long‐term succession in boreal forests. N. Phytol. 205, 1525–1536 (2015).CAS 
Article 

Google Scholar 
Averill, C., Turner, B. L. & Finzi, A. C. Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. Nature 505, 543–545 (2014).CAS 
PubMed 
Article 

Google Scholar 
Staddon, P. L., Ramsey, C. B., Ostle, N., Ineson, P. & Fitter, A. H. Rapid turnover of hyphae of mycorrhizal fungi determined by AMS microanalysis of 14C. Science 300, 1138–1140 (2003).CAS 
PubMed 
Article 

Google Scholar 
Adamczyk, B., Sietiö, O., Biasi, C. & Heinonsalo, J. Interaction between tannins and fungal necromass stabilizes fungal residues in boreal forest soils. N. Phytol. 223, 16–21 (2019).Article 

Google Scholar 
Davison, J. et al. Plant functional groups associate with distinct arbuscular mycorrhizal fungal communities. N. Phytol. 226, 1117–1128 (2020).Article 

Google Scholar 
Liski, J., Palosuo, T., Peltoniemi, M. & Sievänen, R. Carbon and decomposition model Yasso for forest soils. Ecol. Modell. 189, 168–182 (2005).CAS 
Article 

Google Scholar 
Guendehou, G. H. S. et al. Decomposition and changes in chemical composition of leaf litter of five dominant tree species in a West African tropical forest. Trop. Ecol. 55, 207–220 (2014).
Google Scholar 
Paterson, E. et al. Labile and recalcitrant plant fractions are utilised by distinct microbial communities in soil: Independent of the presence of roots and mycorrhizal fungi. Soil Biol. Biochem. 40, 1103–1113 (2008).CAS 
Article 

Google Scholar 
Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E. The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant inputs form stable soil organic matter? Glob. Chang. Biol. 19, 988–995 (2013).PubMed 
Article 

Google Scholar 
Xia, J. et al. Global patterns in Net Primary Production allocation regulated by environmental conditions and forest stand age: a model‐data comparison. J. Geophys. Res. Biogeosciences 124, 2039–2059 (2019).Article 

Google Scholar 
Malhi, Y., Doughty, C. & Galbraith, D. The allocation of ecosystem net primary productivity in tropical forests. Philos. Trans. R. Soc. B Biol. Sci. 366, 3225–3245 (2011).CAS 
Article 

Google Scholar 
Tedersoo, L., May, T. W. & Smith, M. E. Ectomycorrhizal lifestyle in fungi: global diversity, distribution, and evolution of phylogenetic lineages. Mycorrhiza 20, 217–263 (2010).PubMed 
Article 

Google Scholar 
Rinaldi, A. C., Comandini, O. & Kuyper, T. W. Ectomycorrhizal fungal diversity: separating the wheat from the chaff. Fungal Divers 33, 1–45 (2008).
Google Scholar 
Krüger, M., Krüger, C., Walker, C., Stockinger, H. & Schüßler, A. Phylogenetic reference data for systematics and phylotaxonomy of arbuscular mycorrhizal fungi from phylum to species level. N. Phytol. 193, 970–984 (2012).Article 

Google Scholar 
Lee, E.-H., Eo, J.-K., Ka, K.-H. & Eom, A.-H. Diversity of arbuscular mycorrhizal fungi and their roles in ecosystems. Mycobiology 41, 121–125 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Schüβler, A., Schwarzott, D. & Walker, C. A new fungal phylum, the Glomeromycota: phylogeny and evolution. Mycol. Res. 105, 1413–1421 (2001).Article 

Google Scholar 
Declerck, S., Strullu, D. G. & Plenchette, C. Monoxenic culture of the intraradical forms of Glomus sp. isolated from a tropical ecosystem: a proposed methodology for germplasm collection. Mycologia 90, 579 (1998).Article 

Google Scholar 
Voets, L. et al. Extraradical mycelium network of arbuscular mycorrhizal fungi allows fast colonization of seedlings under in vitro conditions. Mycorrhiza 19, 347–356 (2009).PubMed 
Article 

Google Scholar 
von Lützow, M. et al. SOM fractionation methods: Relevance to functional pools and to stabilization mechanisms. Soil Biol. Biochem. 39, 2183–2207 (2007).Article 
CAS 

Google Scholar 
Davidson, E. A., Galloway, L. F. & Strand, M. K. Assessing available carbon: Comparison of techniques across selected forest soils. Commun. Soil Sci. Plant Anal. 18, 45–64 (1987).CAS 
Article 

Google Scholar 
Trumbore, S. E., Vogel, J. S. & Southon, J. R. AMS 14C measurements of fractionated soil organic matter: an approach to deciphering the soil carbon cycle. Radiocarbon 31, 644–654 (1989).Article 

Google Scholar 
Henriksen, T. & Breland, T. Evaluation of criteria for describing crop residue degradability in a model of carbon and nitrogen turnover in soil. Soil Biol. Biochem 31, 1135–1149 (1999).CAS 
Article 

Google Scholar 
Schnitzer, M. & Schuppli, P. Method for the sequential extraction of organic matter from soils and soil fractions. Soil Sci. Soc. Am. J. 53, 1418–1424 (1989).CAS 
Article 

Google Scholar 
Ryan, M. G., Melillo, J. M. & Ricca, A. A comparison of methods for determining proximate carbon fractions of forest litter. Can. J . Res. 20, 166–171 (1990).Article 

Google Scholar 
Wieder, R. K. & Starr, S. T. Quantitative determination of organic fractions in highly organic, Sphagnum peat soils. Commun. Soil Sci. Plant Anal. 29, 847–857 (1998).CAS 
Article 

Google Scholar 
Xu, G. et al. Differential responses of soil hydrolytic and oxidative enzyme activities to the natural forest conversion. Sci. Total Environ. 716, 136414 (2020).CAS 
PubMed 
Article 

Google Scholar 
Viskari, T. et al. Improving Yasso15 soil carbon model estimates with ensemble adjustment Kalman filter state data assimilation. Geosci. Model Dev. 13, 5959–5971 (2020). https://doi.org/10.5194/gmd-13-5959-2020.CAS 
Article 

Google Scholar 
Anderson, M. J. Permutational multivariate analysis of variance (PERMANOVA). Wiley StatsRef: Statistics Reference Online, https://doi.org/10.1002/9781118445112.stat07841 (2014).Anderson, M. J., Ellingsen, K. E. & McArdle, B. H. Multivariate dispersion as a measure of beta diversity. Ecol. Lett. 9, 683–693 (2006).PubMed 
Article 

Google Scholar 
Tomczak, M. & Tomczak, E. The need to report effect size estimates revisited. An overview of some recommended measures of effect size. Trends Sport Sci. 1, 19–25 (2014).
Google Scholar 
Kattge, J. et al. TRY – a global database of plant traits. Glob. Chang. Biol. 17, 2905–2935 (2011).PubMed Central 
Article 

Google Scholar 
Engemann, K. et al. A plant growth form dataset for the New World. Ecology 97, 3243 (2016).CAS 
PubMed 
Article 

Google Scholar  More