Ecology

FAO Food and Agriculture Organization). Fishery Information Data and Statistics Unit. FISHSTAT + Databases and Statistics (Food and Agriculture Organization of the United Nation, 2016).
Google Scholar 
Ke, P. & Wang, W. X. Trace metal contamination in estuarine and coastal environments in China. Sci. Total Environ. 421–422(Apr.1), 3–16 (2012).
Google Scholar 
Jarup, L. Hazards of heavy metal contamination. Brit. Med. Bull. 68(1), 167–182 (2003).PubMed 
Article 

Google Scholar 
Golden, C. et al. Nutrition: Fall in fish catch threatens human health. Nature 534(7607), 317–320 (2016).ADS 
PubMed 
Article 

Google Scholar 
Baki, A. M. et al. Concentration of heavy metals in seafood (fishes, shrimp, lobster and crabs) and human health assessment in Saint Martin Island, Bangladesh. Ecotoxicol. Environ. Saf. 159, 153–163 (2018).PubMed 
Article 
CAS 

Google Scholar 
Saha, N., Mollah, M., Alam, M. F. & Rahman, M. S. Seasonal investigation of heavy metals in marine fishes captured from the Bay of Bengal and the implications for human health risk assessment. Food Control 70, 110–118 (2016).CAS 
Article 

Google Scholar 
Gu, Y. G. et al. Heavy metals in fish tissues/stomach contents in four marine wild commercially valuable fish species from the western continental shelf of south china sea. Mar. Pollut. Bull. 114(2), 1125–1129 (2017).CAS 
PubMed 
Article 

Google Scholar 
Korkmaz, C., Özcan, A., Ersoysal, Y., Köroğlu, M. A. & Erdem, C. Heavy metal levels in muscle tissues of some fish species caught from north-east mediterranean: Evaluation of their effects on human health. J. Food Compos. Anal. 81, 1–9 (2019).CAS 
Article 

Google Scholar 
Rahman, M. S. et al. Assessment of heavy metals contamination in selected tropical marine fish species in Bangladesh and their impact on human health. Environ. Nanotechnol. Monit. Manage. 11, 25 (2019).
Google Scholar 
Zhou, X. J., Zhao, X., Zhang, S. Y. & Lin, J. Marine ranching construction and management in East China Sea: Programs for sustainable fishery and aquaculture. Water 6, 25 (2019).
Google Scholar 
Lu, C. Thoughts on promoting the construction of Dachen Ecological Island. Decis. Mak. Consult. 03, 80–83 (2017).Article 

Google Scholar 
Liu, Y. Y., Ren, M. & Gu, Y. Study on the planning and construction of Taizhou Dachen Marine ecological special reserve. Mar. Dev. Manage. 29(05), 113–115 (2012).
Google Scholar 
Wang, J. Y., Wang, Y. C. & Lou, J. H. Analysis on heavy metal pollution in major seafoods from Zhoushan Fishery, China. Chin. J. Epidemiol. 33(10), 1001–1004 (2012).CAS 

Google Scholar 
Peng, F. et al. Occurrence and risk assessment of heavy metals and polycyclic aromatic hydrocarbons in marine organisms from Yuwai Fishing Ground. Asian J. Ecotoxicol. 14(01), 168–179 (2019).
Google Scholar 
Liu, Q., Liao, Y., Xu, X., Shi, X. & Shou, L. Heavy metal concentrations in tissues of marine fish and crab collected from the middle coast of Zhejiang Province, China. Environ. Monit. Assess. 192, 5 (2020).Article 
CAS 

Google Scholar 
AOAC. Association of Official Analytical Chemists. Official Methods of Analysis 16th edn. (Arlington, 2016).
Google Scholar 
Varol, M., Kaya, G. K. & Sünbül, M. R. Evaluation of health risks from exposure to arsenic and heavy metals through consumption of ten fish species. Environ. Sci. Pollut. Res. 26(32), 33311–33320 (2019).CAS 
Article 

Google Scholar 
SOA. GB 17378–2007 (Standardization Administration of the People’s Republic of China (SAC), 2007).
Google Scholar 
Yi, Y., Yang, Z. & Zhang, S. Ecological risk assessment of heavy metals in sediment and human health risk assessment of heavy metals in fishes in the middle and lower reaches of the Yangtze River Basin. Environ. Pollut. 159(10), 2575–2585 (2011).CAS 
PubMed 
Article 

Google Scholar 
Lie, A., Poa, A., Aaec, D., It, A. & Eob, D. Potential health risk consequences of heavy metal concentrations in surface water, shrimp (Macrobrachium macrobrachion) and fish (Brycinus longipinnis) from Benin river, Nigeria. Toxicol. Rep. 6, 1–9 (2019).Article 
CAS 

Google Scholar 
Liu, Z. et al. Review on the evaluation methods of food safety of edible fish in Meijiang River. Guangdong Chem. Ind. 46(11), 122–123 (2019).
Google Scholar 
Yue, D. D. et al. Relationship between aquatic product consumption and income gap between Chinese urban and rural residents. Fish. Inf. Strat. 33(337), 4–11 (2018).
Google Scholar 
USEPA (U.S. Environmental Protection Agency). Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisories, Volume II. Risk Assessment and Fish Consumption Limits. (EPA 823-B-00-008) (United States Environmental Protection Agency, 2000).
Google Scholar 
Wang, L. et al. Heavy metal pollution and health risk assessment of fish in the Huizhou section of the Dongjiang River. J. Ecol. Rural Environ. 33(01), 70–76 (2017).CAS 

Google Scholar 
Wang, X., Sato, T., Xing, B. & Tao, S. Health risks of heavy metals to the general public in Tianjin, China via consumption of vegetables and fish. Sci. Total Environ. 350(1/3), 28–37 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
USEPA. Risk-Based Concentration Table (United States Environmental Protection Agency, 2009).
Google Scholar 
Shang, D. et al. Safety evaluation of arsenic and arsenic compounds in food. Chin. Fish. Qual. Std. 04, 21–32 (2012).
Google Scholar 
Ahmed, A. S. S., Sultana, S., Habib, A., Ullah, H. & Sarker, M. S. I. Bioaccumulation of heavy metals in some commercially important fishes from a tropical river estuary suggests higher potential health risk in children than adults. PLoS One 14(10), e0219336 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Quanyou, G. et al. Quality differences of large yellow croaker (Pseudosciaena crocea) cultured in deep-water sea cages of two China Regions. Spine 9(9), 1–8 (2018).ADS 

Google Scholar 
Zhu, A. Y., Xie, J. Y., Jiang, L. H. & Lou, B. The nutritional composition and evaluation in muscle of S. marmoratus. Acta Nutr. Sin. 33(06), 621–623 (2011).CAS 

Google Scholar 
Xu, X. H. et al. Analysis and quality evaluation of the muscle nutrients of Wild Pirate Goby in Lianyungang Sea. Jiangsu Agric. Sci. 1, 261–265 (2012).
Google Scholar 
Jiang, X. H. & Yang, P. M. Nutritional composition analysis in muscle of Tapertail Anchovy Coilia nasus from Dayyang River before and after reproduction. Fish. Sci. 40(06), 835–842 (2021).
Google Scholar 
Zeng, S. K., Zhang, C. Y. & Jiang, Z. H. Study on the comparison of the food nutrient contents between the muscle and head of Muraenesox cinereus. Mar. Sci. 05, 13–15 (2002).
Google Scholar 
Guo, H., Xu, M., Shen, Y. C., Ye, N. & Cao, Y. T. Analysis and evaluation of nutritional composition in the muscle of Johnius belangerii. Feed Ind. 37(18), 24–26 (2016).
Google Scholar 
Wang, Y. H., Lv, Z. H., Gao, T. X. & Zheng, G. X. Research on nutritional components of Lateolabrax sp and L. japonicus. Progress Fish. Sci. 02, 35–39 (2003).ADS 
CAS 

Google Scholar 
Nauen, C. E. Compilation of legal limits for hazardous substances in fish and fishery products. FAO Fisheries Circular (FAO) no 764. (1983).JECFA (Joint FAO/WHO Expert Committee on Food Additives). Evaluation of Certain Food Additives and Contaminants. Thirty-Third Report of the Joint FAO/WHO Expert Committee on Food Additives. WHO technical report series, No 776 (World Health Organization, 1989).
Google Scholar 
FAO. The State of the World Fisheries and Aquaculture (FAO Fisheries and Aquaculture Dept, 2014).
Google Scholar 
JECFA (Joint FAO/WHO Expert Committee on Food Additives). Evaluation of Certain Food Additives and Contaminants. Seventythird Report of the Joint FAO/WHO Expert Committee on Food Additives. WHO technical report series, No 960 (World Health Organization, 2011).
Google Scholar 
JECFA (Joint FAO/WHO Expert Committee on Food Additives). Evaluation of Certain Food Additives and Contaminants. Twenty-sixth Report of the Joint FAO/WHO Expert Committee on Food Additives. WHO Technical Report Series, No 683 (World Health Organization, 1982).
Google Scholar 
EFSA (European Food Safety Authority). Scientific opinion on lead in food. EFSA J. 8(4), 1570 (2010).
Google Scholar 
EFSA (European Food Safety Authority). Scientific opinion on dietary reference values for chromium. EFSA J. 12(10), 3845 (2014).Article 
CAS 

Google Scholar 
Younis, E. M. & Abdel-Warithl-Shayia, A. A. A. S. Chemical composition and mineral contents of six commercial fish species from the Arabian Gulf coast of Saudi Arabia. J. Anim. Vet. Adv. 10(23), 3063–3069 (2011).
Google Scholar 
Jakhar, K., Jakhar, J. K., Pal, A. K., Reddy, A. D. & Vardia, H. K. Fatty acids composition of some selected Indian fishes. Afr. J. Basic Appl. Sci. 4(5), 155–160 (2012).CAS 

Google Scholar 
Patrizia, C., Francesca, T. & Rosaria, S. Heavy metal bioaccumulation and metallothionein content in tissues of the sea bream Sparus aurata from three different fish farming systems. Environ. Monit. Assess. 20, 1–4 (2010).
Google Scholar 
Younis, E. M., Abdel-Warith, A., Al-Asgah, N. A., Elthebite, S. A. & Rahman, M. M. Nutritional value and bioaccumulation of heavy metals in muscle tissues of five commercially important marine fish species from the red sea. Saudi J. Biol. Sci. 20, 20 (2020).
Google Scholar 
Nath, A. K. et al. Fatty acid compositions of four edible fishes of Hooghly Estuary, West Bengal, India. Int. J. Curr. Microbiol. Appl. Sci 3, 208–218 (2014).
Google Scholar 
Saeed, S. Impact of environmental parameters on fish condition and quality in Lake Edku, Egypt. Egypt. J. Aquat. Biol. Fish. 17(1), 101–112 (2013).
Google Scholar 
Xiao, M. S., Wang, S., Bao, F. Y. & Feng, C. Enrichment of heavy metals in economic aquatic animals in huaihe river segment of Bengbu sampling points. Res. Environ. Sci. 24(8), 942–948 (2011).CAS 

Google Scholar 
Sun, W. P., Liu, X. Y., Pan, J. M. & Weng, H. X. Levels of heavy metals in commercial fish species from the near-shore of Zhejiang Province. J. Zhejiang Univ. (Sci. Ed.) 26, 1–21 (2012).
Google Scholar 
Djikanovic, V., Skoric, S., Spasic, S., Naunovic, Z. & Lenhardt, M. Ecological risk assessment for different macrophytes and fish species in reservoirs using biota-sediment accumulation factors as a useful tool. Environ. Pollut. 241(4), 1167 (2018).CAS 
PubMed 
Article 

Google Scholar 
Wuana, R., Ogbodo, C., Itodo, U. A. P. D. & Eneji, I. Ecological and human health risk assessment of toxic metals in water, sediment and fish from Lower Usuma Dam Abuja, Nigeria. J. Geosci. Environm. Protect. 08, 82–106 (2020).Article 

Google Scholar 
UNEP Chemicals. Inter-Organization Programm for the sound Management of Chemicals, Global Mercury Assessment (UNEP Chemicals, 2002).
Google Scholar 
Hammerschmidt, C. R. & Fitzgerald, W. Geochemical controls on the production and distribution of methylmercury in near-shore marine sediments. Environ. Sci. Technol. 38(5), 1487–1495 (2004).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Yang, Y. F. et al. Heavy metal characterization of fish species in a typical sea area of Guangdong-Hong Kong-Macau Greater Bay Area. Trans. Oceanol. Limnol. 43(03), 107–116 (2021).
Google Scholar 
Wang, H., Fang, F. & Xie, H. Research situation and outlook on heavy metal pollution in water environment of China. Guangdong Trace Elem. Sci. 17, 14–18 (2010).CAS 

Google Scholar 
Monroy, M., Maceda-Veiga, A. & Sostoa, A. D. Metal concentration in water, sediment and four fish species from lake Titicaca reveals a large-scale environmental concern. Sci. Total Environ. 487(15), JUL.15-244 (2014).
Google Scholar 
Canli, M. & Atli, G. The relationships between heavy metal (Cd, Cr, Cu, Fe, Pb, Zn) levels and the size of six mediterranean fish species. Environ. Pollut. 121(1), 129–136 (2003).CAS 
PubMed 
Article 

Google Scholar 
Makedonski, L., Peycheva, K. & Stancheva, M. Determination of heavy metals in selected black sea fish species. Food Control 72, 313–318 (2017).CAS 
Article 

Google Scholar 
Shinn, C., Dauba, F., Grenouillet, G., Guenard, G. & Lek, S. Temporal variation of heavy metal contamination in fish of the river lot in Southern France. Ecotoxicol. Environ. Saf. 72(7), 1957–1965 (2009).CAS 
PubMed 
Article 

Google Scholar 
Nabavi, S. F., Nabavi, S. M., Latifi, A. M., Eslami, S. & Ebrahimzadeh, M. A. Determination of trace elements level of pikeperch collected from the Caspian sea. Bull. Environ. Contam. Toxicol. 88(3), 401–405 (2012).CAS 
PubMed 
Article 

Google Scholar 
Chen, H. X. et al. Mechanisms of cr(VI) toxicity to fish in aquatic environment: A review. Chin. J. Appl. Ecol. 10, 3226–3234 (2015).
Google Scholar 
Ding, X., Si, Y. E. & Jing, L. The heavy metals distribution pattern and geochemical provinces of the surficial sediments offshore Zhejiang. Mar. Geol. Front. 26(12), 1–8 (2010).ADS 

Google Scholar 
Dai, W. Research progress on the toxicity of lead in aquatic animals. J. Anhui Agric. Sci. 38(011), 5819–5820 (2010).CAS 

Google Scholar 
Lee, K. G. et al. Characterization of tyrosine-rich antheraea pernyi silk fibroin hydrolysate. Int. J. Biol. Macromol. 48(1), 223–226 (2011).CAS 
PubMed 
Article 

Google Scholar 
Li, T. F. Heavy metal content and food risk of aquatic organisms in Jiaojiang district of Taizhou city. J. Food Saf. Qual. 10(16), 5561–5567 (2019).
Google Scholar 
Wu, Z. Y., Yang, S. Y., Su, N., Guo, Y. L. & Bi, L. Distribution and pollution assessment of heavy metals in the sediments of Jiaojiang River. Mar. Geol. Q. Geol. 38(01), 96–107 (2018).CAS 

Google Scholar 
Bravo, A. G. et al. Mercury human exposure through fish consumption in a reservoir contaminated by a chlor-alkali plant: Babeni reservoir (Romania). Environ. Sci. Pollut. R 17(8), 1422–1432 (2010).CAS 
Article 

Google Scholar 
Vu, C. T., Lin, C., Yeh, G. & Villanueva, M. C. Bioaccumulation and potential sources of heavy metal contamination in fish species in Taiwan: Assessment and possible human health implications. Environ. Sci. Pollut. Res. 24, 19422–19434 (2017).CAS 
Article 

Google Scholar 
Li, P. H. et al. Assessing the hazardous risks of vehicle inspection workers’ exposure to particulate heavy metals in their work places. Aerosol. Air Qual. Res. 13, 255–265 (2013).Article 
CAS 

Google Scholar 
Saha, N. & Zaman, M. R. Evaluation of possible health risks of heavy metals by consumption of foodstuffs available in the central market of Rajshahi City, Bangladesh. Environ. Monit. Assess. 185(5), 3867–3878 (2013).CAS 
PubMed 
Article 

Google Scholar 
Miri, M., Akbari, E., Amrane, A., Jafari, S. J. & Taghavi, M. Health risk assessment of heavy metal intake due to fish consumption in the Sistan Region, Iran. Environ. Monit. Assess. 189(11), 583 (2017).PubMed 
Article 
CAS 

Google Scholar 
Kalantzi, I. et al. Metals in tissues of seabass and seabream reared in sites with oxic and anoxic substrata and risk assessment for consumers. Food Chem. 194(1), 659–670 (2016).CAS 
PubMed 
Article 

Google Scholar 
Zhong, W. et al. Health risk assessment of heavy metals in freshwater fish in the central and Eastern North China. Ecotoxicol. Environ. Saf. 157(AUG), 343–349 (2018).CAS 
PubMed 
Article 

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

Drag coefficients of plesiosaurs, ichthyosaurs and modern cetaceansAt equal Reynolds numbers (same body length and same flow velocity), the total drag coefficients of plesiosaurs (Cd) are higher than the estimated values for ichthyosaurs and modern cetaceans (Fig. 1a). The limbless bodies, however, display similar Cd in all three groups and are even lower-than-average in the long-necked plesiosaurs, indicating that the limbs are responsible for the observed high Cd. The limbs of plesiosaurs contribute to more than 20% of their total drag coefficient: up to 32.2% in the basal Meyerasaurus and averaging 25% in derived plesiosaurs, with no major differences between plesiosaur morphotypes. In parvipelvian ichthyosaurs the contribution of the limbs to Cd is 11.2–15.6%, compared to 8.7–14.3% in modern cetaceans. Some of the living taxa we include provide a functional reference for this analysis. Our computed drag coefficient for the bottlenose dolphin model (Cd = 0.00413 at Re = 107) for example, is consistent with the estimates from a gliding living dolphin33 (Cd = 0.0034 at Re = 9.1 × 106) and other static CFD simulations34 (Cd = 0.00413 at Re = 107). It is worth noting that these values are, as expected, lower than estimates obtained from kinematic models, as motion is not accounted for35. In a former study, drag coefficients for a plesiosaur (Cryptoclidus), two ichthyosaurs and various cetaceans were obtained from rigid models in water tanks36. However, the pressure drag component (Cp) was likely overestimated due to the proximity of the models to the air–water interface, and thus are not directly comparable to ours.Fig. 1: Comparison of the drag coefficient of derived plesiosaurs, ichthyosaurs and cetaceans.a Total drag coefficient computed for the full models including the limbs (‘body + limbs’, circles) and the limbless models (‘body’, squares). Average (point) and range (bar) shown for calculations at Re = 5 × 106–107. The derived short-necked plesiosaurs are highlighted in orange; the parvipelvian ichthyosaurs in blue and the extant cetaceans in red. A basal plesiosaur included as a reference is highlighted in purple. b Representative two-dimensional plots of the flow velocity magnitude at Re = 5 × 106 (inlet velocity of 5 ms−1) in lateral view. For dorsal view see Supplementary Fig. 1. Images of Tursiops and the three ichthyosaurs modified from Gutarra et al.29.Full size imageIn all models across the various clades, velocity plots display a stagnation point at the anterior tip of the model, a thin velocity gradient along the body corresponding to the boundary layer, an area of higher velocity around the greatest diameter and a low velocity wake behind the body, characteristic features of a fully developed external flow (Fig. 1b, Supplementary Fig. 1). The acceleration of flow results in areas of low pressure (Supplementary Fig. 2), while high pressure areas are observed where stagnation occurs. Our CFD methodology has been previously validated against experimental data from slender torpedo-like shapes26 and has been shown to provide a reliable distribution of internal drag components29 essential when dealing with streamlined bodies35. In all our simulations, the proportion of frictional and pressure drag was consistent with the expected values for slender geometries31: most of the drag originated from skin friction with a minor pressure drag component (Supplementary Fig. 2). The relatively larger limbs of plesiosaurs (Supplementary Table 1) produce a small increase in skin friction (Supplementary Fig. 2a), but a large increase in the pressure drag coefficient (Supplementary Fig. 2b), indicating that the latter largely explains differences in total drag coefficient between the groups. These effects might be explained by the low local Reynolds number of the flippers (resulting from a small chord length) producing high local Cd relative to the rest of the body31, alongside interference drag (i.e. drag caused by the interaction of flow fields where limbs and body meet), which might be higher for larger flippers.Effect of body shape and body size on drag-related costs of steady swimmingWhen comparing morphologies at the same volume (proxy for body mass) and the same velocity, to focus on the effect of shape alone, derived plesiosaurs produce on average 30% more drag than parvipelvian ichthyosaurs and modern cetaceans (Fig. 2a, Supplementary Table 3; two-sample t-tests p  0.05). In these conditions, the drag-related costs of steady swimming of plesiosaurs fall within the range observed in both modern cetaceans and ichthyosaurs. Normalised against a 2.85 m-long Tursiops, the COTdrag for derived plesiosaurs ranges from 0.42, estimated for the large elasmosaur Thalassomedon, to 1.41 in the medium-sized Dolichorhynchops. In the parvipelvians, COTdrag spans from 0.33 estimated for the large Temnodontosaurus, to 1.76 in a 2.5 m-long Stenopterygius. Cetaceans show a smaller lower limit, because they include the largest animal in our sample, a 16 m-long humpback whale, with a COTdrag of 0.13 compared to Tursiops. The estimated cetacean upper COTdrag limit is 1.54 for a 1.9 m Tursiops. On the other hand, comparisons of the total drag power (Pdrag, i.e., the non-mass normalised version of COTdrag) for the same speed of 1 ms−1 (Fig. 3), show a different trend. Pdrag is highest for Megaptera, higher than in any fossil taxa included in this study, and is lowest in Tursiops. Thalassomedon is comparable both in total drag power and COTdrag to the killer whale. Similarly, the thalassophonean pliosaurid Liopleurodon matches the elasmosaurian Hydrotherosaurus in having a similarly low mass-normalised COTdrag but requiring about 4× more total drag power than Tursiops. Smaller forms like the polycotylid Dolichorhynchops and the thunnosaurian Ophthalmosaurus resemble the extant bottlenose dolphin in having a relatively high COTdrag and low total power.Fig. 3: Comparative plot of mass-normalised drag power and total drag power.Values of mass-normalised drag power (i.e., drag per unit of volume or COTdrag calculated as in Fig. 2b) in grey, and non-mass-normalised total drag power, in black, for an array of derived plesiosaurs, parvipelvian ichthyosaurs and modern cetaceans compared at the same inlet velocity of 1 ms−1. Error bars represent minimum and maximum values accounting for taxon body size variation (see Supplementary Data). Values are normalised to the results for Tursiops.Full size imageThus, in contrast to the volume-normalised simulations, differences between animals at their life-size scale are mainly influenced by size. For example, medium-sized plesiosaurs and ichthyosaurs, such as Dolichorhynchops and Ophthalmosaurus, have values of COTdrag close to that of a dolphin, while large plesiosaurs like Thalassomedon are more like the parvipelvian ichthyosaur Temnodontosaurus and a modern Orcinus. It is worth noting that the inflow velocity of 1 ms−1, is a reference velocity used for comparative purposes, and is not equivalent to the optimal cruising speed (i.e. speed at which COT is minimum16). This parameter is known to vary little in nature, with most vertebrates displaying values of preferred speed between 1–2 ms−1 regardless of body size40,41,42, which means it is reasonable to assume all tested taxa, regardless of their size, were able to swim at this velocity. Using a different reference velocity (2 ms−1) has no effect on the relative values of drag per unit of volume and the mass-normalised drag power (Supplementary Fig. 3; Supplementary Data). A reduction of mass-normalised drag-related costs of cruising as body size increases is selectively advantageous, as energy savings can be used to extend foraging and mating range, increase swimming speed and fuel other activities42,43.Our analysis shows that for highly aquatic tetrapods, size dominates over shape in affecting the drag-related costs of steady locomotion. This is because COTdrag (i.e., the balance of drag to volume) is highly sensitive to surface/volume proportion (Fig. 2f), and so is much influenced by isometry in streamlined animals.Interplay between neck anatomy and body size in plesiosaur dragSimulations at constant Reynolds number (i.e., comparing models at same total length and same flow velocity), show that necks up to 5× the length of the trunk do not increase substantially the total drag coefficient. Longer neck ratios up to 7× were found to impact the drag coefficient by as little as 3% (Fig. 4a). We estimated a 4–10% increase in skin friction drag coefficient for neck ratios of 3–7×, but also a comparable reduction in pressure drag resulting in almost no change in the total drag coefficient. A previous CFD-based study also found no differences in drag coefficient between plesiosaur models with variable neck proportions20, but further comparison is not possible because of great differences in the order of magnitude of Cd, the use of a different scaling reference area and the lack of information on skin and pressure drag20. Here, we have shown that long necks produce only a small increase in skin friction, although not as great as previously speculated25,30, and this is nullified by reduced pressure drag.Fig. 4: Influence of neck length and its interaction with body size on the drag-related costs of swimming in plesiosaurs.a Total drag coefficient and skin friction drag coefficient for an array of hypothetical plesiosaurs with varying neck ratios computed at Re = 5 × 106 (same total length and inflow velocity). b Drag per unit of trunk volume computed for the same array of models scaled at the same trunk length and tested at the same speed of 1 ms−1. The hypothetical models were created by modifying the length in the model of the basal plesiosaur Meyerasaurus victor which has a neck ratio of 0.87×. The limits of the trunk (which extends along the torso and includes the edges of the pectoral and pelvic girdles) are shown in red in the rendered models. c Three-dimensional models of a wide array of plesiosaurs, in dorsal view, at their life-size dimensions, showing the differences in body proportions and sizes. The limits of the trunk in the models (defined as in b) are coloured by group. Basal plesiosaurs are highlighted in purple. Among the derived groups, thalassophonean plesiosaurs (derived pliosaurid plesiosaurs) are highlighted in light orange, polycotylid plesiosaurs in dark orange and elasmosaurid plesiosaurs in green. d Scatterplot of trunk length (cm) and neck ratio showing the relative drag per unit of trunk volume as a gradient of colour for each taxon analysed and for the plot area in between (contour lines represent the interpolated values of drag per unit of volume). e Plot of the relative drag per unit of trunk volume versus the trunk length showing results highlighted by group. Line plots at the right-hand side show the range for each group. The D/Vtr and the trunk length show a significant negative correlation (Pearson’s correlation coefficient calculated with log-transformed variables, p = 2.28 × 10−7, R2 = −0.92). A small version of the fitted power curve (regression equation (y=69.76{x}^{-0.94})) is shown on the right upper corner. The grey area around the curve represents a confidence interval of 95%. All values in b, d and e are normalized to the results for the Meyerasaurus model.Full size imageNext, we explored the impact of neck proportions on drag-related costs of swimming in simulations where the size factor is removed. We found that if trunk dimensions are kept constant while the neck is enlarged, the drag per unit of trunk volume does not change appreciably for neck ratios up to 2×. However, longer neck proportions did impact resistive forces. This was moderate for a 3× ratio, with 12% more drag per unit of trunk volume, but became more substantial for longer necks, with 22%, 35% and 59% excess drag for necks of 4×, 5× and 7× respectively (Fig. 4b). This means that elasmosaurine elasmosaurs, with necks commonly 3–4× the length of the trunk23 might have experienced higher drag than other plesiosaurs of similar trunk dimensions.To test if the ‘long neck effect’ remains when body size is accounted for, we compared the relative amount of drag-per-unit-trunk-volume (D/Vtr) in a wide sample of plesiosaurs (Fig. 4c) at life-size scale for a constant velocity of 1 ms−1, including three species with neck ratios above 2×: Styxosaurus (2.76×), Hydrotherosaurus (3.18×) and Albertonectes (3.72×), the last being the elasmosaur with the longest reported neck44. Our results show great variability in D/Vtr. Small-bodied plesiosaurs such as Plesiosaurus, Meyerasaurus and Dolichorhynchops generated up to six times more D/Vtr than the largest plesiosaurs, Kronosaurus and Aristonectes (Fig. 4d, e). Comparisons per group show that both basal plesiosaurs and derived polycotylids, the groups with the smallest specimens, produced generally higher D/Vtr. Moreover, we did not find substantial differences between elasmosaurs and thalassophonean pliosauroids (Fig. 4e, Supplementary Table 4; all two-sample t-tests p  > 0.05). Both groups had similarly low ranges of D/Vtr regardless of neck length, lower on average than in polycotylids. These results stand even if we exclude Aristonectes, which belongs to the aristonectines, an elasmosaur subfamily with reduced neck length23,45. Further comparisons by morphotype show no significant differences between short-necked pliosauromorphs (here arbitrarily including plesiosaurs with neck ratios below 2×) and long-necked plesiosauromorphs (Supplementary Table 4, all two-sample t-tests p  > 0.05). The highest values of D/Vtr occur in animals with trunk lengths of 100 cm or less, followed by a steep decrease between 100–150 cm and a steadier decrease in longer trunks. This indicates a strong negative correlation between trunk dimensions and D/Vtr (Pearson’s product-moment correlation between the log-transformed variables, adjusted r2 = −0.92, p = 2.28 × 10−7). The curve that best describes this relationship is the power equation, D/Vtr = 69.76 × Trunk length−0.944 (Fig. 4e), an almost inversely proportional relationship, consistent with the streamlined nature of these animals for which skin friction drag is dominant.Polycotylids and thalassophonean pliosaurs, both derived pliosauromorph plesiosaurs9,21, share the same general body proportions9,21,46, but the latter had larger bodies and therefore needed less power in relation to their muscles to move at the same speed. Elasmosaurs on the other hand, despite their disparate morphologies, were no different from thalassophonean pliosaurs in their drag-related costs of forward swimming (Fig. 4c–e) and therefore they were likely to have been equally efficient cruisers.Earlier research suggested that, even if long necks did not add extra drag during forward swimming, speed in elasmosaurs would have been limited to avoid added drag when their necks bent20. However, when the neck is bent in living forms, the course of swimming changes, as does the flow direction, but the body remains streamlined in the direction of incoming flow. For example, sea lions perform non-powered turns initiated by the head in which the body glides smoothly in a curved position, limiting deceleration47. Further biomechanical research is needed to understand the role of plesiosaur necks in manoeuvrability and other aspects of swimming performance, as well as how these were influenced by shape and flexibility. The well-established idea that long-necked plesiosaurs were sluggish, slow swimmers7,30 is thus not supported here, not because long necks did not increase drag20, but because body size overrode this drag excess.Long necks evolved in large-bodied plesiosaurs: implications for dragWe analysed trends of body size and neck proportion in a wider sample of sauropterygians, including plesiosaurian and non-plesiosaurian Triassic sauropterygians. Long necks (neck ratio > 3×) occur in taxa with trunk lengths > 150 cm, whereas most sauropterygians had neck ratios of ≤ 2× (Fig. 5a). The great plasticity of body proportions of sauropterygians before and after their transition to a pelagic lifestyle after the Triassic has been well documented21,23,46, but this is the first time that neck and body size have been explored in the context of swimming performance for such a wide sample. We show that overall, sauropterygians and particularly plesiosaurs, mainly explored neck morphologies with little or no effect on drag costs and did not enter morphospaces that were suboptimal for aquatic locomotion (i.e., corresponding to small trunks with long necks; Fig. 5a). In fact, ancestral state reconstruction for trunk length shows that the ancestor of elasmosaurs was likely around 180 cm long and had a relatively short neck with a ratio smaller than 2× (Fig. 5b, c). This indicates that large trunks preceded neck elongation in elasmosaurs and suggests that extreme proportions might have been favoured by a release of hydrodynamic constraints.Fig. 5: Evolutionary trends of neck proportions and body size in Sauropterygia and their implications for the drag-related costs of swimming.a Bivariate plot of the length of trunk and the neck ratio of 79 sauropterygian taxa. Polygons in different colours show area occupied by the main sauropterygian groups. The functional trends describing the effect of each axis are based on results from flow simulations. On the top of this graph, a univariate plot shows the distribution and mean values of trunk length for each group. b, c Phenograms showing the disparity of trunk length (b) and neck ratio (c) in sauropterygians through time. The branches corresponding to basal Plesiosauria (including Rhomaleosauridae and Plesiosauridae), thalassophonean pliosaurs, polycotylids and elasmosaurs are highlighted (colour coding as in a). d, e Sauropterygian trees showing the evolutionary rates for trunk length (d) and neck ratio (e) represented by colour gradient (see Supplementary Fig. 5 for an alternative analysis to 5d using the log10-transformed trunk length). Consensus trees show average results from analyses of 20 cal3-dated trees (see Supplementary Figs. 4 and 6 for analysis on Hedman-dated trees). Rates are based on the mean scalar evolutionary rate parameter.Full size imageWe next explored evolutionary rates of relative neck length and trunk length in sauropterygians. The pattern of trunk length evolution is consistent with a heterogeneous rates model, not a homogeneous Brownian motion model (log Bayes Factor48 (BF)  > 5 in 100% of the sampled trees and > 10 in 92.5%, Supplementary Table 5). Analysis of non-transformed trunk data shows that through the evolution of Sauropterygia, there was a general increase in trunk length with some higher rates, in Triassic nothosauroids, Jurassic rhomaleosaurids and Cretaceous aristonectine elasmosaurs (Fig. 5d; Supplementary Fig. 4a). Additionally, analysis of the log10-transformed trunk data highlights variation in the small-to-medium size ranges and reveals high rates in Triassic eosauropterygians (Supplementary Figs. 5 and 6). The largest trunks evolved independently in two groups, thalassophonean pliosaurids and elasmosaurid plesiosauroids, with no evidence of high rates in the former. In the plesiosauroids, rates are not particularly high in the basal branches, but they are very high in derived aristonectines, and rates for the whole clade were significantly higher than the background rate in 40% of randomisation tests (Supplementary Fig. 7 and Table 6). A progressive increase in body mass over evolutionary time has been described for various clades of aquatic mammals49 and seems to be a common hallmark of the aquatic adaptation to marine pelagic lifestyles in secondarily aquatic tetrapods44. Whether body size reaches a plateau as is the case in cetaceans49 and what constraints influence the evolutionary patterns of size in plesiosaurs remains unexplored. Against this general trend, some derived plesiosaurs, such as polycotylids, saw a reduction in body size, which might have been related to pressures on niche selection, such as adaptation to specific prey, the need for higher manoeuvrability or other ecological factors. As shown earlier, small sizes require lower amounts of total power for a given speed, and therefore would be favoured if for example food resources were limited. This suggests that, in spite of the energy advantages of large size in terms of reduced mass-specific drag29 and metabolic rates49,50, which make it a common adaptation to the pelagic mode of life, other constraints limiting very large sizes were also at work50,51.A heterogeneous evolutionary rates model for neck proportion is also strongly supported (log BF  > 5 in 100% of the sampled trees and > 10 in 45%, Supplementary Table 5). Fast rates are consistently seen at the base of Pistosauroidea (including some Triassic forms and plesiosaurs) and, interestingly, also within elasmosaurs (Fig. 5e; Supplementary Fig. 4b). The neck proportions of elasmosaurs were found to evolve at a faster pace than the background rate in 90% of analyses (randomisation test p-value < 0.001 in 80% and < 0.01 in 10% of the sampled trees; Supplementary Fig. 7 and Table 6). Very fast rates in elasmosaurs are concentrated in the most derived branches (i.e., Euelasmosauridia from the late Upper Cretaceous52) and represent both rapid neck elongation in elasmosaurines and rapid neck shortening in weddellonectians (i.e., aristonectines and closely related taxa52). Additionally, various other independent instances of relative shortening of the neck occurred during the evolution of Sauropterygia, most notably in placodonts, pliosaurs and polycotylids, but these are not associated with high rates.Our findings contrast with a previous study23 which did not identify any significant evolutionary rate shifts in the neck ratio across Sauropterygia. Here we use a larger number of taxa and a different model fitting approach, which might account for these discrepancies. The association between very long necks and large trunks, along with our flow simulations results and the evidence of high rates in the elongation of necks in elasmosaurines (Fig. 5e), suggests that neck elongation was facilitated by large body sizes. The question remains why neck ratios did not evolve longer than 4×. According to our data, hydrodynamic constraints might have operated against the selection of such long necks. However, it is possible that the primary function for which they were selected, which is still debated30,53, did not require necks with those characteristics. Neck anatomy is likely to be the result of a compromise between different functions/constraints, one of them being hydrodynamic, as shown by the results presented herein. More

Minimum distance to weak efficient frontierBriec and Charnes et al. first proposed the Minimum distance to weak efficient frontier (MinDW) model39,40, which can be expressed as (m + n) linear programming ((m) is the number of input indicators and (n) is the number of output indicators), assuming that the input variable is (x) and the output variable is (y). The specific formula is shown in Eq. (1):$$ begin{aligned} & max beta_{z} ,z = 1,2, ldots ,m + n \ & s.t.left{ begin{gathered} sumnolimits_{j = 1}^{q} {alpha_{j} x_{rj} + beta_{z} e_{r} le x_{rk} ,r = 1,2, ldots ,m} hfill \ sumnolimits_{j = 1}^{q} {alpha_{j} x_{ij} + beta_{z} e_{i} ge y_{ik} ,i = 1,2, ldots ,n} hfill \ alpha_{j} ge 0 hfill \ end{gathered} right. \ end{aligned} $$
(1)
(e_{r}) and (e_{i}) are constants. In the programming formula, only one (e) is equal to 1, and the others are 0, that is shown in Eq. (2):$$ begin{aligned} & e_{r} = 1;{text{ if}}; , r = z; , e_{r} = 0 , ;{text{if}}; , r ne z \ & e_{i} = 1 , ;{text{if}}; , i = z – m; , e_{r} = 0 , ;{text{if}}; , i ne z – m \ end{aligned} $$
(2)
The efficiency value of model is expressed as Eq. (3):$$ theta_{z}^{*} = frac{{1 – frac{1}{m}sumnolimits_{r = 1}^{m} {frac{{beta_{z}^{*} e_{r} }}{{x_{rk} }}} }}{{1 + frac{1}{n}sumnolimits_{i = 1}^{n} {frac{{beta_{z}^{*} e_{i} }}{{y_{ik} }}} }} $$
(3)
The efficiency value of MinDW model is expressed as (theta_{max }^{*} = max (theta_{z}^{*} ,z = 1,2, cdots ,m + n)), and the maximum efficiency value corresponds to the minimum (beta^{*}), that is the nearest distance to the frontier.This paper uses the MinDW model with negative output to conduct empirical analysis. The method can be expressed as (m + n + d) linear programming ((m) is the number of inputs, (n) is the number of desirable output, (d) is the number of unexpected output), assuming that the input variable is (x), the desirable output variable is (y), and the undesirable output variable is (f). The specific formula is shown in Eq. (4):$$ begin{aligned} & max beta_{z} ,z = 1,2, ldots ,m + n + d \ & s.t.left{ begin{gathered} sumnolimits_{j = 1}^{q} {alpha_{j} x_{rj} + beta_{z} e_{r} le x_{rk} ,r = 1,2, ldots ,m} hfill \ sumnolimits_{j = 1}^{q} {alpha_{j} x_{ij} – beta_{z} e_{i} ge y_{ik} ,i = 1,2, ldots ,n} hfill \ sumnolimits_{j = 1}^{q} {alpha_{j} x_{lj} + beta_{z} e_{l} le f_{lk} ,l = 1,2, ldots ,d} hfill \ alpha_{j} ge 0 hfill \ end{gathered} right. \ end{aligned} $$
(4)
(e_{r}), (e_{i}) and (e_{l}) are constants. In the programming formula, only one (e) is equal to 1, and the others are 0, that is shown in Eq. (5):$$ begin{aligned} & e_{r} = 1;{text{ if}}; , r = z; , e_{r} = 0 , ;{text{if}}; , r ne z \ & e_{i} = 1 , ;{text{if }};i = z – m; , e_{r} = 0 , ;{text{if}}; , i ne z – m \ & e_{l} = 1 , ;{text{if}}; , l = z – m – n; , e_{l} = 0 , ;{text{if}}; , l ne z – m – n \ end{aligned} $$
(5)
The efficiency value of model is expressed as Eq. (6):$$ theta_{z}^{*} = frac{{1 – frac{1}{m}sumnolimits_{r = 1}^{m} {frac{{beta_{z}^{*} e_{r} }}{{x_{rk} }}} }}{{1 + frac{1}{n + d}left( {sumnolimits_{i = 1}^{n} {frac{{beta_{z}^{*} e_{i} }}{{y_{ik} }}} + sumnolimits_{l = 1}^{d} {frac{{beta_{z}^{*} e_{l} }}{{f_{lk} }}} } right)}} $$
(6)
The efficiency value of MinDW model is expressed as (theta_{max }^{*} = max (theta_{z}^{*} ,z = 1,2, cdots ,m + n + d)), and the maximum efficiency value corresponds to the minimum (beta^{*}), which means the nearest distance to the frontier.The efficiency value of MinDW model will not be less than the efficiency value of directional distance function model with any direction vector or other distance types (such as radial model and SBM model). In other words, the efficiency value of MinDW model is the largest. Combined with the above process, we can define the common boundary ((beta^{meta*})) and the model is as Eq. (7):$$ begin{aligned} & beta^{meta*} = max frac{{1 – frac{1}{m}sumnolimits_{r = 1}^{m} {frac{{beta_{z} e_{r} }}{{x_{rk} }}} }}{{1 + frac{1}{n + d}left( {sumnolimits_{i = 1}^{n} {frac{{beta_{z} e_{i} }}{{y_{ik} }}} + sumnolimits_{l = 1}^{d} {frac{{beta_{z} e_{l} }}{{f_{lk} }}} } right)}} \ & s.t.left{ begin{gathered} sumnolimits_{j = 1}^{{q_{m} }} {alpha_{j} x_{rj} + beta_{z} e_{r} le x_{rk} ,r = 1,2, cdots ,m} hfill \ sumnolimits_{j = 1}^{{q_{m} }} {alpha_{j} x_{ij} – beta_{z} e_{i} ge y_{ik} ,i = 1,2, cdots ,n} hfill \ sumnolimits_{j = 1}^{{q_{m} }} {alpha_{j} x_{lj} + beta_{z} e_{l} le f_{lk} ,l = 1,2, cdots ,d} hfill \ alpha_{j} ge 0 hfill \ end{gathered} right. \ end{aligned} $$
(7)
Similarly, the efficiency value of DMU relative to the scale frontier ((beta^{scale*})) can be obtained by the Eq. (8):$$ begin{aligned} & beta^{scale*} = max frac{{1 – frac{1}{m}sumnolimits_{r = 1}^{m} {frac{{beta_{z} e_{r} }}{{x_{rk} }}} }}{{1 + frac{1}{n + d}left( {sumnolimits_{i = 1}^{n} {frac{{beta_{z} e_{i} }}{{y_{ik} }}} + sumnolimits_{l = 1}^{d} {frac{{beta_{z} e_{l} }}{{f_{lk} }}} } right)}} \ & s.t.left{ begin{gathered} sumnolimits_{j = 1}^{{q_{s} }} {alpha_{j} x_{rj} + beta_{z} e_{r} le x_{rk} ,r = 1,2, ldots ,m} hfill \ sumnolimits_{j = 1}^{{q_{s} }} {alpha_{j} x_{ij} – beta_{z} e_{i} ge y_{ik} ,i = 1,2, ldots ,n} hfill \ sumnolimits_{j = 1}^{{q_{s} }} {alpha_{j} x_{lj} + beta_{z} e_{l} le f_{lk} ,l = 1,2, ldots ,d} hfill \ alpha_{j} ge 0 hfill \ end{gathered} right. \ end{aligned} $$
(8)
Finally, in the common frontier model, the technology gap ratio (TGR) is equal to the ratio of the efficiency value of the common frontier to the scale frontier41. The formula is as Eq. (9):$$ TGR^{MinDW} = frac{{beta^{meta*} }}{{beta^{scale*} }} $$
(9)
(beta^{meta*}) and (beta^{scale*}) represent the optimal solution of formula (7) and formula (8), respectively. Obviously, (0 le TGR le 1). TGR is used to measure the distance between the optimal production technology and the potential optimal technology of a group, and identify whether there are any differences in LHG under different groups. The closer the TGR is to 1, the closer the technology level is to the optimal potential technology level. Conversely, it shows the larger gap between the technology level and the potential optimal technology level.Metafrontier-Malmquist–Luenberger indexMalmquist productivity index is widely used in the study of dynamic efficiency change trend, and has good adaptability to multiple input–output data and panel data analysis. The actual production process often contains unexpected output. After Chung et al. proposed Malmquist–Luenberger (ML) index, any Malmquist index with undesired output can be called ML index42. Oh constructed the Global-Malmquist–Luenberger index43. All the evaluated DMUs are included in the global reference set, which avoids the phenomenon of infeasible solution in VRS. The global reference set constructed in this paper is as Eqs. (10)–(11):$$ Q^{G} left( x right) = Q^{1} left( {x^{1} } right) cup Q^{2} left( {x^{2} } right) cup cdots cup Q^{T} left( {x^{T} } right) $$
(10)
$$ Q^{t} left( {x^{t} } right) = left{ {left( {y^{t} ,f^{t} } right)left| {x^{t} ;can;produce} right.;left( {y^{t} ,f^{t} } right)} right} $$
(11)
This paper takes MML index as the LHG.$$ begin{aligned} MML_{t – 1}^{t} & = sqrt {frac{{1 – D_{t – 1} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{t – 1} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}} times frac{{1 – D_{t} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{t} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}}} \ & = sqrt {frac{{1 – D_{t – 1} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}}{{1 – D_{t} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}} times frac{{1 – D_{t – 1} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{t} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}} \ & ;;;;; times frac{{1 – D_{t} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{t – 1} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}} \ end{aligned} $$
(12)
Next, it further decompose the MML index into efficiency change (EC) and technology change (TC). The specific formula is shown in Eqs. (13)–(14):$$ TC_{t – 1}^{t} = sqrt {frac{{1 – D_{t – 1} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}}{{1 – D_{t} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}} times frac{{1 – D_{t – 1} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{t} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}} $$
(13)
$$ EC_{t – 1}^{t} = frac{{1 – D_{t} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{t – 1} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}} $$
(14)
where (left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} } right)) and (left( {x^{t} ,y^{t} ,f^{t} } right)) represent the input, expected output and unexpected output of t-1 and t, respectively. (TC_{t – 1}^{t}) is the devotion to LHG raise of DMU’s technical progress from (t – 1) to (t). And (EC_{t – 1}^{t}) represents the devotion to LHG raise of DMU’s efficiency improvement from (t – 1) to (t). The higher the value is, the larger the devotion is. The (MML) index is recorded as (MI). The value of (MI) is the LHG. The green total factor productivity index of laying hens breeding under the common frontier and scale frontier are as Eqs. (15)–(16):$$ metaMI_{t – 1}^{t} = sqrt {frac{{1 – D_{{_{t – 1} }}^{m} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{{_{t – 1} }}^{m} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}} times frac{{1 – D_{{_{t} }}^{m} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{{_{t} }}^{m} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}}} $$
(15)
$$ groupMI_{t – 1}^{t} = sqrt {frac{{1 – D_{{_{t – 1} }}^{g} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{{_{t – 1} }}^{g} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}} times frac{{1 – D_{{_{t} }}^{g} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{{_{t} }}^{g} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}}} $$
(16)
For the DMUs with scale heterogeneity, we can measure the technology gap between the group frontier and the common frontier, which is caused by the specific group structure.Data and variablesBased on the research of the existing literature36, this paper selects five indexes to build the input–output indicator system. Details are as below:

1.

Input variables:

(1)

Quantity of concentrated forage. Mainly includes seeds of crops and their by-products.

(2)

Quantity of grain consumption. Quantity of grain consumed is the quantity of grain consumed by laying hens when they are raised. For example: corn, sorghum, broken rice, wheat, barley, wheat bran, etc.

(3)

Material expenses. The sum of water and fuel power costs, labor costs, and medical epidemic prevention fees. Water and fuel power costs include water, electricity, coal and other fuel power costs; labor costs mean the human management cost of each laying hen from the brood stage to the laying stage; medical and epidemic prevention costs include the cost of disease prevention and control.

2.

Positive output Main product production, which is the egg production per layer.

3.

Negative output Total discharge. According to the calculation method of The Manual of Pollutant Discharge Coefficient, Eq. (17) is used to calculate the COD, TN, and the TP of each layer. Then, according to the calculation method of class GB3838-2002 water quality standard in V, Eq. (18) is used to calculate the total discharge.

$$ POLLUTANTS = FP(FD) times Days $$
(17)
$$ TOTAL , POLLUTANTS = frac{COD}{{40}} + frac{TN}{2} + frac{TP}{{0.4}} $$
(18)
where, (FP(FD)) is the pollution discharge coefficient and the (Days) is the average raising days. Descriptive statistics of input and output indicators are shown in Table 1.Table 1 Descriptive statistics of input and output indicators.Full size tableThe quantity of concentrate, the quantity of food consumed, the cost of labor, the cost of medical treatment all come from “National Agricultural Product Cost and Benefit Data Compilation”. The pollutant discharge coefficient of laying hens is derived from “The Manual of Pollutant Discharge Coefficient”. According to the definition of scale in above two materials, a small scale 300–1000 laying hens, a medium scale 1000–10,000 laying hens, and a large scale greater than 10,000 laying hens are grouped to calculate cost efficiency.From 2004 to 2018, this paper selects 24 major egg-producing provinces (municipalities) in China as samples, after eliminating singular data in the three scales and averaging the missing data, the final small-scale group is left with 7 provinces including Liaoning, Shandong, Henan, Heilongjiang, Jilin, Shanxi, and Shaanxi; the medium-scale group is the remaining 21 provinces of Beijing, Hebei, Jiangsu, Liaoning, Shandong, Tianjin, Zhejiang, Anhui, Henan, Heilongjiang, Jilin, Hubei, Inner Mongolia, Shanxi, Yunnan, Gansu, Ningxia, Shaanxi, Sichuan, Xinjiang, Chongqing; the large-scale group has 18 provinces, including Beijing, Fujian, Guangdong, Henan, Jiangsu, Liaoning, Shandong, Tianjin, Anhui, Henan, Heilongjiang, Hubei, Jilin, Shanxi, Yunnan, Gansu, Sichuan and Chongqing.As is shown in Table 2, after dividing the provinces by region, the eastern region has 10 provinces (municipalities): Liaoning, Shandong, Beijing, Hebei, Jiangsu, Tianjin, Zhejiang, Fujian, Guangdong, Henan. The central region has 7 provinces (autonomous region): Henan, Heilongjiang, Jilin, Shanxi, Anhui, Hubei, Inner Mongolia. The western region has 7 provinces (municipalities): Shaanxi, Gansu, Ningxia, Sichuan, Xinjiang, Chongqing, Yunnan.Table 2 Samples selected from 2004–2018.Full size table More