Ecology

This first national descriptive study used an indicator, which comprehensively reflects incident all-type hospitalized cases coded endometriosis in the French territory up to the municipality scale. We observed an increase in the risk of being hospitalized from 2011 to 2017 and spatial heterogeneity with the identification of 20 scattered hotspots in Metropolitan France as well as in 2 overseas departments.Descriptive resultsThe annual incidence rate (12.9/10,000 PYs) of all-type hospitalized cases coded endometriosis in France in females aged 10–49 years was of the same order of magnitude as the rates observed in other countries (Italy, Iceland) using similar methods29,30. Moreover, a recent meta-analysis2 estimated the pooled incidence rate of endometriosis based on hospital data to be 13.6/10,000 PYs (95% CI: 10.9; 16.3), which situates the French estimation within the confidence interval and close to the pooled value.In our study, 68.3% of all-type cases and 83.2% of non-adenomyosis cases were aged 25–49 years, and only 3.6% (8.5% for non-adenomyosis cases) were under 24 years. In young females, this low percentage could reflect underdiagnosis or delayed diagnosis, because histologic evidence may occur after an interval of 5–10 years following the first signs of endometriosis31. Moreover, many cases are fortuitously diagnosed during fertility check-ups, which rarely take place before 25 years of age. This age distribution in France is close to the distribution observed in a recent Italian study (3.6%  50 years) carried out using similar methods in the population of the Friuli Venezia Giulia region from 2011 to 201330. The Italian authors remarked a noticeable percentage of incident cases over 50 years of age for non-adenomyosis cases (11.5%), close to our results (8.3%), even though endometriosis is expected to attenuate after menopause. They suggested that endometriosis deposits could still be potentially active in older patients and be reactivated in the presence of certain hormones30. This hypothesis seems quite relevant regarding the potential link with EDC exposure. Indeed, the developmental hypothesis supposes that reproductive disorders at adult age could result from early (i.e., prenatal, perinatal, or pubertal) exposure to EDCs in specific exposure windows. In males, this hypothesis has been especially developed according to the so-called “testicular dysgenesis syndrome (TDS)”32. The disruption of fetal androgen action with EDCs, specifically in the “masculinization programming window” (MPW), induces a shorter anogenital distance that is supposed to provide a life-long readout of the level of androgen exposure in the MPW33 and is consistently associated in animals and humans with TDS troubles (cryptorchidism, hypospadias, poor sperm quality)34.In females, the mirror concept of “ovarian dysgenesis syndrome” has been proposed, including a higher risk to develop endometriosis35. Interestingly, endometriosis has recently been associated with a shorter anogenital distance in women36, and this anthropological indicator, measurable using MRI, could be useful for a non-invasive diagnosis of the disease37.In addition, some authors suggest that endometriosis onset could occur in two steps: an early hormonal-developmental step and a second hormonal step at adult age38,39, or a first initiation step with a second promotion step based on experimental tumor production40. Overall, these hypotheses could contribute to the unexpected proportion of hospitalized endometriosis cases identified after menopause. Another explanation could be the large number of fortuitous diagnoses of endometriosis at the same time as hysterectomies performed for diverse indications in women at an older age.Temporal trendsStudies on the temporal trends of endometriosis incidence used diverse methods and delivered differing results according to the country as reviewed in a recent study1. Only three studies carried out with hospital data in the general population are available. A Finnish study showed a decrease in incidence from 1987 to 201241. An Icelandic study did not conclude to any trend from 1981 to 200029, and a recent Korean study only showed an incidence increase in young women aged 15–19 and 20–24 years, but not in other age groups42.In France, the increase in the risk of being hospitalized, observed for both adenomyosis and non-adenomyosis cases, could reflect a real increase in the incidence of endometriosis, consistent with the perception of numerous clinicians. We did not observe an upward trend in females under the age of 25 years, which could reflect the underdiagnosis of this population. The global increase could also relate to the increasing use of non-invasive examinations, like ultrasounds or pelvic MRI during the study period. Pelvic MRI was only recommended by the French Health Authority at the end of the study period43, although clinicians would have anticipated this recommendation, which is supported by the results of the additional analyses (Supplementary Material). In the study period, there was a 69% increase in cases who underwent this examination concurrently with hospitalization, which accounted for around a third of cases. The increasing use of MRI (or ultrasounds) would result in more and more cases treated without hospitalization and could explain the apparent increase of hospitalized incidence at later ages and less at younger ages.Regarding the secondary indicator, the incidence rate in the whole of France during the study period remained steady. However, the trends differed according to each type (Table 4). The risk did not increase for endometrioma, a type of endometriosis that is not expected to depend on the use of pelvic MRI, but it did increase for intestinal endometriosis, expected to be strongly influenced by pelvic MRI. Therefore, these results also support the role of pelvic MRI. As for the divergent evolution of specific types of endometriosis, experts believe that it could depend on shifting practice patterns such as the more frequent tendency to medically treat endometrioma.Table 4 Number of incident cases of hospitalized endometriosis and crude incident rate for specific types of endometriosis for the study period in the whole of France, in females aged 10 years and above.Full size tableAnother factor could also contribute to the global increase in hospitalized endometriosis. Several patient societies (EndoFrance, Endomind, Info-endometriose) have strongly advocated for better detection and care of this disease and provided targeted information, which may have resulted in increased awareness of patients and clinicians regarding the disease during the study period.These factors are likely interlinked with a possible real increase in endometriosis incidence, which could be confirmed by a longer monitoring period.Spatiotemporal and spatial trendsThe spatiotemporal and spatial heterogeneity of the risk of hospitalized endometriosis that we observed in France during the study period could be related to spatial disparities and different evolutions in terms of detection and hospital care. In half of the 20 hotspots in Metropolitan France, we identified a town where an expert clinic for endometriosis was operational during the study period (Fig. 4). In the overseas departments, we identified an expert clinic in the Reunion Island, where we also observed a high incidence. However, we identified expert clinics in areas with a low or moderate risk of hospitalized endometriosis, especially in Paris (four expert clinics), Lyon (two expert clinics), Rennes, Brest, and Angers. Adjusting the spatial model at the department scale with the density of gynecologists and obstetricians using the available data provided by the shared inventory of health professionals from 2011 to 2016 did not change the geographic distribution (data not shown). Adjusting for incident cases of non-endometriotic ovarian cysts only brought about some changes in several departments in the north where the risk attenuated, even though it stayed above 1 (data not shown).Taken together, these results indicate that the activity of local expert clinics could only partially explain the spatial and spatiotemporal heterogeneity of the risk of hospitalized endometriosis. The contribution of environmental factors remains possible and plausible, as we argued above.The results of the exploratory cluster detection performed in Metropolitan France showed a negative relation with the socioeconomic deprivation index. Indeed, a high socioeconomic status (SES) or education level has been associated with a higher frequency of endometriosis44,45, which probably reflects the better detection and patient care of women with high SES. However, this relation was inverted in a recent Swedish study, although the authors partly attribute this inconsistent finding to egalitarian health care in Sweden46.Among the 40 detected clusters (p  More

Verbesselt, J. et al. Remotely sensed resilience of tropical forests. Nat. Clim. Change 6, 1028–1031 (2016).Article 

Google Scholar 
Lovejoy, T. E. & Nobre, C. Amazon tipping point. Sci. Adv. 4, eaat2340 (2018).Article 

Google Scholar 
Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).CAS 
Article 

Google Scholar 
Hirota, M., Holmgren, M., Van Nes, E. H. & Scheffer, M. Global resilience of tropical forest and savanna to critical transitions. Science 334, 232–235 (2011).CAS 
Article 

Google Scholar 
Ciemer, C. et al. Higher resilience to climatic disturbances in tropical vegetation exposed to more variable rainfall. Nat. Geosci. 12, 174–179 (2019).CAS 
Article 

Google Scholar 
Boers, N., Marwan, N. & Barbosa, H. M. J. A deforestation-induced tipping point for the South American monsoon system. Sci. Rep. 49, 41489 (2017).Article 
CAS 

Google Scholar 
Lasslop, G., Brovkin, V., Reick, C. H., Bathiany, S. & Kloster, S. Multiple stable states of tree cover in a global land surface model due to a fire–vegetation feedback. Geophys. Res. Lett. 43, 6324–6331 (2016).Article 

Google Scholar 
Abis, B. & Brovkin, V. Environmental conditions for alternative tree-cover states in high latitudes. Biogeosciences 14, 511–527 (2017).CAS 
Article 

Google Scholar 
Bastiaansen, R. et al. Multistability of model and real dryland ecosystems through spatial self-organization. Proc. Natl Acad. Sci. USA 115, 11256–11261 (2018).CAS 
Article 

Google Scholar 
Lewis, S. L., Wheeler, C. E., Mitchard, E. T. & Koch, A. Restoring natural forests is the best way to remove atmospheric carbon. Nature 568, 25–28 (2019).CAS 
Article 

Google Scholar 
Peterson, G., Allen, C. R. & Holling, C. S. Ecological resilience, biodiversity, and scale. Ecosystems 1, 6–18 (1998).Article 

Google Scholar 
Folke, C. et al. Regime shifts, resilience, in ecosystem management. Annu. Rev. Ecol. Evol. Syst. 35, 557–581 (2004).Article 

Google Scholar 
Arani, B. M., Carpenter, S. R., Lahti, L., van Nes, E. H. & Scheffer, M. Exit time as a measure of ecological resilience. Science 372, eaay4895 (2021).CAS 
Article 

Google Scholar 
Einstein, A. Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen. Ann. der Phys. 322, 549–560 (1905).Article 

Google Scholar 
Nyquist, H. Thermal agitation of electric charge in conductors. Phys. Rev. 32, 110–113 (1928).CAS 
Article 

Google Scholar 
Kubo, R. The fluctuation–dissipation theorem. Rep. Prog. Phys. 29, 255–284 (1966).CAS 
Article 

Google Scholar 
Marconi, U. M. B., Puglisi, A., Rondoni, L. & Vulpiani, A. Fluctuation–dissipation: response theory in statistical physics. Phys. Rep. 461, 111–195 (2008).Article 

Google Scholar 
Groth, A., Ghil, M., Hallegatte, S. & Dumas, P. The role of oscillatory modes in US business cycles. J. Bus. Cycle Meas. Anal. https://doi.org/10.1787/jbcma-2015-5jrs0lv715wl (2015).Groth, A., Dumas, P., Ghil, M. & Hallegatte, S. in Extreme Events: Observations, Modeling, and Economics (eds Chavez, M. et al.) 343–360 (Wiley, 2015).Gritsun, A. & Branstator, G. Climate response using a three-dimensional operator based on the fluctuation-dissipation theorem. J. Atmos. Sci. 64, 2558–2575 (2007).Article 

Google Scholar 
Majda, A. J., Abramov, R. & Gershgorin, B. High skill in low-frequency climate response through fluctuation dissipation theorems despite structural instability. Proc. Natl Acad. Sci. USA 107, 581–586 (2010).CAS 
Article 

Google Scholar 
Carpenter, S. R. & Brock, W. A. Rising variance: a leading indicator of ecological transition. Ecol. Lett. 9, 311–318 (2006).CAS 
Article 

Google Scholar 
Seddon, A. W., Macias-Fauria, M., Long, P. R., Benz, D. & Willis, K. J. Sensitivity of global terrestrial ecosystems to climate variability. Nature 531, 229–232 (2016).CAS 
Article 

Google Scholar 
van der Bolt, B., van Nes, E. H., Bathiany, S., Vollebregt, M. E. & Scheffer, M. Climate reddening increases the chance of critical transitions. Nat. Clim. Change 8, 478–484 (2018).Article 

Google Scholar 
Liu, Y., Kumar, M., Katul, G. G. & Porporato, A. Reduced resilience as an early warning signal of forest mortality. Nat. Clim. Change 9, 880–885 (2019).Article 

Google Scholar 
Van Nes, E. H. & Scheffer, M. Slow recovery from perturbations as a generic indicator of a nearby catastrophic shift. Am. Nat. 169, 738–747 (2007).Article 

Google Scholar 
Dakos, V., Van Nes, E. H., d’Odorico, P. & Scheffer, M. Robustness of variance and autocorrelation as indicators of critical slowing down. Ecology 93, 264–271 (2012).Article 

Google Scholar 
Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009).CAS 
Article 

Google Scholar 
Carpenter, S. R. et al. Early warnings of regime shifts: a whole-ecosystem experiment. Science 332, 1079–1082 (2011).CAS 
Article 

Google Scholar 
Veraart, A. J. et al. Recovery rates reflect distance to a tipping point in a living system. Nature 481, 357–359 (2012).CAS 
Article 

Google Scholar 
Dakos, V. et al. Slowing down as an early warning signal for abrupt climate change. Proc. Natl Acad. Sci. USA 105, 14308–14312 (2008).CAS 
Article 

Google Scholar 
Rypdal, M. Early-warning signals for the onsets of Greenland interstadials and the Younger Dryas-preboreal transition. J. Clim. 29, 4047–4056 (2016).Article 

Google Scholar 
Boers, N. Early-warning signals for Dansgaard–Oeschger events in a high-resolution ice core record. Nat. Commun. 9, 2556 (2018).Lenton, T. M., Livina, V. N., Dakos, V., van Nes, E. H. & Scheffer, M. Early warning of climate tipping points from critical slowing down: comparing methods to improve robustness. Phil. Trans. R. Soc. A 370, 1185–204 (2012).CAS 
Article 

Google Scholar 
Boulton, C. A., Allison, L. C. & Lenton, T. M. Early warning signals of Atlantic Meridional Overturning Circulation collapse in a fully coupled climate model. Nat. Commun. 5, 5752 (2014).CAS 
Article 

Google Scholar 
De Keersmaecker, W. et al. How to measure ecosystem stability? An evaluation of the reliability of stability metrics based on remote sensing time series across the major global ecosystems. Glob. Change Biol. 20, 2149–2161 (2014).Article 

Google Scholar 
De Keersmaecker, W. et al. A model quantifying global vegetation resistance and resilience to short-term climate anomalies and their relationship with vegetation cover. Glob. Ecol. Biogeogr. 24, 539–548 (2015).Article 

Google Scholar 
Pinzon, J. E. & Tucker, C. J. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929–6960 (2014).Article 

Google Scholar 
Moesinger, L. et al. The global long-term microwave vegetation optical depth climate archive (vodca). Earth Syst. Sci. Data 12, 177–196 (2020).Article 

Google Scholar 
Boulton, C. A., Lenton, T. & Boers, N. Pronounced loss of Amazon rainforest resilience since the early 2000s. Nat. Clim. Change 12, 271–278 (2022).Article 

Google Scholar 
Feng, Y. et al. Reduced resilience of terrestrial ecosystems locally is not reflected on a global scale. Commun. Earth Environ. 2, 88 (2021).Article 

Google Scholar 
Friedl, M. & Sulla-Menashe, D. MCD12C1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05 Deg Version 006 (NASA, 2015).Wang, W., Chen, Y., Becker, S. & Liu, B. Linear trend detection in serially dependent hydrometeorological data based on a variance correction Spearman rho method. Water 7, 7045–7065 (2015).CAS 
Article 

Google Scholar 
Boulton, C. A., Good, P. & Lenton, T. M. Early warning signals of simulated Amazon rainforest dieback. Theor. Ecol. 6, 373–384 (2013).Article 

Google Scholar 
Box, E. O., Holben, B. N. & Kalb, V. Accuracy of the AVHRR vegetation index as a predictor of biomass, primary productivity and net CO2 flux. Vegetatio 80, 71–89 (1989).Article 

Google Scholar 
Liu, L., Zhang, Y., Wu, S., Li, S. & Qin, D. Water memory effects and their impacts on global vegetation productivity and resilience. Sci. Rep. 8, 2962 (2018).Article 
CAS 

Google Scholar 
Schwalm, C. R. et al. Global patterns of drought recovery. Nature 548, 202–205 (2017).CAS 
Article 

Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
Article 

Google Scholar 
Chen, J. et al. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens. Environ. 91, 332–344 (2004).Article 

Google Scholar 
Cleveland, R. B., Cleveland, W. S., McRae, J. E. & Terpenning, I. Stl: a seasonal-trend decomposition procedure based on loess. J. Off. Stat. 6, 3–73 (1990).
Google Scholar 
Donner, R. et al. Spatial patterns of linear and nonparametric long-term trends in Baltic sea-level variability. Nonlinear Process. Geophys. 19, 95–111 (2012).Article 

Google Scholar 
Smith, T. & Bookhagen, B. Changes in seasonal snow water equivalent distribution in high mountain Asia (1987 to 2009). Sci. Adv. 4, e1701550 (2018).Article 

Google Scholar 
Smith, T., Boers, N. & Traxl, D. Global vegetation resilience estimation. Zenodo https://doi.org/10.5281/zenodo.5816934 (2022).Rousseau, D.-D. et al. (MIS3 & 2) millennial oscillations in Greenland dust and Eurasian aeolian records—a paleosol perspective. Quat. Sci. Rev. 196, 99–113 (2017).Article 

Google Scholar 
Boulton, C. A. & Lenton, T. M. A new method for detecting abrupt shifts in time series. F1000Research 8, 746 (2019).Article 

Google Scholar 
Savitzky, A. & Golay, M. J. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36, 1627–1639 (1964).CAS 
Article 

Google Scholar 
Scheffer, M., Carpenter, S. R., Dakos, V. & van Nes, E. H. Generic indicators of ecological resilience: inferring the chance of a critical transition. Annu. Rev. Ecol. Evol. Syst. 46, 145–167 (2015).Article 

Google Scholar 
Djikstra, H. Nonlinear Climate Dynamics (Cambridge Univ. Press, 2013).Book 

Google Scholar 
Kendall, M. G. Rank Correlation Methods (Griffin, 1948). More

Schwenk, K., Padilla, D. K., Bakken, G. S. & Full, R. J. Grand challenges in organismal biology. Integr. Comp. Biol. 49, 7–14. https://doi.org/10.1093/icb/icp034 (2009).Article 
PubMed 

Google Scholar 
Chapman, L. J., Galis, F. & Shinn, J. Phenotypic plasticity and the possible role of genetic assimilation: Hypoxia-induced trade-offs in the morphological traits of an African cichlid. Ecol. Lett. 3, 387–393. https://doi.org/10.1046/j.1461-0248.2000.00160.x (2000).Article 

Google Scholar 
Crispo, E. & Chapman, L. J. Geographic variation in phenotypic plasticity in response to dissolved oxygen in an African cichlid fish. J. Evol. Biol. 23, 2091–2103. https://doi.org/10.1111/j.1420-9101.2010.02069.x (2010).CAS 
Article 
PubMed 

Google Scholar 
Fox, R. J., Donelson, J. M., Schunter, C., Ravasi, T. & Gaitan-Espitia, J. D. Beyond buying time: the role of plasticity in phenotypic adaptation to rapid environmental change. Philos. Trans. R. Soc. B-Biol. Sci. 374, 20180174 (2019).Article 

Google Scholar 
Schmidt-Nielsen, K. Animal physiology: adaptation and environment 4th edn. (Cambridge University Press, 1990).
Google Scholar 
Willmer, P., Stone, G. & Johnston, I. A. Environmental physiology of animals (Blackwell, 2000).
Google Scholar 
Begon, M., Townsend, C. R. & Harper, J. L. Ecology from individuals to ecosystems 4th edn. (Blackwell Publishing, 2006).
Google Scholar 
Johnston, I. A. & Bennett, A. F. Animals and temperature. Phenotypic and Evolutionary Adaptation (Cambridge University Press, 2008).
Google Scholar 
Atkinson, D. Temperature and organism size – a biological law for ectotherms. Adv. Ecol. Res. 25, 1–58 (1994).Article 

Google Scholar 
Atkinson, D. & Sibly, R. M. Why are organisms usually bigger in colder environments? Making sense of a life history puzzle. Trends Ecol. Evol. 12, 235–239. https://doi.org/10.1016/s0169-5347(97)01058-6 (1997).CAS 
Article 
PubMed 

Google Scholar 
Bergmann, C. Uber die verhaltnisse der warmeokonomie der thiere zuihrer grosse. Gottinger Studien 1, 595–708 (1847).
Google Scholar 
Blanckenhorn, W. U. & Demont, M. Bergmann and converse Bergmann latitudinal clines in Arthropods: two ends of a continuum?. Integr. Comp. Biol. 44, 413–424 (2004).CAS 
Article 

Google Scholar 
Blackburn, T. M., Gaston, K. & Loder, N. Geographic gradients in body size: a clarification of Bergmann’s rule. Divers. Distrib. 5, 165–174 (1999).Article 

Google Scholar 
Berrigan, D. & Charnov, E. L. Reaction norms for age and size at maturity in response to temperature—a puzzle for life historians. Oikos 70, 474–478 (1994).Article 

Google Scholar 
Angilletta, M. J. & Dunham, A. E. The temperature-size rule in ectotherms: Simple evolutionary explanations may not be general. Am. Nat. 162, 332–342 (2003).Article 

Google Scholar 
Angilletta, M. J. Jr., Steury, T. D. & Sears, M. W. Temperature, growth rate, and body size in ectotherms: Fitting pieces of a life–history puzzle. Integr. Comp. Biol. 44, 498–509 (2004).Article 

Google Scholar 
Clusella-Trullas, S., Blackburn, T. M. & Chown, S. L. Climatic predictors of temperature performance curve paremeters in ectotherms imply complex responses to climate change. Am. Nat. 177, 738–751 (2011).Article 

Google Scholar 
Horne, C. R., Hirst, A. G., Atkinson, D., Neves, A. & Kiorboe, T. A global synthesis of seasonal temperature-size responses in copepods. Glob. Ecol. Biogeogr. 25, 988–999. https://doi.org/10.1111/geb.12460 (2016).Article 

Google Scholar 
Kiełbasa, A., Walczyńska, A., Fiałkowska, E., Pajdak-Stós, A. & Kozłowski, J. Seasonal changes in the body size of two rotifer species living in activated sludge follow the Temperature-Size Rule. Ecol. Evol. 4, 4678–4689. https://doi.org/10.1002/ece3.1292 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Stoks, R., Geerts, A. N. & De Meester, L. Evolutionary and plastic responses of freshwater invertebrates to climate change: Realized patterns and future potential. Evol. Appl. 7, 42–55. https://doi.org/10.1111/eva.12108 (2014).Article 
PubMed 

Google Scholar 
Hassall, C. Time stress and temperature explain continental variation in damselfly body size. Ecography 36, 894–903. https://doi.org/10.1111/j.1600-0587.2013.00018.x (2013).Article 

Google Scholar 
Horne, C. R., Hirst, A. G. & Atkinson, D. Temperature-size responses match latitudinal-size clines in arthropods, revealing critical differences between aquatic and terrestrial species. Ecol. Lett. 18, 327–335. https://doi.org/10.1111/ele.12413 (2015).Article 
PubMed 

Google Scholar 
Merckx, T. et al. Body-size shifts in aquatic and terrestrial urban communities. Nature 558, 113–116. https://doi.org/10.1038/s41586-018-0140-0 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Rollinson, N. & Rowe, L. Oxygen limitation at the larval stage and the evolution of maternal investment per offspring in aquatic environments. Am. Nat. 191, 604–619. https://doi.org/10.1086/696857 (2018).Article 
PubMed 

Google Scholar 
Santilli, J. & Rollinson, N. Toward a general explanation for latitudinal clines in body size among chelonians. Biol. J. Lin. Soc. 124, 381–393. https://doi.org/10.1093/biolinnean/bly054 (2018).Article 

Google Scholar 
Walczyńska, A. & Sobczyk, Ł. The underestimated role of temperature–oxygen relationship in large-scale studies on size-to-temperature response. Ecol. Evol. 7, 7434–7441. https://doi.org/10.1002/ece3.3263 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Czarnoleski, M., Ejsmont-Karabin, J., Angilletta, M. J. Jr. & Kozlowski, J. Colder rotifers grow larger but only in oxygenated waters. Ecosphere https://doi.org/10.1890/es15-00024.1 (2015).Article 

Google Scholar 
Forster, J., Hirst, A. G. & Atkinson, D. Warming-induced reductions in body size are greater in aquatic than terrestrial species. Proc. Natl. Acad. Sci. U.S.A. 109, 19310–19314. https://doi.org/10.1073/pnas.1210460109 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Woods, H. A. Egg-mass size and cell size: Effects of temperature on oxygen distribution. Am. Zool. 39, 244–252 (1999).Article 

Google Scholar 
Verberk, W. C. E. P., Bilton, D. T., Calosi, P. & Spicer, J. I. Oxygen supply in aquatic ectotherms: Partial pressure and solubility together explain biodiversity and size patterns. Ecology 92, 1565–1572 (2011).Article 

Google Scholar 
Berner, R. A., VandenBrooks, J. M. & Ward, P. D. Evolution—Oxygen and evolution. Science 316, 557–558. https://doi.org/10.1126/science.1140273 (2007).CAS 
Article 
PubMed 

Google Scholar 
Verberk, W. C. E. P. & Atkinson, D. Why polar gigantism and Palaeozoic gigantism are not equivalent: Effects of oxygen and temperature on the body size of ectotherms. Funct. Ecol. 27, 1275–1285. https://doi.org/10.1111/1365-2435.12152 (2013).Article 

Google Scholar 
Rollinson, N. & Rowe, L. Temperature-dependent oxygen limitation and the rise of Bergmann’s rule in species with aquatic respiration. Evolution 72, 977–988. https://doi.org/10.1111/evo.13458 (2018).CAS 
Article 
PubMed 

Google Scholar 
Harrison, J. F., Kaiser, A. & VandenBrooks, J. M. Atmospheric oxygen level and the evolution of insect body size. Proc. R. Soc. B 277, 1937–1946. https://doi.org/10.1098/rspb.2010.0001 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Frazier, M. R., Woods, H. A. & Harrison, J. F. Interactive effects of rearing temperature and oxygen on the development of Drosophila melanogaster. Physiol. Biochem. Zool. 74, 641–650. https://doi.org/10.1086/322172 (2001).CAS 
Article 
PubMed 

Google Scholar 
Hoefnagel, K. N. & Verberk, W. Is the temperature-size rule mediated by oxygen in aquatic ectotherms?. J. Therm. Biol 54, 56–65. https://doi.org/10.1016/j.jtherbio.2014.12.003 (2015).Article 
PubMed 

Google Scholar 
Walczyńska, A., Labecka, A. M., Sobczyk, M., Czarnoleski, M. & Kozłowski, J. The Temperature-Size Rule in Lecane inermis (Rotifera) is adaptive and driven by nuclei size adjustment to temperature. J. Therm. Biol 54, 78–85 (2015).Article 

Google Scholar 
Whitman, D. W. & Agrawal, A. A. in Phenotypic plasticity of insects: mechanisms and consequences (eds D.W. Whitman & T.N. Ananthakrishnan) 1–63 (Science Publishers, 2009).Stauffer, J. R. & van Snik Gray, E. Phenotypic plasticity: Its role in trophic radiation and explosive speciation in cichlids (Teleostei: Cichlidae). Animal Biol. 54, 137–158 (2004).Article 

Google Scholar 
Ishikawa, A. et al. Speciation in ninespine stickleback: Reproductive isolation and phenotypic divergence among cryptic species of Japanese ninespine stickleback. J. Evol. Biol. 26, 1417–1430 (2013).CAS 
Article 

Google Scholar 
Gabaldon, C., Fontaneto, D., Carmona, M. J., Montero-Pau, J. & Serra, M. Ecological differentiation in cryptic rotifer species: What we can learn from the Brachionus plicatilis complex. Hydrobiologia 796, 7–18. https://doi.org/10.1007/s10750-016-2723-9 (2017).Article 

Google Scholar 
Mills, S. et al. Fifteen species in one: deciphering the Brachionus plicatilis species complex (Rotifera, Monogononta) through DNA taxonomy. Hydrobiologia 796, 39–58. https://doi.org/10.1007/s10750-016-2725-7 (2017).CAS 
Article 

Google Scholar 
Ortells, R., Gomez, A. & Serra, M. Coexistence of cryptic rotifer species: Ecological and genetic characterisation of Brachionus plicatilis. Freshw. Biol. 48, 2194–2202. https://doi.org/10.1046/j.1365-2427.2003.01159.x (2003).Article 

Google Scholar 
Serra, M. & Fontaneto, D. in Rotifers. Aquaculture, ecology, gerontology, and ecotoxicology (eds A. Hagiwara & T. Yoshinaga) 15–34 (Springer, 2017).Gomez, A., Montero-Pau, J., Lunt, D. H., Serra, M. & Campillo, S. Persistent genetic signatures of colonization in Brachionus manjavacas rotifers in the Iberian Peninsula. Mol. Ecol. 16, 3228–3240. https://doi.org/10.1111/j.1365-294X.2007.03372.x (2007).CAS 
Article 
PubMed 

Google Scholar 
Montero-Pau, J., Ramos-Rodriguez, E., Serra, M. & Gomez, A. Long-term coexistence of rotifer cryptic species. PLoS ONE https://doi.org/10.1371/journal.pone.0021530 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Gomez, A., Carmona, M. J. & Serra, M. Ecological factors affecting gene flow in the Brachionus plicatilis complex (Rotifera). Oecologia 111, 350–356. https://doi.org/10.1007/s004420050245 (1997).ADS 
Article 
PubMed 

Google Scholar 
Serrano, L., Serra, M. & Miracle, M. R. Size variation in Brachionus plicatilis resting eggs. Hydrobiologia 186, 381–386. https://doi.org/10.1007/bf00048936 (1989).Article 

Google Scholar 
Walczyńska, A. & Serra, M. Inter- and intraspecific relationships between performance and temperature in a cryptic species complex of the rotifer Brachionus plicatilis. Hydrobiologia 734, 17–26 (2014).Article 

Google Scholar 
Serra, M. & Miracle, M. R. Bometric variation in three strains of Brachionus plicatilis as a direct response to abiotic variables. Hydrobiologia 147, 83–89. https://doi.org/10.1007/bf00025729 (1987).CAS 
Article 

Google Scholar 
Gomez, A., Temprano, M. & Serra, M. Ecological genetics of a cyclical parthenogen in temporary habitats. J. Evol. Biol. 8, 601–622. https://doi.org/10.1046/j.1420-9101.1995.8050601.x (1995).Article 

Google Scholar 
Walczyńska, A. & Serra, M. Species size affects hatching response to different temperature regimes in a rotifer cryptic species complex. Evol. Ecol. 28, 131–140 (2014).Article 

Google Scholar 
Walczynska, A., Franch-Gras, L. & Serra, M. Empirical evidence for fast temperature-dependent body size evolution in rotifers. Hydrobiologia 796, 191–200. https://doi.org/10.1007/s10750-017-3206-3 (2017).Article 

Google Scholar 
Weider, L. J., Jeyasingh, P. D. & Frisch, D. Evolutionary aspects of resurrection ecology: Progress, scope, and applications-An overview. Evol. Appl. 11, 3–10. https://doi.org/10.1111/eva.12563 (2018).Article 
PubMed 

Google Scholar 
Levis, N. A. & Pfennig, D. W. Evaluating “Plasticity-First” evolution in nature: Key criteria and empirical approaches. Trends Ecol. Evol. 31, 563–574. https://doi.org/10.1016/j.tree.2016.03.012 (2016).Article 
PubMed 

Google Scholar 
O’Rourke, N. & Hatcher, L. A step-by-step approach to using SAS® for Factor Analysis and Structural Equation Modeling 2nd edn. (SAS Institute Inc., 2013).
Google Scholar 
Campillo, S., Garcia-Roger, E. M., Jose Carmona, M. & Serra, M. Local adaptation in rotifer populations. Evolut. Ecol. 25, 933–947. https://doi.org/10.1007/s10682-010-9447-5 (2011).Article 

Google Scholar 
Gomez, A. & Carvalho, G. R. Sex, parthenogenesis and genetic structure of rotifers: Microsatellite analysis of contemporary and resting egg bank populations. Mol. Ecol. 9, 203–214. https://doi.org/10.1046/j.1365-294x.2000.00849.x (2000).CAS 
Article 
PubMed 

Google Scholar 
Gabaldon, C., Montero-Pau, J., Carmona, M. J. & Serra, M. Life-history variation, environmental fluctuations and competition in ecologically similar species: Modeling the case of rotifers. J. Plankton Res. 37, 953–965. https://doi.org/10.1093/plankt/fbv072 (2015).Article 

Google Scholar 
Wetzel, R. G. Limnology. Lake and river ecosystems (Elsevier Academic Press, 2001).
Google Scholar 
Kuhl, M., Cohen, Y., Dalsgaard, T., Jorgensen, B. B. & Revsbech, N. P. Micreoenvironment and photosynthesis of Zooxanthellae in scleractinian corals studied with microsensors for O2, pH and light. Mar. Ecol. Prog. Ser. 117, 159–172. https://doi.org/10.3354/meps117159 (1995).ADS 
Article 

Google Scholar 
Denny, M. W. Air and water. The biology and physics of life’s media (Princeton University Press, 1993).Book 

Google Scholar 
Montero-Pau, J., Serra, M. & Gomez, A. Diapausing egg banks, lake size, and genetic diversity in the rotifer Brachionus plicatilis Muller (Rotifera, Monogononta). Hydrobiologia 796, 77–91. https://doi.org/10.1007/s10750-016-2833-4 (2017).CAS 
Article 

Google Scholar 
Tarazona, E., Garcia-Roger, E. M. & Carmona, M. J. Experimental evolutioin of bet hedging in rotifer diapause traits as a response to environmental unpredictability. Oikos 126, 1162–1172. https://doi.org/10.1111/oik.04186 (2017).Article 

Google Scholar 
Franch-Gras, L., Montero-Pau, J. & Serra, M. The effect of environmental uncertainty and diapause investment on the occurrence of specialist and generalist species. Int. Rev. Hydrobiol. 99, 125–132. https://doi.org/10.1002/iroh.201301712 (2014).Article 

Google Scholar 
Martinez-Ruiz, C. & Garcia-Roger, E. M. Being first increases the probability of long diapause in rotifer resting eggs. Hydrobiologia 745, 111–121. https://doi.org/10.1007/s10750-014-2098-8 (2015).Article 

Google Scholar 
Garcia-Roger, E. M. Analisis demografico de bancos de huevos diapausicos de rotiferos PhD Thesis thesis, University of Valencia, (2006).Lapesa, S. Efecto de la depredación por invertebrados sobre poblaciones simpátricas de especies crípticas de rotíferos PhD thesis, University of Valencia, (2004).Miracle, M. R. & Serra, M. Salinity and temperature influence in rotifer life-history characteristics. Hydrobiologia 186, 81–102. https://doi.org/10.1007/bf00048900 (1989).Article 

Google Scholar 
Fontaneto, D., Giordani, I., Melone, G. & Serra, M. Disentangling the morphological stasis in two rotifer species of the Brachionus plicatilis species complex. Hydrobiologia 583, 297–307. https://doi.org/10.1007/s10750-007-0573-1 (2007).Article 

Google Scholar 
Gabaldon, C., Montero-Pau, J., Serra, M. & Carmona, M. J. Morphological similarity and ecological overlap in two rotifer species. PLoS ONE https://doi.org/10.1371/journal.pone.0057087 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Gabaldon, C. & Carmona, M. J. Allocation patterns in modes of reproduction in two facultatively sexual cryptic rotifer species. J. Plankton Res. 37, 429–440. https://doi.org/10.1093/plankt/fbv012 (2015).Article 

Google Scholar 
Garcia-Roger, E. M., Carmona, M. J. & Serra, M. Deterioration patterns in diapausing egg banks of Brachionus (Muller, 1786) rotifer species. J. Exp. Mar. Biol. Ecol. 314, 149–161. https://doi.org/10.1016/j.jembe.2004.08.023 (2005).Article 

Google Scholar 
Lapesa, S., Snell, T. W., Fields, D. M. & Serra, M. Predatory interactions between a cyclopoid copepod and three sibling rotifer species. Freshw. Biol. 47, 1685–1695. https://doi.org/10.1046/j.1365-2427.2002.00926.x (2002).Article 

Google Scholar 
Serra, M., Gomez, A. & Carmona, M. J. Ecological genetics of Brachionus sympatric sibling species. Hydrobiologia 387, 373–384. https://doi.org/10.1023/a:1017083820908 (1998).Article 

Google Scholar 
Ter Braak, C. J. F. & Šmilauer, P. Canoco reference manual and user’s guide: software for ordination, version 5.0. . 496 (Microcomputer Power, 2012).Ciros-Perez, J., Gomez, A. & Serra, M. On the taxonomy of three sympatric sibling species of the Brachionus plicatilis (Rotifera) complex from Spain, with the description of B. ibericus n. sp. Journal of Plankton Research 23, 1311–1328 (2001).Gomez, A., Serra, M., Carvalho, G. R. & Lunt, D. H. Speciation in ancient cryptic species complexes: Evidence from the molecular phylogeny of Brachionus plicatilis (Rotifera). Evolution 56, 1431–1444 (2002).CAS 
Article 

Google Scholar 
SAS/STAT User’s Guide (Cary NC, SAS Institute Inc., 2013). More

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

Zhang, Y., Wu, J. & Xu, B. Human health risk assessment of groundwater nitrogen pollution in Jinghui canal irrigation area of the loess region, northwest China. Environ. Earth Sci. 77, 273 (2018).Article 
CAS 

Google Scholar 
Zhang, D. et al. Potential spreading risks and disinfection challenges of medical wastewater by the presence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) viral RNA in septic tanks of Fangcang Hospital. Sci. Total Environ. 741, 140445 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ahmed, W. et al. First confirmed detection of SARS-CoV-2 in untreated wastewater in Australia: A proof of concept for the wastewater surveillance of COVID-19 in the community. Sci. Total Environ. 728, 138764 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Haramoto, E., Malla, B., Thakali, O. & Kitajima, M. First environmental surveillance for the presence of SARS-CoV-2 RNA in wastewater and river water in Japan. Sci. Total Environ. 737, 140405 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Naddeo, V. & Liu, H. Editorial Perspectives: 2019 novel coronavirus (SARS-CoV-2): What is its fate in urban water cycle and how can the water research community respond?. Environ. Sci. Water Res. Technol. 6, 1213–1216 (2020).CAS 
Article 

Google Scholar 
Cornelisen, C. D., Gillespie, P. A., Kirs, M., Young, R. G. & Harwood, V. J. Motueka River plume facilitates transport of ruminant faecal contaminants into shellfish growing waters, Tasman Bay, New Zealand. N. Z. J. Mar. Freshw. Res. 45, 477–495 (2011).Article 

Google Scholar 
Devane, M. L., Moriarty, E. M., Wood, D., Webster-Brown, J. & Gilpin, B. J. The impact of major earthquakes and subsequent sewage discharges on the microbial quality of water and sediments in an urban river. Sci. Total Environ. 485–486, 666–680 (2014).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Duttagupta, S. et al. Achieving sustainable development goal for clean water in India: Influence of natural and anthropogenic factors on groundwater microbial pollution. Environ. Manag. 66, 42–755 (2020).Article 

Google Scholar 
Huelsen, T. et al. Domestic wastewater treatment with purple phototrophic bacteria using a novel continuous photo anaerobic membrane bioreactor. Water Res. 100, 486–495 (2016).Article 
CAS 

Google Scholar 
Johnson, D. R. et al. The functional and taxonomic richness of wastewater treatment plant microbial communities are associated with each other and with ambient nitrogen and carbon availability. Environ. Microbiol. 17(12), 4851–4860 (2015).CAS 
PubMed 
Article 

Google Scholar 
Lei, Z. J. M. Effects of phosphorus addition on soil microbial biomass and community composition in three forest types in tropical China. Soil Biol. Biochem. 44(1), 31–38 (2012).Article 
CAS 

Google Scholar 
Jian, L. Effects of nitrogen and phosphorus addition on soil microbial community in a secondary tropical forest of China. Biol. Fertil. Soils 51, 207–215 (2015).Article 
CAS 

Google Scholar 
Yu, S. X., Pang, Y. L., Wang, Y. C., Li, J. L. & Qin, S. Spatial variation of microbial communities in sediments along the environmental gradients from Xiaoqing River to Laizhou Bay. Mar. Pollut. Bull. 76, 1048–1056 (2017).
Google Scholar 
Reidl, J. & Klose, K. E. Vibrio cholerae and cholera: Out of the water and into the host. FEMS Microbiol. Rev. 26(2), 125–139 (2002).CAS 
PubMed 
Article 

Google Scholar 
Chin, C.-S. et al. The origin of the Haitian cholera outbreak strain. N. Engl. J. Med. 364, 33–42 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Minoru, K., Miho, F., Mao, T., Yoko, S. & Kanae, M. KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucl. Acids Res. 45, D353–D361 (2017).Article 
CAS 

Google Scholar 
Zieliński, W. et al. The prevalence of drug-resistant and virulent Staphylococcus spp. in a municipal wastewater treatment plant and their spread in the environment. Environ. Int. 143, 105914 (2020).PubMed 
Article 
CAS 

Google Scholar 
Dietrich, J. E. S. & Doherty, T. M. Interaction of Mycobacterium tuberculosis with the host: Consequences for vaccine development. APMIS 117, 440–457 (2009).CAS 
PubMed 
Article 

Google Scholar 
Velayati, A. A. et al. Identification and genotyping of Mycobacterium tuberculosis isolated from water and soil samples of a metropolitan city. Chest 147, 1094–1102 (2015).PubMed 
Article 

Google Scholar 
Pereira, M. I. & Medeiros, J. A. Role of Helicobacter pylori in gastric mucosa-associated lymphoid tissue lymphomas. World J. Gastroenterol. 20, 684–698 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
West, A. P., Millar, M. R. & Tompkins, D. S. Effect of physical environment on survival of Helicobacter pylori. J. Clin. Pathol. 45, 228–231 (1992).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Miller, W. A. et al. Salmonella spp., Vibrio spp., Clostridium perfringens, and Plesiomonas shigelloides in marine and freshwater invertebrates from coastal California ecosystems. Microb. Ecol. 52, 198–206 (2006).CAS 
PubMed 
Article 

Google Scholar 
McCarthy, S. A. Effects of temperature and salinity on survival of toxigenic Vibrio cholerae O1 in seawater. Microb Ecol 31, 167–175 (1996).CAS 
PubMed 
Article 

Google Scholar 
Heaney, N. et al. Effects of softwood biochar on the status of nitrogen species and elements of potential toxicity in soils. Ecotoxicol. Environ. Saf. 166, 383–389 (2018).CAS 
PubMed 
Article 

Google Scholar 
Wang, Z. B., Miao, M. S., Kong, Q. & Ni, S. Q. Evaluation of microbial diversity of activated sludge in a municipal wastewater treatment plant of northern China by high-throughput sequencing technology. Desalin. Water Treat. 57, 1–6 (2016).Article 
CAS 

Google Scholar 
Wang, Z. et al. Weak magnetic field: A powerful strategy to enhance partial nitrification. Water Res. 120, 190–198 (2017).CAS 
PubMed 
Article 

Google Scholar 
Zhang, X. et al. Reduction of nitrous oxide emissions from partial nitrification process by using innovative carbon source (mannitol). Bioresour. Technol. 218, 789–795 (2016).CAS 
PubMed 
Article 

Google Scholar 
Liu, X. et al. N2O emission and bacterial community dynamics during realization of the partial nitrification process. RSC Adv. 8, 24305–24311 (2018).ADS 
CAS 
Article 

Google Scholar 
Lv, L., Ren, L. F., Ni, S. Q., Gao, B. Y. & Wang, Y. N. The effect of magnetite on the start-up and N2O emission reduction of the anammox process. RSC Adv. 6, 99989–99996 (2016).ADS 
CAS 
Article 

Google Scholar 
Yang, S., Liebner, S., Alawi, M., Ebenhöh, O. & Wagner, D. Taxonomic database and cut-off value for processing mcrA gene 454 pyrosequencing data by MOTHUR. J. Microbiol. Methods 103, 3–5 (2014).CAS 
PubMed 
Article 

Google Scholar 
Xu, F. et al. Electricity production and evolution of microbial community in the constructed wetland-microbial fuel cell. Chem. Eng. J. 339, 479–486 (2018).CAS 
Article 

Google Scholar 
Bu, C. et al. Dissimilatory nitrate reduction to ammonium in the yellow river estuary: Rates, abundance, and community diversity. Sci. Rep. 7, 6830 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zhou, J., Fries, M. R., Cheesanford, J. C. & Tiedje, J. M. Phylogenetic analyses of a new group of denitrifiers capable of anaerobic growth of toluene and description of Azoarcus tolulyticus sp. nov.. Int. J. Syst. Bacteriol. 194, 500–506 (1995).Article 

Google Scholar 
Casanova, L., Rutala, W. A., Weber, D. J. & Sobsey, M. D. Survival of surrogate coronaviruses in water. Water Res. 43, 1893–1898 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Elreedy, A. et al. Unraveling the capability of graphene nanosheets and γ-Fe2O3 nanoparticles to stimulate anammox granular sludge. J. Environ. Manag. 277, 111495 (2021).CAS 
Article 

Google Scholar 
Ismail, S. et al. Response of anammox bacteria to short-term exposure of 1,4-dioxane: Bacterial activity and community dynamics. Sep. Purif. Technol. 266, 118539 (2021).CAS 
Article 

Google Scholar 
Shen, X., Xu, M., Li, M., Zhao, Y. & Shao, X. Response of sediment bacterial communities to the drainage of wastewater from aquaculture ponds in different seasons. Sci. Total Environ. 717, 137180 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ismail, S. et al. Fatigue of anammox consortia under long-term 1,4-dioxane exposure and recovery potential: N-kinetics and microbial dynamics. J. Hazard. Mater. 414, 125533 (2021).CAS 
PubMed 
Article 

Google Scholar 
Li, H. F., Li, B. Z., Wang, E. T., Yang, J. S. & Yuan, H. L. Removal of low concentration of phosphorus from solution by free and immobilized cells of Pseudomonas stutzeri YG-24. Desalination 286, 242–247 (2012).CAS 
Article 

Google Scholar 
Xia, J., Ye, L., Ren, H. & Zhang, X. X. Microbial community structure and function in aerobic granular sludge. Appl. Microbiol. Biotechnol. 102(9), 3967–3979 (2018).CAS 
PubMed 
Article 

Google Scholar 
Akizuki, S. et al. Effects of substrate COD/NO2-N ratio on simultaneous methanogenesis and short-cut denitrification in the treatment of blue mussel using acclimated sludge. Biochem. Eng. J. 99, 16–23 (2015).CAS 
Article 

Google Scholar 
Liao, K. et al. Use of convertible flow cells to simulate the impacts of anthropogenic activities on river biofilm bacterial communities. Sci. Total Environ. 653, 148–156 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Marassi, R. J. et al. Performance and toxicity assessment of an up-flow tubular microbial fuel cell during long-term operation with high-strength dairy wastewater. J. Clean. Prod. 259, 120882 (2020).CAS 
Article 

Google Scholar 
Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Medema, G. J., Schets, F. M., Teunis, P. F. M. & Havelaar, A. H. Sedimentation of free and attached Cryptosporidium oocysts and Giardia cysts in water. Appl. Environ. Microbiol. 64, 4460–4466 (1998).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Igbinosa, E. O., Obi, L. C. & Okoh, A. I. Occurrence of potentially pathogenic vibrios in final effluents of a wastewater treatment facility in a rural community of the Eastern Cape Province of South Africa. Res. Microbiol. 160, 531–537 (2009).CAS 
PubMed 
Article 

Google Scholar 
Goh, S. G., Bayen, S., Burger, D., Kelly, B. C. & Gin, Y. H. Occurrence and distribution of bacteria indicators, chemical tracers and pathogenic vibrios in Singapore coastal waters. Mar. Pollut. Bull. 114, 627–634 (2016).PubMed 
Article 
CAS 

Google Scholar 
Cui, Q., Huang, Y., Wang, H. & Fang, T. Diversity and abundance of bacterial pathogens in urban rivers impacted by domestic sewage. Environ. Pollut. 249, 24–35 (2019).CAS 
PubMed 
Article 

Google Scholar 
Suzuki, Y. et al. Growth and antibiotic resistance acquisition of Escherichia coli in a river that receives treated sewage effluent. Sci. Total Environ. 690, 696–704 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Silva, D. C. V. R. et al. Predicting zebrafish spatial avoidance triggered by discharges of dairy wastewater: An experimental approach based on self-purification in a model river. Environ. Pollut. 266, 115325 (2020).CAS 
PubMed 
Article 

Google Scholar 
Wagner, I. & Zalewski, M. Temporal changes in the abiotic/biotic drivers of selfpurification in a temperate river. Ecol. Eng. 94, 275–285 (2016).Article 

Google Scholar 
Clements, W. H. & Rohr, J. R. Community responses to contaminants: Using basic ecological principles to predict ecotoxicological effects. Environ. Toxicol. Chem. 28, 1789–1800 (2009).CAS 
PubMed 
Article 

Google Scholar 
Ismail, S. & Tawfik, A. Comprehensive study for Anammox process via multistage anaerobic baffled reactors. E3S Web Conf. 22, 4–11 (2017).Article 
CAS 

Google Scholar  More

Shope, R. E., MacNamara, L. G. & Mangold, R. Report on the deer mortality, epizootic hemorrhagic disease of deer. NJ Outdoors 6, 17–21 (1955).
Google Scholar 
Trainer, D. O. Epizootic hemorrhagic disease of deer. J. Wildl. Dis. 28, 377–381 (1964).
Google Scholar 
Shope, R. E., MacNamara, L. G. & Mangold, R. A virus-induced epizootic hemorrhagic disease of the Virginia white-tailed deer (Odocoileus virginianus). J. Exp. Med. 111, 155–170 (1960).CAS 
PubMed 

Google Scholar 
Chalmers, G. A., Vance, H. N. & Mitchell, G. J. An outbreak of epizootic hemorrhagic disease in wild ungulates in Alberta. Wildl. Dis. 4, 1–6 (1964).
Google Scholar 
Stallknecht, D. E. et al. Apparent increase of reported hemorrhagic disease in the midwestern and northeastern USA. J. Wildl. Dis. 51, 348–361 (2015).PubMed 

Google Scholar 
Ruder, M. G. et al. The first 10 years (2006–2015) of epizootic hemorrhagic disease virus serotype 6 in the USA. J. Wildl. Dis. 53, 901–905 (2017).PubMed 

Google Scholar 
Pybus, M. J., Ravi, M. & Pollock, C. Epizootic hemorrhagic disease in Alberta, Canada. J. Wildl. Dis. 50, 720–722 (2014).PubMed 

Google Scholar 
Ruder, M. G. et al. Transmission and epidemiology of bluetongue and epizootic hemorrhagic disease in North America: current perspectives, research gaps, and future directions. Vector-Borne Zoonotic Dis. 15, 348–363 (2015).PubMed 

Google Scholar 
Rivera, N. A. et al. Bluetongue and epizootic hemorrhagic disease in the United States of America at the wildlife: livestock interface. Pathogens 10, 915 (2021).PubMed 

Google Scholar 
Mellor, P. S., Boorman, J. & Baylis, M. Culicoides biting midges: their role as arbovirus vectors. Annu. Rev. Entomol. 45, 307–340 (2000).CAS 
PubMed 

Google Scholar 
Pfannenstiel, R. S. et al. Management of North American Culicoides biting midges: current knowledge and research needs. Vector-Borne Zoonotic Dis. 15, 374–384 (2015).PubMed 

Google Scholar 
Mcgregor, B. L. et al. Vector competence of Florida Culicoides insignis (Diptera: Ceratopogonidae) for epizootic hemorrhagic disease virus serotype-2. (2021). https://doi.org/10.3390/v13030410.Vigil, S. L. et al. Apparent range expansion of Culicoides (Hoffmania) insignis (Diptera: Ceratopogonidae) in the Southeastern United States. https://doi.org/10.1093/jme/tjy036.Mullen, G. R. & Murphree, C. S. Chapter 13-biting midges (Ceratopogonidae). in (eds. Mullen, G. R. & Durden, L. A. B. T.-M. and V. E. (Third E.) 213–236 (Academic Press, 2019). https://doi.org/10.1016/B978-0-12-814043-7.00013-3.Werner, D., Groschupp, S., Bauer, C. & Kampen, H. Breeding Habitat Preferences of major Culicoides Species (Diptera: Ceratopogonidae) in Germany. Int. J. Environ. Res. Public Health 17, 5000 (2020).
Google Scholar 
Tabachnick, W. J., Smartt, C. T. & Rutledge-Connelly, C. R. Bluetongue: ENY-743/IN768, 4/2008. EDIS 2008, (2008).Schmidtmann, E. T., Bobian, R. J. & Belden, R. P. Soil chemistries define aquatic habitats with immature populations of the Culicoides variipennis complex (Diptera: Ceratopogonidae). J. Med. Entomol. 37, 58–64 (2000).CAS 
PubMed 

Google Scholar 
Schmidtmann, E. T. et al. Distribution of Culicoides sonorensis (Diptera: Ceratopogonidae) in Nebraska, South Dakota, and North Dakota: clarifying the epidemiology of bluetongue disease in the Northern great plains region of the United States. J. Med. Entomol. 48, 634–643 (2011).CAS 
PubMed 

Google Scholar 
Mullens, B. A. & Holbrook, F. R. Temperature effects on the gonotrophic cycle of Culicoides variipennis (Diptera: Ceratopogonidae). J. Am. Mosq. Control Assoc. 7, 588–591 (1991).CAS 
PubMed 

Google Scholar 
Lysyk, T. J. & Dergousoff, S. J. Distribution of Culicoides sonorensis (Diptera: Ceratopogonidae) in Alberta, Canada. J. Med. Entomol. 51, 560–571 (2014).CAS 
PubMed 

Google Scholar 
Christensen, S. A., Ruder, M. G., Williams, D. M., Porter, W. F. & Stallknecht, D. E. The role of drought as a determinant of hemorrhagic disease in the eastern United States. Glob. Chang. Biol. 26, 3799–3808 (2020).ADS 
PubMed 

Google Scholar 
Lysyk, T. J. & Danyk, T. Effect of temperature on life history parameters of adult Culicoides sonorensis (Diptera: Ceratopogonidae) in relation to geographic origin and vectorial capacity for bluetongue virus. J. Med. Entomol. 44, 741–751 (2007).CAS 
PubMed 

Google Scholar 
Wittmann, E. J., Mellor, P. S. & Baylis, M. Effect of temperature on the transmission of orbiviruses by the biting midge, Culicoides sonorensis. Med. Vet. Entomol. 16, 147–156 (2002).CAS 
PubMed 

Google Scholar 
Brand, S. P. C. & Keeling, M. J. The impact of temperature changes on vector-borne disease transmission: Culicoides midges and bluetongue virus. J. R. Soc. Interface 14, 20160481 (2017).PubMed 

Google Scholar 
Couvillion, C. E., Nettles, V. F., Davidson, W. R., Pearson, J. E. & Gustafson, G. A. Hemorrhagic disease among white-tailed deer in the Southeast from 1971 through 1980. Proc. US Anim. Hlth. Assoc. 85, 522–537 (1981).
Google Scholar 
Zarnke, R. L. Serologic survey for selected microbial pathogens in Alaskan wildlife. J. Wildl. Dis. 19, 324–329 (1983).CAS 
PubMed 

Google Scholar 
Howerth, E. W., Stallknecht, D. E. & Kirkland, P. D. Bluetongue, epizootic hemorrhagic disease, and other orbivirus-related diseases. Infect. Dis. Wild Mammals https://doi.org/10.1002/9780470344880.ch3 (2001).Article 

Google Scholar 
Stevens, G., McCluskey, B., King, A., O’Hearn, E. & Mayr, G. Review of the 2012 epizootic hemorrhagic disease outbreak in domestic ruminants in the United States. PLoS ONE 10, 1–11 (2015).
Google Scholar 
Fischer, J. R. et al. An epizootic of hemorrhagic disease in white-tailed deer (Odocoileus virginianus) in Missouri: necropsy findings and population impact. J. Wildl. Dis. 31, 30–36 (1995).CAS 
PubMed 

Google Scholar 
Pierce, B. EHD outbreak takes toll on white-tailed deer population. Bozeman Daily Chronicle (2011).Gaydos, J. K., Davidson, W. R., Mead, D. G., Howerth, E. W. & Stallknecht, D. E. Innate resistance to epizootic hemorrhagic disease in white-tailed deer. J. Wildl. Dis. 38, 713–719 (2002).PubMed 

Google Scholar 
Stallknecht, D. E. & Howerth, E. W. Epidemiology of bluetongue and epizootic haemorrhagic disease in wildlife: surveillance methods. Vet. Ital. 40, 203–207 (2004).CAS 
PubMed 

Google Scholar 
Hedman, H. D. et al. Spatial analysis of chronic wasting disease in free-ranging white-tailed deer (Odocoileus virginianus) in Illinois, 2008–2019. Transbound. Emerg. Dis. 68, 2376–2383 (2020).PubMed 

Google Scholar 
Baygents, G. & Bani-Yaghoub, M. Cluster analysis of hemorrhagic disease in Missouri’s white-tailed deer population: 1980–2013. BMC Ecol. 18, 35 (2018).PubMed 

Google Scholar 
French, S. K., Pearl, D. L., Peregrine, A. S. & Jardine, C. M. Spatio-temporal clustering of Baylisascaris procyonis, a zoonotic parasite, in raccoons across different landscapes in southern Ontario. Spat. Spatiotemporal. Epidemiol. 35, 100371 (2020).PubMed 

Google Scholar 
Kulldorff, M., Heffernan, R., Hartman, J., Assunção, R. & Mostashari, F. A space-time permutation scan statistic for disease outbreak detection. PLoS Med. 2, 0216–0224 (2005).
Google Scholar 
Allison, A. B. et al. Detection of a novel reassortant epizootic hemorrhagic disease virus (EHDV) in the USA containing RNA segments derived from both exotic (EHDV-6) and endemic (EHDV-2) serotypes. J. Gen. Virol. 91, 430–439 (2010).CAS 
PubMed 

Google Scholar 
Allen, S. E. et al. Epizootic hemorrhagic disease in white-tailed deer, Canada. Emerg. Infect. Dis. 25, 832–834 (2019).PubMed 

Google Scholar 
Boyer, T. C., Ward, M. P., Wallace, R. L. & Singer, R. S. Regional seroprevalence of bluetongue virus in cattle in Illinois and western Indiana. Am. J. Vet. Res. 68, 1212–1219 (2007).PubMed 

Google Scholar 
Pedersen, K. et al. Serologic Evidence of various arboviruses detected in white-tailed deer (Odocoileus virginianus) in the United States. Am. J. Trop. Med. Hyg. 97, 319–323 (2017).PubMed 

Google Scholar 
Garrett, E. F. et al. Clinical disease associated with epizootic hemorrhagic disease virus in cattle in Illinois. J. Am. Vet. Med. Assoc. 247, 190–195 (2015).PubMed 

Google Scholar 
Boyer, T. C., Ward, M. P. & Singer, R. S. Climate, landscape, and the risk of orbivirus exposure in cattle in Illinois and western Indiana. Am. J. Trop. Med. Hyg. 83, 789–794 (2010).PubMed 

Google Scholar 
Cauvin, A. et al. Antibodies to epizootic hemorrhagic disease virus (EHDV) in farmed and wild Florida white-tailed deer (Odocoileus virginianus). J. Wildl. Dis. 56, 208–213 (2020).CAS 
PubMed 

Google Scholar 
McGregor, B. L. et al. Host use patterns of Culicoides spp. biting midges at a big game preserve in Florida, USA, and implications for the transmission of orbiviruses. Med. Vet. Entomol. 33, 110–120 (2019).CAS 
PubMed 

Google Scholar 
Berke, O. Exploratory disease mapping: kriging the spatial risk function from regional count data. 11, 1–11 (2004).Svoboda, M. et al. The drought monitor. Bull. Am. Meterol. Soc. 83, 1181–1190 (2002).ADS 

Google Scholar 
NOAA National Centers for Environmental Information. State of the Climate: National Climate Report for Annual 2012. https://www.ncdc.noaa.gov/sotc/national/201213. (Accessed: 5th February 2022)Calzolari, M. & Albieri, A. Could drought conditions trigger Schmallenberg virus and other arboviruses circulation?. Int. J. Health Geogr. 12, 6–10 (2013).
Google Scholar 
Zuliani, A. et al. Modelling the northward expansion of Culicoides sonorensis (Diptera: Ceratopogonidae) under future climate scenarios. PLoS ONE 10, 1–23 (2015).
Google Scholar 
Burns, D. Diseases caused by arthropods and other noxious animals. in Rook’s Textbook of Dermatology 1555–1618 (Blackwell Publishing, 2004).Mullens, B. A. A quantitative survey of Culicoides variipennis (Diptera: Ceratopogonidae) in dairy waste water ponds in Southern California. J. Med. Entomol. 26, 559–565 (1989).CAS 
PubMed 

Google Scholar 
Wang, D., Hejazi, M., Cai, X. & Valocchi, A. J. Climate change impact on meteorological, agricultural, and hydrological drought in central Illinois. Water Resour. Res. 47, 9527 (2011).ADS 

Google Scholar 
Tomasek, B. J., Williams, M. M. II. & Davis, A. S. Changes in field workability and drought risk from projected climate change drive spatially variable risks in Illinois cropping systems. PLoS ONE 12, e0172301 (2017).PubMed 

Google Scholar 
Casey, C. L., Rathbun, S. L., Stallknecht, D. E. & Ruder, M. G. Spatial analysis of the 2017 outbreak of hemorrhagic disease and physiographic region in the eastern United States. Viruses 13, 550 (2021).CAS 
PubMed 

Google Scholar 
Berry, B. S., Magori, K., Perofsky, A. C., Stallknecht, D. E. & Park, A. W. Wetland cover dynamics drive hemorrhagic disease patterns in white-tailed deer in the United States. J. Wildl. Dis. 49, 501–509 (2013).PubMed 

Google Scholar 
Uslu, U. & Dik, B. Chemical characteristics of breeding sites of Culicoides species (Diptera: Ceratopogonidae). Vet. Parasitol. 169, 178–184 (2010).CAS 
PubMed 

Google Scholar 
Lysyk, T. J. Abundance and species composition of Culicoides (Diptera : Ceratopogonidae) at cattle facilities in southern Alberta, Canada. (2006).Erram, D., Blosser, E. M. & Cadena, N. B. Habitat associations of Culicoides species (Diptera : Ceratopogonidae) abundant on a commercial cervid farm in Florida, USA. Parasit. Vectors https://doi.org/10.1186/s13071-019-3626-1 (2019).Article 
PubMed 

Google Scholar 
Jones, R. H. Observations on the larval habitats of some North American species of Culicoides (Diptera: Ceratopogonidae). Ann. Entomol. Soc. Am. 54, 702–710 (1961).
Google Scholar 
Schmidtmann, E. T., Jones, C. J. & Gollands, B. Comparative host-seeking activity of Culicoides (Diptera: Ceratopogonidae) attracted to pastured livestock in central New York State, USA. J. Med. Entomol. 17, 221–231 (1980).
Google Scholar 
Schlichting, P. E. Summary of 2019–2020 Illinois deer seasons. Illinois Dep. Nat. Resour. 1–12 (2020).Orange, J. P. et al. Evidence of epizootic hemorrhagic disease virus and bluetongue virus exposure in nonnative ruminant species in northern Florida. J. Zoo Wildl. Med. 51, 745–751 (2021).PubMed 

Google Scholar 
Purse, B. V. et al. Impacts of climate, host and landscape factors on Culicoides species in Scotland. Med. Vet. Entomol. 26, 168–177 (2012).CAS 
PubMed 

Google Scholar 
Searle, K. R. et al. Identifying environmental drivers of insect phenology across space and time: Culicoides in Scotland as a case study. Bull. Entomol. Res. 103, 155–170 (2013).ADS 
CAS 
PubMed 

Google Scholar 
Shimizu, S., Toyota, I., Arishima, T. & Goto, Y. Frequency of serological cross-reactions between Ibaraki and bluetongue viruses using the agar gel immunodiffusion test. Vet. Ital. 40, 583–586 (2004).CAS 
PubMed 

Google Scholar 
Alkhamis, M. A. et al. Global emergence and evolutionary dynamics of bluetongue virus. Sci. Rep. 10, 21677 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Cottingham, S. L., White, Z. S., Wisely, S. M. & Campos-Krauer, J. M. A Mortality-based description of EHDV and BTV prevalence in farmed white-tailed deer (Odocoileus virginianus) in Florida, USA. Viruses 13, 1443 (2021).CAS 
PubMed 

Google Scholar 
Nettles, V. F., Davidson, W. R. & Stallknecht, D. E. Surveillance for hemorrhagic disease in white-tailed deer and other wild ruminants, 1980-1989. In Proceeding of the Annual Conference of the Southeastern Association of Fish and Wildlife Agencies. 46, 138–146 (1992).Maclachlan, N. J., Zientara, S., Wilson, W. C., Richt, J. A. & Savini, G. Bluetongue and epizootic hemorrhagic disease viruses: recent developments with these globally re-emerging arboviral infections of ruminants. Curr. Opin. Virol. 34, 56–62 (2019).PubMed 

Google Scholar 
Savini, G. et al. Epizootic haemorragic disease. Res. Vet. Sci. 91, 1–17 (2011).CAS 
PubMed 

Google Scholar 
Kedmi, M. et al. The association of winds with the spread of EHDV in dairy cattle in Israel during an outbreak in 2006. Prev. Vet. Med. 96, 152–160 (2010).PubMed 

Google Scholar 
Mayo, C. E. et al. Seasonal and interseasonal dynamics of bluetongue virus infection of dairy cattle and Culicoides sonorensis Midges in Northern California: implications for virus overwintering in temperate zones. PLoS ONE 9, e106975 (2014).ADS 
PubMed 

Google Scholar 
USGS National Wildlife Health Center. Wildlife Health Information Sharing Partnership-event reporting system (WHISPers). https://www.nwhc.usgs.gov/whispers/.Lenoch, J. & Nguyen, N. WHISPers, the USGS-NWHC Wildlife Health event reporting system. Proc. Wildl. Dis. Assoc. 8, 2579 (2016).
Google Scholar 
Brooks, J. W. Postmortem changes in animal carcasses and estimation of the postmortem interval. Vet. Pathol. 53, 929–940 (2016).CAS 
PubMed 

Google Scholar 
Pilz, J. & Spöck, G. Why do we need and how should we implement Bayesian Kriging methods. Stoch. Environ. Res. Risk Assess. 22, 621–632 (2007).MathSciNet 

Google Scholar 
Krivoruchko, K. Empirical Bayesian Kriging. ArcUser Fall 6, (2012).Ord, J. K. & Getis, A. Local spatial autocorrelation statistics: distributional issues and an application. Geogr. Anal. 27, 286–306 (1995).
Google Scholar 
Kulldorff, M. & Information Management Services Inc. SaTScanTM v 9.6: Software for the spatial and space-time scan statistics. (2018).Kulldorff, M., Athas, W. F., Feuer, E. J., Miller, B. A. & Key, C. R. Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos, New Mexico. Am. J. Public Health 88, 1377–1380 (1998).CAS 
PubMed 

Google Scholar  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

Animals such as this orangutan in Indonesia are endangered because of illegal deforestation.Credit: Jami Tarris/Future Publishing via Getty

Funding battles stymie plan to protect global biodiversityScientists are frustrated with slow progress towards a new deal to protect the natural world. Government officials from around the globe met in Geneva, Switzerland, on 14–29 March to find common ground on a draft of the deal, known as the post-2020 global biodiversity framework, but discussions stalled.The framework so far sets out 4 broad goals, including slowing species extinction, and 21 mostly quantitative targets, such as protecting at least 30% of the world’s land and seas. It is part of an international treaty known as the United Nations Convention on Biological Diversity, and aims to address the global biodiversity crisis, which could see one million plant and animal species go extinct in the next few decades.Many who were at the meeting say that disagreements over funding for biodiversity conservation were the main hold-up in negotiations. For example, the draft deal proposed that US$10 billion of funding per year should flow from developed nations to low- and middle-income countries to help them to implement the biodiversity framework. But many think this is not enough.Negotiators say they will now have to meet again before a highly anticipated UN biodiversity summit later this year, where the deal was to be signed.‘Collegiality’ influences researchers’ promotion prospectsUniversities in North America often consider how well researchers interact with each other when making decisions about who gets promoted, a study has found, even though these factors are not formally acknowledged in review guidelines.A researcher’s performance is usually assessed according to three pillars: research, teaching and service. But in recent years, there has been a push from some academics to add another pillar: collegiality. Many say that the concepts of cooperation, collaboration and respect, which broadly fall under the definition of collegiality, are important to the functioning of laboratories and research teams.DeDe Dawson, an academic librarian at the University of Saskatchewan in Saskatoon, Canada, and colleagues analysed more than 860 review, promotion and tenure documents from different departments at 129 universities in the United States and Canada to get a sense of how often collegiality is taken into account.The study, published on 6 April (D. Dawson et al. PLoS ONE 17, e0265506; 2022), found that the concept of collegiality was widespread: the word ‘collegiality’ and related terms, such as ‘citizenship’ or ‘professionalism’, appeared 507 times in 213 of the documents, suggesting that it was often taken into account in evaluations. But just 85 documents included a definition of the term, and fewer still explained how it was measured or used in assessments.

Source: D. Dawson et al. PLoS ONE 17, e0265506 (2022)

Collegiality was mentioned most often in research-intensive institutions (see ‘Academia’s fourth pillar’). The authors say that this could be because the behaviour involved is valued in research groups.Dawson and her colleagues warn that relying on collegiality in performance reviews without adequate guidance could introduce bias, as those in charge fill in the blanks with their own definitions.“We need to make sure that we don’t use collegiality to exclude others that may communicate or interact differently,” says Sujay Kaushal, a geologist at the University of Maryland in College Park, who has previously studied collegiality. More