Philippa Kaur

Sharma, S. & Chatterjee, S. Microplastic pollution, a threat to marine ecosystem and human health: A short review. Environ. Sci. Pollut. Res. 24(27), 21530–21547 (2017).Article 

Google Scholar 
Gray, A. D., Wertz, H., Leads, R. R. & Weinstein, J. E. Microplastic in two South Carolina Estuaries: Occurrence, distribution, and composition. Mar. Pollut. Bull. 128, 223–233 (2018).CAS 
PubMed 
Article 

Google Scholar 
Weinstein, J. E., Dekle, J. L., Leads, R. R. & Hunter, R. A. Degradation of bio-based and biodegradable plastics in a salt marsh habitat: Another potential source of microplastics in coastal waters. Mar. Pollut. Bull. 160, 111518 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Robin, R. S. et al. Holistic assessment of microplastics in various coastal environmental matrices, southwest coast of India. Sci. Total Environ. 703, 134947 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Kwon, O. Y., Kang, J. H., Hong, S. H. & Shim, W. J. Spatial distribution of microplastic in the surface waters along the coast of Korea. Mar. Pollut. Bull. 155, 110729 (2020).CAS 
PubMed 
Article 

Google Scholar 
Fendall, L. S. & Sewell, M. A. Contributing to marine pollution by washing your face: Microplastics in facial cleansers. Mar. Pollut. Bull. 58(8), 1225–1228 (2009).CAS 
PubMed 
Article 

Google Scholar 
Hantoro, I., Löhr, A. J., Van Belleghem, F. G., Widianarko, B. & Ragas, A. M. Microplastics in coastal areas and seafood: Implications for food safety. Food Addit. Contam. Part A 36(5), 674–711 (2019).CAS 
Article 

Google Scholar 
Retama, I. et al. Microplastics in tourist beaches of Huatulco Bay, Pacific coast of southern Mexico. Mar. Pollut. Bull. 113(1–2), 530–535 (2016).CAS 
PubMed 
Article 

Google Scholar 
Frias, J. P. G. L., Otero, V. & Sobral, P. Evidence of microplastics in samples of zooplankton from Portuguese coastal waters. Mar. Environ. Res. 95, 89–95 (2014).CAS 
PubMed 
Article 

Google Scholar 
Hosseini, R., Sayadi, M. H., Aazami, J. & Savabieasfehani, M. Accumulation and distribution of microplastics in the sediment and coastal water samples of Chabahar Bay in the Oman Sea, Iran. Mar. Pollut. Bull. 160, 111682 (2020).CAS 
PubMed 
Article 

Google Scholar 
Andrady, A. L. Persistence of Plastic Litter in the Oceans. Marine Anthropogenic Litter 57–72 (Springer, 2015).Leads, R. R. & Weinstein, J. E. Occurrence of tire wear particles and other microplastics within the tributaries of the Charleston Harbor Estuary, South Carolina, USA. Mar. Pollut. Bull. 145, 569–582 (2019).CAS 
PubMed 
Article 

Google Scholar 
Nor, N. H. M. & Obbard, J. P. Microplastics in Singapore’s coastal mangrove ecosystems. Mar. Pollut. Bull. 79(1–2), 278–283 (2014).PubMed 

Google Scholar 
Plastics Europe. Plastics—The Facts 2017. (Plastics Europe, 2017).Lusher, A. L., Welden, N. A., Sobral, P., & Cole, M. Sampling, isolating and identifying microplastics ingested by fish and invertebrates. In Analysis of Nanoplastics and Microplastics in Food 119–148. (CRC Press, 2020).Murray, F. & Cowie, P. R. Plastic contamination in the decapod crustacean Nephrops norvegicus (Linnaeus, 1758). Mar. Pollut. Bull. 62(6), 1207–1217 (2011).CAS 
PubMed 
Article 

Google Scholar 
Gray, A. D. & Weinstein, J. E. Size-and shape-dependent effects of microplastic particles on adult daggerblade grass shrimp (Palaemonetes pugio). Environ. Toxicol. Chem. 36(11), 3074–3080 (2017).CAS 
PubMed 
Article 

Google Scholar 
Au, S. Y., Bruce, T. F., Bridges, W. C. & Klaine, S. J. Responses of Hyalella azteca to acute and chronic microplastic exposures. Environ. Toxicol. Chem. 34(11), 2564–2572 (2015).CAS 
PubMed 
Article 

Google Scholar 
Cole, M. et al. Microplastic ingestion by zooplankton. Environ. Sci. Technol. 47(12), 6646–6655 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Woods, M. N., Stack, M. E., Fields, D. M., Shaw, S. D. & Matrai, P. A. Microplastic fiber uptake, ingestion, and egestion rates in the blue mussel (Mytilus edulis). Mar. Pollut. Bull. 137, 638–645 (2018).CAS 
PubMed 
Article 

Google Scholar 
Scott, N. et al. Particle characteristics of microplastics contaminating the mussel Mytilus edulis and their surrounding environments. Mar. Pollut. Bull. 146, 125–133 (2019).CAS 
PubMed 
Article 

Google Scholar 
Van Cauwenberghe, L., Claessens, M., Vandegehuchte, M. B. & Janssen, C. R. Microplastics are taken up by mussels (Mytilus edulis) and lugworms (Arenicola marina) living in natural habitats. Environ. Pollut. 199, 10e17 (2015).
Google Scholar 
Waite, H. R., Donnelly, M. J. & Walters, L. J. Quantity and types of microplastics in the organic tissues of the eastern oyster Crassostrea virginica and Atlantic mud crab Panopeus herbstii from a Florida estuary. Mar. Pollut. Bull. 129(1), 179–185 (2018).CAS 
PubMed 
Article 

Google Scholar 
Quanbin, L. et al. Uptake and elimination of microplastics by Tigriopus japonicus and its impact on feeding behavior. Asian J. Ecotoxicol. 4, 184–191. https://doi.org/10.7524/AJE.1673-5897.20191216002 (2020).Article 

Google Scholar 
Galloway, T. S. & Lewis, C. N. Marine microplastics spell big problems for future generations. Proc. Natl. Acad. Sci. U.S.A. 113(9), 2331e2333 (2016).Article 
CAS 

Google Scholar 
Galloway, T. S., Cole, M. & Lewis, C. Interactions of microplastic debris throughout the marine ecosystem. Nat. Ecol. Evol. 1, 0116. https://doi.org/10.1038/s41559-017-0116 (2017).Article 

Google Scholar 
Carlos de Sá, L., Luís, L. G. & Guilhermino, L. Effects of microplastics on juveniles of the common goby (Pomatoschistus microps): Confusion with prey, reduction of the predatory performance and efficiency, and possible influence of developmental conditions. Environ. Pollut. 196, 359–362 (2015).PubMed 
Article 
CAS 

Google Scholar 
Cole, M., Lindeque, P., Halsband, C. & Galloway, T. S. Microplastics as contaminants in the marine environment: A review. Mar. Pollut. Bull. 62(12), 2588–2597 (2011).CAS 
PubMed 
Article 

Google Scholar 
Key, P. B., Chung, K. W., West, J. B., Pennington, P. L. & DeLorenzo, M. E. Developmental and reproductive effects in grass shrimp (Palaemon pugio) following acute larval exposure to a thin oil sheen and ultraviolet light. Aquat. Toxicol. 228, 105651 (2020).CAS 
PubMed 
Article 

Google Scholar 
Allen, D. M., Harding, J. M., Stroud, K. B. & Yozzo, K. L. Movements and site fidelity of grass shrimp (Palaemonetes pugio and P. vulgaris) in salt marsh intertidal creeks. Mar. Biol. 162(6), 1275–1285 (2015).Article 

Google Scholar 
Kunz, A. K., Ford, M. & Pung, O. J. Behavior of the grass shrimp Palaemonetes pugio and its response to the presence of the predatory fish Fundulus heteroclitus. Am. Midl. Nat. 155, 286–294. https://doi.org/10.1674/0003-0031 (2006).Article 

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

Google Scholar 
Cozar, A. et al. Plastic debris in the open ocean. PNAS 111, 10239e10244. https://doi.org/10.1073/pnas.1314705111 (2014).CAS 
Article 

Google Scholar 
Leads, R. R., Burnett, K. G. & Weinstein, J. E. The effect of microplastic ingestion on survival of the grass shrimp Palaemonetes pugio (Holthuis, 1949) challenged with Vibrio campbellii. Environ. Toxicol. Chem. 38(10), 2233–2242 (2019).CAS 
PubMed 
Article 

Google Scholar 
Beiras, R., Duran, I., Bellas, J. & Sanchez-Marín, P. Biological effects of contaminants: Paracentrotus lividus sea urchin embryo test with marine sediment elutriates. Int. Counc. Explor. Sea. Technol. Environ. Mar. Sci. 51, 113 (2012).
Google Scholar 
Kögel, T., Bjorøy, Ø., Toto, B., Bienfait, A. M. & Sanden, M. Micro-and nanoplastic toxicity on aquatic life: Determining factors. Sci. Total Environ. 709, 136050 (2020).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Lindeque, P. K. et al. Are we underestimating microplastic abundance in the marine environment? A comparison of microplastic capture with nets of different mesh-size. Environ Pollut. 265(Pt A), 114721. https://doi.org/10.1016/j.envpol.2020.114721 (2020).CAS 
Article 
PubMed 

Google Scholar 
Andrady, A. L. Microplastics in the marine environment. Mar. Pollut. Bull. 62(8), 1596e1605 (2011).Article 
CAS 

Google Scholar 
Leight, A. K., Scott, G. I., Fulton, M. H. & Daugomah, J. W. Long term monitoring of grass shrimp Palaemonetes spp. Population metrics at sites with agricultural runoff influences 1, 2. Integr. Comp. Biol. 45(1), 143–150 (2005).CAS 
PubMed 
Article 

Google Scholar 
Weinstein, J. E. & Garner, T. R. Piperonyl butoxide enhances the bioconcentration and photoinduced toxicity of fluoranthene and benzo [a] pyrene to larvae of the grass shrimp (Palaemonetes pugio). Aquat. Toxicol. 87(1), 28–36 (2008).CAS 
PubMed 
Article 

Google Scholar 
Key, P. B., Chung, K. W., Hoguet, J., Sapozhnikova, Y. & DeLorenzo, M. E. Toxicity of the mosquito control insecticide phenothrin to three life stages of the grass shrimp (Palaemonetes pugio). J. Environ. Sci. Health B 46(5), 426–431 (2011).CAS 
PubMed 
Article 

Google Scholar 
Broad, A. C. Larval development of Palaemonetes pugio Holthuis. Biol. Bull. 112, 144–161 (1957).Article 

Google Scholar 
Broad, A. C. The relationship between diet and larval development of Palaemonetes. Biol. Bull. 112, 162–170 (1957).Article 

Google Scholar 
Sandifer, P. A. Effects of temperature and salinity on larval development of grass shrimp, Palaemonetes vulgaris (Decapoda, Caridea). Fish. Bull. 71(1), 115 (1973).
Google Scholar 
Boston, M. A. & Provenzano, A. J. Attempted hybridization of the grass shrimp Palaemonetes (Caridea, palaemonidae) with an evaluation of taxonomic characters of juveniles. Estuaries 5(3), 165–174 (1982).Article 

Google Scholar 
Anderson, G. S. Species profiles: Life histories and environmental requirements of coastal fishes and invertebrates (Gulf of Mexico): Grass shrimp (No. 4). The Service. (1985).Vikas, P. A. et al. Unraveling the effects of live microalgal enrichment on Artemia nauplii. Indian J. Fish. 59(4), 111–121 (2012).
Google Scholar 
Provenzano, A. J., Schmitz, K. B. & Boston, M. A. Survival, duration of larval stages, and size of postlarvae of grass shrimp, Palaemonetes pugio, reared from Kepone® contaminated and uncontaminated populations in Chesapeake Bay. Estuaries 1(4), 239–244 (1978).Article 

Google Scholar 
Johnson, W. S., & Allen, D. M. Zooplankton of the Atlantic and Gulf Coasts: A Guide to Their Identification and Ecology. (JHU Press, 2012).Hubschman, J. H. The development and function of neurosecretory sites in the eyestalks of larval Palaemonetes (Decapoda: Natantia) (Doctoral dissertation, The Ohio State University, 1962).Wheeler, M. W., Park, R. M. & Bailer, A. J. Comparing median lethal concentration values using confidence interval overlap or ratio tests. Environ. Toxicol. Chem. Int. J. 25(5), 1441–1444 (2006).CAS 
Article 

Google Scholar 
Isobe, A., Kubo, K., Tamura, Y., Nakashima, E. & Fujii, N. Selective transport of microplastics and mesoplastics by drifting in coastal waters. Mar. Pollut. Bull. 89(1–2), 324–330 (2014).CAS 
PubMed 
Article 

Google Scholar 
Syakti, A. D. et al. Beach macro-litter monitoring and floating microplastic in a coastal area of Indonesia. Mar. Pollut. Bull. 122(1–2), 217–225. https://doi.org/10.1016/j.marpolbul.2017.06.046 (2017) (Epub 2017 Jun 20 PMID: 28645761).CAS 
Article 
PubMed 

Google Scholar 
Reisser, J. et al. Marine plastic pollution in waters around Australia: Characteristics, concentrations, and pathways. PLoS One 8(11), e80466 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Järlskog, I. et al. Occurrence of tire and bitumen wear microplastics on urban streets and in sweepsand and washwater. Sci. Total Environ. 729, 138950. https://doi.org/10.1016/j.scitotenv.2020.138950 (2020) (Epub 2020 Apr 26. PMID: 32371211).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Key, P. B., Fulton, M. H., Scott, G. I., Layman, S. L. & Wirth, E. F. Lethal and sublethal effects of malathion on three life stages of the grass shrimp, Palaemonetes pugio. Aquat. Toxicol. 40(4), 311–322 (1998).CAS 
Article 

Google Scholar 
DeLorenzo, M. E., Serrano, L., Chung, K. W., Hoguet, J. & Key, P. B. Effects of the insecticide permethrin on three life stages of the grass shrimp, Palaemonetes pugio. Ecotoxicol. Environ. Saf. 64(2), 122–127 (2006).CAS 
PubMed 
Article 

Google Scholar 
Key, P. B., Meyer, S. L. & Chung, K. W. Lethal and sub-lethal effects of the fungicide chlorothalonil on three life stages of the grass shrimp, Palaemonetes pugio. J. Environ. Sci. Health B 38(5), 539–549 (2003).PubMed 
Article 
CAS 

Google Scholar 
Key, P. B., Chung, K. W., Hoguet, J., Shaddrix, B. & Fulton, M. H. Toxicity and physiological effects of brominated flame retardant PBDE-47 on two life stages of grass shrimp, Palaemonetes pugio. Sci. Total Environ. 399(1–3), 28–32 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ziajahromi, S., Kumar, A., Neale, P. A. & Leusch, F. D. Environmentally relevant concentrations of polyethylene microplastics negatively impact the survival, growth and emergence of sediment-dwelling invertebrates. Environ. Pollut. 236, 425–431 (2018).CAS 
PubMed 
Article 

Google Scholar 
Redondo-Hasselerharm, P. E., Falahudin, D., Peeters, E. T. & Koelmans, A. A. Microplastic effect thresholds for freshwater benthic macroinvertebrates. Environ. Sci. Technol. 52(4), 2278–2286 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lehtiniemi, M. et al. Exposure to leachates from post-consumer plastic and recycled rubber causes stress responses and mortality in a copepod Limnocalanus macrurus. Mar. Pollut. Bull. 173, 113103 (2021).CAS 
PubMed 
Article 

Google Scholar 
Martínez-Gómez, C., León, V. M., Calles, S., Gomáriz-Olcina, M. & Vethaak, A. D. The adverse effects of virgin microplastics on the fertilization and larval development of sea urchins. Mar. Environ. Res. 130, 69–76 (2017).PubMed 
Article 
CAS 

Google Scholar 
Khosrovyan, A., Gabrielyan, B. & Kahru, A. Ingestion and effects of virgin polyamide microplastics on Chironomus riparius adult larvae and adult zebrafish Danio rerio. Chemosphere 259, 127456 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Le Bihanic, F. et al. Organic contaminants sorbed to microplastics affect marine medaka fish early life stages development. Mar Pollut Bull. 154, 111059. https://doi.org/10.1016/j.marpolbul.2020.111059 (2020) (Epub 2020 Mar 31 PMID: 32319895).CAS 
Article 
PubMed 

Google Scholar 
LeMoine, C. M. et al. Transcriptional effects of polyethylene microplastics ingestion in developing zebrafish (Danio rerio). Environ. Pollut. 243, 591–600 (2018).CAS 
PubMed 
Article 

Google Scholar 
Freeman, J. A. Regulation of tissue growth in crustacean larvae by feeding regime. Biol. Bull. 178(3), 217–221 (1990).CAS 
PubMed 
Article 

Google Scholar 
Ma, H. et al. Microplastics in aquatic environments: Toxicity to trigger ecological consequences. Environ. Pollut. 261, 114089 (2020).CAS 
PubMed 
Article 

Google Scholar  More

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

Relevance for design of dFAD recovery programmesOur results provide guidance for implementing effective dFAD recovery programmes. More than 40% of dFAD trajectories in the Indian and Atlantic oceans drifted away from fishing grounds never to return, potentially later stranding in coastal areas (Imzilen et al.5 estimated that 10–20% of all French dFADs eventually strand, whereas 16.0% of our trajectories that definitively leave fishing zones strand). This loss represents at least 529 tonnes yr−1 of marine litter for the French fleet5,14 and probably 2–3 times that weight including all purse seiners in the two oceans28. More than 20% of dFAD trajectories that drifted away from fishing grounds passed within 50 km of a port (ranging from 3.3% to 31.6% for cut-off distances from 10 to 100 km; potentially underestimated due to remote deactivation of GPS buoys by purse seiners). This result suggests that coastal dFAD recovery programmes could be complementary to other mitigation measures, such as dFAD buoy limits already implemented by tRFMOs and spatio-temporal dFAD deployment closures proposed by Imzilen et al.5. Indeed, Imzilen et al.5 showed that prohibiting dFAD deployments in areas that would probably lead to strandings would principally protect coastal areas of the southwestern Indian Ocean and the eastern Gulf of Guinea, whereas we found that dFADs exiting fishing grounds from other areas, such as the northwestern Indian Ocean and the northern Gulf of Guinea, passed close to regional ports and could potentially be recovered at sea. Although our results are specific to the French and associated purse-seine fleet (representing ~1/3–1/2 of catch and dFAD deployments of all fleets28), available data indicate that other purse-seine fleets have similar spatio-temporal patterns of deployments28, suggesting that our results are applicable to the entire tropical tuna purse-seine fishery in the Indian and Atlantic oceans.These results contrast somewhat with existing analyses from the western and central Pacific Ocean, where it was estimated that 36% of dFADs ended up outside fishing grounds, but that the final recorded position of these abandoned dFADs were typically far from ports (502–952 km)29. Although these differences may be related to the larger spatial scales of the Pacific Ocean, additional analyses based on examinations of entire trajectories are needed to assess viability of recovery programmes based on ports.Consequences of spatial and temporal variation of dFAD lossHigh seas recovery could also be structured around our results on where important percentages of buoys exit fishing grounds towards the high seas. In the Indian Ocean, dFADs definitively leaving from the eastern border (70° E) end up stranded in or transiting through the Maldives and the eastern Indian Ocean. This happens relatively less frequently in the period from June to August and becomes much more frequent from October to December. Low loss rates during June to August are consistent with known seasonal patterns in dFAD deployment and fishing during this period4,25. At that time of the year, dFADs are deployed by fishers with the intent that they drift along the eastern African coast until they reach the main dFAD fishing grounds off Somalia, avoiding strong monsoon-driven currents favourable to eastward export of dFADs from July to December27. This is followed by a more intense dFAD fishing season during August–October. Finally, starting in October/November, a period of transition towards fishing further south in the Indian Ocean occurs, with relatively more focus on free-swimming school sets25,30, probably contributing to abandonment of dFADs in the northern Indian Ocean in the last quarter of the year.In the Atlantic Ocean, dFADs lost to the high seas exit fishing grounds mostly from the northwestern border (between 10° and 20° N) and southwestern border (2°–5° S), which is consistent with transport by the North Equatorial and South Equatorial Currents26. Although the seasonality of loss is less marked in the Atlantic Ocean than in the Indian Ocean, the peak months of July and December are associated with transitions in the spatio-temporal distribution of deployments from principally deploying just north of the equator off of West Africa to focusing on the Gulf of Guinea further east30. These transitions could lead to increased dFAD abandonment in areas highly susceptible to export of dFADs, although seasonality in currents may also play a role.Challenges facing recovery programmesWhile the information provided in this paper on spatio-temporal patterns of dFAD loss provides an essential foundation for implementing dFAD recovery strategies, there are several important practical challenges to the success of such efforts. Most efforts towards reducing or removing marine debris after it has been created have so far focused on beach clean-ups31,32. Such operations are costly, time-consuming and only capture a fraction of the overall debris18,33. Recovery at sea is a promising alternative solution34, but this requires consolidating systems to observe these debris35 and understanding their drift36, as well as putting in place appropriate incentives and socio-economic and political frameworks37. Broadly, data availability (for example, access to near-real-time location data from all fleets), equipment availability (for example, appropriately sized and equipped vessels for collecting large debris such as dFADs)32, recovery programme structure (for example, collaboration with local fishers, NGOs and/or nation-states; use of support vessels, and/or chartering of dFAD recovery vessels) and funding sources (for example, reuse of recovered tracking buoys or dFAD plastic floats, and/or polluter-payer systems collected at dFAD deployment or manufacturing) need to be optimized to recover a maximum number of dFADs while minimizing costs and fishing impacts. These considerations highlight the importance of identifying areas leading to losses and multiple ports of different sizes from which operations could potentially be conducted, as we have done above, as well as careful analysis of the possible impediments to implementation of recovery programmes.Some possible impediments to dFAD recovery programmes are environmental, strategic or geopolitical. For instance, although the Somali coast is identified as a dFADs stranding hotspot in winter5 and has potential for a port-based recovery programme as we show here, recovering dFADs along this coast is unlikely to be a priority due to the area’s relatively limited number of sensitive habitats, such as coral reefs, and because of the difficult and dangerous socio-political situation in the country and its adjacent waters. On the other hand, the Maldives archipelago is likely to be a priority given that it is an area with high dFAD stranding rates on coral reefs5 and also has many dFADs that leave fishing grounds and never return. Implementing a recovery programme in this area could be particularly valuable, especially given that the Maldives is well integrated into regional maritime transport and tuna fisheries. However, implementing such a programme for a large island chain composed of >1,000 individual islands will probably be complex. Extensive collaboration with regional stakeholders, such as research institutes, fisher associations and NGOs, as well as buoy manufacturers, would be essential to operationalize a recovery programme in the Maldives and elsewhere.Another major challenge for at-sea dFAD recovery is availability of appropriate vessels to remove dFADs from the water. The vertical subsurface structure of dFADs generally stretches from 50 to 80 m below the surface. The weight of the materials used to build dFADs and the numerous sessile organisms that attach to the ‘dFAD tail’ eventually make dFADs very heavy (up to hundreds of kilograms) and therefore difficult to remove from the water. Complete removal is probably only possible for medium to large vessels with an appropriate crane or winch for hauling heavy material. Purse-seine vessels themselves could participate in dFAD recovery efforts, but this would be costly and disruptive to fishing. For smaller vessels, it may only be possible to remove some parts of the dFAD, potentially aided by natural breakdown of the object or acoustic release systems, such as the GPS buoy, plastic flotation devices and/or surface raft metallic or plastic structural elements. However, this could still be extremely useful as the remaining material will normally sink before reaching coastal environments, thereby potentially avoiding the most important environmental impacts. This strategy would be particularly valuable if the subsurface structure can be made of biodegradable materials9,23,38. Imzilen et al.5 suggested that the removal of GPS buoys by artisanal fishers is already occurring in coastal areas. Therefore, if dFAD tracking information can be made accessible and appropriate incentive mechanisms are put in place to encourage recovery of dFAD elements, this strategy could substantially reduce marine debris from dFADs. Other practical considerations should be taken into account once at port, such as the availability of infrastructure for shipping, disposing of, recycling and/or reusing tracking buoys and other dFAD components. All of these potential impediments can be addressed, but they will require active engagement from fishers, tRFMOs, NGOs and coastal nations.Complementary measuresIn addition to such recovery programmes, existing complementary measures controlling the numbers of dFADs present at sea (for example, limits on the number of operational GPS-tracking buoys and limits on the use of support vessels) may need to be strengthened, as a higher number of dFADs obviously contributes to higher risks of marine debris and stranding. Lowering limits on the number of dFADs may also encourage vessels to increase sharing of buoy information, thereby maximizing use of dFADs and potentially reducing dFAD loss. However, oddly enough, such measures may aggravate problems of ALD dFADs if their consequences are not accurately anticipated. For example, limits on the number of tracked dFADs implemented by tRFMOs have modified the strategy of some components of the purse-seine fishery, encouraging them to remotely deactivate satellite-transmitting GPS-tracking buoys when dFADs leave fishing grounds to maintain the number of operational buoys below authorized limits. The loss of position information prevents the tracking of dFADs outside fishing grounds and may result in under-estimation and spatial bias in estimates of the risks of stranding and loss5,39. A potential solution would be to consider ALD dFADs as part of a stock of ‘recoverable dFADs’ that are not counted as part of the individual vessel’s quota of operational buoys, but for which position information is transmitted and made available to partners involved in recovery programmes39. Other useful options to facilitate the recovery of buoys include limiting the per vessel number of deployments instead of limiting the number of tracked dFADs and/or making new deployments contingent on recovery of an equivalent number of already deployed dFADs. The current tRFMO-implemented reduction in the number of support vessels in the Indian Ocean is also likely to increase the loss of dFADs because these vessels may be used to recover dFADs before they leave fishing grounds, highlighting the urgent need for complementary dFAD management and recovery approaches.Financial considerationsA final question about dFAD recovery programmes is how they could be financed. The logistical challenges described above, such as chartering appropriate recovery vessels, involve substantial costs that cannot be ignored. The most simple and logical financing scheme would be a polluter-payer programme whereby vessels, dFAD manufacturers and/or fishing nations pay some monetary amount per ALD dFAD, potentially in proportion to its expected negative impacts, into an independently run and verified clean-up fund. The basic elements for identifying which vessels, fishing companies and/or nations are deploying dFADs are largely in place via tRFMO reporting requirements, dFAD vessel logbooks and purse-seine observer programmes. The detailed spatio-temporal maps provided here and in Imzilen et al.5 identify where the losses and impacts are occurring, thereby providing a blueprint for apportioning such funds geographically.Missing elementsThe missing elements for reducing dFAD loss are mostly political: facilitating access to tracking and activation-deactivation information for all ALD dFADs (for example, the EU recently objected at the 2nd Indian Ocean Tuna Commission (IOTC) ad hoc working group on dFADs to making dFAD data publicly available for scientific purposes); implementing requirements for appropriate disposal of ALD dFADs; and improving collaboration between industry and regional stakeholders concerned with clean-up programmes. Although these missing elements may seem formidable, there are very promising precedents for rapidly addressing these types of issues. Throughout the 2010s, various initiatives of purse-seine fleets, national scientists, tRFMOs and organizations such as the International Sustainable Seafood Foundation (ISSF) have allowed the rapid adoption of mitigation measures. This was the case for non-entangling dFADs40, best practices guidelines for the release of sensitive species41,42,43, exhaustive observer coverage44,45 and dFAD management plans46, which are all required for ISSF-participating fishing companies if they wish tuna from their fishing vessels to be accepted by ISSF member canneries. A similar approach could be used to address dFAD loss, using the fulcrums of the ISSF, Marine Stewardship Council certification and European Union (EU) environmental regulations to extend the commitments already made by some of the fleets (for example regarding data availability and tests of recovery mechanisms) to other fleets and other areas, and therefore rapidly transform industry behaviour for the benefit of all. More

United Nations. World Population Prospects: The 2017 Revision, Key Findings and Advance Tables. Working Paper No. ESA/P/WP/248 (UN-DESA, 2017).Costello, C. et al. The future of food from the sea. Nature 588, 95–100 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
IPCC. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (2019).FAO. Mapping Supply and Demand for Animal-Source Foods to 2030 (2011).Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
DeFries, R. S., Rudel, T., Uriarte, M. & Hansen, M. Deforestation driven by urban population growth and agricultural trade in the twenty-first century. Nat. Geosci. 3, 178–181 (2010).ADS 
CAS 
Article 

Google Scholar 
Rockström, J. et al. Future water availability for global food production: the potential of green water for increasing resilience to global change. Water Resour. Res. 45, W00A12 (2009).Article 

Google Scholar 
IPCC. IPCC Special Report on Climate Change and Land (2019).Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987–992 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
FAO. The State of World Fisheries and Aquaculture 2020: Sustainability in Action (2020).Bryndum‐Buchholz, A. et al. Twenty-first-century climate change impacts on marine animal biomass and ecosystem structure across ocean basins. Glob. Change Biol. 25, 459–472 (2019).ADS 
Article 

Google Scholar 
Cheung, W. W. L., Dunne, J., Sarmiento, J. L. & Pauly, D. Integrating ecophysiology and plankton dynamics into projected maximum fisheries catch potential under climate change in the Northeast Atlantic. ICES J. Mar. Sci. 68, 1008–1018 (2011).Article 

Google Scholar 
Froehlich, H. E., Gentry, R. R. & Halpern, B. S. Global change in marine aquaculture production potential under climate change. Nat. Ecol. Evol. 2, 1745–1750 (2018).PubMed 
Article 

Google Scholar 
Handisyde, N., Telfer, T. C. & Ross, L. G. Vulnerability of aquaculture-related livelihoods to changing climate at the global scale. Fish Fish. 18, 466–488 (2017).Article 

Google Scholar 
Szuwalski, C. S. & Hollowed, A. B. Climate change and non-stationary population processes in fisheries management. ICES J. Mar. Sci. 73, 1297–1305 (2016).Article 

Google Scholar 
Pinsky, M. L. et al. Preparing ocean governance for species on the move. Science 360, 1189–1191 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Gaines, S. D. et al. Improved fisheries management could offset many negative effects of climate change. Sci. Adv. 4, eaao1378 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Free, C. M. et al. Realistic fisheries management reforms could mitigate the impacts of climate change in most countries. PLoS ONE 15, e0224347 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Clapp, J. Food self-sufficiency: making sense of it, and when it makes sense. Food Policy 66, 88–96 (2017).Article 

Google Scholar 
Barange, M., Bahri, T., Beveridge, M. & Cochrane, K. L. Impacts of Climate Change on Fisheries and Aquaculture: Synthesis of Current Knowledge, Adaptation and Mitigation Options. Fisheries and Aquaculture Technical Paper No. 627 (FAO, 2018).Lester, S. E. et al. Marine spatial planning makes room for offshore aquaculture in crowded coastal waters. Nat. Commun. 9, 945 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cottrell, R. S., Blanchard, J. L., Halpern, B. S., Metian, M. & Froehlich, H. E. Global adoption of novel aquaculture feeds could substantially reduce forage fish demand by 2030. Nat. Food 1, 301–308 (2020).Article 

Google Scholar 
Hua, K. et al. The future of aquatic protein: implications for protein sources in aquaculture diets. One Earth 1, 316–329 (2019).ADS 
Article 

Google Scholar 
Chavanne, H. et al. A comprehensive survey on selective breeding programs and seed market in the European aquaculture fish industry. Aquacult. Int. 24, 1287–1307 (2016).Article 

Google Scholar 
Troell, M., Jonell, M. & Henriksson, P. J. G. Ocean space for seafood. Nat. Ecol. Evol. 1, 1224–1225 (2017).PubMed 
Article 

Google Scholar 
European Union. Commission Regulation (EC) No 710/2009 of 5 August 2009 Amending Regulation (EC) No 889/2008 laying down detailed rules for the implementation of Council Regulation (EC) No 834/2007, as regards laying down detailed rules on organic aquaculture animal and seaweed production. http://data.europa.eu/eli/reg/2009/710/oj (2009).Golden, C. D. et al. Aquatic foods to nourish nations. Nature 598, 315–320 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Davies, I. P. et al. Governance of marine aquaculture: pitfalls, potential, and pathways forward. Mar. Policy 104, 29–36 (2019).Article 

Google Scholar 
Gentry, R. R. et al. Exploring the potential for marine aquaculture to contribute to ecosystem services. Rev. Aquacult. 12, 499–512 (2020).Article 

Google Scholar 
Troell, M. et al. Ecological engineering in aquaculture — potential for integrated multi-trophic aquaculture (IMTA) in marine offshore systems. Aquaculture 297, 1–9 (2009).Article 

Google Scholar 
Froehlich, H. E., Jacobsen, N. S., Essington, T. E., Clavelle, T. & Halpern, B. S. Avoiding the ecological limits of forage fish for fed aquaculture. Nat. Sustain. 1, 298–303 (2018).Article 

Google Scholar 
Øverland, M., Mydland, L. T. & Skrede, A. Marine macroalgae as sources of protein and bioactive compounds in feed for monogastric animals. J. Sci. Food Agric. 99, 13–24 (2019).PubMed 
Article 
CAS 

Google Scholar 
Besson, M. et al. Environmental impacts of genetic improvement of growth rate and feed conversion ratio in fish farming under rearing density and nitrogen output limitations. J. Clean. Prod. 116, 100–109 (2016).Article 

Google Scholar 
Froehlich, H. E., Runge, C. A., Gentry, R. R., Gaines, S. D. & Halpern, B. S. Comparative terrestrial feed and land use of an aquaculture-dominant world. Proc. Natl Acad. Sci. USA 115, 5295–5300 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Aguilar-Manjarrez, J., Soto, D., Brummett, R. E. Aquaculture Zoning, Site Selection and Area Management under the Ecosystem Approach to Aquaculture (FAO, 2017).Soto, D. et al. In Impacts Of Climate Change on Fisheries and Aquaculture: Synthesis of Current Knowledge, Adaptation and Mitigation Options Ch. 26 (FAO, 2018).Darwin, C. The Variation of Animals and Plants Under Domestication (John Murray, 1868).Gjedrem, T., Robinson, N. & Rye, M. The importance of selective breeding in aquaculture to meet future demands for animal protein: a review. Aquaculture 350–353, 117–129 (2012).Article 

Google Scholar 
Antonello, J. et al. Estimates of heritability and genetic correlation for body length and resistance to fish pasteurellosis in the gilthead sea bream (Sparus aurata L.). Aquaculture 298, 29–35 (2009).Article 

Google Scholar 
Saillant, E., Dupont-Nivet, M., Haffray, P. & Chatain, B. Estimates of heritability and genotype–environment interactions for body weight in sea bass (Dicentrarchus labrax L.) raised under communal rearing conditions. Aquaculture 254, 139–147 (2006).Article 

Google Scholar 
Klinger, D. H., Levin, S. A. & Watson, J. R. The growth of finfish in global open-ocean aquaculture under climate change. Proc. R. Soc. B 284, 20170834 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Salayo, N. D., Perez, M. L., Garces, L. R. & Pido, M. D. Mariculture development and livelihood diversification in the Philippines. Mar. Policy 36, 867–881 (2012).Article 

Google Scholar 
Boyce, D. G., Lotze, H. K., Tittensor, D. P., Carozza, D. A. & Worm, B. Future ocean biomass losses may widen socioeconomic equity gaps. Nat. Commun. 11, 2235 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sumaila, U. R. et al. Benefits of the Paris Agreement to ocean life, economies, and people. Sci. Adv. 5, eaau3855 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
United Nations. Transforming Our World: The 2030 Agenda for Sustainable Development (United Nations, 2017).Hilborn, R. et al. Effective fisheries management instrumental in improving fish stock status. Proc. Natl Acad. Sci. USA 117, 2218–2224 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Free, C. M. et al. Impacts of historical warming on marine fisheries production. Science 363, 979–983 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Costello, C. et al. Global fishery prospects under contrasting management regimes. Proc. Natl Acad. Sci. USA 113, 5125–5129 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ye, Y. & Gutierrez, N. L. Ending fishery overexploitation by expanding from local successes to globalized solutions. Nat. Ecol. Evol. 1, 0179 (2017).Article 

Google Scholar 
Leape, J. et al. Technology, Data and New Models for Sustainably Managing Ocean Resources (World Resources Institute, 2020).Anderson, C. R. et al. Scaling up from regional case studies to a global harmful algal bloom observing system. Front. Mar. Sci. 6, 250 (2019).Article 

Google Scholar 
Dunn, D. C., Maxwell, S. M., Boustany, A. M. & Halpin, P. N. Dynamic ocean management increases the efficiency and efficacy of fisheries management. Proc. Natl Acad. Sci. USA 113, 668–673 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
FAO. Aquaculture Development: 7. Aquaculture Governance and Sector Development (2017).Oyinlola, M. A., Reygondeau, G., Wabnitz, C. C. C., Troell, M. & Cheung, W. W. L. Global estimation of areas with suitable environmental conditions for mariculture species. PLoS ONE 13, e0191086 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Jackson, A. Fish in-fish out ratio explained. Aquacult. Eur. 34, 5–10 (2009).
Google Scholar 
Tacon, A. G. J. & Metian, M. Feed matters: satisfying the feed demand of aquaculture. Rev. Fish. Sci. Aquacult. 23, 1–10 (2015).Article 

Google Scholar 
Tacon, A. G. J. & Metian, M. Global overview on the use of fish meal and fish oil in industrially compounded aquafeeds: trends and future prospects. Aquaculture 285, 146–158 (2008).CAS 
Article 

Google Scholar 
World Bank. Population, Total (2020); https://data.worldbank.org/indicator/SP.POP.TOTLEdwards, P., Zhang, W., Belton, B. & Little, D. C. Misunderstandings, myths and mantras in aquaculture: its contribution to world food supplies has been systematically over reported. Mar. Policy 106, 103547 (2019).Article 

Google Scholar 
Roberts, P. Conversion Factors for Estimating the Equivalent Live Weight of Fisheries Products (The Food and Agriculture Organization of the United Nations, 1998).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Kaschner, K. et al. AquaMaps: Predicted Range Maps for Aquatic Species https://www.aquamaps.org/ (2019).García Molinos, J. et al. Climate velocity and the future global redistribution of marine biodiversity. Nat. Clim. Change 6, 83–88 (2016).ADS 
Article 

Google Scholar 
Cashion, T., Le Manach, F., Zeller, D. & Pauly, D. Most fish destined for fishmeal production are food-grade fish. Fish Fish. 18, 837–844 (2017).Article 

Google Scholar 
Froehlich, H. E., Gentry, R. R. & Halpern, B. S. Synthesis and comparative analysis of physiological tolerance and life-history growth traits of marine aquaculture species. Aquaculture 460, 75–82 (2016).Article 

Google Scholar 
Thorson, J. T., Munch, S. B., Cope, J. M. & Gao, J. Predicting life history parameters for all fishes worldwide. Ecol. Appl. 27, 2262–2276 (2017).PubMed 
Article 

Google Scholar 
Froese, R. & Pauly, D. FishBase http://www.fishbase.org (2021).Palomares, M. & Pauly, D. SeaLifeBase http://www.sealifebase.org (2019).FAO. Cultured Aquatic Species (2019).Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon Earth system models. Part I: physical formulation and baseline simulation characteristics. J. Clim. 25, 6646–6665 (2012).ADS 
Article 

Google Scholar 
Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon Earth system models. Part II: carbon system formulation and baseline simulation characteristics. J. Clim. 26, 2247–2267 (2013).ADS 
Article 

Google Scholar 
Song, Z. et al. Centuries of monthly and 3-hourly global ocean wave data for past, present, and future climate research. Sci. Data 7, 226 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Gentry, R. R. et al. Mapping the global potential for marine aquaculture. Nat. Ecol. Evol. 1, 1317–1324 (2017).PubMed 
Article 

Google Scholar 
Barton, A. et al. Impacts of coastal acidification on the Pacific Northwest shellfish industry and adaptation strategies implemented in response. Oceanography 25, 146–159 (2015).Article 

Google Scholar 
Froehlich, H. E., Smith, A., Gentry, R. R. & Halpern, B. S. Offshore aquaculture: I know it when I see it. Front. Mar. Sci. 4, 154 (2017).Article 

Google Scholar 
World Bank. Adjusted Net National Income per Capita (Current US$) (2019); https://data.worldbank.org/indicator/NY.ADJ.NNTY.PC.CDWorld Bank. Pump Price for Diesel Fuel (US$ per liter) (2019); https://data.worldbank.org/indicator/EP.PMP.DESL.CDPiburn, J. wbstats: programmatic access to the World Bank API. R package v.1.0.4 https://cran.r-project.org/web/packages/wbstats/index.html (2018).Rubino, M. (ed.) Offshore Aquaculture in the United States: Economic Considerations, Implications & Opportunities NOAA Technical Memorandum NMFS F/SPO-103 (US Department of Commerce, 2008).Jackson, A. & Newton, R. Project to Model the Use of Fisheries By-products in the Production of Marine Ingredients, with Special Reference to the Omega 3 Fatty Acids EPA and DHA (Institute Of Aquaculture, University Of Stirling And IFFO, 2016). 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

We used the IUCN Red List criteria34,35 and methods developed in other global status-assessment efforts36,37 to assess 10,078 reptile species for extinction risk. We additionally include recommended Red List categories for 118 turtle species38, for a total of 10,196 species covered, representing 89% of the 11,341 described reptile species as of August 202039.Data compilationWe compiled assessment data primarily through regional in-person and remote (that is, through phone and email) workshops with species experts (9,536 species) and consultation with IUCN Species Survival Commission Specialist Groups and stand-alone Red List Authorities (442 species, primarily marine turtles, terrestrial and freshwater turtles, iguanas, sea snakes, mainland African chameleons and crocodiles). We conducted 48 workshops between 2004 and 2019 (Supplementary Table 1). Workshop participants provided information to complete the required species assessment fields (geographical distribution, population abundance and trends, habitat and ecological requirements, threats, use and trade, literature) and draw a distribution map. We then applied the Red List criteria34 to this information to assign a Red List category: extinct, extinct in the wild, critically endangered, endangered, vulnerable, near threatened, least concern and data deficient. Threatened species are those categorized as critically endangered, endangered and vulnerable.TaxonomyWe used The Reptile Database39 as a taxonomic standard, diverging only to follow well-justified taxonomic standards from the IUCN Species Survival Commission40. We could not revisit new descriptions for most regions after the end of the original assessment, so the final species list is not fully consistent with any single release of The Reptile Database.Distribution mapsWhere data allowed, we developed distribution maps in Esri shapefile format using the IUCN mapping guidelines41 (1,003 species). These maps are typically broad polygons that encompass all known localities, with provisions made to show obvious discontinuity in areas of unsuitable habitat. Each polygon is coded according to species’ presence (extant, possibly extant or extinct) and origin (native, introduced or reintroduced)41. For some regions covered in workshops (Caucasus, Southeast Asia, much of Africa, Australia and western South America), we collaborated with the Global Assessment of Reptile Distributions (GARD) (http://www.gardinitiative.org/) to provide contributing experts with a baseline species distribution map for review. Although refined maps were returned to the GARD team, not all of these maps have been incorporated into the GARD.Habitat preferencesWhere known, species habitats were coded using the IUCN Habitat Classification Scheme (v.3.1) (https://www.iucnredlist.org/resources/habitat-classification-scheme). Species were assigned to all habitat classes in which they are known to occur. Where possible, habitat suitability (suitable, marginal or unknown) and major importance (yes or no) was recorded. Habitat data were available for 9,484 reptile species.ThreatsAll known historical, current and projected (within 10 years or 3 generations, whichever is the longest; generation time estimated, when not available, from related species for which it is known; generation time recorded for 76.3% of the 186 species categorized as threatened under Red List criteria A and C1, the only criteria using generation length) threats were coded using the IUCN Threats Classification Scheme v.3.2 (https://www.iucnredlist.org/resources/threat-classification-scheme), which follows a previously published study42. Where possible, the scope (whole ( >90%), majority (50–90%), minority (30%), rapid ( >20%), slow but notable ( More

Szulkin, M. et al. How to quantify urbanization when testing for urban evolution?. Urban Evol. Biol. https://doi.org/10.1093/oso/9780198836841.003.0002 (2020).Article 

Google Scholar 
Slabbekoorn, H. Songs of the city: Noise-dependent spectral plasticity in the acoustic phenotype of urban birds. Anim. Behav. https://doi.org/10.1016/j.anbehav.2013.01.021 (2013).Article 

Google Scholar 
Christiansen, N. A., Fryirs, K. A., Green, T. J. & Hose, G. C. The impact of urbanisation on community structure, gene abundance and transcription rates of microbes in upland swamps of Eastern Australia. PLoS ONE https://doi.org/10.1371/journal.pone.0213275 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Alberti, M. et al. Global urban signatures of phenotypic change in animal and plant populations. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.1606034114 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
McFall-Ngai, M. M. et al. Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.1218525110 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Zilber-Rosenberg, I. & Rosenberg, E. Role of microorganisms in the evolution of animals and plants: the hologenome theory of evolution. FEMS Microbiol. Rev. https://doi.org/10.1111/j.1574-6976.2008.00123.x (2008).Article 
PubMed 

Google Scholar 
Trevelline, B. K., Fontaine, S. S., Hartup, B. K. & Kohl, K. D. Conservation biology needs a microbial renaissance: A call for the consideration of host-associated microbiota in wildlife management practices. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2018.2448 (2019).Article 

Google Scholar 
Jarrett, C., Powell, L. L., McDevitt, H., Helm, B. & Welch, A. J. Bitter fruits of hard labour: diet metabarcoding and telemetry reveal that urban songbirds travel further for lower-quality food. Oecologia https://doi.org/10.1007/s00442-020-04678-w (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Zollinger, S. A. et al. Traffic noise exposure depresses plasma corticosterone and delays offspring growth in breeding zebra finches. Conserv. Physiol. https://doi.org/10.1093/conphys/coz056 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Sprau, P., Mouchet, A. & Dingemanse, N. J. Multidimensional environmental predictors of variation in avian forest and city life histories. Behav. Ecol. https://doi.org/10.1093/beheco/arw130 (2017).Article 

Google Scholar 
Teyssier, A. et al. Inside the guts of the city: Urban-induced alterations of the gut microbiota in a wild passerine. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2017.09.035 (2018).Article 
PubMed 

Google Scholar 
Murray, M. H. et al. Gut microbiome shifts with urbanization and potentially facilitates a zoonotic pathogen in a wading bird. PLoS ONE https://doi.org/10.1371/journal.pone.0220926 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Fuirst, M., Veit, R. R., Hahn, M., Dheilly, N. & Thorne, L. H. Effects of urbanization on the foraging ecology and microbiota of the generalist seabird Larus argentatus. PLoS ONE https://doi.org/10.1371/journal.pone.0209200 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Phillips, J. N., Berlow, M. & Derryberry, E. P. The effects of landscape urbanization on the gut microbiome: An exploration into the gut of urban and rural white-crowned sparrows. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2018.00148 (2018).Article 

Google Scholar 
Berlow, M., Phillips, J. N. & Derryberry, E. P. Effects of urbanization and landscape on gut microbiomes in white-crowned sparrows. Microb. Ecol. https://doi.org/10.1007/s00248-020-01569-8 (2020).Article 
PubMed 

Google Scholar 
Cox, L. M. et al. Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences. Cell https://doi.org/10.1016/j.cell.2014.05.052 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Knutie, S. A., Wilkinson, C. L., Kohl, K. D. & Rohr, J. R. Early-life disruption of amphibian microbiota decreases later-life resistance to parasites. Nat. Commun. 8, 1–8 (2017).CAS 
Article 

Google Scholar 
Sudyka, J., Di Lecce, I., Wojas, L., Rowiński, P. & Szulkin, M. Nest-boxes alter the reproductive ecology of urban cavity-nesters in a species-dependent way. https://doi.org/10.32942/OSF.IO/WP9MN.
Maziarz, M., Broughton, R. K. & Wesołowski, T. Microclimate in tree cavities and nest-boxes: Implications for hole-nesting birds. For. Ecol. Manag. https://doi.org/10.1016/j.foreco.2017.01.001 (2017).Article 

Google Scholar 
Thompson, M. J., Capilla-Lasheras, P., Dominoni, D. M., Réale, D. & Charmantier, A. Phenotypic variation in urban environments: mechanisms and implications. Trends Ecol. Evol. 37, 171–182 (2022).CAS 
Article 

Google Scholar 
Salmón, P. et al. Continent-wide genomic signatures of adaptation to urbanisation in a songbird across Europe. Nat. Commun. 12, 1–14 (2021).ADS 
Article 

Google Scholar 
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. https://doi.org/10.1186/s13059-014-0550-8 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Sackey, B. A., Mensah, P., Collison, E. & Sakyi-Dawson, E. Campylobacter, Salmonella, Shigella and Escherichia coli in live and dressed poultry from metropolitan Accra. Int. J. Food Microbiol. https://doi.org/10.1016/S0168-1605(01)00595-5 (2001).Article 
PubMed 

Google Scholar 
Benskin, C. M. W. H., Wilson, K., Jones, K. & Hartley, I. R. Bacterial pathogens in wild birds: A review of the frequency and effects of infection. Biol. Rev. https://doi.org/10.1111/j.1469-185X.2008.00076.x (2009).Article 
PubMed 

Google Scholar 
Hansell, M. & Overhill, R. Bird nests and construction behaviour. Bird Nests Constr. Behav. https://doi.org/10.1017/cbo9781139106788 (2000).Article 

Google Scholar 
Siddiqui, S. H., Khan, M., Kang, D., Choi, H. W. & Shim, K. Meta-analysis and systematic review of the thermal stress response: Gallus gallus domesticus show low immune responses during heat stress. Front. Physiol. 13, 31 (2022).Article 

Google Scholar 
Sepulveda, J. & Moeller, A. H. The effects of temperature on animal gut microbiomes. Front. Microbiol. https://doi.org/10.3389/fmicb.2020.00384 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Kohl, K. D. & Yahn, J. Effects of environmental temperature on the gut microbial communities of tadpoles. Environ. Microbiol. https://doi.org/10.1111/1462-2920.13255 (2016).Article 
PubMed 

Google Scholar 
Teyssier, A. et al. Diet contributes to urban-induced alterations in gut microbiota: Experimental evidence from a wild passerine. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2019.2182 (2020).Article 

Google Scholar 
Benskin, C. M. W. H., Rhodes, G., Pickup, R. W., Wilson, K. & Hartley, I. R. Diversity and temporal stability of bacterial communities in a model passerine bird, the zebra finch. Mol. Ecol. https://doi.org/10.1111/j.1365-294X.2010.04892.x (2010).Article 
PubMed 

Google Scholar 
Garrett, W. S. et al. Enterobacteriaceae Act in concert with the gut microbiota to induce spontaneous and maternally transmitted colitis. Cell Host Microbe https://doi.org/10.1016/j.chom.2010.08.004 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Videvall, E. et al. Early-life gut dysbiosis linked to juvenile mortality in ostriches. BMC Microbiome 8, 1–13 (2020).Article 

Google Scholar 
Hooper, L. V. & MacPherson, A. J. Immune adaptations that maintain homeostasis with the intestinal microbiota. Nat. Rev. Immunol. https://doi.org/10.1038/nri2710 (2010).Article 
PubMed 

Google Scholar 
Borre, Y. E. et al. Microbiota and neurodevelopmental windows: Implications for brain disorders. Trends Mol. Med. https://doi.org/10.1016/j.molmed.2014.05.002 (2014).Article 
PubMed 

Google Scholar 
Jones, E. L. & Leather, S. R. Invertebrates in urban areas: A review. Eur. J. Entomol. https://doi.org/10.14411/eje.2012.060 (2012).Article 

Google Scholar 
Wilkin, T. A., King, L. E. & Sheldon, B. C. Habitat quality, nestling diet, and provisioning behaviour in great tits Parus major. J. Avian Biol. https://doi.org/10.1111/j.1600-048X.2009.04362.x (2009).Article 

Google Scholar 
Pollock, C. J., Capilla-Lasheras, P., McGill, R. A. R., Helm, B. & Dominoni, D. M. Integrated behavioural and stable isotope data reveal altered diet linked to low breeding success in urban-dwelling blue tits (Cyanistes caeruleus). Sci. Rep. https://doi.org/10.1038/s41598-017-04575-y (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Davidson, G. L. et al. Diet induces parallel changes to the gut microbiota and problem solving performance in a wild bird. Sci. Rep. https://doi.org/10.1038/s41598-020-77256-y (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Bodawatta, K. H. et al. Flexibility and resilience of great tit (Parus major) gut microbiomes to changing diets. Anim. Microbiome 2021(3), 1–14 (2021).
Google Scholar 
Baniel, A. et al. Seasonal shifts in the gut microbiome indicate plastic responses to diet in wild geladas. Microbiome 9, 1–20 (2021).Article 

Google Scholar 
Sullam, K. E. et al. Environmental and ecological factors that shape the gut bacterial communities of fish: A meta-analysis. Mol. Ecol. https://doi.org/10.1111/j.1365-294X.2012.05552.x (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Martiny, J. B. H. et al. Microbial biogeography: Putting microorganisms on the map. Nat. Rev. Microbiol. https://doi.org/10.1038/nrmicro1341 (2006).Article 
PubMed 

Google Scholar 
Lucass, C., Eens, M. & Müller, W. When ambient noise impairs parent-offspring communication. Environ. Pollut. https://doi.org/10.1016/j.envpol.2016.03.015 (2016).Article 
PubMed 

Google Scholar 
Kight, C. R. & Swaddle, J. P. How and why environmental noise impacts animals: An integrative, mechanistic review. Ecol. Lett. https://doi.org/10.1111/j.1461-0248.2011.01664.x (2011).Article 
PubMed 

Google Scholar 
Cui, B., Gai, Z., She, X., Wang, R. & Xi, Z. Effects of chronic noise on glucose metabolism and gut microbiota-host inflammatory homeostasis in rats. Sci. Rep. https://doi.org/10.1038/srep36693 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Campo, J. L., Gil, M. G. & Dávila, S. G. Effects of specific noise and music stimuli on stress and fear levels of laying hens of several breeds. Appl. Anim. Behav. Sci. https://doi.org/10.1016/j.applanim.2004.08.028 (2005).Article 

Google Scholar 
Injaian, A. S., Taff, C. C. & Patricelli, G. L. Experimental anthropogenic noise impacts avian parental behaviour, nestling growth and nestling oxidative stress. Anim. Behav. https://doi.org/10.1016/j.anbehav.2017.12.003 (2018).Article 

Google Scholar 
Cui, B. et al. Effects of chronic noise exposure on the microbiome-gut-brain axis in senescence-accelerated prone mice: Implications for Alzheimer’s disease. J. Neuroinflammation https://doi.org/10.1186/s12974-018-1223-4 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Wei, L. et al. Constant light exposure alters gut microbiota and promotes the progression of steatohepatitis in high fat diet rats. Front. Microbiol. https://doi.org/10.3389/fmicb.2020.01975 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Chatelain, M. et al. Replicated, urban-driven exposure to metallic trace elements in two passerines. Sci. Rep. 11, 1–10 (2021).Article 

Google Scholar 
Chatelain, M. et al. Urban metal pollution explains variation in reproductive outputs in great tits and blue tits. Sci. Total Environ. 776, 145966 (2021).ADS 
CAS 
Article 

Google Scholar 
Rosenfeld, C. S. Gut dysbiosis in animals due to environmental chemical exposures. Front. Cell. Infect. Microbiol. 7, 396 (2017).Article 

Google Scholar 
Sommer, F. & Bäckhed, F. The gut microbiota-masters of host development and physiology. Nat. Rev. Microbiol. https://doi.org/10.1038/nrmicro2974 (2013).Article 
PubMed 

Google Scholar 
Tomiałojć, L. & Wesołowski, T. Diversity of the Białowieza forest avifauna in space and time. J. Ornithol. https://doi.org/10.1007/s10336-003-0017-2 (2004).Article 

Google Scholar 
Corsini, M. et al. Growing in the city: Urban evolutionary ecology of avian growth rates. Evol. Appl. https://doi.org/10.1111/eva.13081 (2021).Article 
PubMed 

Google Scholar 
Teyssier, A., Lens, L., Matthysen, E. & White, J. Dynamics of gut microbiota diversity during the early development of an avian host: Evidence from a cross-foster experiment. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.01524 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Tremblay, I., Thomas, D., Blondel, J., Perret, P. & Lambrechts, M. M. The effect of habitat quality on foraging patterns, provisioning rate and nestling growth in Corsican Blue Tits Parus caeruleus. Ibis (Lond 1859). 147, 17–24 (2005).Article 

Google Scholar 
Corsini, M., Marrot, P. & Szulkin, M. Quantifying human presence in a heterogeneous urban landscape. Behav. Ecol. https://doi.org/10.1093/beheco/arz128 (2019).Article 

Google Scholar 
Corsini, M., Dubiec, A., Marrot, P. & Szulkin, M. Humans and tits in the city: Quantifying the effects of human presence on great tit and blue tit reproductive trait variation. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2017.00082 (2017).Article 

Google Scholar 
Kyba, C. C. M. et al. High-resolution imagery of earth at night: New sources, opportunities and challenges. Remote Sens. https://doi.org/10.3390/rs70100001 (2015).Article 

Google Scholar 
Maraci, Ö. et al. The gut microbial composition is species-specific and individual-specific in two species of estrildid finches, the Bengalese finch and the zebra finch. Front. Microbiol. https://doi.org/10.3389/fmicb.2021.619141 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Engel, K. et al. Individual- and species-specific skin microbiomes in three different estrildid finch species revealed by 16S amplicon sequencing. Microb. Ecol. https://doi.org/10.1007/s00248-017-1130-8 (2017).Article 
PubMed 

Google Scholar 
Magoč, T. & Salzberg, S. L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics https://doi.org/10.1093/bioinformatics/btr507 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal https://doi.org/10.14806/ej.17.1.200 (2011).Article 

Google Scholar 
Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01541-09 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics https://doi.org/10.1093/bioinformatics/btq461 (2010).Article 
PubMed 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. https://doi.org/10.1093/nar/gks1219 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).
Google Scholar 
Clarke, K. R., Gorley, R., Somerfield, P. & Warwick, R. Change in Marine Communities: an Approach to Statistical Analysis and Interpretation 3rd edn (Prim. Plymouth, 2014).Shannon, C. E. The mathematical theory of communication. MD Comput. https://doi.org/10.2307/410457 (1997).Article 
PubMed 

Google Scholar 
Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. https://doi.org/10.1016/0006-3207(92)91201-3 (1992).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Fox, J. et al. The car Package. R (2012).Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. https://doi.org/10.1111/j.2041-210x.2009.00001.x (2010).Article 

Google Scholar 
DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models. https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).Book 

Google Scholar 
McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE https://doi.org/10.1371/journal.pone.0061217 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B https://doi.org/10.1111/j.2517-6161.1995.tb02031.x (1995).Article 
MATH 

Google Scholar 
Whittaker, R. H. Vegetation of the Siskiyou mountains Oregon and California. Ecol. Monogr. https://doi.org/10.2307/1948435 (1960).Article 

Google Scholar 
Paulson, J. metagenomeSeq: Statistical analysis for sparse high-throughput sequencing. Bioconductor.Jp (2014).Bray, J. R. & Curtis, J. T. An ordination of the upland forest communities of southern Wisconsin. Ecol. Monogr. https://doi.org/10.2307/1942268 (1957).Article 

Google Scholar 
Lozupone, C. A., Hamady, M., Kelley, S. T. & Knight, R. Quantitative and qualitative β diversity measures lead to different insights into factors that structure microbial communities. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01996-06 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Oksanen, J. et al. Package ‘vegan’ Title Community Ecology Package Version 2.5-6. cran.ism.ac.jp (2019).Anderson, M. J. & Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. https://doi.org/10.1046/j.1442-9993.2001.01070.x (2001).Article 

Google Scholar 
Clarke, K. R. & Ainsworth, M. A method of linking multivariate community structure to environmental variables. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps092205 (1993).Article 

Google Scholar 
QGIS Development Team. QGIS Geographic Information System (Open Source Geospatial Foundation, 2019).
Google Scholar  More