Philippa Kaur

Beerda, B., Schilder, M. B. H., Van Hooff, J. A., De Vries, H. W. & Mol, J. A. Chronic stress in dogs subjected to social and spatial restriction I. Behavioral responses. Physiol. Behav. 66, 233–242 (1999).CAS 
PubMed 
Article 

Google Scholar 
Rooney, N. J., Gaines, S. A. & Bradshaw, J. W. Behavioural and glucocorticoid responses of dogs (Canis familiaris) to kennelling: investigating mitigation of stress by prior habituation. Physiol. Behav. 92, 847–854. https://doi.org/10.1016/j.physbeh.2007.06.011 (2007).CAS 
PubMed 
Article 

Google Scholar 
Stephen, J. M. & Ledger, R. A. A longitudinal evaluation of urinary cortisol in kennelled dogs Canis familiaris. Physiol. Behav. 87, 911–916. https://doi.org/10.1016/j.physbeh.2006.02.015 (2006).CAS 
PubMed 
Article 

Google Scholar 
Mills, D., Karagiannis, C., Zulch, H. Stress its effects on health and behavior. Vet. Clin. North Am. Small Anim. Pract. 44, 525–541 (2014).Mormède, P. et al. Exploration of the hypothalamic–pituitary–adrenal function as a tool to evaluate animal welfare. Physiol. Behav. 92, 317–339 (2007).PubMed 
Article 

Google Scholar 
Hennessy, M. B. Using hypothalamic–pituitary–adrenal measures for assessing and reducing the stress of dogs in shelters: A review. Appl. Anim. Behav. Sci. 149, 1–12 (2013).Article 

Google Scholar 
Cobb, M. L., Iskandarani, K., Chinchilli, V. M. & Dreschel, N. A. A systematic review and meta-analysis of salivary cortisol measurement in domestic canines. Domest. Anim. Endocrinol. 57, 31–42 (2016).CAS 
PubMed 
Article 

Google Scholar 
Wester, V. L. & van Rossum, E. F. Clinical applications of cortisol measurements in hair. Eur. J. Endocrinol. 173, M1–M10 (2015).CAS 
PubMed 
Article 

Google Scholar 
Heimbürge, S., Kanitz, E. & Otten, W. The use of hair cortisol for the assessment of stress in animals. Gen. Comp. Endocrinol. 270, 10–17 (2019).PubMed 
Article 

Google Scholar 
Meyer, J. S. & Novak, M. A. Minireview: hair cortisol: A novel biomarker of hypothalamic-pituitary-adrenocortical activity. Endocrinology 153, 4120–4127 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Khoury, J. E., Bosquet Enlow, M., Plamondon, A. & Lyons-Ruth, K. The association between adversity and hair cortisol levels in humans: A meta-analysis. Psychoneuroendocrinology 103, 104–117 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Davenport, M. D., Tiefenbacher, S., Lutz, C. K., Novak, M. A. & Meyer, J. S. Analysis of endogenous cortisol concentrations in the hair of rhesus macaques. Gen. Comp. Endocrinol. 147, 255–261 (2006).CAS 
PubMed 
Article 

Google Scholar 
Greff, M. J. E. et al. Hair cortisol analysis: An update on methodological considerations and clinical applications. Clin. Biochem. 63, 1–9 (2019).CAS 
PubMed 
Article 

Google Scholar 
del Rosario, G. et al. Effects of adrenocorticotropic hormone challenge and age on hair cortisol concentrations in dairy cattle. Can. J. Vet. Res. 75, 216–221 (2011).
Google Scholar 
Macbeth, B. J., Cattet, M., Stenhouse, G. B., Gibeau, M. L. & Janz, D. M. Hair cortisol concentration as a noninvasive measure of long-term stress in free-ranging grizzly bears (Ursus arctos): considerations with implications for other wildlife. Can. J. Zool. 88, 935–949 (2010).CAS 
Article 

Google Scholar 
Accorsi, P. A. et al. Cortisol determination in hair and faeces from domestic cats and dogs. Gen. Comp. Endocrinol. 155, 398–402 (2008).CAS 
PubMed 
Article 

Google Scholar 
Bennett, A. & Hayssen, V. Measuring cortisol in hair and saliva from dogs: coat color and pigment differences. Domest. Anim. Endocrinol. 39, 171–180 (2010).CAS 
PubMed 
Article 

Google Scholar 
Bryan, H. M., Adams, A. G., Invik, R. M., Wynne-Edwards, K. E. & Smits, J. E. Hair as a meaningful measure of baseline cortisol levels over time in dogs. J. Am. Assoc. Lab. Anim. Sci. 52, 189–196 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Siniscalchi, M., McFarlane, J. R., Kauter, K. G., Quaranta, A. & Rogers, L. J. Cortisol levels in hair reflect behavioural reactivity of dogs to acoustic stimuli. Res. Vet. Sci. 94, 49–54 (2013).CAS 
PubMed 
Article 

Google Scholar 
Stella, J., Shreyer, T., Ha, J. & Croney, C. Improving canine welfare in commercial breeding (CB) operations: Evaluating rehoming candidates. Appl. Anim. Behav. Sci. 220, 104861. https://doi.org/10.1016/j.applanim.2019.104861 (2019).Article 

Google Scholar 
Nicholson, S. L. & Meredith, J. E. Should stress management be part of the clinical care provided to chronically ill dogs?. J. Vet. Behav. 10, 489–495 (2015).Article 

Google Scholar 
Maxwell, N., Buchanan, C. & Evans, N. Hair cortisol concentrations, as a measure of chronic activity within the hypothalamic-pituitary-adrenal axis, is elevated in dogs farmed for meat, relative to pet dogs South Korea. Anim. Welf. 28, 389–395 (2019).Article 

Google Scholar 
Roth, L. S., Faresjö, Å, Theodorsson, E., Jensen, P. Hair cortisol varies with season and lifestyle and relates to human interactions in German shepherd dogs. Sci. Rep. 6, 19631; https://doi.org/10.1038/srep19631 (2016).Packer, R. M. et al. What can we learn from the hair of the dog? Complex effects of endogenous and exogenous stressors on canine hair cortisol. PLoS ONE 14, e0216000. https://doi.org/10.1371/journal.pone.0216000 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sundman, A. et al. Long-term stress levels are synchronized in dogs and their owners. Sci. Rep. 9, 7391; https://doi.org/10.1038/s41598-019-43851-x (2019).Höglin, A. et al. Long-term stress in dogs is related to the human-dog relationship and personality traits. Sci. Rep. 11, 8612; https://doi.org/10.1038/s41598-021-88201-y (2021).Bowland, G. B. et al. Fur color and nutritional status predict hair cortisol concentrations of dogs in Nicaragua. Front. Vet. Sci. 7, 565346. https://doi.org/10.3389/fvets.2020.565346 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Veronesi, M. C. et al. Coat and claws as new matrices for noninvasive long-term cortisol assessment in dogs from birth up to 30 days of age. Theriogenology 84, 791–796 (2015).CAS 
PubMed 
Article 

Google Scholar 
Davenport, M. D., Lutz, C. K., Tiefenbacher, S., Novak, M. A. & Meyer, J. S. A rhesus monkey model of self-injury: Effects of relocation stress on behavior and neuroendocrine function. Biol. Psychiatry 63, 990–996 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
van der Laan, J. E., Vinke, C. M., van der Borg, J. A. M. & Arndt, S. S. Restless nights? Nocturnal activity as a useful indicator of adaptability of shelter housed dogs. Appl. Anim. Behav. Sci. 241, 105377. https://doi.org/10.1016/j.applanim.2021.105377 (2021).Article 

Google Scholar 
Pollinger, J. P. et al. Genome-wide SNP and haplotype analyses reveal a rich history underlying dog domestication. Nature 464, 898–902 (2010).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Voith, V. L., Ingram, E., Mitsouras, K. & Irizarry, K. Comparison of adoption agency breed identification and DNA breed identification of dogs. J. Appl. Anim. Welf. Sci. 12, 253–262 (2009).CAS 
PubMed 
Article 

Google Scholar 
Gunter, L. M., Barber, R. T. & Wynne, C. D. L. A canine identity crisis: Genetic breed heritage testing of shelter dogs. PLoS ONE 13, e0202633. https://doi.org/10.1371/journal.pone.0202633 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D., R Core Team. Nlme: linear and nonlinear mixed effects models. R package version 3. 1–148 (2020).Protopopova, A. & Gunter, L. Adoption and relinquishment interventions at the animal shelter: a review. Anim. Welf. 26, 35–48 (2017).Article 

Google Scholar 
Müntener, T., Doherr, M. G., Guscetti, F., Suter, M. M. & Welle, M. M. The canine hair cycle – a guide for the assessment of morphological and immunohistochemical criteria. Vet. Dermatol. 22, 383–395 (2011).PubMed 
Article 

Google Scholar 
Wennig, R. Potential problems with the interpretation of hair analysis results. Forensic Sci. Int. 107, 5–12 (2000).CAS 
PubMed 
Article 

Google Scholar 
Heimbürge, S., Kanitz, E., Tuchscherer, A. & Otten, W. Within a hair’s breadth – Factors influencing hair cortisol levels in pigs and cattle. Gen. Comp. Endocrinol. 288, 113359. https://doi.org/10.1016/j.ygcen.2019.113359 (2020).CAS 
PubMed 
Article 

Google Scholar 
Diaz, S. F., Torres, S. M., Dunstan, R. W. & Lekcharoensuk, C. An analysis of canine hair re-growth after clipping for a surgical procedure. Vet. Dermatol. 15, 25–30 (2004).PubMed 
Article 

Google Scholar 
Zeugswetter, F., Bydzovsky, N., Kampner, D. & Schwendenwein, I. Tailored reference limits for urine corticoid:creatinine ratio in dogs to answer distinct clinical questions. Vet. Rec. 167, 997–1001 (2010).CAS 
PubMed 
Article 

Google Scholar 
Jones, S. et al. Use of accelerometers to measure stress levels in shelter dogs. J. Appl. Anim. Welf. Sci. 17, 18–28 (2014).CAS 
PubMed 
Article 

Google Scholar 
Gunter, L. M., Feuerbacher, E. N., Gilchrist, R. J. & Wynne, C. D. Evaluating the effects of a temporary fostering program on shelter dog welfare. PeerJ 7, e6620. https://doi.org/10.7717/peerj.6620 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Van den Brom, W. E. & Biewenga, W. J. Assessment of glomerular filtration rate in normal dogs: analysis of the 51Cr-EDTA clearance and its relation to several endogenous parameters of glomerular filtration. Res. Vet. Sci. 30, 152–157 (1981).PubMed 
Article 

Google Scholar 
Sandri, M., Colussi, A., Perrotta, M. G. & Stefanon, B. Salivary cortisol concentration in healthy dogs is affected by size, sex, and housing context. J. Vet. Behav. 10, 302–306 (2015).Article 

Google Scholar 
Haase, C. G., Long, A. K. & Gillooly, J. F. Energetics of stress: linking plasma cortisol levels to metabolic rate in mammals. Biol. Lett. 12, 20150867. https://doi.org/10.1098/rsbl.2015.0867 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Garnier, F., Benoit, E., Virat, M., Ochoa, R. & Delatour, P. Adrenal cortical response in clinically normal dogs before and after adaptation to a housing environment. Lab. Anim. 24, 40–43 (1990).CAS 
PubMed 
Article 

Google Scholar 
Beerda, B. et al. Chronic stress in dogs subjected to social and spatial restriction. II. Hormonal and immunological responses. Physiol. Behav. 66, 243–254 (1999).Rincón-Cortés, M., Herman, J. P., Lupien, S., Maguire, J. & Shansky, R. M. Stress: Influence of sex, reproductive status and gender. Neurobiol. Stress 10, 100155. https://doi.org/10.1016/j.ynstr.2019.100155 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Oyola, M. G. & Handa, R. J. Hypothalamic–pituitary–adrenal and hypothalamic–pituitary–gonadal axes: sex differences in regulation of stress responsivity. Stress 20, 476–494 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Willen, R. M., Mutwill, A., MacDonald, L. J., Schiml, P. A. & Hennessy, M. B. Factors determining the effects of human interaction on the cortisol levels of shelter dogs. Appl. Anim. Behav. Sci. 186, 41–48 (2017).Article 

Google Scholar 
Protopopova, A. Effects of sheltering on physiology, immune function, behavior, and the welfare of dogs. Physiol. Behav. 159, 95–103 (2016).CAS 
PubMed 
Article 

Google Scholar 
Mesarcova, L., Kottferova, J., Skurkova, L., Leskova, L. & Kmecova, N. Analysis of cortisol in dog hair-a potential biomarker of chronic stress: a review. Vet. Med. (Praha) 62, 363–376 (2017).CAS 
Article 

Google Scholar 
Neumann, A. et al. Predicting hair cortisol levels with hair pigmentation genes: a possible hair pigmentation bias. Sci. Rep. 7, 8529 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Romero, L. M. & Beattie, U. K. Common myths of glucocorticoid function in ecology and conservation. J. Exp. Zool. A. Ecol. Integr. Physiol. https://doi.org/10.1002/jez.2459 (2021).PubMed 
Article 

Google Scholar 
Heimbürge, S., Kanitz, E., Tuchscherer, A. & Otten, W. Is it getting in the hair? – Cortisol concentrations in native, regrown and segmented hairs of cattle and pigs after repeated ACTH administrations. Gen. Comp. Endocrinol. 295, 113534. https://doi.org/10.1016/j.ygcen.2020.113534 (2020).CAS 
PubMed 
Article 

Google Scholar 
Van Ockenburg, S. L. et al. The relationship between 63 days of 24-h urinary free cortisol and hair cortisol levels in 10 healthy individuals. Psychoneuroendocrinology 73, 142–147 (2016).PubMed 
Article 

Google Scholar 
Short, S. J. et al. Correspondence between hair cortisol concentrations and 30-day integrated daily salivary and weekly urinary cortisol measures. Psychoneuroendocrinology 71, 12–18 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mack, Z. & Fokidis, H. B. A novel method for assessing chronic cortisol concentrations in dogs using the nail as a source. Domest. Anim. Endocrinol. 59, 53–57 (2017).CAS 
PubMed 
Article 

Google Scholar  More

Notomi, T. et al. Loop-mediated isothermal amplification of DNA. Nucleic Acids Res. https://doi.org/10.1093/nar/28.12.e63 (2000).Article 

Google Scholar 
Nagamine, K., Hase, T. & Notomi, T. Accelerated reaction by loop-mediated isothermal amplification using loop primers. Mol. Cell. Probes 16, 223–229. https://doi.org/10.1006/mcpr.2002.0415 (2002).CAS 
Article 

Google Scholar 
Nagamine, K., Watanabe, K., Ohtsuka, K., Hase, T. & Notomi, T. Loop-mediated isothermal amplification reaction using a nondenatured template. Clin. Chem. 47, 1742–1743 (2001).CAS 
Article 

Google Scholar 
Thai, H. T. C. et al. Development and evaluation of a novel loop-mediated isothermal amplification method for rapid detection of severe acute respiratory syndrome coronavirus. J. Clin. Microbiol. 42, 1956–1961. https://doi.org/10.1128/jcm.42.5.1956-1961.2004 (2004).CAS 
Article 

Google Scholar 
Geojith, G., Dhanasekaran, S., Chandran, S. P. & Kenneth, J. Efficacy of loop mediated isothermal amplification (LAMP) assay for the laboratory identification of Mycobacterium tuberculosis isolates in a resource limited setting. J. Microbiol. Methods 84, 71–73. https://doi.org/10.1016/j.mimet.2010.10.015 (2011).CAS 
Article 

Google Scholar 
Saengsawang, N. et al. Development of a fluorescent distance-based paper device using loop-mediated isothermal amplification to detect Escherichia coli in urine. Analyst 145, 8077–8086. https://doi.org/10.1039/d0an01306d (2020).CAS 
Article 

Google Scholar 
Yoshikawa, R. et al. Development and evaluation of a rapid and simple diagnostic assay for COVID-19 based on loop-mediated isothermal amplification. Plos Neglect. Trop. Dis. 14, 14. https://doi.org/10.1371/journal.pntd.000885 (2021).Article 

Google Scholar 
Kim, J. et al. Development and evaluation of a multiplex loop-mediated isothermal amplification (LAMP) assay for differentiation of Mycobacterium tuberculosis and non-tuberculosis mycobacterium in clinical samples. PLoS ONE 16, 11. https://doi.org/10.1371/journal.pone.0244753 (2021).CAS 
Article 

Google Scholar 
Hongjaisee, S. et al. Rapid visual detection of hepatitis C virus using a reverse transcription loop-mediated isothermal ampli fi cation assay. Int. J. Infect. Dis. 102, 440–445. https://doi.org/10.1016/j.ijid.2020.10.082 (2021).CAS 
Article 

Google Scholar 
Niessen, L. & Vogel, R. F. Detection of Fusarium graminearum DNA using a loop-mediated isothermal amplification (LAMP) assay. Int. J. Food Microbiol. 140, 183–191. https://doi.org/10.1016/j.ijfoodmicro.2010.03.036 (2010).CAS 
Article 

Google Scholar 
Ren, W. C., Liu, N. & Li, B. H. Development and application of a LAMP method for rapid detection of apple blotch caused by Marssonina coronaria. Crop Prot. 141, 6. https://doi.org/10.1016/j.cropro.2020.105452 (2021).CAS 
Article 

Google Scholar 
Kong, G. H. et al. Detection of Peronophythora litchii on lychee by loop-mediated isothermal amplification assay. Crop Prot. 139, 6. https://doi.org/10.1016/j.cropro.2020.105370 (2021).CAS 
Article 

Google Scholar 
Zhou, Q. J. et al. Simultaneous detection of multiple bacterial and viral aquatic pathogens using a fluorogenic loop-mediated isothermal amplification-based dual-sample microfluidic chip. J. Fish Dis. https://doi.org/10.1111/jfd.13325 (2020).Article 

Google Scholar 
Huang, H. L. et al. Molecular method for rapid detection of the red tide dinoflagellate Karenia mikimotoi in the coastal region of Xiangshan Bay, China. J. Microbiol. Methods 168, 7. https://doi.org/10.1016/j.mimet.2019.105801 (2020).CAS 
Article 

Google Scholar 
Sridapan, T. et al. Rapid detection of Clostridium perfringens in food by loop-mediated isothermal amplification combined with a lateral flow biosensor. PLoS ONE 16, 14. https://doi.org/10.1371/journal.pone.0245144 (2021).CAS 
Article 

Google Scholar 
Xiong, X. et al. Using real time fluorescence loop-mediated isothermal amplification for rapid species authentication of Atlantic salmon (Salmo salar). J. Food Compos. Anal. 95, 7. https://doi.org/10.1016/j.jfca.2020.103659 (2021).CAS 
Article 

Google Scholar 
Huang, C. G., Hsu, J. C., Haymer, D. S., Lin, G. C. & Wu, W. J. Rapid identification of the Mediterranean fruit fly (Diptera: Tephritidae) by loop-mediated isothermal amplification. J. Econ. Entomol. 102, 1239–1246 (2009).CAS 
Article 

Google Scholar 
Ide, T., Kanzaki, N., Ohmura, W. & Okabe, K. Molecular identification of an invasive wood-boring insect Lyctus brunneus (Coleoptera: Bostrichidae: Lyctinae) using frass by loop-mediated isothermal amplification and nested PCR assays. J. Econ. Entomol. 109, 1410–1414. https://doi.org/10.1093/jee/tow030 (2016).CAS 
Article 

Google Scholar 
Stainton, K., Hall, J., Budge, G. E., Boonham, N. & Hodgetts, J. Rapid molecular methods for in-field and laboratory identification of the yellow-legged Asian hornet (Vespa velutina nigrithorax). J. Appl. Entomol. 142, 610–616. https://doi.org/10.1111/jen.12506 (2018).CAS 
Article 

Google Scholar 
Agarwal, A., Cunningham, J. P., Valenzuela, I. & Blacket, M. J. A diagnostic LAMP assay for the destructive grapevine insect pest, phylloxera (Daktulosphaira vitifoliae). Sci. Rep. 10, 10. https://doi.org/10.1038/s41598-020-77928-9 (2020).CAS 
Article 

Google Scholar 
Rizzo, D. et al. Molecular identification of Anoplophora glabripennis (Coleoptera: Cerambycidae) from frass by loop-mediated isothermal amplification. J. Econ. Entomol. 113, 2911–2919. https://doi.org/10.1093/jee/toaa206 (2020).CAS 
Article 

Google Scholar 
Hsieh, C. H., Wang, H. Y., Chen, Y. F. & Ko, C. C. Loop-mediated isothermal amplification for rapid identification of biotypes B and Q of the globally invasive pest Bemisia tabaci, and studying population dynamics. Pest Manag. Sci. 68, 1206–1213. https://doi.org/10.1002/ps.3298 (2012).CAS 
Article 

Google Scholar 
Williams, M. R. et al. Isothermal amplification of environmental DNA (eDNA) for direct field-based monitoring and laboratory confirmation of Dreissena sp. PLoS ONE 12, 18. https://doi.org/10.1371/journal.pone.0186462 (2017).CAS 
Article 

Google Scholar 
Ponting, S., Tomkies, V. & Stainton, K. Rapid identification of the invasive small hive beetle (Aethina tumida) using LAMP. Pest Manag. Sci. 77, 1476–1481. https://doi.org/10.1002/ps.6168 (2020).CAS 
Article 

Google Scholar 
Davis, C. N. et al. Rapid detection of Galba truncatula in water sources on pasture-land using loop-mediated isothermal amplification for control of trematode infections. Parasites Vectors 13, 11. https://doi.org/10.1186/s13071-020-04371-0 (2020).CAS 
Article 

Google Scholar 
Carvalho, J. et al. Faster monitoring of the invasive alien species (IAS) Dreissena polymorpha in river basins through isothermal amplification. Sci. Rep. 11, 10. https://doi.org/10.1038/s41598-021-89574-w (2021).CAS 
Article 

Google Scholar 
Treguier, A. et al. Environmental DNA surveillance for invertebrate species: Advantages and technical limitations to detect invasive crayfish Procambarus clarkii in freshwater ponds. J. Appl. Ecol. 51, 871–879. https://doi.org/10.1111/1365-2664.12262 (2014).CAS 
Article 

Google Scholar 
Cai, W. et al. Using eDNA to detect the distribution and density of invasive crayfish in the Honghe-Hani rice terrace World Heritage site. PLoS ONE https://doi.org/10.1371/journal.pone.0177724 (2017).Article 

Google Scholar 
Wilcox, T. M. et al. Understanding environmental DNA detection probabilities: A case study using a stream-dwelling char Salvelinus fontinalis. Biol. Conserv. 194, 209–216. https://doi.org/10.1016/j.biocon.2015.12.023 (2016).Article 

Google Scholar 
Hunter, M. E., Ferrante, J. A., Meigs-Friend, G. & Ulmer, A. Improving eDNA yield and inhibitor reduction through increased water volumes and multi-filter isolation techniques. Sci. Rep. https://doi.org/10.1038/s41598-019-40977-w (2019).Article 

Google Scholar 
Twardochleb, L. A., Olden, J. D. & Larson, E. R. A global meta-analysis of the ecological impacts of nonnative crayfish. Freshw. Sci. 32, 1367–1382. https://doi.org/10.1899/12-203.1 (2013).Article 

Google Scholar 
Andruszkiewicz, A. E., Zhang, W. G. & Govindarajan, A. F. Environmental DNA shedding and decay rates from diverse animal forms and thermal regimes. Environ. DNA 3, 492–514. https://doi.org/10.1002/edn3.141 (2021).Article 

Google Scholar 
Stedtfeld, R. D. et al. Static self-directed sample dispensing into a series of reaction wells on a microfluidic card for parallel genetic detection of microbial pathogens. Biomed. Microdev. 17, 89. https://doi.org/10.1007/s10544-015-9994-1 (2015).CAS 
Article 

Google Scholar 
Koloren, Z., Sotiriadou, I. & Karanis, P. Investigations and comparative detection of Cryptosporidium species by microscopy, nested PCR and LAMP in water supplies of Ordu, Middle Black Sea, Turkey. Ann. Trop. Med. Parasitol. 105, 607–615. https://doi.org/10.1179/2047773211y.0000000011 (2011).CAS 
Article 

Google Scholar 
Sabike, I. I. et al. Use of direct LAMP screening of broiler fecal samples for Campylobacter jejuni and Campylobacter coli in the positive flock identification strategy. Front. Microbiol. 7, 1582. https://doi.org/10.3389/fmicb.2016.01582 (2016).Article 

Google Scholar 
Gahlawat, S. K., Ellis, A. E. & Collet, B. A sensitive loop-mediated isothermal amplification (LAMP) method for detection of Renibacterium salmoninarum, causative agent of bacterial kidney disease in salmonids. J. Fish Dis. 32, 491–497. https://doi.org/10.1111/j.1365-2761.2009.01005.x (2009).CAS 
Article 

Google Scholar 
Levy, J. et al. Methods for rapid and effective PCR-based detection of ‘Candidatus Liberibacter solanacearum’ from the insect vector Bactericera cockerelli: Streamlining the DNA extraction/purification process. J. Econ. Entomol. 106, 1440–1445. https://doi.org/10.1603/ec12419 (2013).CAS 
Article 

Google Scholar 
Kaneko, H., Kawana, T., Fukushima, E. & Suzutani, T. Tolerance of loop-mediated isothermal amplification to a culture medium and biological substances. J. Biochem. Biophys. Methods 70, 499–501. https://doi.org/10.1016/j.jbbm.2006.08.008 (2007).CAS 
Article 

Google Scholar 
Curtis, A. N., Tiemann, J. S., Douglass, S. A., Davis, M. A. & Larson, E. R. High stream flows dilute environmental DNA (eDNA) concentrations and reduce detectability. Divers. Distrib. 27, 1918–1931. https://doi.org/10.1111/ddi.13196 (2020).Article 

Google Scholar 
Mauvisseau, Q. et al. Environmental DNA as an efficient tool for detecting invasive crayfishes in freshwater ponds. Hydrobiologia 805, 163–175. https://doi.org/10.1007/s10750-017-3288-y (2018).CAS 
Article 

Google Scholar 
RStudioTeam. Boston (ed. PBC) (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).Book 

Google Scholar  More

Magallón, S., Sánchez-Reyes, L. L. & Gómez-Acevedo, S. L. Thirty clues to the exceptional diversification of flowering plants. Ann. Bot. 123, 491–503 (2019).PubMed 
Article 

Google Scholar 
Shi, J. J. & Rabosky, D. L. Speciation dynamics during the global radiation of extant bats. Evolution 69, 1528–1545 (2015).PubMed 
Article 

Google Scholar 
Nicolai, M. P. J. & Matzke, N. J. Trait-based range expansion aided in the global radiation of Crocodylidae. Glob. Ecol. Biogeogr. 28, 1244–1258 (2019).Article 

Google Scholar 
Coyne, J. A. & Orr, H. A. Speciation (Sinauer Associates, 2004).Price, T. & others. Speciation in Birds (Roberts and Co., 2008).Moyle, R. G., Filardi, C. E., Smith, C. E. & Diamond, J. Explosive Pleistocene diversification and hemispheric expansion of a “great speciator”. Proc. Natl Acad. Sci. USA 106, 1863–1868 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Van Bocxlaer, I. et al. Gradual adaptation toward a range-expansion phenotype initiated the global radiation of toads. Science 327, 679–682 (2010).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Phillimore, A. B. & Price, T. D. in Speciation and Patterns on Diversity (eds Butlin, R., Bridle, J. & Schluter, D.) Ch. 13 (Cambridge Univ. Press, 2009).Price, T. D. et al. Niche filling slows the diversification of Himalayan songbirds. Nature 509, 222–225 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Nosil, P. Ecological Speciation (Oxford Univ. Press, 2012).Naciri, Y. & Linder, H. P. The genetics of evolutionary radiations. Biol. Rev. Camb. Philos. Soc. 95, 1055–1072 (2020).Price, T. D. & Sol, D. Introduction: genetics of colonizing species. Am. Nat. 172, S1–S3 (2008).PubMed 
Article 

Google Scholar 
Schluter, D. The Ecology of Adaptive Radiation (Oxford Univ. Press, 2000).Gill, F. & Donsker, D. IOC world bird list (v 8.1). 2018. (2018).Del Hoyo, J., Del Hoyo, J., Elliott, A. & Sargatal, J. Handbook of the Birds of the World Vol. 1 (Lynx edicions, 1992).Cassey, P. Are there body size implications for the success of globally introduced land birds? Ecography 24, 413–420 (2001).Article 

Google Scholar 
Fristoe, T. S., Iwaniuk, A. N. & Botero, C. A. Big brains stabilize populations and facilitate colonization of variable habitats in birds. Nat. Ecol. Evol. 1, 1706–1715 (2017).PubMed 
Article 

Google Scholar 
Sayol, F. et al. Environmental variation and the evolution of large brains in birds. Nat. Commun. 7, 1–8 (2016).Article 
CAS 

Google Scholar 
Sol, D. Revisiting the cognitive buffer hypothesis for the evolution of large brains. Biol. Lett. 5, 130–133 (2009).PubMed 
Article 

Google Scholar 
Lefebvre, L. & Sol, D. Brains, lifestyles and cognition: are there general trends? Brain. Behav. Evol. 72, 135–144 (2008).PubMed 
Article 

Google Scholar 
Jønsson, K. A. et al. A supermatrix phylogeny of corvoid passerine birds (Aves: Corvides). Mol. Phylogenet. Evol. 94, 87–94 (2016).PubMed 
Article 

Google Scholar 
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Marki, P. Z. et al. Breeding system evolution influenced the geographic expansion and diversification of the core Corvoidea (Aves: Passeriformes). Evolution 69, 1874–1924 (2015).PubMed 
Article 

Google Scholar 
KessLer, J. E. Evolution of Corvids and their presence in the neogene and the quaternary in the Carpathian Basin. Ornis Hungarica 28, 121–168 (2020).Article 

Google Scholar 
Olson, S. L. & Rasmussen, P. C., others. Miocene and Pliocene birds from the Lee Creek Mine, North Carolina. Smithson Contrib. Paleobiol. 90, 233–365 (2001).
Google Scholar 
Rabosky, D. L. Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLoS ONE 9, e89543 (2014).Alfaro, M. E. et al. Lineage-specific diversification rates and high turnover in the history of jawed vertebrates. Proc. Natl Acad. Sci. USA 106, 13410–13414 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rabosky, D. L., Donnellan, S. C., Grundler, M. & Lovette, I. J. Analysis and visualization of complex macroevolutionary dynamics: an example from Australian scincid lizards. Syst. Biol. 63, 610–627 (2014).PubMed 
Article 

Google Scholar 
Louca, S. & Pennell, M. W. Extant timetrees are consistent with a myriad of diversification histories. Nature 580, 502–505 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Kulemeyer, C., Asbahr, K., Gunz, P., Frahnert, S. & Bairlein, F. Functional morphology and integration of corvid skulls-a 3D geometric morphometric approach. Front. Zool. 6, 2 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
Zeffer, A., Johansson, L. C. & Marmebro, Å. Functional correlation between habitat use and leg morphology in birds (Aves). Biol. J. Linn. Soc. 79, 461–484 (2003).Article 

Google Scholar 
Wang, X., McGowan, A. J. & Dyke, G. J. Avian wing proportions and flight styles: first step towards predicting the flight modes of Mesozoic birds. PLoS ONE 6, e28672 (2011).Corbin, C. E., Lowenberger, L. K. & Gray, B. L. Linkage and trade-off in trophic morphology and behavioural performance of birds. Funct. Ecol. 29, 808–815 (2015).Article 

Google Scholar 
Kennedy, J. D. et al. The influence of wing morphology upon the dispersal, geographical distributions and diversification of the Corvides (Aves; Passeriformes). Proc. R. Soc. B Biol. Sci. 283, 20161922 (2016).Article 

Google Scholar 
Pigot, A. L. et al. Macroevolutionary convergence connects morphological form to ecological function in birds. Nat. Ecol. Evol. 4, 230–239 (2020).PubMed 
Article 

Google Scholar 
Clavel, J., Escarguel, G. & Merceron, G. mvMORPH: an R package for fitting multivariate evolutionary models to morphometric data. Methods in Ecology and Evolution 6, 1311–1319 (2015).Uyeda, J. C., Caetano, D. S. & Pennell, M. W. Comparative analysis of principal components can be misleading. Syst. Biol. 64, 677–689 (2015).CAS 
PubMed 
Article 

Google Scholar 
Leyequién, E., de Boer, W. F. & Cleef, A. Influence of body size on coexistence of bird species. Ecol. Res. 22, 735–741 (2007).Article 

Google Scholar 
Grant, P. R. Bill size, body size, and the ecological adaptations of bird species to competitive situations on islands. Syst. Biol. 17, 319–333 (1968).CAS 
Article 

Google Scholar 
Meiri, S. & Dayan, T. On the validity of Bergmann’s rule. J. Biogeogr. 30, 331–351 (2003).Article 

Google Scholar 
Friedman, N. R. et al. Evolution of a multifunctional trait: shared effects of foraging ecology and thermoregulation on beak morphology, with consequences for song evolution. Proc. R. Soc. B 286, 20192474 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Friedman, N. R., Harmáčková, L., Economo, E. P. & Remeš, V. Smaller beaks for colder winters: Thermoregulation drives beak size evolution in Australasian songbirds. Evolution 71, 2120–2129 (2017).PubMed 
Article 

Google Scholar 
Sheard, C. et al. Ecological drivers of global gradients in avian dispersal inferred from wing morphology. Nat. Commun. 11, 1–9 (2020).Article 
CAS 

Google Scholar 
Rabosky, D. L. et al. BAMM tools: an R package for the analysis of evolutionary dynamics on phylogenetic trees. Methods Ecol. Evol. 5, 701–707 (2014).Article 

Google Scholar 
Thomas, G. H. & Freckleton, R. P. MOTMOT: models of trait macroevolution on trees. Methods Ecol. Evol. 3, 145–151 (2012).CAS 
Article 

Google Scholar 
O’Meara, B. C., Ané, C., Sanderson, M. J. & Wainwright, P. C. Testing for different rates of continuous trait evolution using likelihood. Evolution 60, 922–933 (2006).PubMed 
Article 

Google Scholar 
Harmon, L. J., Schulte, J. A., Larson, A. & Losos, J. B. Tempo and mode of evolutionary radiation in iguanian lizards. Science 301, 961–964 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Slater, G. J., Price, S. A., Santini, F. & Alfaro, M. E. Diversity versus disparity and the radiation of modern cetaceans. Proc. R. Soc. B Biol. Sci. 277, 3097–3104 (2010).Article 

Google Scholar 
Sullivan, B. L. et al. eBird: A citizen-based bird observation network in the biological sciences. Biol. Conserv. 142, 2282–2292 (2009).Article 

Google Scholar 
Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data. Glob. Ecol. Biogeogr. 21, 481–497 (2012).Article 

Google Scholar 
Heinrich, B. Ravens in Winter (Simon and Schuster, 2014).Taylor, A. H., Hunt, G. R., Medina, F. S. & Gray, R. D. Do new Caledonian crows solve physical problems through causal reasoning? Proc. R. Soc. B Biol. Sci. 276, 247–254 (2009).CAS 
Article 

Google Scholar 
Lefebvre, L., Reader, S. M. & Sol, D. Brains, innovations and evolution in birds and primates. Brain. Behav. Evol. 63, 233–246 (2004).PubMed 
Article 

Google Scholar 
Rensch, B. Increase of learning capability with increase of brain-size. Am. Nat. 90, 81–95 (1956).Article 

Google Scholar 
Roth, T. C., LaDage, L. D., Freas, C. A. & Pravosudov, V. V. Variation in memory and the hippocampus across populations from different climates: a common garden approach. Proc. R. Soc. B Biol. Sci. 279, 402–410 (2012).Article 

Google Scholar 
Olkowicz, S. et al. Birds have primate-like numbers of neurons in the forebrain. Proc. Natl Acad. Sci. USA 113, 7255–7260 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sayol, F., Lefebvre, L. & Sol, D. Relative brain size and its relation with the associative pallium in birds. Brain. Behav. Evol. 87, 69–77 (2016).PubMed 
Article 

Google Scholar 
Garcia-Porta, J. & Ord, T. J. Key innovations and island colonization as engines of evolutionary diversification: a comparative test with the Australasian diplodactyloid geckos. J. Evol. Biol. 26, 2662–2680 (2013).Losos, J. B. & Ricklefs, R. E. Adaptation and diversification on islands. Nature 457, 830–836 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Stuart, Y. E. & Losos, J. B. Ecological character displacement: glass half full or half empty? Trends Ecol. Evol. 28, 402–408 (2013).PubMed 
Article 

Google Scholar 
Jenkins, D. G. et al. Does size matter for dispersal distance? Glob. Ecol. Biogeogr. 16, 415–425 (2007).Article 

Google Scholar 
Sol, D. et al. Evolutionary divergence in brain size between migratory and resident birds. PLoS ONE 5, e9617 (2010).Ducatez, S., Sol, D., Sayol, F. & Lefebvre, L. Behavioural plasticity is associated with reduced extinction risk in birds. Nat. Ecol. Evol. 4, 788–793 (2020).PubMed 
Article 

Google Scholar 
Sayol, F., Sol, D. & Pigot, A. L. Brain size and life history interact to predict urban tolerance in birds. Front. Ecol. Evol. 8, 58 (2020).Article 

Google Scholar 
Baltensperger, A. P. et al. Seasonal observations and machine-learning-based spatial model predictions for the common raven (Corvus corax) in the urban, sub-arctic environment of Fairbanks, Alaska. Polar Biol. 36, 1587–1599 (2013).Article 

Google Scholar 
Kövér, L. et al. Recent colonization and nest site selection of the Hooded Crow (Corvus corone cornix L.) in an urban environment. Landsc. Urban Plan. 133, 78–86 (2015).Article 

Google Scholar 
Oostra, V., Saastamoinen, M., Zwaan, B. J. & Wheat, C. W. Strong phenotypic plasticity limits potential for evolutionary responses to climate change. Nat. Commun. 9, 1–11 (2018).CAS 
Article 

Google Scholar 
Dukas, R. & Ratcliffe, J. M. Cognitive Ecology II (University of Chicago Press, 2009).Huey, R. B., Hertz, P. E. & Sinervo, B. Behavioral drive versus behavioral inertia in evolution: a null model approach. Am. Nat. 161, 357–366 (2003).PubMed 
Article 

Google Scholar 
Fox, R. J., Donelson, J. M., Schunter, C., Ravasi, T. & Gaitán-Espitia, J. D. Beyond buying time: the role of plasticity in phenotypic adaptation to rapid environmental change. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20180174 (2019).Aboitiz, F. Behavior, body types and the irreversibility of evolution. Acta Biotheor. 38, 91–101 (1990).Wcislo, W. T. Behavioral environments and evolutionary change. Annu. Rev. Ecol. Syst. 20, 137–169 (1989).Article 

Google Scholar 
Sol, D., Stirling, D. G. & Lefebvre, L. Behavioral drive or behavioral inhibition in evolution: subspecific diversification in Holarctic passerines. Evolution 59, 2669–2677 (2005).PubMed 
Article 

Google Scholar 
Mayr, E., Mayr, E., Mayr, E. & Mayr, E. Animal Species and Evolution Vol. 797 (Belknap Press of Harvard University Press, 1963).Mayr, E. The emergence of evolutionary novelties. Evol. Darwin 1, 349–380 (1960).
Google Scholar 
Hardy, A. C. The Living Stream: Evolution and Man (Harper & Row, 1967).Wyles, J. S., Kunkel, J. G. & Wilson, A. C. Birds, behavior, and anatomical evolution. Proc. Natl Acad. Sci. USA 80, 4394–4397 (1983).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Plotkin, H. C. The Role of Behavior in Evolution (MIT press, 1988).Lande, R. Models of speciation by sexual selection on polygenic traits. Proc. Natl Acad. Sci. USA 78, 3721–3725 (1981).ADS 
MathSciNet 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
West-Eberhard, M. J. Phenotypic plasticity and the origins of diversity. Annu. Rev. Ecol. Syst. 20, 249–278 (1989).Article 

Google Scholar 
Sol, D. & Price, T. D. Brain size and the diversification of body size in birds. Am. Nat. 172, 170–177 (2008).PubMed 
Article 

Google Scholar 
Sayol, F., Lapiedra, O., Ducatez, S. & Sol, D. Larger brains spur species diversification in birds. Evolution 73, 2085–2093 (2019).PubMed 
Article 

Google Scholar 
Abascal, F., Zardoya, R. & Telford, M. J. TranslatorX: multiple alignment of nucleotide sequences guided by amino acid translations. Nucleic Acids Res. 38, W7–W13 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bouckaert, R. et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 10, e1003537 (2014).Bouckaert, R., Alvarado-Mora, M. V. & Pinho, J. R., others. Evolutionary rates and HBV: issues of rate estimation with Bayesian molecular methods. Antivir. Ther. 18, 497–503 (2013).PubMed 
Article 

Google Scholar 
Rambaut, A. & Drummond, A. J. Tracer v1. 4. (2007).Harmon, L. J., Weir, J. T., Brock, C. D., Glor, R. E. & Challenger, W. GEIGER: investigating evolutionary radiations. Bioinformatics 24, 129–131 (2008).CAS 
PubMed 
Article 

Google Scholar 
Louca, S. & Louca, M. S. Package ‘castor’. (2017).Rasband, W. S. et al. ImageJ. (1997).Rohlf, F. J. & Slice, D. Extensions of the Procrustes method for the optimal superimposition of landmarks. Syst. Biol. 39, 40–59 (1990).
Google Scholar 
Adams, D. C. & Otárola-Castillo, E. geomorph: an R package for the collection and analysis of geometric morphometric shape data. Methods Ecol. Evol. 4, 393–399 (2013).Article 

Google Scholar 
Adams, D. C., Collyer, M., Kaliontzopoulou, A. & Sherratt, E. Geomorph: software for geometric morphometric analyses. (2016).Chira, A. M. & Thomas, G. H. The impact of rate heterogeneity on inference of phylogenetic models of trait evolution. J. Evol. Biol. 29, 2502–2518 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rodríguez Casal, A. & Pateiro López, B. Generalizing the convex hull of a sample: the R package alphahull. J. Stat. Softw. 34, 1–28 (2010).Zelditch, M. L., Swiderski, D. L. & Sheets, H. D. Geometric Morphometrics for Biologists: A Primer (Academic Press, 2012).Clavel, J. & Morlon, H. Reliable phylogenetic regressions for multivariate comparative data: illustration with the MANOVA and application to the effect of diet on mandible morphology in Phyllostomid bats. Syst. Biol. 69, 927–943 (2020).CAS 
PubMed 
Article 

Google Scholar 
Dujardin, J.-P., Le Pont, F. & Baylac, M. Geographical versus interspecific differentiation of sand flies (Diptera: Psychodidae): a landmark data analysis. Bull. Entomol. Res. 93, 87–90 (2003).PubMed 
Article 

Google Scholar 
Sidlauskas, B. Continuous and arrested morphological diversification in sister clades of characiform fishes: a phylomorphospace approach. Evolution 62, 3135–3156 (2008).PubMed 
Article 

Google Scholar 
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

Google Scholar 
International, B. BirdLife International and handbook of the birds of the world (2017) Bird species distribution maps of the world. (2017).Callaghan, C. T., Nakagawa, S. & Cornwell, W. K. Global abundance estimates for 9,700 bird species. Proc. Natl. Acad. Sci. USA 118, e2023170118 (2021).Hijmans, R. & van Etten, J. raster: raster: geographic data analysis and modeling. R. Packag. version 517, 2 (2014).
Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Dray, S. & Dufour, A.-B., others. The ade4 package: implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).Article 

Google Scholar 
Ho, L. S. T. et al. Package ‘phylolm’. (2018).Akaike, H. Selected Papers of Hirotugu Akaike (Springer, 1998).Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 
PubMed 
Article 

Google Scholar  More

RESEARCH BRIEFINGS
20 April 2022

Protected areas are a cornerstone of conservation policies, but many are not benefiting target species. These areas need to be managed better if they are to achieve conservation goals. More

Vote for our episode What’s the isiZulu for dinosaur? to win a People’s Voice Award in this year’s Webbys

Listen to the latest from the world of science, with Benjamin Thompson, Nick Petrić Howe and Shamini Bundell.

Your browser does not support the audio element.

Download MP3

In this episode:00:46 What COP26 promises will do for climateAt COP26 countries made a host of promises and commitments to tackle global warming. Now, a new analysis suggests these pledges could limit warming to below 2˚C – if countries stick to them.BBC News: Climate change: COP26 promises will hold warming under 2C03:48 Efficiency boost for energy storage solutionStoring excess energy is a key obstacle preventing wider adoption of renewable power. One potential solution has been to store this energy as heat before converting it back into electricity, but to date this process has been inefficient. Last week, a team reported the development of a new type of ‘photothermovoltaic’ that increases the efficiency of converting stored heat back into electricity, potentially making the process economically viable.Science: ‘Thermal batteries’ could efficiently store wind and solar power in a renewable grid07:56 Leeches’ lunches help ecologists count wildlifeBlood ingested by leeches may be a way to track wildlife, suggests new research. Using DNA from the blood, researchers were able to detect 86 different species in China’s Ailaoshan Nature Reserve. Their results also suggest that biodiversity was highest in the high-altitude interior of the reserve, suggesting that human activity had pushed wildlife away from other areas.ScienceNews: Leeches expose wildlife’s whereabouts and may aid conservation efforts11:05 How communication evolved in underground cave fishResearch has revealed that Mexican tetra fish are very chatty, and capable of making six distinct sounds. They also showed that fish populations living in underground caves in north-eastern Mexico have distinct accents.New Scientist: Blind Mexican cave fish are developing cave-specific accents14:36 Declassified data hints at interstellar meteorite strikeIn 2014 a meteorite hit the Earth’s atmosphere that may have come from far outside the solar system, making it the first interstellar object to be detected. However, as some of the data needed to confirm this was classified by the US Government, the study was never published. Now the United States Space Command have confirmed the researchers’ findings, although the work has yet to be peer reviewed.LiveScience: An interstellar object exploded over Earth in 2014, declassified government data revealVice: Secret Government Info Confirms First Known Interstellar Object on Earth, Scientists SaySubscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.Never miss an episode: Subscribe to the Nature Podcast on Apple Podcasts, Google Podcasts, Spotify or your favourite podcast app. Head here for the Nature Podcast RSS feed. More

Azam, F. & Malfatti, F. Microbial structuring of marine ecosystems. Nat. Rev. Microbiol. 5, 782–791 (2007).CAS 
Article 

Google Scholar 
Blackburn, N., Fenchel, T. & Mitchell, J. Microscale nutrient patches in planktonic habitats shown by chemotactic bacteria. Science 282, 2254–2256 (1998).CAS 
Article 

Google Scholar 
Stocker, R. Marine microbes see a sea of gradients. Science 338, 628 (2012).CAS 
Article 

Google Scholar 
Levin, S. A. The problem of pattern and scale in ecology. Ecology 73, 1943–1967 (1992).Article 

Google Scholar 
Azam, F. Microbial control of oceanic carbon flux: the plot thickens. Science 280, 694–696 (1998).CAS 
Article 

Google Scholar 
Strom, S. L. Microbial ecology of ocean biogeochemistry: a community perspective. Science 320, 1043–1045 (2008).CAS 
Article 

Google Scholar 
Sarmento, H. & Gasol, J. M. Use of phytoplankton-derived dissolved organic carbon by different types of bacterioplankton. Env. Microbiol. 14, 2348–2360 (2012).CAS 
Article 

Google Scholar 
Grossart, H.-P., Riemann, L. & Azam, F. Bacterial motility in the sea and its ecological implications. Aquat. Microb. Ecol. 25, 247–258 (2001).Article 

Google Scholar 
Brumley, D. R. et al. Bacteria push the limits of chemotactic precision to navigate dynamic chemical gradients. Proc. Natl Acad. Sci. USA 116, 10792–10797 (2019).CAS 
Article 

Google Scholar 
Fenchel, T. Eppur si muove: many water column bacteria are motile. Aquat. Microb. Ecol. 24, 197–201 (2001).Article 

Google Scholar 
Son, K., Menolascina, F. & Stocker, R. Speed-dependent chemotactic precision in marine bacteria. Proc. Natl Acad. Sci. USA 113, 8624–8629 (2016).CAS 
Article 

Google Scholar 
Fenchel, T. Microbial behavior in a heterogeneous world. Science 296, 1068–1071 (2002).CAS 
Article 

Google Scholar 
Kiørboe, T. & Jackson, G. A. Marine snow, organic solute plumes, and optimal chemosensory behavior of bacteria. Limnol. Oceanogr. 46, 1309–1318 (2001).Article 

Google Scholar 
Lambert, B. S., Fernandez, V. I. & Stocker, R. Motility drives bacterial encounter with particles responsible for carbon export throughout the ocean. Limnol. Oceanogr. Lett. 4, 113–118 (2019).Article 

Google Scholar 
Wadhams, G. H. & Armitage, J. P. Making sense of it all: bacterial chemotaxis. Nat. Rev. Mol. Cell. Biol. 5, 1024–1037 (2004).CAS 
Article 

Google Scholar 
Stocker, R., Seymour, J. R., Samadani, A., Hunt, D. E. & Polz, M. F. Rapid chemotactic response enables marine bacteria to exploit ephemeral microscale nutrient patches. Proc. Natl Acad. Sci. USA 105, 4209–4214 (2008).CAS 
Article 

Google Scholar 
Raina, J.-B., Fernandez, V., Lambert, B., Stocker, R. & Seymour, J. R. The role of microbial motility and chemotaxis in symbiosis. Nat. Rev. Microbiol. 17, 284–294 (2019).CAS 
Article 

Google Scholar 
Seymour, J. R., Amin, S. A., Raina, J.-B. & Stocker, R. Zooming in on the phycosphere: the ecological interface for phytoplankton–bacteria relationships. Nat. Microbiol. 2, 17065 (2017).CAS 
Article 

Google Scholar 
Bell, W. & Mitchell, R. Chemotactic and growth responses of marine bacteria to algal extracellular products. Biol. Bull. 143, 265–277 (1972).Article 

Google Scholar 
Smriga, S., Fernandez, V. I., Mitchell, J. G. & Stocker, R. Chemotaxis toward phytoplankton drives organic matter partitioning among marine bacteria. Proc. Natl Acad. Sci. USA 113, 1576–1581 (2016).CAS 
Article 

Google Scholar 
Amin, S. A. et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature 522, 98–101 (2015).CAS 
Article 

Google Scholar 
Lambert, B. S. et al. A microfluidics-based in situ chemotaxis assay to study the behaviour of aquatic microbial communities. Nat. Microbiol. 2, 1344–1349 (2017).CAS 
Article 

Google Scholar 
Larsen, M. H., Blackburn, N., Larsen, J. L. & Olsen, J. E. Influences of temperature, salinity and starvation on the motility and chemotactic response of Vibrio anguillarum. Microbiology 150, 1283–1290 (2004).CAS 
Article 

Google Scholar 
Rinke, C. et al. Validation of picogram- and femtogram-input DNA libraries for microscale metagenomics. PeerJ 4, e2486 (2016).Article 

Google Scholar 
Becker, J. et al. Closely related phytoplankton species produce similar suites of dissolved organic matter. Front. Microbiol. 5, 111 (2014).Article 

Google Scholar 
Vraspir, J. M. & Butler, A. Chemistry of marine ligands and siderophores. Annu. Rev. Mar. Sci. 1, 43–63 (2009).Article 

Google Scholar 
Tagliabue, A. et al. The integral role of iron in ocean biogeochemistry. Nature 543, 51–59 (2017).CAS 
Article 

Google Scholar 
Hopkinson, B. M. & Morel, F. M. M. The role of siderophores in iron acquisition by photosynthetic marine microorganisms. BioMetals 22, 659–669 (2009).CAS 
Article 

Google Scholar 
Amin, S. A. et al. Photolysis of iron–siderophore chelates promotes bacterial–algal mutualism. Proc. Natl Acad. Sci. USA 106, 17071–17076 (2009).CAS 
Article 

Google Scholar 
Croft, M. T., Lawrence, A. D., Raux-Deery, E., Warren, M. J. & Smith, A. G. Algae acquire vitamin B12 through a symbiotic relationship with bacteria. Nature 438, 90–93 (2005).CAS 
Article 

Google Scholar 
Helliwell, K. E. The roles of B vitamins in phytoplankton nutrition: new perspectives and prospects. New Phytol. 216, 62–68 (2017).CAS 
Article 

Google Scholar 
Berg, G. Plant–microbe interactions promoting plant growth and health: perspectives for controlled use of microorganisms in agriculture. Appl. Microbiol. Biotechnol. 84, 11–18 (2009).CAS 
Article 

Google Scholar 
Christie, P. J., Whitaker, N. & González-Rivera, C. Mechanism and structure of the bacterial type IV secretion systems. Biochim. Biophys. Acta 1843, 1578–1591 (2014).CAS 
Article 

Google Scholar 
Preston, G. M. Metropolitan microbes: type III secretion in multihost symbionts. Cell Host Microbe 2, 291–294 (2007).CAS 
Article 

Google Scholar 
Deakin, W. J. & Broughton, W. J. Symbiotic use of pathogenic strategies: rhizobial protein secretion systems. Nat. Rev. Microbiol. 7, 312–320 (2009).CAS 
Article 

Google Scholar 
Luo, H. & Moran, M. A. Evolutionary ecology of the marine Roseobacter clade. Microbiol. Mol. Biol. Rev. 78, 573–587 (2014).Article 

Google Scholar 
Rolland, J. L., Stien, D., Sanchez-Ferandin, S. & Lami, R. Quorum sensing and quorum quenching in the phycosphere of phytoplankton: a case of chemical interactions in ecology. J. Chem. Ecol. 42, 1201–1211 (2016).CAS 
Article 

Google Scholar 
Fei, C. et al. Quorum sensing regulates ‘swim-or-stick’ lifestyle in the phycosphere. Environ. Microbiol. 22, 4761–4778 (2020).CAS 
Article 

Google Scholar 
Landa, M., Burns, A. S., Roth, S. J. & Moran, M. A. Bacterial transcriptome remodeling during sequential co-culture with a marine dinoflagellate and diatom. ISME J. 11, 2677 (2017).CAS 
Article 

Google Scholar 
Rinke, C. et al. A phylogenomic and ecological analysis of the globally abundant Marine Group II archaea (Ca. Poseidoniales ord. nov.). ISME J. 13, 663–675 (2019).CAS 
Article 

Google Scholar 
Fenchel, T. & Blackburn, N. Motile chemosensory behaviour of phagotrophic protists: mechanisms for and efficiency in congregating at food patches. Protist 150, 325–336 (1999).CAS 
Article 

Google Scholar 
Hughes, D. J. et al. Impact of nitrogen availability upon the electron requirement for carbon fixation in Australian coastal phytoplankton communities. 63, 1891–1910 (2018).Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis. Metabolomics 3, 211–221 (2007).CAS 
Article 

Google Scholar 
Chong, J., Wishart, D. S. & Xia, J. Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis. Curr. Protoc. Bioinformatics 68, e86 (2019).Article 

Google Scholar 
Xia, J. & Wishart, D. S. Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nat. Protoc. 6, 743–760 (2011).CAS 
Article 

Google Scholar 
Lambert, B. S. & Raina, J.-B. Fabrication and deployment of the in situ chemotaxis assay (ISCA). protocols.io https://doi.org/10.17504/protocols.io.kztcx6n (2019).Ritchie, R. J. Consistent sets of spectrophotometric chlorophyll equations for acetone, methanol and ethanol solvents. Photosynth. Res. 89, 27–41 (2006).CAS 
Article 

Google Scholar 
Marie, D., Partensky, F., Jacquet, S. & Vaulot, D. Enumeration and cell cycle analysis of natural populations of marine picoplankton by flow cytometry using the nucleic acid stain SYBR Green I. Appl. Environ. Microbiol. 63, 186–193 (1997).CAS 
Article 

Google Scholar 
Bramucci, A. R. et al. Microvolume DNA extraction methods for microscale amplicon and metagenomic studies. ISME Commun. 1, 79 (2021).Article 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
Article 

Google Scholar 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://doi.org/10.48550/arXiv.1303.3997 (2013).Boyd, J. A., Woodcroft, B. J. & Tyson, G. W. GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes. Nucleic Acids Res. 46, e59 (2018).Article 

Google Scholar 
Kuever, J., Rainey, F. A. & Widdel, F. In Bergey’s Manual of Systematics of Archaea and Bacteria https://doi.org/10.1002/9781118960608.obm00084 (2015).Bianchi, D., Weber, T. S., Kiko, R. & Deutsch, C. Global niche of marine anaerobic metabolisms expanded by particle microenvironments. Nat. Geosci. 11, 263–268 (2018).CAS 
Article 

Google Scholar 
Liu, X. et al. Wide distribution of anaerobic ammonium-oxidizing bacteria in the water column of the South China Sea: implications for their survival strategies. Divers. Distrib. 27, 1893–19003 (2021).Article 

Google Scholar 
Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).CAS 
Article 

Google Scholar 
Suzek, B. E., Huang, H., McGarvey, P., Mazumder, R. & Wu, C. H. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics 23, 1282–1288 (2007).CAS 
Article 

Google Scholar 
Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).CAS 
Article 

Google Scholar 
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59 (2014).Article 

Google Scholar 
Paulson, J. N., Stine, O. C., Bravo, H. C. & Pop, M. Differential abundance analysis for microbial marker-gene surveys. Nat. Methods 10, 1200 (2013).CAS 
Article 

Google Scholar 
McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLOS Comput. Biol. 10, e1003531 (2014).Article 

Google Scholar 
Berges, J. A., Franklin, D. J. & Harrison, P. J. Evolution of an artificial seawater medium: improvements in enriched seawater, artificial water over the last two decades. J. Phycol. 37, 1138–1145 (2001).Article 

Google Scholar 
Lane, D. In Nucleic Acid Techniques in Bacterial Systematics (eds Stackebrandt, E. & Goodfellow, M.) 115–175 (1991).Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nature Methods 13, 581–583 (2016).CAS 
Article 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).Article 

Google Scholar 
Oksanen, J. et al. Package ‘Vegan’ Community Ecology Package Version 2 (2013).Durham, B. P. et al. Sulfonate-based networks between eukaryotic phytoplankton and heterotrophic bacteria in the surface ocean. Nat. Microbiol. 4, 1706–1715 (2019).CAS 
Article 

Google Scholar 
Durham, B. P. et al. Recognition cascade and metabolite transfer in a marine bacteria–phytoplankton model system. Environ. Microbiol. 19, 3500–3513 (2017).CAS 
Article 

Google Scholar 
Durham, B. P. et al. Cryptic carbon and sulfur cycling between surface ocean plankton. Proc. Natl Acad. Sci. USA 112, 453–457 (2015).CAS 
Article 

Google Scholar 
Landa, M. et al. Sulfur metabolites that facilitate oceanic phytoplankton–bacteria carbon flux. ISME J. 13, 2536–2550 (2019).CAS 
Article 

Google Scholar  More

To help to achieve ecological recovery worldwide, more multinational corporations are making commitments to biodiversity conservation1–3. According to the most recent assessment in 2018, 31 of the 100 largest companies by revenue worldwide (the global Fortune 100) have done so, from the retail corporation Walmart to the insurance company AXA4.To deliver real gains — in the population sizes of endangered species, say, or in the number of hectares of restored forests, grasslands or wetlands — large organizations need to determine which of their activities have the greatest impacts on biodiversity5. And they need to disclose and mitigate those impacts. Currently, methods for doing this are lacking (see ‘Promises are hard to keep’). (By large organizations, we mean formal entities composed of hundreds of people or more that act towards a certain purpose, whether in the public, private or non-profit sectors.)
Promises are hard to keep

A lack of consensus on methods and metrics means companies are struggling to clearly define — and deliver on — commitments relating to biodiversity.
So far, most studies of the environmental impacts of organizations, such as multinational corporations and universities, have focused on greenhouse-gas emissions.
The G7 group of the world’s largest economies endorsed the new Taskforce on Nature-related Financial Disclosures (TNFD) only last year. This builds on a similar approach used for climate change — the Taskforce on Climate-related Financial Disclosures. The TNFD aims to guide organizations on how to disclose environmental harms tied to their activities, but is still being developed.
The number of corporations making commitments to achieve ‘net gain’ or ‘no net loss’ outcomes in relation to biodiversity has risen steadily in the past two decades3. But some of these promises have subsequently been retracted. In 2016, for example, the mining corporation Rio Tinto moved away from its 2006 agenda-setting ‘net positive impact’ biodiversity commitment, reportedly to focus on minimizing impacts3 (see also go.nature.com/3xtjggo).
Many other commitments are not quantitative. As of 2018, only 5 of the 31 global Fortune 100 companies making biodiversity-related commitments had provided ones that were SMART — specific, measurable, ambitious, realistic and time-bound4 (the global Fortune 100 is an annual list of the 100 largest firms worldwide by revenue, as ranked by Fortune magazine).
When quantitative analyses have been done, they tend to be of limited use, mainly because of inconsistencies in the biodiversity metrics used, and limitations in the scope of the assessment made. Disclosure of results is also limited.

When quantitative analyses have been done, a variety of metrics have been used to quantify impacts. These range from the proportion of local species that would be lost as a result of an activity, to factors such as hectares of habitat affected, or the amount of sustainably sourced paper, fish or palm oil that is used4. But the choice of metric can radically alter the results of an impact assessment, so it is difficult to compare organizations. Likewise, few analyses consider the impacts of activities that are not under the direct control of the organization, such as those associated with supply chains6.As a proof of principle, we conducted a comprehensive assessment of biodiversity losses associated with activities at the University of Oxford, UK. We used data on purchasing, travel bookings, utility bills and other information from the 2018–19 and 2019–20 academic years. The 60 activities we assessed included the day-to-day running of buildings and transport services; travel (including flights) for students and researchers; construction of laboratories and other buildings; consumption of food and beverages at restaurants and cafeterias; and use of medical supplies and other materials in research labs.Our aim was to demonstrate what it would take for a large organization such as the University of Oxford to bring about a net gain in biodiversity — meaning that, thanks to its actions, the world’s biodiversity is left in a better state than it was before. As part of our analysis, we assessed how the university’s various activities and operations also affect greenhouse-gas emissions, and how those, in turn, affect biodiversity by driving climate change.We are confident that the approach we’ve developed for Oxford could be applied more broadly. Indeed, we hope that such a well-known institution disclosing a full assessment of its biodiversity footprint will offer powerful inspiration for others. (All seven of us have a current or previous affiliation with the university.)What we didThe University of Oxford launched an ambitious environmental sustainability strategy in March 2021. Its two main goals are to achieve biodiversity net gain and net-zero carbon, both by 2035. (The latter means that the university will remove as much carbon from the atmosphere as it adds.)To understand how challenging these goals might be to fulfil, we assessed the environmental impacts of the university’s various activities. This covered all those to do with research, education and operations during an academic year for staff and students (see ‘Upstream effects’). For our purposes, operations includes the university transport fleet, consumption of departmental food and utilities, waste disposal and the operational supply chain, including for paper.

Source: J. W. Bull et al.

As a first step, we defined a conceptual framework to systematically categorize the environmental impacts. We grouped activities in research, education and operations according to whether they involved any of five features: travel; food; the built environment (university buildings); the natural environment (any green space or land owned by the university, including managed parks and gardens); and resource use and waste (see ‘What we left out’). Each of these is associated with five general environmental impacts: greenhouse-gas emissions, the use of land and water, and pollution of water and air.
What we left out

Other organizations could assess different types of impact on biodiversity.
Our biodiversity analysis of the University of Oxford, UK, included most upstream impacts — those resulting from consumption of goods and services created outside the university, such as food or medical supplies. We excluded the downstream impacts of research and education, such as those of a discovery in gene editing or chemistry, because it would be impossible to comprehensively account for all of the environmental impacts of knowledge generation. Also not included in our analysis were the university’s 39 colleges, 6 permanent private halls and more than 260 commercial buildings. These are independent legal entities that manage sustainability issues separately.
Other analyses in different sectors might well be able to include downstream impacts. The effects of discarded plastic bottles or clothes could be included for a soft-drinks company or clothing manufacturer, for example.

To further categorize the environmental impacts, we assigned each activity to one of two groups: those under direct university control or influence (through staff and key contractors), and those that the university can influence only indirectly (through students and supply chains). We deemed students buying tuna sandwiches from a university-owned cafe as direct control, for instance, because the university could decide to serve only vegetarian food. However, it can influence only indirectly what happens up the supply chain, before materials are used in a research lab, for example.Using this organizational framework, we worked with administrators to obtain the relevant information, such as travel bookings for staff and students, electricity and water bills, and purchasing records for goods, services and materials used in construction projects.Next, we used various tools to convert all the activities data into estimates of ‘mid-point environmental impacts’ (amount of carbon dioxide emitted, land or water used, and air or water pollutants produced). The database Exiobase 3 is one of the most extensive sources of international supply-chain impacts worldwide7. It shows, for instance, that the roughly US$3.5 million the university spent on paper and paper products in 2019–20 contributed to atmospheric acidification by releasing 2,448 kilograms of sulfur dioxide equivalent. Similarly, the UK Higher Education Supply Chain Emissions Tool uses spending data on goods and services to estimate greenhouse-gas emissions. The roughly $23 million Oxford spent on personal computers, printers and calculators in 2019–20, for example, produced an estimated 20,105 tonnes of CO2 equivalent.We then needed to estimate the extent of biodiversity loss associated with this wide range of broad environmental impacts. So we converted the mid-point environmental impacts into ‘end-point impacts’ that are specifically concerned with biodiversity. To do this, we used an established conversion methodology called ReCiPe8. The output metric ultimately linked to each activity is based on the proportion of local species that would be lost as a result of that activity, relative to the number that exists currently (see Supplementary information for all results and conversion factors).CaveatsWe refined our methods slightly when analysing data from the 2019–20 academic year. This, combined with the disruption caused by the COVID-19 pandemic, makes it difficult to compare years. So for simplicity, we report our results only from the 2019–20 academic year.The biodiversity metric we obtain using ReCiPe is based on strong evidence: the conversion tool is derived from the results of hundreds of studies of the impacts of human pressures on biodiversity8. But, in general, we weren’t able to factor in fine-level variables, such as whether the beef steaks in a university-owned restaurant are sourced from a UK or Brazilian farm. As such, our approach is best seen as a way to evaluate relative impacts, rather than as an indicator of precise absolute impacts.This difficulty aside, it is hard to compare the impact of the University of Oxford on biodiversity with that of similarly sized organizations. As yet, and as far as we know, no other organization has comprehensively evaluated and disclosed its impact on biodiversity, and then had its assessment independently validated. (Ecologists and other stakeholders at the University of Jyväskylä in Finland have begun to explore the impacts of that university’s activities on biodiversity using a similar approach to ours.)Using the greenhouse-gas metric, however, we can compare the impacts of the University of Oxford on emissions (which are related to its impacts on biodiversity) with those of comparably sized organizations.What we foundThe absolute size of the university’s greenhouse-gas footprint is astonishingly large — comparable to that of the eastern Caribbean island nation of Saint Lucia. It is two orders of magnitude smaller than Microsoft’s greenhouse-gas footprint, but one order of magnitude larger than that of the London Stock Exchange, according to estimates publicly disclosed by those organizations.Perhaps the most striking finding in our assessment of impacts specifically on biodiversity is that most of the harms are tied to university activities that are not under its direct control. In fact, the activities with the five biggest impacts on biodiversity are (from biggest to smallest): the supply chain for research activities (such as for chemicals, medical products, organic tissue and plastics); the supply chain for the day-to-day running of buildings (for paper, information technology and so on); food consumption; electricity consumption; and the supply chain for construction. All of these activities are associated with resource use and waste, food and the built environment.

The University of Oxford’s use of laboratory materials has a large impact on biodiversity because of the upstream supply chain.Credit: Peter Nicholls/Reuters

In short, supplies of lab equipment have much greater impacts on biodiversity overall than do international flights, the university’s consumption of electricity or its use of construction materials. (Personal protective equipment used in the lab, for example, requires the extraction and industrial processing of hydrocarbons, often from areas that are rich in biodiversity.)This observation is in line with the results of a handful of studies that suggest that supply chains, not transport or the day-to-day running of buildings, are the main contributors to greenhouse-gas emissions for universities (see, for example, ref. 9). It also aligns with the results of assessments by the fashion giant Kering since 2012, using its Environmental Profit & Loss account — a tool designed to quantify the environmental impacts of the company’s activities. These have revealed that Kering’s procurements of commodities, such as leather, wool and metals, have much more impact on greenhouse-gas emissions, particularly on those from land use, than does the day-to-day running of its factories and offices10.Yet the sustainability strategies of large organizations typically focus not on supply chains, but on recycling, reducing the number of flights people take or the amount of electricity used11–13 (see also Nature 546, 565–567; 2017).Another important finding is the scale of intervention needed. Restoring the university’s owned land (around 1,000 hectares) to native woodland or some other natural habitat would make little difference when it comes to compensating for the impacts on biodiversity that result from just one year of activity. The university colleges own much more land than the university itself — some 50,000 hectares — but we excluded them from our analysis because they are independent legal entities that manage sustainability issues separately.Biodiversity boostHow could the university reverse the biodiversity losses stemming from its activities and operations?Here we consider three options. It could pursue its current environmental sustainability strategy. This entails (among other steps) setting quantitative targets to reduce flights, limiting consumption of all single-use products, making university-catered food vegetarian by default, and achieving 20% net gain for biodiversity in new construction projects. Alternatively, it could focus more heavily on preventing harms to biodiversity. We model a scenario in which all staff flights are prevented, all use of paper and any further construction is stopped, and the purchasing of lab materials is halved. Or the university could focus on compensating for the impacts that its activities and operations have on the planet, by taking steps to increase biodiversity in other places (see ‘Oxford’s options’).

Source: J. W. Bull et al.

Using the 2018–19 academic year results (selected because the COVID-19 pandemic made 2019–20 so unusual), we estimated how far these mitigation strategies might take the university towards biodiversity net gain.Our analysis indicates that the set of preventive measures proposed under the university’s environmental sustainability strategy get it about one-third of the way towards net gain. The findings also indicate that focusing mainly on the prevention of impacts is operationally unfeasible. Activities that have most effect on biodiversity, such as purchasing lab consumables, are central to the university’s existence and cannot simply stop.To achieve net gain, preventive measures, such as reducing flights and paper use, will have to be accompanied by additional and extensive actions to compensate for the remaining impacts on biodiversity.Such actions could include investing in reforestation, wetland restoration, sustainable land-management programmes and prevention of habitat loss caused by independent parties. For example, those directing the Oyu Tolgoi mining project in Mongolia are seeking to achieve biodiversity net gain by spending around 0.6% of the total project cost on actions that benefit biodiversity, such as sustainable grazing practices (see go.nature.com/3tkkbjh). Similarly, the Ambatovy metals mine in Madagascar is on course to offset its impacts on biodiverse eastern rainforests by preventing deforestation of those same habitats through small-scale agriculture14.Achieving true biodiversity net gain will require substantial offsetting that does not necessarily contribute to the university’s reductions in greenhouse-gas emissions. But whatever mix of approaches the institution pursues, it should strive for win–wins on both biodiversity and climate.Many types of action can simultaneously increase biodiversity and reduce greenhouse-gas emissions. For example, restoring mangroves in Bangladesh increased populations of wintering water birds 20-fold in just three years from 2004. And these restored mangroves can absorb carbon four times faster than land-based forests can15. But in other cases, there are trade-offs. Constructing wind turbines and solar photovoltaics to produce renewable energy, for instance, requires extensive mining of metals in places that can be rich in biodiversity16.Net gain for other organizationsOur calculations are likely to be comparable to results for other universities. In our analysis, we do not include the impacts of individual colleges. But because similar kinds of activity occur in colleges as in the rest of the university, their inclusion — or of halls of residence at other universities — is unlikely to qualitatively change our main findings. In fact, because of the colleges’ unusually large land holdings, including them would arguably result in an assessment that doesn’t so easily compare with those of other universities.Crucially, however, the analytical framework we have developed can be applied to a wide range of large organizations — whether they be universities, multinational corporations or government institutions.

Restoring mangroves in western Bangladesh increased populations of wintering water birds, such as this oriental darter (Anhinga melanogaster).Credit: Muhammad Mostafigur Rahman/Alamy

Governments, intergovernmental organizations and multinational corporations are increasingly recognizing that it will not be enough to simply slow the loss of the world’s biodiversity. Damaged habitats and depleted natural resources must be restored to prevent the collapse of ecosystems.Last year, the United Nations called for the urgent revival of nature in farmlands, forests and other ecosystems, declaring 2021–30 to be the Decade on Ecosystem Restoration. Later this year, at a meeting in Kunming, China, it is hoped that 196 nations will agree to the Post-2020 Global Biodiversity Framework of the Convention on Biological Diversity. Among the goals listed in the draft document are a “net gain in the area, connectivity and integrity of natural systems of at least 5 per cent”17.We urge all large organizations, academic or otherwise, to commit to strategies for a net gain in biodiversity — and to adopt formalized approaches that quantify current impacts and allow transparent tracking of progress. Otherwise, the degree of worldwide recovery of natural resources increasingly recognized as crucial for resilient societies to function will not happen.A key challenge is the lack of traceability for commodities. Both our assessment of the University of Oxford and those of others have revealed that large organizations often don’t know which country their commodities (such as cotton, flour or cement) come from — let alone which supplier or what kinds of biodiversity are being affected as a result.According to its 2022 report, for example, even a sector leader such as Kering could trace the source of only about three-quarters of its cotton. Supply chains for other commodities, such as sand, are even more opaque18.Encouragingly, various initiatives are being developed to provide more transparency about environmental impacts across supply chains. These include the supply-chain mapping tool TRASE, which aims to address deforestation.A related challenge, covered extensively elsewhere19,20, is how to ensure that biodiversity offsets are effectively and appropriately implemented such that they lead to conservation outcomes that are truly additional.Currently, there are uncertainties around how long it takes for a restored forest to start delivering biodiversity gains, whether promises to offset harms to biodiversity are actually met, what level of biodiversity gain is delivered by the restoration of a particular habitat, and so on. Take the Ambatovy mine in Madagascar. Its directors began protecting areas of eastern rainforest in 2009 to offset the impacts of deforestation directly caused by the mine. Yet forest gains are not estimated to balance losses until sometime between 2018 and 203314.Despite such challenges, however, we think that a commitment to full transparency, and to improving data collection over time, will enable organizations to compare performance and drive change — both in their own operations and throughout supply chains.Time is too short to let the perfect be the enemy of the good, or to claim that biodiversity net gain is too hard to achieve because there is no universal biodiversity metric. Individual metrics are imperfect but improving, and their limitations should not be a reason to delay measuring, disclosing and tackling impacts on biodiversity. More

Coupling accuracy analysisPrecipitation simulation accuracyThe comparison between annual precipitation simulated by WRF-Hydro and measured precipitation is shown in the following Fig. 3a. From the Fig. 3a, we can get that the correlation between simulated precipitation and measured precipitation is 0.783, which is relatively high and the simulation is good. In addition, the simulated precipitation is less than the measured precipitation value in time. We guess that this error is caused by the precision and quality of precipitation products. WRF-Hydro can easily underestimate the duration of heavy rain when simulating precipitation, so the simulated precipitation is slightly smaller than the measured precipitation in long-term sequence, but the overall accuracy is good.Figure 3(a) Comparison between WRF-HYDRO simulation and measured annual precipitation in Daihai; (b) Comparison of runoff simulation and remote sensing estimation in Daihai Lake; (c) Modified runoff simulation and remote sensing estimation in Daihai Lake.Full size imageThe comparison between the simulated spatial distribution of annual precipitation and the verified products in the study area is shown in the Fig. 4. Generally speaking, the precipitation of interpolation products is slightly higher than the simulation value, which is consistent with the above analysis. In addition, the spatial distribution law of the two is consistent with each other, and the spatial variation law is basically the same. However, the transition of simulation results in areas with severe precipitation changes is relatively gentle, while the transition of interpolation products is more severe. The coverage of the maximum value in the simulation results is smaller than that of interpolation products. The guess is caused by the error of setting the precipitation boundary line. The boundary of interpolation products is China as a whole, and the boundary of simulation results is only Daihai Basin, which fundamentally determines that the precipitation simulation results will be slightly smaller than the interpolation products. Because the climate and hydrology mutual chamber is defined in the model setting from the surrounding grid points, the smaller the area causes some areas with mutual chamber cannot enter the boundary line, resulting in the precipitation simulation results less than the interpolation products. But in terms of the overall spatial differentiation law, the distribution of simulation results in interpolation products is not very different, which has good practical value.Figure 4Spatial comparison of WRF-HYDRO simulation and interpolation of annual precipitation in Daihai.Full size imageSimulation accuracy of runoff into LakeThe comparison between the WRF-Hydro simulation results and remote sensing estimation results of the runoff from Daihai Lake for many years is shown in the Fig. 3b. It can be seen from the figure that the correlation between simulation results and remote sensing estimation results is 0.629, which is better. But it is obvious that the simulation results are higher than those of remote sensing. The reason may be that the model does not set up the parameters of man-made water from the river entering the lake, including agricultural irrigation water and industrial water intake. So the simulation results are overestimated to the runoff into the lake. Therefore, the simulated runoff into the lake is modified in this study to reduce the water consumption ignored by the model.The comparison between the revised simulated runoff and remote sensing estimation is shown in the Fig. 3c. As can be seen from the figure, the correlation is increased to 0.650. Although not much improvement, the simulation results and remote sensing results are distributed evenly around the boundary.Analysis of coupling resultsPrecipitation analysisThe precipitation in Daihai Basin is relatively abundant. Except for some extreme drought years and humid years, the average annual precipitation is 300–600 mm (see Fig. 5a), and the average annual precipitation is about 400 mm. It can be seen from the figure that the minimum annual precipitation is less than 250 mm; The maximum annual diameter is higher than 750 mm. The difference between extreme dry year and extreme wet year is three times.Figure 5(a) Distribution curve of annual precipitation in Daihai Basin; (b) Distribution curve of annual mean monthly precipitation in Daihai Basin.Full size imageThe monthly average of precipitation in the Daihai Basin for many years is shown in the Fig. 5b. It can be seen from the figure that the precipitation in the Daihai Basin is unevenly distributed throughout the year, with the least in January at 1.73 mm and the most in July at 112.10 mm. The precipitation in July–August accounts for more than 50% of the total annual precipitation. In addition, it can be seen from the figure that the precipitation in the Daihai Basin is mainly concentrated in June to September, which is also the flood season in the Daihai Basin, accounting for more than 70% of the total annual precipitation.Combined with Table 3, overall, the average precipitation from 1980 to 1994 is 401.75 mm, with little fluctuation; During the period from 1995 to 2011, except for extreme precipitation in some years (more than 600 mm in both 1995 and 2003), the precipitation decrease, with an average value of 371.39 mm. There are several dry years and wet years, and the fluctuation range was sharp; From 2012 to 2020, the fluctuation range is small, and the average value rises to 451.75 mm.Table 3 Average precipitation (mm) in different periods in Dahai BasinFull size tableThe spatial distribution of annual precipitation in Daihai Basin is shown in the Fig. 6. It is obvious from the figure that the precipitation in 1990, 1995 and 2020 is abundant compared with other years. In addition, it is found that although the annual precipitation in Daihai Basin varies in size, its spatial distribution is basically the same.Figure 6Spatial distribution of annual precipitation in Daihai Basin.Full size imageThe spatial pattern of annual precipitation in Daihai Basin is as follows: the southeast of Liangcheng County and the north of Zuoyun County, the northwest of Liangcheng County and the northwest of Fengzhen county are the three precipitation centers, which gradually decrease outward. And the central effect of Fengzhen county is not obvious in some years. In addition, it is found that the area around Daihai Lake has the least precipitation in the whole Daihai Basin. This may be related to the terrain surrounding the Daihai Basin.In the whole study area, the annual precipitation in the north of Zuoyun County is larger than that in other regions. In some years, the annual precipitation reaches 800 mm, and the extension area is wide. In some years, it extends to the southeast of Liangcheng County. Therefore, it is speculated that mountain torrents, debris flows, rainstorms, snowstorms and other natural disasters are prone to occur here.In addition, combined with the topographic map, it is found that the southeast and northwest of Liangcheng County are the highest elevation in the study area, which coincides with the extreme precipitation. At the same time, it is found that the spatial consistency of precipitation distribution in the whole study area is higher than that of terrain distribution in the study area. Therefore, it is speculated that the precipitation in the study area is seriously affected by the terrain, in other words, the precipitation in the study area is mostly terrain rain or mountain convective rain.Runoff analysisThe Runoff Curve of Daihai Lake is shown in the Fig. 7a. It can be seen from the figure that the flow into the lake shows a downward trend from 1980 to 2020. Although it rebounded in 1996–1999 and 2005–2007, after 2010, the runoff into the lake decreased sharply below 8 × 106m3. From 1980 to 1990, the runoff into the lake decreased linearly with a larger slope and a faster speed; However, from 1990 to 2000, the runoff into the lake appeared the first vibration wave peak, and from 2000 to 2007, the second vibration wave peak. From 2008 to 2012, the decline rate was sharp, and the runoff into the lake had been reduced to 3.95 × 106m3 in 2012; Since 2013, the runoff into the lake tends to be flat, but it has not exceeded 10 × 106m3.Figure 7(a) Change of runoff in Daihai Lake over the years; (b) Changes of lake area in Daihai over the years; (c) Changes of lake water level in Daihai over the years; (d) Changes of volume water in Daihai Lake over the years.Full size imageThe change curve of Daihai Lake area is shown in the Fig. 7b. It can be seen from the figure that the area of Daihai Lake is declining in a straight line. In a short period of 40 years, the lake area has shrunk nearly 100 km2. In addition, we found that the shrinkage rate of Daihai Lake area slowed down from 1980 to 1985, but the lake area shrank sharply from 1995 to 2000. After 2005, the atrophy curve almost coincided with the fitting curve, and the overall fitting R2 was as high as 0.958.The water level variation curve of Daihai Lake is shown in the Fig. 7c. As can be seen from the figure, the variation trend of water level in Daihai Lake is very similar to that of lake area. However, the slope of lake water level change is less than the change rate of lake area. In the 40 years since 1975, the water level in Daihai has dropped by nearly 10 m. In addition, the water level rose slightly in 1995–1996 and 2003–2006. And after 2006, Daihai water level decline rate also accelerated. Since 2006, the water level of Daihai has dropped nearly 6 m, with a rate of 0.45 m/year.The trend of the volume water volume of the Daihai Lake is shown in the Fig. 7d. It can be clearly seen from the figure that the decline curve of the Daihai Lake water volume is close to a straight line, especially from 2005 to the present, the fitting degree is as high as 0.981. There should be some geometrical relationship among the lake area, water level and water volume, and this relationship should be related to the digital elevation model of the lake bottom. In addition, the changes of lake bottom topography are not linear, so there are still subtle differences between the three changes.The annual surface runoff of Daihai Basin is shown in the Fig. 8. It can be seen from the figure that the Gongba River, the Wuhao River, the Buliang River and the Tiancheng River in the south of Daihai Lake supply the Daihai Lake for a long time, and the Bantanzi River in the West also flows into the Dai sea in some years. Combined with the spatial distribution of annual precipitation, it can be concluded that surface runoff is seriously affected by precipitation. The annual distribution is uneven. The surface runoff from the southeast of Liangcheng County generally flows into Daihai Lake to the north, but in some drought years, it will be stopped and cannot flow into Daihai Lake. Bantanzi River in the west of Daihai Lake also supplies Daihai Lake in the year of more precipitation.Figure 8Spatial distribution of surface runoff in Daihai Basin.Full size imageTaking the surface runoff of Daihai Basin in January, April, July and October 2015 as an example, the distribution of surface runoff in different seasons of the year is analyzed, as shown in the Fig. 9. It can be seen from the figure that the rivers in Daihai Basin are seasonal rivers, which are prone to be cut off in autumn and winter. In winter (December–February), there will be different degrees of snowfall events in Daihai Basin, but due to the river freezing period and small snowfall, there will be no runoff. In spring (March to May), the precipitation in Daihai Basin began to increase, and the surface runoff also began to increase, mainly from the southeast and northwest of Liangcheng County. Gongba River, Wuhao River, buliang River, Tiancheng River and Bantanzi River in the south of Daihai Lake will supply Daihai Lake, but these rivers have small flow in spring, which is easy to break. Summer (June–August) is the main period of precipitation in Daihai Basin, and the surface runoff will also surge. In July 2015, the runoff in some areas reached 2000 mm, which was prone to flood disaster. The rivers in the west and south of Daihai Lake will supply it, but the runoff into Daihai Lake is not high, and most of the runoff is concentrated in the upper and middle reaches. In autumn (from September to November), the precipitation in Daihai Basin decreases. Before the freezing period, the precipitation may form runoff, but it is difficult to flow into Daihai Lake due to the small flow.Figure 9Spatial distribution of surface runoff in different seasons in Daihai Basin.Full size imageStatistical analysis of other factorsClimatic factors

(1)

Evaporation capacity

The variation curve of annual evaporation in Daihai is shown in the Fig. 10a. It can be seen from the figure that although the evaporation in Daihai Basin fluctuates, it shows an upward trend, with an upward slope of 8.855 and R2 of 0.560. From 1980 to 1986, the annual evaporation fluctuated around 1000 mm; From 1987 to 1992, the annual evaporation of Daihai Basin decreased sharply, but from 1993 to 2000, the annual evaporation increased sharply with a very high rate of increase; But after 2000, the annual evaporation fluctuated and remained at 1250 mm.

(2)

Average temperature

Figure 10Perennial (a) evaporation (b) annual average temperature (c) annual average wind speed change in Daihai Basin.Full size imageThe variation curve of annual average temperature in Daihai is shown in the Fig. 10b. It can be seen from the figure that the annual average temperature in Daihai Basin presents an obvious fluctuating upward trend, and the fitting upward slope is 0.040, R2 is 0.406. In addition, it can be observed that in a 10-year cycle, there will be two small fluctuations and one large fluctuation, and the fluctuation will rise.

(3)

Wind speed

The curve of annual average wind speed in Daihai is shown in the Fig. 10c. It can be seen from the figure that the annual average wind speed of Daihai Basin presents a fluctuating downward trend, and the fitting downward slope is 0.036, R2 is 0.368. In addition, it can be observed that the annual average wind speed fluctuated with a mean line of 6.2 from 1980 to 1987; In 1988 and 1990, it dropped sharply with a large slope; From 1990 to 2003, the fluctuation decreased. From 2003 to 2011, the fluctuation was stable at 4.5, and rose sharply in 2012. So far, the fluctuation has been stable at 5.2.Human factors

(1)

Cultivated land area

The change curve of cultivated land area in Daihai Basin is shown in the figure. It can be seen from the Fig. 11a that the annual average wind speed in Daihai Basin presents an upward trend, with the fitting rising rate of 0.017 and R2 of 0.970, almost in a straight line. In addition, it can be observed that from 1996 to 2005, the rising rate appeared a trough, that is, the rising rate first increased rapidly and then decreased. From 2000 to 2005, the rising rate was very slow and approached zero; But since 2006, it has returned to a straight-line rise.

(2)

Industrial water consumption

Figure 11Perennial (a) cultivated land area (b) industrial water consumption (c) total population change curve in Daihai Basin.Full size imageThe change curve of industrial water consumption in Daihai Basin is shown in the Fig. 11b. It can be seen from the figure that the industrial water consumption of Daihai Basin presents an upward trend, and the fitting rising rate is 0.433, R2 is 0.794. In addition, it can be observed that from 1975 to 1993, the industrial water consumption of Daihai Basin was below 3 × 106m3; From 1994 to 2005, except for the decrease in 1998–2000, it has been on the rise, and the rising speed is fast, which has increased five times in ten years; Since 2005, the industrial water consumption in Daihai Basin has been stable at about 15 × 106m3.

(3)

Total population

The change curve of total population in Daihai Basin is shown in the Fig. 11c. It can be seen from the figure that the total population of Daihai Basin presents an upward trend, and the fitting rising rate is 0.074, R2 is 0.864. In addition, it can be observed that the total population of Daihai Basin increased slowly from 1975 to 1985; From 1986 to 1990, the total population remained flat; It fluctuated from 1990 to 2000; Since 2000, the total population has risen sharply.Analysis of driving factors of hydrological informationIn this study, the average temperature, annual precipitation, annual evaporation, average wind speed in natural factors and cultivated land area, agricultural water consumption, industrial water consumption and population in human factors are considered as the influencing factors of runoff change in Daihai Lake. Therefore, the flow into the lake and the above elements constitute a variable sequence, and the correlation matrix is calculated. See the Table 4 for details.Table 4 Correlation matrix between lake inflow and influencing factors.Full size tableIt can be seen from the Table 4 that the cultivated land area has the highest correlation with the runoff into the lake, with a correlation of − 0.777, which is highly significant, followed by the wind speed, with a correlation of 0.690, which is highly significant; In addition, the total population, industrial water consumption, evaporation and average temperature were significantly correlated. Therefore, the discharge of Daihai Lake is influenced by both nature and human. It can be seen from the table that industrial water consumption, total population, cultivated land area, evaporation and annual average temperature have a negative impact on the flow into the lake, while wind speed has a positive impact.At the same time, the correlation between different factors can be obtained from the Table. For example, the correlation between industrial water consumption and population, cultivated land area and evaporation is as high as 0.8, which is highly significant; The correlation between population and cultivated land, cultivated land and wind speed and evaporation is also about 0.8, which is highly significant; In addition, the correlations between industrial water consumption and annual average temperature, population and annual average temperature, wind speed, evaporation, cultivated land, cultivated land and annual average temperature, evaporation and wind speed, wind speed and annual average temperature are all over 0.5.It can be clearly observed from the table that except for agricultural water consumption, precipitation and evaporation, the annual average temperature is significantly correlated with other factors, and the correlation is more than 0.5. The correlation between annual precipitation and other factors is small and not significant. Therefore, it can be determined that there is data redundancy between different elements. In order to eliminate the data redundancy and get the determinants of the discharge into the lake, the correlation analysis of the variable sequence is carried out, as shown in the table.It can be seen from the Table 5 that the cumulative variance of the first three principal components has reached 87.016%, and the eigenvalues of the first two principal components are greater than 1, which has met the standard. The variance contribution rate of the first principal component was 59.641%, and the order of load rate was cultivated land (0.967), industrial water (0.950), population (0.859), evaporation (0.856), wind speed (0.841), and the load rate was greater than 0.8; In the first principal component, the influence of human factors is greater than that of natural factors. In the second principal component, the variance contribution rate is 18.821%, in which the annual precipitation (− 0.875) and agricultural water consumption (0.736) have higher load rate, and the influence of natural factors is greater than that of human factors.Table 5 Component matrix of principal component analysis of different influencing factorsFull size tableFuture forecastAccording to the analysis in Sect. 3.4, we find that human factors have a huge impact on the lake inflow. In lake water balance, precipitation and evaporation are determined by climate. Now, the Inner Mongolian government has taken a series of measures to protect the Daihai Lake. Therefore, when we predict the future lake water volume, we consider two situations: (1) the future lake water volume in the natural state without any interference (protection or destruction) measures; (2) keeping the existing water volume unchanged future lake water volume in the case.Situation IFor the Situation I, we use two forecasting methods. Method I is to directly predict the future lake water volume by using the variation law of lake volume water volume with time. Method II is to use the lake water balance equation to estimate the change in lake water volume, and then estimate the future lake water volume. The results obtained by these two calculation methods are shown in the Table 6.Table 6 Future prediction of Daihai Lake in situation I.Full size tableWhen estimating the dry years of the Daihai Lake, the results obtained by using the time-varying laws of lake area, water volume and lake depth are inconsistent. Among them, the dry year of the Daihai Lake obtained by using the water volume is 2031, the lake area is 2047, and the water depth is 2096. The three are vastly different. The reason is the uncertainty of our modeling data. As Daihai Lake is a lake in an arid area, data is extremely scarce, and there is almost no continuous measurement of water level, depth, and water volume. The lake area is interpreted from remote sensing images and is an annual average, which results in neglect of inter-annual hydrological changes. Similarly, the water depth is also obtained by remote sensing. The resolution of the remote sensing image is 30 m. We use the interpolation method to control the accuracy to about 5 m. However, in the later stage of the prediction, when the lake depth is lower than 10 m, the results begin to become inaccurate. The modeling data of lake water volume were obtained from WRF-Hydro simulations, so the uncertainty of the data led to the inconsistency of the results. We choose the most recent year as the final result of method I, that is, the forecast result of water volume.From the Table 6, we can observe that the calculation results of the two methods are quite different. The reason is that in method I, we assume that the volume of water in the lake changes linearly, and there is only one variable; in method II, the number of variables increases and the uncertainty increases. However, the years when the Daihai Lake is predicted to dry up are basically the same. Method I predicts that the Daihai Lake will be depleted in 2031, and method II is 2033, which is not much different.Situation IIFor the situation II, we control the agricultural water consumption and industrial water consumption to remain unchanged, estimate the change of volume water at this time, and then estimate the future lake water volume. Among them, the change in water consumption is only evaporation, and the change in water replenishment is precipitation and runoff. The future lake inflow and lake water volume calculated by using the water balance equation are shown in the Table 7:Table 7 Future prediction of Daihai Lake in situation II.Full size tableFrom the Table 7, we can see that under human control, although the of lake inflow will continue to decline compared with no measures, the rate of decline will be significantly slower. And the lake inflow will drop to 0 in 2060. Similarly, the water volume in the Daihai Lake will decline. But the rate is significantly slower compared with situation I. And the water volume will drop to 0 in 2140, nearly 110 years later than 2032–3033 without any control. This shows that man-made protection of the Daihai Lake is extremely important. More