Philippa Kaur

Arunrat, N., Sereenonchai, S., Chaowiwat, W. & Wang, C. Climate change impact on major crop yield and water footprint under CMIP6 climate projections in repeated drought and flood areas in Thailand. Sci. Total Environ. 807, 150741 (2022).ADS 
CAS 

Google Scholar 
Chandio, A. A., Shah, M. I., Sethi, N. & Mushtaq, Z. Assessing the effect of climate change and financial development on agricultural production in ASEAN-4: the role of renewable energy, institutional quality, and human capital as moderators. Environ. Sci. Pollut. Res. 29, 13211–13225 (2022).
Google Scholar 
Masood, N., Akram, R., Fatima, M., Mubeen, M., Hussain, S., Shakeel, M., Khan, N., Adnan, M., Wahid, A., Shah, A. N. and Ihsan, M. Z. (2022) Insect pest management under climate change. In Building climate resilience in agriculture. Springer, ChamOzdemir, D. The impact of climate change on agricultural productivity in Asian countries: A heterogeneous panel data approach. Environ. Sci. Pollut. Res. 29, 8205–8217 (2022).
Google Scholar 
Aidoo, O. F. et al. Climate-induced range shifts of invasive species (Diaphorina citri Kuwayama). Pest Manag. Sci. 78, 2534–2549 (2022).CAS 

Google Scholar 
Hebbar, K. B. et al. Predicting the Potential Suitable Climate for Coconut (Cocos nucifera L.) Cultivation in India under Climate Change Scenarios Using the MaxEnt Model. Plants. 11, 731 (2022).
Google Scholar 
Martín-Vélez, V. & Abellán, P. Effects of climate change on the distribution of threatened invertebrates in a Mediterranean hotspot. Insect Conserv. Divers. 15, 370–379 (2022).
Google Scholar 
Williams, J. J., Freeman, R., Spooner, F. & Newbold, T. Vertebrate population trends are influenced by interactions between land use, climatic position, habitat loss and climate change. Glob. Chang. Biol. 28, 797–815 (2022).CAS 

Google Scholar 
Aidoo, O. F. et al. Lethal yellowing disease: insights from predicting potential distribution under different climate change scenarios. J. Plant Dis. Prot. 128, 1313–1325 (2021).
Google Scholar 
Sofaer, H. R. et al. Development and delivery of species distribution models to inform decision-making. Bioscience 69, 544–557 (2019).
Google Scholar 
Mead FW, The Asiatic citrus psyllid, Diaphorina citri Kuwayama (Homoptera: Psyllidae). Florida Department of Agriculture Conservation Service, Division of Plant Industry Entomological Circular No. 180.Bové, J. M. Huanglongbing: A destructive, newly-emerging, century-old disease of citrus. Plant Pathol. J. 1, 7–37 (2006).
Google Scholar 
Li, S., Wu, F., Duan, Y., Singerman, A. & Guan, Z. Citrus greening: Management strategies and their economic impact. HortScience 55, 604–612 (2020).
Google Scholar 
Jia, H. et al. Genome editing of the disease susceptibility gene Cs LOB 1 in citrus confers resistance to citrus canker. Plant Biotechnol. J. 15, 817–823 (2017).CAS 

Google Scholar 
Ehsani, R., Dewdney, M. & Johnson, E. Controlling HLB with thermotherapy: What have we learned so far?. Citrus Ind. News 9, 26–28 (2016).
Google Scholar 
Spreen, T. H., Baldwin, J. P. & Futch, S. H. An economic assessment of the impact of Huanglongbing on citrus tree plantings in Florida. J. Hortic. Sci. 49, 1052–1055 (2014).
Google Scholar 
Djeddour, D., Pratt, C., Constantine, K., Rwomushana, I. and Day, R., (2021) The Asian citrus greening disease (Huanglongbing). Evidence note on invasiveness and potential economic impacts for East Africa. CABI Working Paper, 24, 94Hu, J., Jiang, J. & Wang, N. Control of citrus Huanglongbing via trunk injection of plant defense activators and antibiotics. Phytopathology 108, 186–195 (2018).CAS 

Google Scholar 
Fan, G. C. et al. Evaluation of thermotherapy against Huanglongbing (citrus greening) in the greenhouse. J. Integr. Agric. 15, 111–119 (2016).
Google Scholar 
Nguyen, V. A., Bartels, D. & Gilligan, C. Modelling the spread and mitigation of an emerging vector-borne pathogen: citrus greening in the US. Biorxiv https://doi.org/10.1101/2022.05.04.490566 (2022).Article 

Google Scholar 
Milosavljević, I. et al. Post-release evaluation of Diaphorencyrtus aligarhensis (Hymenoptera: Encyrtidae) and Tamarixia radiata (Hymenoptera: Eulophidae) for biological control of Diaphorina citri (Hemiptera: Liviidae) in Urban California, USA. Agronomy 12, 583 (2022).
Google Scholar 
Maluta, N., Castro, T. & Lopes, J. R. S. Entomopathogenic fungus disrupts the phloem-probing behavior of Diaphorina citri and may be an important biological control tool in citrus. Sci. Rep. 12, 1–10 (2022).
Google Scholar 
Hall, D. G., Richardson, M. L., Ammar, E. D. & Halbert, S. E. Asian citrus psyllid, Diaphorina citri, vector of citrus huanglongbing disease. Entomol. Exp. Appl. 146, 207–223 (2013).
Google Scholar 
Vázquez-García, M. et al. Insecticide resistance in adult Diaphorina citri Kuwayama1 from lime orchards in central west Mexico. Southwest. Entomol. 38, 579–596 (2013).
Google Scholar 
Naeem, A., Freed, S., Jin, F. L., Akmal, M. & Mehmood, M. Monitoring of insecticide resistance in Diaphorina citri Kuwayama (Hemiptera: Psyllidae) from citrus groves of Punjab Pakistan. Crop Prot. 86, 62–68 (2016).CAS 

Google Scholar 
Hulme, P. E. et al. Grasping at the routes of biological invasions: A framework for integrating pathways into policy. J. Appl. Ecol. 45, 403–414 (2008).
Google Scholar 
Oke, A. O., Oladigbolu, A. A., Kunta, M., Alabi, O. J. & Sétamou, M. First report of the occurrence of Asian citrus psyllid Diaphorina citri (Hemiptera: Liviidae), an invasive species in Nigeria. West Africa. Sci. Rep. 10, 1–8 (2020).
Google Scholar 
Tang, Y.Q. (1990) On the parasite complex of Diaphorina citri Kuwayama (Homoptera: Psyllidae) in Asian-Pacific and other areas. In proceedings 4th international conference on citrus rehabilitation, Chiang Mai, Thailand. 4: 240 245Chien, C. C., Chiu, S. C. & Ku, S. C. Biological control of Diaphorina citri in Taiwan. Fruits 44, 401–407 (1989).
Google Scholar 
Hoddle, M. S. Foreign exploration for natural enemies of Asian citrus psyllid, Diaphorina citri (Hemiptera: Psyllidae), in the Punjab of Pakistan for use in a classical biological control program in California USA. Pakistan Entomol. 34, 1–5 (2012).
Google Scholar 
Étienne, J., Quilici, S., Marival, D., Franck, A. & Gonzalez Fernandez, C. Biological control of Diaphorina citri (Hemiptera: Psyllidae) in Guadeloupe by imported Tamarixia radiata (Hymenoptera: Eulophidae). Fruits 56, 307–315 (2001).
Google Scholar 
Qureshi, J. A., Rogers, M. E., Hall, D. G. & Stansly, P. A. Incidence of invasive Diaphorina citri (Hemiptera: Psyllidae) and its introduced parasitoid Tamarixia radiata (Hymenoptera: Eulophidae) in Florida citrus. J. Econ. Entomol. 102, 247–256 (2009).
Google Scholar 
Chen, X., Triana, M. & Stansly, P. A. Optimizing production of Tamarixia radiata (Hymenoptera: Eulophidae), a parasitoid of the citrus greening disease vector Diaphorina citri (Hemiptera: Psylloidea). Biol. Control. 105, 13–18. https://doi.org/10.1016/j.biocontrol.2016.10.010 (2017).Article 

Google Scholar 
Kistner, E. J., Amrich, R., Castillo, M., Strode, V. & Hoddle, M. S. Phenology of Asian citrus psyllid (Hemiptera: Liviidae), with special reference to biological control by Tamarixia radiata, in the residential landscape of southern California. J. Econ. Entomol. 109, 1047–1057. https://doi.org/10.1093/jee/tow021 (2016).Article 

Google Scholar 
Ramos Aguila, L. C. et al. Temperature-dependent biological control effectiveness of Tamarixia radiata (Hymenoptera: Eulophidea) under laboratory conditions. J. Econ. Entomol. 114, 2009–2017 (2021).
Google Scholar 
Ramos Aguila, L. C. et al. Temperature-dependent demography and population projection of Tamarixia radiata (Hymenoptera: Eulophidea) reared on Diaphorina citri (Hemiptera: Liviidae). J. Econ. Entomol. 113, 55–63 (2020).
Google Scholar 
Ashraf, H. J. et al. Comparative microbiome analysis of Diaphorina citri and its associated parasitoids Tamarixia radiata and Diaphorencyrtus aligarhensis reveals Wolbachia as a dominant endosymbiont. Environ. Microbiol. 24, 1638–1652 (2022).CAS 

Google Scholar 
Chow, A. & Sétamou, M. Parasitism of Diaphorina citri (Hemiptera: Liviidae) by Tamarixia radiata (Hymenoptera: Eulophidae) on residential citrus in Texas: Importance of colony size and instar composition. Biol. Control 165, 104796 (2022).
Google Scholar 
Ajene, I. J. et al. Habitat suitability and distribution potential of Liberibacter species (“Candidatus Liberibacter asiaticus” and “Candidatus Liberibacter africanus”) associated with citrus greening disease. Environ. Microbiol. 26, 575–588 (2020).
Google Scholar 
Shabani, F., Kumar, L. & Ahmadi, M. A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area. Ecol. Evol. 6, 5973–5986 (2016).
Google Scholar 
Kearney, M. & Porter, W. Mechanistic niche modelling: Combining physiological and spatial data to predict species’ ranges. Ecol 12, 334–350 (2009).
Google Scholar 
Byeon, D. H., Jung, S. & Lee, W. H. Review of CLIMEX and MaxEnt for studying species distribution in South Korea. J. Asia-Pac. Biodivers. 1, 325–333 (2018).
Google Scholar 
Kriticos, D. J., Yonow, T. & McFadyen, R. E. The potential distribution of Chromolaena odorata (Siam weed) in relation to climate. Weed Res 45, 246–254 (2005).
Google Scholar 
Wharton, T. N. & Kriticos, D. J. The fundamental and realized niche of the Monterey pine aphid, Essigella californica (Essig) (Hemiptera: Aphididae): implications for managing softwood plantations in Australia. Divers. Distrib. 10, 253–262 (2004).
Google Scholar 
Sutherst, R., Maywald, G. and Kriticos, D., CLIMEX version 3: user’s guide. (2007).Ramirez-Cabral, N. Y., Kumar, L. & Shabani, F. Global alterations in areas of suitability for maize production from climate change and using a mechanistic species distribution model (CLIMEX). Sci. Rep. 7, 1–3 (2017).CAS 

Google Scholar 
McCalla, K. A., Keçeci, M., Milosavljević, I., Ratkowsky, D. A. & Hoddle, M. S. The influence of temperature variation on life history parameters and thermal performance curves of Tamarixia radiata (Hymenoptera: Eulophidae), a parasitoid of the Asian citrus psyllid (Hemiptera: Liviidae). J. Econ. Entomol. 112, 1560–1574 (2019).
Google Scholar 
Gonzalez-Cabrera, J., Moreno-Carrillo, G., Sanchez-Gonzalez, J. A. & Bernal, H. C. Natural and augmented parasitism of tamarixia radiata (Hymenoptera Eulophidae) in Urban Areas of western Mexico. Entomol. Sci. 53, 486–492. https://doi.org/10.18474/JES17-112.1 (2018).Article 

Google Scholar 
Chavez, Y. et al. Tamarixia radiata (Waterston) and Cheilomenes sexmaculata (Fabricius) as biological control agents of Diaphorina citri Kuwayama in Ecuador. Chil. J. Agric. Res. 77, 180–184. https://doi.org/10.4067/S0718-58392017000200180 (2017).Article 

Google Scholar 
Flores, D. & Ciomperlik, M. Biological control using the ectoparasitoid, Tamarixia radiata, against the Asian citrus psyllid, Diaphorina citri, in the lower Rio Grande valley of Texas. Southwest. Entomol. 42, 49–59. https://doi.org/10.3958/059.042.0105 (2017).Article 

Google Scholar 
Parra, J. R., Alves, G. R., Diniz, A. J. & Vieira, J. M. Tamarixia radiata (Hymenoptera: Eulophidae) × Diaphorina citri (Hemiptera: Liviidae): Mass rearing and potential use of the parasitoid in Brazil. J. Integr. Pest. Manag. https://doi.org/10.1093/jipm/pmw003 (2016).Article 

Google Scholar 
Diniz, A. J. F., Otimização da criação de Diaphorina citri Kuwayama, 1908 (Hemiptera: Liviidae) e de Tamarixia radiata (Waterston, 1922) (Hymenoptera: Eulophidae), visando a produção em larga escala do parasitoide e avalliação do seu estabelecimento em campo. Tese (Doutorado em Entomologia)—Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de São Paulo, São Paulo. (2013)Hoddle, M. S. & Pandey, R. Host range testing of Tamarixia radiata (Hymenoptera: Eulophidae) sourced from the Punjab of Pakistan for classical biological control of Diaphorina citri (Hemiptera: Liviidae: Euphyllurinae: Diaphorinini) in California. J. Econ. Entomol. 107, 125–136. https://doi.org/10.1603/EC13318 (2014).Article 

Google Scholar 
Gómez-Torres, M. L., Nava, D. E. & Parra, J. R. Thermal hygrometric requirements for the rearing and release of Tamarixia radiata (Waterston) (Hymenoptera, Eulophidae). Rev. Bras. Entomol. 58, 291–295. https://doi.org/10.1590/S0085-56262014000300011 (2014).Article 

Google Scholar 
Gómez-Torres, M. L., Nava, D. E. & Parra, J. R. Life table of Tamarixia radiata (Hymenoptera: Eulophidae) on Diaphorina citri (Hemiptera: Psyllidae) at different temperatures. J. Econ. Entomol. 105, 338–343 (2012).
Google Scholar 
Chong, J. H., Roda, A. L. & Mannion, C. M. Density and natural enemies of the Asian Citrus Psyllid, Diaphorina citri (Hemiptera: Psyllidae), in the residential landscape of Southern Florida. J. Agric. Urban Entomol. 27, 33–49. https://doi.org/10.3954/11-05.1 (2010).Article 

Google Scholar 
Pluke, R. W., Qureshi, J. A. & Stansly, P. A. Citrus flushing patterns, Diaphorina citri (Hemiptera: Psyllidae) populations and parasitism by Tamarixia radiata (Hymenoptera: Eulophidae) in Puerto Rico. Florida Entomol. 91, 36–42 (2008).
Google Scholar 
Ashraf, H. J. et al. Genetic diversity of Tamarixia radiata populations and their associated endosymbiont Wolbachia species from China. Agronomy 11, 2018 (2021).CAS 

Google Scholar 
Jung, J. M., Lee, W. H. & Jung, S. Insect distribution in response to climate change based on a model: Review of function and use of CLIMEX. Entomol. Res. 46, 223–235 (2016).
Google Scholar 
Kriticos, D. J. et al. CLIMEX Version 4, 184p (2015).
Google Scholar 
Gomez-Marco, F., Gebiola, M., Baker, B. G., Stouthamer, R. & Simmons, G. S. Impact of the temperature on the phenology of Diaphorina citri (Hemiptera: Liviidae) and on the establishment of Tamarixia radiata (Hymenoptera: Eulophidae) in urban areas in the lower Colorado Desert in Arizona. Environ. Entomol. 48, 514–523 (2019).
Google Scholar 
Vieira, J. M. Biologia em temperaturas alternantes e exigências térmicas de Diaphorina citri Kuwayama, 1908 (Hemiptera: Liviidae) e Tamarixia radiata (Waterston, 1922) (Hymenoptera: Eulophidae) visando ao seu zoneamento em regiões citrícolas do estado (Doctoral dissertation, Universidade de São Paulo).Castillo, J., Jacas, J. A., Peña, J. E., Ulmer, B. J. & Hall, D. G. Effect of temperature on life history of Quadrastichus haitiensis (Hymenoptera: Eulophidae), an endoparasitoid of Diaprepes abbreviatus (Coleoptera: Curculionidae). Biol. Control. 36, 189–196 (2006).
Google Scholar 
McFarland, C. D. & Hoy, M. A. Survival of Diaphorina citri (Homoptera: Psyllidae), and its two parasitoids, Tamarixia radiata (Hymenoptera: Eulophidae) and Diaphorencyrtus aligarhensis (Hymenoptera: Encyrtidae), under different relative humidities and temperature regimes. Fla. Entomol. 84, 227–233 (2001).
Google Scholar 
Fauvergue, X. & Quilici, S. Etude de certains parametres de la biologie de Tamarixia radiata (Waterston, 1992)(Hymenoptera: Eulophidae), ectoparasitoide primaire de Diaphorina citri Kuwayama (Hemiptera: Psyllidae) vecteur du greening des agrumes. Paris Fruits 46, 179–179 (1991).
Google Scholar 
Araújo, F. H. et al. Modelling climate suitability for Striga asiatica, a potential invasive weed of cereal crops. Crop Prot. 1(160), 106050 (2022).
Google Scholar 
Silva, D. A. & RS, Kumar L, Shabani F and Picanço MC,. Potential risk levels of invasive Neoleucinodes elegantalis (small tomato borer) in areas optimal for open-field Solanum lycopersicum (tomato) cultivation in the present and under predicted climate change. Pest Manag. Sci 73, 616–627 (2017).
Google Scholar 
Kumar, S., Neven, L. G. & Yee, W. L. Evaluating correlative and mechanistic niche models for assessing the risk of pest establishment. Ecosphere 5, 1–23. https://doi.org/10.1890/ES14-00050.1 (2014).Article 
CAS 

Google Scholar 
Kriticos, D. J. et al. CliMond: global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods Ecol. Evol. 1, 53–64 (2012).
Google Scholar 
Santana Júnior PA, Worldwide spatial distribution of Tuta absoluta (Lepidoptera: Gelechiidae) and its natural enemies under current and future climatic change conditions through modelling. 136 f 2019 (Tese (Doutorado em Fitotecnia) – Universidade Federal de Viçosa, 2019).
Google Scholar 
Kriticos, D. J., Maywald, G. F., Yonow, T., Zurcher, E. J., Herrmann, N. I. and Sutherst, R. W., CLIMEX Version 4: Exploring the effects of climate on plants, animals and diseases. CSIRO, Canberra.156, (2015)Ramos Aguila, L. C. et al. Temperature-dependent demography and population projection of Tamarixia radiata (Hymenoptera: Eulophidea) reared on Diaphorina citri (Hemiptera: Liviidae). J. Econ. Entomol. 113, 55–63 (2019).
Google Scholar 
Oliveira, R. C., Modelagem de nicho ecológico para Helicoverpa punctigera (Wallengren, 1860) (Lepidoptera: Noctuidae) no mundo: Potencial invasão e riscos diante das mudanças climáticas. (2021). http://www.repositorio.ufc.br/handle/riufc/61961Bazzocchi, G. G., Lanzoni, A., Burgio, G. & Fiacconi, M. R. Effects of temperature and host on the pre-imaginal development of the parasitoid Diglyphus isaea (Hymenoptera: Eulophidae). Biol. Control 26, 74–82 (2003).
Google Scholar 
Hondo, T., Koike, A. & Sugimoto, T. Comparison of thermal tolerance of seven native species of parasitoids (Hymenoptera: Eulophidae) as biological control agents against Liriomyza trifolii (Diptera: Agromyzidae) in Japan. Appl. Entomol. Zool. 41, 73–82 (2006).
Google Scholar 
Duale, A. Effect of temperature and relative humidity on the biology of the stem borer parasitoid Pediobius furvus (Gahan) (Hymenoptera: Eulophidae) for the management of stem borers. Environ. Entomol. 34, 1–5 (2005).
Google Scholar 
Ashraf, H. J. et al. Comparative transcriptome analysis of Tamarixia radiata (Hymenoptera: Eulophidae) reveals differentially expressed genes upon heat shock. Comp. Biochem. Physiol. D: Genom. Proteom. 41, 100940 (2022).CAS 

Google Scholar 
van Doan, C. et al. Natural enemies of herbivores maintain their biological control potential under short-term exposure to future CO2, temperature, and precipitation patterns. Ecol. Evol. 11, 4182–4192 (2021).
Google Scholar 
Thomson, L. J., Macfadyen, S. & Hoffmann, A. A. Predicting the effects of climate change on natural enemies of agricultural pests. Biol. Control. 52, 296–306 (2010).
Google Scholar 
Rosenblatt, A. E. & Schmitz, O. J. Climate change, nutrition, and bottom-up and top-down food web processes. Trends Ecol. Evol. 31, 965–975 (2016).
Google Scholar 
Aidoo, O. F. et al. A machine learning algorithm-based approach (MaxEnt) for predicting invasive potential of Trioza erytreae on a global scale. Ecol. Inform. 71, 101792 (2022).
Google Scholar 
Aidoo, O. F. et al. The Impact of Climate Change on Potential Invasion Risk of Oryctes monoceros Worldwide. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2022.895906 (2022).Article 

Google Scholar 
Hao, M. et al. Global potential distribution of Oryctes rhinoceros, as predicted by Boosted Regression Tree model. Glob. Ecol. Conserv. 1(37), e02175 (2022).
Google Scholar 
Aidoo, O. F. et al. Model-based prediction of the potential geographical distribution of the invasive coconut mite, Aceria guerreronis Keifer (Acari: Eriophyidae) based on MaxEnt. Agric. For. Entomol. 24, 390–404 (2022).
Google Scholar  More

Anna Lena Bercht interviewed fishers in Lofoten, Norway, to assess how climate change was affecting their livelihoods.Credit: Anna Lena Bercht

I remember February 2011, when, in the Chinese megacity of Guangzhou, an older man finally overcame his scepticism about being interviewed and invited me to sit down next to him on a stone bench under a shady tree. I held my notebook on my lap, and we sat on either side of a translator and talked about his life and world for more than two hours. It was one of the most informative and revealing interviews that I had done during my fieldwork in the city.
Making it in the megacity
One of the most fundamental challenges in qualitative fieldwork is gaining access to research participants. This is often time-consuming and labour-intensive, particularly when the topic requires in-depth methods and addresses a sensitive subject.Advice that goes beyond the usual recommendations of establishing relationships with gatekeepers, ensuring anonymity for interviewees and relying on the snowball sampling technique (in which one research participant suggests further ones) is rare. In this light, I’m happy to share some simple, but often neglected, examples from my qualitative fieldwork in the lively Guangzhou (where I worked for 12 months)1 and on the remote, Arctic island chain of Lofoten, Norway (done over 4 months)2, that might offer some inspiration and encouragement.I have a background in human geography, and did my PhD on experiences of stress, coping and resilience among the Chinese population of Guangzhou in the face of the city’s rapid urbanization. I travelled there five times to help to establish research cooperation with Chinese scholars, make field observations, select a case-study site and interview locals. I, together with other PhD students, stayed in a typical Chinese high-rise apartment in a neighbourhood that wasn’t a common choice for expatriates. Living side-by-side with the locals gave us a perfect opportunity to experience genuine everyday life and Chinese culture.My first postdoctoral project after my PhD brought me to Lofoten, where I looked at psychological barriers to climate adaptation in small-scale coastal fisheries. I went to Lofoten twice. On my first visit, I travelled across the whole archipelago by bus for one month to get a profound overview of the fishing villages and local living conditions, and to conduct first interviews. During my second visit, I stayed for a total of three months in rental locations near fishing harbours, and conducted more extensive interviews.In both China and Norway, I used in-depth interviews to learn about the challenges that people face. I asked people about unemployment, about the possibility of being forced to move elsewhere and about how climate change might affect their livelihoods. This required a sensitive and thoughtful approach to ‘getting invited’ into people’s lives. In Guangzhou, German- and English-speaking Chinese students assisted me as translators (and interpreters, when needed). On Lofoten, I conducted the interviews myself in English.There are two ways to access research participants: physical access, which refers to the ability of the researcher to get in direct face-to-face contact with people, and mental access. Successful mental access means that interlocutors open up about why they think, feel and behave as they do. Physical access is a necessary condition for mental access; however, in my experience, both are equally valuable.

Chinese interviewees in Guangzou shared their feelings about the rapid urbanization of their city.Credit: Anna Lena Bercht

Compared with Lofoten, it took longer to get physical access to local inhabitants in China. Presumably, this was because of the language barrier and reliance on translators, as well as cultural differences. Trust is considered a central tenet in Chinese relationships, and time and effort are needed to let it grow. During my time in Guangzhou, I occasionally benefited from being a foreigner: people were touched that someone from abroad showed genuine interest in their well-being. In Lofoten, fishers appreciated talking to a social scientist instead of a natural scientist who would have mainly asked questions about fishing quotas and catch volume.My advice for other social scientists hoping to gain access to research participants falls into those two categories.How to get good physical accessUse local public transport. Using local public transport creates many unexpected opportunities to bump into people, get into conversations and gain relevant information. For example, while waiting at a bus stop in Lofoten, I came across an art-gallery owner from a fishing village. He wondered why I was travelling out of the peak tourism season. I ended up with an invitation to his gallery, where he introduced me to two retired fishers whom he had also invited. Without the gallerist and his proactive networking, I probably would not have been given the chance to interview these two very informative and engaging fishers.In a metro station in Guangzhou, a toddler kept staring at me and tried to touch my light hair. This small interaction led me to chat to the toddler’s father, who recommended that I talk to a local teacher to learn more about the area’s history. His advice opened up important insights into urban-restructuring processes that I would have missed otherwise.
Nine ‘brain food’ tips for researchers
Use local media. In Norway, a journalist was at the harbour to get first-hand information on the year’s cod catch, when he saw me interviewing fishers. He became curious and eager to learn more about my work. In the end, he wrote an article about my research, which was published a few days later across Lofoten. His article was a door-opener for me.People recognized me from my photo in the article and contacted me to tell me about their lives and the cod fisheries. They also invited me on their vessels and put me in touch with other key informants.Change your workplace. During fieldwork, a workplace is often needed for interview transcription, literature research and interim data analysis. Moving the workplace outside wherever you are staying during a field trip allows you to immerse yourself in the daily lives of local people and interact with them more easily. For me, such agile ‘mini-office’ locations were cafes, public libraries and picnic tables. In this way, I was able to recruit interview partners on the spot.How to create deeper mental accessWear appropriate outfits. First impressions count, always. Researchers are judged not only on what they say and how they say it, but also on how they look. Certain clothes, such as those with a political slogan or religious symbol, have certain meanings and connotations. Depending on the context and whom you talk to, your appearance could promote or impede making connections and building rapport. For instance, whereas my practical ‘outdoorsy’ get-dirty outfit was appropriate for interviews on fishing vessels, a modest appearance (non-branded clothes and a simple style) was useful in rural areas of Guangzhou.Show respect. Just like in any other relationship, respect and humility play a crucial part in building a trustworthy interviewer–interviewee relationship. Showing respect can be subtly embedded in conversations in many ways, including in the content of questions and the manner in which they are asked. When interviewees started to close down when asked about painful issues, such as underemployment or loss of identity, I upheld their privacy, comfort and security by not probing when given an evasive answer. Instead, I changed the interview focus and, when appropriate, cautiously reapproached the sensitive issue by using interview techniques such as roleplaying. Interviewees were asked to put themselves in the position of someone else, such as a spatial planner or politician, and assess the issue at hand from this perspective. Taking such an imaginary role can help to make the interviewees feel more secure and face pain more openly.Be humble. Having a modest view of yourself is essential to communicate at eye level with people. As a scientist, you can easily fall into the trap of thinking that your thoughts and concepts are somehow more valuable because you are well-educated and established. However, you are the one asking questions — and the interviewees, whether they are fishers, farmers or homeless people, often know more about many things than you do. Being aware of this is an expression of humility. I let the interviewees know that they were the local experts and I was the foreign learner.Use small talk. Small talk — including non-verbal communication, such as smiling, or connective gestures, for example handing out a handkerchief or offering some tea — has an essential bonding function. Talking about ‘safe’ topics can help the interviewee to overcome the feelings of otherness, newness and discomfort that can emerge in an interview, and fosters social cohesiveness. This can help to counteract the asymmetrical power relationship between the researcher (who asks) and the researched (who answers). For example, before substantive questioning, I created shared experiences by talking about last night’s storm or the world cod-fishing championship, which takes place every year in Lofoten. This took the relationship to a greater level of intimacy and togetherness — which small talk after finishing the interview can strengthen. I remember joking about my stamina for eating properly with chopsticks to one interviewee.Use self-disclosure. Revealing selected information about yourself and sharing your own thoughts with interlocutors can help to create and reaffirm a sphere of confidentiality and trust. Fishers in Norway would, for instance, often ask “What interested you in Lofoten coastal fisheries?” or “Why do you ask me and not the scientists from Tromsø University?” I answered such questions honestly, which assisted in creating a more balanced relationship, encouraging the interviewees to address sensitive subjects more openly and readily.Change interview sites. In several interviews, I found that the answers given tended to depend on where the interview was held and which identity that site evoked for the interviewee. For example, a fisher did not talk about climate-change concerns on his fishing vessel (any concern was masked by his existential fear of losing his livelihood as a coastal fisher), but he later that day freely discussed his worries in his home. Changing the interview site can be a helpful technique to access hidden thoughts and feelings.Above all, be realistic. You will probably make mistakes; I regretted not dressing warmly enough on a fishing vessel in Arctic weather. Locals will find you amusing, weird or impolite. They will keep out of your way, and you will never know why. And they will terminate interviews prematurely with no excuse. And that’s all right. In the end, fieldwork is a combination of planning, resources, time, skills, hard work, commitment, headache, joy — and luck. Learn from your mistakes, and accept the things you cannot change. More

Analytical method validationThe results of the precision study with relative standard deviation (RSD), and accuracy are shown in Table 1. Through the precision study we found the value of RSD as less than 5%. Moreover, accuracy was done with percent recovery experiments. The results showed that the percentage recoveries for spiked samples were in the range of 95.7–103.7%.Table 1 Shows percent (%) recovery and relative standard deviation.Full size tablePhysicochemical properties and water quality indexThe investigations of the water quality properties of the Narora channel are shown in Table 2. The temperature, TDS, turbidity, and alkalinity were within the standards of the country18 and WHO19 (taken from UNEPGEMS). While pH and dissolved oxygen (D.O) were above the recommended standards indicating poor water quality. Moreover, the detected heavy metals were in the following order Ni  > Fe  > Cd  > Zn  > Cr  > Cu  > Mn. Among these heavy metals Mn, Cu, and Zn were within the recommended limits whereas Cr, Fe, Ni, and Cd were crossing the limits18 contributing to the poor quality. Furthermore, the WQI calculation will give more insights into the overall quality of water as it explains the combined effect of several physicochemical properties12. Its calculation is done simply by converting numerous variables of water quality into a single number12,20. In addition to this, WQI simplifies all the data and helps in clarifying water quality issues by combining the complex data and producing a score that shows the status of water quality2,12,21. The WQI classifies water quality status into five groups such as if WQI  Cu  > Zn  > Fe  > Zn  > Ni  > Cr from root to stalk; and Mn  > Cd  > Zn  > Cu  > Fe  > Ni  > Cr from stalk to leaves.Table 5 Heavy metal concentrations in Eichhornia crassipes (mg/kg.dw).Full size tableFigure 3MPI values in E. crassipes.Full size imageTable 6 Bioaccumulation factor (BAF), transfer factor (TF), and mobility factor (MF) in plant E. crassipes.Full size tableThese factors BAF, TF, and MF are utilized to monitor the level of anthropogenic pollution in plants and their surrounding medium2,15,32,34,35. BAF shows the concentrations of heavy metals bioaccumulated by plants from the water. If the BAF  > 1 it indicates hyperaccumulation36. So, in the present study, all the concerned heavy metals were hyperaccumulated in the plant. The TF elucidates the capability of the plant to translocate the accumulated metals to its other parts. The roots of E. crassipes showed the highest translocation capacity for Ni (1.57) as well as Zn (1.30) to other parts. If the value of TF exceeds 1, then it represents the high accumulation efficiency37,38, therefore, plants will be considered as the hyperaccumulators for the Ni and Zn. Although the Cd was the highest accumulated metal in the plant, it could have been because of its may be because of its low TF. Whereas, TF values lower than 1 for Cr, Mn, Fe, Cu, and Cd pointed out that this plant’s roots act as a non-hyperaccumulator for these heavy metals. Furthermore, the highest MF values were depicted for Mn in both cases which reflects that E. crassipes can suitably be used for phytoextraction of Mn as well as for Cd, Zn, Fe, Ni, and Cu. The BAF, TF, and MF of Cr are low in the present study, which implies that roots are limiting the Cr. Moreover, if the BAF ≤ 1.00 then it shows the capability of absorption only rather than accumulation36,37. In addition, if the values of BAF, TF, and MF exceed 1, plants can also work for phytoextraction. Furthermore, if the BAF  > 1 and TF  More

Daskalova, G. N. et al. Landscape-scale forest loss as a catalyst of population and biodiversity change. Science 368(6497), 1341–1347 (2020).ADS 
CAS 

Google Scholar 
Betts, M. G. et al. Extinction filters mediate the global effects of habitat fragmentation on animals. Science 366(6470), 1236–1239 (2019).ADS 
CAS 

Google Scholar 
Siddig, A. A., Ellison, A. M., Ochs, A., Villar-Leeman, C. & Lau, M. K. How do ecologists select and use indicator species to monitor ecological change? Insights from 14 years of publication in Ecological Indicators. Ecol. Ind. 60, 223–230 (2016).
Google Scholar 
Thancharoen, A. Well managed firefly tourism: A good tool for firefly conservation in Thailand. Lampyrid. 2, 142–148 (2012).
Google Scholar 
Hwang, Y. T., Moon, J., Lee, W. S., Kim, S. A. & Kim, J. Evaluation of firefly as a tourist attraction and resource using contingent valuation method based on a new environmental paradigm. J. Qual. Assur. Hosp. Tour. 21(3), 320–336 (2019).Carlson, A. D. & Copeland, J. Flash communication in fireflies. Q. Rev. Biol. 60(4), 415–436 (1985).
Google Scholar 
Evans, T. R., Salvatore, D., van de Pol, M. & Musters, C. J. M. Adult firefly abundance is linked to weather during the larval stage in the previous year. Ecol. Entomol. 44(2), 265–273 (2018).
Google Scholar 
Lewis, S. M. et al. A global perspective on firefly extinction threats. Bioscience 70(2), 157–167 (2020).
Google Scholar 
Cao, C. Q., Zhang, Y., Wang, Y. Z. & He, H. Progress in the research, protection, development and utilization of fireflies. J. Environ. Entomol.1–36 (2022).Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403(6772), 853–858 (2000).ADS 
CAS 

Google Scholar 
Thorn, J. S., Nijman, V., Smith, D. & Nekaris, K. A. I. Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates:Nycticebus). Divers. Distrib. 15(2), 289–298 (2009).
Google Scholar 
Elith, J. & Leathwick, J. R. Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40(1), 677–697 (2009).
Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190(3–4), 231–259 (2006).
Google Scholar 
Hirzel, A. H., Hausser, J., Chessel, D. & Perrin, N. Ecological-Niche Factor Analysis: How to compute habitat-suitability maps without absence data?. Ecology 83(7), 2027–2036 (2002).
Google Scholar 
Nelder, J. A. & Wedderburn, R. W. Generalized linear models. J. R. Stat. Soc. Ser. A (General). 135(3), 370–384 (1972).
Google Scholar 
Hastie, T. J. Generalized additive models. Statistical models in S. Routledge. 249–307 (2017).Stockwell, D. R. & Noble, I. R. Induction of sets of rules from animal distribution data: A robust and informative method of data analysis. Math. Comput. Simul. 33(5–6), 385–390 (1992).
Google Scholar 
Beaumont, L. J., Hughes, L. & Poulsen, M. Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions. Ecol. Model. 186(2), 251–270 (2005).
Google Scholar 
Jung, J. M., Lee, W. H. & Jung, S. Insect distribution in response to climate change based on a model: Review of function and use of CLIMEX. Entomol. Res. 46(4), 223–235 (2016).
Google Scholar 
Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31(2), 161–175 (2008).
Google Scholar 
Moreno, R., Zamora, R., Molina, J. R., Vasquez, A. & Herrera, M. Á. Predictive modeling of microhabitats for endemic birds in South Chilean temperate forests using Maximum entropy (Maxent). Eco. Inform. 6(6), 364–370 (2011).
Google Scholar 
Wang, Z. et al. Prediction of potential distribution of the invasive Chrysanthemum Lace Bug, Corythucha marmorata in China based on Maxent. J. Environ. Entomol. 41(3), 626–633 (2019).
Google Scholar 
Li, A. et al. MaxEnt modeling to predict current and future distributions of Batocera lineolata (Coleoptera: Cerambycidae) under climate change in China. Ecoscience 27(1), 23–31 (2020).
Google Scholar 
Sutherland, L. N., Powell, G. S. & Bybee, S. M. Validating species distribution models to illuminate coastal fireflies in the South Pacific (Coleoptera: Lampyridae). Sci. Rep. 11(1), 1–12 (2021).ADS 

Google Scholar 
Fu, X. H., Ballantyne, L. A. & Lambkin, C. Emeia gen. nov., a new genus of Luciolinae fireflies from China (Coleoptera: Lampyridae) with an unusual trilobite-like larva, and a redescription of the genus Curtos Motschulsky. Zootaxa. 3403(1), 1–53 (2012).Idris, N. S. et al. The dynamics of landscape changes surrounding a firefly ecotourism area. Glob. Ecol. Conserv. 29, e01741 (2021).
Google Scholar 
Santiago-Blay, J. A. Silent Sparks: The Wondrous World of Fireflies. Life: The Excitement of Biology. (2016).Picchi, M. S., Avolio, L., Azzani, L., Brombin, O. & Camerini, G. Fireflies and land use in an urban landscape: the case of Luciola italica L.(Coleoptera: Lampyridae) in the city of Turin. J. Insect Conserv. 17(4), 797–805 (2013).Pearsons, K. A., Lower, S. E. & Tooker, J. F. Toxicity of clothianidin to common Eastern North American fireflies. PeerJ 9, e12495 (2021).
Google Scholar 
Madruga Rios, O. & Hernández Quinta, M. Larval Feeding Habits of the Cuban Endemic FireflyAlecton discoidalisLaporte (Coleoptera: Lampyridae). Psyche J. Entomol. 2010, 1–5 (2010).Roberge, J. M. & Angelstam, P. E. R. Usefulness of the umbrella species concept as a conservation tool. Conserv. Biol. 18(1), 76–85 (2004).
Google Scholar 
Bowen-Jones, E. & Entwistle, A. Identifying appropriate flagship species: The importance of culture and local contexts. Oryx 36(2), 189–195 (2002).
Google Scholar 
Walpole, M. J. & Leader-Williams, N. Tourism and flagship species in conservation. Biodivers. Conserv. 11(3), 543–547 (2002).Zhejiang Provincial Bureau of Statistics. Zhejiang physical geography profile, http://tjj.zj.gov.cn/col/col1525489/index.html (2022).Zhejiang Provincial Forestry Department. Announcement of Forest Resources and Their Ecological Function Value in Zhejiang Province. Zhejiang Daily. https://doi.org/10.38328/n.cnki.nzjrb.2016.002829 (2016).Boria, R. A., Olson, L. E., Goodman, S. M. & Anderson, R. P. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol. Model. 275, 73–77 (2014).
Google Scholar 
Brown, J. L. SDM toolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 5(7), 694–700 (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(12), 4302–4315 (2017).
Google Scholar 
Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F.-C. & Taneja, J. Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019. Remote Sens. 13(5), 922 (2021).ADS 

Google Scholar 
WAN, J. et al. Predicting the potential geographic distribution of Bactrocera bryoniae and Bactrocera neohumeralis (Diptera: Tephritidae) in China using MaxEnt ecological niche modeling. J. Integr. Agric. 19(8), 2072–2082 (2020).Zhou, R. et al. Projecting the potential distribution of glossina morsitans (Diptera: Glossinidae) under climate change using the MaxEnt model. Biology. 10(11), 1150 (2021).
Google Scholar 
Hill, M. P., Hoffmann, A. A., McColl, S. A. & Umina, P. A. Distribution of cryptic blue oat mite species in Australia: current and future climate conditions. Agric. For. Entomol. 14(2), 127–137 (2011).
Google Scholar 
Su, H., Bista, M. & Li, M. Mapping habitat suitability for Asiatic black bear and red panda in Makalu Barun National Park of Nepal from Maxent and GARP models. Sci. Rep. 11(1), 1 (2021).ADS 
CAS 

Google Scholar 
Proosdij, A. J., Sosef, M., Wieringa, J. & Raes, N. Minimum required number of specimen records to develop accurate species distribution models. Ecography 39, 542–552 (2016).
Google Scholar 
Lobo, J. M., Jiménez-Valverde, A. & Real, R. AUC: A misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17(2), 145–151 (2008).
Google Scholar 
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43(6), 1223–1232 (2006).
Google Scholar 
Liu, C., Newell, G. & White, M. On the selection of thresholds for predicting species occurrence with presence-only data. Ecol. Evol. 6(1), 337–348 (2016).
Google Scholar 
Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240(4857), 1285–1293 (1988).ADS 
CAS 
MATH 

Google Scholar 
Pearce, J. & Ferrier, S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Model. 133(3), 225–245 (2000).
Google Scholar 
Gama, M., Crespo, D., Dolbeth, M. & Anastácio, P. M. Ensemble forecasting of Corbicula fluminea worldwide distribution: projections of the impact of climate change. Aquat. Conserv. Mar. Freshwat. Ecosyst. 27(3), 675–684 (2017).
Google Scholar 
Zhao, Y., Deng, X., Xiang, W., Chen, L. & Ouyang, S. Predicting potential suitable habitats of Chinese fir under current and future climatic scenarios based on Maxent model. Eco. Inform. 64, 101393 (2021).
Google Scholar 
Evans, T. R., Salvatore, D., van de Pol, M. & Musters, C. J. M. Adult firefly abundance is linked to weather during the larval stage in the previous year. Ecol. Entomol. 44(2), 265–273 (2018).Chettri, B., Bhupathy, S. & Acharya, B. K. Distribution pattern of reptiles along an eastern Himalayan elevation gradient India. Acta Oecol. 36(1), 16–22 (2010).ADS 

Google Scholar 
Brown, J. H. Mammals on mountainsides: elevational patterns of diversity. Global Ecol. Biogeogr. 10(1), 101–109 (2001).Gairola, S., Sharma, C. M., Ghildiyal, S. K. & Suyal, S. Tree species composition and diversity along an altitudinal gradient in moist tropical montane valley slopes of the Garhwal Himalaya India. For. Sci. Technol. 7(3), 91–102 (2011).
Google Scholar 
Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J. Biogeogr. 34(1), 102–117 (2007).Hernandez, P. A., Graham, C. H., Master, L. L. & Albert, D. L. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29(5), 773–785 (2006).
Google Scholar 
Abe, N. Kansei estimation on luminescence of Firefly-Kansei information measurement and welfare utilization. J. Japan Soc. Kansei Eng. 3(2), 41–50 (2004).
Google Scholar 
Buckley, R. et al. Economic value of protected areas via visitor mental health. Nat. Commun. 10(1), 1 (2019).
Google Scholar 
Lewis, S. M. et al. Firefly tourism: Advancing a global phenomenon toward a brighter future. Conserv. Sci. Pract. 3(5), 1 (2021).
Google Scholar  More

Lovegrove, B. G. A phenology of the evolution of endothermy in birds and mammals. Biol. Rev. 92, 1213–1240 (2017).
Google Scholar 
Cuthill, I. C. et al. The biology of color. Science 357, 1–7 (2017).
Google Scholar 
Stuart-Fox, D., Newton, E. & Clusella-Trullas, S. Thermal consequences of colour and near-infrared reflectance. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160345 (2017).
Google Scholar 
Smith, K. R. et al. Color change for thermoregulation versus camouflage in free-ranging lizards. Am. Nat. 188, 668–678 (2016).
Google Scholar 
Rudh, A. & Qvarnström, A. Adaptive colouration in amphibians. Semin. Cell Dev. Biol. 24, 553–561 (2013).
Google Scholar 
Geen, M. R. S. & Johnston, G. R. Coloration affects heating and cooling in three color morphs of the Australian bluetongue lizard, Tiliqua scincoides. J. Therm. Biol. 43, 54–60 (2014).
Google Scholar 
Tattersall, G. J., Eterovick, P. C. & de Andrade, D. V. Tribute to R. G. Boutilier: skin colour and body temperature changes in basking Bokermannohyla alvarengai (Bokermann 1956). J. Exp. Biol. 209, 1185–1196 (2006).
Google Scholar 
Tattersall, G. J., Hillman, S. S., Drewes, R. C. & Sokol, O. M. The thermogenesis of digestion in rattlesnakes. J. Exp. Biol. 207, 579–585 (2004).
Google Scholar 
Seebacher, F. & Murray, S. A. Transient receptor potential ion channels control thermoregulatory behaviour in reptiles. PLoS One 2, e281, 1–7 (2007).Forget-Klein, É. & Green, D. M. Toads use the subsurface thermal gradient for temperature regulation underground. J. Therm. Biol. 99, 1–9 (2021).
Google Scholar 
Kiefer, M. C., Van Sluys, M. & Rocha, C. F. D. Thermoregulatory behaviour in Tropidurus torquatus (Squamata, Tropiduridae) from Brazilian coastal populations: an estimate of passive and active thermoregulation in lizards. Acta Zool. 88, 81–87 (2007).
Google Scholar 
Spencer, K. et al. Growth at cold temperature increases the number of motor neurons to optimize locomotor function. Curr. Biol. 29, 1787–1799.e5 (2019).CAS 

Google Scholar 
Herrel, A. & Bonneaud, C. Temperature dependence of locomotor performance in the tropical clawed frog, Xenopus tropicalis. J. Exp. Biol. 215, 2465–2470 (2012).
Google Scholar 
Casterlin, M. E. & Reynolds, W. W. Diel activity and thermoregulatory behavior of a fully aquatic frog: Xenopus laevis. Hydrobiologia 75, 189–191 (1980).
Google Scholar 
Guo, K. et al. The thermal dependence and molecular basis of physiological color change in Takydromus septentrionalis (Lacertidae). Biol. Open 10, 1–9 (2021).
Google Scholar 
De Velasco, J. B. & Tattersall, G. J. The influence of hypoxia on the thermal sensitivity of skin colouration in the bearded dragon, Pogona vitticeps. J. Comp. Physiol. B. 178, 867–875 (2008).CAS 

Google Scholar 
Stuart-Fox, D. & Moussalli, A. Camouflage, communication and thermoregulation: lessons from colour changing organisms. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 364, 463–470 (2009).
Google Scholar 
Sanabria, E. A., Vaira, M., Quiroga, L. B., Akmentins, M. S. & Pereyra, L. C. Variation of thermal parameters in two different color morphs of a diurnal poison toad, Melanophryniscus rubriventris (Anura: Bufonidae). J. Therm. Biol. 41, 1–5 (2014).
Google Scholar 
Clusella-Trullas, S., van Wyk, J. H. & Spotila, J. R. Thermal benefits of melanism in cordylid lizards: a theoretical and field test. Ecology 90, 2297–2312 (2009).
Google Scholar 
Duarte, R. C., Flores, A. A. V. & Stevens, M. Camouflage through colour change: mechanisms, adaptive value and ecological significance. Philos. Trans. R. Soc. B: Biol. Sci. 372, 1–7 (2017).Bertolesi, G. E. & McFarlane, S. Seeing the light to change colour: an evolutionary perspective on the role of melanopsin in neuroendocrine circuits regulating light-mediated skin pigmentation. Pigment Cell Melanoma Res. 31, 354–373 (2018).CAS 

Google Scholar 
Bertolesi, G. E. et al. The regulation of skin pigmentation in response to environmental light by pineal type II opsins and skin melanophore melatonin receptors. J. Photochem. Photobiol. B Biol. 212, 112024 (2020).CAS 

Google Scholar 
Bagnara, J. T. Pineal regulation of the body lightening reaction in amphibian larvae. Sci. (80-.). 132, 1481–1483 (1960).CAS 

Google Scholar 
Bertolesi, G. E., Song, Y. N., Atkinson-Leadbeater, K., Yang, J.-L. J. & McFarlane, S. Interaction and developmental activation of two neuroendocrine systems that regulate light-mediated skin pigmentation. Pigment Cell Melanoma Res. 30, 413–423 (2017).CAS 

Google Scholar 
Wang, H. & Siemens, J. TRP ion channels in thermosensation, thermoregulation and metabolism. Temp. (Austin, Tex.) 2, 178–187 (2015).
Google Scholar 
Hoffstaetter, L. J., Bagriantsev, S. N. & Gracheva, E. O. TRPs et al.: a molecular toolkit for thermosensory adaptations. Pflug. Arch. Eur. J. Physiol. 470, 745–759 (2018).CAS 

Google Scholar 
Kashio, M. Thermosensation involving thermo-TRPs. Mol. Cell. Endocrinol. 520, 1–8 (2021).
Google Scholar 
Señarís, R., Ordás, P., Reimúndez, A. & Viana, F. Mammalian cold TRP channels: impact on thermoregulation and energy homeostasis. Pflug. Arch. 470, 761–777 (2018).
Google Scholar 
Guo, H., Carlson, J. A. & Slominski, A. Role of TRPM in melanocytes and melanoma. Exp. Dermatol. 21, 650–654 (2012).CAS 

Google Scholar 
Kadowaki, T. Evolutionary dynamics of metazoan TRP channels. Pflug. Arch. 467, 2043–2053 (2015).CAS 

Google Scholar 
Saito, S. & Tominaga, M. Evolutionary tuning of TRPA1 and TRPV1 thermal and chemical sensitivity in vertebrates. Temp. (Austin, Tex.) 4, 141–152 (2017).
Google Scholar 
Saito, S. et al. Analysis of transient receptor potential ankyrin 1 (TRPA1) in frogs and lizards illuminates both nociceptive heat and chemical sensitivities and coexpression with TRP vanilloid 1 (TRPV1) in ancestral vertebrates. J. Biol. Chem. 287, 30743–30754 (2012).CAS 

Google Scholar 
Saito, S. et al. Evolution of heat sensors drove shifts in thermosensation between xenopus species adapted to different thermal niches. J. Biol. Chem. 291, 11446–11459 (2016).CAS 

Google Scholar 
Gracheva, E. O. et al. Molecular basis of infrared detection by snakes. Nature 464, 1006–1011 (2010).CAS 

Google Scholar 
Laursen, W. J., Anderson, E. O., Hoffstaetter, L. J., Bagriantsev, S. N. & Gracheva, E. O. Species-specific temperature sensitivity of TRPA1. Temp. (Austin, Tex.) 2, 214–226 (2015).
Google Scholar 
Bertolesi, G. E., Hehr, C. L. & McFarlane, S. Melanopsin photoreception in the eye regulates light-induced skin colour changes through the production of α-MSH in the pituitary gland. Pigment Cell Melanoma Res. 28, 559–571 (2015).CAS 

Google Scholar 
Bagnara, J. T. The pineal and the body lightening reaction of larval amphibians. Gen. Comp. Endocrinol. 3, 86–100 (1963).CAS 

Google Scholar 
Nisembaum, L. et al. In the heat of the night: thermo-TRPV channels in the salmonid pineal photoreceptors and modulation of melatonin secretion. Endocrinology 156, 4629–4638 (2015).CAS 

Google Scholar 
Schartl, M. et al. What is a vertebrate pigment cell? Pigment Cell Melanoma Res. 29, 8–14 (2016).
Google Scholar 
Slominski, A. Cooling skin cancer: menthol inhibits melanoma growth. Focus on ‘TRPM8 activation suppresses cellular viability in human melanoma’. Am. J. Physiol. – Cell Physiol. 295, C293–C295 (2008).CAS 

Google Scholar 
Yamamura, H., Ugawa, S., Ueda, T., Morita, A. & Shimada, S. TRPM8 activation suppresses cellular viability in human melanoma. Am. J. Physiol. Cell Physiol. 295, C296–C301 (2008).CAS 

Google Scholar 
Knowlton, W. M. et al. A sensory-labeled line for cold: TRPM8-expressing sensory neurons define the cellular basis for cold, cold pain, and cooling-mediated analgesia. J. Neurosci. 33, 2837–2848 (2013).CAS 

Google Scholar 
Weyer-Menkhoff, I., Pinter, A., Schlierbach, H., Schänzer, A. & Lötsch, J. Epidermal expression of human TRPM8, but not of TRPA1 ion channels, is associated with sensory responses to local skin cooling. Pain 160, 2699–2709 (2019).Kumasaka, M., Sato, S., Yajima, I. & Yamamoto, H. Isolation and developmental expression of tyrosinase family genes in Xenopus laevis. Pigment Cell Res. 16, 455–462 (2003).CAS 

Google Scholar 
Rodionov, V. I., Hope, A. J., Svitkina, T. M. & Borisy, G. G. Functional coordination of microtubule-based and actin-based motility in melanophores. Curr. Biol. 8, 165–169 (1998).CAS 

Google Scholar 
Session, A. M. et al. Genome evolution in the allotetraploid frog Xenopus laevis. Nature 538, 336–343 (2016).CAS 

Google Scholar 
Gosset, J. R. et al. A cross-species translational pharmacokinetic-pharmacodynamic evaluation of core body temperature reduction by the TRPM8 blocker PF-05105679. Eur. J. Pharm. Sci. 109S, S161–S167 (2017).
Google Scholar 
Winchester, W. J. et al. Inhibition of TRPM8 channels reduces pain in the cold pressor test in humans. J. Pharmacol. Exp. Ther. 351, 259–269 (2014).
Google Scholar 
Bianchi, B., Smith, P. A. & Abriel, H. The ion channel TRPM4 in murine experimental autoimmune encephalomyelitis and in a model of glutamate-induced neuronal degeneration. Mol. Brain 11, 1–10 (2018).
Google Scholar 
Li, K., Shi, Y., Gonye, E. C. & Bayliss, D. A. TRPM4 contributes to subthreshold membrane potential oscillations in multiple mouse pacemaker neurons. eNeuro 8, 1–13 (2021).
Google Scholar 
Dong, W. et al. Visual avoidance in Xenopus tadpoles is correlated with the maturation of visual responses in the optic tectum. J. Neurophysiol. 101, 803–815 (2009).
Google Scholar 
Bertolesi, G. E., Debnath, N., Atkinson-Leadbeater, K., Niedzwiecka, A. & McFarlane, S. Distinct type II opsins in the eye decode light properties for background adaptation and behavioural background preference. Mol. Ecol. 30, 6659–6676 (2021).CAS 

Google Scholar 
Viczian, A. S. & Zuber, M. E. A simple behavioral assay for testing visual function in xenopus laevis. J. Vis. Exp. 12, 51726 (2014).
Google Scholar 
Myers, B. R., Sigal, Y. M. & Julius, D. Evolution of thermal response properties in a cold-activated TRP channel. PLoS One 4, e5741 (2009).
Google Scholar 
Furman, B. L. S. et al. Pan-African phylogeography of a model organism, the African clawed frog ‘Xenopus laevis’. Mol. Ecol. 24, 909–925 (2015).CAS 

Google Scholar 
Wilson, R. S., James, R. S. & Johnston, I. A. Thermal acclimation of locomotor performance in tadpoles and adults of the aquatic frog Xenopus laevis. J. Comp. Physiol. B. 170, 117–124 (2000).CAS 

Google Scholar 
Kashiwagi, K. et al. Xenopus tropicalis: an ideal experimental animal in amphibia. Exp. Anim. 59, 395–405 (2010).CAS 

Google Scholar 
Martínez-Freiría, F., Toyama, K. S., Freitas, I. & Kaliontzopoulou, A. Thermal melanism explains macroevolutionary variation of dorsal pigmentation in Eurasian vipers. Sci. Rep. 10, 72871–1 (2020).Tanaka, K. Does the thermal advantage of melanism produce size differences in color-dimorphic snakes? Zool. Sci. 26, 698–703 (2009).
Google Scholar 
Moreno Azócar, D. L., Nayan, A. A., Perotti, M. G. & Cruz, F. B. How and when melanic coloration is an advantage for lizards: the case of three closely-related species of Liolaemus. Zool. (Jena.) 141, 125774 (2020).
Google Scholar 
Azócar, D. L. M. et al. Effect of body mass and melanism on heat balance in Liolaemus lizards of the goetschi clade. J. Exp. Biol. 219, 1162–1171 (2016).
Google Scholar 
Smith, K. R. et al. Colour change on different body regions provides thermal and signalling advantages in bearded dragon lizards. Proc. R. Soc. B Biol. Sci. 283, 20160626 (2016).
Google Scholar 
Rowe, J. W. et al. Thermal and substrate color-induced melanization in laboratory reared red-eared sliders (Trachemys scripta elegans). J. Therm. Biol. 61, 125–132 (2016).
Google Scholar 
Larsen, E. H. Dual skin functions in amphibian osmoregulation. Comp. Biochem. Physiol. A. Mol. Integr. Physiol. 253, 110869 (2021).CAS 

Google Scholar 
Franco-Belussi, L., Sköld, H. N. & De Oliveira, C. Internal pigment cells respond to external UV radiation in frogs. J. Exp. Biol. 219, 1378–1383 (2016).
Google Scholar 
Langhelle, A., Lindell, M. J. & Nyström, P. Effects of ultraviolet radiation on amphibian embryonic and larval development. J. Herpetol. 33, 449–456 (1999).
Google Scholar 
Mueller, K. P. & Neuhauss, S. C. F. Sunscreen for fish: co-option of UV light protection for camouflage. PLoS One 9, e87372 (2014).
Google Scholar 
Perotti, M. G., Diéguez, M. & Del, C. Effect of UV-B exposure on eggs and embryos of patagonian anurans and evidence of photoprotection. Chemosphere 65, 2063–2070 (2006).CAS 

Google Scholar 
Nilsson Sköld, H., Aspengren, S. & Wallin, M. Rapid color change in fish and amphibians – function, regulation, and emerging applications. Pigment Cell Melanoma Res. 26, 29–38 (2013).
Google Scholar 
Vences, M. et al. Field body temperatures and heating rates in a montane frog population: the importance of black dorsal pattern for thermoregulation on JSTOR. Ann. Zool. Fennici 39, 209–220 (2002).
Google Scholar 
Lindgren, J. et al. Skin pigmentation provides evidence of convergent melanism in extinct marine reptiles. Nature 506, 484–488 (2014).CAS 

Google Scholar 
Bonino, M. F., Cruz, F. B. & Perotti, M. G. Does temperature at local scale explain thermal biology patterns of temperate tadpoles? J. Therm. Biol. 94, 102744 (2020).
Google Scholar 
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 

Google Scholar 
Liu, T. et al. RNA interference-mediated depletion of TRPM8 enhances the efficacy of epirubicin chemotherapy in prostate cancer LNCaP and PC3 cells. Oncol. Lett. 15, 4129–4136 (2018).
Google Scholar 
Kashina, A. S. et al. Protein Kinase A, which regulates intracellular transport, forms complexes with molecular motors on organelles. Curr. Biol. 14, 1877–1881 (2004).CAS 

Google Scholar  More

Brown, J. S., Laundre, J. W. & Gurung, M. The ecology of fear: optimal foraging, game theory, and trophic interactions. J. Mammal. 80, 385–399 (1999).
Google Scholar 
Laundré, J. W., Hernández, L. & Altendorf, K. B. Wolves, elk, and bison: reestablishing the ‘landscape of fear’ in Yellowstone National Park, US.A. Can. J. Zool. 79, 1401–1409 (2001).
Google Scholar 
Atkins, J. L. et al. Cascading impacts of large-carnivore extirpation in an African ecosystem. Science 364, 173–177 (2019).CAS 

Google Scholar 
Laundre, J. W., Hernandez, L. & Ripple, W. J. The landscape of fear: ecological implications of being afraid. Open Ecol. J. 3, 1–7 (2010).
Google Scholar 
Loggins, A. A., Shrader, A. M., Monadjem, A. & McCleery, R. A. Shrub cover homogenizes small mammals’ activity and perceived predation risk. Sci. Rep. 9, 16857 (2019).
Google Scholar 
Whittingham, M. J. & Evans, K. L. The effects of habitat structure on predation risk of birds in agricultural landscapes. Ibis 146, 210–220 (2004).
Google Scholar 
Marshall, K. L. A., Philpot, K. E. & Stevens, M. Microhabitat choice in island lizards enhances camouflage against avian predators. Sci. Rep. 6, 19815 (2016).CAS 

Google Scholar 
Stevens, M., Troscianko, J., Wilson-Aggarwal, J. K. & Spottiswoode, C. N. Improvement of individual camouflage through background choice in ground-nesting birds. Nat. Ecol. Evol. 1, 1325–1333 (2017).
Google Scholar 
Wilson-Aggarwal, J. K., Troscianko, J. T., Stevens, M. & Spottiswoode, C. N. Escape distance in ground-nesting birds differs with individual level of camouflage. Am. Nat. 188, 231–239 (2016).
Google Scholar 
Troscianko, J., Wilson-Aggarwal, J., Stevens, M. & Spottiswoode, C. N. Camouflage predicts survival in ground-nesting birds. Sci. Rep. 6, 19966 (2016).CAS 

Google Scholar 
Gaston, K. J., Duffy, J. P., Gaston, S., Bennie, J. & Davies, T. W. Human alteration of natural light cycles: causes and ecological consequences. Oecologia 176, 917–931 (2014).
Google Scholar 
Gaston, K. J., Davies, T. W., Nedelec, S. L. & Holt, L. A. Impacts of artificial light at night on biological timings. Annu. Rev. Ecol. Evol. Syst. 48, 49–68 (2017).
Google Scholar 
Falchi, F. et al. The new world atlas of artificial night sky brightness. Sci. Adv. 2, e1600377 (2016).
Google Scholar 
Gaston, K. J. et al. Pervasiveness of biological impacts of artificial light at night. Integr. Comp. Biol. 61, 1098–1110 (2021).
Google Scholar 
Sanders, D., Frago, E., Kehoe, R., Patterson, C. & Gaston, K. J. A meta-analysis of biological impacts of artificial light at night. Nat. Ecol. Evol. 5, 74–81 (2021).
Google Scholar 
Kronfeld-Schor, N., Visser, M. E., Salis, L. & van Gils, J. A. Chronobiology of interspecific interactions in a changing world. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160248 (2017).
Google Scholar 
Underwood, C. N., Davies, T. W. & Queir Os, A. M. Artificial light at night alters trophic interactions of intertidal invertebrates. J. Anim. Ecol. 86, 781–789 (2017).
Google Scholar 
Burger, J., Howe, M. A., Hahn, D. C. & Chase, J. Effects of tide cycles on habitat selection and habitat partitioning by migrating shorebirds. Auk 94, 743–758 (1977).
Google Scholar 
Granadeiro, J. P., Dias, M. P., Martins, R. C. & Palmeirim, J. M. Variation in numbers and behaviour of waders during the tidal cycle: implications for the use of estuarine sediment flats. Acta Oecologica 29, 293–300 (2006).
Google Scholar 
Lourenço, P. M. et al. The energetic importance of night foraging for waders wintering in a temperate estuary. Acta Oecologica 34, 122–129 (2008).
Google Scholar 
McNeil, R., Drapeau, P. & Goss-Custard, J. D. The occurrence and adaptive significance of nocturnal habits in waterfowl. Biol. Rev. 67, 381–419 (1992).
Google Scholar 
Martin, G. R. Visual fields and their functions in birds. J. Ornithol. 148, 547–562 (2007).
Google Scholar 
Martin, G. R. What is binocular vision for? A birds’ eye view. J. Vis. 9, 1–19 (2009).
Google Scholar 
Davies, T. W., Duffy, J. P., Bennie, J. & Gaston, K. J. The nature, extent, and ecological implications of marine light pollution. Front. Ecol. Environ. 12, 347–355 (2014).
Google Scholar 
Leopold, M. F., Philippart, C. J. M. & Yorio, P. Nocturnal feeding under artificial light conditions by Brown-hooded Gull (Larus maculipennis) in Puerto Madryn harbour (Chubut Province, Argentina). Hornero 25, 55–60 (2010).
Google Scholar 
Pugh, A. R. & Pawson, S. M. Artificial light at night potentially alters feeding behaviour of the native southern black-backed gull (Larus dominicanus). Notornis 63, 37–39 (2016).
Google Scholar 
Santos, C. D. et al. Effects of artificial illumination on the nocturnal foraging of waders. Acta Oecologica 36, 166–172 (2010).
Google Scholar 
Montevecchi, W. A. Influences of Artificial Light on Marine Birds. in Ecological Consequences of Artificial Night Lighting (eds. Rich, C. & Longcore, T.) 94–113 (Island Press, 2006).Dwyer, R. G., Bearhop, S., Campbell, H. A. & Bryant, D. M. Shedding light on light: benefits of anthropogenic illumination to a nocturnally foraging shorebird. J. Anim. Ecol. 82, 478–485 (2013).
Google Scholar 
Blumstein, D. T. Developing an evolutionary ecology of fear: how life history and natural history traits affect disturbance tolerance in birds. Anim. Behav. 71, 389–399 (2006).
Google Scholar 
Stankowich, T. & Blumstein, D. T. Fear in animals: a meta-analysis and review of risk assessment. Proc. R. Soc. B Biol. Sci. 272, 2627–2634 (2005).
Google Scholar 
Caro, T. Antipredator Defenses in Birds and Mammals. (University of Chicago Press, 2005).Tillmann, J. E. Fear of the dark: night-time roosting and anti-predation behaviour in the grey partridge (Perdix perdix L.). Behaviour 146, 999–1023 (2009).
Google Scholar 
IUCN. The IUCN Red List of Threatened Species. Version 2022-1. https://www.iucnredlist.org/species/22693190/117917038 (2022).Brown, D. et al. The Eurasian Curlew—the most pressing bird conservation priority in the UK? Br. Birds 108, 660–668 (2015).
Google Scholar 
Franks, S. E., Douglas, D. J. T., Gillings, S. & Pearce-Higgins, J. W. Environmental correlates of breeding abundance and population change of Eurasian Curlew Numenius arquata in Britain. Bird. Study 64, 393–409 (2017).
Google Scholar 
Desholm, M. & Kahlert, J. Avian collision risk at an offshore wind farm. Biol. Lett. 1, 296–298 (2005).
Google Scholar 
Clarke, J. A. Moonlight’s influence on predator/prey interactions between short-eared owls (Asio flammeus) and Deermice (Peromyscus maniculatus). Behav. Ecol. Sociobiol. 13, 205–209 (1983).
Google Scholar 
Mandelik, Y., Jones, M. & Dayan, T. Structurally complex habitat and sensory adaptations mediate the behavioural responses of a desert rodent to an indirect cue for increased predation risk. Evol. Ecol. Res. 5, 501–515 (2003).
Google Scholar 
Alexander, R. D. The Evolution of Social Behavior | Annual Review of Ecology, Evolution, and Systematics. Annu. Rev. Ecol. Syst. 5, 325–383 (1974).
Google Scholar 
Pulliam, H. R. On the advantages of flocking. J. Theor. Biol. 38, 419–422 (1973).CAS 

Google Scholar 
Barnard, C. J. Flock feeding and time budgets in the house sparrow (Passer domesticus L.). Anim. Behav. 28, 295–309 (1980).
Google Scholar 
Cooper, W. E. Jr. et al. Effects of risk, cost, and their interaction on optimal escape by nonrefuging Bonaire whiptail lizards, Cnemidophorus murinus. Behav. Ecol. 14, 288–293 (2003).
Google Scholar 
Lagos, P. A. et al. Flight initiation distance is differentially sensitive to the costs of staying and leaving food patches in a small-mammal prey. Can. J. Zool. 87, 1016–1023 (2009).
Google Scholar 
Ydenberg, R. C. & Dill, L. M. The economics of fleeing from predators. Adv. Study Behav. 16, 229–249 (1986).
Google Scholar 
Tucker, V. A., Tucker, A. E., Akers, K. & Enderson, J. H. Curved flight paths and sideways vision in peregrine falcons (Falco peregrinus). J. Exp. Biol. 203, 3755–3763 (2000).CAS 

Google Scholar 
Carr, J. M. & Lima, S. L. Wintering birds avoid warm sunshine: predation and the costs of foraging in sunlight. Oecologia 174, 713–721 (2014).
Google Scholar 
van den Hout, P. J. & Martin, G. R. Extreme head-tilting in shorebirds: predator detection and sun avoidance. Wader Study Group Bull. 118, 18–21 (2011).
Google Scholar 
Ferguson, J. W. H., Galpin, J. S. & de Wet, M. J. Factors affecting the activity patterns of black-backed jackals Canis mesomelas. J. Zool. 214, 55–69 (1988).
Google Scholar 
Pyke, G. H. Optimal foraging theory: a critical review. Annu. Rev. Ecol. Syst. 15, 523–575 (1984).
Google Scholar 
Stephens, D. W. & Krebs, J. R. Foraging Theory. (Princeton University Press, 1986).Mouritsen, K. N. Predator avoidance in night-feeding dunlins calidris alpina: a matter of concealment. Ornis Scand. 23, 195–198 (1992).
Google Scholar 
Blumstein, D. T. Flight-initiation distance in birds is dependent on intruder starting distance. J. Wildl. Manag. 67, 852–857 (2003).
Google Scholar 
Troscianko, J. OSpRad; an open-source, low-cost, high-sensitivity spectroradiometer (p. 2022.12.09.519768). bioRxiv https://doi.org/10.1101/2022.12.09.519768 (2022).Article 

Google Scholar 
Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.4.4. http://florianhartig.github.io/DHARMa/ (2022).Core Team, R. R: a Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2022).
Google Scholar  More

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Saxon E, Bertozzi C. Cell surface engineering by a modified Staudinger reaction. Science. 2000;287:2007–10.CAS 

Google Scholar 
Staudinger H, Meyer J. Über neue organische Phosphorverbindungen III. Phosphinmethylenderivate und Phosphinimine. Helv Chim Acta. 1919;2:635–46. https://doi.org/10.1002/hlca.19190020164.Article 
CAS 

Google Scholar 
Laughlin ST, Bertozzi CR. Metabolic labeling of glycans with azido sugars and subsequent glycan-profiling and visualization via Staudinger ligation. Nat Protoc. 2007;2:2930–44.CAS 

Google Scholar 
Oliveira BL, Guo Z, Bernardes GJL. Inverse electron demand Diels–Alder reactions in chemical biology. Chem Soc Rev. 2017;46:4895–950.CAS 

Google Scholar 
Lang K, Chin JW. Bioorthogonal reactions for labeling proteins. ACS Chem Biol. 2014;9:16–20. https://doi.org/10.1021/cb4009292.Article 
CAS 

Google Scholar 
Kolb HC, Finn MG, Sharpless K. Click chemistry: diverse chemical function from a few good reactions. Angew Chemie-Int Ed. 2001;40:2004–21.CAS 

Google Scholar 
Tornøe C, Christensen C, Meldal M. Peptidotriazoles on Solid Phase: [1,2,3]-Triazoles by regiospecific Copper(I)-Catalyzed 1,3-Dipolar Cycloadditions of Terminal Alkynes to Azides. J Org Chem. 2002;67:3057–64. https://doi.org/10.1021/jo011148j.Article 
CAS 

Google Scholar 
Bakkum T, Leeuwen T, van, Sarris AJC, Elsland DM, van, Poulcharidis D, Overkleeft HS, et al. Quantification of bioorthogonal stability in immune phagocytes using flow cytometry reveals rapid degradation of strained alkynes. ACS Chem Biol. 2018;13:1173–9. https://doi.org/10.1021/acschembio.8b0035.Article 
CAS 

Google Scholar 
Wang Q, Chan T, Hilgraf R, Fokin R, Sharpless K, Finn M. Bioconjugation by copper(I)-catalyzed azide-alkyne [3 + 2] cycloaddition. J Am Chem Soc. 2003;125:3192–3.CAS 

Google Scholar 
Link A, Tirrell D. Cell surface labeling of Escherichia coli via copper(I)-catalyzed [3+2] cycloaddition. J Am Chem Soc. 2003;125:11164–5.CAS 

Google Scholar 
Dieterich D, Link A, Tirrell D, Schuman E. Selective identification of newly synthesized proteins in mammalian cells using bioorthogonal noncanonical amino acid tagging (BONCAT). Proc Natl Acad Sci USA. 2006;103:9482–7.CAS 

Google Scholar 
McKay C, Finn M. Click chemistry in complex mixtures: bioorthogonal bioconjugation. Chem Biol. 2014;21:1075–101.CAS 

Google Scholar 
Agard N, Prescher J, Bertozzi C. A strain-promoted [3 + 2] Azide−Alkyne cycloaddition for covalent modification of biomolecules in living systems. J Am Chem Soc. 2004;126:15046–7. https://doi.org/10.1021/ja044996f.Article 
CAS 

Google Scholar 
Weissleder R, Hilderbrand S. Tetrazine-based cycloadditions: application to pretargeted live cell imaging. Bioconjug Chem. 2008;19:2297–9.
Google Scholar 
Scinto SL, Bilodeau DA, Hincapie R, Lee W, Nguyen SS, Xu M, et al. Bioorthogonal chemistry. Nat Rev Methods. 2021;1:1–23.
Google Scholar 
Sletten E, Bertozzi C. Bioorthogonal chemistry: fishing for selectivity in a sea of functionality. Angew Chem Int Ed Engl. 2009;48:6974–98.CAS 

Google Scholar 
Moses JE, Moorhouse AD. The growing applications of click chemistry. Chem Soc Rev. 2007;36:1249–62.CAS 

Google Scholar 
Banahene N, Kavunja HW, Swarts BM. Chemical reporters for bacterial glycans: development and applications. Chem Rev. 2021;122:3336–413. https://doi.org/10.1021/acs.chemrev.1c00729.Article 
CAS 

Google Scholar 
Hatzenpichler R, Krukenberg V, Spietz RL, Jay ZJ. Next-generation physiology approaches to study microbiome function at single cell level. Nat Rev Microbiol. 2020;184:241–56.
Google Scholar 
Siegrist M, Whiteside S, Jewett J, Aditham A, Cava F, Bertozzi C. (D)-Amino acid chemical reporters reveal peptidoglycan dynamics of an intracellular pathogen. ACS Chem Biol. 2013;8:500–5.CAS 

Google Scholar 
Liechti G, Kuru E, Hall E, Kalinda A, Brun YV, VanNieuwenhze M, et al. A new metabolic cell wall labeling method reveals peptidoglycan in Chlamydia trachomatis. Nature. 2014;506:507. https://doi.org/10.1038/nature12892.Article 
CAS 

Google Scholar 
Pilhofer M, Aistleitner K, Biboy J, Gray J, Kuru E, Hall E, et al. Discovery of chlamydial peptidoglycan reveals bacteria with murein sacculi but without FtsZ. Nat Commun. 2013;4:1–7.
Google Scholar 
Taylor JA, Bratton BP, Sichel SR, Blair KM, Jacobs HM, Demeester KE, et al. Distinct cytoskeletal proteins define zones of enhanced cell wall synthesis in helicobacter pylori. Elife. 2020;9:e52482.CAS 

Google Scholar 
Kuru E, Hughes HV, Brown PJ, Hall E, Tekkam S, Cava F, et al. In situ probing of newly synthesized peptidoglycan in live bacteria with fluorescent D-amino acids. Angew Chemie Int Ed. 2012;51:12519–23. https://doi.org/10.1002/anie.201206749.Article 
CAS 

Google Scholar 
van Teeseling MCF, Mesman RJ, Kuru E, Espaillat A, Cava F, Brun YV, et al. Anammox Planctomycetes have a peptidoglycan cell wall. Nat Commun. 2015;6:6878. https://doi.org/10.1038/ncomms7878.Article 
CAS 

Google Scholar 
Wang W, Yang Q, Du Y, Zhou X, Du X, Wu Q. et al. Metabolic labeling of Peptidoglycan with NIR-II dye enables in vivo imaging of gut microbiota. Angew Chemie Int Ed. 2020;59:2628–33. https://doi.org/10.1002/anie.201910555.Article 
CAS 

Google Scholar 
Wang W, Zhu Y, Chen X. imaging of gram-negative and gram-positive microbiotas in the mouse gut. Biochemistry. 2017;56:3889–93.CAS 

Google Scholar 
Geva-Zatorsky N, Alvarez D, Hudak JE, Reading NC, Erturk-Hasdemir D, Dasgupta S, et al. In vivo imaging and tracking of host-microbiota interactions via metabolic labeling of gut anaerobic bacteria. Nat Med. 2015;21:1091–100.CAS 

Google Scholar 
Besanceney-Webler C, Jiang H, Wang W, Baughn AD, Wu P. Metabolic labeling of fucosylated glycoproteins in Bacteroidales species. Bioorg Med Chem Lett. 2011;21:4989–92.CAS 

Google Scholar 
Han Z, Thuy-Boun PS, Pfeiffer W, Vartabedian VF, Torkamani A, Teijaro JR, et al. Identification of an N-acetylneuraminic acid-presenting bacteria isolated from a human microbiome. Sci Rep. 2021;11:1–12.
Google Scholar 
Becam J, Walter T, Burgert A, Schlegel J, Sauer M, Seibel J, et al. Antibacterial activity of ceramide and ceramide analogs against pathogenic Neisseria. Sci Rep. 2017;7:1–12.CAS 

Google Scholar 
Nilsson I, Lee SY, Sawyer WS, Baxter Rath CM, Lapointe G, Six DA. Metabolic phospholipid labeling of intact bacteria enables a fluorescence assay that detects compromised outer membranes. J Lipid Res. 2020;61:870–83.CAS 

Google Scholar 
Evershed RP, Crossman ZM, Bull ID, Mottram H, Dungait JAJ, Maxfield PJ, et al. 13C-Labelling of lipids to investigate microbial communities in the environment. Curr Opin Biotechnol. 2006;17:72–82.CAS 

Google Scholar 
Salic A, Mitchison TJ. A chemical method for fast and sensitive detection of DNA synthesis in vivo. Proc Natl Acad Sci USA. 2008;105:2415–20. https://doi.org/10.1073/pnas.0712168105.Article 

Google Scholar 
Smriga S, Samo TJ, Malfatti F, Villareal J, Azam F. Individual cell DNA synthesis within natural marine bacterial assemblages as detected by ‘click’ chemistry. Aquat Microb Ecol. 2014;72:269–80.
Google Scholar 
Beauchemina ET, Hunter C, Maurice CF. Actively replicating gut bacteria identified by 5-ethynyl-2’-deoxyuridine (EdU) click chemistry and cell sorting. bioRxiv. 2022. https://www.biorxiv.org/content/10.1101/2022.07.20.500840v2.Sinclair L, Barthelemy C, Cantrell D. Single cell glucose uptake assays: a cautionary tale. Immunometabolism. 2020;2. https://pubmed.ncbi.nlm.nih.gov/32879737/.Hu F, Chen DZ, Zhang DL, Shen Y, Wei L, Min PW. Vibrational imaging of glucose uptake activity in live cells and tissues by stimulated Raman scattering. Angew Chem Int Ed Engl. 2015;54:9821.CAS 

Google Scholar 
Kiick K, Saxon E, Tirrell D, Bertozzi C. Incorporation of azides into recombinant proteins for chemoselective modification by the Staudinger ligation. Proc Natl Acad Sci USA. 2002;99:19–24.CAS 

Google Scholar 
Kiick K, Tirrell D. Protein engineering by in vivo incorporation of non-natural amino acids: control of incorporation of methionine analogues by Methionyl-tRNA Synthetase. Tetrahedron. 2000;56:9487–93.CAS 

Google Scholar 
Ignacio B, Bakkum T, Bonger K, Martin N, van Kasteren S. Metabolic labeling probes for interrogation of the host-pathogen interaction. Org Biomol Chem. 2021;19:2856–70.CAS 

Google Scholar 
Bagert JD, Kessel JC, van, Sweredoski MJ, Feng L, Hess S, Bassler BL, et al. Time-resolved proteomic analysis of quorum sensing in Vibrio harveyi. Chem Sci. 2016;7:1797–806.CAS 

Google Scholar 
Babin BM, Atangcho L, Van Eldijk MB, Sweredoski MJ, Moradian A, Hess S, et al. Selective proteomic analysis of antibiotic-tolerant cellular subpopulations in pseudomonas aeruginosa biofilms. 2017. https://doi.org/10.1128/mBio.01593-17.Hatzenpichler R, Scheller S, Tavormina PL, Babin BM, Tirrell DA, Orphan VJ. In situ visualization of newly synthesized proteins in environmental microbes using amino acid tagging and click chemistry. Environ Microbiol. 2014;16:2568–90. https://doi.org/10.1111/1462-2920.12436.Article 
CAS 

Google Scholar 
Samo TJ, Smriga S, Malfatti F, Sherwood BP, Azam F. Broad distribution and high proportion of protein synthesis active marine bacteria revealed by click chemistry at the single cell level. Front Mar Sci. 2014;0:48.
Google Scholar 
Hatzenpichler R, Connon SA, Goudeau D, Malmstrom RR, Woyke T, Orphan VJ. Visualizing in situ translational activity for identifying and sorting slow-growing archaeal-bacterial consortia. Proc Natl Acad Sci USA. 2016;113:E4069–78. https://doi.org/10.1073/pnas.1603757113.Article 
CAS 

Google Scholar 
Couradeau E, Sasse J, Goudeau D, Nath N, Hazen TC, Bowen BP, et al. Probing the active fraction of soil microbiomes using BONCAT-FACS. Nat Commun. 2019;10:1–10.CAS 

Google Scholar 
Leizeaga A, Estrany M, Forn I, Sebastián M. Using click-chemistry for visualizing in situ changes of translational activity in planktonic marine bacteria. Front Microbiol. 2017;0:2360.
Google Scholar 
Lindivat M, Larsen A, Hess-Erga OK, Bratbak G, Hoell IA. Bioorthogonal non-canonical amino acid tagging combined with flow cytometry for determination of activity in aquatic microorganisms. Front Microbiol. 2020;0:1929.
Google Scholar 
Chen L, Zhao B, Li X, Cheng Z, Wu R, Xia Y. Isolating and characterizing translationally active fraction of anammox microbiota using bioorthogonal non-canonical amino acid tagging. Chem Eng J. 2021;418:129411.CAS 

Google Scholar 
McKay LJ, Smith HJ, Barnhart EP, Schweitzer HD, Malmstrom RR, Goudeau D, et al. Activity-based, genome-resolved metagenomics uncovers key populations and pathways involved in subsurface conversions of coal to methane. ISME J. 2021;16:915–26.
Google Scholar 
Du Z, Behrens SF. Tracking de novo protein synthesis in the activated sludge microbiome using BONCAT-FACS. Water Res. 2021;205:117696.CAS 

Google Scholar 
Valentini TD, Lucas SK, Binder KA, Cameron LC, Motl JA, Dunitz JM, et al. Bioorthogonal non-canonical amino acid tagging reveals translationally active subpopulations of the cystic fibrosis lung microbiota. Nat Commun. 2020;11:1–11.
Google Scholar 
Taguer M, Shapiro BJ, Maurice CF. Translational activity is uncoupled from nucleic acid content in bacterial cells of the human gut microbiota. Gut Microbes. 2021;13:1–15.
Google Scholar 
Banahene N, Kavunja HW, Swarts BM. Chemical reporters for bacterial glycans: development and applications. Chem Rev. 2021;122:3336–413. https://doi.org/10.1021/acs.chemrev.1c00729.Article 
CAS 

Google Scholar 
Kavunja HW, Piligian BF, Fiolek TJ, Foley HN, Nathan TO, Swarts BM. A chemical reporter strategy for detecting and identifying O-mycoloylated proteins in Corynebacterium. Chem Commun. 2016;52:13795–8.CAS 

Google Scholar 
Demeester KE, Liang H, Jensen MR, Jones ZS, D’Ambrosio EA, Scinto SL, et al. Synthesis of functionalized N-Acetyl Muramic acids to probe bacterial cell wall recycling and biosynthesis. J Am Chem Soc. 2018;140:9458–65. https://doi.org/10.1021/jacs.8b03304.Article 
CAS 

Google Scholar 
Moulton KD, Adewale AP, Carol HA, Mikami SA, Dube DH. Metabolic glycan labeling-based screen to identify bacterial glycosylation genes. ACS Infect Dis. 2020;6:3247–59. https://doi.org/10.1021/acsinfecdis.0c00612.Article 
CAS 

Google Scholar 
Keller LJ, Babin BM, Lakemeyer M, Bogyo M. Activity-based protein profiling in bacteria: Applications for identification of therapeutic targets and characterization of microbial communities. Curr Opin Chem Biol. 2020;54:45–53.CAS 

Google Scholar 
Speers AE, Adam GC, Cravatt BF. Activity-based protein profiling in vivo using a copper(I)-catalyzed azide-alkyne [3 + 2] cycloaddition. J Am Chem Soc. 2003;125:4686–7. https://doi.org/10.1021/ja034490.Article 
CAS 

Google Scholar 
Krysiak J, Sieber SA. Activity-based protein profiling in bacteria. Methods Mol Biol. 2017;1491:57–74.CAS 

Google Scholar 
Jariwala PB, Pellock SJ, Cloer EW, Artola M, Simpson JB, Bhatt AP, et al. Discovering the microbial enzymes driving drug toxicity with activity-based protein profiling. ACS Chem Biol. 2020;15:217–25. https://doi.org/10.1021/acschembio.9b00788.Article 
CAS 

Google Scholar 
Kovalyova Y, Hatzios SK. Activity-based protein profiling at the host-pathogen interface. Curr Top Microbiol Immunol. 2019;420:73–91.CAS 

Google Scholar 
Sakoula D, Smith GJ, Frank J, Mesman RJ, Kop LFM, Blom P, et al. Universal activity-based labeling method for ammonia- and alkane-oxidizing bacteria. ISME J. 2021;16:958–71.
Google Scholar 
Fan Y, Pedersen O. Gut microbiota in human metabolic health and disease. Nat Rev Microbiol. 2020;19:55–71.
Google Scholar 
Fitzpatrick CR, Salas-González I, Conway JM, Finkel OM, Gilbert S, Russ D, et al. The plant microbiome: from ecology to reductionism and beyond. 101146/annurev-micro-022620-014327. 2020;74:81–100. https://www.annualreviews.org/doi/abs/10.1146/annurev-micro-022620-014327.Kawecki TJ, Lenski RE, Ebert D, Hollis B, Olivieri I, Whitlock MC. Experimental evolution. Trends Ecol Evol. 2012;27:547–60.
Google Scholar 
Lenski RE. Experimental evolution and the dynamics of adaptation and genome evolution in microbial populations. ISME J. 2017;11:2181–94.CAS 

Google Scholar 
Rodríguez-Verdugo A. Evolving Interactions and Emergent Functions in Microbial Consortia. mSystems. 2021;6. https://pubmed.ncbi.nlm.nih.gov/34427521/.Pascual-García A, Bonhoeffer S, Bell T. Metabolically cohesive microbial consortia and ecosystem functioning. Philos Trans R Soc B. 2020;375. https://royalsocietypublishing.org/doi/full/10.1098/rstb.2019.0245.Ackermann M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat Rev Microbiol. 2015;13:497–508.CAS 

Google Scholar 
Balaban NQ, Helaine S, Lewis K, Ackermann M, Aldridge B, Andersson DI, et al. Definitions and guidelines for research on antibiotic persistence. Nat Rev Microbiol. 2019;17:441–8.CAS 

Google Scholar 
Vermeersch L, Perez-Samper G, Cerulus B, Jariani A, Gallone B, Voordeckers K, et al. On the duration of the microbial lag phase. Curr Genet. 2019;65:721–7.CAS 

Google Scholar 
Solopova A, van Gestel J, Weissing FJ, Bachmann H, Teusink B, Kok J, et al. Bet-hedging during bacterial diauxic shift. Proc Natl Acad Sci USA. 2014;111:7427–32.CAS 

Google Scholar 
Zhang Z, Du C, de Barsy F, Liem M, Liakopoulos A, van Wezel GP, et al. Antibiotic production in Streptomyces is organized by a division of labor through terminal genomic differentiation. Sci Adv. 2020;6:eaay5781.CAS 

Google Scholar 
Mavridou DAI, Gonzalez D, Kim W, West SA, Foster KR. Bacteria use collective behavior to generate diverse combat strategies. Curr Biol. 2018;28:345–355.e4.CAS 

Google Scholar 
Levin AM, de Vries RP, Conesa A, de Bekker C, Talon M, Menke HH, et al. Spatial differentiation in the vegetative mycelium of Aspergillus niger. Eukaryot Cell. 2007;6:2311–22.CAS 

Google Scholar 
Zacchetti B, Wösten HAB, Claessen D. Multiscale heterogeneity in filamentous microbes. Biotechnol Adv. 2018;36:2138–49.CAS 

Google Scholar 
Bleichrodt R-J, Vinck A, Read ND, Wösten HAB. Selective transport between heterogeneous hyphal compartments via the plasma membrane lining septal walls of Aspergillus niger. Fungal Genet Biol. 2015;82:193–200.CAS 

Google Scholar 
Nürnberg DJ, Mariscal V, Bornikoel J, Nieves-Morión M, Krauß N, Herrero A, et al. Intercellular diffusion of a fluorescent sucrose analog via the septal junctions in a Filamentous Cyanobacterium. MBio. 2015;6. https://journals.asm.org/doi/full/10.1128/mBio.02109-14.Pasulka AL, Thamatrakoln K, Kopf SH, Guan Y, Poulos B, Moradian A, et al. Interrogating marine virus-host interactions and elemental transfer with BONCAT and nanoSIMS-based methods. Environ Microbiol. 2018;20:671–92. https://doi.org/10.1111/1462-2920.13996.Article 
CAS 

Google Scholar 
Berjón-Otero M, Duponchel S, Hackl T, Fischer M. Visualization of giant virus particles using BONCAT labeling and STED microscopy. bioRxiv. 2020;2020.07.14.202192. https://www.biorxiv.org/content/10.1101/2020.07.14.202192v1.Steward KF, Eilers B, Tripet B, Fuchs A, Dorle M, Rawle R, et al. Metabolic implications of using BioOrthogonal Non-Canonical Amino Acid Tagging (BONCAT) for tracking protein synthesis. Front Microbiol. 2020;0:197.
Google Scholar 
van Elsland DM, Pujals S, Bakkum T, Bos E, Oikonomeas-Koppasis N, Berlin I, et al. Ultrastructural Imaging of Salmonella–Host interactions using super-resolution correlative light-electron microscopy of bioorthogonal pathogens. ChemBioChem. 2018;19:1766–70. https://doi.org/10.1002/cbic.201800230.Article 
CAS 

Google Scholar 
Michels DE, Lomenick B, Chou T-F, Sweredoski MJ, Pasulka A. Amino acid analog induces stress response in marine Synechococcus. Appl Environ Microbiol. 2021;87:1–18. https://doi.org/10.1128/AEM.00200-21.Article 

Google Scholar 
Hong V, Steinmetz NF, Manchester M, Finn MG. Labeling live cells by copper-catalyzed alkyne−azide click chemistry. Bioconjug Chem. 2010;21:1912–6. https://doi.org/10.1021/bc100272z.Article 
CAS 

Google Scholar 
van Geel R, Pruijn G, van Delft F, Boelens W. Preventing thiol-yne addition improves the specificity of strain-promoted azide-alkyne cycloaddition. Bioconjug Chem. 2012;23:392–8.
Google Scholar 
Patterson DM, Nazarova LA, Prescher JA. Finding the Right (Bioorthogonal) Chemistry. ACS Chem Biol. 2014;9:592–605. https://doi.org/10.1021/cb400828a.Article 
CAS 

Google Scholar 
Ignacio BJ, Dijkstra J, Garcia NM, Slot EFJ, van Weijsten MJ, Storkebaum E, et al. THRONCAT: Efficient metabolic labeling of newly synthesized proteins using a bioorthogonal threonine analog. bioRxiv. 2022. https://www.biorxiv.org/content/10.1101/2022.03.29.486210v1.Wright MH. Chemical proteomics of host–microbe interactions. Proteomics. 2018;18:1700333. https://doi.org/10.1002/pmic.201700333.Article 
CAS 

Google Scholar 
Yu H, Schomaker J. Recent developments and strategies for mutually orthogonal bioorthogonal reactions. Chembiochem. 2021;22:3254–62.
Google Scholar 
Willems LI, Li N, Florea BI, Ruben M, van der Marel GA, Overkleeft HS. Triple bioorthogonal ligation strategy for simultaneous labeling of multiple enzymatic activities. Angew Chemie Int Ed. 2012;51:4431–4. https://doi.org/10.1002/anie.201200923.Article 
CAS 

Google Scholar 
Simon C, Lion C, Spriet C, Baldacci-Cresp F, Hawkins S, Biot C. One, two, three: a bioorthogonal triple labelling strategy for studying the dynamics of plant cell wall formation in vivo. Angew Chemie Int Ed. 2018;57:16665–71. https://doi.org/10.1002/anie.201808493.Article 
CAS 

Google Scholar 
Chio TI, Gu H, Mukherjee K, Tumey LN, Bane SL. Site-specific bioconjugation and multi-bioorthogonal labeling via rapid formation of a boron–nitrogen heterocycle. Bioconjug Chem. 2019;30:1554–64. https://doi.org/10.1021/acs.bioconjchem.9b0024.Article 
CAS 

Google Scholar 
Bakkum T, Heemskerk MT, Bos E, Groenewold M, Oikonomeas-Koppasis N, Walburg KV, et al. Bioorthogonal correlative light-electron microscopy of mycobacterium tuberculosis in macrophages reveals the effect of antituberculosis drugs on subcellular bacterial distribution. ACS Cent Sci. 2020;6:1997–2007. https://doi.org/10.1021/acscentsci.0c00539.Article 
CAS 

Google Scholar  More