Philippa Kaur

Estes, J. A. Predators and ecosystem management. Wildl. Soc. Bull. 24, 390–396 (1996).
Google Scholar 
Licht, D. S., Millspaugh, J. J., Kunkel, K. E., Kochanny, C. O. & Peterson, R. O. Using small populations of wolves for ecosystem restoration and stewardship. Bioscience 60, 147–153 (2010).Article 

Google Scholar 
Schwartz, C. C., Swenson, J. E. & Miller, S. D. Large carnivores, moose, and humans: A changing paradigm of predator management in the 21st century. Alces J. Devot. Biol. Manag. Moose 39, 41–63 (2003).
Google Scholar 
Valente, A. M., Acevedo, P., Figueiredo, A. M., Fonseca, C. & Torres, R. T. Overabundant wild ungulate populations in Europe: Management with consideration of socio-ecological consequences. Mammal Rev. 50, 353–366 (2020).Article 

Google Scholar 
Lee, S. D. & Miller-Rushing, A. J. Degradation, urbanization, and restoration: A review of the challenges and future of conservation on the Korean Peninsula. Biol. Cons. 176, 262–276 (2014).Article 

Google Scholar 
Kodera, Y. Habitat selection of Japanese wild boar in Iwami district, Shimane Prefecture, western Japan. Wildl. Conserv. Jpn. 6, 119–129 (2001).
Google Scholar 
Ohashi, H. et al. Differences in the activity pattern of the wild boar Sus scrofa related to human disturbance. Eur. J. Wildl. Res. 59, 167–177 (2013).Article 

Google Scholar 
Ministry of Environment Republic of Korea. Management Plan of Pest Wild Boars. (Sejong, 2010).National Institute of Biological Resources. Analysis of Hunting Effect on Pest Animals and its Monitoring Techniques. (Incheon, 2017).Lee, S. M. & Lee, E. J. Diet of the wild boar (Sus scrofa): Implications for management in forest-agricultural and urban environments in South Korea. PeerJ 7, e7835 (2019).Article 

Google Scholar 
Bieber, C. & Ruf, T. Population dynamics in wild boar Sus scrofa: ecology, elasticity of growth rate and implications for the management of pulsed resource consumers. J. Appl. Ecol. 42, 1203–1213 (2005).Article 

Google Scholar 
Brogi, R. et al. Capital-income breeding in wild boar: A comparison between two sexes. Sci. Rep. 11, 1–10 (2021).Article 

Google Scholar 
Frauendorf, M., Gethöffer, F., Siebert, U. & Keuling, O. The influence of environmental and physiological factors on the litter size of wild boar (Sus scrofa) in an agriculture dominated area in Germany. Sci. Total Environ. 541, 877–882 (2016).Article 
ADS 
CAS 

Google Scholar 
Massei, G., Genov, P. V. & Staines, B. W. Diet, food availability and reproduction of wild boar in a Mediterranean coastal area. Acta Theriol. 41, 307–320 (1996).Article 

Google Scholar 
Sabrina, S., Jean-Michel, G., Carole, T., Serge, B. & Eric, B. Pulsed resources and climate-induced variation in the reproductive traits of wild boar under high hunting pressure. J. Anim. Ecol. 78, 1278–1290 (2009).Article 

Google Scholar 
Fonseca, C. et al. Reproduction in the wild boar (Sus scrofa Linnaeus, 1758) populations of Portugal. Galemys 16, 53–65 (2004).
Google Scholar 
Morreti, M. Birth distribution, structure and dynamics of a hunted mountain populatin of wild boars (Sus scrofa L.), Ticino, Switzerland. J. Mt. Ecol. 3, 192–196 (1995).
Google Scholar 
Rosell, C., Navas, F. & Romero, S. Reproduction of wild boar in a cropland and coastal wetland area: Implications for management. Anim. Biodivers. Conserv. 35, 209–217 (2012).Article 

Google Scholar 
Cellina, S. Effects of supplemental feeding on the body condition and reproductive state of wild boar Sus scrofa in Luxembourg PhD Dissertation thesis, University of Sussex, (2008).Gamelon, M. et al. High hunting pressure selects for earlier birth date: Wild boar as a case study. Evol. Int. J. Org. Evol. 65, 3100–3112 (2011).Article 

Google Scholar 
Gethöffer, F., Sodeikat, G. & Pohlmeyer, K. Reproductive parameters of wild boar (Sus scrofa) in three different parts of Germany. Eur. J. Wildl. Res. 53, 287–297 (2007).Article 

Google Scholar 
Fonseca, C., Da Silva, A., Alves, J., Vingada, J. & Soares, A. Reproductive performance of wild boar females in Portugal. Eur. J. Wildl. Res. 57, 363–371 (2011).Article 

Google Scholar 
Gaillard, J.-M., Brandt, S. & Jullien, J.-M. Body weight effect on reproduction of young wild boar (Sus scrofa) females: A comparative analysis. Folia Zool. (Brno) 42, 204–212 (1993).
Google Scholar 
Poteaux, C. et al. Socio-genetic structure and mating system of a wild boar population. J. Zool. 278, 116–125 (2009).Article 

Google Scholar 
Spitz, F., Valet, G. & Lehr Brisbin, I. Jr. Variation in body mass of wild boars from southern France. J. Mammal. 79, 251–259 (1998).Article 

Google Scholar 
Trivers, R. L. & Willard, D. E. Natural selection of parental ability to vary the sex ratio of offspring. Science 179, 90–92 (1973).Article 
ADS 
CAS 

Google Scholar 
Clutton-Brock, T. H., Albon, S. & Guinness, F. Parental investment in male and female offspring in polygynous mammals. Nature 289, 487–489 (1981).Article 
ADS 

Google Scholar 
Hewison, A. M. & Gaillard, J.-M. Successful sons or advantaged daughters? The Trivers-Willard model and sex-biased maternal investment in ungulates. Trends Ecol. Evol. 14, 229–234 (1999).Article 
CAS 

Google Scholar 
Clutton-Brock, T. H. & Iason, G. R. Sex ratio variation in mammals. Q. Rev. Biol. 61, 339–374 (1986).Article 
CAS 

Google Scholar 
Fernández-Llario, P. & Mateos-Quesada, P. Body size and reproductive parameters in the wild boar Sus scrofa. Acta Theriol. 43, 439–444 (1998).Article 

Google Scholar 
Meikle, D. B., Drickamer, L. C., Vessey, S. H., Rosenthal, T. L. & Fitzgerald, K. S. Maternal dominance rank and secondary sex ratio in domestic swine. Anim. Behav. 46, 79–85 (1993).Article 

Google Scholar 
Servanty, S., Gaillard, J.-M., Allainé, D., Brandt, S. & Baubet, E. Litter size and fetal sex ratio adjustment in a highly polytocous species: The wild boar. Behav. Ecol. 18, 427–432 (2007).Article 

Google Scholar 
Mendl, M., Zanella, A. J., Broom, D. M. & Whittemore, C. T. Maternal social status and birth sex ratio in domestic pigs: An analysis of mechanisms. Anim. Behav. 50, 1361–1370 (1995).Article 

Google Scholar 
Cameron, E. Z. Facultative adjustment of mammalian sex ratios in support of the Trivers-Willard hypothesis: Evidence for a mechanism. Proc. R. Soc. Lond. Ser. B Biol. Sci. 271, 1723–1728 (2004).Article 

Google Scholar 
Clutton-Brock, T., Albon, S. & Guinness, F. Maternal dominance, breeding success and birth sex ratios in red deer. Nature 308, 358–360 (1984).Article 
ADS 

Google Scholar 
Maillard, D. & Fournier, P. Timing and synchrony of births in the wild boar (Sus scrofa Linnaeus, 1758) in a Mediterranean habitat: The effect of food availability. Galemys 16, 67–74 (2004).
Google Scholar 
Bywater, K. A., Apollonio, M., Cappai, N. & Stephens, P. A. Litter size and latitude in a large mammal: the wild boar Sus scrofa. Mammal Rev. 40, 212–220 (2010).
Google Scholar 
Orłowska, L., Rembacz, W. & Florek, C. Carcass weight, condition and reproduction of wild boars harvested in north-western Poland. Pest Manag. Sci. 69, 367–370 (2013).Article 

Google Scholar 
Carranza, J. Sexual selection for male body mass and the evolution of litter size in mammals. Am. Nat. 148, 81–100 (1996).Article 

Google Scholar 
FernáNdez-Llario, P., Carranza, J. & Mateos-Quesada, P. Sex allocation in a polygynous mammal with large litters: The wild boar. Anim. Behav. 58, 1079–1084 (1999).Article 

Google Scholar 
McBride, G. The” teat order” and communication in young pigs. Animal Behaviour (1963).McBride, G., James, J. & Hodgens, N. Social behaviour of domestic animals. IV. Growing pigs. Anim. Sci. 6, 129–139 (1964).Article 

Google Scholar 
McBride, G., James, J. & Wyeth, G. Social behaviour of domestic animals VII. Variation in weaning weight in pigs. Anim. Sci. 7, 67–74 (1965).Article 

Google Scholar 
Geochang County. Geochang Statistical yearbook. (Geochang, 2015).Seoul Metropolitan Government. Seoul Statistical Yearbook. (Seoul, 2017).Animal and Plant Quarantine Agency and Ministry of Food and Drug Safety. Institutional Animal Care and Use Committee Standard Operation Guideline. (Gimcheon, 2020).Magnell, O. & Carter, R. The chronology of tooth development in wild boar – A guide to age determination of linear enamel hypoplasia in prehistoric and medieval pigs. Verterrinarija Ir Zootechnika. T. 40, 43–48 (2007).
Google Scholar 
Oroian, T. E., Oroian, R. G., Pasca, I., Oroian, E. & Covrig, I. Methods of age estimation by dentition in Sus scrofa ferus sp. Bull. UASVM Anim. Sci. Biotechnol. 67, 1–2 (2010).
Google Scholar 
Vericad, R. Fetal age, conception and birth period estimation on wild boar (Sus scrofa) in West Pyrenees. in Actas del XV Congresso Int. Fauna Cinegética y Silvestre. (Trujillo, 1983).Rosell, C., Fernández-Llario, P. & Herrero, J. The wild boar (Sus scrofa Linnaeus, 1758). Galemys 13, 1–25 (2001).
Google Scholar 
R core team. R: A language and environment for statistical computing v. 3.6.0 (Austria, 2019). More

Siegel, J. M. Do all animals sleep? Trends Neurosci. 31, 208–213 (2008).Article 
CAS 

Google Scholar 
Lima, S. L., Rattenborg, N. C., Lesku, J. A. & Amlaner, C. J. Sleeping under the risk of predation. Anim. Behav. 70, 723–736 (2005).Article 

Google Scholar 
Tougeron, K. & Abram, P. K. An Ecological Perspective on Sleep Disruption. Am. Nat. 190, 55–66 (2017).Article 

Google Scholar 
Lesku, J. A., Aulsebrook, A. E., Kelly, M. L. & Tisdale, R. K. Evolution of Sleep and Adaptive Sleeplessness. Handbook of Behavioral Neuroscience vol. 30 (Elsevier B.V., 2019).Smeltzer, E. A. et al. Social sleepers: The effects of social status on sleep in terrestrial mammals. Horm. Behav. 143, 105181 (2022).Article 
CAS 

Google Scholar 
Chu, H. S., Oh, J. & Lee, K. The Relationship between Living Arrangements and Sleep Quality in Older Adults: Gender Differences. Int. J. Environ. Res. Public Health 19, 3893 (2022).Karamihalev, S., Flachskamm, C., Eren, N., Kimura, M. & Chen, A. Social context and dominance status contribute to sleep patterns and quality in groups of freely-moving mice. Sci. Rep. 9, 1–9 (2019).Article 
CAS 

Google Scholar 
Capellini, I., Barton, R. A., McNamara, P., Preston, B. T. & Nunn, C. L. Phylogenetic analysis of the ecology and evolution of mammalian sleep. Evolution 62, 1764–1776 (2008).Article 

Google Scholar 
Ogawa, H., Idani, G., Moore, J., Pintea, L. & Hernandez-Aguilar, A. Sleeping Parties and nest distribution of chimpanzees in the Savanna woodland, Ugalla, Tanzania. Int. J. Primatol. 28, 1397–1412 (2007).Article 

Google Scholar 
Mulavwa, M. N. et al. Nest groups of wild bonobos at Wamba: Selection of vegetation and tree species and relationships between nest group size and party size. Am. J. Primatol. 72, 575–586 (2010).
Google Scholar 
Matsuda, I., Tuuga, A. & Higashi, S. Effects of water level on sleeping-site selection and inter-group association in proboscis monkeys: Why do they sleep alone inland on flooded days? Ecol. Res. 25, 475–482 (2010).Article 

Google Scholar 
Schreier, A. L. & Swedell, L. Ecology and sociality in a multilevel society: Ecological determinants of spatial cohesion in hamadryas baboons. Am. J. Phys. Anthropol. 148, 580–588 (2012).Article 

Google Scholar 
Kummer, H. & Kurt, F. Social units of free-living population of hamadryas baboons. Folia Primotol. 1, 4–19 (1963).Ogawa, H. & Takahashi, H. Triadic positions of Tibetan macaques huddling at a sleeping site. Int. J. Primatol. 24, 591–606 (2002).Article 

Google Scholar 
Snyder-Mackler, N., Beehner, J. C. & Bergman, T. J. Defining Higher Levels in the Multilevel Societies of Geladas (Theropithecus gelada). Int. J. Primatol. 33, 1054–1068 (2012).Article 

Google Scholar 
Mochida, K. & Nishikawa, M. Sleep duration is affected by social relationships among sleeping partners in wild Japanese macaques. Behav. Process. 103, 102–104 (2014).Article 

Google Scholar 
Di Bitetti, M. S., Vidal, E. M. L., Baldovino, M. C. & Benesovsky, V. Sleeping site preferences in tufted capuchin monkeys (Cebus apella nigritus). Am. J. Primatol. 50, 257 (2000).Article 

Google Scholar 
Takahashi, H. Huddling relationships in night sleeping groups among wild Japanese macaques in Kinkazan Island during winter. Primates 38, 57–68 (1997).Article 

Google Scholar 
Park, O., Barden, A. & Williams, E. Studies in Nocturnal Ecology, IX. Further Analysis of Activity of Panama Rain Forest Animals. Ecology 21, 122 (1940).Article 

Google Scholar 
Gaston, K. J. Nighttime ecology: The “nocturnal problem” revisited. Am. Nat. 193, 481–502 (2019).Article 

Google Scholar 
Börger, L. et al. Biologging Special Feature. J. Anim. Ecol. 89, 6–15 (2020).Article 

Google Scholar 
Krause, J. et al. Reality mining of animal social systems. Trends Ecol. Evol. 28, 541–551 (2013).Article 

Google Scholar 
Zeus, V. M., Puechmaille, S. J. & Kerth, G. Conspecific and heterospecific social groups affect each other’s resource use: a study on roost sharing among bat colonies. Anim. Behav. 123, 329–338 (2017).Article 

Google Scholar 
Wey, T. W., Burger, J. R., Ebensperger, L. A. & Hayes, L. D. Reproductive correlates of social network variation in plurally breeding degus (Octodon degus). Anim. Behav. 85, 1407–1414 (2013).Article 

Google Scholar 
Hirsch, B. T., Prange, S., Hauver, S. A. & Gehrt, S. D. Genetic relatedness does not predict racoon social network structure. Anim. Behav. 85, 463–470 (2013).Article 

Google Scholar 
Robitaille, A. L., Webber, Q. M. R., Turner, J. W. & Wal Eric, V. The problem and promise of scale in multilayer animal social networks. Curr. Zool. 67, 113–123 (2021).Article 

Google Scholar 
Smith, J. E. et al. Split between two worlds: Automated sensing reveals links between above- and belowground social networks in a free-living mammal. Philos. Trans. R. Soc. B Biol. Sci. 373, 20170249 (2018).Silk, M. J. et al. Seasonal variation in daily patterns of social contacts in the European badger Meles meles. Ecol. Evol. 7, 9006–9015 (2017).Article 

Google Scholar 
Gaynor, K. M., Hojnowski, C. E., Carter, N. H. & Brashares, J. S. The influence of human disturbance on wildlife nocturnality. Science 360, 1232–1235 (2018).Article 
CAS 

Google Scholar 
Barry, R. E. & Mundy, P. J. Seasonal variation in the degree of heterospecific association of two syntopic hyraxes (Heterohyrax brucei and Procavia capensis) exhibiting synchronous parturition. Behav. Ecol. Sociobiol. 52, 177–181 (2002).Article 

Google Scholar 
Barocas, A., Ilany, A., Koren, L., Kam, M. & Geffen, E. Variance in centrality within rock hyrax social networks predicts adult longevity. PLoS ONE 6, 1–8 (2011).Article 

Google Scholar 
Ilany, A., Barocas, A., Koren, L., Kam, M. & Geffen, E. Structural balance in the social networks of a wild mammal. Anim. Behav. 85, 1397–1405 (2013).Article 

Google Scholar 
Gravett, N., Bhagwandin, A., Lyamin, O. I., Siegel, M. & Manger, P. R. Sleep in the Rock Hyrax, Procavia capensis. Brain Behav. Evol. 79, 155–169 (2012).Coe, M. J. Notes on the habits of the mount kenya hyrax (Procavia johnstoni mackinderi thomas). Proc. Zool. Soc. Lond. 138, 638–644 (1961).
Google Scholar 
Viblanc, V. A., Pasquaretta, C., Sueur, C., Boonstra, R. & Dobson, F. S. Aggression in Columbian ground squirrels: relationships with age, kinship, energy allocation, and fitness. Behav. Ecol. 27, arw098 (2016).Article 

Google Scholar 
Wolf, J. B. W., Mawdsley, D., Trillmich, F. & James, R. Social structure in a colonial mammal: unravelling hidden structural layers and their foundations by network analysis. Anim. Behav. 74, 1293–1302 (2007).Article 

Google Scholar 
Podgórski, T., Lusseau, D., Scandura, M., Sönnichsen, L. & Jȩdrzejewska, B. Long-lasting, kin-directed female interactions in a spatially structured wild boar social network. PLoS ONE 9, 1–11 (2014).Article 

Google Scholar 
Druce, D. J. et al. Scale-dependent foraging costs: Habitat use by rock hyraxes (Procavia capensis) determined using giving-up densities. Oikos 115, 513–525 (2006).Article 

Google Scholar 
Goll, Y. et al. Sex-associated and context-dependent leadership in the rock hyrax. iScience 104063 https://doi.org/10.1016/j.isci.2022.104063 (2022).Kelley, J. L., Morrell, L. J., Inskip, C., Krause, J. & Croft, D. P. Predation risk shapes social networks in fission-fusion populations. PLoS One 6, e24280 (2011).Article 
CAS 

Google Scholar 
Brown, K. J. Seasonal variation in the thermal biology of the rock hyrax (Procavia capensis) (Document N° 10413/10124) [Master Dissertation, University of KwaZulu-Natal]. ResearchSpace Digital Library for UKZN scholarly research. http://hdl.handle.net/10413/10124.Bar Ziv, E. et al. Individual, social, and sexual niche traits affect copulation success in a polygynandrous mating system. Behav. Ecol. Sociobiol. 70, 901–912 (2016).Article 

Google Scholar 
McDonald, G. C., Spurgin, L. G., Fairfield, E. A., Richardson, D. S. & Pizzari, T. Differential female sociality is linked with the fine-scale structure of sexual interactions in replicate groups of red junglefowl, Gallus gallus. Proc. R. Soc. B Biol. Sci. 286, 20191734 (2019).Stanley, C. R., Liddiard Williams, H. & Preziosi, R. F. Female clustering in cockroach aggregations—A case of social niche construction? Ethology 124, 706–718 (2018).Article 

Google Scholar 
Pilastro, A., Benetton, S. & Bisazza, A. Female aggregation and male competition reduce costs of sexual harassment in the mosquitofish Gambusia holbrooki. Anim. Behav. 65, 1161–1167 (2003).Article 

Google Scholar 
Schoepf, I. & Schradin, C. Better off alone! Reproductive competition and ecological constraints determine sociality in the African striped mouse (Rhabdomys pumilio). J. Anim. Ecol. 81, 649–656 (2012).Article 

Google Scholar 
Brent, L. J. N., MacLarnon, A., Platt, M. L. & Semple, S. Seasonal changes in the structure of rhesus macaque social networks. Behav. Ecol. Sociobiol. 67, 349–359 (2013).Article 

Google Scholar 
Sundaresan, S. R., Fischhoff, I. R., Dushoff, J. & Rubenstein, D. I. Network metrics reveal differences in social organization between two fission-fusion species, Grevy’s zebra and onager. Oecologia 151, 140–149 (2007).Article 

Google Scholar 
Hasenjager, M. J. & Dugatkin, L. A. Fear of predation shapes social network structure and the acquisition of foraging information in guppy shoals. Proc. R. Soc. B Biol. Sci. 284, 20172020 (2017).Heathcote, R. J. P., Darden, S. K., Franks, D. W., Ramnarine, I. W. & Croft, D. P. Fear of predation drives stable and differentiated social relationships in guppies. Sci. Rep. 7, 1–10 (2017).Article 

Google Scholar 
Dunbar, R. I. M. Social structure as a strategy to mitigate the costs of group living: a comparison of gelada and guereza monkeys. Anim. Behav. 136, 53–64 (2018).Article 
CAS 

Google Scholar 
Sutcliffe, A., Dunbar, R., Binder, J. & Arrow, H. Relationships and the social brain: Integrating psychological and evolutionary perspectives. Br. J. Psychol. 103, 149–168 (2012).Article 

Google Scholar 
Brown, M. R. Comparing the Fission-Fusion Dynamics of Spider Monkeys (Ateles geoffroyi) From Day to Night. https://doi.org/10.11575/PRISM/25371 (2014).Fanson, K. V., Fanson, B. G. & Brown, J. S. Using path analysis to explore vigilance behavior in the rock hyrax (Procavia capensis). J. Mammal. 92, 78–85 (2011).Article 

Google Scholar 
Santema, P. & Clutton-Brock, T. Meerkat helpers increase sentinel behaviour and bipedal vigilance in the presence of pups. Anim. Behav. 85, 655–661 (2013).Article 

Google Scholar 
Wright, J., Berg, E., De Kort, S. R., Khazin, V. & Maklakov, A. A. Cooperative sentinel behaviour in the Arabian babbler. Anim. Behav. 62, 973–979 (2001).Article 

Google Scholar 
Moscovice, L. R., Sueur, C. & Aureli, F. How socio-ecological factors influence the differentiation of social relationships: An integrated conceptual framework. Biol. Lett. 16, 20200384 (2020).Kotler, B. P., Brown, J. S. & Knight, M. H. Habitat and patch use by hyraxes: There’s no place like home? Ecol. Lett. 2, 82–88 (1999).Article 

Google Scholar 
Margolis, E. Dietary composition of the wolf Canis lupus in the Ein Gedi area according to analysis of their droppings (in Hebrew). In Proceedings of 45th Meeting of the Israel Zoological Society, Isr. J. Ecol. Evol. 55, 157–180 (2008).Firth, J. A. & Sheldon, B. C. Social carry-over effects underpin trans-seasonally linked structure in a wild bird population. Ecol. Lett. 19, 1324–1332 (2016).Article 

Google Scholar 
Olds, N. & Shoshani, J. Procavia capensis. Mammalian Species 171, 1–7 (2016).Fourie, L. J. & Perrin, M. R. Social behaviour and spatial relationships of the rock hyrax. South 17, 91–98 (1987).Montiglio, P.-O., Ferrari, C. & Réale, D. Social niche specialization under constraints: Personality, social interactions and environmental heterogeneity. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120343 (2013).Article 

Google Scholar 
Dunbar, R. I. M. Time: a hidden constraint on the behavioural ecology of baboons. Behav. Ecol. Sociobiol. 31, 35–49 (1992).Article 

Google Scholar 
Dunbar, R. I. M., Korstjens, A. H. & Lehmann, J. Time as an ecological constraint. Biol. Rev. 84, 413–429 (2009).Article 
CAS 

Google Scholar 
Zahavi, A. Arabian babbler. In Cooperative Breeding in Birds (eds. Staceyp, B. & Koenigw, D.) 103-130 (Cambridge University Press, 1990).Smith, J. E. et al. Greetings promote cooperation and reinforce social bonds among spotted hyenas. Anim. Behav. 81, 401–415 (2011).Article 

Google Scholar 
Aureli, F. & Schaffner, C. M. Aggression and conflict management at fusion in spider monkeys. Biol. Lett. 3, 147–149 (2007).Article 

Google Scholar 
Deag, J. M. The diurnal patterns of behaviour of the wild Barbary macaque Macaca sylvanus. J. Zool. 206, 403–413 (1985).Article 

Google Scholar 
Canteloup, C., Cera, M. B., Barrett, B. J. & van de Waal, E. Processing of novel food reveals payoff and rank-biased social learning in a wild primate. Sci. Rep. 11, 1–13 (2021).Article 

Google Scholar 
Dragić, N., Keynan, O. & Ilany, A. Multilayer social networks reveal the social complexity of a cooperatively breeding bird. iScience 24, 103336 (2021).Kulahci, I. G., Ghazanfar, A. A. & Rubenstein, D. I. Knowledgeable Lemurs Become More Central in Social Networks. Curr. Biol. 28, 1306–1310.e2 (2018).Article 
CAS 

Google Scholar 
Schino, G. Grooming and agonistic support: A meta-analysis of primate reciprocal altruism. Behav. Ecol. 18, 115–120 (2007).Article 

Google Scholar 
Kutsukake, N. & Clutton-Brock, T. H. Social functions of allogrooming in cooperatively breeding meerkats. Anim. Behav. 72, 1059–1068 (2006).Article 

Google Scholar 
Schweinfurth, M. K., Stieger, B. & Taborsky, M. Experimental evidence for reciprocity in allogrooming among wild-type Norway rats. Sci. Rep. 7, 1–8 (2017).Article 
CAS 

Google Scholar 
Nandini, S., Keerthipriya, P. & Vidya, T. N. C. Group size differences may mask underlying similarities in social structure: A comparison of female elephant societies. Behav. Ecol. 29, 145–159 (2018).Article 

Google Scholar 
Hamede, R. K., Bashford, J., McCallum, H. & Jones, M. Contact networks in a wild Tasmanian devil (Sarcophilus harrisii) population: using social network analysis to reveal seasonal variability in social behaviour and its implications for transmission of devil facial tumour disease. Ecol. Lett. 12, 1147–1157 (2009).Article 

Google Scholar 
Henkel, S., Heistermann, M. & Fischer, J. Infants as costly social tools in male Barbary macaque networks. Anim. Behav. 79, 1199–1204 (2010).Article 

Google Scholar 
Prehn, S. G. et al. Seasonal variation and stability across years in a social network of wild giraffe. Anim. Behav. 157, 95–104 (2019).Article 

Google Scholar 
Borgeaud, C., Sosa, S., Sueur, C. & Bshary, R. The influence of demographic variation on social network stability in wild vervet monkeys. Anim. Behav. 134, 155–165 (2017).Article 

Google Scholar 
Kerth, G., Perony, N. & Schweitzer, F. Bats are able to maintain long-term social relationships despite the high fission-fusion dynamics of their groups. Proc. R. Soc. B 278, 2761–2767 (2011).Article 

Google Scholar 
Silk, J. B. et al. The benefits of social capital: Close social bonds among female baboons enhance offspring survival. Proc. R. Soc. B Biol. Sci. 276, 3099–3104 (2009).Article 

Google Scholar 
Riehl, C. & Strong, M. J. Stable social relationships between unrelated females increase individual fitness in a cooperative bird. Proc. R. Soc. B Biol. Sci. 285, 20180130 (2018).Shizuka, D. & Johnson, A. E. How demographic processes shape animal social networks. Behav. Ecol. 31, 1–11 (2020).Article 

Google Scholar 
Sick, C. et al. Evidence for varying social strategies across the day in chacma baboons. Biol. Lett. 10, 3–6 (2014).Article 

Google Scholar 
Barrett, L., Peter Henzi, S. & Lusseau, D. Taking sociality seriously: The structure of multi-dimensional social networks as a source of information for individuals. Philos. Trans. R. Soc. B Biol. Sci. 367, 2108–2118 (2012).Article 

Google Scholar 
Henzi, S. P., Lusseau, D., Weingrill, T., Van Schaik, C. P. & Barrett, L. Cyclicity in the structure of female baboon social networks. Behav. Ecol. Sociobiol. 63, 1015–1021 (2009).Article 

Google Scholar 
Ripperger, S. P. & Carter, G. G. Social foraging in vampire bats is predicted by long-term cooperative relationships. PLoS Biol. 19, 1–17 (2021).Article 

Google Scholar 
Wittemyer, G., Douglas-Hamilton, I. & Getz, W. M. The socioecology of elephants: analysis of the processes creating multitiered social structures. Anim. Behav. 69, 1357–1371 (2005).Article 

Google Scholar 
Wittemyer, G., Getz, W. M., Vollrath, F. & Douglas-Hamilton, I. Social dominance, seasonal movements, and spatial segregation in African elephants: A contribution to conservation behavior. Behav. Ecol. Sociobiol. 61, 1919–1931 (2007).Article 

Google Scholar 
Gelardi, V., Fagot, J., Barrat, A. & Claidière, N. Detecting social (in)stability in primates from their temporal co-presence network. Anim. Behav. 157, 239–254 (2019).Article 

Google Scholar 
Hobson, E. A., Ferdinand, V., Kolchinsky, A. & Garland, J. Rethinking animal social complexity measures with the help of complex systems concepts. Anim. Behav. 155, 287–296 (2019).Article 

Google Scholar 
Kappeler, P. M. A framework for studying social complexity. Behav. Ecol. Sociobiol. 73, 13 (2019).Ballerini, M. et al. Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study. Proc. Natl Acad. Sci. USA 105, 1232–1237 (2008).Article 
CAS 

Google Scholar 
Bonabeau, E., Theraulaz, G., Deneubourg, J.-L., Aron, S. & Camazine, S. Self-organization in social insects. Trends Ecol. Evol. 12, 188–193 (1997).Article 
CAS 

Google Scholar 
Wickramasinghe, A. & Muthukumarana, S. Assessing the impact of the density and sparsity of the network on community detection using a Gaussian mixture random partition graph generator. Int. J. Inf. Technol. 14, 607–618 (2022).
Google Scholar 
Motalebi, N., Stevens, N. T. & Steiner, S. H. Hurdle Blockmodels for Sparse Network Modeling. Am. Stat. 75, 383–393 (2021).Article 

Google Scholar 
Gokcekus, S., Cole, E. F., Sheldon, B. C. & Firth, J. A. Exploring the causes and consequences of cooperative behaviour in wild animal populations using a social network approach. Biol. Rev. 96, 2355–2372 (2021).Article 

Google Scholar 
Koren, L., Mokady, O. & Geffen, E. Social status and cortisol levels in singing rock hyraxes. Horm. Behav. 54, 212–216 (2008).Article 
CAS 

Google Scholar 
Boyland, N. K., James, R., Mlynski, D. T., Madden, J. R. & Croft, D. P. Spatial proximity loggers for recording animal social networks: Consequences of inter-logger variation in performance. Behav. Ecol. Sociobiol. 67, 1877–1890 (2013).Article 

Google Scholar 
Drewe, J. A. et al. Performance of proximity loggers in recording Intra- and Inter-species interactions: A laboratory and field-based validation study. PLoS ONE 7, e39068 (2012).Hoppitt, W. & Farine, D. Association Indices For Quantifying Social Relationships: How To Deal With Missing Observations Of Individuals Or Groups. Anim. Behav. 136, 227–238 (2018).Article 

Google Scholar 
Farine, D. R. Animal social network inference and permutations for ecologists in R using asnipe. Methods Ecol. Evol. 4, 1187–1194 (2013).Bejder, L., Fletcher, D. & Bräger, S. A method for testing association patterns of social animals. Anim. Behav. 56, 719–725 (1998).Article 
CAS 

Google Scholar 
Kalinka, A. T. & Tomancak, P. linkcomm: An R package for the generation, visualization, and analysis of link communities in networks of arbitrary size and type. Bioinformatics 27, 2011–2012 (2011).Article 
CAS 

Google Scholar 
R Core Team, R. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2020).Wild, F. lsa: Latent Semantic Analysis. R package version 0.73.2. https://CRAN.R-project.org/package=lsa (2020).Han, J., Kamber, M. & Pei, J. Getting to Know Your Data. An R Companion Third Ed. Fundam. Polit. Sci. Res. https://doi.org/10.1016/B978-0-12-381479-1.00002-2 (2021).Benjamini, Y. Controlling the false discovery rate – A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).
Google Scholar 
Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal, Complex Syst. 1695, 1–9 (2006).Dai, H., Leeder, J. S. & Cui, Y. A modified generalized fisher method for combining probabilities from dependent tests. Front. Genet. 20, 2–7 (2014).
Google Scholar  More

Valiela, I., Bowen, J. L. & York, J. K. Mangrove Forests: One of the World’s Threatened Major Tropical Environments: At least 35% of the area of mangrove forests has been lost in the past two decades, losses that exceed those for tropical rain forests and coral reefs, two other well-known threatened environments. Bioscience 51, 807–815. https://doi.org/10.1641/0006-3568(2001)051[0807:MFOOTW]2.0.CO;2 (2001).Article 

Google Scholar 
Kuenzer, C., Bluemel, A., Gebhardt, S., Quoc, T. V. & Dech, S. Remote sensing of mangrove ecosystems: A review. Remote Sens. 3, 1. https://doi.org/10.3390/rs3050878 (2011).Article 

Google Scholar 
Turschwell, M. P. et al. Multi-scale estimation of the effects of pressures and drivers on mangrove forest loss globally. Biol. Cons. 247, 108637. https://doi.org/10.1016/j.biocon.2020.108637 (2020).Article 

Google Scholar 
Millennium Ecosystem Assessment. Ecosystems and Human Well-being: Synthesis. (2005).Nagelkerken, I. et al. The habitat function of mangroves for terrestrial and marine fauna: A review. Aquat. Bot. 89, 155–185. https://doi.org/10.1016/j.aquabot.2007.12.007 (2008).Article 

Google Scholar 
Hamilton, S. E. & Casey, D. Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21). Glob. Ecol. Biogeogr. 25, 729–738. https://doi.org/10.1111/geb.12449 (2016).Article 

Google Scholar 
Friess, D. A. et al. The state of the world’s Mangrove forests: Past, present, and future. Annu. Rev. Environ. Resour. 44, 89–115. https://doi.org/10.1146/annurev-environ-101718-033302 (2019).Article 

Google Scholar 
Zeng, Y., Friess, D. A., Sarira, T. V., Siman, K. & Koh, L. P. Global potential and limits of mangrove blue carbon for climate change mitigation. Curr. Biol. 31, 1737-1743.e1733. https://doi.org/10.1016/j.cub.2021.01.070 (2021).Article 
CAS 

Google Scholar 
zu Ermgassen, P. S. E. et al. Fishers who rely on mangroves: Modelling and mapping the global intensity of mangrove-associated fisheries. Estuar. Coast. Shelf Sci. 247, 106975. https://doi.org/10.1016/j.ecss.2020.106975 (2020).Article 

Google Scholar 
Walters, A. D. et al. Do hotspots fall within protected areas? A geographic approach to planning analysis of regional freshwater biodiversity. Freshw. Biol. 64, 2046–2056. https://doi.org/10.1111/fwb.13394 (2019).Article 

Google Scholar 
Blasco, F., Saenger, P. & Janodet, E. Mangroves as indicators of coastal change. CATENA 27, 167–178. https://doi.org/10.1016/0341-8162(96)00013-6 (1996).Article 

Google Scholar 
Gilman, E. L., Ellison, J., Duke, N. C. & Field, C. Threats to mangroves from climate change and adaptation options: A review. Aquat. Bot. 89, 237–250. https://doi.org/10.1016/j.aquabot.2007.12.009 (2008).Article 

Google Scholar 
Hamilton, S. Assessing the role of commercial aquaculture in displacing mangrove forest. Bull. Mar. Sci. 89, 585–601 (2013).Article 

Google Scholar 
Lovelock, C. E. et al. The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature 526, 559–563. https://doi.org/10.1038/nature15538 (2015).Article 
ADS 
CAS 

Google Scholar 
Richards Daniel, R. & Friess Daniel, A. Rates and drivers of mangrove deforestation in Southeast Asia, 2000–2012. Proc. Natl. Acad. Sci. 113, 344–349. https://doi.org/10.1073/pnas.1510272113 (2016).Article 
ADS 
CAS 

Google Scholar 
Appeltans, W. et al. The magnitude of global marine species diversity. Curr. Biol. 22, 2189–2202. https://doi.org/10.1016/j.cub.2012.09.036 (2012).Article 
CAS 

Google Scholar 
Ward, R. D., Friess, D. A., Day, R. H. & MacKenzie, R. A. Impacts of climate change on mangrove ecosystems: A region by region overview. Ecosyst. Health Sustain. 2, e01211. https://doi.org/10.1002/ehs2.1211 (2016).Article 

Google Scholar 
Van der Stocken, T., Vanschoenwinkel, B., Carroll, D., Cavanaugh, K. C. & Koedam, N. Mangrove dispersal disrupted by projected changes in global seawater density. Nat. Clim. Chang. 12, 685–691. https://doi.org/10.1038/s41558-022-01391-9 (2022).Article 
ADS 

Google Scholar 
Alongi, D. M. The impact of climate change on Mangrove forests. Curr. Clim. Change Rep. 1, 30–39. https://doi.org/10.1007/s40641-015-0002-x (2015).Article 

Google Scholar 
Giri, C. et al. Status and distribution of mangrove forests of the world using earth observation satellite data. Glob. Ecol. Biogeogr. 20, 154–159. https://doi.org/10.1111/j.1466-8238.2010.00584.x (2011).Article 

Google Scholar 
Kristensen, E. Mangrove crabs as ecosystem engineers; with emphasis on sediment processes. J. Sea Res. 59, 30–43. https://doi.org/10.1016/j.seares.2007.05.004 (2008).Article 
ADS 

Google Scholar 
Penha-Lopes, G. et al. Are fiddler crabs potentially useful ecosystem engineers in mangrove wastewater wetlands?. Mar. Pollut. Bull. 58, 1694–1703. https://doi.org/10.1016/j.marpolbul.2009.06.015 (2009).Article 
CAS 

Google Scholar 
Sharifian, S., Kamrani, E. & Saeedi, H. Global biodiversity and biogeography of mangrove crabs: Temperature, the key driver of latitudinal gradients of species richness. J. Therm. Biol 92, 102692. https://doi.org/10.1016/j.jtherbio.2020.102692 (2020).Article 
CAS 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006).Article 

Google Scholar 
Guisan, A. & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecol. Model. 135, 147–186. https://doi.org/10.1016/S0304-3800(00)00354-9 (2000).Article 

Google Scholar 
Guisan, A. et al. Predicting species distributions for conservation decisions. Ecol. Lett. 16, 1424–1435. https://doi.org/10.1111/ele.12189 (2013).Article 

Google Scholar 
Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat Suitability and Distribution Models: With Applications in R. (Cambridge University Press, 2017).Luan, J., Zhang, C., Xu, B., Xue, Y. & Ren, Y. Modelling the spatial distribution of three Portunidae crabs in Haizhou Bay, China. PLoS ONE 13, e0207457. https://doi.org/10.1371/journal.pone.0207457 (2018).Article 
CAS 

Google Scholar 
Kafash, A. et al. The Gray Toad-headed Agama, Phrynocephalus scutellatus, on the Iranian Plateau: The degree of niche overlap depends on the phylogenetic distance. Zool. Middle East 64, 47–54. https://doi.org/10.1080/09397140.2017.1401309 (2018).Article 

Google Scholar 
Yousefi, M., Shabani, A. A. & Azarnivand, H. Reconstructing distribution of the Eastern Rock Nuthatch during the Last Glacial Maximum and Last Interglacial. Avian Biol. Res. 13, 3–9. https://doi.org/10.1177/1758155919874537 (2019).Article 

Google Scholar 
De Rock, P. et al. Predicting large-scale habitat suitability for cetaceans off Namibia using MinxEnt. Mar. Ecol. Prog. Ser. 619, 149–167 (2019).Article 
ADS 

Google Scholar 
Saeedi, H., Basher, Z. & Costello, M. J. Modelling present and future global distributions of razor clams (Bivalvia: Solenidae). Helgol. Mar. Res. 70, 23. https://doi.org/10.1186/s10152-016-0477-4 (2016).Article 

Google Scholar 
Bosso, L. et al. The rise and fall of an alien: why the successful colonizer Littorina saxatilis failed to invade the Mediterranean Sea. Biol. Invas. 24, 3169–3187. https://doi.org/10.1007/s10530-022-02838-y (2022).Article 

Google Scholar 
Moradmand, M. & Yousefi, M. Ecological niche modelling and climate change in two species groups of huntsman spider genus Eusparassus in the Western Palearctic. Sci. Rep. 12, 4138. https://doi.org/10.1038/s41598-022-08145-9 (2022).Article 
ADS 
CAS 

Google Scholar 
Compton, T. J., Leathwick, J. R. & Inglis, G. J. Thermogeography predicts the potential global range of the invasive European green crab (Carcinus maenas). Divers. Distrib. 16, 243–255. https://doi.org/10.1111/j.1472-4642.2010.00644.x (2010).Article 

Google Scholar 
Kafash, A., Ashrafi, S. & Yousefi, M. Modeling habitat suitability of bats to identify high priority areas for field monitoring and conservation. Environ. Sci. Pollut. Res. 29, 25881–25891. https://doi.org/10.1007/s11356-021-17412-7 (2022).Article 

Google Scholar 
Leathwick, J. et al. Novel methods for the design and evaluation of marine protected areas in offshore waters. Conserv. Lett. 1, 91–102. https://doi.org/10.1111/j.1755-263X.2008.00012.x (2008).Article 

Google Scholar 
Charrua, A. B., Bandeira, S. O., Catarino, S., Cabral, P. & Romeiras, M. M. Assessment of the vulnerability of coastal mangrove ecosystems in Mozambique. Ocean Coast. Manag. 189, 105145. https://doi.org/10.1016/j.ocecoaman.2020.105145 (2020).Article 

Google Scholar 
Khajoei Nasab, F., Mehrabian, A. & Mostafavi, H. Mapping the current and future distributions of Onosma species endemic to Iran. J. Arid Land 12, 1031–1045. https://doi.org/10.1007/s40333-020-0080-z (2020).Article 

Google Scholar 
Allyn, A. J. et al. Comparing and synthesizing quantitative distribution models and qualitative vulnerability assessments to project marine species distributions under climate change. PLoS ONE 15, e0231595. https://doi.org/10.1371/journal.pone.0231595 (2020).Article 
CAS 

Google Scholar 
Makki, T., Mostafavi, H., Matkan, A. & Aghighi, H. Modelling Climate-Change Impact on the Spatial Distribution of Garra Rufa (Heckel, 1843) (Teleostei: Cyprinidae). Iran. J. Sci. Technol. Trans. A: Sci. 45, 795–804. https://doi.org/10.1007/s40995-021-01088-2 (2021).Article 

Google Scholar 
Bolon, I. et al. What is the impact of snakebite envenoming on domestic animals? A nation-wide community-based study in Nepal and Cameroon. Toxicon: X 9–10, 100068. https://doi.org/10.1016/j.toxcx.2021.100068 (2021).Sharma, A., Dubey, V. K., Johnson, J. A., Rawal, Y. K. & Sivakumar, K. Is there always space at the top? Ensemble modeling reveals climate-driven high-altitude squeeze for the vulnerable snow trout Schizothorax richardsonii in Himalaya. Ecol. Ind. 120, 106900. https://doi.org/10.1016/j.ecolind.2020.106900 (2021).Article 

Google Scholar 
Yousefi, M., Naderloo, R. & Keikhosravi, A. Freshwater crabs of the Near East: Increased extinction risk from climate change and underrepresented within protected areas. Glob. Ecol. Conserv. 38, e02266. https://doi.org/10.1016/j.gecco.2022.e02266 (2022).Article 

Google Scholar 
Sheykhi Ilanloo, S. et al. Applying opportunistic observations to model current and future suitability of the Kopet Dagh Mountains for a Near Threatened avian scavenger. Avian Biol. Res. 14, 18–26. https://doi.org/10.1177/1758155920962750 (2020).Article 

Google Scholar 
Naderloo, R. Grapsoid crabs (Decapoda: Brachyura: Thoracotremata) of the Persian Gulf and the Gulf of Oman. Zootaxa 3048(1), 1. https://doi.org/10.11646/zootaxa.3048.1.1 (2011).Article 

Google Scholar 
Naderloo, R. Atlas of crabs of the Persian Gulf. (2017).Innocenti, G., Schubart, C. D. & Fratini, S. Description of Metopograpsus cannicci, new species, a pseudocryptic crab species from East Africa and the Western Indian Ocean (Decapoda: Brachyura: Grapsidae). Raffles Bull. Zool. (RBZ) 68, 619–628 (2020).
Google Scholar 
Hemmati, M. R., Shojaei, M. G., Taheri Mirghaed, A., Mashhadi Farahani, M. & Weigt, M. Food sources for camptandriid crabs in an arid mangrove ecosystem of the Persian Gulf: a stable isotope approach. Isotop. Environ. Health Stud. 57, 457–469. https://doi.org/10.1080/10256016.2021.1925665 (2021).Article 
CAS 

Google Scholar 
Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101. https://doi.org/10.1038/nature09329 (2010).Article 
ADS 
CAS 

Google Scholar 
Kordas, R. L., Harley, C. D. G. & O’Connor, M. I. Community ecology in a warming world: The influence of temperature on interspecific interactions in marine systems. J. Exp. Mar. Biol. Ecol. 400, 218–226. https://doi.org/10.1016/j.jembe.2011.02.029 (2011).Article 

Google Scholar 
Hall, S. & Thatje, S. Temperature-driven biogeography of the deep-sea family Lithodidae (Crustacea: Decapoda: Anomura) in the Southern Ocean. Polar Biol. 34, 363–370. https://doi.org/10.1007/s00300-010-0890-0 (2011).Article 

Google Scholar 
Hannah, L. Climate Change Biology. Academic Press (2015).Ali, H. et al. Expanding or shrinking? range shifts in wild ungulates under climate change in Pamir-Karakoram mountains, Pakistan. PLoS ONE 16, e0260031. https://doi.org/10.1371/journal.pone.0260031 (2022).Article 
CAS 

Google Scholar 
Yousefi, M. et al. Climate change is a major problem for biodiversity conservation: A systematic review of recent studies in Iran. Contemp. Probl. Ecol. 12, 394–403. https://doi.org/10.1134/S1995425519040127 (2019).Article 

Google Scholar 
Doney, S. C. et al. Climate Change Impacts on Marine Ecosystems. Ann. Rev. Mar. Sci. 4, 11–37. https://doi.org/10.1146/annurev-marine-041911-111611 (2011).Article 

Google Scholar 
Worm, B. & Lotze, H. K. in Climate Change (Second Edition) (ed Trevor M. Letcher) 195–212 (Elsevier, 2016).Ramírez, F., Afán, I., Davis, L. S. & Chiaradia, A. Climate impacts on global hot spots of marine biodiversity. Sci. Adv. 3, e1601198. https://doi.org/10.1126/sciadv.1601198 (2017).Article 
ADS 

Google Scholar 
Worm, B. et al. Impacts of Biodiversity Loss on Ocean Ecosystem Services. Science 314, 787–790. https://doi.org/10.1126/science.1132294 (2006).Article 
ADS 
CAS 

Google Scholar 
Lester, S. E. et al. Biological effects within no-take marine reserves: a global synthesis. Mar. Ecol. Prog. Ser. 384, 33–46 (2009).Article 
ADS 

Google Scholar 
Daru, B. H. & le Roux, P. C. Marine protected areas are insufficient to conserve global marine plant diversity. Glob. Ecol. Biogeogr. 25, 324–334. https://doi.org/10.1111/geb.12412 (2016).Article 

Google Scholar 
Sala, E. et al. Protecting the global ocean for biodiversity, food and climate. Nature https://doi.org/10.1038/s41586-021-03371-z (2021).Article 

Google Scholar 
Embling, C. B. et al. Using habitat models to identify suitable sites for marine protected areas for harbour porpoises (Phocoena phocoena). Biol. Cons. 143, 267–279. https://doi.org/10.1016/j.biocon.2009.09.005 (2010).Article 

Google Scholar 
Magris, R. A. & Déstro, G. F. G. Predictive modeling of suitable habitats for threatened marine invertebrates and implications for conservation assessment in Brazil. Braz. J. Oceanogr. 58, 57–68 (2010).Article 

Google Scholar 
Welch, H., Pressey, R. L. & Reside, A. E. Using temporally explicit habitat suitability models to assess threats to mobile species and evaluate the effectiveness of marine protected areas. J. Nat. Conserv. 41, 106–115. https://doi.org/10.1016/j.jnc.2017.12.003 (2018).Article 

Google Scholar 
Rhoden, C. M., Peterman, W. E. & Taylor, C. A. Maxent-directed field surveys identify new populations of narrowly endemic habitat specialists. PeerJ 5, e3632–e3632. https://doi.org/10.7717/peerj.3632 (2017).Article 

Google Scholar 
Ancillotto, L., Mori, E., Bosso, L., Agnelli, P. & Russo, D. The Balkan long-eared bat (Plecotus kolombatovici) occurs in Italy—First confirmed record and potential distribution. Mamm. Biol. 96, 61–67. https://doi.org/10.1016/j.mambio.2019.03.014 (2019).
Article 

Google Scholar 
Imtiyaz, B. B., Sweta, P. D., Prakash, K. K. Threats to marine biodiversity. Mar. Biodivers.: Present Status Prospects (2011).Robinson, N. M., Nelson, W. A., Costello, M. J., Sutherland, J. E. & Lundquist, C. J. A systematic review of marine-based species distribution models (SDMs) with recommendations for best practice. Front. Mar. Sci. 4, 421 (2017).Article 

Google Scholar 
Fabri-Ruiz, S., Danis, B., David, B. & Saucède, T. Can we generate robust species distribution models at the scale of the Southern Ocean?. Divers. Distrib. 25, 21–37. https://doi.org/10.1111/ddi.12835 (2019).Article 

Google Scholar 
Maxwell, D. L., Stelzenmüller, V., Eastwood, P. D. & Rogers, S. I. Modelling the spatial distribution of plaice (Pleuronectes platessa), sole (Solea solea) and thornback ray (Raja clavata) in UK waters for marine management and planning. J. Sea Res. 61, 258–267. https://doi.org/10.1016/j.seares.2008.11.008 (2009).Article 
ADS 

Google Scholar 
Marshall, C. E., Glegg, G. A. & Howell, K. L. Species distribution modelling to support marine conservation planning: The next steps. Mar. Policy 45, 330–332. https://doi.org/10.1016/j.marpol.2013.09.003 (2014).Article 

Google Scholar 
GBIF. GBIF Occurrence Download https://doi.org/10.15468/dl.khpu28. GBIF (2021).Spalding, M. D. et al. Marine ecoregions of the world: A bioregionalization of coastal and shelf areas. Bioscience 57, 573–583. https://doi.org/10.1641/B570707 (2007).Article 

Google Scholar 
Basher, Z., Bowden, D. A. & Costello, M. J. Global marine environment datasets (GMED). World Wide Web Electron. Publ. 14, 1 (2018).
Google Scholar 
Barnes, D. Ecology of subtropical hermit crabs in SW Madagascar: short-range migrations. Mar. Biol. 142, 549–557. https://doi.org/10.1007/s00227-002-0968-5 (2003).Article 

Google Scholar 
Naimullah, M. et al. Association of environmental factors in the Taiwan Strait with distributions and habitat characteristics of three swimming crabs. Remote Sens. 12, 1. https://doi.org/10.3390/rs12142231 (2020).Article 

Google Scholar 
Malvé, M. E., Rivadeneira, M. M. & Gordillo, S. Northward range expansion of the European green crab Carcinus maenas in the SW Atlantic: a synthesis after ~20 years of invasion history. bioRxiv, 2020.2011.2004.368761, doi:https://doi.org/10.1101/2020.11.04.368761 (2020).Merow, C., Smith, M. J. & Silander, J. A. Jr. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36, 1058–1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x (2013).Article 

Google Scholar 
Naimi, B. & Araújo, M. B. sdm: a reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375. https://doi.org/10.1111/ecog.01881 (2016).Article 

Google Scholar 
Team, R. C. R: A Language and Environment for Statistical Computing (2020).Fielding, A. H. & Bell, J. F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49. https://doi.org/10.1017/S0376892997000088 (1997).Article 

Google Scholar 
Swets John, A. Measuring the Accuracy of Diagnostic Systems. Science 240, 1285–1293. https://doi.org/10.1126/science.3287615 (1988).Article 
ADS 
MathSciNet 
MATH 

Google Scholar 
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: an open-source release of Maxent. Ecography 40, 887–893. https://doi.org/10.1111/ecog.03049 (2017).Article 

Google Scholar 
Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.3–7 (2020).UNEP-WCMC and IUCN. Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM). UNEP-WCMC and IUCN (2021). More

Mild shading facilitates sesquiterpenoid accumulation and growth in Atractylodes lancea rhizomeTo determine a concrete shading value for the production of high-quality and high-yielding AR, we examined the major compounds, including the sesquiterpenoids hinesol (Hin), β-eudesmol (Edu), and atractylone (Atl), and the polyacetylene atractylodin (Atd), as well as the biomass of AR at different growth stages (Fig. 1A–C) under various light intensities. The sum of these four volatile oils as the total volatile oil content was subsequently analyzed. The results revealed that the accumulation of volatile oils was significantly different (p  More

Lyman, C. P., Willis, J. S., Malan, A. & Wang, L. C. H. Hibernation and Torpor in Mammals and Birds (Academic Press, 1982).
Google Scholar 
Boyles, J. G. et al. A global heterothermic continuum in mammals. Glob. Ecol. Biogeogr. 22, 1029–1039. https://doi.org/10.1111/geb.12077 (2013).Article 

Google Scholar 
Geiser, F. Ecological Physiology of Daily Torpor and Hibernation (Springer, 2021). https://doi.org/10.1007/978-3-030-75525-6.Book 

Google Scholar 
Buck, C. L. & Barnes, B. M. Effects of ambient temperature on metabolic rate, respiratory quotient and torpor in an arctic hibernator. Am. J. Physiol. Reg. Integr. Comp. Physiol 279, R255–R262. https://doi.org/10.1152/ajpregu.2000.279.1.R255 (2000).Article 
CAS 

Google Scholar 
Ortmann, S. & Heldmaier, G. Regulation of body temperature and energy requirements of hibernating Alpine marmots (Marmota marmota). Am. J. Physiol. Regul. Integr. Comp. Physiol. 278, R698–R704. https://doi.org/10.1152/ajpregu.2000.278.3.R698 (2000).Article 
CAS 

Google Scholar 
Swoap, S. J. & Gutilla, M. J. Cardiovascular changes during daily torpor in the laboratory mouse. Am. J. Physiol. Regul. Integr. Comp. Physiol 297, R769–R774. https://doi.org/10.1152/ajpregu.00131.2009 (2009).Article 
CAS 

Google Scholar 
Kirsch, R., Ouarour, A. & Pévet, P. Daily torpor in the Djungarian hamster (Phodopus sungorus): photoperiodic regulation, characteristics and circadian organization. J. Comp. Physiol. A 168, 121–128. https://doi.org/10.1007/BF00217110 (1991).Article 
CAS 

Google Scholar 
Nowack, J., Stawski, C. & Geiser, F. More functions of torpor and their roles in a changing world. J. Comp. Physiol. (B) 187, 889–897. https://doi.org/10.1007/s00360-017-1100-y (2017).Article 

Google Scholar 
Nowack, J., Levesque, D. L., Reher, S. & Dausmann, K. H. Variable climates lead to varying phenotypes: “Weird” mammalian torpor and lessons from non-holarctic species. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2020.00060 (2020).Article 

Google Scholar 
Hoelzl, F. et al. How to spend the summer? Free-living dormice (Glis glis) can hibernate for 11 months in non-reproductive years. J. Comp. Physiol. B 185, 931–939. https://doi.org/10.1007/s00360-015-0929-1 (2015).Article 

Google Scholar 
Geiser, F. Seasonal expression of avian and mammalian daily torpor and hibernation: not a simple summer-winter affair. F. Phys. 11, 436. https://doi.org/10.3389/fphys.2020.00436 (2020).Article 

Google Scholar 
Jonasson, K. A. & Willis, C. K. R. Hibernation energetics of free-ranging little brown bats. J. Exp. Biol. 215, 2141–2149. https://doi.org/10.1242/jeb.066514 (2012).Article 

Google Scholar 
Dietz, M., Kalko, E. K. V. Seasonal changes in daily torpor patterns of free-ranging female and male Daubenton’s bats (Myotis daubentonii). J. Comp. Physiol. B. 176(3), 223–231. https://doi.org/10.1007/s00360-005-0043-x (2006).Article 

Google Scholar 
Kobbe, S., Ganzhorn, J. U. & Dausmann, K. H. Extreme individual flexibility of heterothermy in free-ranging Malagasy mouse lemurs (Microcebus griseorufus). J. Comp. Physiol. B 181, 165–173. https://doi.org/10.1007/s00360-010-0507-5 (2011).Article 

Google Scholar 
Ruf, T. & Geiser, F. Daily torpor and hibernation in birds and mammals. Biol. Rev. 90, 891–926. https://doi.org/10.1111/brv.12137 (2015).Article 

Google Scholar 
Geiser, F. Metabolic rate and body temperature reduction during hibernation and daily torpor. Annu. Rev. Physiol. 66, 239–274 (2004).Article 
ADS 
CAS 

Google Scholar 
Storey, K. B. & Storey, J. M. Metabolic rate depression: the biochemistry of mammalian hibernation. Adv. Clin. Chem. 52, 77–108 (2010).Article 
CAS 

Google Scholar 
Stawski, C., Willis, C. K. R. & Geiser, F. The importance of temporal heterothermy in bats. J. Zool. 292, 86–100. https://doi.org/10.1111/jzo.12105 (2014).Article 

Google Scholar 
Bondarenco, A., Körtner, G. & Geiser, F. Some like it cold: summer torpor by freetail bats in the Australian arid zone. J. Comp. Physiol. (B) 183, 1113–1122. https://doi.org/10.1007/s00360-013-0779-7 (2013).Article 

Google Scholar 
O’Mara, M. T. et al. Heart rate reveals torpor at high body temperatures in lowland tropical free-tailed bats. R. Soc. Open Sci. 4, 171359. https://doi.org/10.1098/rsos.171359 (2017).Article 
ADS 
CAS 

Google Scholar 
Reher, S., Ehlers, J., Rabarison, H. & Dausmann, K. H. Short and hyperthermic torpor responses in the Malagasy bat Macronycteris commersoni reveal a broader hypometabolic scope in heterotherms. J. Comp. Physiol. B https://doi.org/10.1007/s00360-018-1171-4 (2018).Article 

Google Scholar 
Geiser, F. et al. Hibernation and daily torpor in Australian and New Zealand bats: Does the climate zone matter?. Aust. J. Zool https://doi.org/10.1071/ZO20025 (2020).Article 

Google Scholar 
Stawski, C., Turbill, C. & Geiser, F. Hibernation by a free-ranging subtropical bat (Nyctophilus bifax). J. Comp. Physiol. (B) 179, 284–292. https://doi.org/10.1007/s00360-008-0328-y (2009).Article 

Google Scholar 
Levin, E. et al. Subtropical mouse-tailed bats use geothermally heated caves for winter hibernation. Proc. R. Soc. Lond. B Biol. Sci. 282, 20142781. https://doi.org/10.1098/rspb.2014.2781 (2015).Article 

Google Scholar 
Bartholomew, G. A., Dawson, W. R. & Lasiewski, R. C. Thermoregulation and heterothermy in some of the smaller flying foxes (Megachiroptera) of New Guinea. Z. Vergl. Physiol. 70, 196–209 (1970).Article 

Google Scholar 
Bartels, W., Law, B. S. & Geiser, F. Daily torpor and energetics in a tropical mammal, the northern blossom-bat Macroglossus minimus (Megachiroptera). J. Comp. Physiol. (B) 168, 233–239. https://doi.org/10.1007/s003600050141 (1998).Article 
CAS 

Google Scholar 
Geiser, F., Coburn, D. K., Körtner, G. & Law, B. S. Thermoregulation, energy metabolism, and torpor in blossom-bats, Syconycteris australis (Megachiroptera). J. Zool. 239, 538–590. https://doi.org/10.1111/j.1469-7998.1996.tb05944.x (1996).Article 

Google Scholar 
Geiser, F. & Coburn, D. K. Field metabolic rates and water uptake in the blossom-bat Syconycteris australis (Megachiroptera). J. Comp. Physiol. (B) 169, 133–138. https://doi.org/10.1007/s003600050203 (1999).Article 
CAS 

Google Scholar 
Turbill, C. Roosting and thermoregulatory behaviour of male Gould’s long-eared bats, Nyctophilus gouldi: energetic benefits of thermally unstable tree roosts. Aust. J. Zool. 54, 57–60. https://doi.org/10.1071/ZO05068 (2006).Article 

Google Scholar 
Currie, S. E. No effect of season on the electrocardiogram of long-eared bats (Nyctophilus gouldi) during torpor. J. Comp. Physiol. B 188, 695–705. https://doi.org/10.1007/s00360-018-1158-1 (2018).Article 

Google Scholar 
Stawski, C. & Geiser, F. Do season and distribution affect thermal energetics of a hibernating bat endemic to the tropics and subtropics?. Am. J. Physiol. Regul. Integr. Comp. Physiol 301, R542–R547. https://doi.org/10.1152/ajpregu.00792.2010 (2011).Article 
CAS 

Google Scholar 
Currie, S. E., Stawski, C. & Geiser, F. Cold-hearted bats: uncoupling of heart rate and metabolism during torpor at subzero temperatures. J. Exp. Biol. https://doi.org/10.1242/jeb.170894 (2018).Article 

Google Scholar 
Churchill, S. Australian Bats 2nd edn. (Allen and Unwin, 2008).
Google Scholar 
Geiser, F., Law, B. S. & Körtner, G. Daily torpor in relation to photoperiod in a subtropical blossom-bat, Syconycteris australis (Megachiroptera). J. Therm. Biol. 30, 574–579. https://doi.org/10.1016/j.jtherbio.2005.08.002 (2005).Article 

Google Scholar 
Coburn, D. K. & Geiser, F. Seasonal changes in energetics and torpor patterns in the subtropical blossom-bat Syconycteris australis (Megachiroptera). Oecologia 113, 467–473 (1998).Article 
ADS 

Google Scholar 
Dietz, M. & Kalko, E. K. V. Seasonal changes in daily torpor patterns of free-ranging female and male Daubenton’s bats (Myotis daubentonii). J. Comp. Physiol. (B) 176, 223–231. https://doi.org/10.1007/s00360-005-0043-x (2006).Article 

Google Scholar 
Andrews, M. T. Advances in molecular biology of hibernation in mammals. BioEssays 29, 431–440. https://doi.org/10.1002/bies.20560 (2007).Article 
CAS 

Google Scholar 
Twente, J. W. & Twente, J. Autonomic regulation of hibernation by Citellus and Eptesicus. In Strategies in Cold: Natural Torpidity and Thermogenesis (eds Wang, L. C. H. & Hudson, J. W.) 327–373 (Academic Press, 1978).Chapter 

Google Scholar 
Davis, W. H. & Reite, O. B. Responses of bats from temperate regions to changes in ambient temperature. Biol. Bull. 132, 320–328 (1967).Article 
CAS 

Google Scholar 
Alston, J. M., Dillon, M. E., Keinath, D. A., Abernethy, I. M. & Goheen, J. R. Daily torpor reduces the energetic consequences of microhabitat selection for a widespread bat. Ecology 103, e3677. https://doi.org/10.1002/ecy.3677 (2022).Article 

Google Scholar 
Humphries, M. M., Thomas, D. W. & Speakman, J. R. Climate-mediated energetic constraints on the distribution of hibernating mammals. Nature 418, 313–316. https://doi.org/10.1038/nature00828 (2002).Article 
ADS 
CAS 

Google Scholar 
Heller, H. C. Hibernation: neural aspects. Annu. Rev. Physiol. 41, 305–321. https://doi.org/10.1038/nature00828 (1979).Article 
CAS 

Google Scholar 
McKechnie, A. E. & Wolf, B. O. The energetics of the rewarming phase of avian torpor. In Life in the Cold: Evolution, Mechanisms, Adaptation and Application (eds Barnes, B. M. & Carey, H. V.) 265–267 (University of Alaska, 2004).

Google Scholar 
Geiser, F. & Baudinette, R. V. The relationship between body mass and rate of rewarming from hibernation and daily torpor in mammals. J. Exp. Biol. 151, 349–359. https://doi.org/10.1242/jeb.151.1.349 (1990).Article 
CAS 

Google Scholar 
Voigt, C. C., Kelm, D. H. & visser, G. H.,. Field metabolic rates of phytophagous bats: do pollination strategies of plants make life of nectar-feeders spin faster?. J. Comp. Physiol. (B) 176, 213–222. https://doi.org/10.1007/s00360-005-0042-y (2006).Article 

Google Scholar 
Bullen, R. D., McKenzie, N. L., Bullen, K. E. & Williams, M. R. Bat heart mass: correlation with foraging niche and roost preference. Aust. J. Zool. 57, 399–408. https://doi.org/10.1071/ZO09053 (2009).Article 

Google Scholar 
Law, B. S. Climatic limitation of the southern distribution of the common blossom bat Syconycteris australis in New South Wales. Aust. J. Ecol. 19, 366–374. https://doi.org/10.1111/j.1442-9993.1994.tb00502.x (1994).Article 

Google Scholar 
Bonaccorso, F. J. & McNab, B. K. Plasticity of energetics in blossom bats (Pteropodidae): impact on distribution. J. Mammal. 78, 1073–1088. https://doi.org/10.2307/1383050 (1997).Article 

Google Scholar 
Geiser, F. & Brigham, R. M. Torpor, thermal biology and energetics in Australian long-eared bats (Nyctophilus). J. Comp. Physiol. (B) 170, 153–162. https://doi.org/10.1007/s003600050270 (2000).Article 
CAS 

Google Scholar 
Withers, P. C. Metabolic, respiratory and haematological adjustments of the little pocket mouse to circadian torpor cycles. Respir. Physiol. 31, 295–307. https://doi.org/10.1016/0034-5687(77)90073-1 (1977).Article 
CAS 

Google Scholar 
Bartholomew, G. A. & Tucker, V. A. Control of changes in body temperature, metabolism and circulation by the Agamid lizard, Amphibolurus barbatus. Physiol. Zool. 36, 199–218 (1963).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26. https://doi.org/10.18637/jss.v082.i13 (2017).Article 

Google Scholar 
Warton, D. I., Duursma, R. A., Falster, D. S. & Taskinen, S. smatr 3- an R package for estimation and inference about allometric lines. Methods Ecol. Evol. 3, 257–259. https://doi.org/10.1111/j.2041-210X.2011.00153.x (2012).Article 

Google Scholar 
Halsey, L. G. et al. Flexibility, variability and constraint in energy management patterns across vertebrate taxa revealed by long-term heart rate measurements. Funct. Ecol. 33, 260–272. https://doi.org/10.1111/1365-2435.13264 (2019).Article 

Google Scholar  More

Bennett, A. et al. Nat. Food https://doi.org/10.1038/s43016-022-00642-4 (2022).Article 

Google Scholar 
Simmance, F. A. et al. Nat. Commun. 3, 174 (2022).
Google Scholar 
Kolding, J., van Zwieten, P., Martin, F., Funge-Smith, S. & Poulain, F. Freshwater Small Pelagic Fish and Their Fisheries in the Major African Lakes and Reservoirs in Relation to Food Security and Nutrition (Food and Agriculture Organization of the United Nations, 2019).Pradhan, S. K., Nayak, P. K. & Armitage, D. Curr. Res. Environ. Sustain. 4, 100128 (2022).Article 

Google Scholar 
Byrd, K. A., Pincus, L., Pasqualino, M. M., Muzofa, F. & Cole, S. M. Matern. Child Nutr. 17, e13192 (2021).Article 

Google Scholar 
Chiwaula, L. S., Chirwa, G. C., Binauli, L. S., Banda, J. & Nagoli, J. Agric. Food Econ. 6, 1–15 (2018).Article 

Google Scholar 
Cole, S. M. et al. Ecol. Soc. 23, 18 (2018).Article 

Google Scholar 
Manyungwa, C. L., Hara, M. M. & Chimatiro, S. K. Marit. Stud. 18, 275–285 (2019).Article 

Google Scholar 
Coates, J. et al. Food Policy 81, 82–94 (2018).Article 

Google Scholar 
Stevens, G. A. et al. Lancet Glob. Health 10, e1590–e1599 (2022).Article 

Google Scholar 
Hicks, C. C. et al. Nat. Food 3, 851–861 (2022).Article 

Google Scholar  More

Welch and colleagues analysed 3.7 billion AIS messages recorded between 2017 and 2019 in the global Fishing Watch AIS dataset, identifying more than 55,000 suspected intentional disabling events in waters more than 50 nautical miles from shore, amounting to 6% ( >4.9 million hours) of obscured vessel activity. Hotspots of disabling activity were located near several regions of IUU concern and transshipment hotspots, including in the exclusive economic zones of Argentina and West African nations and in the Northwest Pacific. Using individual boosted regression tree models for the four dominant gear types (squid jiggers, trawlers, tuna purse seines and drifting longlines) and a full model that included all suspected disabling events (that is, the four gear types listed above and additional gears such as gillnet and troll), Welch and colleagues found that loitering by transshipment vessels (a proxy for potential transshipment events) was the most important driver in the full model and squid jigger model and more than half of the disabling events by squid jiggers were close enough to undertake transshipment to refrigerated cargo vessels. More

Kulahci, I. G., Dornhaus, A. & Papaj, D. R. Multimodal signals enhance decision making in foraging bumble-bees. Proc. Biol. Sci. 275, 797–802 (2008).
Google Scholar 
Goldshtein, A. et al. Reinforcement learning enables resource partitioning in foraging bats. Curr. Biol. 30, 4096–4102.e4096 (2020).CAS 

Google Scholar 
Skogen, K. A., Overson, R. P., Hilpman, E. T. & Fant, J. B. Hawkmoth pollination facilitates long-distance pollen dispersal and reduces isolation across a gradient of land-use change. Ann. Mo. Bot. Gard. 104, 495–511 (2019). 417.
Google Scholar 
Deng, J.-Y., van Noort, S., Compton, S. G., Chen, Y. & Greeff, J. M. Conservation implications of fine scale population genetic structure of Ficus species in South African forests. Ecol. Manag. 474, 118387 (2020).
Google Scholar 
Galizia, C. G. et al. Relationship of visual and olfactory signal parameters in a food-deceptive flower mimicry system. Behav. Ecol. 16, 159–168 (2004).
Google Scholar 
Gibernau, M., HossaertMcKey, M., Frey, J. & Kjellberg, F. Are olfactory signals sufficient to attract fig pollinators. Ecoscience 5, 306–311 (1998).
Google Scholar 
Kapustjansky, A., Chittka, L. & Spaethe, J. Bees use three-dimensional information to improve target detection. Naturwissenschaften 97, 229–233 (2010).ADS 
CAS 

Google Scholar 
Hempel de Ibarra, N., Langridge, K. V. & Vorobyev, M. More than colour attraction: behavioural functions of flower patterns. Curr. Opin. Insect Sci. 12, 64–70 (2015).
Google Scholar 
Boff, S., Henrique, J. A., Friedel, A. & Raizer, J. Disentangling the path of pollinator attraction in temporarily colored flowers. Int. J. Trop. Insect Sci. 41, 1305–1311 (2021).
Google Scholar 
Leonard, A. S. & Papaj, D. R. ‘X’ marks the spot: the possible benefits of nectar guides to bees and plants. Funct. Ecol. 25, 1293–1301 (2011).
Google Scholar 
Dobson, H. E. M. & Bergström, G. The ecology and evolution of pollen odors. Plant Syst. Evol. 222, 63–87 (2000).CAS 

Google Scholar 
Raguso, R. A. Why are some floral nectars scented? Ecology 85, 1486–1494 (2004).
Google Scholar 
Corbet, S. A., Kerslake, C. J. C., Brown, D. & Morland, N. E. Can bees select nectar-rich flowers in a patch. J. Apic. Res. 23, 234–242 (1984).
Google Scholar 
Policha, T. et al. Disentangling visual and olfactory signals in mushroom-mimicking Dracula orchids using realistic three-dimensional printed flowers. N. Phytol. 210, 1058–1071 (2016).CAS 

Google Scholar 
Stout, J. C., Goulson, D. & Allen, J. A. Repellent scent-marking of flowers by a guild of foraging bumblebees (Bombus spp.). Behav. Ecol. Sociobiol. 43, 317–326 (1998).
Google Scholar 
Howell, A. D. & Alarcón, R. Osmia bees (Hymenoptera: Megachilidae) can detect nectar-rewarding flowers using olfactory cues. Anim. Behav. 74, 199–205 (2007).von Arx, M. Floral humidity and other indicators of energy rewards in pollination biology. Commun. Integr. Biol. 6, e22750–e22750 (2013).
Google Scholar 
Goyret, J. The breath of a flower: CO2 adds another channel-and then some-to plant-pollinator interactions. Commun. Integr. Biol. 1, 66–68 (2008).CAS 

Google Scholar 
Bradbury, J. W. & Vehrencamp, S. L. Principles of Animal Communication 2nd edn (Sinauer Associates, 2011).McMeniman, C. J., Corfas, R. A., Matthews, B. J., Ritchie, S. A. & Vosshall, L. B. Multimodal integration of carbon dioxide and other sensory cues drives mosquito attraction to humans. Cell 156, 1060–1071 (2014).CAS 

Google Scholar 
Smith, J. M. & Harper, D. Animal Signals (Oxford Univ. Press, 2003).Smith, M. J. & Harper, D. G. C. Animal signals: models and terminology. J. Theor. Biol. 177, 305–311 (1995).ADS 

Google Scholar 
Laidre, M. E. & Johnstone, R. A. Animal signals. Curr. Biol. 23, R829–R833 (2013).CAS 

Google Scholar 
Smith, J. M. Must reliable signals always be costly? Anim. Behav. 47, 1115–1120 (1994).
Google Scholar 
Guerenstein, P. G., A.Yepez, E., van Haren, J., Williams, D. G. & Hildebrand, J. G. Floral CO2 emission may indicate food abundance to nectar-feeding moths. Naturwissenschaften 91, 329–333 (2004).ADS 
CAS 

Google Scholar 
Goyret, J., Markwell, P. M. & Raguso, R. A. Context- and scale-dependent effects of floral CO2 on nectar foraging by Manduca sexta. Proc. Natl Acad. Sci. USA 105, 4565–4570 (2008).ADS 
CAS 

Google Scholar 
Thom, C., Guerenstein, P. G., Mechaber, W. L. & Hildebrand, J. G. Floral CO2 reveals flower profitability to moths. J. Chem. Ecol. 30, 1285–1288 (2004).CAS 

Google Scholar 
Gilbert, F. S., Haines, N. & Dickson, K. Empty flowers. Funct. Ecol. 5, 29–39 (1991).
Google Scholar 
von Arx, M., Goyret, J., Davidowitz, G. & Raguso, R. A. Floral humidity as a reliable sensory cue for profitability assessment by nectar-foraging hawkmoths. Proc. Natl Acad. Sci. USA 109, 9471–9476 (2012).ADS 

Google Scholar 
Harrap, M. J. M., Hempel de Ibarra, N., Knowles, H. D., Whitney, H. M. & Rands, S. A. Floral humidity in flowering plants: A preliminary survey. Front. Plant Sci. https://doi.org/10.3389/fpls.2020.00249 (2020).Harrap, M. J. M. & Rands, S. A. The role of petal transpiration in floral humidity generation. Planta 255, 78 (2022).CAS 

Google Scholar 
Harrap, M. J. M., Hempel de Ibarra, N., Knowles, H. D., Whitney, H. M. & Rands, S. A. Bumblebees can detect floral humidity. J. Exp. Biol. https://doi.org/10.1242/jeb.240861 (2021).Hebets, E. A. & Papaj, D. R. Complex signal function: developing a framework of testable hypotheses. Behav. Ecol. Sociobiol. 57, 197–214 (2005).
Google Scholar 
Bronstein, J. L., Huxman, T., Horvath, B., Farabee, M. & Davidowitz, G. Reproductive biology of Datura wrightii: the benefits of a herbivorous pollinator. Ann. Bot. 103, 1435–1443 (2009).
Google Scholar 
Johnson, C. A. et al. Coevolutionary transitions from antagonism to mutualism explained by the co-opted antagonist hypothesis. Nat. Commun. https://doi.org/10.1038/s41467-021-23177-x (2021).Clark, C. J. The role of power versus energy in courtship: what is the ‘energetic cost’ of a courtship display? Anim. Behav. 84, 269–277 (2012).
Google Scholar 
Willmott, A. P. & Ellington, C. P. The mechanics of flight in the hawkmoth Manduca sexta. I. Kinematics of hovering and forward flight. J. Exp. Biol. 200, 2705–2722 (1997).CAS 

Google Scholar 
Shields, V. D. C. & Hildebrand, J. G. Fine structure of antennal sensilla of the female sphinx moth, Manduca sexta (Lepidoptera: Sphingidae). II. Auriculate, coeloconic, and styliform complex sensilla. Can. J. Zool. 77, 302–313 (1999).
Google Scholar 
Lee, J. K. & Strausfeld, N. J. Structure, distribution and number of surface sensilla and their receptor cells on the olfactory appendage of the male moth Manduca sexta. J. Neurocytol. 19, 519–538 (1990).CAS 

Google Scholar 
Shields, V. D. & Hildebrand, J. G. Recent advances in insect olfaction, specifically regarding the morphology and sensory physiology of antennal sensilla of the female sphinx moth Manduca sexta. Microsc. Res. Tech. 55, 307–329 (2001).CAS 

Google Scholar 
Tichy, H. & Loftus, R. Hygroreceptors in insects and a spider: Humidity transduction models. Naturwissenschaften 83, 255–263 (1996).ADS 
CAS 

Google Scholar 
Ahrens, M., Huang, K.-H., Narayan, S., Mensh, B. & Engert, F. Two-photon calcium imaging during fictive navigation in virtual environments. Front. Neural Circuits https://doi.org/10.3389/fncir.2013.00104 (2013).Lacher, V. Elektrophysiologische untersuchungen an einzelnen rezeptoren für geruch, kohlendioxyd, luftfeuchtigkeit und tempratur auf den antennen der arbeitsbiene und der drohne (Apis mellifica L.). Z. f.ür. Vgl. Physiologie 48, 587–623 (1964).
Google Scholar 
Waldow, U. Elektrophysiologische untersuchungen an feuchte-, trocken- und kälterezeptoren auf der antenne der wanderheuschrecke Locusta. Z. f.ür. Vgl. Physiologie 69, 249–283 (1970).
Google Scholar 
Yokohari, F. & Tateda, H. Moist and dry hygroreceptors for relative humidity of the cockroach, Periplaneta americana L. J. Comp. Physiol. 106, 137–152 (1976).
Google Scholar 
Tichy, H. Low rates of change enhance effect of humidity on the activity of insect hygroreceptors. J. Comp. Physiol. A Neuroethol. Sens Neural Behav. Physiol. 189, 175–179 (2003).CAS 

Google Scholar 
Tichy, H., Hellwig, M. & Kallina, W. Revisiting theories of humidity transduction: a focus on electrophysiological data. Front. Physiol. 8, 650 (2017).
Google Scholar 
Tichy, H. & Kallina, W. Insect hygroreceptor responses to continuous changes in humidity and air pressure. J. Neurophysiol. 103, 3274–3286 (2010).CAS 

Google Scholar 
Wolfin, M. S., Raguso, R. A., Davidowitz, G. & Goyret, J. Context dependency of in-flight responses by Manduca sexta moths to ambient differences in relative humidity. J. Exp. Biol. https://doi.org/10.1242/jeb.177774 (2018).Smith, G., Kim, C. & Raguso, R. A. Pollen accumulation on hawkmoths varies substantially among moth-pollinated flowers. Preprint at bioRxiv https://doi.org/10.1101/2022.07.15.500245 (2022).Haverkamp, A., Bing, J., Badeke, E., Hansson, B. S. & Knaden, M. Innate olfactory preferences for flowers matching proboscis length ensure optimal energy gain in a hawkmoth. Nat. Commun. 7, 11644 (2016).ADS 
CAS 

Google Scholar 
Harrison, A. S. & Rands, S. A. The ability of bumblebees Bombus terrestris (hymenoptera: Apidae) to detect floral humidity is dependent upon environmental humidity. Environ. Entomol. 51, 1010–1019 (2022).
Google Scholar 
Kelber, A. What a hawkmoth remembers after hibernation depends on innate preferences and conditioning situation. Behav. Ecol. 21, 1093–1097 (2010).
Google Scholar 
Riffell, J. A. et al. Flower discrimination by pollinators in a dynamic chemical environment. Science 344, 1515–1518 (2014).ADS 
CAS 

Google Scholar 
Schellenberg, R. The trouble with humidity: the hidden challenge of RH calibration. Cal. Lab. 9, 40–42 (2002).
Google Scholar 
Roddy, A. B., Brodersen, C. R. & Dawson, T. E. Hydraulic conductance and the maintenance of water balance in flowers. Plant Cell Environ. 39, 2123–2132 (2016).CAS 

Google Scholar 
Sane, S. P. & Jacobson, N. P. Induced airflow in flying insects. II. Measurement of induced flow. J. Exp. Biol. 209, 43–56 (2006).
Google Scholar 
Daly, K. C., Kalwar, F., Hatfield, M., Staudacher, E. & Bradley, S. P. Odor detection in Manduca sexta is optimized when odor stimuli are pulsed at a frequency matching the wing beat during flight. PLoS ONE 8, e81863 (2013).ADS 

Google Scholar 
Yokohari, F. Hygroreceptor mechanism in the antenna of the cockroach. Periplaneta. J. Comp. Physiol. 124, 153 (1978).
Google Scholar 
Loftus, R. Temperature-dependent dry receptor on antenna of Periplaneta. Tonic response. J. Comp. Physiol. 111, 153–170 (1976).
Google Scholar 
Tichy, H. & Kallina, W. Sensitivity of honeybee hygroreceptors to slow humidity changes and temporal humidity variation detected in high resolution by mobile measurements. PLoS ONE 9, e99032 (2014).ADS 

Google Scholar 
Galen, C., Sherry, R. A. & Carroll, A. B. Are flowers physiological sinks or faucets? Costs and correlates of water use by flowers of Polemonium viscosum. Oecologia 118, 461–470 (1999).ADS 

Google Scholar 
Elle, E., van Dam, N. M. & Hare, J. D. Cost of glandular trichomes, a “resistance” character in Datura wrightii regel (solanaceae). Evolution 53, 22–35 (1999).
Google Scholar 
Elle, E. & Hare, J. D. Environmentally induced variation in floral traits affects the mating system in Datura wrightii. Funct. Ecol. 16, 79–88 (2002).
Google Scholar 
Marler, C. A. & Ryan, M. J. Energetic constraints and steroid hormone correlates of male calling behaviour in the túngara frog. J. Zool. 240, 397–409 (1996).
Google Scholar 
Bernal, X. E., Rand, A. S. & Ryan, M. J. Acoustic preferences and localization performance of blood-sucking flies (Corethrella Coquillett) to túngara frog calls. Behav. Ecol. 17, 709–715 (2006).
Google Scholar 
Raguso, R. A. Flowers as sensory billboards: progress towards an integrated understanding of floral advertisement. Curr. Opin. Plant Biol. 7, 434–440 (2004).
Google Scholar 
Peach, D. A. H., Gries, R., Zhai, H., Young, N. & Gries, G. Multimodal floral cues guide mosquitoes to tansy inflorescences. Sci. Rep. 9, 3908 (2019).ADS 

Google Scholar 
Riffell, J. A. & Alarcón, R. Multimodal floral signals and moth foraging decisions. PLoS ONE 8, e72809 (2013).ADS 
CAS 

Google Scholar 
van der Kooi, C. J., Kevan, P. G. & Koski, M. H. The thermal ecology of flowers. Ann. Bot. 124, 343–353 (2019).
Google Scholar 
Terry, L. I., Roemer, R. B., Walter, G. H., Booth, D. & Lee, K. P. Thrips’ responses to thermogenic associated signals in a cycad pollination system: the interplay of temperature, light, humidity and cone volatiles. Funct. Ecol. 28, 857–867 (2014).
Google Scholar 
Bronstein, J. L., Alarcón, R. & Geber, M. The evolution of plant–insect mutualisms. N. Phytol. 172, 412–428 (2006).
Google Scholar 
Schaefer, H. M. & Ruxton, G. D. Deception in plants: mimicry or perceptual exploitation. Trends Ecol. Evol. 24, 676–685 (2009).
Google Scholar 
Franchi, G. G., Nepi, M. & Pacini, E. Is flower/corolla closure linked to decrease in viability of desiccation-sensitive pollen? Facts and hypotheses: a review of current literature with the support of some new experimental data. Plant Syst. Evol. 300, 577–584 (2014).
Google Scholar 
Safavian, D. et al. High humidity partially rescues the Arabidopsis thaliana exo70A1 stigmatic defect for accepting compatible pollen. Plant Reprod. 27, 121–127 (2014).CAS 

Google Scholar 
Shivanna, K. R. & Cresti, M. Effects of high humidity and temperature stress on pollen membrane integrity and pollen vigour in Nicotiana tabacum. Sex. Plant Reprod. 2, 137–141 (1989).
Google Scholar 
Richman, S. K. et al. The sensory and cognitive ecology of nectar robbing. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2021.698137 (2021).Raguso, R. A. et al. Trumpet flowers of the Sonoran Desert: floral biology of Peniocereus Cacti and Sacred Datura. Int. J. Plant Sci. 164, 877–892 (2003).CAS 

Google Scholar 
Carazo, P. & Font, E. ‘Communication breakdown’: the evolution of signal unreliability and deception. Anim. Behav. 87, 17–22 (2014).
Google Scholar 
Schemske, D. W. Evolution of floral display in the orchid Brassavola nodosa. Evolution 34, 489–493 (1980).
Google Scholar 
Haber, W. A. Pollination by deceit in a mass-flowering tropical tree Plumeria rubra L. (apocynaceae). Biotropica 16, 269–275 (1984).
Google Scholar 
Brandenburg, A., Kuhlemeier, C. & Bshary, R. Hawkmoth pollinators decrease seed set of a low-nectar Petunia axillaris line through reduced probing time. Curr. Biol. 22, 1635–1639 (2012).CAS 

Google Scholar 
Bye, R. & Sosa, V. Molecular phylogeny of the jimsonweed genus Datura (solanaceae). Syst. Bot. 38, 818–829 (2013).
Google Scholar 
Kariñho-Betancourt, E., Agrawal, A. A., Halitschke, R. & Núñez-Farfán, J. Phylogenetic correlations among chemical and physical plant defenses change with ontogeny. N. Phytol. 206, 796–806 (2015).
Google Scholar 
Kawahara, A. Y. et al. Evolution of Manduca sexta hornworms and relatives: biogeographical analysis reveals an ancestral diversification in Central America. Mol. Phylogenet. Evol. 68, 381–386 (2013).
Google Scholar 
Contreras, H. L. et al. The effect of ambient humidity on the foraging behavior of the hawkmoth Manduca sexta. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 199, 1053–1063 (2013).
Google Scholar 
Cardoso, J. C. F., Gonzaga, M. O., Cavalleri, A., Maruyama, P. K. & Alves-Silva, E. The role of floral structure and biotic factors in determining the occurrence of florivorous thrips in a dystilous shrub. Arthropod-Plant Interact. 10, 477–484 (2016).
Google Scholar 
Nicolson, S. W. Sweet solutions: nectar chemistry and quality. Philos. Trans. R. Soc. Lond. B Biol. Sci. 377, 20210163 (2022).CAS 

Google Scholar 
Pellmyr, O. & Thien, L. B. Insect reproduction and floral fragrances: keys to the evolution of the Angiosperms. Taxon 35, 76–85 (1986).
Google Scholar 
Enjin, A. et al. Humidity sensing in Drosophila. Curr. Biol. 26, 1352–1358 (2016).CAS 

Google Scholar 
Knecht, Z. A. et al. Distinct combinations of variant ionotropic glutamate receptors mediate thermosensation and hygrosensation in Drosophila. eLife 5, e17879 (2016).
Google Scholar 
Knecht, Z. A. et al. Ionotropic receptor-dependent moist and dry cells control hygrosensation in Drosophila. eLife 6, e26654 (2017).
Google Scholar 
Croset, V. et al. Ancient protostome origin of chemosensory ionotropic glutamate receptors and the evolution of insect taste and olfaction. PLoS Genet. 6, e1001064–e1001064 (2010).
Google Scholar 
Dahake, A. et al. MATLAB codes: a signal-like role for floral humidity in a nocturnal pollination system. Zenodo https://doi.org/10.5281/zenodo.7320037 (2022).Pereira, T. D. et al. Fast animal pose estimation using deep neural networks. Nat. Methods 16, 117–125 (2019).CAS 

Google Scholar 
Nilsson, S. R. et al. Simple behavioral analysis (SimBA) – an open source toolkit for computer classification of complex social behaviors in experimental animals. Preprint at bioRxiv https://doi.org/10.1101/2020.04.19.049452 (2020).Casey, T. M. Flight energetics of sphinx moths: power input during hovering flight. J. Exp. Biol. 64, 529–543 (1976).CAS 

Google Scholar 
Riffell, J. A. et al. Behavioral consequences of innate preferences and olfactory learning in hawkmoth-flower interactions. Proc. Natl Acad. Sci. USA 105, 3404–3409 (2008).ADS 
CAS 

Google Scholar 
Lott, G. K., Johnson, B. R., Bonow, R. H., Land, B. R. & Hoy, R. R. g-PRIME: a free, windows based data acquisition and event analysis software package for physiology in classrooms and research labs. J. Undergrad. Neurosci. Educ. 8, A50–A54 (2009).
Google Scholar 
Chaure, F. J., Rey, H. G. & Quiroga, R. Q. A novel and fully automatic spike-sorting implementation with variable number of features. J. Neurophysiol. 120, 1859–1871 (2018).CAS 

Google Scholar 
Tichy, H. Humidity-dependent cold cells on the antenna of the stick insect. J. Neurophysiol. 97, 3851–3858 (2007).
Google Scholar 
Campbell, R. raacampbell/shadedErrorBar. https://github.com/raacampbell/shadedErrorBar (2022).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).Broadhead, G. T. & Raguso, R. A. Associative learning of non-sugar nectar components: amino acids modify nectar preference in a hawkmoth. J. Exp. Biol. https://doi.org/10.1242/jeb.234633 (2021). More