Ecology

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

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

Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).Article 
CAS 

Google Scholar 
Frolicher, T. L., Fischer, E. M. & Gruber, N. Marine heatwaves under global warming. Nature 560, 360–364 (2018).Article 
CAS 

Google Scholar 
Riegl, B. & Purkis, S. Coral population dynamics across consecutive mass mortality events. Glob. Chang Biol. 21, 3995–4005 (2015).Article 

Google Scholar 
Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373−+ (2017).Article 

Google Scholar 
Hoegh-Guldberg, O. Climate change, coral bleaching and the future of the world’s coral reefs. Mar. Freshw. Res. 50, 839–866 (1999).
Google Scholar 
van Hooidonk, R., Maynard, J. A. & Planes, S. Temporary refugia for coral reefs in a warming world. Nat. Clim. Change 3, 508–511 (2013).Article 

Google Scholar 
Hoegh-Guldberg, O. et al. Coral reefs under rapid climate change and ocean acidification. Science 318, 1737–1742 (2007).Article 
CAS 

Google Scholar 
Frieler, K. et al. Limiting global warming to 2 degrees C is unlikely to save most coral reefs. Nat. Clim. Change 3, 165–170 (2013).Article 

Google Scholar 
Guest, J. R. et al. Contrasting patterns of coral bleaching susceptibility in 2010 suggest an adaptive response to thermal stress. PLoS ONE 7, https://doi.org/10.1371/journal.pone.0033353 (2012).Oliver, T. A. & Palumbi, S. R. Do fluctuating temperature environments elevate coral thermal tolerance? Coral Reefs 30, 429–440 (2011).Article 

Google Scholar 
Howells, E. J. et al. Coral thermal tolerance shaped by local adaptation of photosymbionts. Nat. Clim. Change 2, 116–120 (2012).Article 

Google Scholar 
Bay, R. A., Rose, N. H., Logan, C. A. & Palumbi, S. R. Genomic models predict successful coral adaptation if future ocean warming rates are reduced. Sci. Adv. 3, https://doi.org/10.1126/sciadv.1701413 (2017).Loya, Y. et al. Coral bleaching: the winners and the losers. Ecol. Lett. 4, 122–131 (2001).Article 

Google Scholar 
Edmunds, P. J., Gates, R. D. & Gleason, D. F. The biology of larvae from the reef coral Porites astreoides, and their response to temperature disturbances. Mar. Biol. 139, 981–989 (2001).Article 

Google Scholar 
Lager, C. V. A., Hagedorn, M., Rodgers, K. S. & Jokiel, P. L. The impact of short-term exposure to near shore stressors on the early life stages of the reef building coral Montipora capitata. Peerj 8, https://doi.org/10.7717/peerj.9415 (2020).Ross, C., Ritson-Williams, R., Olsen, K. & Paul, V. J. Short-term and latent post-settlement effects associated with elevated temperature and oxidative stress on larvae from the coral Porites astreoides. Coral Reefs 32, 71–79 (2013).Article 

Google Scholar 
Putnam, H. M. & Gates, R. D. Preconditioning in the reef-building coral Pocillopora damicornis and the potential for trans-generational acclimatization in coral larvae under future climate change conditions. J. Exp. Biol. 218, 2365–2372 (2015).Article 

Google Scholar 
Baird, A. H. & Marshall, P. A. Mortality, growth and reproduction in scleractinian corals following bleaching on the Great Barrier Reef. Mar. Ecol. Prog. Ser. 237, 133–141 (2002).Article 

Google Scholar 
Fisch, J., Drury, C., Towle, E. K., Winter, R. N. & Miller, M. W. Physiological and reproductive repercussions of consecutive summer bleaching events of the threatened Caribbean coral Orbicella faveolata. Coral Reefs 38, 863–876 (2019).Article 

Google Scholar 
Michalek-Wagner, K. & Willis, B. L. Impacts of bleaching on the soft coral Lobophytum compactum. I. Fecundity, fertilization and offspring viability. Coral Reefs 19, 231–239 (2001).Article 

Google Scholar 
Paxton, C. W., Baria, M. V. B., Weis, V. M. & Harii, S. Effect of elevated temperature on fecundity and reproductive timing in the coral Acropora digitifera. Zygote 24, 511–516 (2016).Article 

Google Scholar 
Nozawa, Y. & Harrison, P. L. Effects of elevated temperature on larval settlement and post-settlement survival in scleractinian corals, Acropora solitaryensis and Favites chinensis. Mar. Biol. 152, 1181–1185 (2007).Article 

Google Scholar 
Hughes, T. P. & Tanner, J. E. Recruitment failure, life histories, and long-term decline of Caribbean corals. Ecology 81, 2250–2263 (2000).Article 

Google Scholar 
Edmunds, P. J. Juvenile coral population dynamics track rising seawater temperature on a Caribbean reef. Mar. Ecol. Prog. Ser. 269, 111–119 (2004).Article 

Google Scholar 
Davis, K. & Marshall, D. J. Offspring size in a resident species affects community assembly. J. Anim. Ecol. 83, 322–331 (2014).Article 

Google Scholar 
Chua, C. M., Leggat, W., Moya, A. & Baird, A. H. Temperature affects the early life history stages of corals more than near future ocean acidification. Mar. Ecol. Prog. Ser. 475, 85–92 (2013).Article 

Google Scholar 
Schnitzler, C. E., Hollingsworth, L. L., Krupp, D. A. & Weis, V. M. Elevated temperature impairs onset of symbiosis and reduces survivorship in larvae of the Hawaiian coral, Fungia scutaria. Mar. Biol. 159, 633–642 (2012).Article 

Google Scholar 
Ward, S., Harrison, P. & Hoegh-Guldberg, O. in: Proceedings of the Ninth International Coral Reef Symposium, Bali, 23–27 October 2000. 1123–1128 (2000).Foster, T. & Gilmour, J. Egg size and fecundity of biannually spawning corals at Scott Reef. Sci Rep-Uk 10, https://doi.org/10.1038/s41598-020-68289-4 (2020).Negri, A. P., Marshall, P. A. & Heyward, A. J. Differing effects of thermal stress on coral fertilization and early embryogenesis in four Indo Pacific species. Coral Reefs 26, 759–763 (2007).Article 

Google Scholar 
Randall, C. J. & Szmant, A. M. Elevated temperature affects development, survivorship, and settlement of the Elkhorn Coral, Acropora palmata (Lamarck 1816). Biol. Bull. 217, 269–282 (2009).Article 

Google Scholar 
Anlauf, H., D’Croz, L. & O’Dea, A. A corrosive concoction: the combined effects of ocean warming and acidification on the early growth of a stony coral are multiplicative. J. Exp. Mar. Biol. Ecol. 397, 13–20 (2011).Article 

Google Scholar 
Foster, T., Gilmour, J. P., Chua, C. M., Falter, J. L. & McCulloch, M. T. Effect of ocean warming and acidification on the early life stages of subtropical Acropora spicifera. Coral Reefs 34, 1217–1226 (2015).Article 

Google Scholar 
Randall, C. J. & Szmant, A. M. Elevated temperature reduces survivorship and settlement of the larvae of the Caribbean scleractinian coral, Favia fragum (Esper). Coral Reefs 28, 537–545 (2009).Article 

Google Scholar 
Gilmour, J. P., Smith, L. D., Heyward, A. J., Baird, A. H. & Pratchett, M. S. Recovery of an isolated coral reef system following severe disturbance. Science 340, 69–71 (2013).Article 

Google Scholar 
Doropoulos, C., Ward, S., Roff, G., Gonzalez-Rivero, M. & Mumby, P. J. Linking Demographic Processes of Juvenile Corals to Benthic Recovery Trajectories in Two Common Reef Habitats. PLoS ONE 10, https://doi.org/10.1371/journal.pone.0128535 (2015).Donelson, J. M., Munday, P. L., McCormick, M. I. & Pitcher, C. R. Rapid transgenerational acclimation of a tropical reef fish to climate change. Nat. Clim. Change 2, 30–32 (2012).Article 

Google Scholar 
Yuyama, I., Nakamura, T., Higuchi, T. & Hidaka, M. Different stress tolerances of juveniles of the Coral Acropora tenuis associated with clades C1 and D symbiodinium. Zool. Stud. 55, https://doi.org/10.6620/Zs.2016.55-19 (2016).Mumby, P. J. Can Caribbean coral populations be modelled at metapopulation scales? Mar. Ecol. Prog. Ser. 180, 275–288 (1999).Article 

Google Scholar 
Raymundo, L. J. & Maypa, A. P. Getting bigger faster: mediation of size-specific mortality via fusion in jevenile coral transplants. Ecol. Appl. 14, 281–295 (2004).Article 

Google Scholar 
Heyward, A. J. & Negri, A. P. Plasticity of larval pre-competency in response to temperature: observations on multiple broadcast spawning coral species. Coral Reefs 29, 631–636 (2010).Article 

Google Scholar 
Figueiredo, J., Baird, A. H., Harii, S. & Connolly, S. R. Increased local retention of reef coral larvae as a result of ocean warming. Nat. Clim. Change 4, 498–502 (2014).Article 

Google Scholar 
Ward, S. & Harrison, P. Changes in gametogenesis and fecundity of acroporid corals that were exposed to elevated nitrogen and phosphorus during the ENCORE experiment. J. Exp. Mar. Biol. Ecol. 246, 179–221 (2000).Article 
CAS 

Google Scholar 
Jones, A. M. & Berkelmans, R. Tradeoffs to thermal acclimation: energetics and reproduction of a reef coral with heat tolerant Symbiodinium type-D. J. Mar. Biol. 2011, 185890 (2011).Harriott, V. Reproductive ecology of four scleratinian species at Lizard Island, Great Barrier Reef. Coral Reefs 2, 9–18 (1983).Article 

Google Scholar 
Hall, V. R. & Hughes, T. P. Reproductive strategies of modular organisms: Comparative studies of reef-building corals. Ecology 77, 950–963 (1996).Article 

Google Scholar 
Smith, C. C. & Fretwell, S. D. The optimal balance between size and number of offspring. Am. Naturalist 108, 499–506 (1974).Article 

Google Scholar 
Hoegh-Guldberg, O. & Smith, G. J. Influence of the population density of zooxanthellae and supply of ammonium on the biomass and metabolic characteristics of the reef corals Seriatopora hystrix and Stylophora pistillata. Mar. Ecol. Prog. Ser. 57, 173–186 (1989).Lesser, M. P. Elevated temperatures and ultraviolet radiation cause oxidative stress and inhibit photosynthesis in ymbiotic dinoflagellates. Limnol. Oceanogr. 41, 271–283 (1996).Article 
CAS 

Google Scholar 
Jones, R. J., Hoegh-Guldberg, O., Larkum, A. W. D. & Schreiber, U. Temperature-induced bleaching of corals begins with impairment of the CO2 fixation mechanism in zooxanthellae. Plant Cell Environ. 21, 1219–1230 (1998).Article 
CAS 

Google Scholar 
Warner, M. E., Fitt, W. K. & Schmidt, G. W. Damage to photosystem II in symbiotic dinoflagellates: A determinant of coral bleaching. Proc. Natl Acad. Sci. USA 96, 8007–8012 (1999).Article 
CAS 

Google Scholar 
McRae, C. J., Huang, W. B., Fan, T. Y. & Côté, I. M. Effects of thermal conditioning on the performance of Pocillopora acuta adult coral colonies and their offspring. Coral Reefs 40, 1491–1503 (2021).Article 

Google Scholar 
Howells, E. J. et al. Species-specific trends in the reproductive output of corals across environmental gradients and bleaching histories. Mar. Pollut. Bull. 105, 532–539 (2016).Article 
CAS 

Google Scholar 
Galanto, N., Sartor, C., Moscato, V., Lizama, M. & Lemer, S. Effects of elevated temperature on reproduction and larval settlement in Leptastrea purpurea. Coral Reefs 41, 293–302 (2022).Article 

Google Scholar 
Hazraty-Kari, S., Masaya, M., Kawachi, M. & Harii, S. The early acquisition of symbiotic algae benefits larval survival and juvenile growth in the coral Acropora tenuis. J. Exp. Zool. A Ecol. Integr. Physiol. https://doi.org/10.1002/jez.2589 (2022).Moran, A. L. & Manahan, D. T. Energy metabolism during larval development of green and white abalone, Haliotis fulgens and H. sorenseni. Biol. Bull. 204, 270–277 (2003).Article 

Google Scholar 
Sewell, M. A. Utilization of lipids during early development of the sea urchin Evechinus chloroticus. Mar. Ecol. Prog. Ser. 304, 133–142 (2005).Article 
CAS 

Google Scholar 
Alexander, G., Hancock, J., Huffmyer, A. & Matsuda, S. Larval thermal conditioning does not improve post-settlement thermal tolerance in the dominant reef-building coral, Montipora capitata. Coral Reefs 41, 333–342 (2022).Rivest, E. B., Chen, C. S., Fan, T. Y., Li, H. H. & Hofmann, G. E. Lipid consumption in coral larvae differs among sites: a consideration of environmental history in a global ocean change scenario. Proc. Biol. Sci. 284, https://doi.org/10.1098/rspb.2016.2825 (2017).Graham, E. M., Baird, A. H., Connolly, S. R., Sewell, M. A. & Willis, B. L. Uncoupling temperature-dependent mortality from lipid depletion for scleractinian coral larvae. Coral Reefs 36, 97–104 (2017).Article 

Google Scholar 
Graham, E. M., Baird, A. H., Connolly, S. R., Sewell, M. A. & Willis, B. L. Rapid declines in metabolism explain extended coral larval longevity. Coral Reefs 32, 539–549 (2013).Article 

Google Scholar 
Reid, E. C. et al. Internal waves influence the thermal and nutrient environment on a shallow coral reef. Limnol. Oceanogr. 64, 1949–1965 (2019).Article 

Google Scholar 
Maynard, J. A., Anthony, K. R. N., Marshall, P. A. & Masiri, I. Major bleaching events can lead to increased thermal tolerance in corals. Mar. Biol. 155, 173–182 (2008).Article 

Google Scholar 
Siebeck, U. E., Marshall, N. J., Kluter, A. & Hoegh-Guldberg, O. Monitoring coral bleaching using a colour reference card. Coral Reefs 25, 453–460 (2006).Article 

Google Scholar 
Veal, C. J., Holmes, G., Nunez, M., Hoegh-Guldberg, O. & Osborn, J. A comparative study of methods for surface area and three-dimensional shape measurement of coral skeletons. Limnol. Oceanogr.-Meth 8, 241–253 (2010).Article 

Google Scholar 
Jeffrey, S. W. & Humphrey, G. F. New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton. Biochem. Physiol. Pflanz. 167, 191–194 (1975).Article 
CAS 

Google Scholar 
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).Article 
CAS 

Google Scholar 
R Foundation for Statistical Computing. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria 2021). More

Sampling strategies and dataThe inner Oslofjorden phytoplankton dataset is a compilation of data mostly assembled from the monitoring program, financed since 1978 by a cooperation between the municipalities around the fjord, united in the counsel for technical water and sewage cooperation called “Fagrådet for Vann- og avløpsteknisk samarbeid i Indre Oslofjord”. The monitoring program started in 1973 and is ongoing. The program has sampled environmental parameters and chlorophyll since 1973, but for the first 25 years, phytoplankton data is only reported for the years 1973, 1974, 1988/9, 1990, 1994 and 1995. Since 1998, yearly sampling has been conducted, and from 2006 to 2019, the sampling frequency was approximately monthly. In addition, we have compiled research and monitoring data from researchers at the University of Oslo from 1896 and 1916, 1933–34 and 1962–1965.The records from 1896 and 1897 were collected using zoo-plankton net13. The phytoplankton collection in 1916–1917 used buckets or Nansen flasks for sampling. From 1933 to 1984, phytoplankton samples were collected using Nansen bottles and then from 1985–2020 with Niskin bottles from research vessels. The exception is the period from 2006 to 2018 when samples were also collected with FerryBox- equipped ships of opportunity14 with refrigerated autosamplers (Table 2).Since the 1990s, quantitative phytoplankton samples have mostly been preserved in Lugol’s solution, except for spring and autumn samples in the period 1990–2000 that were preserved in formalin. The records from 1896, 1897 and 1916 were preserved in ethanol, and between 1933 and 1990, samples were preserved in formalin. Sampling strategies and methods are listed in Table 2.The records from 1896 and 1897 were quantified by weight, and taxon abundance is categorised as “rare” (r), “rather common” (+), “common” (c) and “very common” (cc)13. In 1916 and 1917, Grans filtration method15 was used, and the number was given in cell counts per litre. From 1916 to 1993, the data is reported only as phytoplankton abundance (N, number of cells per litre). For most years after 1994, the dataset includes both abundance and biomass (μg C per litre), except for 2003, 2004, 2017 and 2018. Phytoplankton was identified and quantified using the sedimentation method of Utermöhl (1958)16. Biovolume for each species is calculated according to HELCOM 200617 and converted to biomass (μg C) following Menden-Deuer & Lessards (2000)18.Data inventoryThe inner Oslofjorden Phytoplankton dataset was compiled in 2020, comprising quantitative phytoplankton cell counts from inner Oslofjorden since 1896. Previously, parts of the data have been available as handwritten or printed tables in reports and published sources19,20,21 (Fig. 2). All sources are digitally available from the University of Oslo Library, the website for “Fagrådet” (http://www.indre-oslofjord.no/) or the NIVA online report database (https://www.niva.no/rapporter). Data from 1994 and onwards have been accessed digitally from the NIVA’s databases. They are also available from client reports from the monitoring project for inner Oslofjorden from the online sites listed above.The first known, published investigation of hydrography and plankton in the upper water column of the inner Oslofjorden was by Hjort & Gran (1900)13. Samples were collected during a hydrographical and biological investigation covering both the Skagerrak and Oslofjorden. There is only one sampling event from Steilene (Dk 1), but some phytoplankton data were obtained at Drøbak, just south of the shallow sill separating the inner and outer Oslofjorden, from winter 1896 to autumn 1897. Twenty years later, Gran and Gaarder (1927)22 conducted a study that included culture experiments at Drøbak field station (at the border between the inner and outer Oslofjorden) in March – April 1916 and August – September 1917. A higher frequency investigation was carried out from June 1933 to May 1934, covering 12 stations in inner and outer Oslofjorden where phytoplankton was analysed by microscopic examination23. The extensive program (the Oslofjord Project) conducted from 1962–1964 covered many parameters, and we have extracted the data for phytoplankton. From 1973 and onward, the research vessel-based monitoring program was financed by the municipalities around the fjord, and since 2006 NIVA has supplemented the monitoring program using FerryBox ships of opportunity. Samples from 4 m depth were collected using a refrigerated autosampler system (Teledyne ISCO) connected to a FerryBox system on M/S Color Festival and M/S Color Fantasy through cooperation between NIVA and Color Line A/S. Since 2018, the FerryBox has been part of the Norwegian Ships of Opportunity Program research infrastructure funded by the Research Council of Norway.The indicated depth of 3.5–4 m is an estimated average, as the actual sampling depth depends on shipload and sea conditions.Several other research projects have sampled from inner Oslofjorden between 1886 and 2000 with different aims. Data from relevant projects reporting on the whole phytoplankton community have also been included in this database.Data compilationThe data already digitalised were compiled from MS Excel files, and other data were manually entered into the standard format in MS Excel files. All collected data were then integrated into one MS Excel database, and this file was used for upload into GBIF. Data can be downloaded from GBIF in different formats and be linked together by the measurementsorfacts table.Quality control and standardisationAfter compilation, the data were checked for errors that could occur during manual digitalisation or just the compilation process. Duplicates and zero values were removed (Fig. 2). The major quantitative unit is phytoplankton abundance in cells per litre. Due to varying scopes of sampling and the development of gear and instruments, the number of species identified may vary between projects. Some of the earliest records were registered as “present”, indicating the amount in comments.Metadata, such as geographical reference, depth and methodology accessed from papers and reports, were accessible from the data source. When data was accessed from the NIVA internal databases, the metadata information was provided by the database owners/researchers.TaxonomyThe taxonomy of microalgae is in constant revision as new knowledge and techniques for identification are developing. Several historical species names recorded in this database are synonyms of accepted names in 2021. We have used the original names in our database and matched them to accepted names and Aphia ID using the taxon match tool available in the open-access reference system; World Register of Marine Species (Worms)24. The taxon match was conducted in March 2021.The nomenclature in Worms is quality assured by a wide range of taxonomic specialists. The Aphia ID is a unique and stable identifier for each available name in the database24. We also cross-checked the last updated nomenclature in Algaebase25 (March 2022) to assign species to a valid taxon name. When Algaebase and Worms were not in accordance, Algaebase taxonomy was usually chosen except in the case of Class Bacillariophyceae.Before matching the species list, the original species names were cleaned from spelling mistakes or just spelling mismatches like spaces, commas, etc. The original name is, however, left in one column in the database. For registrations where a species identification is uncertain, e.g. Alexandrium cf. tamarense, we used only Alexandrium. For registrations where the full name is uncertain, e.g. cf. Alexandrium tamarense, we used the name and Aphia ID for higher taxa, in this case, order. For others, e.g. “pennate diatoms” or “centric diatoms“, we used the name and Aphia ID for class. When names for, e.g. order and class were not recognised automatically by the matching tool in World Register of Marine Species (WoRMS), these were matched manually. Only very few records, mostly “cysts” and “unidentified monads”, could not be matched neither automatically nor manually but were assigned to general “protists” with affiliated ID. More

Schendel, V., Rash, L. D., Jenner, R. A. & Undheim, E. A. The diversity of venom: The importance of behavior and venom system morphology in understanding its ecology and evolution. Toxins 11(11), 666 (2019).Article 
CAS 

Google Scholar 
Casewell, N. R., Wüster, W., Vonk, F. J., Harrison, R. A. & Fry, B. G. Complex cocktails: The evolutionary novelty of venoms. Trends Ecol. Evol. 28(4), 219–229 (2013).Article 

Google Scholar 
Pineda, S. S. et al. Structural venomics reveals evolution of a complex venom by duplication and diversification of an ancient peptide-encoding gene. Proc. Natl. Acad. Sci. USA 117(21), 11399–11408 (2020).Article 
ADS 
CAS 

Google Scholar 
Chippaux, J. P., Williams, V. & White, J. Snake venom variability: Methods of study, results and interpretation. Toxicon 29(11), 1279–1303 (1991).Article 
CAS 

Google Scholar 
Lyons, K., Dugon, M. M. & Healy, K. Diet breadth mediates the prey specificity of venom potency in snakes. Toxins 12(2), 74 (2020).Article 

Google Scholar 
Pekár, S. et al. Venom gland size and venom complexity—essential trophic adaptations of venomous predators: A case study using spiders. Mol. Ecol. 27(21), 4257–4269 (2018).Article 

Google Scholar 
Phuong, M. A., Mahardika, G. N. & Alfaro, M. E. Dietary breadth is positively correlated with venom complexity in cone snails. BMC Genom. 17(1), 401 (2016).Article 

Google Scholar 
Holding, M. L., Biardi, J. E. & Gibbs, H. L. Coevolution of venom function and venom resistance in a rattlesnake predator and its squirrel prey. Proc. R. Soc. B. 283(1829), 20152841 (2016).Article 

Google Scholar 
Pekár, S., Líznarová, E., Bočánek, O. & Zdráhal, Z. Venom of prey-specialized spiders is more toxic to their preferred prey: A result of prey-specific toxins. J. Anim. Ecol. 87(6), 1639–1652 (2018).Article 

Google Scholar 
Pekár, S., Coddington, J. A. & Blackledge, T. A. Evolution of stenophagy in spiders (Araneae): Evidence based on the comparative analysis of spider diets. Evolution 66(3), 776–806 (2012).Article 

Google Scholar 
Herzig, V., King, G. F. & Undheim, E. A. Can we resolve the taxonomic bias in spider venom research?. Toxicon: X 1, 100005 (2019).Article 
CAS 

Google Scholar 
Platnick, N. A relimitation and revision of the Australasian ground spider family Lamponidae (Araneae: Gnaphosoidea). Bull. Am. Mus. Nat. Hist. 2000(245), 1–328 (2000).Article 

Google Scholar 
World Spider Catalog. Version 22.0. Natural History Museum Bern. http://wsc.nmbe.ch. Accessed 15 Mar 2021 (2021).White, J. & Weinstein, S. A. A phoenix of clinical toxinology: White-tailed spider (Lampona spp.) bites. A case report and review of medical significance. Toxicon 87, 76–80 (2014).Article 
CAS 

Google Scholar 
Rash, L. D., King, R. G. & Hodgson, W. C. Sex differences in the pharmacological activity of venom from the white-tailed spider (Lampona cylindrata). Toxicon 38, 1111–1127 (2000).Article 
CAS 

Google Scholar 
Young, A. R. & Pincus, S. J. Comparison of enzymatic activity from three species of necrotising arachnids in Australia: Loxosceles rufescens, Badumna insignis and Lampona cylindrata. Toxicon 39, 391–400 (2001).Article 
CAS 

Google Scholar 
Michálek, O., Petráková, L. & Pekár, S. Capture efficiency and trophic adaptations of a specialist and generalist predator: A comparison. Ecol. Evol. 7(8), 2756–2766 (2017).Article 

Google Scholar 
Klint, J. K. et al. Spider-venom peptides that target voltage-gated sodium channels: Pharmacological tools and potential therapeutic leads. Toxicon 60(4), 478–491 (2012).Article 
CAS 

Google Scholar 
Diniz, M. R. et al. An overview of Phoneutria nigriventer spider venom using combined transcriptomic and proteomic approaches. PLoS ONE 13(8), e0200628 (2018).Article 

Google Scholar 
Wilson, D. et al. The aromatic head group of spider toxin polyamines influences toxicity to cancer cells. Toxins 9(11), 346 (2017).Article 

Google Scholar 
Herzig, V. & King, G. F. The cystine knot is responsible for the exceptional stability of the insecticidal spider toxin ω-hexatoxin-Hv1a. Toxins 7(10), 4366–4380 (2015).Article 
CAS 

Google Scholar 
Wang, X. H. et al. Discovery and characterization of a family of insecticidal neurotoxins with a rare vicinal disulfide bridge. Nat. Struct. Biol. 7(6), 505–513 (2000).Article 
CAS 

Google Scholar 
Yuan, C. H. et al. Discovery of a distinct superfamily of Kunitz-type toxin (KTT) from tarantulas. PLoS ONE 3(10), e3414 (2008).Article 
ADS 

Google Scholar 
Luo, J. et al. Molecular diversity and evolutionary trends of cysteine-rich peptides from the venom glands of Chinese spider Heteropoda venatoria. Sci. Rep. 11, 3211 (2021).Article 
ADS 
CAS 

Google Scholar 
Cole, J., Buszka, P. A., Mobley, J. A. & Hataway, R. A. Characterization of the venom proteome for the wandering spider, Ctenus hibernalis (Aranea: Ctenidae). J. Proteom. Bioinform. 9, 196–199 (2016).Article 

Google Scholar 
Korolkova, Y. et al. New Insectotoxin from Tibellus Oblongus Spider venom presents novel daptation of ICK Fold. Toxins 13(1), 29 (2021).Article 
CAS 

Google Scholar 
Koua, D. et al. Proteotranscriptomic insights into the venom composition of the wolf spider Lycosa tarantula. Toxins 12(8), 501 (2020).Article 
CAS 

Google Scholar 
Liberato, T., Troncone, L. R. P., Yamashiro, E. T., Serrano, S. M. & Zelanis, A. High-resolution proteomic profiling of spider venom: Expanding the toxin diversity of Phoneutria nigriventer venom. Amino Acids 48(3), 901–906 (2016).Article 
CAS 

Google Scholar 
Oldrati, V. et al. Peptidomic and transcriptomic profiling of four distinct spider venoms. PLoS ONE 12(3), e0172966 (2017).Article 

Google Scholar 
King, G. F. & Hardy, M. C. Spider-venom peptides: Structure, pharmacology, and potential for control of insect pests. Annu. Rev. Entomol. 58, 475–496 (2013).Article 
CAS 

Google Scholar 
Turner, A. J., Isaac, R. E. & Coates, D. The neprilysin (NEP) family of zinc metalloendopeptidases: Genomics and function. BioEssays 23(3), 261–269 (2001).Article 
CAS 

Google Scholar 
Casewell, N. R., Harrison, R. A., Wüster, W. & Wagstaff, S. C. Comparative venom gland transcriptome surveys of the saw-scaled vipers (Viperidae: Echis) reveal substantial intra-family gene diversity and novel venom transcripts. BMC Genom. 10(1), 1–12 (2009).Article 

Google Scholar 
Tan, C. H., Tan, K. Y., Fung, S. Y. & Tan, N. H. Venom-gland transcriptome and venom proteome of the Malaysian king cobra (Ophiophagus hannah). BMC Genom. 16(1), 1–21 (2015).Article 

Google Scholar 
Tan, K. Y., Tan, C. H., Chanhome, L. & Tan, N. H. Comparative venom gland transcriptomics of Naja kaouthia (monocled cobra) from Malaysia and Thailand: Elucidating geographical venom variation and insights into sequence novelty. PeerJ 5, e3142 (2017).Article 

Google Scholar 
Undheim, E. A. et al. A proteomics and transcriptomics investigation of the venom from the barychelid spider Trittame loki (brush-foot trapdoor). Toxins. 5(12), 2488–2503 (2013).Article 
CAS 

Google Scholar 
do Nascimento, S. M., de Oliveira, U. C., Nishiyama-Jr, M. Y., Tashima, A. K. & Silva Junior, P. I. D. Presence of a neprilysin on Avicularia juruensis (Mygalomorphae: Theraphosidae) venom. Toxin Rev. 41(2), 370–379 (2021).Article 

Google Scholar 
Zobel-Thropp, P. A. et al. Not so dangerous after all? Venom composition and potency of the Pholcid (daddy long-leg) spider Physocyclus mexicanus. Front. Ecol. Evol. 7, 256 (2019).Article 

Google Scholar 
Diniz, M. R. et al. An overview of Phoneutria nigriventer spider venom using combined transcriptomic and proteomic approaches. PLoS ONE 13(8), e0200628 (2018).Article 

Google Scholar 
He, Q. et al. The venom gland transcriptome of Latrodectus tredecimguttatus revealed by deep sequencing and cDNA library analysis. PLoS ONE 8(11), e81357 (2013).Article 
ADS 

Google Scholar 
Haney, R. A., Ayoub, N. A., Clarke, T. H., Hayashi, C. Y. & Garb, J. E. Dramatic expansion of the black widow toxin arsenal uncovered by multi-tissue transcriptomics and venom proteomics. BMC Genom. 15(1), 1–18 (2014).Article 

Google Scholar 
Haney, R. A., Matte, T., Forsyth, F. S. & Garb, J. E. Alternative transcription at venom genes and its role as a complementary mechanism for the generation of venom complexity in the common house spider. Front. Ecol. Evol. 7, 85 (2019).Article 

Google Scholar 
Lüddecke, T. et al. An economic dilemma between molecular weapon systems may explain an arachno-atypical venom in wasp spiders (Argiope bruennichi). Biomolecules 10(7), 978 (2020).Article 

Google Scholar 
Fainzilber, M., Gordon, D., Hasson, A., Spira, M. E. & Zlotkin, E. Mollusc-specific toxins from the venom of Conus textile neovicarius. Eur. J. Biochem. 202(2), 589–595 (1991).Article 
CAS 

Google Scholar 
Pawlak, J. et al. Denmotoxin, a three-finger toxin from the colubrid snake Boiga dendrophila (Mangrove Catsnake) with bird-specific activity. J. Biol. Chem. 281(39), 29030–29041 (2006).Article 
CAS 

Google Scholar 
Krasnoperov, V. G., Shamotienko, O. G. & Grishin, E. V. Isolation and properties of insect and crustacean-specific neurotoxins from the venom of the black widow spider (Latrodectus mactans tredecimguttatus). J. Nat. Toxins 1, 17–23 (1992).CAS 

Google Scholar 
Xu, X. et al. A comparative analysis of the venom gland transcriptomes of the fishing spiders Dolomedes mizhoanus and Dolomedes sulfurous. PLoS ONE 10(10), e0139908 (2015).Article 

Google Scholar 
Kuzmenkov, A. I., Sachkova, M. Y., Kovalchuk, S. I., Grishin, E. V. & Vassilevski, A. A. Lachesana tarabaevi, an expert in membrane-active toxins. Biochem. J. 473(16), 2495–2506 (2016).Article 
CAS 

Google Scholar 
Pekár, S. & Toft, S. Trophic specialisation in a predatory group: The case of prey-specialised spiders (Araneae). Biol. Rev. 90(3), 744–761 (2015).Article 

Google Scholar 
Nyffeler, M. & Pusey, B. J. Fish predation by semi-aquatic spiders: A global pattern. PLoS ONE 9(6), e99459 (2014).Article 
ADS 

Google Scholar 
Pekár, S. & Lubin, Y. Prey and predatory behavior of two zodariid species (Araneae, Zodariidae). J. Arachnol. 37(1), 118–121 (2009).Article 

Google Scholar 
Michálek, O., Kuhn-Nentwig, L. & Pekár, S. High specific efficiency of venom of two prey-specialized spiders. Toxins 11(12), 687 (2019).Article 

Google Scholar 
Modahl, C. M., Mrinalini, Frietze, S. & Mackessy, S. P. Adaptive evolution of distinct prey-specific toxin genes in rear-fanged snake venom. Proc. R. Soc. B. 285(1884), 20181003 (2018).Article 

Google Scholar 
Harris, R. J., Zdenek, C. N., Harrich, D., Frank, N. & Fry, B. G. An appetite for destruction: Detecting prey-selective binding of α-neurotoxins in the venom of Afro-Asian elapids. Toxins 12(3), 205 (2020).Article 
CAS 

Google Scholar 
Duran, L. H., Rymer, T. L. & Wilson, D. T. Variation in venom composition in the Australian funnel-web spiders Hadronyche valida. Toxicon: X 8, 100063 (2020).Article 
CAS 

Google Scholar 
Kuhn-Nentwig, L., Schaller, J. & Nentwig, W. Purification of toxic peptides and the amino acid sequence of CSTX-1 from the multicomponent venom of Cupiennius salei (Araneae: Ctenidae). Toxicon 32(3), 287–302 (1994).Article 
CAS 

Google Scholar 
Friedel, T. & Nentwig, W. Immobilizing and lethal effects of spider venoms on the cockroach and the common mealbeetle. Toxicon 27(3), 305–316 (1989).Article 
CAS 

Google Scholar 
Eggs, B., Wolff, J. O., Kuhn-Nentwig, L., Gorb, S. N. & Nentwig, W. Hunting without a web: How lycosoid spiders subdue their prey. Ethology 121(12), 1166–1177 (2015).Article 

Google Scholar 
Andrews, S. FastQC: A quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc (2015).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30(15), 2114–2120 (2014).Article 
CAS 

Google Scholar 
Song, L. & Florea, L. Rcorrector: Efficient and accurate error correction for Illumina RNA-seq reads. GigaScience 4(1), s13742–s14015 (2015).Article 

Google Scholar 
Grabherr, M. G. et al. Trinity: Reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nat. Biotechnol. 29(7), 644 (2011).Article 
CAS 

Google Scholar 
Gilbert, D. EvidentialGene: Evidence directed gene predictions for eukaryotes. Available online at: http://arthropods.eugenes.org/EvidentialGene/ (2010).Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10(3), 1–10 (2009).Article 

Google Scholar 
Seppey, M., Manni, M. & Zdobnov, E. M. BUSCO: Assessing genome assembly and annotation completeness. In Gene Prediction (ed. Kollmar, M.) 227–245 (Humana, 2019).
Google Scholar 
Haas, B. TransDecoder. Available online at: https://github.com/TransDecoder/TransDecoder (2015).Petersen, T. N., Brunak, S., Von Heijne, G. & Nielsen, H. SignalP 4.0: Discriminating signal peptides from transmembrane regions. Nat. Methods 8(10), 785–786 (2011).Article 
CAS 

Google Scholar 
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 25(17), 3389–3402 (1997).Article 
CAS 

Google Scholar 
UniProt. The universal protein knowledgebase in 2021. Nucleic Acids Res. 49(1), 480–489 (2021).
Google Scholar 
Eddy, S. R. A probabilistic model of local sequence alignment that simplifies statistical significance estimation. PLoS Comput. Biol. 4(5), e1000069 (2008).Article 
ADS 
MathSciNet 

Google Scholar 
Finn, R. D. et al. Pfam: The protein families database. Nucleic Acids Res. 42(1), 222–230 (2014).Article 

Google Scholar 
Wong, E. S., Hardy, M. C., Wood, D., Bailey, T. & King, G. F. SVM-based prediction of propeptide cleavage sites in spider toxins identifies toxin innovation in an Australian tarantula. PLoS ONE 8(7), e66279 (2013).Article 
ADS 
CAS 

Google Scholar 
King, G. F., Gentz, M. C., Escoubas, P. & Nicholson, G. M. A rational nomenclature for naming peptide toxins from spiders and other venomous animals. Toxicon 52(2), 264–276 (2008).Article 
CAS 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available online at: https://www.R-project.org/ (2019).Venables, W. N. & Ripley, B. D. Random and mixed effects in Modern Applied Statistics with S 271–300 (Springer, New York, 2002).Pekár, S. & Brabec, M. Modern Analysis of Biological Data: Generalized Linear Models in R (Masaryk University Press, 2016).
Google Scholar 
Halekoh, U., Højsgaard, S. & Yan, J. The R package geepack for generalized estimating equations. J. Stat. Softw. 15(2), 1–11 (2006).Article 

Google Scholar 
Pekár, S. & Brabec, M. Generalized estimating equations: A pragmatic and flexible approach to the marginal GLM modelling of correlated data in the behavioural sciences. Ethology 124(2), 86–93 (2018).Article 

Google Scholar  More

Gruninger, R. J., Ribeiro, G. O., Cameron, A. & McAllister, T. A. Invited review: Application of meta-omics to understand the dynamic nature of the rumen microbiome and how it responds to diet in ruminants. Animal 13, 1843–1854 (2019).CAS 

Google Scholar 
Morgavi, D. P., Kelly, W. J., Janssen, P. H. & Attwood, G. T. Rumen microbial (meta)genomics and its application to ruminant production. Animal 7, 184–201 (2013).CAS 

Google Scholar 
Bergman, E. N. Energy contributions of volatile fatty acids from the gastrointestinal tract in various species. Physiol. Rev. 70, 567–590 (1990).CAS 

Google Scholar 
Flint, H. J. The rumen microbial ecosystem—Some recent developments. Trends Microbiol. 5, 483–488 (1997).CAS 

Google Scholar 
Hobson, P. N. & Stewart, C. S. The Rumen Microbial Ecosystem. (Springer, 2012).Moraïs, S. & Mizrahi, I. The road not taken: The rumen microbiome, functional groups, and community states. Trends Microbiol. 27, 538–549 (2019).
Google Scholar 
Cheng, K. J., Forsberg, C. W., Minato, H. & Costerton, J. W. in Physiological Aspects of Digestion and Metabolism in Ruminants (eds T. Tsuda, Y. Sasaki, & R. Kawashima) 595–624 (Academic Press, 1991).McSweeney, C. S., Palmer, B., McNeill, D. M. & Krause, D. O. Microbial interactions with tannins: Nutritional consequences for ruminants. Anim. Feed Sci. Technol. 91, 83–93 (2001).CAS 

Google Scholar 
Skene, I. K. & Brooker, J. D. Characterization of tannin acylhydrolase activity in the ruminal bacterium Selenomonas ruminantium. Anaerobe 1, 321–327 (1995).CAS 

Google Scholar 
Khanbabaee, K. & van Ree, T. Tannins: Classification and definition. Nat. Prod. Rep. 18, 641–649 (2001).CAS 

Google Scholar 
Makkar, H. P. S. & Becker, K. Isolation of tannins from leaves of some trees and shrubs and their properties. J. Agric. Food Chem. 42, 731–734 (1994).CAS 

Google Scholar 
Bhat, T. K., Kannan, A., Singh, B. & Sharma, O. P. Value addition of feed and fodder by alleviating the antinutritional effects of tannins. Agr. Res. 2, 189–206 (2013).CAS 

Google Scholar 
Shimada, T. Salivary proteins as a defense against dietary tannins. J. Chem. Ecol. 32, 1149–1163 (2006).CAS 

Google Scholar 
Zhu, J., Filippich, L. J. & Alsalami, M. T. Tannic acid intoxication in sheep and mice. Res. Vet. Sci. 53, 280–292 (1992).CAS 

Google Scholar 
Kohl, K. D., Stengel, A. & Dearing, M. D. Inoculation of tannin-degrading bacteria into novel hosts increases performance on tannin-rich diets. Environ. Microbiol. 18, 1720–1729 (2016).CAS 

Google Scholar 
Kumar, K., Chaudhary, L. C., Agarwal, N. & Kamra, D. N. Isolation and characterization of tannin-degrading bacteria from the rumen of goats fed oak (Quercus semicarpifolia) leaves. Agr. Res. 3, 377–385 (2014).
Google Scholar 
Odenyo, A. A. et al. Characterization of tannin-tolerant bacterial isolates from East African ruminants. Anaerobe 7, 5–15 (2001).CAS 

Google Scholar 
Grilli, D. J. et al. Analysis of the rumen bacterial diversity of goats during shift from forage to concentrate diet. Anaerobe 42, 17–26 (2016).
Google Scholar 
Tong, J. et al. Illumina sequencing analysis of the ruminal microbiota in high-yield and low-yield lactating dairy cows. PLoS ONE 13, e0198225 (2018).
Google Scholar 
Pope, P. B. et al. Metagenomics of the Svalbard reindeer rumen microbiome reveals abundance of polysaccharide utilization loci. PLoS ONE 7, e38571 (2012).ADS 
CAS 

Google Scholar 
Østbye, K., Wilson, R. & Rudi, K. Rumen microbiota for wild boreal cervids living in the same habitat. FEMS Microbiol. Lett. 363, fnw233 (2016).
Google Scholar 
Gruninger, R. J., Sensen, C. W., McAllister, T. A. & Forster, R. J. Diversity of rumen bacteria in Canadian cervids. PLoS ONE 9, e89682 (2014).ADS 

Google Scholar 
Henderson, G. et al. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci. Rep. 5, 14567 (2015).CAS 

Google Scholar 
Reese, A. T. & Kearney, S. M. Incorporating functional trade-offs into studies of the gut microbiota. Curr. Opin. Microbiol. 50, 20–27 (2019).CAS 

Google Scholar 
Moeller, A. H. et al. Social behavior shapes the chimpanzee pan-microbiome. Sci. Adv. 2, e1500997 (2016).ADS 

Google Scholar 
Okano, T. & Matsuda, H. Biocultural diversity of Yakushima Island: Mountains, beaches, and sea. J. Mar. Isl. Cult. 2, 69–77 (2013).
Google Scholar 
Agetsuma, N., Agetsuma-Yanagihara, Y. & Takafumi, H. Food habits of Japanese deer in an evergreen forest: Litter-feeding deer. Mamm. Biol. 76, 201–207 (2011).
Google Scholar 
Higashi, Y., Hirota, S. K., Suyama, Y. & Yahara, T. Geographical and seasonal variation of plant taxa detected in faces of Cervus nippon yakushimae based on plant DNA analysis in Yakushima Island. Ecol. Res. 37, 582–597 (2022).CAS 

Google Scholar 
Kuroiwa, A. Nutritional ecology of the Yakushika (Cervus nippon yakushimae) population under high density Ph.D. thesis, Kyushu University, (2017).Koda, R., Agetsuma, N., Agetsuma-Yanagihara, Y., Tsujino, R. & Fujita, N. A proposal of the method of deer density estimate without fecal decomposition rate: A case study of fecal accumulation rate technique in Japan. Ecol. Res. 26, 227–231 (2011).
Google Scholar 
Yahara, T. in Deer eats world heritages: Ecology of deer and forets (eds T. Yumoto & H. Matsuda) 168–187 (Bunichi-Sogo-Shuppan, 2006).Onoda, Y. & Yahara, T. in Challenges for Conservation Ecology in Space and Time. (eds T. Miyashita & J. Nishihiro) 126–149 (University of Tokyo Press, 2015).Kagoshima Prefecture Nature Conservation Division. The current status of Yakusika in FY 2020, available at https://www.rinya.maff.go.jp/kyusyu/fukyu/shika/attach/pdf/yakushikaWG_R3_2-23.pdf (2020).Kuroiwa, A., Kuroe, M. & Yahara, T. Effects of density, season, and food intake on sika deer nutrition on Yakushima Island, Japan. Ecol. Res. 32, 369–378 (2017).
Google Scholar 
Hiura, T., Hashidoko, Y., Kobayashi, Y. & Tahara, S. Effective degradation of tannic acid by immobilized rumen microbes of a sika deer (Cervus nippon yesoensis) in winter. Anim. Feed Sci. Technol. 155, 1–8 (2010).CAS 

Google Scholar 
Kawarai, S. et al. Seasonal and geographical differences in the ruminal microbial and chloroplast composition of sika deer (Cervus nippon) in Japan. Sci. Rep. 12, 6356 (2022).ADS 
CAS 

Google Scholar 
Li, Z. et al. Response of the rumen microbiota of sika deer Cervus nippon fed different concentrations of tannin rich plants. PLoS ONE 10, e0123481 (2015).
Google Scholar 
McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012).CAS 

Google Scholar 
Kim, M., Morrison, M. & Yu, Z. Status of the phylogenetic diversity census of ruminal microbiomes. FEMS Microbiol. Ecol. 76, 49–63 (2011).CAS 

Google Scholar 
Weimer, P. J. Redundancy, resilience, and host specificity of the ruminal microbiota: Implications for engineering improved ruminal fermentations. Front. Microbiol. 6, 296 (2015).
Google Scholar 
Scott, K. P., Gratz, S. W., Sheridan, P. O., Flint, H. J. & Duncan, S. H. The influence of diet on the gut microbiota. Pharmacol. Res. 69, 52–60 (2013).CAS 

Google Scholar 
Tapio, I. et al. Taxon abundance, diversity, co-occurrence and network analysis of the ruminal microbiota in response to dietary changes in dairy cows. PLoS ONE 12, e0180260 (2017).
Google Scholar 
Ohara, M. in Agriculture in Hokkaido v2 (ed K. Iwama, Ohara, M., Araki, H., Yamada, T., Nakatsuji, H., Kataoka, T., Yamamoto, Y.) 1–18(Faculty of Agriculture, Hokkaido Univ., 2009).Igota, H., Sakuragi, M. & Uno, H. in Sika Deer: Biology and Management of Native and Introduced Populations (eds. Dale R. McCullough, Seiki Takatsuki, & Koichi Kaji) 251–272 (Springer Japan, 2009).Fernando, S. C. et al. Rumen microbial population dynamics during adaptation to a high-grain diet. Appl. Environ. Microbiol. 76, 7482–7490 (2010).ADS 
CAS 

Google Scholar 
Hu, X. et al. High-throughput analysis reveals seasonal variation of the gut microbiota composition within forest musk deer (Moschus berezovskii). Front. Microbiol. 9, (2018).Artzi, L., Morag, E., Shamshoum, M. & Bayer, E. A. Cellulosomal expansion: Functionality and incorporation into the complex. Biotechnol. Biofuels 9, 61 (2016).
Google Scholar 
Biddle, A., Stewart, L., Blanchard, J. & Leschine, S. Untangling the genetic basis of fibrolytic specialization by Lachnospiraceae and Ruminococcaceae in diverse gut communities. Diversity 5, (2013).Eisenhauer, N., Scheu, S. & Jousset, A. Bacterial diversity stabilizes community productivity. PLoS ONE 7, e34517 (2012).ADS 
CAS 

Google Scholar 
Miller, A. W., Oakeson, K. F., Dale, C. & Dearing, M. D. Effect of dietary oxalate on the gut microbiota of the mammalian herbivore Neotoma albigula. Appl. Environ. Microbiol. 82, 2669–2675 (2016).ADS 
CAS 

Google Scholar 
Adams, J. M., Rehill, B., Zhang, Y. & Gower, J. A test of the latitudinal defense hypothesis: Herbivory, tannins and total phenolics in four North American tree species. Ecol. Res. 24, 697–704 (2009).CAS 

Google Scholar 
Nabeshima, E., Murakami, M. & Hiura, T. Effects of herbivory and light conditions on induced defense in Quercus crispula. J. Plant Res. 114, 403–409 (2001).
Google Scholar 
Yang, C.-M., Yang, M.-M., Hsu, J.-M. & Jane, W.-N. Herbivorous insect causes deficiency of pigment–protein complexes in an oval-pointed cecidomyiid gall of Machilus thunbergii leaf. Bot. Bull. Acad. Sin. 44, 315–321 (2003).
Google Scholar 
Agetsuma, N., Agetsuma-Yanagihara, Y., Takafumi, H. & Nakaji, T. Plant constituents affecting food selection by sika deer. J. Wildl. Manag. 83, 669–678 (2019).
Google Scholar 
Couch, C. E. et al. Diet and gut microbiome enterotype are associated at the population level in African buffalo. Nat. Commun. 12, 2267 (2021).ADS 
CAS 

Google Scholar 
Goel, G., Puniya, A. K. & Singh, K. Tannic acid resistance in ruminal streptococcal isolates. J. Basic Microbiol. 45, 243–245 (2005).CAS 

Google Scholar 
Jiménez, N. et al. Genetic and biochemical approaches towards unravelling the degradation of gallotannins by Streptococcus gallolyticus. Microb. Cell Fact. 13, 154 (2014).
Google Scholar 
Nelson, K. E., Thonney, M. L., Woolston, T. K., Zinder, S. H. & Pell, A. N. Phenotypic and phylogenetic characterization of ruminal tannin-tolerant bacteria. Appl. Environ. Microbiol. 64, 3824–3830 (1998).ADS 
CAS 

Google Scholar 
Selwal, M. K. et al. Optimization of cultural conditions for tannase production by Pseudomonas aeruginosa IIIB 8914 under submerged fermentation. World J. Microbiol. Biotechnol. 26, 599–605 (2010).CAS 

Google Scholar 
Kohl, K. D., Weiss, R. B., Cox, J., Dale, C. & Denise Dearing, M. Gut microbes of mammalian herbivores facilitate intake of plant toxins. Ecol. Lett. 17, 1238–1246 (2014).
Google Scholar 
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Method 7, 335–336 (2010).CAS 

Google Scholar 
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).CAS 

Google Scholar 
Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2009).
Google Scholar 
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).ADS 

Google Scholar 
R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2020).Osawa, R. Formation of a clear zone on tannin-treated brain heart infusion agar by a Streptococcus sp. isolated from feces of koalas. Appl. Environ. Microbiol. 56, 829–831 (1990).ADS 
CAS 

Google Scholar 
Hamamura, N., Olson, S. H., Ward, D. M. & Inskeep, W. P. Diversity and functional analysis of bacterial communities associated with natural hydrocarbon seeps in acidic soils at Rainbow Springs, Yellowstone National Park. Appl. Environ. Microbiol. 71, 5943–5950 (2005).ADS 
CAS 

Google Scholar 
Benson, D. A. et al. GenBank. Nucleic Acids Res. 41, D36–D42 (2012).ADS 

Google Scholar 
Chen, I.-M. A. et al. The IMG/M data management and analysis system v.6.0: new tools and advanced capabilities. Nucleic Acids Res. 49, D751–D763 (2020)Suzuki, M. T., Taylor, L. T. & Delong, E. F. Quantitative analysis of small-subunit rRNA genes in mixed microbial populations via 5 ’-nuclease assays. Appl. Environ. Microbiol. 66, 4605–4614 (2000).ADS 
CAS 

Google Scholar  More

Study area and soil collectionTwenty NRCS map units were selected across Hawaii Commercial & Sugar Company (HC&S) in central Maui that represented seven soil orders, 10 NRCS soil series, and approximately 77% of the total plantation area (Fig. 1). Soil heterogeneity across the landscape allowed for the comparison of a continuum of soil and soil properties that have experienced the same C4 grass inputs and agricultural treatment under sugarcane production for over 100 years. Conventional sugarcane production involved 2-year growth followed by harvest burn, collection of remaining stalks by mechanical ripper, deep tillage to 40 cm, no crop rotations, and little to no residue return. The sampled soils, collected from September-August 2015, thus represent a baseline of SOC after input-intensive tropical agriculture and long-term soil disturbance. Elemental analyses from this work show consistent agricultural disturbances led to degraded SOC content ranging from 0.23 to 2.91% SOC of soil mass with an average of only 1.16% SOC across all locations and depths.Figure 1Hawaiian Commercial and Sugar in central Maui with main Hawaiian Islands inset (left). Soil series identified by NRCS across HC&S fields (right) with black dots indicating 20 locations where soils were sampled to test landscape level differences in topical soil kinetics and associated soil properties under conventional sugarcane. Maps from Ref.19 created using ESRI ArcGIS with soil series data from: Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture, Web Soil Survey, Available online at http://websoilsurvey.nrcs.usda.gov/. Accessed [07/30/2016]19.Full size imageThe homogenized land use history allowed focused investigation of soil property effects on SOC storage across heterogenous soils (Table 1). Though soil inputs (e.g. water, nutrients, root inputs, residue removal) and disturbance regimes (e.g. burn, rip, till, compaction, no crop rotation) were consistent across the 20 field locations, average annual surface temperatures varied from 22.9 to 25.1 °C with a mean of 24.4 °C, average annual relative humidity varied from 70.4 to 79.2% with a mean of 73.4%, and average annual rainfall varied from 306 to 1493 mm with a mean of 575 mm. Gradients of rainfall, relative humidity, and elevation across the site generally increase in an east/north-east direction towards the prevailing winds and up the western slope of Haleakalā. In contrast, surface temperatures increase in the opposite direction towards Kihei and the southern tip of the West Maui Mountains.Table 1 NRCS soil classification and environmental conditions at 20 field sites.Full size tableaSoil descriptions26.bInterpolated estimates from Ref.25.Soil sampling and analysisPit locations were identified with a handheld GPS and were sampled using NRCS Rapid Carbon Assessment methods27. A total of 75 horizons were identified from the 20 selected map units to a depth of 1 m28,29. The central depth of each horizon was sampled using volumetric bulk density cores up to 50 cm. After 50 cm, a hand auger was used to check for any further horizon changes. The bulk density of horizons past 50 cm were estimated using collected soil mass and known auger size. Collected soils were air dried, processed through a 2 mm sieve, and analyzed for total C and nitrogen percent, SOC percent, soil texture, iron (Fe) and aluminum (Al) minerals, pH, cation and anion exchange capacity, extractable cations, wet and dry size classes, aggregate stability, and soil water potential at -15 kPa. Total C and nitrogen were measured by elemental analysis (Costech, ECS 4010, Valencia, CA), with SOC content determined by elemental analysis after hydrochloric acid digestion to remove carbonates. Soil texture was measured using sedimentary separation, while a 10:1 soil slurry in water was used to test soil pH. Soil pressure plates were used to measure soil water potential at -15 kPa.Fe and Al oxides were quantified in mineral phases using selective dissolutions of collected soils, including: (1) a 20:1 sodium citrate to sodium dithionite extraction, shaken 16 h, to quantify total free minerals30, (2) 0.25 M hydroxylamine hydrochloride and hydrochloric acid extraction, shaken 16 h, to quantify amorphous minerals31, and (3) 0.1 M sodium pyrophosphate (pH 10), shaken 16 h and centrifuged at 20,000g, to quantify organo-bound metals30. Extracted Fe, Al, and Si from al extractions were measured by inductively coupled plasma analysis (PerkinElmer, Optima ICP-OES, Norwalk, CT). Exploratory ratios of Fe/Al, Fe/Si, and Al/Si for the citrate/dithionite (c), hydroxylamine (h), and pyrophosphate (p) extractions were calculated. Crystalline Fe, operationally-defined as the difference between the citrate dithionite and hydroxylamine extraction, and Al + ½ Fe32 were calculated for each extraction.Plant-available phosphorus was extracted by the Olsen method using 0.5 M sodium bicarbonate adjusted to pH 8.5 and measured by continuous flow colorimetry (Hach, Lachat Quickchem 8500, Loveland, CO). Exchangeable cations (i.e. calcium, magnesium, potassium, and sodium), effective cation exchange capacity, and anion exchange capacity were measured by compulsive exchange using barium chloride and magnesium sulfate33. Cations were quantified by continuous flow colorimetry and flame-spectroscopy (Hach, Lachat Quickchem 8500, Loveland, CO). Field soils were air dried and initially passed through a 2 mm sieve before size classes of macroaggregate (2 mm – 250 µm) and microaggregate ( More

Human-modified forests, such as selectively logged forests, are often characterized as degraded ecosystems because of their altered structure and low biomass. The concept of ecosystem degradation can be a double-edged sword. It rightly draws attention to the conservation value of old-growth systems and the importance of ecosystem restoration. However, it can also suggest that human-modified ecosystems are of low ecological value and therefore, in some cases, suitable for conversion to agriculture (such as oil palm plantations) and other land uses3,4,5.Selectively logged and other forms of structurally altered forests are becoming the prevailing vegetation cover in much of the tropical forest biome2. Such disturbance frequently leads to a decline in old-growth specialist species1, and also in non-specialist species in some contexts6,7,8. However, species-focused biodiversity metrics are only one measure of ecosystem vitality and functionality, and rarely consider the collective role that suites of species play in maintaining ecological functions9.An alternative approach is to focus on the energetics of key taxonomic groups, and the number and relative dominance of species contributing to each energetic pathway. Energetic approaches to examining ecosystem structure and function have a long history in ecosystem ecology10. Virtually all ecosystems are powered by a cascade of captured sunlight through an array of autotroph tissues and into hierarchical assemblages of herbivores, carnivores and detritivores. Energetic approaches shine light on the relative significance of energy flows among key taxa and provide insight into the processes that shape biodiversity and ecosystem function. The common currency of energy enables diverse guilds and taxa to be compared in a unified and physically meaningful manner: dominant energetic pathways can be identified, and the resilience of each pathway to the loss of individual species can be assessed. Quantitative links can then be made between animal communities and the plant-based ecosystem productivity on which they depend. The magnitude of energetic pathways in particular animal groups can often be indicators of key associated ecosystem processes, such as nutrient cycling, seed dispersal and pollination, or trophic factors such as intensity of predation pressure or availability of resource supply, all unified under the common metric of energy flux11,12.Energetics approaches have rarely been applied in biodiverse tropical ecosystems because of the range of observations they require11,12,13. Such analyses rely on: population density estimates for a very large number of species; understanding of the diet and feeding behaviour of the species; and reliable estimation of net primary productivity (NPP). Here we take advantage of uniquely rich datasets to apply an energetics lens to examine and quantify aspects of the ecological function and vitality of habitats in Sabah, Malaysia, that comprise old-growth forests, logged forest and oil palm plantation (Fig. 1 and Extended Data Fig. 1). Our approach is to calculate the short-term equilibrium production or consumption rates of food energy by specific species, guilds or taxonomic groups. We focus on three taxonomic groups (plants, birds and mammals) that are frequently used indicators of biodiversity and are relatively well understood ecologically.Fig. 1: Maps of the study sites in Sabah, Borneo.a–d, Maps showing locations of NPP plots and biodiversity surveys in old-growth forest, logged forest and oil palm plantations in the Stability of Altered Forest Ecosystems Project landscape (a), Maliau Basin (b), Danum Valley (c) and Sepilok (d). The inset in a shows the location of the four sites in Sabah. The shade of green indicates old-growth (dark green), twice-logged (intermediate green) or heavily logged (light green) forests. The camera and trap grid includes cameras and small mammal traps. White areas indicate oil palm plantations.Full size imageWe are interested in the fraction of primary productivity consumed by birds and mammals, and how it varies along the disturbance gradient, and how and why various food energetic pathways in mammals and birds, and the diversity of species contributing to those pathways, vary along the disturbance gradient. To estimate the density of 104 mammal and 144 bird species in each of the three habitat types, we aggregated data from 882 camera sampling locations (a total of 42,877 camera trap nights), 508 bird point count locations, 1,488 small terrestrial mammal trap locations (34,058 live-trap nights) and 336 bat trap locations (Fig. 1 and Extended Data Fig. 1). We then calculated daily energetic expenditure for each species based on their body mass, assigned each species to a dietary group and calculated total food consumption in energy units. For primary productivity, we relied on 34 plot-years (summation of plots multiplied by the number of years each plot is monitored) of measurements of the key components of NPP (canopy litterfall, woody growth, fine root production) using the protocols of the Global Ecosystem Monitoring Network14,15,16 across old-growth (n = 4), logged (n = 5) and oil palm (n = 1) plots. This dataset encompasses more than 14,000 measurements of litterfall, 20,000 tree diameter measurements and 2,700 fine root samples.Overall bird species diversity is maintained across the disturbance gradient and peaks in the logged forest; for mammals, there is also a slight increase in the logged forest, followed by rapid decline in the oil palm (Fig. 2b,c). Strikingly, both bird and mammal biomass increases substantially (144% and 231%, respectively) in the logged forest compared to the old-growth forest, with mammals contributing about 75% of total (bird plus mammal) biomass in both habitat types (Fig. 2b,c).Fig. 2: Variation of ecosystem energetics along the disturbance gradient from old-growth forest through logged forest to oil palm.a, Total NPP along the gradient (mean of intensive 1-ha plots; n = 4 for old growth (OG), n = 5 for logged and n = 1 for oil palm (OP); error bars are 95% confidence intervals derived from propagated uncertainty in the individually measured NPP components), with individual plot data points overlaid. b,c, Total body mass (bars, left axis) and number of species counted (blue dots and line, right axis) of birds (b) and mammals (c). d,e, Total direct energetic food intake by birds (d) and mammals (e). f,g, Percentage of NPP directly consumed by birds (f) and mammals (g). In b–e, body mass and energetics were estimated for individual bird and mammal species, with the bars showing the sum. Error bars denote 95% confidence intervals derived from 10,000 Monte Carlo simulation estimates incorporating uncertainty in body mass, population density, the daily energy expenditure equation, assimilation efficiency of the different food types, composition of the diet of each species and NPP. In f,g, the grey bars indicate direct consumption of NPP, white bars denote the percentage of NPP indirectly supporting bird and mammal food intake when the mean trophic level of consumed invertebrates is assumed to be 2.5, with the error bars denoting assumed mean trophic levels of 2.4 and 2.6. Note the log scale of the y axis in f,g. Numbers for d,e provided in Supplementary Data Tables 1, 2.Full size imageThe total flow of energy through consumption is amplified across all energetic pathways by a factor of 2.5 (2.2–3.0; all ranges reported are 95% confidence intervals) in logged forest relative to old-growth forest. In all three habitat types, total energy intake by birds is much greater than by mammals (Fig. 2d,e and Extended Data Table 1). Birds account for 67%, 68% and 90% of the total direct consumption by birds and mammals combined in old-growth forests, logged forests and oil palm, respectively. Although mammal biomass is higher than bird biomass in the old-growth and logged forests, the metabolism per unit mass is much higher in birds because of their small body size; hence, in terms of the energetics and consumption rates, the bird community dominates. The total energy intake by birds alone increases by a factor of 2.6 (2.1–3.2) in the logged forest relative to old-growth forest. This is mainly driven by a 2.5-fold (1.7–2.8) increase in foliage-gleaning insectivory (the dominant energetic pathway), and most other feeding guilds also show an even larger increase (Figs. 2d and 3). However, total bird energy intake in the oil palm drops back to levels similar to those in the old-growth forest, with a collapse in multiple guilds. For mammals, there is a similar 2.4-fold (1.9–3.2) increase in total consumption when going from old-growth to logged forest, but this declines sharply in oil palm plantation. Most notable is the 5.7-fold (3.2–10.2) increase in the importance of terrestrial mammal herbivores in the logged relative to old-growth forests. All four individual old-growth forest sites show consistently lower bird and mammal energetics than the logged forests (Extended Data Fig. 5).Fig. 3: Magnitude and species diversity of energetic pathways in old-growth forest, logged forest and oil palm.The size of the circles indicates the magnitude of energy flow, and the colour indicates birds or mammals. S, number of species; E, ESWI, an index of species redundancy and, therefore, resilience (high values indicate high redundancy; see main text). For clarity, guilds with small energetic flows are not shown, but are listed in Supplementary Data 4. Images created by J. Bentley.Full size imageThe fraction of NPP flowing through the bird and mammal communities increases by a factor of 2.1 (1.5–3.0) in logged forest relative to old-growth forest. There is very little increase in NPP in logged relative to old-growth forests (Fig. 2a) because increased NPP in patches of relatively intact logged forest is offset by very low productivity in more structurally degraded areas such as former logging platforms14,15. In oil palm plantations, oil palm fruits account for a large proportion of NPP, although a large fraction of these is harvested and removed from the ecosystem17. As a proportion of NPP, 1.62% (1.35–2.13%) is directly consumed by birds and mammals in the old-growth forest; this rises to 3.36% (2.57–5.07%) in the logged forest but drops to 0.89% (0.57–1.44%) in oil palm (Fig. 2f,g and Extended Data Table 2).If all invertebrates consumed are herbivores or detritivores (that is, at a trophic level of 2.0), and trophic efficiency is 10% (ref. 10), the total amount of NPP supporting the combined bird and mammal food intake would be 9%, 16% and 5% for old-growth forest, logged forest and oil palm, respectively. However, if the mean trophic level of consumed invertebrates is 2.5 (that is, a mix of herbivores and predators), the corresponding proportions would be 27%, 51% and 17% (Fig. 2f,g). As insectivory is the dominant feeding mode for the avian community, these numbers are dominated by bird diets. For birds in the old-growth forests, 0.35% of NPP supports direct herbivory and frugivory, but around 22% of NPP (assumed invertebrate trophic level 2.5) is indirectly required to support insectivory. The equivalent numbers for birds in logged forest are 0.83% and 46%. Hence, birds account for a much larger indirect consumption of NPP. Bird diet studies in old-growth and logged forest in the region suggest that consumed invertebrates have a mean trophic level of 2.5 (ref. 18; K. Sam, personal communication), indicating that the higher-end estimates of indirect NPP consumption (that is, around 50% in logged forests) are plausible.It is interesting to compare such high fractions of NPP to direct estimates of invertebrate herbivory. Scans of tree leaf litter from these forests suggest that just 7.0% of tree canopy leaf area (1–3% of total NPP) is removed by tree leaf herbivory14,16, but such estimates do not include other pathways available to invertebrates, including herbivory of the understorey, aboveground and belowground sap-sucking, leaf-mining, fruit- and wood-feeding, and canopy, litter and ground-layer detritivory. An increase in invertebrate biomass and herbivory in logged forest compared to old-growth forest has previously been reported in fogging studies in this landscape19. Such high levels of consumption of NPP by invertebrates could have implications on ecosystem vegetation biomass production, suggesting, first, that invertebrate herbivory has a substantial influence on recovery from logging and, second, that insectivorous bird densities may exert substantial indirect controls on ecosystem recovery.The distributions of energy flows among feeding guilds are remarkably stable among habitat types (Fig. 3), indicating that the amplified energy flows in the logged forests do not distort the overall trophic structure of vertebrate communities. Overall bird diet energetics are dominated by insectivory, which accounts for a strikingly invariant 66%, 63% and 66% of bird energetic consumption in old-growth forest, logged forest and oil palm, respectively. Foliage-gleaning dominates as a mode of invertebrate consumption in all three habitat types, with frugivory being the second most energetically important feeding mode (26%, 27% and 19%, respectively). Mammal diet is more evenly distributed across feeding guilds, but frugivory (31%, 30%, 30%) and folivory (24%, 38%, 26%) dominate. Small mammal insectivores are probably under-sampled (see Methods) so the contribution of mammal insectivory may be slightly greater than that estimated here. The apparent constancy of relative magnitude of feeding pathways across the intact and disturbed ecosystems is noteworthy and not sensitive to plausible shifts in feeding behaviour between habitat types (see Supplementary Discussion). There is no evidence of a substantial shift in dominant feeding guild: the principal feeding pathways present in the old-growth forest are maintained in the logged forest.When examining change at species level in the logged forests, the largest absolute increases in bird food consumption were in arboreal insectivores and omnivores (Fig. 4a and Extended Data Fig. 2a). In particular, this change was characterized by large increases in the abundance of bulbul species (Pycnonotus spp.). No bird species showed a significant or substantial reduction in overall energy consumption. In the oil palm plantation, total food consumption by birds was less than in logged forests, but similar to that in old-growth forests. However, this was driven by very high abundance of a handful of species, notably a single arboreal omnivore (yellow-vented bulbul Pycnonotus goiavier) and three arboreal insectivores (Mixornis bornensis, Rhipidura javanica, Copsychus saularis), whereas energy flows through most other bird species were greatly reduced (Fig. 4b and Extended Data Fig. 2b).Fig. 4: Changes in energy consumption by species in logged forest and oil palm relative to old-growth forest.a,b, Changes in energy consumption by species in logged forest relative to old-growth forest (a) and in oil palm relative to old-growth forest (b). The 20 species experiencing the largest increase (red) and decrease (blue) in both habitat types are shown. Bird species are shown in a lighter tone and mammal species are shown in a darker tone. The error bars denote 95% confidence intervals, derived from 10,000 Monte Carlo simulation estimates incorporating uncertainty in body mass, population density, the daily energy expenditure equation, assimilation efficiency of the different food types and composition of the diet of each species.Full size imageFor mammals, the increase in consumption in logged forests is dominated by consumption by large terrestrial herbivores increasing by a factor of 5.7 (3.2–10.2), particularly sambar deer (Rusa unicolor) and Asian elephant (Elephas maximus; Fig. 4a and Extended Data Figs. 2b and 3), along with that by small omnivores, predominantly rodents (native spiny rats, non-native black rat; Fig. 4). A few rainforest species show a strong decline (for example, greater mouse-deer Tragulus napu and brown spiny rat Maxomys rajah). In the oil palm, most mammal species collapse (Fig. 4b) and the limited consumption is dominated by a few disturbance-tolerant habitat generalists (for example, red muntjac Muntiacus muntjak, black rat Rattus rattus, civets), albeit these species are at lower densities than observed in old-growth forest (Extended Data Fig. 2).With very few exceptions, the amplified energy flows in logged forest seem to retain the same level of resilience as in old-growth forest. The diversity and dominance of species within any pathway can be a measure of the resilience of that pathway to loss of species. We assessed energetic dominance within individual pathways by defining an energetic Shannon–Wiener index (ESWI) to examine distribution of energy flow across species; low ESWI indicates a pathway with high dependence on a few species and hence potential vulnerability (Fig. 3). The overall ESWI across guilds does not differ between the old-growth and logged forest (t2,34 = −0.363, P = 0.930), but does decline substantially from old-growth forest to oil palm (t2,34 = −3.826, P = 0.0015), and from logged forest to oil palm (t2,34 = −3.639, P = 0.0025; linear mixed-effects models, with habitat type as fixed effect and guild as random effect; for model coefficients see Supplementary Table 3).Hence, for birds, the diversity of species contributing to dominant energetic pathways is maintained in the transition from old-growth to logged forests but declines substantially in oil palm. Mammals generally show lower diversity and ESWI than birds, but six out of ten feeding guilds maintain or increase ESWI in logged forest relative to the old-growth forests but collapse in oil palm (Fig. 3). Terrestrial herbivory is the largest mammal pathway in the logged forest but is dependent on only four species and is probably the most vulnerable of the larger pathways: a few large mammals (especially sambar deer) play a dominant terrestrial herbivory role in the logged forest. In parallel, bearded pigs (Sus barbatus), the only wild suid in Borneo, form an important and functionally unique component of the terrestrial omnivory pathway. These larger animals are particularly sensitive to anthropogenic pressures such as hunting, or associated pathogenic pressures as evidenced by the recent precipitous decline of the bearded pig in Sabah due to an outbreak of Asian swine fever (after our data were collected)20.Vertebrate populations across the tropics are particularly sensitive to hunting pressure21. Our study site has little hunting, but as a sensitivity analysis we explored the energetic consequences of 50% reduction in population density of those species potentially affected by targeted and/or indiscriminate hunting (Extended Data Fig. 4). Targeted hunted species include commercially valuable birds, and gun-hunted mammals (bearded pig, ungulates, banteng and mammals with medicinal value). Indiscriminately hunted species include birds and mammals likely to be trapped with nets and snares. Hunting in the logged forests lowers both bird and mammal energy flows but still leaves them at levels higher than in faunally intact old-growth forests. Such hunting brings bird energetics levels close to (but still above) those of old-growth forests. For mammals, however, even intensively hunted logged forests seem to maintain higher energetic flows than the old-growth forests. Hence, only very heavy hunting is likely to ‘offset’ the amplified energetics in the logged forest.The amplified energetic pathways in our logged forest probably arise as a result of bottom-up trophic factors including increased resource supply, palatability and accessibility. The more open forest structure in logged forest results in more vegetation being near ground level22,23 and hence more accessible to large generalist mammal herbivores, which show the most striking increase of the mammal guilds. The increased prioritization by plants of competition for light and therefore rapid vegetation growth strategies in logged forests results in higher leaf nutrient content and reduced leaf chemical defences against herbivory24,25, along with higher fruiting and flowering rates19 and greater clumping in resource supply9. This increased resource availability and palatability probably supports high invertebrate and vertebrate herbivore densities25. The act of disturbance displaces the ecosystem from a conservative chemically defended state to a more dynamic state with amplified energy and nutrient flow, but not to an extent that causes heavy disruption in animal community composition. Top-down trophic factors might also play a role in amplifying the energy flows in intermediate trophic levels, through mechanisms such as increased protection of ground-dwelling or nesting mammals and birds from aerial predators in the dense vegetation ground layer. This might partially explain the increased abundance of rodents, but there is little evidence of trophic release at this site because of the persisting high density of mammal carnivores26. Overall, the larger number of bottom-up mechanisms and surge in invertebrate consumption suggest that increased resource supply and palatability largely explains the amplification of consumption pathways in the logged forest. An alternative possibility is that the amplified vertebrate energetics do not indicate amplified overall animal energetics but rather a large diversion of energy from unmeasured invertebrate predation pathways (for example, parasitoids); this seems unlikely but warrants further exploration.Oil palm plantations show a large decline in the proportion of NPP consumed by mammals and birds compared to logged forests12. Mammal populations collapse because they are more vulnerable and avoid humans, and there is no suite of mammal generalists that can step in27,28. Birds show a more modest decline, to levels similar to those observed in old-growth forests, as there is a broad suite of generalist species that are able to adapt to and exploit the habitat types across the disturbance gradient, and because their small size and mobility render them less sensitive to human activity29. There is a consistent decline in the oil palm in ESWI for birds and especially for mammals, indicating a substantial increase in ecosystem vulnerability in many pathways.In conclusion, our analysis demonstrates the tremendously dynamic and ecologically vibrant nature of the studied logged forests, even heavily and repeatedly logged forests such as those found across Borneo. It is likely that the patterns, mechanisms and basic ecological energetics we describe are general to most tropical forests; amplification of multiple ecosystem processes after logging has also been reported for logged forests in Kenya9, but similar detailed analyses are needed for a range of tropical forests to elucidate the importance of biogeographic, climatic or other factors. We stress that our findings do not diminish the importance of protecting structurally intact old-growth forests, but rather question the meaning of degradation by shining a new light on the ecological value of logged and other structurally ‘degraded’ forests, reinforcing their significance to the conservation agenda30. We have shown that a wide diversity of species not only persist but thrive in the logged forest environment. Moreover, such ecological vibrancy probably enhances the prospects for ecosystem structural recovery. In terms of faunal intactness, our study landscape is close to a best-case scenario because hunting pressures were low. If logged forests can be protected from heavy defaunation, our analysis demonstrates that they can be vibrant ecosystems, providing many key ecosystem functions at levels much higher than in old-growth forests. Conservation of logged forest landscapes has an essential role to play in the in the protection of global biodiversity and biosphere function. More