Philippa Kaur

Aizen, M. A., Sabatino, M. & Tylianakis, J. M. Specialization and rarity predict nonrandom loss of interactions from mutualist networks. Science 335, 1486–1489 (2012).CAS 
PubMed 
Article 

Google Scholar 
Aanen, D. K. et al. The evolution of fungus-growing termites and their mutualistic fungal symbionts. Proc. Natl Acad. Sci. USA 99, 14887–14892 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lello, J., Boag, B., Fenton, A., Stevenson, I. R. & Hudson, P. J. Competition and mutualism among the gut helminths of a mammalian host. Nature 428, 840–844 (2004).CAS 
PubMed 
Article 

Google Scholar 
Jaeggi, A. V. & Gurven, M. Natural cooperators: food sharing in humans and other primates. Evol. Anthropol. 22, 186–195 (2013).PubMed 
Article 

Google Scholar 
Van Der Maas, H. L., Kan, K.-J., Marsman, M. & Stevenson, C. E. Network models for cognitive development and intelligence. J. Intell. 5, 16 (2017).PubMed Central 
Article 

Google Scholar 
Bascompte, J. & Jordano, P. Plant-animal mutualistic networks: the architecture of biodiversity. Annu. Rev. Ecol. Evol. Syst. 38, 567–593 (2007).Article 

Google Scholar 
Bastolla, U. et al. The architecture of mutualistic networks minimizes competition and increases biodiversity. Nature 458, 1018 (2009).CAS 
PubMed 
Article 

Google Scholar 
Valverde, S. et al. The architecture of mutualistic networks as an evolutionary spandrel. Nat. Ecol. Evol. 2, 94–99 (2018).PubMed 
Article 

Google Scholar 
Vizentin-Bugoni, J. et al. Structure, spatial dynamics, and stability of novel seed dispersal mutualistic networks in Hawai’i. Science 364, 78–82 (2019).CAS 
PubMed 
Article 

Google Scholar 
Bascompte, J. Disentangling the web of life. Science 325, 416–419 (2009).CAS 
PubMed 
Article 

Google Scholar 
Liu, X. et al. Network resilience. Phys. Rep. 971, 1–108 (2022).Article 

Google Scholar 
Rezende, E. L., Lavabre, J. E., Guimarães, P. R., Jordano, P. & Bascompte, J. Non-random coextinctions in phylogenetically structured mutualistic networks. Nature 448, 925–928 (2007).CAS 
PubMed 
Article 

Google Scholar 
Pocock, M. J., Evans, D. M. & Memmott, J. The robustness and restoration of a network of ecological networks. Science 335, 973–977 (2012).CAS 
PubMed 
Article 

Google Scholar 
Fowler, J. H. & Christakis, N. A. Cooperative behavior cascades in human social networks. Proc. Natl Acad. Sci. USA 107, 5334–5338 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
May, R. M., Levin, S. A. & Sugihara, G. Complex systems: ecology for bankers. Nature 451, 893–894 (2008).CAS 
PubMed 
Article 

Google Scholar 
Thébault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856 (2010).PubMed 
Article 
CAS 

Google Scholar 
Berdugo, M. et al. Global ecosystem thresholds driven by aridity. Science 367, 787–790 (2020).CAS 
PubMed 
Article 

Google Scholar 
Diaz, R. J. & Rosenberg, R. Spreading dead zones and consequences for marine ecosystems. Science 321, 926–929 (2008).CAS 
PubMed 
Article 

Google Scholar 
Biggs, R. O., Peterson, G. & Rocha, J. C. The regime shifts database: a framework for analyzing regime shifts in social-ecological systems. Ecol. Soc. 23, 3 (2018).Article 

Google Scholar 
Walker, B. & Meyers, J. A. Thresholds in ecological and social-ecological systems: a developing database. Ecol. Soc. 9, 2 (2004).
Google Scholar 
Hirota, M., Holmgren, M., Van Nes, E. H. & Scheffer, M. Global resilience of tropical forest and savanna to critical transitions. Science 334, 232–235 (2011).CAS 
PubMed 
Article 

Google Scholar 
Barnosky, A. D. et al. Approaching a state shift in earth’s biosphere. Nature 486, 52–58 (2012).CAS 
PubMed 
Article 

Google Scholar 
Dakos, V. & Bascompte, J. Critical slowing down as early warning for the onset of collapse in mutualistic communities. Proc. Natl Acad. Sci. USA 111, 17546–17551 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lever, J. J., van Nes, E. H., Scheffer, M. & Bascompte, J. The sudden collapse of pollinator communities. Ecol. Lett. 17, 350–359 (2014).PubMed 
Article 

Google Scholar 
Lever, J. J. et al. Foreseeing the future of mutualistic communities beyond collapse. Ecol. Lett. 23, 2–15 (2020).PubMed 
Article 

Google Scholar 
Hillebrand, H. et al. Thresholds for ecological responses to global change do not emerge from empirical data. Nat. Ecol. Evol. 4, 1502–1509 (2020).PubMed 
Article 

Google Scholar 
Dudney, J. & Suding, K. N. The elusive search for tipping points. Nat. Ecol. Evol. 4, 1449–1450 (2020).PubMed 
Article 

Google Scholar 
Scheffer, M. et al. Anticipating critical transitions. Science 338, 344–348 (2012).CAS 
PubMed 
Article 

Google Scholar 
Martin, S., Deffuant, G. & Calabrese, J. M. in Viability and Resilience of Complex Systems (eds. Deffuant, G., & Gilbert, N.) 15–36 (Springer, 2011).Cohen, R., Erez, K., Ben-Avraham, D. & Havlin, S. Resilience of the internet to random breakdowns. Phys. Rev. Lett. 85, 4626–4628 (2000).CAS 
PubMed 
Article 

Google Scholar 
Gao, J., Barzel, B. & Barabási, A.-L. Universal resilience patterns in complex networks. Nature 530, 307–312 (2016).CAS 
PubMed 
Article 

Google Scholar 
Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009).CAS 
PubMed 
Article 

Google Scholar 
Boettiger, C. & Hastings, A. Quantifying limits to detection of early warning for critical transitions. J. R. Soc. Interface 9, 2527–2539 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Blanchard, J. L. A rewired food web. Nature 527, 173–174 (2015).CAS 
PubMed 
Article 

Google Scholar 
Campbell, C., Yang, S., Shea, K. & Albert, R. Topology of plant-pollinator networks that are vulnerable to collapse from species extinction. Phys. Rev. E 86, 021924 (2012).Article 
CAS 

Google Scholar 
Revilla, T. A., Encinas-Viso, F. & Loreau, M. Robustness of mutualistic networks under phenological change and habitat destruction. Oikos 124, 22–32 (2015).Article 

Google Scholar 
Vizentin-Bugoni, J. et al. Ecological correlates of species’ roles in highly invaded seed dispersal networks. Proc. Natl Acad. Sci. USA 118, (2021).Whanpetch, N. et al. Temporal changes in benthic communities of seagrass beds impacted by a tsunami in the Andaman Sea, Thailand. Estuar. Coast. Shelf Sci. 87, 246–252 (2010).Article 

Google Scholar 
Orth, R. J. et al. Restoration of seagrass habitat leads to rapid recovery of coastal ecosystem services. Sci. Adv. 6, eabc6434 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Veraart, A. J. et al. Recovery rates reflect distance to a tipping point in a living system. Nature 481, 357–359 (2012).CAS 
Article 

Google Scholar 
Dai, L., Vorselen, D., Korolev, K. S. & Gore, J. Generic indicators for loss of resilience before a tipping point leading to population collapse. Science 336, 1175–1177 (2012).CAS 
PubMed 
Article 

Google Scholar 
Dakos, V., van Nes, E. H., d’Odorico, P. & Scheffer, M. Robustness of variance and autocorrelation as indicators of critical slowing down. Ecology 93, 264–271 (2012).PubMed 
Article 

Google Scholar 
van Belzen, J. et al. Vegetation recovery in tidal marshes reveals critical slowing down under increased inundation. Nat. Commun. 8, 15811 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Rohr, R. P., Saavedra, S. & Bascompte, J. On the structural stability of mutualistic systems. Science 345, 1253497 (2014).PubMed 
Article 
CAS 

Google Scholar 
Wright, D. H. A simple, stable model of mutualism incorporating handling time. Am. Nat.134, 664–667 (1989).Article 

Google Scholar 
Newman, M. E. J. Networks: An Introduction (Oxford Univ. Press, 2010).Jiang, J. et al. Predicting tipping points in mutualistic networks through dimension reduction. Proc. Natl Acad. Sci. USA 115, E639–E647 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gao, J., Buldyrev, S. V., Stanley, H. E. & Havlin, S. Networks formed from interdependent networks. Nat. Phys. 8, 40–48 (2012).CAS 
Article 

Google Scholar 
May, R. M. Thresholds and breakpoints in ecosystems with a multiplicity of stable states. Nature 269, 471–477 (1977).Article 

Google Scholar 
Moreno, Y., Pastor-Satorras, R., Vázquez, A. & Vespignani, A. Critical load and congestion instabilities in scale-free networks. Europhys. Lett. 62, 292–298 (2003).CAS 
Article 

Google Scholar 
Martinez, N. D., Williams, R. J., Dunne, J. A. & Pascual, M. in Ecological Networks: Linking Structure to Dynamics in Food Webs (eds. Pascual, M., Dunne, J. A., & Dunne, J. A.) 163–185 (Oxford University Press, 2006).Chen, S., O’Dea, E. B., Drake, J. M. & Epureanu, B. I. Eigenvalues of the covariance matrix as early warning signals for critical transitions in ecological systems. Sci. Rep. 9, 1–14 (2019).Article 
CAS 

Google Scholar 
Suweis, S., Simini, F., Banavar, J. R. & Maritan, A. Emergence of structural and dynamical properties of ecological mutualistic networks. Nature 500, 449–452 (2013).CAS 
PubMed 
Article 

Google Scholar 
Mariani, M. S., Ren, Z.-M., Bascompte, J. & Tessone, C. J. Nestedness in complex networks: observation, emergence, and implications. Phys. Rep. 813, 1–90 (2019).Article 

Google Scholar 
Staniczenko, P. P., Kopp, J. C. & Allesina, S. The ghost of nestedness in ecological networks. Nat. Commun. 4, 1–6 (2013).Article 
CAS 

Google Scholar 
Marsh, H. et al. Optimizing allocation of management resources for wildlife. Conserv. Biol. 21, 387–399 (2007).PubMed 
Article 

Google Scholar 
Dakos, V. et al. Slowing down as an early warning signal for abrupt climate change. Proc. Natl Acad. Sci. USA 105, 14308–14312 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Reyer, C. P. et al. Forest resilience and tipping points at different spatio-temporal scales: approaches and challenges. J. Ecol. 103, 5–15 (2015).Article 

Google Scholar 
Dakos, V. et al. Ecosystem tipping points in an evolving world. Nat. Ecol. Evol. 3, 355–362 (2019).PubMed 
Article 

Google Scholar 
Hurwicz, L. The design of mechanisms for resource allocation. Am. Econ. Rev. 63, 1–30 (1973).
Google Scholar 
Almeida-Neto, M. & Ulrich, W. A straightforward computational approach for measuring nestedness using quantitative matrices. Environ. Model. Softw. 26, 173–178 (2011).Article 

Google Scholar 
Atmar, W. & Patterson, B. D. The measure of order and disorder in the distribution of species in fragmented habitat. Oecologia 96, 373–382 (1993).PubMed 
Article 

Google Scholar 
Kéfi, S. et al. Spatial vegetation patterns and imminent desertification in Mediterranean arid ecosystems. Nature 449, 213–217 (2007).PubMed 
Article 
CAS 

Google Scholar 
Dakos, V., van Nes, E. H., Donangelo, R., Fort, H. & Scheffer, M. Spatial correlation as leading indicator of catastrophic shifts. Theor. Ecol. 3, 163–174 (2010).Article 

Google Scholar 
Buldyrev, S. V., Parshani, R., Paul, G., Stanley, H. E. & Havlin, S. Catastrophic cascade of failures in interdependent networks. Nature 464, 1025–1028 (2010).CAS 
PubMed 
Article 

Google Scholar 
Web of Life, Ecological Networks Database (Bascompte Lab, accessed 12 June 2017); http://www.web-of-life.es/map.php?type=5/Gleeson, J. P., Melnik, S., Ward, J. A., Porter, M. A. & Mucha, P. J. Accuracy of mean-field theory for dynamics on real-world networks. Phys. Rev. E 85, 026106 (2012).Article 
CAS 

Google Scholar 
Strogatz, S. H. Nonlinear Dynamics and Chaos: with Applications to Physics, Biology, Chemistry, and Engineering (CRC Press, 2018).Vázquez, D. P. Interactions Among Introduced Ungulates, Plants, and Pollinators: a Field Study in the Temperate Forest of the Southern Andes PhD thesis, University of Tennessee (2002).Kaiser-Bunbury, C. N., Vázquez, D. P., Stang, M. & Ghazoul, J. Determinants of the microstructure of plant-pollinator networks. Ecology 95, 3314–3324 (2014).Article 

Google Scholar 
Memmott, J. The structure of a plant-pollinator food web. Ecol. Lett. 2, 276–280 (1999).CAS 
PubMed 
Article 

Google Scholar 
Dicks, L., Corbet, S. & Pywell, R. Compartmentalization in plant-insect flower visitor webs. J. Anim. Ecol. 71, 32–43 (2002).Article 

Google Scholar 
SMITH-RAMÍREZ, C., Martinez, P., Nunez, M., González, C. & Armesto, J. J. Diversity, flower visitation frequency and generalism of pollinators in temperate rain forests of Chiloé Island, Chile. Bot. J. Linn. Soc. 147, 399–416 (2005).Article 

Google Scholar 
Dupont, Y. L., Hansen, D. M. & Olesen, J. M. Structure of a plant-flower-visitor network in the high-altitude sub-alpine desert of Tenerife, Canary Islands. Ecography 26, 301–310 (2003).Article 

Google Scholar 
Dupont, Y. L. & Olesen, J. M. Ecological modules and roles of species in heathland plant-insect flower visitor networks. J. Anim. Ecol. 78, 346–353 (2009).PubMed 
Article 

Google Scholar  More

Koenig, J. E. et al. Succession of microbial consortia in the developing infant gut microbiome. Proc. Natl. Acad. Sci. 108, 4578–4585 (2011).ADS 
PubMed 
Article 

Google Scholar 
Bäckhed, F. et al. Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe 17, 690–703 (2015).PubMed 
Article 
CAS 

Google Scholar 
Tanaka, M. & Nakayama, J. Development of the gut microbiota in infancy and its impact on health in later life. Allergol. Int. 66, 515–522 (2017).PubMed 
Article 
CAS 

Google Scholar 
Xu, C., Zhu, H. & Qiu, P. Aging progression of human gut microbiota. BMC Microbiol. 19, 1–10 (2019).Article 

Google Scholar 
Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Nagpal, R. et al. Ontogenesis of the gut microbiota composition in healthy, full-term, vaginally born and breast-fed infants over the first 3 years of life: A quantitative bird’s-eye view. Front. Microbiol. 8, 1–9 (2017).Article 

Google Scholar 
Smith, S. C., Chalker, A., Dewar, M. L. & Arnould, J. P. Y. Age-related differences revealed in Australian fur seal Arctocephalus pusillus doriferus gut microbiota. FEMS Microbiol. Ecol. 86, 246–255 (2013).PubMed 
Article 
CAS 

Google Scholar 
Janiak, M. C. et al. Age and sex-associated variation in the multi-site microbiome of an entire social group of free-ranging rhesus macaques. Microbiome 9, (2021).Toro-Valdivieso, C., Toro, F., Stubbs, S., Castro-Nallar, E. & Blacklaws, B. Patterns of the fecal microbiota in the Juan Fernández fur seal (Arctocephalus philippii). MicrobiologyOpen 10, 1–19 (2021).Article 
CAS 

Google Scholar 
Medeiros, A. W. et al. Characterization of the faecal bacterial community of wild young South American (Arctocephalus australis) and Subantarctic fur seals (Arctocephalus tropicalis). FEMS Microbiol. Ecol. 92, 1–8 (2016).Article 
CAS 

Google Scholar 
Bik, E. M. et al. Marine mammals harbor unique microbiotas shaped by and yet distinct from the sea. Nat. Commun. 7, 10516 (2016).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Numberger, D., Herlemann, D. P. R., Jürgens, K., Dehnhardt, G. & Schulz-Vogt, H. Comparative analysis of the fecal bacterial community of five harbor seals (Phoca vitulina). MicrobiologyOpen 5, 782–792 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Pacheco-Sandoval, A. et al. The Pacific harbor seal gut microbiota in Mexico: Its relationship with diet and functional inferences. PlosOne 14, (2019).Nelson, T. M., Rogers, T. L., Carlini, A. R. & Brown, M. V. Diet and phylogeny shape the gut microbiota of Antarctic seals: A comparison of wild and captive animals. Environ. Microbiol. 15, 1132–1145 (2013).PubMed 
Article 
CAS 

Google Scholar 
Glad, T. et al. Ecological characterisation of the colonic microbiota in Arctic and sub-Arctic seals. Microbiol. Ecol. 60, 320–330 (2010).Article 
CAS 

Google Scholar 
Delport, T. C., Power, M. L., Harcourt, R. G., Webster, K. N. & Tetu, S. G. Colony location and captivity influence the gut microbial community composition of the Australian sea lion (Neophoca cinerea). Appl. Environ. Microbiol. 82, 3440–3349 (2016).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Stoffel, M. A. et al. Early sexual dimorphism in the developing gut microbiome of northern elephant seals. Mol. Ecol. 29, 2109–2122 (2020).PubMed 
Article 

Google Scholar 
Tian, J., Du, J., Han, J., Song, X. & Lu, Z. Age-related differences in gut microbial community composition of captive spotted seals (Phoca largha). Mar. Mamm. Sci. 36, 1231–1240 (2020).Article 

Google Scholar 
Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bigg, M. A. Harbour seal: Phoca vitulina and P. largha. In Handbook of Marine Mammals Vol. 2 (eds Ridgeway, S. H. & Harrison, R. J.) 1–27 (Academic Press, 1981).
Google Scholar 
Parracho, H., McCartney, A. L. & Gibson, G. R. Probiotics and prebiotics in infant nutrition. Proc. Nutr. Society 66, 405–411 (2007).Article 

Google Scholar 
Marques, T. M. et al. Programming infant gut microbiota: Influence of dietary and environmental factors. Curr. Opin. Biotechnol. 21, 149–156 (2010).PubMed 
Article 
CAS 

Google Scholar 
Palmer, C., Bik, E. M., DiGiulio, D. B., Relman, D. A. & Brown, P. O. Development of the human infant intestinal microbiota. PLoS Biol. 5, 1556–1573 (2007).Article 
CAS 

Google Scholar 
Mitsuoka, T. Intestinal flora and aging. Nutr. Rev. 50, 438–446 (1992).PubMed 
Article 
CAS 

Google Scholar 
Bowen, W., Oftedal, O. & Boness, D. Mass and energy transfer during lactation in a small phocid, the harbor seal (Phoca vitulina). Physiol. Zool. 65, 844–866 (1992).Article 

Google Scholar 
Bowen, W. D., Boness, D. J. & Iverson, S. J. Diving behaviour of lactating harbour seals and their pups during maternal foraging trips. Can. J. Zool. 77, 978–988 (1999).Article 

Google Scholar 
Jørgensen, C., Lydersen, C., Brix, O. & Kovacs, K. M. Diving development in nursing harbour seal pups. J. Exp. Biol. 204, 3993–4004 (2001).PubMed 
Article 

Google Scholar 
Muelbert, M. M. C. & Bowen, W. D. Duration of lactation and postweaning changes in mass and body composition of harbour seal, Phoca vitulina, pups. Can. J. Zool. 71, 1405–1414 (1993).Article 

Google Scholar 
Kim, M., Cho, H. & Lee, W. Y. Distinct gut microbiotas between southern elephant seals and Weddell seals of Antarctica. J. Microbiol. 58, 1018–1026 (2020).PubMed 
Article 
CAS 

Google Scholar 
Kershaw, J. L. & Hall, A. J. Seasonal variation in harbour seal (Phoca vitulina) blubber cortisol—A novel indicator of physiological state?. Sci. Rep. 6, 1–9 (2016).Article 
CAS 

Google Scholar 
Madison, A. & Kiecolt-Glaser, J. K. Stress, depression, diet, and the gut microbiota: Human–bacteria interactions at the core of psychoneuroimmunology and nutrition. Curr. Opin. Behav. Sci. 28, 105–110 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Thompson, P. M., Miller, D., Cooper, R. & Hammond, P. S. Changes in the distribution and activity of female harbour seals during the breeding season: implications for their lactation strategy and mating patterns. J. Anim. Ecol. 63, 24 (1994).Article 

Google Scholar 
Raulo, A. et al. Social behaviour and gut microbiota in red-bellied lemurs (Eulemur rubriventer): In search of the role of immunity in the evolution of sociality. J. Anim. Ecol. 87, 388–399 (2018).PubMed 
Article 

Google Scholar 
Song, S. J. et al. Cohabiting family members share microbiota with one another and with their dogs. Elife 2013, 1–22 (2013).
Google Scholar 
Fernández-Martin, E. M., Heckel, G., Schramm, Y. & García-Aguilar, M. C. The timing of pupping and molting of the Pacific harbor seal, Phoca vitulina richardii, at Punta Banda Estuary, Baja California, Mexico. Cienc. Mar. 42, 195–208 (2016).Article 

Google Scholar 
Oates, S. C. Survival, movements, and diet of juvenile harbor seals along central California. [Master’s thesis, San Jose State University]. (2005). https://doi.org/10.31979/etd.ra96-xhge.Germain, L. R., Mccarthy, M. D., Koch, P. L. & Harvey, J. T. Stable carbon and nitrogen isotopes in multiple tissues of wild and captive harbor seals (Phoca vitulina) off the California coast. Mar. Mamm. Sci. 28, 542–560 (2012).Article 
CAS 

Google Scholar 
Brassea-Pérez, E., Schramm, Y., Heckel, G., Chong-Robles, J. & Lago-Lestón, A. Metabarcoding analysis of the Pacific harbor seal diet in Mexico. Mar. Biol. 166, (2019).Davis, T. A., Nguyen, H. V., Costa, D. P. & Reeds, P. J. Amino acid composition of pinniped milk. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 110, 633–639 (1995).PubMed 
Article 
CAS 

Google Scholar 
Sauvé, C. C., van de Walle, J., Hammill, M. O., Arnould, J. P. Y. & Beauplet, G. Stomach temperature records reveal nursing behaviour and transition to solid food consumption in an unweaned mammal, the harbour seal pup (Phoca vitulina). PLoS ONE 9, (2014).Fernández Martín, E. M. Fenología de los nacimientos, estado de salud de las crías, y estructura genética poblacional de Phoca vitulina richardii en México [Doctoral thesis, Universidad Autónoma de Baja California, Mexico]. (2018).Gresse, R. et al. Gut microbiota dysbiosis in postweaning piglets: Understanding the keys to health. Trends Microbiol. 25, 851–873 (2017).PubMed 
Article 
CAS 

Google Scholar 
Sommer, F. et al. The gut microbiota modulates energy metabolism in the hibernating brown bear Ursus arctos. Cell Rep. 14, 1655–1661 (2016).PubMed 
Article 
CAS 

Google Scholar 
Ni, Y. et al. Distinct composition and metabolic functions of human gut microbiota are associated with cachexia in lung cancer patients. ISME J. 15, 3207–3220 (2021).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Pacífico, C. et al. Unveiling the bovine epimural microbiota composition and putative function. Microorganisms 9, 1–23 (2021).Article 
CAS 

Google Scholar 
Fenn, K. et al. Quinones are growth factors for the human gut microbiota. Microbiome 5, 161 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Rodríguez, J. M. et al. The composition of the gut microbiota throughout life, with an emphasis on early life. Microb. Ecol. Health Disease 26, (2015).Thompson, P. M., Mackay, A., Tollit, D. J., Enderby, S. & Hammond, P. S. The influence of body size and sex on the characteristics of harbour seal foraging trips. Can. J. Zool. 76, 1044–1053 (1998).Article 

Google Scholar 
van Parijs, S. M., Thompson, P. M., Tollit, D. J. & Mackay, A. Distribution and activity of male harbour seals during the mating season. Anim. Behav. 54, 35–43 (1997).Article 

Google Scholar 
Bjorkland, R. H. et al. Stable isotope mixing models elucidate sex and size effects on the diet of a generalist marine predator. Mar. Ecol. Prog. Ser. 526, 213–225 (2015).ADS 
Article 

Google Scholar 
Schwarz, D. et al. Large-scale molecular diet analysis in a generalist marine mammal reveals male preference for prey of conservation concern. Ecol. Evol. 8, 9889–9905 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Boulva, J. Temporal variations in birth period and characteristics of newborn harbour seals. Rapports et procPs-verbaux, Reunions du Conseil International pour I’Exploration de la Mer 169, 405–408 (1975).
Google Scholar 
Bhute, S. S., Ghaskadbi, S. S. & Shouche, Y. S. Rare biosphere in human gut: A less explored component of human gut microbiota and its association with human health. In Mining of Microbial Wealth and MetaGenomics (eds Kalia, V. C. et al.) 133–142 (Springer Nature Singapore Ptd Ltd, 2017). https://doi.org/10.1007/978-981-10-5708-3.Chapter 

Google Scholar 
Brown, R. F. & Mate, B. R. Abundance, movements, and feeding habits of harbor seals, Phoca vitulina, at Netarts and Tillamook Bays, Oregon. Fishery Bull. 81, 291–301 (1983).
Google Scholar 
Higgins, R. Bacteria and fungi of marine mammals: A review. Can. Veterinary J. 41, 105–116 (2000).CAS 

Google Scholar 
Gilbert, M. J. et al. Campylobacter blaseri sp. nov., isolated from common seals (Phoca vitulina). Int. J. Syst. Evolut. Microbiol. 68, 1787–1794 (2018).Article 
CAS 

Google Scholar 
Agnese, E. D. et al. Comparative microbial community analysis of fur seals and salmon aquaculture in Tasmania. Authorea. https://doi.org/10.22541/au.160253843.32636436/v1 (2020).Article 

Google Scholar 
Rivas, A. J., Lemos, M. L. & Osorio, C. R. Photobacterium damselae subsp. damselae, a bacterium pathogenic for marine animals and humans. Front. Microbiol. 4, 1–6 (2013).Article 

Google Scholar 
Fouz, B., Toranzo, A. E., Milan, M. & Amaro, C. Evidence that water transmits the disease caused by the fish pathogen Photobacterium damselae subsp. damselae. J. Appl. Microbiol. 88, 531–535 (2000).PubMed 
Article 
CAS 

Google Scholar 
Hundenborn, J., Thurig, S., Kommerell, M., Haag, H. & Nolte, O. Severe Wound Infection with Photobacterium damselae ssp. damselae and Vibrio harveyi, following a laceration injury in marine environment: A case report and review of the literature. Case Rep. Med. 2013, (2013).Lubinsky-Jinich, D., Schramm, Y. & Heckel, G. The Pacific Harbor Seal’s (Phoca vitulina richardii) breeding colonies in Mexico: Abundance and distribution. Aquat. Mamm. 43, 73–81 (2017).Article 

Google Scholar 
Arias-Del Razo, A. et al. Distribution of four pinnipeds (Zalophus californianus, Arctocephalus philippii townsendi, Phoca vitulina richardii, and Mirounga angustirostris) on Islands off the west coast of the Baja California Peninsula, Mexico. Aquat. Mamm. 43, 40–51 (2017).Article 

Google Scholar 
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. USA. 108, 4516–4522 (2011).ADS 
PubMed 
Article 

Google Scholar 
Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Robertson, K. M., Lauf, M. L. & Morin, P. A. Genetic sexing of pinnipeds: A real-time, single step qPCR technique. Conserv. Genet. Resour. 10, 213–218 (2018).Article 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).Article 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/ (2019). Accessed 3 June 2021.McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, (2013).Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-4. https://cran.r-project.org/package=vegan (2019). Accessed 3 June 2021.Andersen, K. S., Kirkegaard, R. H., Karst, S. M. & Albertsen, M. ampvis2: An R package to analyse and visualise 16S rRNA amplicon data. bioRxiv. https://doi.org/10.1101/299537 (2018).Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).MATH 
Book 

Google Scholar 
Salinas, H. & Ramirez-Delgado, D. ecolTest: Community Ecology Tests. (2021).Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).Article 
CAS 

Google Scholar 
Lozupone, C. & Knight, R. UniFrac: A new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J. & Knight, R. UniFrac: An effective distance metric for microbial community comparison. ISME J. 5, 169–172 (2011).PubMed 
Article 

Google Scholar 
Martinez Arbizu, P. pairwiseAdonis: Pairwise multilevel comparison using adonis. R package version 0.4. (2020).Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 38, 685–688 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes-A 2019 update. Nucleic Acids Res. 48, D455–D463 (2020).Article 
CAS 

Google Scholar  More

Fly population and maintenanceWe used an outbred population of Drosophila melanogaster established from 160 Wolbachia-infected fertilised females collected in Azeitão, Portugal54, and given to us by Élio Sucena. For at least 13 generations prior to the start of the experiments the flies were maintained on standard sugar yeast agar medium (SYA medium: 970 ml water, 100 g brewer’s yeast, 50 g sugar, 15 g agar, 30 ml 10% Nipagin solution and 3 ml propionic acid; ref. 61), in a population cage containing at least 5000 flies, with non-overlapping generations of 15 days. They were maintained at 24.3 ± 0.2 °C, on a 12:12 h light-dark cycle, at 60–80 % relative humidity. The experimental flies were kept under the same conditions. No ethical approval or guidance is required for experiments with D. melanogaster.Bacterial speciesWe used the Gram positive Lactococcus lactis (gift from Brian Lazzaro), Gram negative Enterobacter cloacae subsp. dissolvens (hereafter called E. cloacae; German collection of microorganisms and cell cultures, DSMZ; type strain: DSM-16657), Providencia burhodogranariea strain B (gift from Brian Lazzaro, DSMZ; type strain: DSM-19968) and Pseudomonas entomophila (gift from Bruno Lemaitre). L. lactis43, Pr. burhodogranariea44 and Ps. entomophila45 were isolated from wild-collected D. melanogaster and can be considered as opportunistic pathogens. E. cloacae was isolated from a maize plant, but has been detected in the microbiota of D. melanogaster46. All bacterial species were stored in 34.4% glycerol at −80 °C and new cultures were grown freshly for each experimental replicate.Experimental designFor each bacterial species, flies were exposed to one of seven treatments: no injection (naïve), injection with Drosophila Ringer’s (injection control) or injection with one of five concentrations of bacteria ranging from 5 × 106 to 5 × 109 colony forming units (CFUs)/mL, corresponding to doses of approximately 92, 920, 1,840, 9200 and 92,000 CFUs per fly. The injections were done in a randomised block design by two people. Each bacterial species was tested in three independent experimental replicates. Per experimental replicate we treated 252 flies, giving a total of 756 flies per bacterium (including naïve and Ringer’s injection control flies). Per experimental replicate and treatment, 36 flies were checked daily for survival until all flies were dead. A sub-set of the dead flies were homogenised upon death to test whether the infection had been cleared before death or not. To evaluate bacterial load in living flies, per experimental replicate, four of the flies were homogenised per treatment, for each of nine time points: one, two, three, four, seven, 14, 21, 28- and 35-days post-injection.Infection assayBacterial preparation was performed as in Kutzer et al.24, except that we grew two overnight liquid cultures of bacteria per species, which were incubated overnight for approximately 15 h at 30 °C and 200 rpm. The overnight cultures were centrifuged at 2880 × g at 4 °C for 10 min and the supernatant removed. The bacteria were washed twice in 45 mL sterile Drosophila Ringer’s solution (182 mmol·L-1 KCl; 46 mol·L-1 NaCl; 3 mmol·L-1 CaCl2; 10 mmol·L-1 Tris·HCl; ref. 62) by centrifugation at 2880 × g at 4 °C for 10 min. The cultures from the two flasks were combined into a single bacterial solution and the optical density (OD) of 500 µL of the solution was measured in a Ultrospec 10 classic (Amersham) at 600 nm. The concentration of the solution was adjusted to that required for each injection dose, based on preliminary experiments where a range of ODs between 0.1 and 0.7 were serially diluted and plated to estimate the number of CFUs. Additionally, to confirm post hoc the concentration estimated by the OD, we serially diluted to 1:107 and plated the bacterial solution three times and counted the number of CFUs.The experimental flies were reared at constant larval density for one generation prior to the start of the experiments. Grape juice agar plates (50 g agar, 600 mL red grape juice, 42 mL Nipagin [10% w/v solution] and 1.1 L water) were smeared with a thin layer of active yeast paste and placed inside the population cage for egg laying and removed 24 h later. The plates were incubated overnight then first instar larvae were collected and placed into plastic vials (95 × 25 mm) containing 7 ml of SYA medium. Each vial contained 100 larvae to maintain a constant density during development. One day after the start of adult eclosion, the flies were placed in fresh food vials in groups of five males and five females, after four days the females were randomly allocated to treatment groups and processed as described below.Before injection, females were anesthetised with CO2 for a maximum of five minutes and injected in the lateral side of the thorax using a fine glass capillary (Ø 0.5 mm, Drummond), pulled to a fine tip with a Narishige PC-10, and then connected to a Nanoject II™ injector (Drummond). A volume of 18.4 nL of bacterial solution, or Drosophila Ringer’s solution as a control, was injected into each fly. Full controls, i.e., naïve flies, underwent the same procedure but without any injection. After being treated, flies were placed in groups of six into new vials containing SYA medium, and then transferred into new vials every 2–5 days. Maintaining flies in groups after infection is a standard method in experiments with D. melanogaster that examine survival and bacterial load (e.g. refs. 22, 63, 64). At the end of each experimental replicate, 50 µL of the aliquots of bacteria that had been used for injections were plated on LB agar to check for potential contamination. No bacteria grew from the Ringer’s solution and there was no evidence of contamination in any of the bacterial replicates. To confirm the concentration of the injected bacteria, serial dilutions were prepared and plated before and after the injections for each experimental replicate, and CFUs counted the following day.Bacterial load of living fliesFlies were randomly allocated to the day at which they would be homogenised. Prior to homogenisation, the flies were briefly anesthetised with CO2 and removed from their vial. Each individual was placed in a 1.5 mL microcentrifuge tube containing 100 µL of pre-chilled LB media and one stainless steel bead (Ø 3 mm, Retsch) on ice. The microcentrifuge tubes were placed in a holder that had previously been chilled in the fridge at 4 °C for at least 30 min to reduce further growth of the bacteria. The holders were placed in a Retsch Mill (MM300) and the flies homogenised at a frequency of 20 Hz for 45 s. Then, the tubes were centrifuged at 420 × g for one minute at 4 °C. After resuspending the solution, 80 µL of the homogenate from each fly was pipetted into a 96-well plate and then serially diluted 1:10 until 1:105. Per fly, three droplets of 5 μL of every dilution were plated onto LB agar. Our lower detection limit with this method was around seven colony-forming units per fly. We consider bacterial clearance by the host to be when no CFUs were visible in any of the droplets, although we note that clearance is indistinguishable from an infection that is below the detection limit. The plates were incubated at 28 °C and the numbers of CFUs were counted after ~20 h. Individual bacterial loads per fly were back calculated using the average of the three droplets from the lowest countable dilution in the plate, which was usually between 10 and 60 CFUs per droplet.D. melanogaster microbiota does not easily grow under the above culturing conditions (e.g. ref. 42) Nonetheless we homogenised control flies (Ringer’s injected and naïve) as a control. We rarely retrieved foreign CFUs after homogenising Ringer’s injected or naïve flies (23 out of 642 cases, i.e., 3.6 %). We also rarely observed contamination in the bacteria-injected flies: except for homogenates from 27 out of 1223 flies (2.2 %), colony morphology and colour were always consistent with the injected bacteria (see methods of ref. 65). Twenty one of these 27 flies were excluded from further analyses given that the contamination made counts of the injected bacteria unreliable; the remaining six flies had only one or two foreign CFUs in the most concentrated homogenate dilution, therefore these flies were included in further analyses. For L. lactis (70 out of 321 flies), P. burhodogranaeria (7 out of 381 flies) and Ps. entomophila (1 out of 71 flies) there were too many CFUs to count at the highest dilution. For these cases, we denoted the flies as having the highest countable number of CFUs found in any fly for that bacterium and at the highest dilution23. This will lead to an underestimate of the bacterial load in these flies. Note that because the assay is destructive, bacterial loads were measured once per fly.Bacterial load of dead fliesFor two periods of time in the chronic infection phase, i.e., between 14 and 35 days and 56 to 78 days post injection, dead flies were retrieved from their vial at the daily survival checks and homogenised in order to test whether they died whilst being infected, or whether they had cleared the infection before death. The fly homogenate was produced in the same way as for live flies, but we increased the dilution of the homogenate (1:1 to 1:1012) because we anticipated higher bacterial loads in the dead compared to the live flies. The higher dilution allowed us more easily to determine whether there was any obvious contamination from foreign CFUs or not. Because the flies may have died at any point in the 24 h preceding the survival check, and the bacteria can potentially continue replicating after host death, we evaluated the infection status (yes/no) of dead flies instead of the number of CFUs. Dead flies were evaluated for two experimental replicates per bacteria, and 160 flies across the whole experiment. Similar to homogenisation of live flies, we rarely observed contamination from foreign CFUs in the homogenate of dead bacteria-injected flies (3 out of 160; 1.9 %); of these three flies, one fly had only one foreign CFU, so it was included in the analyses. Dead Ringer’s injected and naïve flies were also homogenised and plated as controls, with 6 out of 68 flies (8.8%) resulting in the growth of unidentified CFUs.Statistical analysesStatistical analyses were performed with R version 4.2.166 in RStudio version 2022.2.3.49267. The following packages were used for visualising the data: “dplyr”68, “ggpubr”69, “gridExtra”70, “ggplot2”71, “plyr”72, “purr”73, “scales”74, “survival”75,76, “survminer”77, “tidyr”78 and “viridis”79, as well as Microsoft PowerPoint for Mac v16.60 and Inkscape for Mac v 1.0.2. Residuals diagnostics of the statistical models were carried out using “DHARMa”80, analysis of variance tables were produced using “car”81, and post-hoc tests were carried out with “emmeans”82. To include a factor as a random factor in a model it has been suggested that there should be more than five to six random-effect levels per random effect83, so that there are sufficient levels to base an estimate of the variance of the population of effects84. In our experimental designs, the low numbers of levels within the factors ‘experimental replicate’ (two to three levels) and ‘person’ (two levels), meant that we therefore fitted them as fixed, rather than random factors84. However, for the analysis of clearance (see below) we included species as a random effect because it was not possible to include it as a fixed effect because PPP is already a species-level predictor. Below we detail the statistical models that were run according to the questions posed. All statistical tests were two-sided.Do the bacterial species differ in virulence?To test whether the bacterial species differed in virulence, we performed a linear model with the natural log of the maximum hazard as the dependent variable and bacterial species as a factor. Post-hoc multiple comparisons were performed using “emmeans”82 and “magrittr”85, using the default Tukey adjustment for multiple comparisons. Effect sizes given as Cohen’s d, were also calculated using “emmeans”, using the sigma value of 0.4342, as estimated by the package. The hazard function in survival analyses gives the instantaneous failure rate, and the maximum hazard gives the hazard at the point at which this rate is highest. We extracted maximum hazard values from time of death data for each bacterial species/dose/experimental replicate. Each maximum hazard per species/dose/experimental replicate was estimated from an average of 33 flies (a few flies were lost whilst being moved between vials etc.). To extract maximum hazard values we defined a function that used the “muhaz” package86 to generate a smooth hazard function and then output the maximum hazard in a defined time window, as well as the time at which this maximum is reached. To assess the appropriate amount of smoothing, we tested and visualised results for four values (1, 2, 3 and 5) of the smoothing parameter, b, which was specified using bw.grid87. We present the results from b = 2, but all of the other values gave qualitatively similar results (see Supplementary Table 2). We used bw.method = “global” to allow a constant smoothing parameter across all times. The defined time window was zero to 20 days post injection. We removed one replicate (92 CFU for E. cloacae infection) because there was no mortality in the first 20 days and therefore the maximum hazard could not be estimated. This gave final sizes of n = 14 for E. cloacae and n = 15 for each of the other three species.$${{{{{rm{Model}}}}}},1:,{{log }}left({{{{{rm{maximum}}}}}},{{{{{rm{hazard}}}}}}right), sim ,{{{{{rm{bacterial}}}}}},{{{{{rm{species}}}}}}$$Are virulence differences due to variation in pathogen exploitation or PPP?To test whether the bacterial species vary in PPP, we performed a linear model with the natural log of the maximum hazard as the dependent variable, bacterial species as a factor, and the natural log of infection intensity as a covariate. We also included the interaction between bacterial species and infection intensity: a significant interaction would indicate variation in the reaction norms, i.e., variation in PPP. The package “emmeans”82 was used to test which of the reaction norms differed significantly from each other. We extracted maximum hazard values from time of death data for each bacterial species/dose/experimental replicate as described in section “Do the bacterial species differ in virulence?”. We also calculated the maximum hazard for the Ringer’s control groups, which gives the maximum hazard in the absence of infection (the y-intercept). We present the results from b = 2, but all of the other values gave qualitatively similar results (see results). We wanted to infer the causal effect of bacterial load upon host survival (and not the reverse), therefore we reasoned that the bacterial load measures should derive from flies homogenised before the maximum hazard had been reached. For E. cloacae, L. lactis, and Pr. burhodogranariea, for all smoothing parameter values, the maximum hazard was reached after two days post injection, although for smoothing parameter value 1, there were four incidences where it was reached between 1.8- and 2-days post injection. Per species/dose/experimental replicate we therefore calculated the geometric mean of infection intensity combined for days 1 and 2 post injection. In order to include flies with zero load, we added one to all load values before calculating the geometric mean. Geometric mean calculation was done using the R packages “dplyr”68, “EnvStats”88, “plyr”72 and “psych”89. Each mean was calculated from the bacterial load of eight flies, except for four mean values for E. cloacae, which derived from four flies each.For Ps. entomophila the maximum hazard was consistently reached at around day one post injection, meaning that bacterial sampling happened at around the time of the maximum hazard, and we therefore excluded this bacterial species from the analysis. We removed two replicates (Ringer’s and 92 CFU for E. cloacae infection) because there was no mortality in the first 20 days and therefore the maximum hazard could not be estimated. One replicate was removed because the maximum hazard occurred before day 1 for all b values (92,000 CFU for E. cloacae) and six replicates were removed because there were no bacterial load data available for day one (experimental replicate three of L. lactis). This gave final sample sizes of n = 15 for E. cloacae and n = 12 for L. lactis, and n = 18 for Pr. burhodogranariea.$${{{{{rm{Model}}}}}},2 :,{{log }}({{{{{rm{maximum}}}}}},{{{{{rm{hazard}}}}}}), sim ,{{log }}({{{{{rm{geometric}}}}}},{{{{{rm{mean}}}}}},{{{{{rm{bacterial}}}}}},{{{{{rm{load}}}}}}),\ times ,{{{{{rm{bacterial}}}}}},{{{{{rm{species}}}}}}$$To test whether there is variation in pathogen exploitation (infection intensity measured as bacterial load), we performed a linear model with the natural log of infection intensity as the dependent variable and bacterial species as a factor. Similar to the previous model, we used the geometric mean of infection intensity combined for days 1 and 2 post injection, for each bacterial species/dose/experimental replicate. The uninfected Ringer’s replicates were not included in this model. Post-hoc multiple comparisons were performed using “emmeans”, using the default Tukey adjustment for multiple comparisons. Effect sizes given as Cohen’s d, were also calculated using “emmeans”, using the sigma value of 2.327, as estimated by the package. Ps. entomophila was excluded for the reason given above. The sample sizes per bacterial species were: n = 13 for E. cloacae, n = 10 for L. lactis and n = 15 for Pr. burhodogranariea.$${{{{{rm{Model}}}}}},3:,{{log }}({{{{{rm{geometric}}}}}},{{{{{rm{mean}}}}}},{{{{{rm{bacterial}}}}}},{{{{{rm{load}}}}}}), sim ,{{{{{rm{bacterial}}}}}},{{{{{rm{species}}}}}}$$Are persistent infection loads dose-dependent?We tested whether initial injection dose is a predictor of bacterial load at seven days post injection22,25. We removed all flies that had a bacterial load that was below the detection limit as they are not informative for this analysis. The response variable was natural log transformed bacterial load at seven days post-injection and the covariate was natural log transformed injection dose, except for P. burhodogranariea, where the response variable and the covariate were log-log transformed. Separate models were carried out for each bacterial species. Experimental replicate and person were fitted as fixed factors. By day seven none of the flies injected with 92,000 CFU of L. lactis were alive. The analysis was not possible for Ps. entomophila infected flies because all flies were dead by seven days post injection.$${{{{{rm{Model}}}}}},4:,{{log }}({{{{{rm{day}}}}}},7,{{{{{rm{bacterial}}}}}},{{{{{rm{load}}}}}}), sim ,{{log }}({{{{{rm{injection}}}}}},{{{{{rm{dose}}}}}}),+,{{{{{rm{replicate}}}}}},+,{{{{{rm{person}}}}}}$$Calculation of clearance indicesTo facilitate the analyses of clearance we calculated clearance indices, which aggregate information about clearance into a single value for each bacterial species/dose/experimental replicate. All indices were based on the estimated proportion of cleared infections (defined as samples with a bacterial load that was below the detection limit) of the whole initial population. For this purpose, we first used data on bacterial load in living flies to calculate the daily proportion of cleared infections in live flies for the days that we sampled. Then we used the data on fly survival to calculate the daily proportion of flies that were still alive. By multiplying the daily proportion of cleared flies in living flies with the proportion of flies that were still alive, we obtained the proportion of cleared infections of the whole initial population – for each day on which bacterial load was measured. We then used these data to calculate two different clearance indices, which we used for different analyses. For each index we calculated the mean clearance across several days. Specifically, the first index was calculated across days three and four post injection (clearance index3,4), and the second index was calculated from days seven, 14 and 21 (clearance index7,14,21).Do the bacterial species differ in clearance?To test whether the bacterial species differed in clearance, we used clearance index3,4, which is the latest timeframe for which we could calculate this index for all four species: due to the high virulence of Ps. entomophila we were not able to assess bacterial load and thus clearance for later days. The distribution of clearance values did not conform to the assumptions of a linear model. We therefore used a Kruskal-Wallis test with pairwise Mann-Whitney-U post hoc tests. Note that the Kruskal-Wallis test uses a Chi-square distribution for approximating the H test statistic. To control for multiple testing we corrected the p-values of the post hoc tests using the method proposed by Benjamini and Hochberg90 that is implemented in the R function pairwise.wilcox.test.$${{{{{rm{Model}}}}}},5:,{{{{{{rm{clearance}}}}}},{{{{{rm{index}}}}}}}_{3,4}, sim ,{{{{{rm{bacterial}}}}}},{{{{{rm{species}}}}}}$$Do exploitation or PPP predict variation in clearance?To assess whether exploitation or PPP predict variation in clearance we performed separate analyses for clearance index3,4 and clearance index7,14,21. As discussed above, this precluded analysing Ps. entomophila. For each of the two indices we fitted a linear mixed effects model with the clearance index as the response variable. As fixed effects predictors we used the replicate-specific geometric mean log bacterial load and the species-specific PPP. In addition, we included species as a random effect.In our analysis we faced the challenge that many measured clearance values were at, or very close to zero. In addition, clearance values below zero do not make conceptual sense. To appropriately account for this issue, we used a logit link function (with Gaussian errors) in our model, which restricts the predicted clearance values to an interval between zero and one. Initial inspections of residuals indicated violations of the model assumption of homogenously distributed errors. To account for this problem, we included the log bacterial load and PPP as predictors of the error variance, which means that we used a model in which we relaxed the standard assumption of homogenous errors and account for heterogenous errors by fitting a function of how errors vary. For this purpose, we used the option dispformula when fitting the models with the function glmmTMB91.$${{{{{rm{Model}}}}}},6 :,{{{{{{rm{clearance}}}}}},{{{{{rm{index}}}}}}}_{3,4},{{{{{rm{or}}}}}},{{{{{{rm{clearance}}}}}},{{{{{rm{index}}}}}}}_{7,14,21}, \ sim ,{{log }}({{{{{rm{geometric}}}}}},{{{{{rm{mean}}}}}},{{{{{rm{bacterial}}}}}},{{{{{rm{load}}}}}}),+,{{{{{rm{PPP}}}}}}+{{{{{{rm{bacterial}}}}}},{{{{{rm{species}}}}}}}_{{{{{{rm{random}}}}}}}$$Does longer-term clearance depend upon the injection dose?In contrast to the analyses described above, we additionally aimed to assess the long-term dynamics of clearance based on the infection status of dead flies collected between 14 and 35 days and 56 to 78 days after injection. Using binomial logistic regressions, we tested whether initial injection dose affected the propensity for flies to clear an infection with E. cloacae or Pr. burhodogranariea before they died. The response variable was binary whereby 0 denoted that no CFUs grew from the homogenate and 1 denoted that CFUs did grow from the homogenate. Log-log transformed injection dose was included as a covariate as well as its interaction with the natural log of day post injection, and person was fitted as a fixed factor. Replicate was included in the Pr. burhodogranariea analysis only, because of unequal sampling across replicates for E. cloacae. L. lactis injected flies were not analysed because only 4 out of 39 (10.3%) cleared the infection. Ps. entomophila infected flies were not statistically analysed because of a low sample size (n = 12). The two bacterial species were analysed separately.$${{{{{rm{Model}}}}}},7 :,{{{{{rm{CFU}}}}}},{{{{{{rm{presence}}}}}}/{{{{{rm{absence}}}}}}}_{{{{{{rm{dead}}}}}}}, sim ,{{log }}({{log }}({{{{{rm{injection}}}}}},{{{{{rm{dose}}}}}})),\ times ,{{log }}({{{{{rm{day}}}}}},{{{{{rm{post}}}}}},{{{{{rm{injection}}}}}}),+,{{{{{rm{replicate}}}}}},+,{{{{{rm{person}}}}}}$$To test whether the patterns of clearance were similar for live and dead flies we tested whether the proportion of live uninfected flies was a predictor of the proportion of dead uninfected flies. We separately summed up the numbers of uninfected and infected flies for each bacterial species and dose, giving us a total sample size of n = 20 (four species × five doses). For live and for dead homogenised flies we had a two-vector (proportion infected and proportion uninfected) response variable, which was bound into a single object using cbind. The predictor was live flies, and the response variable was dead flies, and it was analysed using a generalized linear model with family = quasibinomial.$${{{{{rm{Model}}}}}},8:,{{{{{rm{cbind}}}}}}({{{{{rm{dead}}}}}},{{{{{rm{uninfected}}}}}},,{{{{{rm{dead}}}}}},{{{{{rm{infected}}}}}}), sim ,{{{{{rm{cbind}}}}}}({{{{{rm{live}}}}}},{{{{{rm{uninfected}}}}}},,{{{{{rm{live}}}}}},{{{{{rm{infected}}}}}})$$Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

MaterialsStudy areaThe Sinbilawin town is located southeast of Dakahleia Governorate, Egypt. It is bounded to the east by the Timai El-Amded city, west by the Aga city, north by the Mansoura city and to the south by the Diarb Negm city. The Sinbilawin lies between 31° 27′ 38.07″ E longitude and 30° 53′ 1.55″ N latitude (Google Earth) (Fig. 1). The total area of Sinbilawin town is about 304.5 km2 with total cultivated area of Sinbilawin is about 64,362.28 Faddens5. The Sinbilawin town is characterized a flat land.Figure 1Map of the Sinbilawin city, 2015 (study area).Full size imageRice strawThe total area of rice crop in Egypt is 1,215,830 faddan and the production of rice is 4,817,964 tons. The average of productivity is 3.963 tons5. The total area of rice crop in Sinbilawin center is 34,078.12167 faddan and the production of rice straw is 148,376.1417 tons. The rice area map is shown in Fig. 2.Figure 2Rice area map.Full size imageDataGIS is a powerful tool which used for computerized mapping and spatial analysis. GIS is used in many applications such as geology, protection, natural resource management, risk management, urban planning, transportation, and various aspects of modeling in the environment. Also, it is using for decision making22. In this study GIS is used to select the best site to be suggested to collect the rice straw as shown in flowchart of Fig. 3.Figure 3Flowchart of rice straw collecting from Sinbilawin center.Full size imageSoftware programs

a.

Google Earth program
Google Earth combines the power of Google Search with satellite imagery, maps, Terrain and 3D buildings to put the world’s geographic information at your fingertips. It displays satellite images of varying resolution of the Earth’s surface, allowing users to see things like cities and houses looking perpendicularly down or at an oblique angle, with perspective23.

b.

Image Processing and Analysis Software (ENVI) program
It has been used to separate layers from the satellite image as layer of road, layer of urban, layer of canal and layer of sites to the rice crop planting. ENVI 5.6.2 Classic is the ideal software for the visualization, analysis and presentation of all types of digital imagery. ENVI Classic’s complete image-processing package includes advanced, yet easy-to-use, spectral tools, geometric correction, terrain analysis, radar analysis, raster and vector GIS capabilities, extensive support for images from a wide variety of sources, and much more24.

c.

GIS program
ArcGIS Desktop 10.1 will be using in the present study. It is the newest version of a popular GIS software which produced by ESRI. ArcGIS Desktop is comprised of a set of integrated applications. All figure numbers were created using GIS software.

Design a model for assembling rice strawArcGIS10.1 was selected in this study to design a model for selecting the suitable sites to collect rice straw amounts in Sinbilawin center. To achieve the former goal must be gotten the satellite images (landsat 8) for the province of Dakahleia and the Sinbilawin center. These images were called operation land imager (OLI). Thus, layers will be obtained from the satellite images such as water channels, drainages, urban areas, main and sub- roads, rice crop areas and sites. ENVI program has been used to separate layers and place it in a file which named (Shp. file) for easy insertion in ArcGIS10.1 program. In this present study, design a model will be done on the main layers which will be obtained from the satellite image as follows:

Location and the administrative limits of Dakahleia Governorate and Sinbilawin center.

The rice crop area and sites in Dakahleia governorate as the main layer.

Layer of rice area and their sites in Sinbilawin center. Sinbilawin center was selected in the study because it is cultivated largest rice area in Dakahleia and Dakahleia biggest governorate cultivates rice.

Layer of roads network in Sinbilawin center. The network of roads was included the main roads and submain to aggregation rice straw. Given the problems associated with transport cost, disposal, and issues that arise from inadequate agriculture crop residues management, the collect units become essential to be nearest of the network of road to facilitate the process of transportation and minimize cost.

Layer of the urban locations in Sinbilawin center. Crop residues collection sites have an enormous impact on urban in general due to contamination and fires. This study proposes the collecting rice straw sites not be near of the urban, because it causes many health problems for the population.

Layer of the canal locations in Sinbilawin center. Collecting rice straw sites must be nearest from the source of water as canal for safety, protect it from fire and important for any recycle operation.

Layer of the drain locations in Sinbilawin center. Also, drain is important as the source of water but less than canal.

Arc GIS 10.1 to select the suitable sites for assembling rice strawThree Scenarios were suggesting for completing the design of the modeling to select best sites for collecting rice straw. From the three scenarios wall be reached to the best collecting sites for rice straw in Sinbilawin center as follows:

The first scenario: Modeling for Sinbilawin center
In this case, modeling was running on the Sinbilawin center as the whole unit.

The second scenario: Modeling for the village in Sinbilawin center.
The Sinbilawin center consists of 97 villages and some other area surrounding. In this case, modeling was running on each village and each accessory in Sinbilawin center.

The third scenario: Modeling for the best site in each village in Sinbilawin center.
In this case, the modeling was running on each best site which located in each village (on the 97 sites in Sinbilawin center).

MethodsTo achieve the former objective in this study wall be done as follows:

Location and the administrative limits of Dakahleia Governorate and Sinbilawin center were uploaded as map by Google earth program.

The rice crop area and sites in Dakahleia governorate. The data of area and sites to rice crop in Dakahleia governorate were collected from the Ministry of Agricultural—Central Administration of Economy and Statistics as numerical data for each center in Dakahleia governorate. Map for Dakahleia governorate was obtained via satellite image from the Remote Sensing Authority.

Rice production (ton) = Cultivated area(fed)*Average production (4.354 ton/fed)5.

Total rice straw (ton) = Rice production (ton) / 2.5.

Satellite image layersAreas and sites of satellite layers for rice in Sinbilawin centerArea and sites of rice crop in Sinbilawin center as the database were obtained and collected Extraction layer from the Ministry of Agricultural. Central Administration of Economy and Statistics as numerical data for each village. Sinbilawin map as layer of molding was obtained via satellite image from the Remote Sensing Authority. It was used with ArcGIS 10.1 software to inference the sites and area of rice crop in the Sinbilawin center villages.Layer for the road network in Sinbilawin centerThe network of roads is very important factor and effective for collecting rice straw. The network roads map as the layer was extracted from satellite image via the Remote Sensing Authority. It was used with ArcGIS 10.1 software to inference the main and sub roads in the Sinbilawin center.Layer for the urban locations in Sinbilawin centerCrop residues collection sites have an enormous impact on urban general due to contamination, environmental pollution and fires, which are causing many health problems for the population. The urban map as the layer was extracted from satellite image via the Remote Sensing Authority. It was used with ArcGIS 10.1 software to appear all the urban sites in the Sinbilawin center.Layer for the water source in Sinbilawin centerRice straw collection sites must be nearest from the source of water as canal for safety and protect it from fire also water is very important for any recycle operation. The canal map as the layer was extracted from satellite image via the Remote Sensing Authority. It was used with ArcGIS 10.1 software to appear all source of water as canal in the Sinbilawin center.Layer for the drain locations in Sinbilawin centerThe drain is important as the source of water but less than canal. The drain map as the layer was extracted from satellite image via the Remote Sensing Authority. It was used with ArcGIS 10.1 software to appear all drain in the Sinbilawin center.ArcGIS 10.1 to select the suitable sites for collecting rice strawModeling was designed as shown in Fig. 4 to apply with the three scenarios.Figure 4Short form for modeling to select suitable sites to assembly rice straw.Full size imageFrom the three scenarios shall be reached to the best collecting sites for recycling rice straw in Sinbilawin center as follows:

The first scenario was running modeling for Sinbilawin center.

The second scenario was running modeling for the village in it.

The third scenario was running modeling for the best site in each village in it.

Different steps were running with modeling to select the best sites to assembly rice straw in Sinbilawin center: 1- Euclidean distance. 2- Reclassify (or changes). 3-Weighted overlay. Assuming common measurement scale and weights for each layer according to its importance as follows:—Roads 50%, Channels 40%, Urban 10% so that the total is 100%0.4- Select Layer by Location (Data Management). In this step, order of selecting layer sites was given through Arc tool box at ArcGIS10.1 for selecting sites through the Arc toolbox at ArcGIS10.1 software as follow: 1- Intersection with roads. 2- Intersection with canals water.Total cost of collecting rice strawTransportation for collecting crop residues is important factors because it affects the success or failure of crop residues utilization. GIS was used to determine suitable sites for collecting rice straw and converting it through given parameters as:

Total length of road (km).

Total weight of rice straw (ton).

Speed of tractor in sub roads (30 km/h)

Total time of transfer (h).

All experimental protocols were approved by Benha University Research Committee and all methods used in this study was carried out according to the guidelines regulations of Benha University. This work is approved by the ethic committee at Benha University. More

Callie Veelenturf measured the pH, conductivity and temperature near a leatherback sea turtle’s nest during research in Equatorial Guinea.Credit: Jonah Reenders

This picture of marine conservation biologist Callie Veelenturf won the Nature Careers photo competition in 2018 — an event Veelenturf credits with kick-starting her career. She went on to assist in drafting a law that will help to protect species and habitats in Panama.Since 2021, editors at Nature have been tracking instances such as this, in which our journalism and opinion articles have had an impact. Here, we look at three times when content on biodiversity affected researchers, communities or policies. As well as shaping Veelenturf’s conservation work, Nature articles have raised the profile of a proposal to protect part of the Antarctic Ocean and fuelled discussions of carbon-tax proposals to fund tropical-forest conservation.Protect PanamaIn the prize-winning photo, Veelenturf was pictured with a leatherback sea turtle (Dermochelys coriacea) in Equatorial Guinea, where she was collecting data for her master’s degree at Purdue University Fort Wayne, Indiana, in 2016. She and biologist Jonah Reenders, now a photographer based in San Francisco, California, spent nearly half a year there, living in tents on Bioko Island, and Reenders took the picture of her as she measured the pH, conductivity and temperature of the sand near the leatherback’s nest.After the photo was published, a deluge of e-mails and messages “gave me this network, almost overnight, of other sea-turtle conservationists doing similar things around the world”, says Veelenturf, who is now based in Arraiján, Panama. “All of a sudden I was an ‘us’.”The photo award also validated her hard work, Veelenturf says, contradicting a common assumption that sea-turtle research just meant relaxing on the beach. Karla Barrientos-Muñoz, a Colombian sea-turtle conservationist at the Fundación Tortugas del Mar, based in Medellín, wrote that Veelenturf’s win was for all women in sea-turtle conservation. “It made me feel part of this community,” Veelenturf says.Inspired, she founded a non-profit organization called the Leatherback Project, based in Norfolk, Massachusetts, and later won a National Geographic Explorers grant, allowing her to perform the first scientific survey of sea turtles in Panama’s Pearl Islands archipelago. Here, her team worked with local communities to study the nesting sites and foraging grounds of olive ridley (Lepidochelys olivacea), green (Chelonia mydas), hawksbill (Eretmochelys imbricata) and eastern Pacific leatherback sea turtles.While doing fieldwork, Veelenturf read David Boyd’s book The Rights of Nature (2017), which described how some lawyers had fought to earn legal rights for nature. Such laws, which now exist in at least nine countries, make it easier to conserve the environment, because organizations can sue to protect a rainforest or stream. She went on to work with environmentally minded congress member Juan Diego Vásquez Gutiérrez and Panamanian legal advisers to draft a similar law for Panama, which is especially rich in biodiversity. Vásquez sponsored the legislation, and after more than a year of debate and revision by the public and in the national assembly, it was signed into law on 24 February 2022.Protect the AntarcticIn October 2020, a Comment article argued that the seas around the western Antarctic Peninsula should be designated a marine protected area. Overfishing there is removing large numbers of shrimp-like crustaceans called Antarctic krill (Euphausia superba), affecting the region’s entire web of species, including penguins, whales and seals, which feed on krill. The peninsula is also one of the fastest-warming ecosystems on the planet.A proposal for a marine protected area in the Antarctic must be approved by the groups of governments that make up the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR). Cassandra Brooks, a marine scientist at University of Colorado Boulder who co-authored the Nature piece and sits on CCAMLR’s non-voting science delegation, says that the Comment was sent to all the commission’s government delegations and observer groups. “If we can raise the issue in the public,” Brooks says, “it does help raise the issue within that diplomatic space.”The western Antarctic Peninsula proposal is one of three on the table for the next CCAMLR meeting in October 2022. It took ten years for CCAMLR to declare the Ross Sea a marine protected area. “The Antarctic does not have ten years,” says Comment co-author Carolyn Hogg, a conservation biologist at the University of Sydney in Australia.News stories about the article were published globally, including in China, India, South Korea and Malaysia. Hogg says it increased her visibility and further raised her profile with the Australian government. She is working with the government to ensure that the country’s threatened-species policy is informed by the latest genomic research. The goal is to give endangered populations the best chance of survival by preserving as much genetic diversity as possible.Hogg and Brooks wrote the piece with other women, some of whom were part of Homeward Bound, a global leadership programme for women in science, technology, engineering, mathematics and medicine. Many Homeward Bound participants and alumnae — 288 women from at least 30 countries — co-signed it and worked to translate it into many languages, “showing CCAMLR that this large community of women scientists from all over the world is watching, and going to hold them accountable”, Brooks says.Antarctica tends to be “both diplomatically and scientifically dominated by men”, she notes, and the impact of this global community of women was inspiring.Carbon tax for tropical forestsTropical countries should adopt a carbon tax, urged another Comment in February 2020, creating a levy on fossil fuels that should be used to conserve tropical forests. Costa Rica and Colombia had already adopted such a tax, and several other countries, including Indonesia, Brazil and Peru, are now considering implementing one, says Sebastian Troëng, executive vice-president of conservation partnerships at Conservation International who is based in Brussels and co-authored the piece.After the article was published, the authors made sure it was widely discussed. One of them, environmental economist Edward Barbier at Colorado State University in Fort Collins, presented the proposal at major meetings. These included the World Bank–International Monetary Fund forum in April 2022 and the Global Peatlands Initiative of the United Nations Framework Convention on Climate Change, at the 2021 climate summit COP26, in Glasgow, UK. The carbon-pricing proposal can be applied to any ecosystem, Barbier says. “Peatlands are ideal, because you’re saving probably the most carbon-dense ecosystem on our planet.”Meanwhile, Troëng’s colleagues presented the proposal to representatives from the finance and environment ministries of Chile, Mexico, Peru, Ecuador, Colombia and Costa Rica. “Since then, we’ve been working directly with government ministries,” he says, to strengthen the existing carbon-tax system in Colombia and to establish similar systems in Peru and Singapore. “I think what people appreciate the most is the fact that two countries have already done it, so it’s not just a theory or a wild idea, but it’s actually working,” Barbier says.“It’s always challenging to say, was it this paper that made something happen?” notes Troëng, on the impact of the article. “But it’s part of this growing consensus that nature plays an extremely important role in how we address climate change.” More

Vaurie, C. Taxonomy and geographical distribution of the Furnariidae (Aves, Passeriformes). Bulletin of the AMNH; v. 166, article 1. Bull. Am. Museum Nat. Hist. 166, 1–357 (1980).
Google Scholar 
Hahn, I. & Römer, U. New observations of the Masafuera Rayadito Aphrastura masafuerae. Cotinga 6, 17–19 (1996).
Google Scholar 
Hahn, I. & Römer, U. Threatened avifauna of the Juan Fernández Archipelago, Chile: The impact of introduced mammals and conservation priorities. Cotinga 17, 66–72 (2002).
Google Scholar 
Remsen, J. V. Family Furnariidae (Ovenbirds) Vol 8 162–348 (Lynx Edicions, 2003).
Google Scholar 
Moreno, J., Merino, S., Lobato, E., Rodríguez-Gironés, M. A. & Vásquez, R. A. Sexual dimorphism and parental roles in the Thorn-tailed Rayadito (Furnariidae). Condor 109, 312–320 (2007).Article 

Google Scholar 
Moreno, J., Merino, S., Vásquez, R. A. & Armesto, J. J. Breeding biology of the Thorn-tailed Rayadito (Furnariidae) in south-temperate rainforests of Chile. Condor 107, 69–77 (2005).Article 

Google Scholar 
Rozzi, R. & Jiménez, J. Sub-Antarctic Magellanic Ornithology: The First Decade of Long-term Bird Studies at the Omora Ethnobotanical Park, Cape Horn Biosphere Reserve, Chile (Universidad de Magallanes, Chile-University of North Texas Press, 2014).
Google Scholar 
Schlatter, R. & Riveros, G. Historia natural del Archipiélago Diego Ramírez, Chile. Ser. Cie. Ina. 47, 87–112 (1997).
Google Scholar 
Barroso, O. et al. Scientific collaboration with the Chilean Navy for long-term ornithological studies in the Diego Ramírez Archipelago: First year-round monitoring of Gonzalo Island’s bird assemblage. Anal. Inst. Patagonia 48, 149–168 (2020).Article 

Google Scholar 
Botero-Delgadillo, E. et al. Range-wide genetic structure in the thorn-tailed rayadito suggests limited gene flow towards peripheral populations. Sci. Rep. 10, 1–14 (2020).Article 
CAS 

Google Scholar 
Aguirre, F. et al. Gradientes climáticos y su influyente rol sobre los ecosistemas terrestres de la Reserva de Biosfera Cabo de Hornos, Chile. Anal. Inst. Patagonia (In press).Rozzi, R. et al. Parque Marino Cabo de Hornos-Diego Ramírez, Informe Técnico para la Propuesta de Creación (Universidad de Magallanes, 2017).
Google Scholar 
Johnson, A. W. & Goodall, J. The Birds of Chile and Adjacent Regions of Argentina, Bolivia and Peru (Platt Establecimientos Graficos, 1967).
Google Scholar 
Tomasevic, J. A., Hodum, P. J. & Estades, C. F. On the ecology and conservation of the critically endangered Masafuera Rayadito (Aphrastura masafuerae). Ornitol. Neotrop. 21, 535–543 (2010).
Google Scholar 
Ippi, S., Anderson, C. B., Rozzi, R. & Elphick, C. S. Annual variation of abundance and composition in forest bird assemblages on Navarino Island, Cape Horn Biosphere Reserve, Chile. Ornitol. Neotrop. 20, 231–245 (2009).
Google Scholar 
Rozzi, R., Martínez, D., Willson, M. F. & Sabag, C. In Ecología de los Bosques Nativos de Chile (eds Armesto, J. J. et al.) 135–152 (Editorial Universitaria, 1996).
Google Scholar 
Hahn, I., Römer, U. & Schlatter, R. Distribution, habitat use, and abundance patterns of land bird communities on the Juan Fernández Islands, Chile. Ornitol. Neotrop. 16, 371–385 (2005).
Google Scholar 
Vergara, P. M. & Marquet, P. A. On the seasonal effect of landscape structure on a bird species: The thorn-tailed rayadito in a relict forest in northern Chile. Landsc. Ecol. 22, 1059–1071 (2007).Article 

Google Scholar 
Kelt, D. A. et al. The avifauna of Bosque Fray Jorge National Park and Chile’s Norte Chico. J. Arid Environ. 126, 23–36 (2016).ADS 
Article 

Google Scholar 
Espíndola-Hernández, P., Castaño-Villa, G. J., Vásquez, R. A. & Quirici, V. Sex-specific provisioning of nutritious food items in relation to brood sex ratios in a non-dimorphic bird. Behav. Ecol. Sociobiol. 71, 65 (2017).Article 

Google Scholar 
Pisano Valdés, E. & Schlatter, R. P. Vegetación y flora de las islas Diego Ramírez (Chile). 1. Características y relaciones de la flora Vascular. Anal. Inst. Patagonia 12, 183–194 (1981).
Google Scholar 
Pisano Valdés, E. & Schlatter, R. P. Vegetación y flora de las islas Diego Ramírez (Chile). 2. Comunidades vegetales vasculares. Anal. Inst. Patagonia 12, 195–204 (1981).
Google Scholar 
Mackenzie, R. et al. Vascular flora and vegetational types at the long-term socio-ecological studies site, Gonzalo Island, Diego Ramírez Archipelago (56°31’S), Chile. Anal. Inst. Patagonia 48, 139–148 (2020).Article 

Google Scholar 
Rozzi, R. et al. Un centinela para el monitoreo del cambio climático y su impacto sobre la biodiversidad en la cumbre austral de América: La nueva red de estudios a largo Plazo Cabo de Hornos. Anal. Inst. Patagonia 48, 45–81 (2020).Article 

Google Scholar 
Robertson, G. et al. Continued increase in the number of black-browed albatrosses (Thalassarche melanophris) at Diego Ramírez, Chile. Polar Biol. 40, 1035–1042 (2017).Article 

Google Scholar 
Arroniz-Crespo, M. et al. Bryophyte-cyanobacteria associations during primary succession in recently deglaciated areas of Tierra del Fuego (Chile). PLoS One 9, e96081 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Rozzi, R. et al. Cape Horn Biosphere Reserve: A challenge for biodiversity conservation, and implementation of sustainable development in southernmost South America. Anal. Inst. Patagonia 36, 55–70 (2007).
Google Scholar 
Rozzi, R. et al. principles for biocultural conservation at the southern tip of the Americas: The approach of the Omora Ethnobotanical Park. Ecol. Soc. 11, 25 (2006).Article 

Google Scholar 
R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).Gonzalez, J. & Wink, M. Genetic differentiation of the Thorn-tailed Rayadito Aphrastura spinicauda (Furnariidae: Passeriformes) revealed by ISSR profiles suggests multiple palaeorefugia and high recurrent gene flow. Ibis 152, 761–774 (2010).Article 

Google Scholar 
Filatov, D. A. ProSeq: A software for preparation and evolutionary analysis of DNA sequence data sets. Mol. Ecol. Notes 2, 621–624 (2002).CAS 
Article 

Google Scholar 
Excoffier, L. & Lischer, H. E. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).PubMed 
Article 

Google Scholar 
Hammer, Ø., Harper, D. A. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
Google Scholar 
Bandelt, H.-J., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48 (1999).CAS 
PubMed 
Article 

Google Scholar 
Pons, O. & Petit, R. Measwring and testing genetic differentiation with ordered versus unordered alleles. Genetics 144, 1237–1245 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Botero-Delgadillo, E. et al. Variation in fine-scale genetic structure and local dispersal patterns between peripheral populations of a South American passerine bird. Ecol. Evol. 7, 8363–8378 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Botero-Delgadillo, E., Quirici, V., Vásquez, R. A. & Kempenaers, B. Heterozygosity-fitness correlations in a continental island population of Thorn-tailed Rayadito. J. Hered. 111, 628–639 (2020).PubMed 
Article 

Google Scholar 
Goudet, J. & Jombart, T. hierfstat: Estimation and tests of hierarchical F-statistics. R package version 0.04-22. https://CRAN.R-project.org/package=hierfstat (2015).Jombart, T. adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
PubMed 
Article 

Google Scholar 
Beugin, M. P., Gayet, T., Pontier, D., Devillard, S. & Jombart, T. A fast likelihood solution to the genetic clustering problem. Methods Ecol. Evol. 9, 1006–1016 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Peakall, R. & Smouse, P. E. GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).Article 

Google Scholar 
Piry, S. et al. GENECLASS2: A software for genetic assignment and first-generation migrant detection. J. Hered. 95, 536–539 (2004).CAS 
PubMed 
Article 

Google Scholar 
Paetkau, D., Calvert, W., Stirling, I. & Strobeck, C. Microsatellite analysis of population structure in Canadian polar bears. Mol. Ecol. 4, 347–354 (1995).CAS 
PubMed 
Article 

Google Scholar 
Paetkau, D., Slade, R., Burden, M. & Estoup, A. Genetic assignment methods for the direct, real-time estimation of migration rate: A simulation-based exploration of accuracy and power. Mol. Ecol. 13, 55–65 (2004).CAS 
PubMed 
Article 

Google Scholar 
Du Sert, N. P. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 20. PLoS Biol. 18, e3000411 (2020).Article 
CAS 

Google Scholar 
Linnaeus, C. Systema Naturae per regna tria naturae. Secundum classes, ordines, genera, species, cum characteribus, differentiis, synonymis, locis. Vol. 1 (Impensis Direct Laurentii Salvii, 1758).Gray, G. R. A List of the Genera of Birds, with an Indication of the Typical Species of Each Genus, Compiled from Various Sources (Richard and John E. Taylor, 1940).
Google Scholar 
Oberholser, H. C. Some untenable names in ornithology. Proc. Acad. Nat. Sci. Philadelphia 20, 201–216 (1899).
Google Scholar 
Derryberry, E. P. et al. Lineage diversification and morphological evolution in a large-scale continental radiation: The Neotropical ovenbirds and woodcreepers (Aves: Furnariidae). Evol. Int. J. Organ. Evol. 65, 2973–2986 (2011).Article 

Google Scholar 
Fjeldsa, J., Christidis, L. & Ericson, P. G. The Largest Avian Radiation: The Evolution of Perching Birds, or the Order Passeriformes (Lynx Edicions, 2020).
Google Scholar 
Munsell Color Charts. Munsell Soil Color Charts (Munsell Color Company, 2000).
Google Scholar 
Mittermeier, R. A. et al. Wilderness and biodiversity conservation. Proc. Natl. Acad. Sci. 100, 10309–10313 (2003).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lomolino, M., Riddle, B. & Whittaker, R. (Oxford University Press, 2016).Whittaker, R. J. Island Biogeography: Ecology, Evolution, and Conservation (Oxford University Press, 1998).
Google Scholar 
Botero-Delgadillo, E. et al. Ecological and social correlates of natal dispersal in female and male Thorn-tailed Rayadito (Aphrastura spinicauda) in a naturally isolated and fragmented habitat. Auk Ornithol. Adv. 136, ukz016 (2019).
Google Scholar 
Botero-Delgadillo, E., Serrano, D., Orellana, N., Poblete, Y. & Vásquez, R. A. Effects of temperature and time constraints on the seasonal variation in nest morphology of the Thorn-tailed Rayadito (Aphrastura spinicauda). Emu-Austral Ornithol. 117, 181–187 (2017).Article 

Google Scholar 
Cornelius, C. Spatial variation in nest-site selection by a secondary cavity-nesting bird in a human-altered landscape. Condor 110, 615–626 (2008).Article 

Google Scholar 
Quilodrán, C. S., Estades, C. F. & Vásquez, R. A. Conspecific effect on habitat selection of a territorial cavity-nesting bird. Wilson J. Ornithol. 126, 534–543 (2014).Article 

Google Scholar 
Quilodrán, C. S., Vásquez, R. A. & Estades, C. F. Nesting of the Thorn-tailed Rayadito (Aphrastura spinicauda) in a pine plantation in southcentral Chile. Wilson J. Ornithol. 124, 737–742 (2012).Article 

Google Scholar 
Wright, N. A., Steadman, D. W. & Witt, C. C. Predictable evolution toward flightlessness in volant island birds. Proc. Natl. Acad. Sci. 113, 4765–4770 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sandvig, E. M., Coulson, T. & Clegg, S. M. The effect of insularity on avian growth rates and implications for insular body size evolution. Proc. R. Soc. B 286, 20181967 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Reznick, D. N. et al. Eco-evolutionary feedbacks predict the time course of rapid life-history evolution. Am. Nat. 194, 671–692 (2019).PubMed 
Article 

Google Scholar 
Clavel, J. & Morlon, H. Accelerated body size evolution during cold climatic periods in the Cenozoic. Proc. Natl. Acad. Sci. 114, 4183–4188 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Philippi, R. & Landbeck, L. Beitrage zur Fauna Chiles. Arch Naturgesch 32, 121–132 (1866).
Google Scholar 
Vaurie, C. Taxonomy and geographical distribution of the Furnariidae (Aves, Passeriformes). Bull. AMNH 166, 1 (1980).
Google Scholar 
Vuilleumier, F. A quantitative survey of speciation phenomena in Patagonian birds. Ornitol. Neotrop. 2, 5–28 (1991).
Google Scholar 
Ippi, S., Vasquez, R. A., van Dongen, W. F. & Lazzoni, I. Geographical variation in the vocalizations of the suboscine Thorn-tailed Rayadito Aphrastura spinicauda. Ibis 153, 789–805 (2011).Article 

Google Scholar 
Imberti, S. Internet Bird Collection: Thorn-tailed Rayadito (Aphrastura spinicauda). https://macaulaylibrary.org/asset/204019791 (2001).Mikula, P. et al. A global analysis of song frequency in passerines provides no support for the acoustic adaptation hypothesis but suggests a role for sexual selection. Ecol. Lett. 24, 477–486 (2021).PubMed 
Article 

Google Scholar 
Meirmans, P. G. & Hedrick, P. W. Assessing population structure: FST and related measures. Mol. Ecol. Resour. 11, 5–18 (2011).PubMed 
Article 

Google Scholar 
Davies, B. J. et al. The evolution of the Patagonian Ice Sheet from 35 ka to the present day (PATICE). Earth Sci. Rev. 204, 103152 (2020).Article 

Google Scholar 
Lamy, F. et al. Glacial reduction and millennial-scale variations in Drake Passage throughflow. Proc. Natl. Acad. Sci. 112, 13496–13501 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rozzi, R. et al. Integrating ecology and environmental ethics: Earth stewardship in the southern end of the Americas. Bioscience 62, 226–236 (2012).Article 

Google Scholar 
Collins, R. & Cruickshank, R. H. The seven deadly sins of DNA barcoding. Mol. Ecol. Resour. 13, 969–975 (2013).CAS 
PubMed 

Google Scholar 
De Queiroz, K. Species concepts and species delimitation. Syst. Biol. 56, 879–886 (2007).PubMed 
Article 

Google Scholar 
Sendell-Price, A. T. et al. The genomic landscape of divergence across the speciation continuum in island-colonising silvereyes (Zosterops lateralis). G3 Genes Genomes Genet. 10, 3147–3163 (2020).CAS 

Google Scholar 
Päckert, M., Martens, J., Wink, M., Feigl, A. & Tietze, D. T. Molecular phylogeny of Old World swifts (Aves: Apodiformes, Apodidae, Apus and Tachymarptis) based on mitochondrial and nuclear markers. Mol. Phylogenet. Evol. 63, 606–616 (2012).PubMed 
Article 

Google Scholar 
Lerner, H. et al. Phylogeny and new taxonomy of the booted eagles (Accipitriformes: Aquilinae). Zootaxa 4216, 301–320 (2017).Article 

Google Scholar 
De Silva, T. N., Peterson, A. T., Bates, J. M., Fernando, S. W. & Girard, M. G. Phylogenetic relationships of weaverbirds (Aves: Ploceidae): A first robust phylogeny based on mitochondrial and nuclear markers. Mol. Phylogenet. Evol. 109, 21–32 (2017).PubMed 
Article 

Google Scholar 
Schüttler, E. et al. New records of invasive mammals from the sub-Antarctic Cape Horn Archipelago. Polar Biol. 42, 1093–1105 (2019).Article 

Google Scholar 
Martin, A. & Richardson, M. Rodent eradication scaled up: Clearing rats and mice from South Georgia. Oryx 53, 27–35 (2019).Article 

Google Scholar 
Schüttler, E., Klenke, R., McGehee, S., Rozzi, R. & Jax, K. Vulnerability of ground-nesting waterbirds to predation by invasive American mink in the Cape Horn Biosphere Reserve, Chile. Biol. Conserv. 142, 1450–1460 (2009).Article 

Google Scholar  More

Having introduced our novel explore-and-fuse method and the Physarum Steiner Algorithm we shall dedicate this section to discussing how the algorithm’s parameters influence the model, and how the method can be used towards diverse applications.In what follows we shall consider how different parameters such as the different shapes of cells, as well as their number, influence the results obtained by the Physarum Steiner Algorithm. We shall then conclude the section by studying different applications that our methods have.Cell shapeAlthough13 and6 considered diamond shaped CELLs, we shall consider here CELLs with other shapes. The primary benefit of square cells is that their shape allows for more cytoplasm to be placed on the grid. As a result, the foraging phase is very fast so using square cells tends to result in shorter run times than using diamond-shaped cells. In addition, large square cells are able to more completely cover the standard square grid than diamond-shaped cells. On the other hand, diamond-shaped cells result in less cytoplasm and more time spent in the foraging stage. This gives the cytoplasm time to move towards a centralized location which results in better solutions.Example A In order to illustrate the above point, in Fig. 3a.i., we begin with squares that are tightly packed. Since the squares are so tightly packed (1 apart), if any piece of cytoplasm in a square is moved, it will lead to a connection with a neighboring cell. As a result, all the points are found very quickly. In fact, many of the squares are connected and part of the network even if they are not close to any of the points, as shown in Fig. 3 (a.ii.). Shrinking these extra squares takes a long time and can also result in long paths which are far out of the way as seen in Fig. 3a.iii.Example B In contrast to Example A, in Fig. 3b, we consider diamond-shaped cells. The cells start off diamond-shaped and with less overall cytoplasm than the square cells. The cells then spend quite a few iterations in the foraging phase. Although this does take time, it allows the cytoplasm to move towards a centralized location around the active zones as seen in Fig. 3 (b.ii.). When the cell finally proceeds to the shrinking phase, there is less cytoplasm to remove and no out of the way paths, resulting in shorter solutions. The downside to this is the increased time which in some cases can be very long (over 100 million iterations) and in some cases the algorithm may not even complete.The effect of multiple cellsIn what follows we shall examine the effects of the number of cells used. We run 10 trials on 10 grids for a total of 100 trials on each cell size and number of cells. For each trial, we measure the total amount or area of cytoplasm that is initially spawned. This is used to normalize the search area which is the number of squares in the grid (for example a (100 times 100) grid has search area 10,000).Success rate: The algorithm may sometimes be unsuccessful at connecting all the points. For example, the cells may miss a point early on and move far away from that point, making it almost impossible to ever find that point. There may also simply not be enough cytoplasm for two far away cells to fuse into one. For each number of cells (1, 9, 25, 100), we try various sizes/amounts of cytoplasm and compute the proportion of trials (out of 100) that successfully terminate within 10 million iterations.Figure 4(a) Proportion of trials that are successful versus the search area as a percentage of cytoplasm for trials with 1, 9, 25, and 100 cells. (b) Length of solutions versus the search area as a percentage of cytoplasm. (c) Number of iterations versus the search area as a percentage of cytoplasm. Failed trails excluded from graphs.Full size imageIn Fig. 4a, we see that the black line (100 cells) extends much further to the right than the cyan line (one cell). Thus, the more cells there are, the larger of a search area we can explore. This is mainly because with more cells, we can spread out our cytoplasm instead of having it be concentrated in certain areas.Solution length Another important metric to consider is the solution length. We measure how good the solution is by counting the amount of cytoplasm when the algorithm terminates. We ignore any cytoplasm that is part of a disjoint cell that does not contain an active zone, or in other words is separate from the cell that actually forms the tree. In Fig. 4b, we see that as the search area as a percentage of cytoplasm increases, the quality of the solution improves. This is because there is comparatively less cytoplasm to begin with. In addition, we see that as the number of cells increases, it is possible to find a better solution. This correlates with the earlier result shown in Fig. 4a that using more cells allows solutions to be found with less cytoplasm. Trials with 100 cells found the shortest solutions (rightmost data point).Run time The last metric we consider is the run time. We consider the true number of iterations the algorithm runs for. By true iterations, we account for the fact that in a parallel algorithm or set of real-world Physarum organisms, multiple cells will be introducing and moving bubbles at the same time. As a result, the iteration count is scaled by the number of disjoint cells. In Fig. 4c, we see that the more cells there are, the lower the number of iterations. This may be because with more cells, the cytoplasm is more spread out and therefore there are less out of the way points which may take a very long time to find. From the above analysis, we see that using more cells allows us to explore bigger search areas, find shorter solutions, and solve problems faster.ApplicationsThe behavior of Physarum and the models it has inspired have found many different uses among which are drug repositioning, developing bio-computing chips, approximating highways layouts, and designing subway systems2,8,9,10. In order to illustrate the operation of the Physarum Steiner Algorithm and demonstrate its applicability to real world problems, we consider the following:

sep0em

Network design We use the algorithm to develop a road network in the United States.

Obstacle-avoidance We use the algorithm to solve the obstacle-avoiding Euclidean Steiner tree problem.

VLSI routing We use the algorithm to route connections between pads in chip design.

Topological surfaces We discuss the algorithm’s adaptability to varying surfaces and boundaries by considering topological surfaces such as the sphere, torus, Klein bottle, and (mathbb{RP}mathbb{}^2).

Road networks The Physarum Steiner Algorithm can be used to build a road network between the largest one hundred cities in the lower 48 United States (excluding Alaska and Hawaii). We use data32 containing the longitude and latitude of the 100 cities with the highest population to generate a rectangular grid of active zones.We spawn diamond-shaped cells of size 7 with a spacing of 1 as shown in Fig. 3. After many iterations, the final road network is shown in Fig. 5a. The algorithm is particularly suited to the problem of designing transportation systems because it first connects all the points before optimizing the network into a tree. The algorithm can thus be terminated early depending on how much redundant connectivity is desired in the transportation network.For example, in Fig. 5b, we have a network that still contains loops in high-traffic routes between the Bay Area, Los Angeles, and Las Vegas. If we allow the algorithm to continue running, we will get networks with fewer loops and eventually a tree.Figure 5Road network generated by the algorithm. (a) shows the final solution with no loops while (b) displays a solution that has some redundancy resulting from terminating the algorithm early.Full size imageWe believe that this algorithm can be applied to many similar problems such as designing fiber optic or electric cable networks. Moreover, as discussed in the last section, it will be very interesting to compare this study to that of33, where in vitro slime mold is used to investigate the construction of transportation networks over a USA map.Obstacle avoidance Due to the cellular automaton nature of this algorithm, it is straightforward to define boundaries or other obstacles that need to be avoided. This is very useful in cases where certain areas need to be avoided such as a lake or the boundary of a county. And, unlike the current standard obstacle-avoiding Euclidean Steiner algorithm27 which takes multiple hours for graphs with only 150 points, the run time of the Physarum Steiner Algorithm is not affected by the need to avoid obstacles.As an example, consider the boundary given in Fig. 6a. Here, the grey area represents the search area and the 100 white squares outlined in dark grey are the points. There are many possible real world situations similar to this. For example, the grey area could be a county and all the points represent homes that subscribe to a certain Internet service provider (ISP). The big white area in the center could be a lake and the smaller white area could be a dog park. The ISP company could utilize the Physarum Steiner Algorithm to find networks to lay fiber optic cables.Figure 6(a) Sample boundary map. Grey area is search area and small white squares are points. (b) Initial deployment of Physarum. (c) Solution at the end of the foraging stage. (d) The final network.Full size imageWe begin by deploying square Physarum cells of size 7 in Fig. 6b. In Fig. 6c, the cells begin to fuse, share intelligence, and find all the points. We choose a solution that still has some loops to increase reliability and ease of future modification to the network. Our final solution is shown in Fig. 6d. This solution is generated in 300,000 iterations and less than 30 seconds.VLSI Routing for VLSI (very large-scale integration) chip design19 is one of the largest real-world manifestations of the Steiner tree problem, especially as modern chips may contain upwards of 10 billion transistors. Solving the VLSI problem would require additional modification to the Physarum Steiner Algorithm since VLSI design is typically presented as a group Steiner tree problem and has very large problem sizes, the Physarum Steiner Algorithm. Due to the usage of a square grid in the Physarum Steiner Algorithm, the algorithm is easily applied to find rectilinear networks such as those required for routing chips. In addition, our empirical results suggest that it should scale well to the large problem sizes common in chip design. Using data from34, we consider a set of pads that need to be connected. In Fig. 7, we represent the pads as active zones and generate a tree between them.Figure 7(a) Graphical representation of 131-point VLSI data set34. (b) Routing solution obtained by the Physarum Steiner Algorithm.Full size imageTopological surfaces Finally, the Physarum Steiner Algorithm is easily applicable to finding Steiner trees on other topological surfaces. Given the nature of the algorithm, we are able to map coordinates on one edge to another. In Fig. 8, we use square identification spaces to find Steiner trees on the torus, sphere, Klein bottle, and (mathbb{RP}mathbb{}^2). These solutions on identification spaces can be seen on a torus and a sphere in Fig. 8a,b.Figure 8Steiner trees on topological surfaces we defined by identification space and obtained through our code. (a) Torus. (b) Sphere. (c) Klein Bottle. (d) (mathbb{RP}mathbb{}^2). Images generated using manim35.Full size imageConcluding remarksWe have presented here a novel explore-and-fuse approach to solve problems that cannot be solved by traditional divide-and-conquer.Our approach is inspired by Physarum, a unicellular slime mold capable of solving the traveling salesman and Steiner tree problems. Besides exhibiting individual intelligence, Physarum can also share information with other Physarum organisms through fusion. These characteristics of Physarum inspire us to spawn many Physarum organisms to independently explore the problem space and collect information in parallel before sharing the information with other organisms through fusion. Eventually, all the organisms fuse into one large Physarum that can then globally optimize using the knowledge collected earlier. Explore-and-fuse can be seen as a less rigid form of divide-and-conquer that can better handle problems that cannot be decomposed into independent subproblems.We demonstrate the explore-and-fuse approach on the Steiner tree problem by creating the Physarum Steiner Algorithm. This algorithm has the ability to incrementally find Steiner trees. The first solution tends to contain many loops that are removed with additional iterations of the algorithm. This incremental improvement is particularly useful for applications such as road and cable networks where some degree of redundancy in the connectivity is desired. In particular, it will be very interesting to compare our work to the the one done in33 where a protoplasmic network created by in vivo Physarum is considered to study and asses show the slime mold imitates the United States Interstate System. We foresee several applications of our algorithm in this direction, leading to similar findings to those appearing in the studies done in33.The algorithm operates on a rectilinear grid and is particularly applicable to rectilinear Steiner tree problems such as those that often arise in VLSI design. In addition, the algorithm performs well on the obstacle-avoidance Euclidean Steiner tree problem.In comparison to the existing Physarum-inspired Steiner tree algorithms described in Section “The Steiner tree problem”, the Physarum Steiner Algorithm uses a completely different mechanism. While the existing algorithms use a system of equations modeling the thickening of tubes as protoplasm flows through them, the Physarum Steiner Algorithm is based on modeling Physarum spatially moving around a grid and finding a tree between squares of the grid. In addition, it should be noted that the approach taking in existing algorithms would not work on the Euclidean Steiner tree problem as in the Euclidean Steiner tree problem, there are an infinite number of possible points that could be part of the Steiner tree (essentially any point in the plane). It would not be possible to write a system of equations representing the infinite possible points and edges. In the future, we believe further work could be done to improve the Physarum Steiner Algorithm. Since the Physarum Steiner Algorithm is an approximate algorithm, future improvements could be made so its approximations are closer to the actual optimal solution. In addition, it would be interesting to see this approach applied to other problems Physarum has been able to solve such as the traveling salesmen problem. More

Different proxies for changes in structure and/or function of microbiomes have been developed, allowing assessing microbiome dynamics at multiple levels. However, the lack and differences in understanding the microbiome dynamics are due to the differences in the choice of proxies in different studies and the limitations of proxies themselves. Here, using both amplicon and metatranscriptomic sequencings, we compared four different proxies (16/18S rRNA genes, 16/18S rRNA transcripts, mRNA taxonomy and mRNA function) to reveal the impact of a severe summer drought in 2018 on prokaryotic and eukaryotic microbiome structures and functions in two rewetted fen peatlands in northern Germany. We found that both prokaryotic and eukaryotic microbiome compositions were significantly different between dry and wet months. Interestingly, mRNA proxies showed stronger and more significant impacts of drought for prokaryotes, while 18S rRNA transcript and mRNA taxonomy showed stronger drought impacts for eukaryotes. Accordingly, by comparing the accuracy of microbiome changes in predicting dry and wet months under different proxies, we found that mRNA proxies performed better for prokaryotes, while 18S rRNA transcript and mRNA taxonomy performed better for eukaryotes. In both cases, rRNA gene proxies showed much lower to the lowest accuracy, suggesting the drawback of DNA based approaches. To our knowledge, this is the first study comparing all these proxies to reveal the dynamics of both prokaryotic and eukaryotic microbiomes in soils. This study shows that microbiomes are sensitive to (extreme) weather changes in rewetted fens, and the associated microbial changes might contribute to ecological consequences. More