Ecology

Davis, K. F., Downs, S. & Gephart, J. A. Towards food supply chain resilience to environmental shocks. Nat. Food 2, 54–65 (2021).Article 

Google Scholar 
Lempert, R. J. & Collins, M. T. Managing the risk of uncertain threshold responses: comparison of robust, optimum, and precautionary approaches. Risk Anal. 27, 1009–1026 (2007).Article 

Google Scholar 
Garnett, P., Doherty, B. & Heron, T. Vulnerability of the United Kingdom’s food supply chains exposed by COVID-19. Nat. Food 1, 315–318 (2020).Article 
CAS 

Google Scholar 
Abson, D. J. et al. Leverage points for sustainability transformation. Ambio 46, 30–39 (2017).Article 

Google Scholar 
Westley, F. et al. Tipping toward sustainability: emerging pathways of transformation. Ambio 40, 762–780 (2011).Article 

Google Scholar 
Steffen, W., Broadgate, W., Deutsch, L., Gaffney, O. & Ludwig, C. The trajectory of the Anthropocene: the Great Acceleration. Anthr. Rev. 2, 81–98 (2015).
Google Scholar 
Jouffray, J.-B., Blasiak, R., Norström, A. V., Österblom, H. & Nyström, M. The blue acceleration: the trajectory of human expansion into the ocean. One Earth 2, 43–54 (2020).Article 

Google Scholar 
Adger, W. N., Eakin, H. & Winkels, A. Nested and teleconnected vulnerabilities to environmental change. Front. Ecol. Environ. 7, 150–157 (2009).Article 

Google Scholar 
Nyström, M. et al. Anatomy and resilience of the global production ecosystem. Nature 575, 98–108 (2019).Article 

Google Scholar 
Mason, W. & Watts, D. J. Collaborative learning in networks. Proc. Natl Acad. Sci. USA 109, 764–769 (2012).Article 
CAS 

Google Scholar 
Helbing, D. Globally networked risks and how to respond. Nature 497, 51–59 (2013).Article 
CAS 

Google Scholar 
Worm, B. & Paine, R. T. Humans as a hyperkeystone species. Trends Ecol. Evol. 31, 600–607 (2016).Article 

Google Scholar 
Crutzen, P. J. & Stoermer, E. F. in The Future of Nature (eds Robin, L. et al.) 479–490 (Yale Univ. Press, 2017); https://doi.org/10.12987/9780300188479-041Ellis, E. C. Anthropogenic transformation of the terrestrial biosphere. Phil. Trans. R. Soc. A 369, 1010–1035 (2011).Article 

Google Scholar 
Senevirante, S. I. et al. in Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) 1513–1766 (IPCC, Cambridge Univ. Press, 2021).Frank, A. B. et al. Dealing with femtorisks in international relations. Proc. Natl Acad. Sci. USA 111, 17356–17362 (2014).Article 
CAS 

Google Scholar 
Folke, C. et al. Our future in the Anthropocene biosphere. Ambio 50, 834–869 (2021).Article 

Google Scholar 
Walker, B. & Salt, D. Resilience Practice: Building Capacity to Absorb Disturbance and Maintain Function (Island Press/Center for Resource Economics, 2012); https://doi.org/10.5822/978-1-61091-231-0Biggs, R., Schlüter, M. & Schoon, M. L. (eds) Principles for Building Resilience: Sustaining Ecosystem Services in Social–Ecological Systems (Cambridge Univ. Press, 2015); https://doi.org/10.1017/CBO9781316014240Cervantes Saavedra, M. de & Rutherford, J. Don Quixote: The Ingenious Hidalgo de la Mancha (Penguin, 2003).Coronese, M., Lamperti, F., Keller, K., Chiaromonte, F. & Roventini, A. Evidence for sharp increase in the economic damages of extreme natural disasters. Proc. Natl Acad. Sci. USA 116, 21450–21455 (2019).Article 
CAS 

Google Scholar 
Cottrell, R. S. et al. Food production shocks across land and sea. Nat. Sustain. 2, 130–137 (2019).Article 

Google Scholar 
Elmqvist, T. et al. Response diversity, ecosystem change, and resilience. Front. Ecol. Environ. 1, 488–494 (2003).Article 

Google Scholar 
Arrow, K. J. & Fisher, A. C. Environmental preservation, uncertainty, and irreversibility. Q. J. Econ. 88, 312–319 (1974).Article 

Google Scholar 
Dixit, A. K. & Pindyck, R. S. Investment under Uncertainty (Princeton Univ. Press, 1994).Markowitz, H. Portfolio selection. J. Finance 7, 77–91 (1952).
Google Scholar 
Sharpe, W. F. Capital asset prices: a theory of market equilibrium under conditions of risk. J. Finance 19, 425–442 (1964).
Google Scholar 
Cifdaloz, O., Regmi, A., Anderies, J. M. & Rodriguez, A. A. Robustness, vulnerability, and adaptive capacity in small-scale social–ecological systems: the Pumpa Irrigation System in Nepal. Ecol. Soc. 15, art39 (2010).Article 

Google Scholar 
Levin, S. A. et al. Governance in the face of extreme events: lessons from evolutionary processes for structuring interventions, and the need to go beyond. Ecosystems 25, 697–711 (2022).Article 

Google Scholar 
Peterson, G., Allen, C. R. & Holling, C. S. Ecological resilience, biodiversity, and scale. Ecosystems 1, 6–18 (1998).Article 

Google Scholar 
Nyström, M. Redundancy and response diversity of functional groups: implications for the resilience of coral reefs. Ambio 35, 30–35 (2006).Article 

Google Scholar 
Kummu, M. et al. Interplay of trade and food system resilience: gains on supply diversity over time at the cost of trade independency. Glob. Food Secur. 24, 100360 (2020).Article 

Google Scholar 
Hedblom, M., Andersson, E. & Borgström, S. Flexible land-use and undefined governance: from threats to potentials in peri-urban landscape planning. Land Use Policy 63, 523–527 (2017).Article 

Google Scholar 
Haldane, A. Rethinking the Financial Network—Speech by Andy Haldane (Bank of England, 2009); https://www.bankofengland.co.uk/speech/2009/rethinking-the-financial-networkHaldane, A. G. & May, R. M. Systemic risk in banking ecosystems. Nature 469, 351–355 (2011).Article 
CAS 

Google Scholar 
Carpenter, S. R., Brock, W. A., Folke, C., van Nes, E. H. & Scheffer, M. Allowing variance may enlarge the safe operating space for exploited ecosystems. Proc. Natl Acad. Sci. USA 112, 14384–14389 (2015).Article 
CAS 

Google Scholar 
Mouillot, D., Graham, N. A. J., Villéger, S., Mason, N. W. H. & Bellwood, D. R. A functional approach reveals community responses to disturbances. Trends Ecol. Evol. 28, 167–177 (2013).Article 

Google Scholar 
Leslie, P. & McCabe, J. T. Response diversity and resilience in social–ecological systems. Curr. Anthropol. 54, 114–143 (2013).Article 

Google Scholar 
Biggs, R. et al. Toward principles for enhancing the resilience of ecosystem services. Annu. Rev. Environ. Resour. 37, 421–448 (2012).Article 

Google Scholar 
Anderies, J. M. Managing variance: key policy challenges for the Anthropocene. Proc. Natl Acad. Sci. USA 112, 14402–14403 (2015).Article 
CAS 

Google Scholar 
Csete, M. E. & Doyle, J. C. Reverse engineering of biological complexity. Science 295, 1664–1669 (2002).Article 
CAS 

Google Scholar 
Carlson, J. M. & Doyle, J. Highly optimized tolerance: robustness and design in complex systems. Phys. Rev. Lett. 84, 2529–2532 (2000).Article 
CAS 

Google Scholar 
Kitano, H. Biological robustness. Nat. Rev. Genet. 5, 826–837 (2004).Article 
CAS 

Google Scholar 
Csete, M. & Doyle, J. Bow ties, metabolism and disease. Trends Biotechnol. 22, 446–450 (2004).Article 
CAS 

Google Scholar 
Anderies, J. M., Rodriguez, A. A., Janssen, M. A. & Cifdaloz, O. Panaceas, uncertainty, and the robust control framework in sustainability science. Proc. Natl Acad. Sci. USA 104, 15194–15199 (2007).Article 
CAS 

Google Scholar 
Rodriguez, A. A., Cifdaloz, O., Anderies, J. M., Janssen, M. A. & Dickeson, J. Confronting management challenges in highly uncertain natural resource systems: a robustness–vulnerability trade-off approach. Environ. Model. Assess. 16, 15–36 (2011).Article 

Google Scholar 
Charpentier, A. Insurability of climate risks. Geneva Pap. Risk Insur. Issues Pract. 33, 91–109 (2008).Article 

Google Scholar 
Alfieri, L., Feyen, L. & Di Baldassarre, G. Increasing flood risk under climate change: a pan-European assessment of the benefits of four adaptation strategies. Climatic Change 136, 507–521 (2016).Article 

Google Scholar 
Isakson, S. R. Derivatives for development? Small-farmer vulnerability and the financialization of climate risk management: small-farmer vulnerability and financialization. J. Agrar. Change 15, 569–580 (2015).Article 

Google Scholar 
Müller, B. & Kreuer, D. Ecologists should care about insurance, too. Trends Ecol. Evol. 31, 1–2 (2016).Article 

Google Scholar 
Walker, B. et al. Looming global-scale failures and missing institutions. Science 325, 1345–1346 (2009).Article 
CAS 

Google Scholar 
Berkes, F. et al. Globalization, roving bandits, and marine resources. Science 311, 1557–1558 (2006).Article 
CAS 

Google Scholar 
Walker, B. H., Langridge, J. L. & McFarlane, F. Resilience of an Australian savanna grassland to selective and non-selective perturbations. Austral Ecol. 22, 125–135 (1997).Article 

Google Scholar 
Polasky, S. et al. Corridors of clarity: four principles to overcome uncertainty paralysis in the Anthropocene. BioScience 70, 1139–1144 (2020).Article 

Google Scholar 
Engström, G. et al. Carbon pricing and planetary boundaries. Nat. Commun. 11, 4688 (2020).Article 

Google Scholar 
Sun, J. C., Ugolini, S. & Vivier, E. Immunological memory within the innate immune system. EMBO J. https://doi.org/10.1002/embj.201387651 (2014).Vély, F. et al. Evidence of innate lymphoid cell redundancy in humans. Nat. Immunol. 17, 1291–1299 (2016).Article 

Google Scholar 
Grimm, N., Cook, E., Hale, R. & Iwaniec, D. in The Routledge Handbook of Urbanization and Global Environmental Change (eds Seto, K. et al.) Ch. 14 (Routledge, 2015).Jiang, B., Mak, C. N. S., Zhong, H., Larsen, L. & Webster, C. J. From broken windows to perceived routine activities: examining impacts of environmental interventions on perceived safety of urban alleys. Front. Psychol. 9, 2450 (2018).Article 

Google Scholar 
Andersson, E. et al. Urban climate resilience through hybrid infrastructure. Curr. Opin. Environ. Sustain. 55, 101158 (2022).Article 

Google Scholar 
Douglas, M. & Wildavsky, A. Risk and Culture: An Essay on the Selection of Technological and Environmental Dangers (Univ. of California Press, 1983).Weber, E. U., Ames, D. R. & Blais, A.-R. ‘How do I choose thee? Let me count the ways’: a textual analysis of similarities and differences in modes of decision-making in China and the United States. Manage. Organ. Rev. 1, 87–118 (2005).Article 

Google Scholar 
Kunreuther, H. et al. in Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) Ch. 2 (IPCC, Cambridge Univ. Press, 2014); https://www.ipcc.ch/site/assets/uploads/2018/02/ipcc_wg3_ar5_chapter2.pdfMeadows, D. H. Thinking in Systems: A Primer (Earthscan, 2009).Nyborg, K. et al. Social norms as solutions. Science 354, 42–43 (2016).Article 
CAS 

Google Scholar 
Hall, P. A. & Lamont, M. (eds) Social Resilience in the Neoliberal Era (Cambridge Univ. Press, 2013).Norström, A. V. et al. Principles for knowledge co-production in sustainability research. Nat. Sustain. 3, 182–190 (2020).Article 

Google Scholar 
United Nations Conference on Trade and Development Review of Maritime Transport 2017 (United Nations, 2017).United Nations Conference on Trade and Development Review of Maritime Transport 2018 (United Nations, 2019).Bailey, R. & Wellesley, L. Chatham House Report 2017: Chokepoints and Vulnerabilities in Global Food Trade (Energy, Environment and Resources Department, Chatham House, The Royal Institute of International Affairs, 2017); https://www.chathamhouse.org/sites/default/files/publications/research/2017-06-27-chokepoints-vulnerabilities-global-food-trade-bailey-wellesley-final.pdfKhoury, C. K. et al. Increasing homogeneity in global food supplies and the implications for food security. Proc. Natl Acad. Sci. USA 111, 4001–4006 (2014).Article 
CAS 

Google Scholar 
Hendrickson, M. K. Resilience in a concentrated and consolidated food system. J. Environ. Stud. Sci. 5, 418–431 (2015).Article 

Google Scholar 
Öborn, I. et al. Restoring rangelands for nutrition and health for humans and livestock. in The XXIV International Grassland Congress / XI International Rangeland Congress (Sustainable Use of Grassland and Rangeland Resources for Improved Livelihoods) (ed. National Organizing Committee of 2021 IGC/IRC Congress) (Kenya Agricultural and Livestock Research Organization, 2022).Vulnerable Supply Chains—Interim Report (Productivity Commission, Australian Government, 2021); https://www.pc.gov.au/inquiries/completed/supply-chains/interim More

Habitat loss is one of main threats to biodiversity worldwide and in general is perceived as something to be avoided. However, the prevalence of negative effects of forest fragmentation is less clear. Fragmentation creates edges between once-pristine forest and the adjacent non-forest system or systems (for example, agricultural lands, cities or water reservoirs), but the effects of these edges on biodiversity are not always clear. By performing a robust study of the interaction between insect pollinators and flowering plants at forest edges and within the forest, Ren et al.1 add a new piece to this puzzle by showing that forest edges can have a positive buffering effect on interaction networks. More

A small number of C. rodentium founders initiates enteric infectionTo enable monitoring of the pathogen population’s diversity during infection, we introduced short, random, ~20 nucleotide DNA tags (barcodes) at a neutral location in the C. rodentium genome. As previously described5, monitoring barcode diversity using high-throughput DNA sequencing and the STAMP (Sequence Tag-based Analysis of Microbial Populations) computational framework can quantify the constriction of the pathogen population that often occurs during establishment of infection (schematized in Fig. 1a). We created two independent STAMP libraries of barcoded bacteria. Library “STAMP-CR253” contains 253 unique barcodes integrated in the intergenic region between genes ROD_05521 and selU. The neutrality of the barcode insertions was confirmed by measuring growth in lysogeny broth (LB; Supplemental Fig. 1a). Library “STAMP-CR69K” contains approximately 69,000 unique barcodes inserted into the genome on a Tn7 vector, which integrates at a neutral site downstream of the glmS gene6,7. While the libraries were not directly compared, both yielded similar results in our studies.To validate that these barcoded STAMP libraries can quantify the population effects of a bottleneck, we created in vitro bottlenecks by plating serial dilutions of the libraries grown in culture. The number of colony forming units (CFU) per plate provides a true measure of the number of founders, i.e., the number of cells from the initial population (culture) that gave rise to the observed population (plated colonies). Bacteria were harvested from the plates, the barcodes were amplified and sequenced, and barcode frequencies were analyzed using the recently updated STAMP analysis pipeline “STAMPR”8. The size of the founding population (founders) was calculated by comparing the diversity and frequency of barcodes recovered from plated samples to those in the initial cultures. There was a strong correlation between the counted founders (CFU) and the calculated founders (Nr for STAMP-CR253 and Ns for STAMP-CR69K) up to 104 founders (Supplemental Fig. 1b). These data were also used as standard curves to increase the resolution of the experiments described below to approximately 106 founders.Contraction of a barcoded population during colonization changes the frequency and number of barcodes relative to the inoculum. To determine when a C. rodentium infection is founded, C57BL/6 J (B6) mice were orally gavaged with 4 × 108 CFUs (enumerated by serial dilution and plating). Remarkably, despite this relatively large dose, within 24 hours (h) there was an average of only 9 founders (geometric mean), and as few as one founder per mouse (a single barcode; Fig. 1b). Thus, only ~1 of every 4 × 107 cells in the inoculum establishes infection, revealing that host bottlenecks result in a massive constriction of the pathogen population. Beyond 24 h, the founding population remained stable at ~10 founders. The diminutive C. rodentium founding population indicates that the vast majority of the inoculum does not survive to give rise to detectable offspring and is thus either killed by the host or passes through the intestine and is excreted in feces. Consistent with the latter possibility, 5 h after inoculation there were 1 × 107 CFU C. rodentium per gram of feces with 9×104 founders, suggesting that at this early point a numerous and diverse population has already reached the colon and cecum but failed to become founders. Surprisingly, the contraction of the pathogen population continued beyond 5 h, when the pathogen had already reached the principal sites of colonization. Despite the profound bottleneck to infection, the ~10 founders were capable of replication, and by 5 days post inoculation the C. rodentium burden in the feces was on average 9 × 108 CFU/gram. Together, these observations reveal that there is a severe bottleneck to infection with this natural, mouse enteric pathogen; however, even though the restrictive bottleneck leaves a founding population that is a miniscule fraction of the inoculum, the founders robustly replicate, creating a total pathogen burden that ultimately exceeds the inoculum (Fig. 1b).The size of the founding population increases with doseWe reasoned that determining how dose impacts the number of founders could provide insight into the mechanisms underlying the bottleneck9,10. For example, one explanation proposed for the C. rodentium bottleneck is that it is created by finite niches or resources (e.g., sugar or amino acids) whose scarcity limits the size of the population11,12. At doses where the pathogen saturates this limited resource, the ‘finite resource’ hypothesis predicts that increasing dose will not increase the number of founders (schematized in Fig. 2a). An alternate possibility is that the bottleneck eliminates potential founders through a mechanism such as acid killing in the stomach13,14, which is expected to result in a founding population that increases with dose.Fig. 2: The C. rodentium bottleneck is defined by a fractional relationship between dose and founders.a Models for the relationship between dose and founding population. In the absence of a bottleneck, all bacteria from the inoculum become founders. If the inoculum contracts due to the limited availability of finite nutrients or niches (e.g. iron, sugar, binding sites), the diversity of the population and thus the size of the founding population will remain fixed once those nutrients are saturated. If increasing dose increases the number of founders, then the underlying mechanism is not due to a limited resource; instead, the bottleneck acts proportionally on the inoculum by eliminating potential founders. b, c C57BL/6 J mice were inoculated with doses ranging from 107 to 1010 CFU of STAMP-CR253 and the C. rodentium population was monitored in the feces (geometric means and standard deviations; ND not detected counted as 0.5). Additional shedding analysis in Supplemental Fig. 2. c The bottleneck impeding B6 colonization is described 5 days post inoculation by comparing dose and founders with a linear regression of the log10-transformed data (regression line with 95% confidence intervals; not detected counted as 0.8; x-intercept “ID50” 107.2–107.9 CFU). 4–8 animals per dose. Source data are provided as a Source Data file.Full size imageTo characterize the bottleneck, we orally inoculated B6 mice with C. rodentium doses ranging 1000-fold from 107 to 1010 CFUs. Doses (ge)108 CFUs led to infection, with the founding population decreasing for ~2 days before reaching a steady value that persisted until the infection began clearing, as indicated by a simultaneous decrease in the total population (burden) and founding population (Fig. 2b). Lower doses of C. rodentium resulted in fewer founders and a longer period to reach peak shedding, with a correspondingly longer time from inoculation to pathogen elimination. As the delay in shedding at lower doses correlated with the delay in clearance, all mice were infected for a similar number of days and had similar total fecal burdens, regardless of dose (Supplemental Fig. 2).The founding population was small in number. Even at the maximum inoculum of 1010 CFUs relatively few founders were detected (83, geometric mean; Fig. 2b). While founders were never numerous, increasing the size of the inoculum always increased the size of the founding population. The bottleneck eliminated a proportion of the C. rodentium population, resulting in a founding population that scaled with dose (increasing dose 100-fold also increased founders ~100-fold). These observations indicate that the number of founders is likely not dictated by limited space or resources, contradicting the finite resource hypothesis (Fig. 2a).As an increase in dose resulted in a proportional increase in the number of founders, we represented their relationship as a line by plotting log10-transformed dose and founding population data from 5 days post inoculation (Fig. 2c). This line indicates that the bottleneck is not fixed, but rather functions by eliminating a fraction of potential founders, as schematized in Fig. 2a (‘elimination bottleneck’). Since our findings conform to a simple fractional relationship between dose and founding population, we will use this relationship to define the bottleneck: in B6 mice 1 of every ~108 inoculated C. rodentium establish a replicative niche.The x-intercept of the log-linear relationship between dose and founders can be used to calculate the dose at which we expect 1 founder. This dose corresponds to the ID50 – the dose that leads to infection of ~50% of animals. Thus, for C. rodentium infection, the ID50, a critical parameter describing a pathogen’s infectivity, is a property biologically defined by the infection bottleneck. For B6 mice, the x-intercept of this line is between 107.2 and 107.9 CFU (95% confidence-intervals) and explains why infection did not result from an inoculum of 107 CFU (Fig. 2b). Surprisingly, even though C. rodentium is a natural mouse pathogen, at least ~100-million organisms are required to routinely establish infection.Stomach acid contributes a 10- to 100-fold bottleneck to C. rodentium colonizationWe next probed the contribution of stomach acid to the highly restrictive B6 enteric colonization bottleneck. The acidity of the stomach is thought to be a potent barrier against ingested bacteria; human studies find that taking stomach acid reducing drugs increases the risk of contracting multiple enteric pathogens15. Notably, it has been observed that eliminating stomach acid decreases the minimum infectious dose for C. rodentium and increases the size of the founding population13,14. Further, acid is mechanistically consistent with the fractional relationship which we observe between dose and founding population (Fig. 2). To test the role of stomach acid in restricting C. rodentium enteric colonization, we treated mice with the fast-acting, irreversible H2-antagonist Loxtidine (aka Lavoltidine)16. 3–5 h after Loxtidine treatment, the pH of the stomach rose from 2.5 to 4.7 (Fig. 3a). Importantly, a pH of 2.5 sterilized 1010 CFUs of C. rodentium in under 15 minutes (min), whereas pH 4.7 did not kill C. rodentium even after a 1 h exposure (Fig. 3b).Fig. 3: Stomach acid constricts the C. rodentium population by 10- to 100-fold.a The effect of Loxtidine on stomach acid in C57BL/6 J mice 3–5 h after intraperitoneal administration of 1 mg in 0.1 ml PBS. pH determined post-mortem in aspirated stomach fluid. Boxes arithmetic mean (2.5 for mock and 4.7 for Loxtidine). Two-tailed t test with p-value 0.0002. Animals are 7 (vehicle) and 5 (Loxtidine). b The acid tolerance of STAMP-CR69K in culture measured by diluting cells in LB at pH 2.5 or 4.7 and incubating at 37 °C with shaking. pH 2.5 sterilized 1010 CFU in 15 min. c, d 3–5 h after intraperitoneal administration of PBS (vehicle) or Loxtidine (1 mg), C57BL/6 J mice were orally gavaged with doses ranging from 107 to 1010 CFU of STAMP-CR69K. c Bacterial burden monitored in the feces for 5 days following inoculation (geometric means and standard deviations; ND not detected counted as 0.5). d Bottleneck to colonization measured 5 days post inoculation by comparing dose and founders with linear regression of the log10-transformed data (regression line and 95% confidence intervals; significance compares elevation with p-value 5.5 × 10−7; not detected counted as 0.8). 4 animals per dose per group. Source data are provided as a Source Data file.Full size imageLoxtidine treatment prior to inoculating B6 mice resulted in infection at a lower dose, a higher pathogen burden in the feces 1 day post inoculation, and more founders on day 5 (Fig. 3c, d). The fractional relationship between dose and founding population was also observed in the absence of stomach acid, but the line depicting this relationship was shifted upward. Loxtidine treatment increased the number of C. rodentium founders approximately 10-fold at every dose, reducing the ID50 computed from the founding population from 107.3 to 105.4 CFUs. Thus, stomach acid significantly contributes to the bottleneck restricting C. rodentium colonization. However, the magnitude of stomach acid’s contribution is relatively small, between 10- and 100-fold of the observed ~108-fold B6 bottleneck to C. rodentium colonization. In the absence of stomach acid, the C. rodentium population constricts >106-fold prior to establishing a replicative niche, indicating that other factors must more potently contribute to the bottleneck.Constriction of the C. rodentium inoculum occurs distal to the stomach, at the sites of infectionTo further define the C. rodentium population dynamics and host barriers that accompany establishment of infection, we probed where and when the bottleneck occurs. Five days post-inoculation, the largest pathogen burdens were detected in the cecum and distal colon, with less numerous populations in the small intestine (SI) (Fig. 4a), consistent with previous observations17. Within individual mice (intra-mouse) the cecum, colon, and feces contained related populations of C. rodentium, with approximately the same number of founders and similar barcodes (Fig. 4b–e). Importantly, the near identity of the barcodes found in the fecal population to those in the cecum and colon indicates that fecal samples can be used to report on the pathogen population at these primary infection sites, facilitating longitudinal monitoring. While intra-mouse populations were related, comparisons of barcodes between cohoused mice (inter-mouse) inoculated with the same inoculum revealed that each mouse contained a distinct C. rodentium population (Fig. 4c–e). The distinct identities of the founding populations in each of five cohoused, co-inoculated mice was apparent when comparing pathogen barcode frequencies with principal component analysis (PCA), where intra-mouse samples formed their own tight clusters (Fig. 4c). Similarly, analysis of barcode genetic distances showed that the intra-mouse pathogen populations were highly similar (low genetic distance), whereas they were dissimilar to the populations in cohoused, co-inoculated mice (Fig. 4d, e). A notable exception were the C. rodentium populations from some SI samples that were more closely related to cage-mates than other intra-mouse samples, likely reflecting recent inter-mouse exchange via coprophagy (Supplemental Fig. 3). These data suggest that despite the consumption of C. rodentium-laden feces, C. rodentium infection leads to super-colonization resistance at the primary infection sites in the cecum and colon, preventing transmission to cohoused, co-infected mice.Fig. 4: Infection is initiated by related populations of C. rodentium in the cecum and colon.a–e C. rodentium populations in whole organ homogenates from 5 cohoused (intra-cage) C57BL/6 J mice 5 days post inoculation with 4 × 108 CFU of STAMP-CR253. Within a mouse (intra-mouse) the C. rodentium populations at the primary sites of colonization (cecum, proximal colon, distal colon, feces) share founders (number, identity, and frequency of barcodes). a, b Lines connect intra-mouse samples. c Clustering of barcode populations by principal component analysis (PCA). d, e Relatedness determined by comparing the barcode frequencies by genetic distance (arithmetic means) with zero indicating no difference between populations (identical). e Two-tailed t test with p-value 5.8 × 10−48. p proximal, m mid, d distal, SI small intestine. Heatmap depicting genetic distance relationships of all intra-cage populations in Supplemental Fig. 3. Source data are provided as a Source Data file. f, g To determine when/where C. rodentium establishes a replicative niche, C57BL/6 J mice were orally gavaged with between 3 × 109 and 6 × 109 CFU STAMP-CR253. Following dissection, the cecum and colon were flushed to separate organ adherent (f) and luminal (g) bacteria. Burden and founders display geometric means and standard deviations. Bacteria not detected (ND) counted as 0.5. Relatedness of populations was determined by comparing the barcode frequencies of colon and cecal populations from within the same animal (intra-mouse) by genetic distance (arithmetic mean and standard deviation). 22 animals. Source data are provided as a Source Data file. h Model depicting how related C. rodentium populations could initiate infection in both the cecum and colon: (1) the inoculum minorly constricts passing through the stomach and SI to deposit diverse populations in the cecum and colon, (2) the populations in the cecum and colon contract separately over the first 24–48 h becoming dissimilar, and then (3) expansion occurs in either the cecum or colon moving to both locations. We depict the movement from cecum to colon as we judge this to be more likely, but the opposite is possible.Full size imageTo test this super-colonization resistance hypothesis, we separately infected two groups of ‘seed’ mice with different sets (A and B) of barcoded C. rodentium (Supplemental Fig. 4a). At the peak of colonization in the seed mice, 7 days post-inoculation, they were cohoused for 16 h along with an uninfected ‘contact’ mouse, three mice per cage. After 16 h, the mice were separated back into 3 cages containing mice originally inoculated with the A barcodes, inoculated with the B barcodes, or uninoculated. No transmission of barcodes was detected between the animals originally inoculated with the A and B barcodes (Supplemental Fig. 4b), confirming that C. rodentium infection prevents super-colonization. In marked contrast, the contact mice became infected with founders from seed A and/or B, demonstrating the ready transmission of C. rodentium from infected to uninfected mice. Furthermore, the co-infection of contact mice with barcodes from A and B confirms a previous report from super-infection experiments in mice lacking a microbiota18 that immunity to super-colonization takes time, providing a window for co-colonization. Importantly, super-colonization resistance indicates that founders are more likely to originate from the inoculum than other cohoused, infected animals.Based on the high burdens of C. rodentium in the cecum but not the colon during the first 3 days following inoculation, prior studies proposed that infection begins with pathogen expansion in the cecum, followed by subsequent spread to the colon17; a hypothesis that is consistent with the closely related intra-mouse C. rodentium populations that we observe in the cecum and colon 5 days after inoculation (Fig. 4a–e). To determine when and where C. rodentium initiates infection, we monitored the luminal and adherent C. rodentium populations in the cecum and colon. Within the first 5 h a large burden ( >107 CFU) and numerous founders ( >105 Nr’) were detected in both locations (Fig. 4f, g). Since a large founding population was observed in the cecum and colon early after inoculation, we can discount the model that the primary bottleneck occurs proximal to these locations (e.g., stomach acid or bile). The number of founders and the total burden contracted over the first 24 h, resulting in small (0.4) populations in the cecum and colon one day post inoculation. Expansion was detected first in the cecum, on day 2. Concomitant with cecal expansion, the populations in the cecum and colon became increasingly similar; i.e., the genetic distance between the populations became smaller. The most plausible model to fit these data (depicted in Fig. 4h) is that (1) within hours many bacteria pass through the stomach, reaching the cecum and colon, and then (2) these populations diverge as they separately constrict, and finally, (3) spread to both locations when a small number of founders begin to replicate. We propose that the initial population expansion begins in the cecum and then spreads to the colon, but we cannot rule out the opposite directionality because we were unable to serially sample the internal populations from a single mouse. However, displacement of the cecal population by bacteria from the colon seems unlikely because it would require non-flagellated C. rodentium to move against the bulk flow of the gut and thus we favor the model that infection initiates in the cecum.C3H/HeOuJ mice have a less restrictive bottleneck than C57BL/6 JWe next interrogated the host’s contribution to the bottleneck impeding C. rodentium colonization by quantifying the bottleneck in a more disease susceptible genotype of mice. While C. rodentium causes self-limited diarrhea in B6 mice, infection leads to a lethal diarrheal disease in C3H/HeOuJ (C3Ou) mice (Fig. 5a, b)19. We found that increased vulnerability to disease correlated with a less restrictive bottleneck. C. rodentium is 10- to 100-fold more infectious in C3Ou than B6 mice, infecting at a ~10-fold lower dose and producing ~10-times more founders at every dose (Fig. 5c). While the bottleneck was relaxed in C3Ou mice, a fractional relationship remained between dose and founding population, suggesting a similar underlying mechanism restricts colonization in both mouse genotypes. Also, as in B6 animals, higher doses and more founders accelerated the dynamics of pathogen shedding in C3Ou mice (Fig. 5a). These observations demonstrate that in addition to dose, the size of the founding population is determined in part by host genetics, which may impact the bottleneck through several mechanisms. Notably, changing host genotype caused a more lethal disease while only alleviating ~10-fold of the ~108-fold B6 bottleneck.Fig. 5: Host genotype impacts the bottleneck to C. rodentium colonization.C3H/HeOuJ (a) or C57BL/6 J (b) mice were inoculated with doses ranging from 106 to 1010 CFU of STAMP-CR253. The C. rodentium population was monitored in the feces (geometric means and standard deviations; ND not detected counted as 0.5) and animal health assessed by weight loss (percent compared to pre-inoculation; arithmetic means and standard deviations) and body condition. For survival, lines are percent of initial animals not moribund. c The bottleneck to C3Ou and B6 colonization is described 5 days post inoculation by comparing dose and founders with a linear regression of the log10-transformed data (regression line with 95% confidence intervals; significance compares elevation with p-value 5.5 × 10−7; not detected counted as 0.8). B6 bottleneck data is repeated from Fig. 2. 4 animals per dose. Source data are provided as a Source Data file.Full size imageThe bottleneck to C. rodentium enteric colonization is microbiota dependentAs shown above, a large portion of the restrictive, fractional, B6 bottleneck to C. rodentium colonization occurs distal to the stomach, at the chief sites of infection in the cecum and colon. These data strongly suggest that the principal step limiting colonization occurs during the pathogen’s establishment of a replicative niche in the cecum and/or colon. One factor present at these sites and previously linked to limiting C. rodentium colonization is the microbiota12,18. We therefore tested whether acute microbiota depletion eliminated the bottleneck to C. rodentium colonization. Treating mice with the antibiotic streptomycin for the 3 days prior to inoculation with streptomycin-resistant C. rodentium greatly accelerated pathogen population expansion, with mice shedding >109 CFUs per gram of feces within the first day (Fig. 6a). Further, streptomycin pretreatment almost completely ablated the bottleneck, with colonization at doses as low as ~100 CFUs; at this low dose, we measured an average of 25 founders 5 days post inoculation, indicating that C. rodentium experiences less than a 10-fold bottleneck following microbiota depletion (Fig. 6b). Significantly, streptomycin treatment does not alter the acidity of the animal’s stomach (Supplemental Fig. 5). Since an ~10-fold bottleneck remains after microbiota depletion and an ~10-fold bottleneck is stomach acid dependent (Fig. 3d), these data suggest that the combination of the microbiota and stomach acid can account for the majority of factors restricting C. rodentium colonization.Fig. 6: Streptomycin treatment ablates most of the bottleneck preventing C. rodentium colonization.The microbiota of conventional C57BL/6 J mice was reduced by treatment with the antibiotic streptomycin for the 3 days prior to inoculation with a streptomycin resistant library of STAMP-CR69K. a Founding population and bacterial burden monitored in the feces, and (b, c) the bottleneck to colonization measured by comparing dose and founders (geometric means and standard deviations; resolution limit is ~106 founders). (a) Animal health monitored by weight loss (percent compared to pre-inoculation; arithmetic means and standard deviations) and body condition. For survival, lines are percent of initial animals not moribund. Untreated B6 bottleneck data is repeated from Fig. 2 for comparison. 4 animals per dose. Streptomycin treatment does not impact stomach acidity (Supplemental Fig. 5). Source data are provided as a Source Data file.Full size imageTo confirm that streptomycin’s ablation of the bottleneck to C. rodentium colonization occurs because of microbiota depletion rather than an off-target effect, we also determined the bottleneck in B6 mice lacking a microbiota (germ-free). In germ-free mice, like streptomycin pretreated animals, there was almost no bottleneck to C. rodentium colonization (Fig. 7a, b). Animals lacking a microbiota were colonized at a dose of 150 CFU and shed numerous C. rodentium within 1 day of inoculation. Together, experiments with germ-free and streptomycin-pretreated mice reveal that the primary barrier to enteric colonization is linked to the microbiota.Fig. 7: The bottleneck to C. rodentium colonization is microbiota dependent.Germ-free C57BL/6 J mice were orally inoculated with doses ranging from 102 to 1010 CFU of STAMP-CR69K. 4 animals per dose, cohoused with animals receiving the same dose in sterile cages. Measurement of germ-free stomach acidity in Supplemental Fig. 5. Source data are provided as a Source Data file. a Bacterial burden and founding population monitored in the feces (geometric means and standard deviations; resolution limit is ~106 founders). Animal health monitored by weight loss (percent compared to pre-inoculation; arithmetic means and standard deviations) and body condition. No animals became moribund. b, c Bottleneck to colonization measured by comparing dose and founders (geometric means and standard deviations). SPF B6 bottleneck data is repeated from Fig. 2 for comparison. d To determine if colonization was accompanied by changes in the C. rodentium genome, whole genome sequencing was performed on 3 clones (colonies) from the STAMP-CR69K input library and compared to clones isolated from feces of infected mice (1 colony per mouse). Boxes represent the genome status of the LEE pathogenicity island. No LEE genomic changes wild-type (wt), deletion of the entire LEE region (del), insertion within the LEE (ins). Mice with a conventional microbiota SPF specific pathogen free. Other genomic changes listed in Supplemental table 1. e Depiction of a ~100,000 base-pair region of the C. rodentium genome containing the LEE pathogenicity island. Read depth from STAMP-CR69K inoculum and 5 clones isolated after 20 days passage in otherwise germ-free animals. Large deletions in 3 of 5 clones revealed by lack of specifically mapped reads in regions of up to 97,691 base-pairs. Non-specific reads map to multiple loci in the genome (primarily transposons).Full size imageMicrobiota disruption also impaired the capacity of mice to clear C. rodentium infection (Figs. 6a, 7a)12. Pathogen burden in the feces of germ-free mice did not decrease over time, in marked contrast to mice with an intact microbiota (specific pathogen free; SPF). Similarly, most cages of streptomycin-pretreated mice failed to clear the pathogen, with heterogeneity presumably caused by variation in the rebound of the microbiota after streptomycin treatment (Fig. 6a). Despite high fecal burdens, germ-free animals only exhibited mild diarrhea and did not lose weight for the 30 days of observation. These data indicate that the microbiota is the primary impediment to C. rodentium replication in the gastrointestinal tract, antagonizing the pathogen’s capacity to initiate a replicative niche and promoting its clearance.In germ-free and streptomycin pretreated animals the number of C. rodentium founders ceased to be fractionally related to dose; doses ranging 10,000-fold, from 106 to 1010 CFUs, all yielded a similar number of founders 5-days post-inoculation (Figs. 6b, 7b). These data suggest that there is an upper limit to the size of the C. rodentium founding population of ~105 on day 5 (i.e., a bottleneck caused by limited resources as illustrated in Fig. 2a). Furthermore, in the absence of a microbiota dependent bottleneck, the maximum size of the founding population continuously decreased for the 20 days of observation (Figs. 6c, 7c). Although there was no contraction in the C. rodentium burden following infection of germ-free animals, the maximum number of founders decreased from ~105 on day 5 to ~102 on day 20 (Fig. 7a, c). A decrease in diversity without a decrease in abundance suggests that C. rodentium adapts to the germ-free environment, introducing a new bottleneck caused by intra-pathogen competition.To test the hypothesis that C. rodentium evolved during colonization of germ-free animals, we sequenced the genomes of single C. rodentium colonies isolated from infected SPF or germ-free mice 5 or 20 days post inoculation. 5 days post-inoculation, the C. rodentium genomes isolated from SPF and germ-free mice were similar to the inoculum, with 6/10 colonies lacking detectable variations (Supplemental table 1). These data indicate that the initial contraction of the C. rodentium population observed during establishment of infection is not caused by selection of a genetically distinct subpopulation of the inoculum. By contrast, 20 days growth in the absence of a microbiota was always accompanied by changes in the C. rodentium genome. Notably, C. rodentium with structural variations in the LEE pathogenicity island became dominant in 4 out of 5 cages of infected germ-free animals (Fig. 7d, Supplemental table 1). These variations included large deletions of up to 97,691 bps (Fig. 7e, isolate from mouse F1) that eliminated the entire island, which is essential for colonization of SPF mice20. These genome alterations suggest that in the absence of a microbiota, a common mechanism for C. rodentium adaption to the host environment is to lose the LEE pathogenicity island. Thus, we conclude that competition among C. rodentium constricts the diversity of the population in the absence of a microbiota-dependent bottleneck, with organisms that lose the LEE virulence island outcompeting bacteria possessing the LEE. Additional mutations were also detected in the C. rodentium isolated on day 20 from germ-free animals, including in the galactonate operon, which have previously been observed in Escherichia coli colonizing microbiota depleted mice21 (Supplemental Table 1). Thus, there may be common evolutionary strategies for pathogenic and non-pathogenic bacteria to adapt to growth without competition in the host intestine.Collectively these experiments show that of the multiple host factors protecting against enteric infection, the microbiota is by far the most restrictive. Diminution of the microbiota markedly increases host susceptibility, permitting infection at almost any dose. In the absence of competition with the microbiota, a new slow-acting bottleneck constricted the C. rodentium population as the pathogen evolved increased fitness, notably through loss of the LEE pathogenicity island. More

Chala, B. & Hamde, F. Emerging and re-emerging vector-borne infectious diseases and the challenges for control: A review. Front. Public Health https://doi.org/10.3389/fpubh.2021.715759 (2021).Article 

Google Scholar 
Jongejan, F. & Uilenberg, G. The global importance of ticks. Parasitology 129, S3–S14 (2004).
Google Scholar 
Hornok, S., Kováts, D., Horváth, G., Kontschán, J. & Farkas, R. Checklist of the hard tick (Acari: Ixodidae) fauna of Hungary with emphasis on host-associations and the emergence of Rhipicephalus sanguineus. Exp. Appl. Acarol. 80, 311–328 (2020).
Google Scholar 
ECDC. Surveillance and disease data—Tick maps. https://www.ecdc.europa.eu/en/diseasevectors/surveillance-and-disease-data/tick-maps (2022). Accessed: 2022–09–02.Brites-Neto, J., Duarte, K. M. R. & Martins, T. F. Tick-borne infections in human and animal population worldwide. Vet. World 8, 301 (2015).
Google Scholar 
Hubálek, Z. Epidemiology of Lyme borreliosis. Lyme Borreliosis 37, 31–50 (2009).
Google Scholar 
Rizzoli, A. et al. Lyme borreliosis in Europe. Eurosurveillance 16, 19906 (2011).
Google Scholar 
Marques, A. R., Strle, F. & Wormser, G. P. Comparison of Lyme disease in the United States and Europe. Emerg. Infect. Dis. 27, 2017 (2021).
Google Scholar 
Jaenson, T. G., Jaenson, D. G., Eisen, L., Petersson, E. & Lindgren, E. Changes in the geographical distribution and abundance of the tick Ixodes ricinus during the past 30 years in Sweden. Parasit. Vectors 5, 1–15 (2012).
Google Scholar 
Medlock, J. M. et al. Driving forces for changes in geographical distribution of Ixodes ricinus ticks in Europe. Parasit. Vectors 6, 1–11 (2013).
Google Scholar 
Semenza, J. C. & Suk, J. E. Vector-borne diseases and climate change: a European perspective. FEMS Microbiol. Lett. https://doi.org/10.1093/femsle/fnx244 (2018).Article 

Google Scholar 
Sutherst, R. W. Global change and human vulnerability to vector-borne diseases. Clin. Microbiol. Rev. 17, 136–173 (2004).
Google Scholar 
Tabachnick, W. Challenges in predicting climate and environmental effects on vector-borne disease episystems in a changing world. J. Exp. Biol. 213, 946–954 (2010).CAS 

Google Scholar 
Sonenshine, D. E., Kocan, K. M. & de la Fuente, J. Tick control: Further thoughts on a research agenda. Trends Parasitol. 22, 550–551 (2006).
Google Scholar 
Willadsen, P. Tick control: Thoughts on a research agenda. Vet. Parasitol. 138, 161–168 (2006).
Google Scholar 
Goolsby, J. A. et al. Rationale for classical biological control of cattle fever ticks and proposed methods for field collection of natural enemies. Subtrop. Agric. Environ. 66, 7–15 (2016).
Google Scholar 
Singh, N. et al. Effect of immersion time on efficacy of entomopathogenic nematodes against engorged females of cattle fever tick, Rhipicephalus (= Boophilus) microplus. Southwest. Entomol. 43, 19–28 (2018).
Google Scholar 
Černý, J. et al. Management options for Ixodes ricinus-associated pathogens: A review of prevention strategies. Int. J. Environ. Res. Public Health 17, 1830 (2020).
Google Scholar 
Kapranas, A. et al. Encyrtid parasitoids of soft scale insects: Biology, behavior, and their use in biological control. Annu. Rev. Entomol. 60, 195–211 (2015).CAS 

Google Scholar 
Chirinos, D. T. & Kondo, T. Description and biological studies of a new species of Metaphycus Mercet, 1917 (Hymenoptera: Encyrtidae), a parasitoid of Capulinia linarosae Kondo & Gullan. Int. J. Insect Sci. 11, 1179543319857962 (2019).
Google Scholar 
Polaszek, A., Noyes, J. S., Russell, S. & Ramadan, M. M. Metaphycus macadamiae (Hymenoptera: Encyrtidae)–a biological control agent of macadamia felted coccid Acanthococcus ironsidei (Hemiptera: Eriococcidae) in Hawaii. PLoS ONE 15, e0230944 (2020).CAS 

Google Scholar 
Howard, L. Another chalcidoid parasite of a tick. Can. Entomol. 40, 239–241 (1908).
Google Scholar 
Hu, R., Hyland, K. & Oliver, J. A review on the use of Ixodiphagus wasps (Hymenoptera: Encyrtidae) as natural enemies for the control of ticks (Acari: Ixodidae). Syst. Appl. Acarol. 3, 19–28 (1998).
Google Scholar 
Collatz, J. et al. A hidden beneficial: Biology of the tick-wasp Ixodiphagus hookeri in Germany. J. Appl. Entomol. 135, 351–358 (2011).
Google Scholar 
Takasu, K. & Nakamura, S. Life history of the tick parasitoid Ixodiphagus hookeri (Hymenoptera: Encyrtidae) in Kenya. Biol. Control. 46, 114–121 (2008).
Google Scholar 
Collatz, J. et al. Being a parasitoid of parasites: host finding in the tick wasp Ixodiphagus hookeri by odours from mammals. Entomol. Experimentalis et Applicata 134, 131–137 (2010).
Google Scholar 
Krawczyk, A. I. et al. Tripartite interactions among Ixodiphagus hookeri, Ixodes ricinus and deer: Differential interference with transmission cycles of tick-borne pathogens. Pathogens 9, 339 (2020).
Google Scholar 
Plaire, D., Puaud, S., Marsolier-Kergoat, M.-C. & Elalouf, J.-M. Comparative analysis of the sensitivity of metagenomic sequencing and PCR to detect a biowarfare simulant (Bacillus atrophaeus) in soil samples. PLoS ONE 12, e0177112 (2017).
Google Scholar 
Wang, C.-X. et al. Comparison of broad-range polymerase chain reaction and metagenomic next-generation sequencing for the diagnosis of prosthetic joint infection. Int. J. Infect. Dis. 95, 8–12 (2020).CAS 

Google Scholar 
Tóth, A. G. et al. Ixodes ricinus tick bacteriome alterations based on a climatically representative survey in Hungary. bioRxiv (2022).Estrada-Peña, A., Mihalca, A. D. & Petney, T. N. Ticks of Europe and North Africa: A Guide to Species Identification (Springer, 2018).
Google Scholar 
Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).CAS 

Google Scholar 
Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 1–13 (2019).
Google Scholar 
Pruitt, K. D., Tatusova, T. & Maglott, D. R. NCBI Reference Sequence (RefSeq): A curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 33, D501–D504 (2005).CAS 

Google Scholar 
NCBI Resource Coordinators. Database resources of the national center for biotechnology information. Nucleic Acids Res. 44, D7 (2016).
Google Scholar 
Katoh, K. & Standley, D. M. Mafft multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 

Google Scholar 
Tennekes, M. tmap: Thematic maps in R. J. Stat. Softw. 84, 1–39 (2018).
Google Scholar 
Bodenhofer, U., Bonatesta, E., Horejš-Kainrath, C. & Hochreiter, S. msa: An R package for multiple sequence alignment. Bioinformatics 31, 3997–3999 (2015).CAS 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2022).Alfeev, N. & Klimas, Y. On the possibility of developing ichneumon flies, Hunterellus hookeri in climatic conditions of the USSR. Sovet. Vet. 15, 55 (1938).
Google Scholar 
Buczek, A., Buczek, W., Bartosik, K., Kulisz, J. & Stanko, M. Ixodiphagus hookeri wasps (Hymenoptera: Encyrtidae) in two sympatric tick species Ixodes ricinus and Haemaphysalis concinna (Ixodida: Ixodidae) in the Slovak Karst (Slovakia): Ecological and biological considerations. Sci. Rep. 11, 1–10 (2021).
Google Scholar 
Slovák, M. Finding of the endoparasitoid Ixodiphagus hookeri (Hymenoptera, Encyrtidae) in Haemaphysalis concinna ticks in Slovakia. Biol. Bratislava 58, 890–894 (2003).
Google Scholar 
Rehacek, J. & Kocianova, E. Attempt to infect Hunterellus hookeri Howard (Hymenoptera, Encyrtidae), an endoparasite of ticks, with Coxiella burnetti. Acta Virol. 36, 492–492 (1992).CAS 

Google Scholar 
Bohacsova, M., Mediannikov, O., Kazimirova, M., Raoult, D. & Sekeyova, Z. Arsenophonus nasoniae and Rickettsiae infection of Ixodes ricinus due to parasitic wasp Ixodiphagus hookeri. PLoS ONE 11, e0149950 (2016).
Google Scholar 
Boucek, Z. & Verny, V. A parasite of ticks, the chalcid Hunterellus hookeri in Czechoslovakia. Zool. Listy 3, 109–111 (1954).
Google Scholar 
Sormunen, J. J., Sippola, E., Kaunisto, K. M., Vesterinen, E. J. & Sääksjärvi, I. E. First evidence of Ixodiphagus hookeri (Hymenoptera: Encyrtidae) parasitization in Finnish castor bean ticks (Ixodes ricinus). Exp. Appl. Acarol. 79, 395–404 (2019).CAS 

Google Scholar 
Doby, J. & van Laere, G. Hunterellus hookeri howard, 1907, Hymenoptère Chalcididae parasite de la tique Ixodes ricinus dans l’ouest et le centre de la France. Bull. de la Société française de parasitologie 11, 265–270 (1993).
Google Scholar 
Plantard, O. et al. Detection of Wolbachia in the tick Ixodes ricinus is due to the presence of the hymenoptera endoparasitoid Ixodiphagus hookeri. PLoS ONE 7, e30692 (2012).ADS 
CAS 

Google Scholar 
Japoshvili, G. New records of Encyrtids (Hymenoptera: Chalcidoidea: Encyrtidae) from Georgia, with description of seven new species. J. Asia-Pacific Entomol. 20, 866–877 (2017).
Google Scholar 
Walter, G. Beitrag zur Biologie der Schlupfwespe Hunterellus hookeri Howard (Hymenoptera: Encyrtidae) in Norddeutschland. Beitr. Naturkunde Niedersachsens 33, 129–133 (1980).
Google Scholar 
Ramos, R. A. N. et al. Occurrence of Ixodiphagus hookeri (Hymenoptera: Encyrtidae) in Ixodes ricinus (Acari: Ixodidae) in Southern Italy. Ticks Tick-borne Dis. 6, 234–236 (2015).
Google Scholar 
Tijsse-Klasen, E., Braks, M., Scholte, E.-J. & Sprong, H. Parasites of vectors—Ixodiphagus hookeri and its Wolbachia symbionts in ticks in the Netherlands. Parasit. Vectors 4, 1–7 (2011).
Google Scholar 
Luu, L. et al. Bacterial pathogens and symbionts harboured by Ixodes ricinus ticks parasitising red squirrels in the United Kingdom. Pathogens 10, 458 (2021).CAS 

Google Scholar 
Pervomaisky, G. S. On the infestation of Ixodes persulcatus by Hunterellus hookeri How. (Hymenoptera). Zool. Zhurnal 22, 211–213 (1943).
Google Scholar 
Klyushkina, E. A parasite of the ixodid ticks, Hunterellus hookeri. How in the Crimea. Zool. Zh. 37, 1561–1563 (1958).
Google Scholar 
Gorman, M., Xu, R., Prakoso, D., Salvador, L. C. & Rajeev, S. Leptospira enrichment culture followed by ONT metagenomic sequencing allows better detection of Leptospira presence and diversity in water and soil samples. PLOS Neglected Trop. Dis. 16, e0010589 (2022).CAS 

Google Scholar 
Ranjan, R., Rani, A., Metwally, A., McGee, H. S. & Perkins, D. L. Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. Biochem. Biophys. Res. Commun. 469, 967–977 (2016).CAS 

Google Scholar 
Laudadio, I. et al. Quantitative assessment of shotgun metagenomics and 16S rDNA amplicon sequencing in the study of human gut microbiome. OMICS 22, 248–254 (2018).CAS 

Google Scholar 
Munaf, H. et al. The first record of Hunterellus hookeri parasitizing Rhipicephalus sanguineus in Indonesia. Southeast Asian J. Trop. Medicine Public Heal. 7, 492 (1976).CAS 

Google Scholar 
Stafford, K. C. III., Denicola, A. J. & Kilpatrick, H. J. Reduced abundance of Ixodes scapularis (Acari: Ixodidae) and the tick parasitoid Ixodiphagus hookeri (Hymenoptera: Encyrtidae) with reduction of white-tailed deer. J. Med. Entomol. 40, 642–652 (2003).
Google Scholar 
Stafford, K. C. Jr., Denicola, A. J. & Magnarelli, L. A. Presence of Ixodiphagus hookeri (Hymenoptera: Encyrtidae) in two Connecticut populations of Ixodes scapularis (Acari: Ixodidae). J. Med. Entomol. 33, 183–188 (1996).
Google Scholar 
Gillespie, J., Johnston, J., Cannone, J. & Gutell, R. Characteristics of the nuclear (18S, 5.8 S, 28S and 5S) and mitochondrial (12S and 16S) rRNA genes of Apis mellifera (Insecta: Hymenoptera): Structure, organization, and retrotransposable elements. Insect Mol. Biol. 15, 657–686 (2006).CAS 

Google Scholar 
Zhao, Y., Zhang, W.-Y., Wang, R.-L. & Niu, D.-L. Divergent domains of 28S ribosomal RNA gene: DNA barcodes for molecular classification and identification of mites. Parasit. Vectors 13, 1–12 (2020).
Google Scholar 
Larrousse, F., King, A. G. & Wolbach, S. The overwintering in Massachusetts of Ixodiphagus caucurtei. Science 67, 351–353 (1928).ADS 
CAS 

Google Scholar 
Smith, C. N. et al. Studies of parasites of the American dog tick. J. Econ. Entomol. https://doi.org/10.1093/jee/36.4.569 (1943).Article 

Google Scholar 
Hu, R., Hyland, K. E. & Mather, T. N. Occurrence and distribution in Rhode Island of Hunterellus hookeri (Hymenoptera: Encyrtidae), a wasp parasitoid of Ixodes dammini. J. Med. Entomol. 30, 277–280 (1993).CAS 

Google Scholar 
Scatolini, D. & Penteado-Dias, A. A fauna de Braconidae (hymenoptera) como bioindicadora do grau de preservação de duas localidades do Estado do Paraná. Revista Brasileira de Ecol. 1, 84–87 (1997).
Google Scholar 
Anderson, A. et al. The potential of parasitoid Hymenoptera as bioindicators of arthropod diversity in agricultural grasslands. J. Appl. Ecol. 48, 382–390 (2011).
Google Scholar  More

We used a custom designed biologging tag package with onboard video to describe a 3D high-resolution pursuit between a solitary sailfish and an individual small tuna in open water, representing the first time such an interaction has been documented. The sailfish was tagged at 09:53 on 18 October 2019, and the tag package remained attached to the sailfish for 67 h. However, analyses here are limited to the 24 h period in which the predation event took place (19 October–20 October; ~ 14 h after tagging and ~ 9 h after post-release recovery18) because this coincides with the time period the video camera was recording during daylight hours (on at 0600, off at 1800, sunrise and sunset, respectively) enabling us to ground-truth acceleration signals. Biologging data and accompanying video show the sailfish performing oscillatory dives between the surface and depths of 40–50 m during daylight hours. At night, fewer dives were performed and the sailfish generally remained within the top 10–20 m of the water column (Fig. 1a), leading to a greater range of temperatures experienced during the day (day 20.9–27.9 °C; night 26.5–28.2 °C). Due to the temperature dependence of the estimated active metabolic rate (AMRE), the cooler temperatures at depth led to a reduced AMRE during daylight hours (212.9 ± 89.1 mgO2 kg−1 h−1) compared to night (224.7 ± 44.4 mgO2 kg−1 h−1). Additionally, AMRE initially increases with depth due to increased swim speeds during diving (Fig. 1b), until the thermocline is reached in the 30–40 m depth bin, at which point AMRE decreases with further increased depth (Fig. 1b, c). However, due to thermal inertia of large-bodied fishes19,20,21, it is possible that the sailfish’s body retained heat during the short (14.7 ± 1.7 min) excursions below the thermocline and did not drop to ambient temperature. As such, the metabolic rate calculated at depth may be underestimated with the temperature correction performed here. For example, during the dive in which the predation event occurred (Fig. 1; Table 1), if body temperature was assumed equivalent to surface temperature throughout the dive, estimated metabolic rates would increase by 18% compared to if the metabolic rates were temperature corrected according to the tag’s external temperature reading (Table S2). Yet, because the majority ( > 90%) of time over the 24 h was spent above the thermocline, the temperature correction has little impact on the daily calculated AMRE and subsequent energy expenditure ( More

Eckert, K. L. & Eckert, A. E. An Atlas of Sea Turtle Nesting Habitat for the Wider Caribbean Region Revised edn. WIDECAST Technical Report no. 19 (2019).Guzmán-Hernández, V. Informe técnico 2017 del Programa de conservación de tortugas marinas en Laguna de Términos, Campeche, México. Contiene información de: 1. CPCTM Isla Aguada y 2. Reseña estatal (APFFLT/RPCyGM/CONANP/SEMARNAT, 2018).Guzmán-Hernández, V. et al. Recovery of green turtle populations and their interactions with coastal dune as a baseline for an integral ecological restoration. Acta Bot. Mex. 129, 1. https://doi.org/10.21829/abm129.2022.1954 (2022).Article 

Google Scholar 
Garduño-Andrade, M., Guzmán, V., Miranda, E., Briseño-Dueñas, R. & Abreu-Grobois, F. A. Increases in hawksbill turtle (Eretmochelys imbricata) nestings in the Yucatán Peninsula, Mexico, 1977–1996: Data in support of successful conservation?. Chelonian Conserv. Biol. 3, 286–295 (1999).
Google Scholar 
Guzmán-Hernández, V., Escanero-Figueroa, G. & Márquez, R. Programa tortuguero en el Centro Regional de Investigación Pesquera de Ciudad del Carmen, Campeche: Retrospectiva, avances y perspectivas. In Tortugas Marinas (eds Márquez, R. & Garduño-Dionate, M.) (Instituto Nacional de Pesca, 2014).
Google Scholar 
Guzmán-Hernández, V. Informe técnico 2021 del Programa de conservación de tortugas marinas en Laguna de Términos, Campeche, México. Contiene información de: 1. CPCTM Isla Aguada y 2. Reseña estatal (APFFLT/RPCyGM/CONANP/SEMARNAT, 2022).Troëng, S. & Rankin, E. Long-term conservation efforts contribute to positive green turtle Chelonia mydas nesting trend at Tortuguero, Costa Rica. Biol. Conserv. 121, 111–116 (2005).Article 

Google Scholar 
Lira-Reyes, D. et al. Informe final de la temporada de anidación 2021 del programa para la conservación de la tortuga marina en El Cuyo, Yucatán e Isla Holbox, Quintana Roo (Dirección General de Vida Silvestre de SEMARNAT/Pronatura Península de Yucatán, 2021).Piacenza, S. E., Balazs, G. H., Hargrove, S. K., Richards, P. M. & Heppell, S. S. Trends and variability in demographic indicators of a recovering population of green sea turtles Chelonia mydas. Endanger. Species Res. 31, 103–117 (2016).Article 

Google Scholar 
Iles, T. C. & Beverton, R. J. H. Stock, recruitment and moderating processes in flatfish. J. Sea Res. 39, 41–55 (1998).Article 
ADS 

Google Scholar 
Heppell, S. S., Crowder, L. B., Crouse, D. T., Epperly, S. P. & Frazer, N. B. Population models for Atlantic Loggerheads: Past, present, and future. In Loggerhead Sea Turtles (eds Bolten, A. & Witherington, B.) 255–273 (Smithsonian Institution Press, 2003).
Google Scholar 
Beverton, R. J. H. & Holt, S. J. On the dynamics of exploited fish populations (Fishery Investigation Series II, 1957).Ricker, W. E. Stock and recruitment. J. Fish. Res. Board Can. 11, 559–623 (1954).Article 

Google Scholar 
Cushing, D. H. The dependence of recruitment on parent stock in different groups of fishes. ICES J. Mar. Sci. 33, 340–362. https://doi.org/10.1093/icesjms/33.3.340 (1971).Article 

Google Scholar 
Iles, T. C. A review of stock-recruitment relationships with reference to flatfish populations. Neth. J. Sea Res. 32, 399–420 (1994).Article 

Google Scholar 
Hilborn, R. & Walters, C. Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty (Chapman & Hall, 1992).Book 

Google Scholar 
Subbey, S., Devine, J. A., Schaarscmidt, U. & Nash, R. D. M. Modeling and forecasting stock recruitment: Current and future perspectives. ICES J. Mar. Sci. 71, 2307–2322 (2014).Article 

Google Scholar 
del Monte-Luna, P., Villalobos-Ortíz, H. & Arreguín-Sánchez, F. Variability of sea surface temperature in the southwestern Gulf of Mexico. Cont. Shelf Res. 102, 73–79 (2015).Article 
ADS 

Google Scholar 
Van Houtan, K. S. & Halley, J. M. Long-term climate forcing in loggerhead sea turtle nesting. PLoS ONE 6, e19043. https://doi.org/10.1371/journal.pone.0019043 (2011).Article 
ADS 
CAS 

Google Scholar 
SWOT Scientific Advisory Board. The State of the World’s Sea Turtles (SWOT) Minimum Data Standards for Nesting Beach Monitoring, version 1.0 (2011).Cuevas, D., Garrido-Chávez, E. & Raymundo-Sánchez, A. Monitoreo del género Chelonia en playas con alta densidad de anidación: reporte final (PROCER/DGOR/15/2013) para la Comisión Nacional de Áreas Naturales Protegidas (Pronatura Península de Yucatán-Comisión Nacional de Áreas Naturales Protegidas, 2013).Chim-Vera, Y. A. Evaluación del esfuerzo de monitoreo del éxito de emergencia en nidos de tortuga carey y blanca en la Península de Yucatán (Instituto Tecnológico de Conkal, 2009).Guzmán-Hernández, V., Cuevas-Flores, E., García-Alvarado, P. & González-Ruíz, T. Biological monitoring of sea turtles on nesting beaches: Datasets and basic evaluations. In Successful Conservation Strategies for Sea Turtles. Achievements and Challenges (eds Lara-Uc, M. M. et al.) 41–78 (Nova Science Publishers, 2015).
Google Scholar 
Méndez-Matos, V. C., Guzmán-Hernández, V. & Rivas-Hernández, G. Dinámica poblacional de hembras de tortuga blanca (Chelonia mydas) en el estado de Campeche, México. In El uso del conocimiento de las tortugas marinas como herramienta para la restauración de sus poblaciones y hábitats asociados (eds Cuevas, E. A. et al.) 171–187 (Universidad Autónoma del Carmen, 2019).
Google Scholar 
Xavier, R., Cortez, L. P., Cuevas, E., Barata, A. & Queiroz, N. Hawksbill turtle (Eretmochelys imbricata Linnaeus 1766) and green turtle (Chelonia mydas Linnaeus 1754) nesting activity (2002–2004) at El Cuyo beach, Mexico. Amphibia-Reptilia 27, 539–547 (2006).Article 

Google Scholar 
Whiting, A. U., Chaloupka, M. & Limpus, C. J. Sampling nesting sea turtles: Impact of survey error on trend detection. Mar. Ecol. Prog. Ser. 634, 213–223 (2020).Article 
ADS 

Google Scholar 
Harrell, F. E. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Springer Series in Statistics (Springer, 2015).Book 
MATH 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2021).Florida Fish and Wildlife Conservation Commission. Index Nesting Beach Survey Totals (1989–2020). https://myfwc.com/research/wildlife/sea-turtles/nesting/beach-survey-totals. Accessed 2022-05-04 (2022).Marjomäki, T. J. Analysis of the spawning stock-recruitment relationship of vendace (Coregonus albula (L.)) with evaluation of alternative models, additional variables, biases and errors. Ecol. Freshw. Fish 13, 46–60 (2004).Article 

Google Scholar 
Arendt, M. D., Schwenter, J. A., Witherington, B. E., Meylan, A. B. & Saba, V. S. Historical versus contemporary climate forcing on the annual nesting variability of loggerhead sea turtles in the Northwest Atlantic Ocean. PLoS ONE 8, e81097. https://doi.org/10.1371/journal.pone.0081097 (2013).Article 
ADS 
CAS 

Google Scholar 
del Monte-Luna, P., Guzmán-Hernández, V., Cuevas, E. A., Arreguín-Sánchez, F. & Lluch-Belda, D. Effect of North Atlantic climate variability on hawksbill turtles in the Southern Gulf of Mexico. J. Exp. Mar. Biol. Ecol. 412, 103–109 (2012).Article 

Google Scholar 
Howard, R., Bell, I. & Pike, D. A. Thermal tolerances of sea turtle embryos: Current understanding and future directions. Endanger. Species Res. 26, 75–86. https://doi.org/10.3354/esr00636 (2014).Article 

Google Scholar 
Broderick, A. C., Godley, B. J. & Hays, G. C. Trophic status drives interannual variability in nesting numbers of marine turtles. Proc. R. Soc. Lond. B. Biol. 268, 1481–1487 (2001).Article 
CAS 

Google Scholar 
Manzano-Sarabia, M. M. & Salinas-Zavala, C. A. Variabilidad estacional e interanual de la concentración de clorofila y temperatura superficial del mar en la región occidental del Golfo de México: 1996–2007. Interciencia 33, 628–634 (2008).
Google Scholar 
Hays, G. C. The implications of variable remigration intervals for the assessment of population size in marine turtles. J. Theor. Biol. 206, 221–227. https://doi.org/10.1006/jtbi.2000.2116 (2000).Article 
ADS 
CAS 

Google Scholar 
Saragoça-Bruno, R., Restrepo, J. A. & Valverde, R. A. Effects of El Niño Southern Oscillation and local ocean temperature on the reproductive output of green turtles (Chelonia mydas) nesting at Tortuguero, Costa Rica. Mar. Biol. 167, 1 (2020).Article 

Google Scholar 
Patrício, A. R., Hawkes, L. A., Monsinjon, J. R., Godley, B. J. & Fuentes, M. M. P. B. Climate change and marine turtles: Recent advances and future directions. Endanger. Species Res. 44, 363–395. https://doi.org/10.3354/esr01110 (2021).Article 

Google Scholar 
Valverde-Cantillo, V., Robinson, N. J. & Santidrián Tomillo, P. Influence of oceanographic conditions on nesting abundance, phenology and internesting periods of east Pacific green turtles. Mar. Biol. 166, 93. https://doi.org/10.1007/s00227-019-3541-1 (2019).Article 

Google Scholar 
Chaloupka, M. Encouraging outlook for recovery of a once severely exploited marine megaherbivore. Glob. Ecol. Biogeogr. 17, 297–304 (2008).Article 

Google Scholar 
Ariza-Gallego, M., Herrera-Carmona, J., Payán, L. & Giraldo, A. Relationship between sea surface temperature and the nesting of the Olive Ridley sea turtle Lepidochelys olivacea (Testudines: Cheloniidae) in Gorgona Island, Colombian Pacific. Rev. Biol. Trop. 68, 528–540. https://doi.org/10.15517/RBT.V68I2.38642 (2020).Article 

Google Scholar 
Ceriani, S. A., Casale, P., Brost, M., Leone, E. H. & Witherington, B. E. Conservation implications of sea turtle nesting trends: Elusive recovery of a globally important loggerhead population. Ecosphere 10, e02936. https://doi.org/10.1002/ecs2.2936 (2019).Article 

Google Scholar 
Arendt, M. D., Schwenter, J. A., Owens, D. W. & Valverde, R. A. Theoretical modeling and neritic monitoring of loggerhead Caretta caretta [Linnaeus, 1758] sea turtle sex ratio in the southeast United States do not substantiate fears of a male-limited population. Glob. Change Biol. 27, 4849–4859. https://doi.org/10.1111/gcb.15808 (2021).Article 
CAS 

Google Scholar 
Doi, T., Márquez, R., Kimoto, H. & Azeno, N. Diagnosis and Conservation of Hawksbill Turtle Population in the Cuban Archipelago. Technical Report 40 (Japan Bekko Association, 1992).Broderick, A. C. Are green turtles globally endangered?. Glob. Ecol. Biogeogr. 14, 21–26 (2006).Article 

Google Scholar 
Mazaris, A. D., Schofield, G., Gkazinou, C., Almpanidou, V. & Hays, G. C. Global sea turtle conservation successes. Sci. Adv. 3, e1600730. https://doi.org/10.1126/sciadv.1600730 (2017).Article 
ADS 

Google Scholar 
Early-Capistrán, M. M. et al. Integrating local ecological knowledge, ecological monitoring, and computer simulation to evaluate conservation outcomes. Conserv. Lett.https://doi.org/10.1111/conl.12921 (2022).Article 

Google Scholar 
Taylor, M. A., Stephenson, T. S., Chen, A. A. & Stephenson, K. A. Climate change and the Caribbean: Review and response. Caribb. Stud. 40, 169–200 (2012).Article 

Google Scholar  More

Charlesworth, B. & Charlesworth, D. Population genetics from 1966 to 2016. Heredity 118, 2–9 (2017).CAS 

Google Scholar 
Orsini, L., Vanoverbeke, J., Swillen, I., Mergeay, J. & Meester, L. Drivers of population genetic differentiation in the wild: Isolation by dispersal limitation, isolation by adaptation and isolation by colonization. Mol. Ecol. 22, 5983–5999 (2013).
Google Scholar 
Vendrami, D. L. J. et al. RAD sequencing resolves fine-scale population structure in a benthic invertebrate: Implications for understanding phenotypic plasticity. R. Soc. Open Sci. 4, 160548 (2017).ADS 

Google Scholar 
Dufresnes, C., Rodrigues, N. & Savary, R. Slow and steady wins the race: Contrasted phylogeographic signatures in two Alpine amphibians. Integr. Zool. 17, 181–190 (2021).
Google Scholar 
Frankham, R. Genetics and extinction. Biol. Conserv. 126, 131–140 (2005).
Google Scholar 
Schwartz, M. K., Luikart, G. & Waples, R. S. Genetic monitoring as a promising tool for conservation and management. Trends in Ecol. Evol. 22, 25–33 (2007).
Google Scholar 
Ottewell, K. M., Bickerton, D. C., Byrne, M. & Lowe, A. J. Bridging the gap: A genetic assessment framework for population-level threatened plant conservation prioritization and decision-making. Divers. Distrib. 22, 174–188 (2016).
Google Scholar 
Frankham, R., Bradshaw, C. J. A. & Brook, B. W. Genetics in conservation management: Revised recommendations for the 50/500 rules, red list criteria and population viability analyses. Biol. Conserv. 170, 56–63 (2014).
Google Scholar 
Hohenlohe, P. A., Funk, C. W. & Rajora, O. P. Population genomics for wildlife conservation and management. Mol. Ecol. 30, 62–82 (2020).
Google Scholar 
Angelone, S. & Holderegger, R. Population genetics suggests effectiveness of habitat connectivity measures for the European tree frog in Switzerland. J. Appl. Ecol. 46, 879–887 (2009).
Google Scholar 
Griffiths, S. M., Taylor-Cox, E. D., Behringer, D. C., Butler, M. J. IV. & Preziosi, R. F. Using genetics to inform restoration and predict resilience in declining populations of a keystone marine sponge. Biodivers. Conserv. 29, 1383–1410 (2020).
Google Scholar 
Moritz, C. Conservation units and translocations: Strategies for conserving evolutionary processes. Hereditas 130, 217–228 (1999).
Google Scholar 
Bohonak, A. J. Dispersal, gene flow, and population structure. Q. Rev. Biol. 74, 21–45 (1999).CAS 

Google Scholar 
Arguedas, N. & Parker, P. G. Seasonal migration and genetic population structure in house wrens. Condor 102, 517–528 (2000).
Google Scholar 
Quillfeldt, P. et al. Does genetic structure reflect differences in non-breeding movements? A case study in small, highly mobile seabirds. BMC Evol. Biol. 17, 160 (2017).
Google Scholar 
Charlesworth, B., Sniegowski, P. & Stephan, W. The evolutionary dynamics of repetitive DNA in eukaryotes. Nature 371, 215–220 (1994).ADS 
CAS 

Google Scholar 
Schlötterer, C. Evolutionary dynamics of microsatellite DNA. Chromosoma 109, 365–371 (2000).
Google Scholar 
Ellegren, H. Microsatellites: Simple sequences with complex evolution. Nat. Rev. Genet. 5, 435–445 (2004).CAS 

Google Scholar 
Hodel, R. G. J. et al. The report of my death was an exaggeration: A review for researchers using microsatellites in the 21st century. Appl. Plant Sci. 4, 1600025 (2016).
Google Scholar 
Dufresnes, C. & Litvinchuk, S. N. Diversity, distribution and molecular species delimitation in frogs and toads from the Eastern Palearctic. Zool. J. Linn. Soc. 195, 695–760 (2022).
Google Scholar 
Galtier, N., Nabholz, B., Glémin, S. & Hurst, G. D. D. Mitochondrial DNA as a marker of molecular diversity: A reappraisal. Mol. Evol. 18, 4541–4550 (2009).CAS 

Google Scholar 
Zink, R. M. & Barrowclough, G. Mitochondrial DNA under siege in avian phylogeography. Mol. Ecol. 17, 2107–2121 (2008).CAS 

Google Scholar 
Toews, D. P. L. & Brelsford, A. The biogeography of mitochondrial and nuclear discordance in animals. Mol. Ecol. 21, 3907–3930 (2012).CAS 

Google Scholar 
Bonnet, T., Leblois, R., Rousset, F. & Crochet, P.-A. A reassessment of explanations for discordant introgressions of mitochondrial and nuclear genomes. Evolution 71, 2140–2218 (2017).
Google Scholar 
Davey, J. W. & Blaxter, M. L. RADSeq: Next-generation population genetics. Brief Funct. Genomics 9, 416–423 (2010).CAS 

Google Scholar 
Lexer, C. et al. ‘Next generation’ biogeography: Towards understanding the drivers of species diversification and persistence. J. Biogeogr. 40, 1013–1022 (2013).
Google Scholar 
Baird, N. A. et al. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS ONE 3, e3376 (2008).ADS 

Google Scholar 
Peterson, B. K., Weber, J. N., Kay, E. H., Fisher, H. S. & Hoekstra, H. E. Double Digest RADseq: An inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS ONE 7, e37135 (2012).ADS 
CAS 

Google Scholar 
Dufresnes, C. et al. Phylogeography of a cryptic speciation continuum in Eurasian spadefoot toads (Pelobates). Mol. Ecol. 28, 3257–3270 (2019).
Google Scholar 
Sunde, J., Yıldırım, Y., Tibblin, P. & Forsman, A. Comparing the performance of microsatellites and RADseq in population genetic studies: Analysis of data for pike (Esox Lucius) and a synthesis of previous studies. Front. Genet. 11, 218 (2020).
Google Scholar 
Moussy, C. et al. Migration and dispersal patterns of bats and their influence on genetic structure. Mammal Rev. 43, 183–195 (2013).
Google Scholar 
Berthier, P., Excoffier, L. & Ruedi, M. Recurrent replacement of mtDNA and cryptic hybridization between two sibling bat species Myotis myotis and Myotis blythii. Proc. R. Soc. B: Biol. Sci. 273, 3101–3109 (2007).
Google Scholar 
Wright, P. G. R. et al. Hydrogen isotopes reveal evidence of migration of Miniopterus schreibersii in Europe. BMC Ecol. 20, 52 (2020).CAS 

Google Scholar 
Schnetter, W. Beringungsergebnisse an der Langflügelfledermaus (Miniopterus schreibersi Kühl) im Kaiserstuhl. Bonn. Zool. Beitr. 11, 150–165 (1960).
Google Scholar 
Rodrigues, L. Miniopterus schreibersii. In The Atlas of European Mammals (eds Mitchell-Jones, A. J. et al.) 154–155 (Academic Press, 1999).
Google Scholar 
Rodrigues, L., Ramos Pereira, M. J., Rainho, A. & Palmeirim, J. M. Behavioral determinants of gene flow in the bat Miniopterus schreibersii. Behav. Ecol. Sociobiol. 64, 835–843 (2010).
Google Scholar 
Rodrigues, L. & Palmeirim, J. M. Migratory behaviour of Miniopterus schreibersii (Chiroptera): When, where, and why do cave bats migrate in a Mediterranean region?. J. Zool. 274, 116–125 (2008).
Google Scholar 
Ramos Pereira, M. J., Salgueiro, P., Rodrigues, L., Coelho, M. M. & Palmeirim, J. M. Population structure of a cave-dwelling bat, Miniopterus schreibersii: Does it reflect history and social organization?. J. Hered. 100, 533–544 (2009).
Google Scholar 
Bilgin, R. et al. Circum-Mediterranean phylogeography of a bat coupled with past environmental niche modeling: A new paradigm for the recolonization of Europe?. Mol. Phylogenet. Evol. 99, 323–336 (2016).
Google Scholar 
Gürün, K. et al. A continent-scale study of the social structure and phylogeography of the bent-wing bat, Miniopterus schreibersii (Mammalia: Chiroptera), using new microsatellite data. J. Mammal. 100, 1865–1878 (2019).
Google Scholar 
Gazaryan, S., Bücs, S., Çoraman, E. Miniopterus schreibersii (errata version published in 2021). The IUCN Red List of Threatened Species 2020: e.T81633057A195856522 (2020).Miller-Butterworth, C. M., Jacobs, D. S. & Harley, E. H. Isolation and characterization of highly polymorphic microsatellite loci in Schreibers’ long-fingered bat, Miniopterus schreibersii (Chiroptera: Vespertilionidae). Mol. Ecol. Notes 2, 139–141 (2002).CAS 

Google Scholar 
Wood, R., Weyeneth, N. & Appleton, B. Development and characterisation of 20 microsatellite loci isolated from the large bent-wing bat, Miniopterus schreibersii (Chiroptera: Miniopteridae) and their cross-taxa utility in the family Miniopteridae. Mol. Ecol. Resour. 11, 675–685 (2011).
Google Scholar 
Witsenburg, F. et al. How a haemosporidian parasite of bats gets around: The genetic structure of a parasite, vector and host compared. Mol. Ecol. 24, 926–940 (2015).CAS 

Google Scholar 
Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. micro-checker: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).
Google Scholar 
Parchman, T. L. et al. Genome wide association mapping of an adaptive trait in lodgepole pine. Mol. Ecol. 21, 2991–3005 (2012).CAS 

Google Scholar 
Catchen, J. M., Amores, A., Hohenlohe, P., Cresko, W. & Postlethwait, J. H. Stacks: Building and genotyping loci de novo from short-read sequences. G3 1, 171–182 (2011).CAS 

Google Scholar 
Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analyses of population structure. Evolution 38, 1358–1370 (1984).CAS 

Google Scholar 
Goudet, J. hierfstat, a package for r to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186 (2005).
Google Scholar 
Frankham, R., Ballou, J. D. & Briscoe, D. A. A Primer of Conservation Genetics (Cambridge University Press, 2004).
Google Scholar 
Weir, B. S. & Goudet, J. A unified characterization of population structure and relatedness. Genetics 206, 2085–2103 (2017).
Google Scholar 
Mantel, N. A. The detection of disease clustering and a generalized regression approach. Cancer Res. 27, 209–220 (1967).CAS 

Google Scholar 
Wright, S. Isolation by distance. Genetics 28, 114–138 (1943).CAS 

Google Scholar 
Cavalli-Sforza, L. L. & Edwards, A. W. F. Phylogenetic analysis: Model and estimation procedures. Am. J. Hum. Genet. 19, 233–257 (1967).CAS 

Google Scholar 
Paradis, E. & Schliep, K. ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).CAS 

Google Scholar 
Goudet, J., Perrin, N. & Waser, P. Tests for sex-biased dispersal using bi-parentally inherited genetic markers. Mol. Ecol. 11, 1103–1114 (2002).CAS 

Google Scholar 
Frichot, E. & François, O. lea: An r package for landscape and ecological association studies. Methods Ecol. Evol. 6, 925–929 (2015).
Google Scholar 
Yannic, G. et al. High connectivity in a long-lived High-Arctic seabird, the ivory gull Pagophila eburnea. Polar Biol. 39, 221–236 (2016).
Google Scholar 
Cumer, T. et al. Landscape and climatic variations of the Quaternary shaped multiple secondary contacts among barn owls (Tyto alba) of the Western Palearctic. Mol. Biol. Evol. 39, msab343 (2022).CAS 

Google Scholar 
Boston, E. S. M., Montgomery, W. I., Hynes, R. & Prodöhl, P. A. New insights on postglacial colonization in western Europe: The phylogeography of the Leisler’s bat (Nyctalus leisleri). Proc. R. Soc. B: Biol. Sci. 282, 20142605 (2015).
Google Scholar 
Razgour, O. et al. The shaping of genetic variation in edge-of-range populations under past and future climate change. Ecol. Lett. 16, 1258–1266 (2013).
Google Scholar 
Petit, E., Balloux, F. & Goudet, J. Sex-biased dispersal in a migratory bat: A characterization using sex-specific demographic parameters. Evolution 55, 635–640 (2001).CAS 

Google Scholar 
Moussy, C. et al. Population genetic structure of serotine bats (Eptesicus serotinus) across Europe and implications for the potential spread of bat rabies (European bat lyssavirus EBLV-1). Heredity 115, 83–92 (2015).CAS 

Google Scholar 
Rossiter, S. J., Benda, P., Dietz, C., Zhang, S. & Jones, G. Rangewide phylogeography in the greater horseshoe bat inferred from microsatellites: Implications for population history, taxonomy and conservation. Mol. Ecol. 16, 4699–4714 (2007).CAS 

Google Scholar 
Dool, S. E. et al. Phylogeography and postglacial recolonization of Europe by Rhinolophus hipposideros: Evidence from multiple genetic markers. Mol. Ecol. 22, 4055–4070 (2013).CAS 

Google Scholar 
Kerth, G. et al. Communally breeding Bechstein’s bats have a stable social system that is independent from the postglacial history and location of the populations. Mol. Ecol. 17, 2368–2381 (2008).CAS 

Google Scholar 
Garrick, R. C., Banusiewicz, J. D., Burgess, S., Hyseni, C. & Symula, R. E. Extending phylogeography to account for lineage fusion. J. Biogeogr. 46, 268–278 (2019).
Google Scholar 
Burri, R. et al. The genetic basis of color-related local adaptation in a ring-like colonization around the Mediterranean. Evolution 70, 140–153 (2016).
Google Scholar 
Taberlet, P., Fumagalli, L., Wust-Saucy, A.-G. & Cosson, J.-F. Comparative phylogeography and postglacial colonization routes in Europe. Mol. Ecol. 7, 453–464 (1998).CAS 

Google Scholar 
Hewitt, G. M. Post-glacial re-colonization of European biota. Biol. J. Linn. Soc. 68, 87–112 (1999).
Google Scholar 
Gómez, A. & Lunt, D. H. Refugia within refugia: Patterns of phylogeographic concordance in the Iberian Peninsula. In Phylogeography of Southern European Refugia (eds Weiss, S. & Ferrand, N.) 155–188 (Springer, 2007).
Google Scholar 
Vonhof, M. J., Russell, A. L. & Miller-Butterworth, M. Range-wide genetic analysis of little brown bat (Myotis lucifugus) populations: Estimating the risk of spread of white-nose syndrome. PLoS ONE 10, e0128713 (2015).
Google Scholar 
Auteri, G. G. & Knowles, L. L. Decimated little brown bats show potential for adaptive change. Sci. Rep. 10, 3023 (2020).ADS 
CAS 

Google Scholar 
Gignoux-Wolfsohn, S. A. et al. Genomic signatures of selection in bats surviving white-nose syndrome. Mol. Ecol. 30, 5643–5657 (2021).
Google Scholar 
Rivers, N. M., Butlin, R. K. & Altringham, J. D. Autumn swarming behaviour of Natterer’s bats in the UK: Population size, catchment area and dispersal. Biol. Conserv. 127, 215–226 (2006).
Google Scholar 
Reis, N. R., Fregonezi, M. N., Peracchi, A. L. & Rossaneis, B. K. Metapopulation in bats of Southern Brazil. Braz. J. Biol. 72, 605–609 (2012).CAS 

Google Scholar 
Humphrey, S. R. & Oli, M. K. Population dynamics and site fidelity of the cave bat, Myotis velifer, Oklahoma. J. Mammal. 96, 946–956 (2015).
Google Scholar 
Jeffries, D. L. et al. Comparing RADseq and microsatellites to infer complex phylogeographic patterns, an empirical perspective in the Crucian carp, Carassius carassius. L. Mol. Ecol. 25, 2997–3018 (2016).
Google Scholar 
Hodel, R. G. J. et al. Adding loci improves phylogeographic resolution in red mangroves despite increased missing data: Comparing microsatellites and RAD-Seq and investigating loci filtering. Sci. Rep. 7, 17598 (2017).ADS 

Google Scholar 
Lemopoulos, A. et al. Comparing RADseq and microsatellites for estimating genetic diversity and relatedness—Implications for brown trout conservation. Ecol. Evol. 9, 2106–2120 (2019).
Google Scholar 
Zimmerman, S. J., Aldridge, C. L. & Oyler-McCance, S. J. An empirical comparison of population genetic analyses using microsatellite and SNP data for a species of conservation concern. BMC Genom. 21, 382 (2020).CAS 

Google Scholar 
Hale, M. L., Burg, T. M. & Steeves, T. E. Sampling for microsatellite-based population genetic studies: 25 to 30 individuals per population is enough to accurately estimate allele frequencies. PLoS ONE 7, e45170 (2012).ADS 
CAS 

Google Scholar 
Quetglas, J., Gonzalez, F. & Paz, O. Estudian la extraña mortandad de miles de murcielago de cuevas. Quercus 203, 50 (2003).
Google Scholar 
Negredo, A. et al. Discovery of an ebolavirus-like filovirus in Europe. PLoS Pathog. 7, e1002304 (2011).CAS 

Google Scholar 
Reed, D. H. & Frankham, R. Correlation between fitness and genetic diversity. Conserv. Biol. 17, 230–237 (2003).
Google Scholar 
Alcalde, J. T., Artácoz, A. & Meijide, F. Recuperación de la colonia de Miniopterus schreibersii de la cueva de Cueva de Ágreda (Soria). Barbastella 5, 32–35 (2012).
Google Scholar 
Kemenesi, G. et al. Re-emergence of Lloviu virus in Miniopterus schreibersii bats, Hungary, 2016. Emerg. Microbes Infect. 7, 66 (2018).
Google Scholar 
Kemenesi, et al. Isolation of infectious Lloviu virus from Schreiber’s bats in Hungary. Nat. Commun. 13, 1706 (2022).ADS 
CAS 

Google Scholar 
Stoffel, C. et al. Genetic consequences of population expansions and contractions in the common hippopotamus (Hippopotamus amphibius) since the late Pleistocene. Mol. Ecol. 24, 2507–2520 (2015).
Google Scholar  More

Quality controls for the validation of MICODE protocolTo validate the MICODE experimental approach in the version of fecal batch of the human proximal colon, we chose to monitor and check some parameters as quality controls (QC) related to metabolites and microbes at the end of fermentations, and in comparison, to the baseline. QCs adopted were; (i) the Firmicutes/Bacteroidetes ratio (F/B), which is related to health and disease11, was maintained at a low level, confirming the capacity to simulate a healthy in vivo condition for 24 h. (ii) The presence of Archea (e.g., Methanobrevibacter smithii and Methanosphaera stadtmanae), which are pretty sensible to oxygen content12, was retained from the baseline to the end point in each vessel and repetition, indicating that the environmental conditions were strictly maintained. (iii) Good’s rarity index of alpha biodiversity remained similar during time of fermentation (p  > 0.05), indicating enough support to the growth of rare species. (iv) Observed OTUs richness index scored approximately 400 OTUs at the end point. (v) The paradigm of prebiotics was confirmed when the positive control (FOS) was applied on MICODE; high probiotic and SCFAs increases and limitation of enteropathogens. (vi) Each GC/MS analysis had quantified some stool-related compounds (urea, 1-propanol, and butylated hydroxy toluene), that ranged across the complete chromatogram and were adsorbed at the same retention times.Changes in bacterial alpha and beta diversitiesThe microbiota diversity indices were analyzed to study the impact of HPBA on microbial population, to assess population’s stability during fermentation, and to compare its microbiota to that of other bioreactors (Figure S1). The baseline of value was compared to the endpoints of fermentation of different treatments. It is undisputable that a part of the effect of reduction in richness (Observed OTUs) was derived by the passage from in vivo to in vitro condition, but the focus must be set on the different trend that other alpha diversity indices had. For example, abundance (Chao 1) for HBPA was significantly higher at the end of fermentation (p  0.05) and HPBA (p  0.05), while oppositely, FOS decreased in evenness (p  > 0.05) and raised in dominance (p  0.05). Among these, 31 variables were significant and their Log2 fold changes in respect to the baseline were compared by post-hoc test (Table 1). The 41 OTUs selected were those that recorded shifts after fermentation and that from literature are susceptible to the effect of prebiotic or fiber substrates. We have included even three OTUs of Archea relative to QC of the experiments (previously discussed).Table 1 Abundances (% ± S.D.) and changes in phylum taxa (Log2 F/C) after 24 h in vitro fecal batch culture fermentations from healthy donors and administrated with HBPA, HB, and FOS as the substrates, and also including a blank control.Full size tableThe first group of OTUs included beneficial or commensal bacteria that usually respond to prebiotics. In this group, three Bifidobacterium were picked showing increases on the substrates and reduction on the blank control. HB and HBPA fostered Bif. bifidum, but just the latter did it significantly, making this taxon grew up to the 3.30% of relative abundance (p  More