Ecology

Millard, J. et al. Global effects of land-use intensity on local pollinator biodiversity. Nat. Commun. 12, 2902 (2021).Article 
CAS 

Google Scholar 
Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, e1500052 (2015).Article 

Google Scholar 
Valiente-Banuet, A. et al. Beyond species loss: the extinction of ecological interactions in a changing world. Funct. Ecol. 29, 299–307 (2015).Article 

Google Scholar 
Rybicki, J., Abrego, N. & Ovaskainen, O. Habitat fragmentation and species diversity in competitive communities. Ecol. Lett. 23, 506–517 (2020).Article 

Google Scholar 
Chase, J. M., Blowes, S. A., Knight, T. M., Gerstner, K. & May, F. Ecosystem decay exacerbates biodiversity loss with habitat loss. Nature 584, 238–243 (2020).Article 
CAS 

Google Scholar 
Ewers, R. M. & Didham, R. K. Confounding factors in the detection of species responses to habitat fragmentation. Biol. Rev. 81, 117–142 (2006).Article 

Google Scholar 
Didham, R. K. Ecological consequences of habitat fragmentation. In Encyclopedia of Life Sciences (ed Jansson, R.), 61, 1–39 (Wiley, UK2010).Aizen, M. A., Sabatino, M. & Tylianakis, J. M. Specialization and rarity predict nonrandom loss of interactions from mutualist networks. Science 335, 1486–1489 (2012).Article 
CAS 

Google Scholar 
Spiesman, B. J. & Inouye, B. D. Habitat loss alters the architecture of plant-pollinator interaction networks. Ecology 94, 2688–2696 (2013).Article 

Google Scholar 
Aizen, M. A. et al. The phylogenetic structure of plant-pollinator networks increases with habitat size and isolation. Ecol. Lett. 19, 29–36 (2016).Article 

Google Scholar 
Emer, C. et al. Seed-dispersal interactions in fragmented landscapes-a metanetwork approach. Ecol. Lett. 21, 484–493 (2018).Article 

Google Scholar 
Fortuna, M. A. & Bascompte, J. Habitat loss and the structure of plant-animal mutualistic networks. Ecol. Lett. 9, 278–283 (2006).Article 

Google Scholar 
Grass, I., Jauker, B., Steffan-Dewenter, I., Tscharntke, T. & Jauker, F. Past and potential future effects of habitat fragmentation on structure and stability of plant-pollinator and host-parasitoid networks. Nat. Ecol. Evol. 2, 1408–1417 (2018).Article 

Google Scholar 
Glenn R. Matlack & John A. Litvaitis. Forest edges. In Maintaining Biodiversity in Forest Ecosystems (ed Hunter, M.) 6, 210–233 (Cambridge Univ. Press, 1999).Hadley, A. S. & Betts, M. G. The effects of landscape fragmentation on pollination dynamics: absence of evidence not evidence of absence. Biol. Rev. 87, 526–544 (2012).Article 

Google Scholar 
Ibanez, I., Katz, D. S. W., Peltier, D., Wolf, S. M. & Barrie, B. T. C. Assessing the integrated effects of landscape fragmentation on plants and plant communities: the challenge of multiprocess-multiresponse dynamics. J. Ecol. 102, 882–895 (2014).Article 

Google Scholar 
Morreale, L. L., Thompson, J. R., Tang, X., Reinmann, A. B. & Hutyra, L. R. Elevated growth and biomass along temperate forest edges. Nat. Commun. 12, 7181 (2021).Article 
CAS 

Google Scholar 
Martinez-Ramos, M., Alvarez-Buylla, E. & Sarukhan, J. Tree demography and gap dynamics in a tropical rain forest. Ecology 70, 555–558 (1989).Article 

Google Scholar 
Yamamoto, S. I. Forest gap dynamics and tree regeneration. J. For. Res. 5, 223–229 (2000).Article 

Google Scholar 
Schnitzer, S. A. & Carson, W. P. Treefall gaps and the maintenance of species diversity in a tropical forest. Ecology 82, 913–919 (2001).Article 

Google Scholar 
Kricher, J. A Shifting Mosaic: Rain Forest Development and Dynamics. In Tropical Ecology 6, 188–226 (Princeton Univ. Press, 2011).Gayer, C. et al. Flowering fields, organic farming and edge habitats promote diversity of plants and arthropods on arable land. J. Appl. Ecol. 58, 1155–1166 (2021).Article 

Google Scholar 
Bailey, S. et al. Distance from forest edge affects bee pollinators in oilseed rape fields. Ecol. Evol. 4, 370–380 (2014).Article 

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

Google Scholar 
Hagen, M. et al. Biodiversity, species interactions and ecological networks in a fragmented world. Adv. Ecol. Res. 46, 89–210 (2012).Article 

Google Scholar 
Traveset, A., Castro-Urgal, R., Rotllan-Puig, X. & Lazaro, A. Effects of habitat loss on the plant-flower visitor network structure of a dune community. Oikos 127, 45–55 (2018).Article 

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

Google Scholar 
Staddon, P., Lindo, Z., Crittenden, P. D., Gilbert, F. & Gonzalez, A. Connectivity, non-random extinction and ecosystem function in experimental metacommunities. Ecol. Lett. 13, 543–552 (2010).Article 

Google Scholar 
Wardle, D. A., Bardgett, R. D., Callaway, R. M. & Van der Putten, W. H. Terrestrial ecosystem responses to species gains and losses. Science 332, 1273–1277 (2011).Article 
CAS 

Google Scholar 
Sargent, R. D. & Ackerly, D. D. Plant-pollinator interactions and the assembly of plant communities. Trends Ecol. Evol. 23, 123–130 (2008).Article 

Google Scholar 
Bastolla, U. et al. The architecture of mutualistic networks minimizes competition and increases biodiversity. Nature 458, 1018–1020 (2009).Article 
CAS 

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

Google Scholar 
Dunne, J. A., Williams, R. J. & Martinez, N. D. Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecol. Lett. 5, 558–567 (2002).Article 

Google Scholar 
Pawar, S. Why are plant-pollinator networks nested? Science 345, 383–383 (2014).Article 
CAS 

Google Scholar 
Kaiser-Bunbury, C. N., Muff, S., Memmott, J., Muller, C. B. & Caflisch, A. The robustness of pollination networks to the loss of species and interactions: a quantitative approach incorporating pollinator behaviour. Ecol. Lett. 13, 442–452 (2010).Article 

Google Scholar 
Evans, D. M., Pocock, M. J. O. & Memmott, J. The robustness of a network of ecological networks to habitat loss. Ecol. Lett. 16, 844–852 (2013).Article 

Google Scholar 
Ponisio, L. C., Gaiarsa, M. P. & Kremen, C. Opportunistic attachment assembles plant-pollinator networks. Ecol. Lett. 20, 1261–1272 (2017).Article 

Google Scholar 
Wilson, M. C. et al. Habitat fragmentation and biodiversity conservation: key findings and future challenges. Landsc. Ecol. 31, 219–227 (2016).Article 

Google Scholar 
Zhong, L., Didham, R. K., Liu, J., Jin, Y. & Yu, M. Community re-assembly and divergence of woody plant traits in an island-mainland system after more than 50 years of regeneration. Divers. Distrib. 27, 1435–1448 (2021).Article 

Google Scholar 
Liu, J. et al. The asymmetric relationships of the distribution of conspecific saplings and adults in forest fragments. J. Plant Ecol. 13, 398–404 (2020).Article 
CAS 

Google Scholar 
Ewers, R. M., Bartlam, S. & Didham, R. K. Altered species interactions at forest edges: contrasting edge effects on bumble bees and their phoretic mite loads in temperate forest remnants. Insect Conserv. Divers. 6, 598–606 (2013).Article 

Google Scholar 
Wardhaugh, C. W. The spatial and temporal distributions of arthropods in forest canopies: uniting disparate patterns with hypotheses for specialisation. Biol. Rev. Camb. Philos. Soc. 89, 1021–1041 (2015).Article 

Google Scholar 
Lowman, M. Life in the treetops – an overview of forest canopy science and its future directions. Plants People Planet 3, 16–21 (2021).Article 

Google Scholar 
Nakamura, A. et al. Forests and their canopies: achievements and horizons in canopy science. Trends Ecol. Evol. 32, 438–451 (2017).Article 

Google Scholar 
Lennartsson, T. Extinction thresholds and disrupted plant-pollinator interactions in fragmented plant populations. Ecology 83, 3060–3072 (2002).
Google Scholar 
Aguilar, R., Ashworth, L., Galetto, L. & Aizen, M. A. Plant reproductive susceptibility to habitat fragmentation: review and synthesis through a meta-analysis. Ecol. Lett. 9, 968–980 (2006).Article 

Google Scholar 
Kremen, C. et al. Pollination and other ecosystem services produced by mobile organisms: a conceptual framework for the effects of land-use change. Ecol. Lett. 10, 299–314 (2007).Article 

Google Scholar 
Goulson, D., Nicholls, E., Botias, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).Article 

Google Scholar 
Gathmann, A. & Tscharntke, T. Foraging ranges of solitary bees. J. Anim. Ecol. 71, 757–764 (2002).Article 

Google Scholar 
Winfree, R., Bartomeus, I. & Cariveau, D. P. Native pollinators in anthropogenic habitats. Annu. Rev. Entomol. 42, 1–22 (2011).
Google Scholar 
Torné-Noguera, A. et al. Determinants of spatial distribution in a bee community: nesting resources, flower resources, and body size. PLoS ONE 9, e97255 (2014).Article 

Google Scholar 
Roswell, M., Dushoff, J. & Winfree, R. A conceptual guide to measuring species diversity. Oikos 130, 321–338 (2021).Article 

Google Scholar 
Schoereder, J. H. et al. Should we use proportional sampling for species-area studies? J. Biogeogr. 31, 1219–1226 (2004).Article 

Google Scholar 
Jordano, P. Patterns of mutualistic interactions in pollination and seed dispersal: connectance, dependence asymmetries, and coevolution. Am. Nat. 129, 657–677 (1987).Article 

Google Scholar 
Devoto, M., Medan, D. & Montaldo, N. H. Patterns of interaction between plants and pollinators along an environmental gradient. Oikos 109, 461–472 (2005).Article 

Google Scholar 
Petanidou, T., Kallimanis, A. S., Tzanopoulos, J., Sgardelis, S. P. & Pantis, J. D. Long-term observation of a pollination network: fluctuation in species and interactions, relative invariance of network structure and implications for estimates of specialization. Ecol. Lett. 11, 564–575 (2008).Article 

Google Scholar 
Brodie, J. F. et al. Secondary extinctions of biodiversity. Trends Ecol. Evol. 29, 664–672 (2014).Article 

Google Scholar 
Vazquez, D. P. & Aizen, M. A. Asymmetric specialization: a pervasive feature of plant-pollinator interactions. Ecology 85, 1251–1257 (2004).Article 

Google Scholar 
Memmott, J., Waser, N. M. & Price, M. V. Tolerance of pollination networks to species extinctions. Proc. R. Soc. Lond. B 271, 2605–2611 (2004).
Google Scholar 
Malhi, Y., Gardner, T. A., Goldsmith, G. R., Silman, M. R. & Zelazowski, P. Tropical forests in the Anthropocene. Annu. Rev. Environ. Resour. 39, 125–159 (2014).Article 

Google Scholar 
Lewis, S. L., Edwards, D. P. & Galbraith, D. Increasing human dominance of tropical forests. Science 349, 827–832 (2015).Article 
CAS 

Google Scholar 
Fletcher, R. J. Jr et al. Is habitat fragmentation good for biodiversity? Biol. Conserv. 226, 9–15 (2018).Article 

Google Scholar 
Ren, P., Si, X. & Ding, P. Stable species and interactions in plant-pollinator networks deviate from core position in fragmented habitats. Ecography 2022, e06102 (2022).Article 

Google Scholar 
Fortuna, M. A. et al. Nestedness versus modularity in ecological networks: two sides of the same coin? J. Anim. Ecol. 79, 811–817 (2010).
Google Scholar 
Bascompte, J., Jordano, P., Melian, C. J. & Olesen, J. M. The nested assembly of plant-animal mutualistic networks. Proc. Natl Acad. Sci. USA 100, 9383–9387 (2003).Article 
CAS 

Google Scholar 
Almeida-Neto, M., Guimaraes, P., Guimaraes, P. R. Jr, Loyola, R. D. & Ulrich, W. A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos 117, 1227–1239 (2008).Article 

Google Scholar 
Ulrich, W., Almeida-Neto, M. & Gotelli, N. J. A consumer’s guide to nestedness analysis. Oikos 118, 3–17 (2009).Article 

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

Google Scholar 
Beckett, S. J. Improved community detection in weighted bipartite networks. R. Soc. Open Sci. 3, 140536 (2016).Article 

Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7 (2020). https://CRAN.R-project.org/package=veganDormann, C. F. et al. bipartite: Visualising Bipartite Networks and Calculating Some (Ecological) Indices. R package version 2.16 (2021). https://CRAN.R-project.org/package=bipartitePocock, M. J. O., Evans, D. M. & Memmott, J. The robustness and restoration of a network of ecological networks. Science 335, 973–977 (2012).Article 
CAS 

Google Scholar 
Scherber, C. et al. Bottom-up effects of plant diversity on multitrophic interactions in a biodiversity experiment. Nature 468, 553–556 (2010).Article 
CAS 

Google Scholar 
Schleuning, M. et al. Ecological networks are more sensitive to plant than to animal extinction under climate change. Nat. Commun. 7, 13965 (2016).Article 
CAS 

Google Scholar 
Shipley, B. Confirmatory path analysis in a generalized multilevel context. Ecology 90, 363–368 (2009).Article 

Google Scholar 
Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
Grace, J. B., Scheiner, S. M. & Schoolmaster, D. R. Jr. Structural equation modeling: building and evaluating causal models. In Ecological Statistics: From Principles to Applications (eds Fox, G. A. et al.), 8, 168–199 (Oxford Univ. Press, 2015).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2014).
Google Scholar 
Shipley, B. The AIC model selection method applied to path analytic models compared using a d-separation test. Ecology 94, 560–564 (2013).Article 

Google Scholar 
Murphy, M. semEff: Automatic Calculation of Effects for Piecewise Structural Equation Models. R package version 0.6.0 (2021). https://CRAN.R-project.org/package=semEffDudgeon, P. A comparative investigation of confidence intervals for independent variables in linear regression. Multivar. Behav. Res. 51, 139–153 (2016).Article 

Google Scholar 
Gotelli, N. J. & Graves, G. R. Null Models in Ecology (Smithsonian Inst. Press, 1996).Jung, V., Violle, C., Mondy, C., Hoffmann, L. & Muller, S. Intraspecific variability and trait-based community assembly. J. Ecol. 98, 1134–1140 (2010).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020). More

Ceballos, G., Ehrlich, P. R. & Dirzo, R. Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines. PNAS 114, E6089–E6096 (2017).ADS 
CAS 

Google Scholar 
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).ADS 
CAS 

Google Scholar 
Ceballos, G., Ehrlich, P. R. & Raven, P. H. Vertebrates on the brink as indicators of biological annihilation and the sixth mass extinction. PNAS 117, 13596–13602 (2020).ADS 
CAS 

Google Scholar 
Butchart, S. H. et al. Global biodiversity: Indicators of recent declines. Science 328, 1164–1168 (2010).ADS 
CAS 

Google Scholar 
Excoffier, L., Foll, M. & Petit, R. J. Genetic consequences of range expansions. Annu. Rev. Ecol. Evol. Syst. 40, 481–501 (2009).
Google Scholar 
Arenas, M., Ray, N., Currat, M. & Excoffier, L. Consequences of range contractions and range shifts on molecular diversity. Mol. Biol. Evol. 29, 207–218 (2012).CAS 

Google Scholar 
Banks, S. C. et al. How does ecological disturbance influence genetic diversity?. Trends Ecol. Evol. 28, 670–679 (2013).
Google Scholar 
Branco, C., Ray, N., Currat, M. & Arenas, M. Influence of Paleolithic range contraction, admixture and long-distance dispersal on genetic gradients of modern humans in Asia. Mol. Ecol. 29, 2150–2159 (2020).
Google Scholar 
Lomolino, M. V. & Channell, R. Splendid isolation: Patterns of geographic range collapse in endangered mammals. J. Mammal. 76(2), 335–347 (1995).
Google Scholar 
Lomolino, M. V. & Channell, R. Range collapse, re-introductions, and biogeographic guidelines for conservation. Conserv. Biol. 12, 481–484 (1998).
Google Scholar 
Channell, R. & Lomolino, M. V. Dynamic biogeography and conservation of endangered species. Nature 403, 84–86 (2000).ADS 
CAS 

Google Scholar 
Channell, R. & Lomolino, M. V. Trajectories to extinction: Spatial dynamics of the contraction of geographical ranges. J. Biogeogr. 27, 169–179 (2000).
Google Scholar 
Laliberte, A. S. & Ripple, W. J. Range contractions of North American carnivores and ungulates. Bioscience 54, 123–138 (2004).
Google Scholar 
Donald, P. F. & Greenwood, J. J. Spatial patterns of range contraction in British breeding birds. Ibis 143, 593–601 (2001).
Google Scholar 
Boakes, E. H., Isaac, N. J., Fuller, R. A., Mace, G. M. & McGowan, P. J. Examining the relationship between local extinction risk and position in range. Conserv. Biol. 32, 229–239 (2018).
Google Scholar 
Spielman, D., Brook, B. W. & Frankham, R. Most species are not driven to extinction before genetic factors impact them. PNAS 101(42), 15261–15264 (2004).ADS 
CAS 

Google Scholar 
Hoelzel, A. R. et al. Elephant seal genetic variation and the use of simulation models to investigate historical population bottlenecks. J. Hered. 84, 443–449 (1993).CAS 

Google Scholar 
Amos, W. & Balmford, A. When does conservation genetics matter?. Heredity 87, 257–265 (2001).CAS 

Google Scholar 
Reed, D. H. & Frankham, R. Correlation between fitness and genetic diversity. Conserv. Biol. 17, 230–237 (2003).
Google Scholar 
Carvalho, C. D. S. et al. Habitat loss does not always entail negative genetic consequences. Front. Genet. 10, 1101 (2019).CAS 

Google Scholar 
Wheeler, B. A., Prosen, E., Mathis, A. & Wilkinson, R. F. Population declines of a long-lived salamander: A 20+-year study of hellbenders, Cryptobranchus alleganiensis. Biol. Cons. 109, 151–156 (2003).
Google Scholar 
Walkup, D. K., Leavitt, D. J. & Fitzgerald, L. A. Effects of habitat fragmentation on population structure of dune-dwelling lizards. Ecosphere 8, e01729 (2017).
Google Scholar 
Mikle, N., Graves, T. A., Kovach, R., Kendall, K. C. & Macleod, A. C. Demographic mechanisms underpinning genetic assimilation of remnant groups of a large carnivore. Proc. R. Soc. B Biol. Sci. 283, 20161467 (2016).
Google Scholar 
DeWoody, J. A., Harder, A. M., Mathur, S. & Willoughby, J. R. The long-standing significance of genetic diversity in conservation. Mol. Ecol. 30(17), 4147–4154 (2021).
Google Scholar 
Kardos, M., Armstrong, E. E., Fitzpatrick, S. W. & Funk, W. C. The crucial role of genome-wide genetic variation in conservation. PNAS 118(48), e210462118 (2021).
Google Scholar 
García-Dorado, A. & Caballero, A. Neutral genetic diversity as a useful tool for conservation biology. Conserv. Genet. 22, 541–545 (2021).
Google Scholar 
Charlesworth, B. Effective population size and patterns of molecular evolution and variation. Nat. Rev. Genet. 10, 195–205 (2009).CAS 

Google Scholar 
Eyre-Walker, A. & Keightley, P. D. The distribution of fitness effects of new mutations. Nat. Rev. Genet. 8, 610–618 (2007).CAS 

Google Scholar 
Haller, B. C., Galloway, J., Kelleher, J., Messer, P. W. & Ralph, P. L. Tree-sequence recording in SLiM opens new horizons forward-time simulation of whole genomes. Mol. Ecol. Resour. 19, 552–566 (2018).
Google Scholar 
Kelleher, J., Thornton, K. R., Ashander, J. & Ralph, P. L. Efficient pedigree recording for fast population genetics simulation. PLoS Comput. Biol. 14, e1006581 (2018).ADS 

Google Scholar 
Haller, B. C. & Messer, P. W. SLiM 3: Forward genetic simulations beyond the Wright–Fisher model. Mol. Biol. Evol. 36, 632–637 (2019).CAS 

Google Scholar 
Rodríguez, J. P. Range contraction in declining North American bird populations. Ecol. Appl. 12, 238–248 (2002).
Google Scholar 
Fisher, D. O. Trajectories from extinction: where are missing mammals rediscovered?. Glob. Ecol. Biogeogr. 20, 415–425 (2011).
Google Scholar 
Lino, A., Fonseca, C., Rojas, D., Fischer, E. & Pereira, M. J. R. A meta-analysis of the effects of habitat loss and fragmentation on genetic diversity in mammals. Mamm. Biol. 94, 69–76 (2019).
Google Scholar 
Vandergast, A. G., Bohonak, A. J., Weissman, D. B. & Fisher, R. N. Understanding the genetic effects of recent habitat fragmentation in the context of evolutionary history: Phylogeography and landscape genetics of a southern California endemic Jerusalem cricket (Orthoptera: Stenopelmatidae: Stenopelmatus). Mol. Ecol. 16, 977–992 (2007).CAS 

Google Scholar 
Young, A., Boyle, T. & Brown, T. The population genetic consequences of habitat fragmentation for plants. Trends Ecol. Evol. 11, 413–418 (1996).CAS 

Google Scholar 
Wilkins, J. F. & Wakeley, J. The coalescent in a continuous, finite, linear population. Genetics 161, 873–888 (2002).
Google Scholar 
Ringbauer, H., Coop, G. & Barton, N. H. Inferring recent demography from isolation by distance of long shared sequence blocks. Genetics 205, 1335–1351 (2017).
Google Scholar 
Bradburd, G. S. & Ralph, P. L. Spatial population genetics: It’s about time. Annu. Rev. Ecol. Evol. Syst. 50, 427–429 (2019).
Google Scholar 
Barton, N. H., Etheridge, A. M., Kelleher, J. & Véber, A. Inference in two dimensions: Allele frequencies versus lengths of shared sequence blocks. Theor. Popul. Biol. 87, 105–119 (2013).CAS 
MATH 

Google Scholar 
Aguillon, S. M. et al. Deconstructing isolation-by-distance: The genomic consequences of limited dispersal. PLoS Genet. 13, e1006911 (2017).
Google Scholar 
Blanco-Pastor, J. L., Fernández-Mazuecos, M. & Vargas, P. Past and future demographic dynamics of alpine species: Limited genetic consequences despite dramatic range contraction in a plant from the Spanish Sierra Nevada. Mol. Ecol. 22, 4177–4195 (2013).CAS 

Google Scholar 
Chen, N. et al. Allele frequency dynamics in a pedigreed natural population. PNAS 116, 2158–2164 (2019).ADS 
CAS 

Google Scholar 
Exposito-Alonso, M., Booker, T. A., Czech, L., Fukami, T., Gillespie, L., Hateley, S. et al. Quantifying the scale of genetic diversity extinction in the Anthropocene. bioRxiv (2021).Keller, I. & Largiadèr, C. R. Recent habitat fragmentation caused by major roads leads to reduction of gene flow and loss of genetic variability in ground beetles. Proc. R. Soc. B Biol. Sci. 270, 417–423 (2003).CAS 

Google Scholar 
Chan, L. M. et al. Phylogeographic structure of the dunes sagebrush lizard, an endemic habitat specialist. PLoS ONE 15, 0238194 (2020).
Google Scholar 
Wang, I. J. & Bradburd, G. S. Isolation by environment. Mol. Ecol. 23, 5649–5662 (2014).
Google Scholar 
Cayuela, H. et al. Demographic and genetic approaches to study dispersal in wild animal populations: A methodological review. Mol. Ecol. 27, 3976–4010 (2018).
Google Scholar 
Battey, C. J., Ralph, P. L. & Kern, A. D. Space is the place: Effects of continuous spatial structure on analysis of population genetic data. Genetics 215, 193–214 (2020).CAS 

Google Scholar 
Stubbs, D. & Swingland, I. R. The ecology of a Mediterranean tortoise (Testudo hermanni): A declining population. Can. J. Zool. 63, 169–180 (1985).
Google Scholar 
Channell, R. The conservation value of peripheral populations: The supporting science. in Proceedings of the Species at Risk 2004 Pathways to Recovery Conference. 1–17. (Species at Risk 2004 Pathways to Recovery Conference Organizing Committee, 2004).Brown, J. H. On the relationship between abundance and distribution of species. Am. Nat. 124(2), 255–279 (1984).
Google Scholar 
Brown, J. H. Macroecology (University of Chicago Press, 1995).
Google Scholar 
Brown, J. H., Stevens, G. C. & Kaufman, D. M. The geographic range: Size, shape, boundaries, and internal structure. Annu. Rev. Ecol. Syst. 27(1), 597–623 (1996).
Google Scholar 
Sagarin, R. D. & Gaines, S. D. The ‘abundant centre’distribution: To what extent is it a biogeographical rule?. Ecol. Lett. 5, 137–147 (2002).
Google Scholar 
Eckert, C. G., Samis, K. E. & Lougheed, S. C. Genetic variation across species’ geographical ranges: The central-marginal hypothesis and beyond. Mol. Ecol. 17, 1170–1188 (2008).CAS 

Google Scholar 
Yackulic, C. B., Sanderson, E. W. & Uriarte, M. Anthropogenic and environmental drivers of modern range loss in large mammals. PNAS 108, 4024–4029 (2011).ADS 
CAS 

Google Scholar 
Fitzgerald L.A., Walkup, D. Chyn, K. Buchholtz, E. Angeli, N. & Parker M. The future for reptiles: Advances and challenges in the Anthropocene. in Encyclopedia of the Anthropocene. (eds. Dellasala, D.A., & Goldstein, M.I.). 163–174 (Elsevier, 2018).Segelbacher, G., Höglund, J. & Storch, I. From connectivity to isolation: Genetic consequences of population fragmentation in capercaillie across Europe. Mol. Ecol. 12, 1773–1780 (2003).CAS 

Google Scholar 
Cegelski, C. C., Waits, L. P. & Anderson, N. J. Assessing population structure and gene flow in Montana wolverines (Gulo gulo) using assignment-based approaches. Mol. Ecol. 12, 2907–2918 (2003).CAS 

Google Scholar 
Proctor, M. F., McLellan, B. N., Strobeck, C. & Barclay, R. M. Genetic analysis reveals demographic fragmentation of grizzly bears yielding vulnerably small populations. Proc. R. Soc. B Biol. Sci. 272, 2409–2416 (2005).
Google Scholar 
Leavitt, D. J. & Fitzgerald, L. A. Disassembly of a dune–dwelling lizard community due to landscape fragmentation. Ecosphere 4, 97 (2013).
Google Scholar 
Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).
Google Scholar 
Rogan, J.E., & Lacher Jr., T.E. Impacts of habitat loss and fragmentation on terrestrial biodiversity. in Reference Modules in Earth Systems and Environmental Sciences. 1–18 (Elsevier, 2018).Hurtado, L. A., Santamaria, C. A. & Fitzgerald, L. A. Conservation genetics of the critically endangered St. Croix ground lizard (Ameiva polops Cope 1863). Conserv. Genet. 13, 665–679 (2012).
Google Scholar 
Lawton, J. H. Range, population abundance and conservation. Trends Ecol. Evol. 8, 409–413 (1993).CAS 

Google Scholar 
Purvis, A., Gittleman, J. L., Cowlishaw, G. & Mace, G. M. Predicting extinction risk in declining species. Proc. R. Soc. B Biol. Sci. 267, 1947–1952 (2000).CAS 

Google Scholar 
Cardillo, M. et al. The predictability of extinction: Biological and external correlates of decline in mammals. Proc. R. Soc. B Biol. Sci. 275, 1441–1448 (2008).
Google Scholar 
Templeton, A. R. Coadaptation and outbreeding depression. in Conservation Biology: The Science of Scarcity and Diversity. (ed. Soulé, M.E.). 105–116 (Sinauer, 1986). Lomolino, M. V. & Smith, G. A. Dynamic biogeography of prairie dog (Cynomys ludovicianus) towns near the edge of their range. J. Mammal. 82, 937–945 (2001).
Google Scholar 
Wright, S. Isolation by distance. Genetics 28, 114 (1943).CAS 

Google Scholar 
Maruyama, T. Rate of decrease of genetic variability in a two-dimensional continuous population of finite size. Genetics 4(1), 639–651 (1972).
Google Scholar 
Wright, S. Coefficients of inbreeding and relationship. Am. Nat. 645, 330–338 (1922).
Google Scholar 
Kelleher, J. & EtheridgeMcVean, A. M. G. Efficient coalescent simulation and genealogical analysis for large sample sizes. PLoS Comput. Biol. 12, e1004842 (2016).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. http://www.R-project.org/ (R Foundation for Statistical Computing, 2019).Greenstein, B. J. & Pandolfi, J. M. Escaping the heat: Range shifts of reef coral taxa in coastal Western Australia. Glob. Change Biol. 14, 513–528 (2008).ADS 

Google Scholar 
Wilcove, D. S. & Terborgh, J. W. Patterns of population decline in birds. Am. Birds 38, 10–13 (1984).
Google Scholar 
Gabelli, F. M. et al. Range contraction in the Pampas meadowlark Sturnella defilippii in the southern Pampas grasslands of Argentina. Oryx 38, 164–170 (2004).
Google Scholar 
Pomara, L. Y., LeDee, O. E., Martin, K. J. & Zuckerberg, B. Demographic consequences of climate change and land cover help explain a history of extirpations and range contraction in a declining snake species. Glob. Change Biol. 20, 2087–2099 (2014).ADS 

Google Scholar 
Towns, D. R. & Daugherty, C. H. Patterns of range contractions and extinctions in the New Zealand herpetofauna following human colonisation. N. Z. J. Zool. 21, 325–339 (1994).
Google Scholar 
Rudolph, D. C., Burgdorf, S. J., Schaefer, R. R., Conner, R. N. & Maxey, R. W. Status of Pituophis ruthveni (Louisiana pine snake). Southeast. Nat. 5(3), 463–472 (2006).
Google Scholar 
Russell, R. W., Lipps, G. J. Jr., Hecnar, S. J. & Haffner, G. D. Persistent organic pollutants in Blanchard’s cricket frogs (Acris crepitans blanchardi) from Ohio. Ohio J. Sci. 102, 119–122 (2002).CAS 

Google Scholar 
Fellers, G. M. & Drost, C. A. Disappearance of the Cascades frog Rana cascadae at the southern end of its range, California, USA. Biol. Cons. 65, 177–181 (1993).
Google Scholar 
Franco, A. M. et al. Impacts of climate warming and habitat loss on extinctions at species’ low-latitude range boundaries. Glob. Change Biol. 12, 1545–1553 (2006).ADS 

Google Scholar 
Stewart, J. A., Wright, D. H. & Heckman, K. A. Apparent climate-mediated loss and fragmentation of core habitat of the American pika in the Northern Sierra Nevada, California, USA. PLoS ONE 12, e0181834 (2017).
Google Scholar 
Rodríguez, A. & Delibes, M. Internal structure and patterns of contraction in the geographic range of the Iberian lynx. Ecography 25, 314–328 (2002).
Google Scholar 
Kattan, G. et al. Range fragmentation in the spectacled bear Tremarctos ornatus in the northern Andes. Oryx 38(2), 155–163 (2004).
Google Scholar 
Jones, S. J., Lima, F. P. & Wethey, D. S. Rising environmental temperatures and biogeography: poleward range contraction of the blue mussel, Mytilus edulis L., in the western Atlantic. J. Biogeogr. 37, 2243–2259 (2010).
Google Scholar 
Smale, D. A. & Wernberg, T. Extreme climatic event drives range contraction of a habitat-forming species. P. R. Soc. B Biol. Sci. 280, 20122829 (2013).
Google Scholar  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

Mohn WW, Mertens B, Neufeld JD, Verstraete W, de Lorenzo V. Distribution and phylogeny of hexachlorocyclohexane-degrading bacteria in soils from Spain. Environ Microbiol. 2006;8:60–8.CAS 

Google Scholar 
Sharma P, Raina V, Kumari R, Malhotra S, Dogra C, Kumari H, et al. Haloalkane Dehalogenase LinB is responsible for β- and δ-hexachlorocyclohexane transformation in Sphingobium indicum B90A. Appl Environ Microbiol. 2006;72:5720–7.CAS 

Google Scholar 
Lal R, Dogra C, Malhotra S, Sharma P, Pal R. Diversity, distribution and divergence of lin genes in hexachlorocyclohexane-degrading sphingomonads. Trends Biotechnol. 2006;24:121–30.CAS 

Google Scholar 
Singh A, Lal R. Sphingobium ummariense sp. nov., a hexachlorocyclohexane (HCH)-degrading bacterium, isolated from HCH-contaminated soil. Int J Syst Evol Microbiol. 2009;59:162–6.CAS 

Google Scholar 
Singh BK, Kuhad RC. Biodegradation of lindane (γ-hexachlorocyclohexane) by the white-rot fungus Trametes hirsutus. Lett Appl Microbiol. 1999;28:238–41.CAS 

Google Scholar 
Kumar D, Pannu R. Perspectives of lindane (γ-hexachlorocyclohexane) biodegradation from the environment: a review. Bioresour Bioprocess. 2018;5:29.CAS 

Google Scholar 
Lal R, Pandey G, Sharma P, Kumari K, Malhotra S, Pandey R, et al. Biochemistry of microbial degradation of hexachlorocyclohexane and prospects for bioremediation. Microbiol Mol Biol Rev. 2010;74:58–80.CAS 

Google Scholar 
Nayyar N, Lal R. Hexachlorocyclohexane contamination and solutions: Brief history and beyond. J Bioremed Biodeg. 2016;07:338.Bumpus JA, Tien M, Wright D, Aust SD. Oxidation of persistent environmental pollutants by a white rot fungus. Science. 1985;228:1434–6.CAS 

Google Scholar 
Phillips TM, Seech AG, Lee H, Trevors JT. Biodegradation of hexachlorocyclohexane (HCH) by microorganisms. Biodegr. 2005;16:363–92.CAS 

Google Scholar 
Dogra C, Raina V, Pal R, Suar M, Lal S, Gartemann KH, et al. Organization of lin genes and IS6100 among different strains of hexachlorocyclohexane-degrading Sphingomonas paucimobilis: Evidence for horizontal gene transfer. J Bacteriol. 2004;186:2225–35.CAS 

Google Scholar 
Pal R, Bala S, Dadhwal M, Kumar M, Dhingra G, Prakash O, et al. Hexachlorocyclohexane-degrading bacterial strains Sphingomonas paucimobilis B90A, UT26 and Sp+, having similar lin genes, represent three distinct species, Sphingobium indicum sp. nov., Sphingobium japonicum sp. nov. and Sphingobium francense sp. nov., and reclassification of [Sphingomonas] chungbukensis as Sphingobium chungbukense comb. nov. Int J Syst Evol Microbiol. 2005;55:1965–72.CAS 

Google Scholar 
Rijnaarts HHM, Bachmann A, Jumelet JC, Zehnder AJB. Effect of desorption and intraparticle mass transfer on the aerobic biomineralization of. alpha-hexachlorocyclohexane in a contaminated calcareous soil. Environ Sci Technol. 1990;24:1349–54.CAS 

Google Scholar 
Harms H, Schlosser D, Wick LY. Untapped potential: exploiting fungi in bioremediation of hazardous chemicals. Nat Rev Microbiol. 2011;9:177–92.CAS 

Google Scholar 
Wick LY Bioavailability as a microbial system property: Lessons learned from biodegradation in the mycosphere. In: Ortega-Calvo JJ, Parsons JR, (eds). Bioavailability of organic chemicals in soil and sediment. Cham: Springer International Publishing; 2020 p. 267–89.Jennings DH. Translocation of Solutes in fungi. Biol Rev. 1987;62:215–43.CAS 

Google Scholar 
Nazir R, Warmink JA, Boersma H, van Elsas JD. Mechanisms that promote bacterial fitness in fungal-affected soil microhabitats. FEMS Microbiol Ecol. 2010;71:169–85.CAS 

Google Scholar 
Worrich A, Stryhanyuk H, Musat N, König S, Banitz T, Centler F, et al. Mycelium-mediated transfer of water and nutrients stimulates bacterial activity in dry and oligotrophic environments. Nat Commun. 2017;8:15472.CAS 

Google Scholar 
Deveau A, Bonito G, Uehling J, Paoletti M, Becker M, Bindschedler S, et al. Bacterial–fungal interactions: ecology, mechanisms and challenges. FEMS Microbiol Rev. 2018;42:335–52.CAS 

Google Scholar 
Kohlmeier S, Smits THM, Ford RM, Keel C, Harms H, Wick LY. Taking the fungal highway: Mobilization of pollutant-degrading bacteria by fungi. Environ Sci Technol. 2005;39:4640–6.CAS 

Google Scholar 
Wick LY, Furuno S, Harms H Fungi as transport vectors for contaminants and contaminant-degrading bacteria. In: Timmis KN, (eds). Handbook of hydrocarbon and lipid microbiology. Berlin, Heidelberg: Springer Berlin Heidelberg; 2010. p. 1555–61.Espinosa-Ortiz EJ, Rene ER, Gerlach R. Potential use of fungal-bacterial co-cultures for the removal of organic pollutants. Crit Rev Biotechnol. 2021;0:1–23.
Google Scholar 
Haq IU, Zhang M, Yang P, van Elsas JD The interactions of bacteria with fungi in soil. In: Adv Appl Microbiol. Elsevier; 2014. p. 185–215.Furuno S, Foss S, Wild E, Jones KC, Semple KT, Harms H, et al. Mycelia promote active transport and spatial dispersion of polycyclic aromatic hydrocarbons. Environ Sci Technol. 2012;46:5463–70.CAS 

Google Scholar 
Schamfuß S, Neu TR, van der Meer JR, Tecon R, Harms H, Wick LY. Impact of mycelia on the accessibility of fluorene to PAH-degrading bacteria. Environ Sci Technol. 2013;47:6908–15.
Google Scholar 
Schamfuß S, Neu TR, Harms H, Wick LY. A Whole Cell Bioreporter approach to assess transport and bioavailability of organic contaminants in water unsaturated systems. J Vis Exp. 2014;24:52334.
Google Scholar 
You X, Kallies R, Kühn I, Schmidt M, Harms H, Chatzinotas A, et al. Phage co-transport with hyphal-riding bacteria fuels bacterial invasion in a water-unsaturated microbial model system. ISME J. 2022;16:1275–83.CAS 

Google Scholar 
Wick LY, Remer R, Würz B, Reichenbach J, Braun S, Schäfer F, et al. Effect of fungal hyphae on the access of bacteria to phenanthrene in soil. Environ Sci Technol. 2007;41:500–5.CAS 

Google Scholar 
Boer W, de, Folman LB, Summerbell RC, Boddy L. Living in a fungal world: impact of fungi on soil bacterial niche development. FEMS Microbiol Rev. 2005;29:795–811.
Google Scholar 
Frey‐Klett P, Garbaye J, Tarkka M. The mycorrhiza helper bacteria revisited. N. Phytol. 2007;176:22–36.
Google Scholar 
Hazen TC Cometabolic Bioremediation. In: Timmis KN, editor. Handbook of hydrocarbon and lipid microbiology. Berlin, Heidelberg: Springer; 2010. p. 2505–14.Jehmlich N, Vogt C, Lünsmann V, Richnow HH, von Bergen M. Protein-SIP in environmental studies. Curr Opin Biotechnol. 2016;41:26–33.CAS 

Google Scholar 
Sachsenberg T, Herbst FA, Taubert M, Kermer R, Jehmlich N, von Bergen M, et al. MetaProSIP: automated inference of stable isotope incorporation rates in proteins for functional metaproteomics. J Proteome Res. 2015;14:619–27.CAS 

Google Scholar 
Taubert M, Jehmlich N, Vogt C, Richnow HH, Schmidt F, von Bergen M, et al. Time resolved protein-based stable isotope probing (Protein-SIP) analysis allows quantification of induced proteins in substrate shift experiments. Proteomics 2011;11:2265–74.CAS 

Google Scholar 
Lünsmann V, Kappelmeyer U, Benndorf R, Martinez-Lavanchy PM, Taubert A, Adrian L, et al. In situ protein-SIP highlights Burkholderiaceae as key players degrading toluene by para ring hydroxylation in a constructed wetland model: Protein-SIP in a toluene-degrading wetland. Environ Microbiol. 2016;18:1176–86.
Google Scholar 
Khan N, Toscan RB, Lunayo A, Wamalwa B, Muge E, Mulaa FJ, et al. Draft genome sequence of Fusarium equiseti K3, a fungal species isolated from hexachlorocyclohexane-contaminated soil. Microbiol Resour Announc. 2021;10:e00885–21.CAS 

Google Scholar 
Khan N, Brizola Toscan R, Lunayo A, Wamalwa B, Muge E, Mulaa FJ, et al. Draft genome sequences of two Sphingobium species associated with hexachlorocyclohexane (HCH) degradation isolated from an HCH-Contaminated Soil. Microbiol Resour Announc. 2022;11:e00886–21.
Google Scholar 
Yang CH, Menge JA, Cooksey DA. Mutations Affecting Hyphal Colonization and Pyoverdine Production in Pseudomonads Antagonistic toward Phytophthora parasitica. Appl Environ Microbiol. 1994;9:473–81.Senoo K, Wada H. Isolation and identification of an aerobic γ-HCH-decomposing bacterium from soil. Soil Sci Plant Nutr. 1989;35:79–87.CAS 

Google Scholar 
Jehmlich N, Schmidt F, Taubert M, Seifert J, Bastida F, von Bergen M, et al. Protein-based stable isotope probing. Nat Protoc. 2010;5:1957–66.CAS 

Google Scholar 
Graves S, Dorai-Raj HPP and LS with help from S. multcompView: Visualizations of paired comparisons. 2019. Available from: https://CRAN.R-project.org/package=multcompViewWickham H, François R, Henry L, Müller K, RStudio. dplyr: A Grammar of data manipulation. 2022. Available from: https://CRAN.R-project.org/package=dplyrggplot2: Create elegant data visualisations using the grammar of graphics—ggplot2-package. Available from: https://ggplot2.tidyverse.org/reference/ggplot2-package.htmlSchramm FD, Heinrich K, Thüring M, Bernhardt J, Jonas K. An essential regulatory function of the DnaK chaperone dictates the decision between proliferation and maintenance in Caulobacter crescentus. PLoS Genet. 2017;13:e1007148.
Google Scholar 
Li SX, Wu HT, Liu YT, Jiang YY, Zhang YS, Liu WD, et al. The F1Fo-ATP synthase β subunit is required for Candida albicans pathogenicity due to its role in carbon flexibility. Front Microbiol. 2018;9:1025.
Google Scholar 
Godlewska R, Wiśniewska K, Pietras Z, Jagusztyn-Krynicka EK. Peptidoglycan-associated lipoprotein (Pal) of Gram-negative bacteria: function, structure, role in pathogenesis and potential application in immunoprophylaxis. FEMS Microbiol Lett. 2009;298:1–11.CAS 

Google Scholar 
Taubert M, Vogt C, Wubet T, Kleinsteuber S, Tarkka MT, Harms H, et al. Protein-SIP enables time-resolved analysis of the carbon flux in a sulfate-reducing, benzene-degrading microbial consortium. ISME J. 2012;6:2291–301.CAS 

Google Scholar 
Little AEF, Robinson CJ, Peterson SB, Raffa KF, Handelsman J. Rules of engagement: Interspecies interactions that regulate microbial communities. Annu Rev Microbiol. 2008;62:375–401.CAS 

Google Scholar 
Morris BEL, Henneberger R, Huber H, Moissl-Eichinger C. Microbial syntrophy: interaction for the common good. FEMS Microbiol Rev. 2013;37:384–406.CAS 

Google Scholar 
Van Hees PAW, Rosling A, Essén S, Godbold DL, Jones DL, Finlay RD. Oxalate and ferricrocin exudation by the extramatrical mycelium of an ectomycorrhizal fungus in symbiosis with Pinus sylvestris. N Phytol. 2006;169:367–78.
Google Scholar 
Sun YP, Unestam T, Lucas SD, Johanson KJ, Kenne L, Finlay R. Exudation-reabsorption in a mycorrhizal fungus, the dynamic interface for interaction with soil and soil microorganisms. Mycorrhiza 1999;9:137–44.CAS 

Google Scholar 
Leveau JHJ, Preston GM. Bacterial mycophagy: definition and diagnosis of a unique bacterial-fungal interaction. N Phytol. 2008;177:859–76.
Google Scholar 
Bassler BL, Losick R. Bacterially speaking. Cell 2006;125:237–46.CAS 

Google Scholar 
Kim HJ, Boedicker JQ, Choi JW, Ismagilov RF. Defined spatial structure stabilizes a synthetic multispecies bacterial community. Proc Natl Acad Sci. 2008;105:18188–93.CAS 

Google Scholar 
Nayyar N, Sangwan N, Kohli P, Verma H, Kumar R, Negi V, et al. Hexachlorocyclohexane: persistence, toxicity and decontamination. Rev Environ Health. 2014;29:49–52.CAS 

Google Scholar 
Willett KL, Ulrich EM, Hites RA. Differential toxicity and environmental fates of hexachlorocyclohexane isomers. Environ Sci Technol. 1998;32:2197–207.CAS 

Google Scholar 
Ellegaard-Jensen L, Knudsen BE, Johansen A, Albers CN, Aamand J, Rosendahl S. Fungal-bacterial consortia increase diuron degradation in water-unsaturated systems. Sci Total Environ. 2014;466–467:699–705.
Google Scholar 
Knudsen BE, Ellegaard-Jensen L, Albers CN, Rosendahl S, Aamand J. Fungal hyphae stimulate bacterial degradation of 2,6-dichlorobenzamide (BAM). Environ Pollut. 2013;181:122–7.CAS 

Google Scholar 
Saez JM, Alvarez A, Fuentes MS, Amoroso MJ, Benimeli CS An Overview on Microbial Degradation of Lindane. In: Singh SN, (eds). Microbe-induced degradation of pesticides. Cham: Springer International Publishing; 2017. p. 191–212.Nzila A. Update on the cometabolism of organic pollutants by bacteria. Environ Pollut. 2013;178:474–82.CAS 

Google Scholar 
Benimeli CS, González AJ, Chaile AP, Amoroso MJ. Temperature and pH effect on lindane removal by Streptomyces sp. M7 in soil extract. J Basic Microbiol. 2007;47:468–73.CAS 

Google Scholar 
Álvarez A, Yañez ML, Benimeli CS, Amoroso MJ. Maize plants (Zea mays) root exudates enhance lindane removal by native Streptomyces strains. Int Biodeterior Biodegrad. 2012;66:14–8.
Google Scholar 
Boltner D, Moreno-Morillas S, Ramos JL. 16S rDNA phylogeny and distribution of lin genes in novel hexachlorocyclohexane-degrading Sphingomonas strains. Environ Microbiol. 2005;7:1329–38.CAS 

Google Scholar  More

Elrick, M. et al. Major Early-Middle Devonian oceanic oxygenation linked to early land plant evolution detected using high-resolution U isotopes of marine limestones. Earth Planet. Sci. Lett. 581, 117410 (2022).CAS 

Google Scholar 
Algeo, T. J. & Scheckler, S. E. Terrestrial-marine teleconnections in the Devonian: Links between the evolution of land plants, weathering processes, and marine anoxic events. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 353(1365), 113–130 (1998).
Google Scholar 
Capel, E. et al. The Silurian-Devonian terrestrial revolution: Diversity patterns and sampling bias of the vascular plant macrofossil record. Earth Sci. Rev. 231, 104085 (2022).
Google Scholar 
Racki, G., Joachimski, M. M. & Morrow, J. R. A major perturbation of the global carbon budget in the Early-Middle Frasnian transition (Late Devonian). Palaeogeogr. Palaeoclimatol. Palaeoecol. 269(3–4), 127–129 (2008).
Google Scholar 
Stein, W. E., Berry, C. M., Hernick, L. V. & Mannolini, F. Surprisingly complex community discovered in the mid-Devonian fossil forest at Gilboa. Nature 483(7387), 78–81 (2012).ADS 
CAS 

Google Scholar 
Retallack, G. J. & Huang, C. Ecology and evolution of Devonian trees in New York, USA. Palaeogeogr. Palaeoclimatol. Palaeoecol. 299(1–2), 110–128 (2011).
Google Scholar 
Qie, W., Algeo, T. J., Luo, G. & Herrmann, A. Global events of the late Paleozoic (early Devonian to Middle Permian): A review. Palaeogeogr. Palaeoclimatol. Palaeoecol. 531, 109259 (2019).
Google Scholar 
Smart, M. S., Filippelli, G., Gilhooly III, W. P., Marshall, J. E. & Whiteside, J. H. Enhanced terrestrial nutrient release during the Devonian emergence and expansion of forests: Evidence from lacustrine phosphorus and geochemical records. GSA Bulletin. Nov. 9 (2022).Śliwiński, M. G., Whalen, M. T. & Day, J. Trace element variations in the Middle Frasnian punctata zone (Late Devonian) in the western Canada sedimentary basin— changes in oceanic bioproductivity and paleoredox spurred by a pulse of terrestrial afforestation?. Geol. Belg. 4, 459–482 (2010).
Google Scholar 
Filippelli, G. M. & Souch, C. Effects of climate and landscape development on the terrestrial phosphorus cycle. Geology 27(2), 171–174 (1999).ADS 
CAS 

Google Scholar 
Filippelli, G. M., Souch, C., Horn, S. P. & Newkirk, D. The pre-Colombian footprint on terrestrial nutrient cycling in Costa Rica: Insights from phosphorus in a lake sediment record. J. Paleolimnol. 43(4), 843–856 (2010).ADS 

Google Scholar 
Pisarzowska, A. & Racki, G. Comparative carbon isotope chemostratigraphy of major Late Devonian biotic crises. In Stratigraphy & Timescales. 387–466, vol. 5. (Academic Press, 2020).Mortlock, R. A. & Froelich, P. N. A simple method for the rapid determination of biogenic opal in pelagic marine sediments. Deep Sea Res. Part A Oceanogr. Res. Pap. 36(9), 1415–1426 (1989).ADS 
CAS 

Google Scholar 
Schieber, J., Krinsley, D. & Riciputi, L. Diagenetic origin of quartz silt in mudstones and implications for silica cycling. Nature 406(6799), 981–985 (2000).ADS 
CAS 

Google Scholar 
Buckman, J., Mahoney, C., März, C., Wagner, T. & Blanco, V. Identifying biogenic silica: Mudrock micro-fabric explored through charge contrast imaging. Am. Miner. 102(4), 833–844 (2017).ADS 

Google Scholar 
Gao, P., He, Z., Lash, G. G., Zhou, Q. & Xiao, X. Controls on silica enrichment of Lower Cambrian organic-rich shale deposits. Mar. Pet. Geol. 130, 105126 (2021).CAS 

Google Scholar 
Schieber, J. Early diagenetic silica deposition in algal cysts and spores; a source of sand in black shales?. J. Sediment. Res. 66(1), 175–183 (1996).
Google Scholar 
Śliwiński, M. G., Whalen, M. T., Newberry, R. J., Payne, J. H. & Day, J. E. Stable isotope (δ13Ccarb and org, δ15Norg) and trace element anomalies during the Late Devonian ‘punctata Event’in the Western Canada Sedimentary Basin. Palaeogeogr. Palaeoclimatol. Palaeoecol. 307(1–4), 245–271 (2011).
Google Scholar 
Papazis, P. K. & Milliken, K. Cathodoluminescent textures and the origin of quartz in the Mississippian Barnett Shale, Fort Worth Basin, Texas. In AAPG Annual Meeting, Volume Abstracts: Calgary, Alberta, American Association of Petroleum Geologists A, 105 (2005).Ross, D. J. & Bustin, R. M. Investigating the use of sedimentary geochemical proxies for paleoenvironment interpretation of thermally mature organic-rich strata: Examples from the Devonian-Mississippian shales, Western Canadian Sedimentary Basin. Chem. Geol. 260(1–2), 1–19 (2009).ADS 
CAS 

Google Scholar 
Götze, J., Plötze, M. & Habermann, D. Origin, spectral characteristics and practical applications of the cathodoluminescence (CL) of quartz–a review. Mineral. Petrol. 71(3), 225–250 (2001).ADS 

Google Scholar 
Milliken, K. L., Ergene, S. M. & Ozkan, A. Quartz types, authigenic and detrital, in the Upper Cretaceous Eagle Ford Formation, south Texas, USA. Sed. Geol. 339, 273–288 (2016).CAS 

Google Scholar 
Blatt, H. Perspectives; Oxygen isotopes and the origin of quartz. J. Sediment. Res. 57(2), 373–377 (1987).ADS 
CAS 

Google Scholar 
Rowe, H. D., Loucks, R. G., Ruppel, S. C. & Rimmer, S. M. Mississippian Barnett Formation, Fort Worth Basin, Texas: Bulk geochemical inferences and Mo–TOC constraints on the severity of hydrographic restriction. Chem. Geol. 257(1–2), 16–25 (2008).ADS 
CAS 

Google Scholar 
Wright, A. M., Ratcliffe, K. T., Zaitlin, B. A. & Wray, D. S. The application of chemostratigraphic techniques to distinguish compound incised valleys in low-accommodation incised-valley systems in a foreland-basin setting: An example from the Lower Cretaceous Mannville Group and Basal Colorado Sandstone (Colorado Group), Western Canadian Sedimentary Basin, in K.T. Ratcliffe, and B.A. Zaitlin (eds.), Application of Modern Stratigraphic Techniques: Theory and Case Histories: SEPM SP PUB no. 94 (2010).Murata, K. J. & Norman, M. B. An index of crystallinity for quartz. Am. J. Sci. 276(9), 1120–1130 (1976).ADS 
CAS 

Google Scholar 
Tréguer, P. J. et al. Reviews and syntheses: The biogeochemical cycle of silicon in the modern ocean. Biogeosciences 18(4), 1269–1289 (2021).ADS 

Google Scholar 
Rivard, B., Harris, N. B., Feng, J. & Dong, T. Inferring total organic carbon and major element geochemical and mineralogical characteristics of shale core from hyperspectral imagery. AAPG Bull. 102(10), 2101–2121 (2018).
Google Scholar 
Lippincott, E. R., Van Valkenburg, A., Weir, C. E. & Bunting, E. N. Infrared studies on polymorphs of silicon dioxide and germanium dioxide. J. Res. Natl. Bur. Stand 61(1), 61–70 (1958).CAS 

Google Scholar 
Salisbury, J. W., D’Aria, D. M. & Jarosewich, E. Midinfrared (2.5–13.5 μm) reflectance spectra of powdered stony meteorites. Icarus 92(2), 280–297 (1991).ADS 
CAS 

Google Scholar 
Wong, P. K., Weissenberger, J. A. W., Gilhooly, M. G., Playton, T. E. & Kerans, C. Revised regional Frasnian sequence stratigraphic framework, Alberta outcrop and subsurface. New Adv. Devonian Carbonates: Outcrop Analogs, Reservoirs, and Chronostratigr. 49(1), 37–85 (2016).
Google Scholar 
Wendte, J. C. Cooking Lake platform evolution and its control on Late Devonian Leduc reef inception and localization, Redwater, Alberta. Bull. Can. Pet. Geol. 42(4), 499–528 (1994).
Google Scholar 
Wendte, J., Stoakes, F. A. & Campbell, C. V. Cyclicity of Devonian strata in the Western Canada Sedimentary Basin. In: Devonian-Early Mississippian Carbonates of the Western Canada Sedimentary Basin: A sequence stratigraphic framework. J. Wendte (ed.). Society of Economic Paleontologists and Mineralogists, Short Course no. 28, p. 25–40 (1995).Stoakes, F. A. Nature and control of shale basin fill and its effect on reef growth and termination: Upper Devonian Duvernay and Ireton Formations of Alberta, Canada. Bull. Can. Pet. Geol. 28(3), 345–410 (1980).
Google Scholar 
Alberta Energy Regulator Duvernay Reserves and Resources Report: A Comprehensive Analysis of Alberta’s Foremost Liquids-Rich Shale Resource, December 2016.Knapp, L. J., McMillan, J. M. & Harris, N. B. A depositional model for organic-rich Duvernay Formation mudstones. Sed. Geol. 347, 160–182 (2017).CAS 

Google Scholar 
Andrichuk, J. M. Stratigraphic evidence for tectonic and current control of Upper Devonian reef sedimentation, Duhamel area, Alberta, Canada. AAPG Bull. 45(5), 612–632 (1961).
Google Scholar 
Harris, N. B., McMillan, J. M., Knapp, L. J. & Mastalerz, M. Organic matter accumulation in the Upper Devonian Duvernay Formation, Western Canada Sedimentary Basin, from sequence stratigraphic analysis and geochemical proxies. Sed. Geol. 376, 185–203 (2018).CAS 

Google Scholar 
Hildred, G. V., Ratcliffe, K. T., Wright, A. M., Zaitlin, B. A. & Wray, D. S. Chemostratigraphic applications to low-accommodation fluvial incised-valley settings: An example from the Lower Mannville Formation of Alberta, Canada. J. Sedim. Res. 80(11), 1032–1045 (2010).
Google Scholar 
Wedepohl, K. H. Environmental influences on the chemical composition of shales and clays. Phys. Chem. Earth 8, 307–333 (1971).ADS 

Google Scholar 
Pearce, T. J., Martin, J. H., Cooper, D. & Wray, D. S. Chemostratigraphy of upper carboniferous (Pennsylvanian) sequences from the Southern North Sea (United Kingdom). Application of Modern Stratigraphic Techniques: Theory and Case Histories. SEPM Spec. Publ. 94, 109–127 (2010).
Google Scholar 
Adachi, M., Yamamoto, K. & Sugisaki, R. Hydrothermal chert and associated siliceous rocks from the northern Pacific their geological significance as indication of ocean ridge activity. Sed. Geol. 47(1–2), 125–148 (1986).CAS 

Google Scholar 
Abercrombie, H. J., Hutcheon, I. E., Bloch, J. D. & Caritat, P. D. Silica activity and the smectite-illite reaction. Geology 22(6), 539–542 (1994).ADS 
CAS 

Google Scholar 
McLennan, S. M. Weathering and global denudation. J. Geol. 101(2), 295–303 (1993).ADS 

Google Scholar 
Nesbitt, H. W. & Young, G. M. Formation and diagenesis of weathering profiles. J. Geol. 97(2), 129–147 (1989).ADS 
CAS 

Google Scholar 
Fedo, C. M., Wayne Nesbitt, H. & Young, G. M. Unraveling the effects of potassium metasomatism in sedimentary rocks and paleosols, with implications for paleoweathering conditions and provenance. Geology 23(10), 921–924 (1995).ADS 
CAS 

Google Scholar 
Nesbitt, H. W. & Young, G. M. Early Proterozoic climates and plate motions inferred from major element chemistry of lutites. Nature 299, 715–717 (1982).ADS 
CAS 

Google Scholar 
von Eynatten, H., Barceló-Vidal, C. & Pawlowsky-Glahn, V. Modelling compositional change: The example of chemical weathering of granitoid rocks. Math. Geol. 35(3), 231–251 (2003).
Google Scholar 
Clark, R. N. & Rencz, A. N. Spectroscopy of rocks and minerals, and principles of spectroscopy. Manual Remote Sens. 3(11), 3–58 (1999).
Google Scholar 
Kump, L. R. & Arthur, M. A. Interpreting carbon-isotope excursions: Carbonates and organic matter. Chem. Geol. 161(1–3), 181–198 (1999).ADS 
CAS 

Google Scholar 
Holmden, C. et al. Carbon isotope chemostratigraphy of Frasnian sequences in Western Canada. Saskatchewan Geol. Surv. Summary Investig. 1, 1–6 (2006).
Google Scholar 
Pisarzowska, A. & Racki, G. Isotopic chemostratigraphy across the Early-Middle Frasnian transition (Late Devonian) on the South Polish carbonate shelf: A reference for the global punctata Event. Chem. Geol. 334, 199–220 (2012).ADS 
CAS 

Google Scholar 
Racki, G. & Bultynck, P. Conodont biostratigraphy of the Middle to Upper Devonian boundary Beds in the Kielce area of the Holy Cross Mts. Acta Geol. Pol. 44, 1–25 (1993).
Google Scholar 
Ziegler and Sandberg. The Late Devonian standard conodont zonation CFS, Cour. Forschungsinst. Senckenberg, 121 (1990).Klapper, G., The Montagne Noire Frasnian (Upper Devonian) conodont succession. In McMillan, N.J., et al., eds., Devonian of the world, Volume III: Canadian Society of Petroleum Geologists Memoir 14, p. 449–468 (1988).Jiao, X. et al. Mixed biogenic and hydrothermal quartz in Permian lacustrine shale of Santanghu Basin, NW China: Implications for penecontemporaneous transformation of silica minerals. Int. J. Earth Sci. 107(6), 1989–2009 (2018).CAS 

Google Scholar 
Peltonen, C., Marcussen, Ø., Bjørlykke, K. & Jahren, J. Clay mineral diagenesis and quartz cementation in mudstones: The effects of smectite to illite reaction on rock properties. Mar. Pet. Geol. 26(6), 887–898 (2009).CAS 

Google Scholar 
Pearce, T. J., Besly, B. M., Wray, D. S. & Wright, D. K. Chemostratigraphy: A method to improve interwell correlation in barren sequences—a case study using onshore Duckmantian/Stephanian sequences (West Midlands, UK). Sed. Geol. 124(1–4), 197–220 (1999).CAS 

Google Scholar 
Calvert, S. E. & Pedersen, T. F. Chapter fourteen elemental proxies for palaeoclimatic and palaeoceanographic variability in marine sediments: Interpretation and application. Dev. Mar. Geol. 1, 567–644 (2007).
Google Scholar 
Perri, F., Cirrincione, R., Critelli, S., Mazzoleni, P. & Pappalardo, A. Clay mineral assemblages and sandstone compositions of the Mesozoic Longobucco Group, northeastern Calabria: Implications for burial history and diagenetic evolution. Int. Geol. Rev. 50(12), 1116–1131 (2008).
Google Scholar 
Johnson, J. G., Klapper, G. & Sandberg, C. A. Devonian eustatic fluctuations in Euramerica. Geol. Soc. Am. Bull. 96(5), 567–587 (1985).ADS 

Google Scholar 
Warme, J. E. & Sandberg, C. A. Alamo megabreccia: Record of a Late Devonian impact in southern Nevada. GSA Today 6(1), 1–7 (1996).
Google Scholar 
Ernst, R. E., Rodygin, S. A. & Grinev, O. M. Age correlation of Large Igneous Provinces with Devonian biotic crises. Glob. Planet. Change 185, 103097 (2020).
Google Scholar 
Schiffbauer, J. D. et al. Decoupling biogeochemical records, extinction, and environmental change during the Cambrian SPICE event. Sci. Adv. 3(3), e1602158 (2017).ADS 

Google Scholar 
Duller, R. A., Armitage, J. J., Manners, H. R., Grimes, S. & Jones, T. D. Delayed sedimentary response to abrupt climate change at the Paleocene-Eocene boundary, northern Spain. Geology 47(2), 159–162 (2019).ADS 
CAS 

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