Philippa Kaur

White, L. A., Forester, J. D. & Craft, M. E. Using contact networks to explore mechanisms of parasite transmission in wildlife. Biol. Rev. 92, 389–409 (2017).PubMed 
Article 

Google Scholar 
Silk, M. J. et al. Using social network measures in wildlife disease ecology, epidemiology, and management. Bioscience 67, 245–257 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Albery, G. F., Kirkpatrick, L., Firth, J. A. & Bansal, S. Unifying spatial and social network analysis in disease ecology. J. Anim. Ecol. 90, 1–17 (2021).Article 

Google Scholar 
Evans, J. C., Silk, M. J., Boogert, N. J. & Hodgson, D. J. Infected or informed? Social structure and the simultaneous transmission of information and infectious disease. Oikos 129, 1271–1288 (2020).Article 

Google Scholar 
Aplin, L. M., Sheldon, B. C. & Morand-Ferron, J. Milk bottles revisited: social learning and individual variation in the blue tit, Cyanistes caeruleus. Anim. Behav. 85, 1225–1232 (2013).Article 

Google Scholar 
Silk, J. B. The adaptive value of sociality in mammalian groups. Phil. Trans. R. Soc. Lond. B 362, 539–559 (2007).Article 

Google Scholar 
Snyder-Mackler, N. et al. Social determinants of health and survival in humans and other animals. Science 368, eaax9553 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Machanda, Z. P. & Rosati, A. G. Shifting sociality during primate ageing. Phil. Trans. R. Soc. B 375, 20190620 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Nussey, D. H., Coulson, T., Festa-Bianchet, M. & Gaillard, J. M. Measuring senescence in wild animal populations: towards a longitudinal approach. Funct. Ecol. 22, 393–406 (2008).Article 

Google Scholar 
van de Pol, M. & Verhulst, S. Age-dependent traits: a new statistical model to separate within- and between-individual effects. Am. Nat. 167, 766–773 (2006).PubMed 
Article 

Google Scholar 
Froy, H. et al. Declining home range area predicts reduced late-life survival in two wild ungulate populations. Ecol. Lett. 21, 1001–1009 (2018).PubMed 
Article 

Google Scholar 
Rosati, A. G. et al. Social selectivity in aging wild chimpanzees. Science 370, 473–476 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kim, S.-Y., Torres, R., Rodriguez, C. & Drummond, H. Effects of breeding success, mate fidelity and senescence on breeding dispersal of male and female blue-footed boobies. J. Anim. Ecol. 76, 471–479 (2007).PubMed 
Article 

Google Scholar 
Webber, Q. M. R. & Vander Wal, E. An evolutionary framework outlining the integration of individual social and spatial ecology. J. Anim. Ecol. 87, 113–127 (2018).PubMed 
Article 

Google Scholar 
Webber, Q. M. R. & Vander Wal, E. Trends and perspectives on the use of animal social network analysis in behavioural ecology: a bibliometric approach. Anim. Behav. 149, 77–87 (2019).Article 

Google Scholar 
Siracusa, E. R., Higham, J. P., Snyder-mackler, N. & Brent, L. J. N. Social ageing: exploring the drivers of late-life changes in social behaviour in mammals. Biol. Lett. 18, 20210643 (2022).PubMed 
PubMed Central 
Article 

Google Scholar 
Elliott, K. H. et al. Ageing gracefully: physiology but not behaviour declines with age in a diving seabird. Funct. Ecol. 29, 219–228 (2015).Article 

Google Scholar 
Aartsen, M. J., Van Tilburg, T., Smits, C. H. M. & Knipscheer, K. C. P. M. A longitudinal study of the impact of physical and cognitive decline on the personal network in old age. J. Soc. Pers. Relat. 21, 249–266 (2004).Article 

Google Scholar 
Brent, L. J. N., Ruiz-Lambides, A. & Platt, M. L. Family network size and survival across the lifespan of female macaques. Proc. R. Soc. B 284, 20170515 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Turner, J. W., Robitaille, A. L., Bills, P. S. & Holekamp, K. E. Early-life relationships matter: social position during early life predicts fitness among female spotted hyenas. J. Anim. Ecol. 90, 183–196 (2021).PubMed 
Article 

Google Scholar 
Almeling, L., Hammerschmidt, K., Sennhenn-Reulen, H., Freund, A. M. & Fischer, J. Motivational shifts in aging monkeys and the origins of social selectivity. Curr. Biol. 26, 1744–1749 (2016).CAS 
PubMed 
Article 

Google Scholar 
Albery, G. F. et al. Multiple spatial behaviours govern social network positions in a wild ungulate. Ecol. Lett. 24, 676–686 (2021).PubMed 
Article 

Google Scholar 
Sanchez, J. N. & Hudgens, B. R. Interactions between density, home range behaviors, and contact rates in the Channel Island fox (Urocyon littoralis). Ecol. Evol. 5, 2466–2477 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Shizuka, D. & Johnson, A. E. How demographic processes shape animal social networks. Behav. Ecol. https://doi.org/10.1093/beheco/arz083 (2019).Krause, J., James, R., Franks, D. W. & Croft, D. P. Animal Social Networks (Oxford Univ. Press, 2015).Firth, J. A. et al. Wild birds respond to flockmate loss by increasing their social network associations to others. Proc. R. Soc. B 284, 20170299 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Evans, J. C., Liechti, J. I., Boatman, B. & König, B. A natural catastrophic turnover event: individual sociality matters despite community resilience in wild house mice. Proc. R. Soc. B 287, 20192880 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Rathke, E. & Fischer, J. Social aging in male and female Barbary macaques. Am. J. Primatol. https://doi.org/10.1002/ajp.23272 (2021).Kroeger, S. B., Blumstein, D. T. & Martin, J. G. A. A. How social behaviour and life-history traits change with age and in the year prior to death in female yellow-bellied marmots. Phil. Trans. R. Soc. B 376, 20190745 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Brambilla, A., von Hardenberg, A., Sueur, C., Canedoli, C. & Stanley, C. Long term analysis of social structure: evidence of age-based consistent associations in Alpine ibex. bioRxiv 1–42 (2021).González, N. T. et al. Age-related change in adult chimpanzee social network integration. Evol. Med. Public Health 9, 448–459 (2021).Article 

Google Scholar 
Clutton-Brock, T. H., Guinness, F. E. & Albon, S. D. Red Deer: Behavior and Ecology of Two Sexes. Vol. 15 (Univ. Chicago Press, 1982).Nussey, D. H., Kruuk, L. E. B., Donald, A., Fowlie, M. & Clutton-Brock, T. H. The rate of senescence in maternal performance increases with early-life fecundity in red deer. Ecol. Lett. 9, 1342–1350 (2006).PubMed 
Article 

Google Scholar 
Croft, D. P., James, R. & Krause, J. Exploring Animal Social Networks (Princeton Univ. Press, 2008).Tobler, W. R. A computer movie simulating urban growth in the Detroit region. Econ. Geogr. 46, 234 (1970).Article 

Google Scholar 
Firth, J. A. & Sheldon, B. C. Social carry-over effects underpin trans-seasonally linked structure in a wild bird population. Ecol. Lett. 19, 1324–1332 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Spiegel, O., Leu, S. T., Sih, A. & Bull, C. M. Socially interacting or indifferent neighbours? Randomization of movement paths to tease apart social preference and spatial constraints. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.12553 (2016).Nussey, D. H. et al. The relationship between tooth wear, habitat quality and late-life reproduction in a wild red deer population. J. Anim. Ecol. 76, 402–412 (2007).PubMed 
Article 

Google Scholar 
Loe, L. E., Mysterud, A., Langvatn, R. & Stenseth, N. C. Decelerating and sex-dependent tooth wear in Norwegian red deer. Oecologia 135, 346–353 (2003).PubMed 
Article 

Google Scholar 
Peignier, M. et al. Space use and social association in a gregarious ungulate: testing the conspecific attraction and resource dispersion hypotheses. Ecol. Evol. 9, 5133–5145 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Franks, D. W., Ruxton, G. D. & James, R. Sampling animal association networks with the gambit of the group. Behav. Ecol. Sociobiol. 64, 493–503 (2010).Article 

Google Scholar 
Patterson, S. K., Strum, S. C. & Silk, J. B. Resource competition shapes female–female aggression in olive baboons, Papio anubis. Anim. Behav. 176, 23–41 (2021).Article 

Google Scholar 
Kays, R., Crofoot, M. C., Jetz, W. & Wikelski, M. Terrestrial animal tracking as an eye on life and planet. Science 348, aaa2478 (2015).PubMed 
Article 
CAS 

Google Scholar 
Gilbertson, M. L. J., White, L. A. & Craft, M. E. Trade‐offs with telemetry‐derived contact networks for infectious disease studies in wildlife. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13355 (2020).Froy, H. et al. Senescence in immunity against helminth parasites predicts adult mortality in a wild mammal. Science 365, 1296–1298 (2019).CAS 
PubMed 
Article 

Google Scholar 
Siracusa, E. R. et al. Familiar neighbors, but not relatives, enhance fitness in a territorial mammal. Curr. Biol. 31, 438–445.e3 (2021).CAS 
PubMed 
Article 

Google Scholar 
Nussey, D. H., Kruuk, L. E. B., Morris, A. & Clutton-Brock, T. H. Environmental conditions in early life influence ageing rates in a wild population of red deer. Curr. Biol. 17, 1000–1001 (2007).Article 
CAS 

Google Scholar 
Castles, M. et al. Social networks created with different techniques are not comparable. Anim. Behav. 96, 59–67 (2014).Article 

Google Scholar 
Froy, H., Walling, C. A., Pemberton, J. M., Clutton-brock, T. H. & Kruuk, L. E. B. Relative costs of offspring sex and offspring survival in a polygynous mammal. Biol. Lett. 12, 20160417 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Clutton-Brock, T. H., Albon, S. D. & Guinness, F. E. Fitness costs of gestation and lactation in wild mammals. Nature 337, 260–262 (1989).CAS 
PubMed 
Article 

Google Scholar 
Cairns, S. J. & Schwager, S. J. A comparison of association indices. Anim. Behav. 35, 1454–1469 (1987).Article 

Google Scholar 
Brent, L. J. N. Friends of friends: are indirect connections in social networks important to animal behaviour? Anim. Behav. 103, 211–222 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Whitehead, H. Analyzing Animal Societies: Quantitative Methods for Vertebrate Social Analysis (Univ. Chicago Press, 2008).Calenge, C. Home range estimation in R: the adehabitatHR package. https://cran.r-project.org/web/packages/adehabitatHR/index.html (2011).Lindgren, F. & Rue, H. Bayesian spatial modelling with R-INLA. J. Stat. Softw. 63, 1–25 (2015).Article 

Google Scholar 
Rue, H. & Martino, S. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Stat. Methodol. 71, 319–392 (2009).Article 

Google Scholar 
Bakka, H. et al. Spatial modelling with R-INLA: a review. WIREs Comput. Stat. 10, e1443 (2018).Article 

Google Scholar 
Lindgren, F., Rue, H. & Lindstrom, J. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. J. R. Stat. Soc. B 73, 423–498 (2011).Article 

Google Scholar  More

Bengtsson, J. Biological control as an ecosystem service: partitioning contributions of nature and human inputs to yield. Ecol. Entomol. 40, 45–44 (2015).Article 

Google Scholar 
Zalucki, M., Furlong, M. J., Schellhorn, N. A., Macfadyen, S. & Davies, A. P. Assessing the impact of natural enemies in agroecosystems: toward “real” IPM or in quest of Holy Grail? Insect. Sci. 22, 1–5 (2015).PubMed 
Article 

Google Scholar 
Van Lenteren, J. C., Bolckmans, K., Kohl, J., Ravensberg, W. J. & Urabaneja, A. Biological control using invertebrates and microorganisms: plenty of new opportunities. BioControl 63, 39–59 (2018).Article 

Google Scholar 
Symondson, W. O. C., Sunderland, K. D. & Greenstone, M. H. Can generalist predators be effective biological control agents. Annu. Rev. Entomol. 47, 561–594 (2002).CAS 
PubMed 
Article 

Google Scholar 
Bianchi, F. J. J. A., Booij, C. J. H. & Tscharntke, T. Sustainable pest regulation in agricultural landscapes: a review on landscape composition, biodiversity and natural pest control. Proc. R. Soc. B. 273, 1715–1727 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Van Nouhuys, S., Niemikapee, S. & Hanski, I. Variation in a host-parasitoid interaction across independent populations. Insects 3, 1236–1256 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Hedlund, K., Vet, L. E. M. & Dicke, M. Generalist and specialist parasitoid strategies of using odours of adult drosophilid flies when searching for larval hosts. Oikos 77, 390–398 (1996).Article 

Google Scholar 
Evans, E. W., Stevenson, A. T. & Richards, D. R. Essential versus alternative foods of insect predators: benefits of a mixed diet. Oelcologia 121, 107–112 (1999).Article 

Google Scholar 
Lovei, G. L. & Sunderland, K. M. Ecology and behavior of ground beetles (Coleoptera: Carabidae). Annu. Rev. Entomol. 41, 231–256 (1996).CAS 
PubMed 
Article 

Google Scholar 
Kromp, B. Carabid beetles in sustainable agriculture: a review on pest control efficacy, cultivation impacts and enhancement. Agric. Ecosyt. Environ. 74, 187–228 (1999).Article 

Google Scholar 
Tuf, H., Dedek, P. & Vesley, M. Does the diurnal activity pattern of carabid beetles depend on season, ground temperature, or habitat? Arch. Biol. Sci. 64, 721–732 (2012).Article 

Google Scholar 
Firlej, A., Doyon, J., Harwood, J. D. & Brodeur, J. A multi-approach study to delineate interaction between carabid beetles and soybean aphids. Environ. Entomol. 42, 89–96 (2013).PubMed 
Article 

Google Scholar 
Clark, M. S., Luna, J. M., Stone, N. D. & Youngman, R. R. Generalist predator consumption of armyworm (Lepidoptera: Noctuidae) and effect of predator removal and damage in no-till corn. Environ. Entomol. 23, 617–622 (1994).Article 

Google Scholar 
Floate, K. D., Doane, J. F. & Gillot, C. Carabid predators of the wheat midge (Diptera: Cecidomyiidae) in Saskatchewan. Environ. Entomol. 19, 1503–1511 (1990).Article 

Google Scholar 
Barsics, F., Haubruge, E. & Verheggen, F. J. Wireworms’ management: an overview of the existing methods, with particular regards to Agriotis spp. (Coleoptera: Elateridae). Insects 4, 117–152 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Oberholzer, F., Escher, N. & Frank, T. The potential of carabid beetles (Coleoptera) to reduce slug damage to oilseed rape in the laboratory. Eur. J. Entomol. 100, 81–85 (2003).Article 

Google Scholar 
Honek, A., Martinkova, Z. & Jarosik, V. Ground beetles Carabidae as seed predators. Eur. J. Entomol. 100, 531–544 (2003).Article 

Google Scholar 
Lundgren, J. G. Relationship of Natural Enemies and Non-prey Foods 1–460 (Springer, 2009).Carbonne, B. et al. The resilience of weed seedbank regulation by carabid beetles, at continental scales, to alternative prey. Sci. Rep. 10, 1935 (2020).Article 
CAS 

Google Scholar 
Wilder, S. M., Norris, M., Lee, R. W., Raubenheimer, D. & Simpson, S. J. Arthropod food webs become increasingly lipid-limited at higher trophic levels. Ecol. Lett. 16, 895–902 (2013).PubMed 
Article 

Google Scholar 
Denno, R. F. & Fagan, W. F. Might nitrogen limitation promote omnivory among carnivorous arthropods? Ecology 84, 2522–2531 (2003).Article 

Google Scholar 
Saska, P. & Jarosik, V. Laboratory study of larval food requirements in nine species of Amara (Coleoptera: Carabidae). Plant Prot. 37, 103–110 (2001).
Google Scholar 
Saska, P., Van der Werf, W. & Westerman, P. Spatial and temporal patterns of carabid activity-density in cereals do not explain levels of weed seed predation. Bull. Entomological Res. 98, 169–181 (2008).CAS 
Article 

Google Scholar 
Talarico, F., Giglio, A., Pizzolotto, R. & Brandmayr, P. P. A synthesis of the feeding habits and reproductive rhythms in Italian seed feeding ground beetles (Coleoptera: Carabidae). Eur. J. Entomol. 113, 325–336 (2016).Article 

Google Scholar 
Fawki, S., Bak, S. S. & Toft, S. Food preference and food value for the carabid beetles Pterostichus melanarius, P. versicolor, and Carabus nemoralis. Eur. Carabidol. 114, 99–109 (2003).
Google Scholar 
Frei, B., Guenay, Y., Bohan, B. A., Traugett, M. & Wallinger, C. Molecular analysis indicates high levels of carabid weed seed consumption in cereal fields across central Europe. J. Plant Sci. 92, 935–942 (2019).
Google Scholar 
Kulkarni, S. S., Dosdall, L. M., Spence, J. R. & Willenborg, C. J. Brassicaceous weed seed predation by ground beetles (Coleoptera: Carabidae). Weed. Sci. 64, 294–302 (2016).Article 

Google Scholar 
Saska, P., Honek, A., Foffova, H. & Martinkova, Z. Burial-induced changes in the seed preferences of carabid beetles (Coleoptera: Carabidae). Eur. J. Entomol. 116, 113–140 (2019).Article 

Google Scholar 
Saska, P., Honek, A. & Martinkova, Z. Preference of carabid beetles (Coleoptera: Carabidae) for herbaceous seeds. Acta Zool. Acad. Sci. Hung. 65, 57–76 (2019).Article 

Google Scholar 
Sih, A. & Christensen, B. Optimal diet theory: when does it work, and when and why does it fail? Anim. Behav. 61, 379–390 (2001).Article 

Google Scholar 
Barron, A. B., Gurney, K. N., Meah, L. F. S., Vasilaki, E. & Marshall, J. A. R. Decision-making and action selection in insects: inspiration from vertebrate-based theories. Front. Behav. Neurosci. 9, 216 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Kulkarni, S. S., Dosdall, L. M., Spence, J. R. & Willenborg, C. J. C. J. The role of ground beetles (Coleoptera: Carabidae) in weed seed consumption: a review. Weed. Sci. 63, 355–376 (2015).Article 

Google Scholar 
Kulkarni, S. S., Dosdall, L. M., Spence, J. R. & Willenborg, C. J. Seed detection and discrimination by ground beetles (Coleoptera: Carabidae) are associated with olfactory cues. PLoS One 12, e0170593 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Law, J. J. & Gallagher, R. S. The role of imbibition on seed selection by Harpalus pensylvanicus. Appl. Soil. Ecol. 87, 118–124 (2015).Article 

Google Scholar 
Davis, A. S., Schutte, B. J., Iannuzzi, J. & Renner, K. A. Chemical and physical defenses of weed seeds in relation to soil seedbank persistence. Weed Sci. 56, 676–684 (2008).CAS 
Article 

Google Scholar 
Ali, K. A. & Willneborg., C. J. C. J. The biology of seed discrimination and its role in shaping the foraging ecology of carabids: a review. Ecol. Evol. 11, 13702–13722 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Wheater, C. P. Prey detection by some predatory Coleoptera (Carabidae and Staphylinidae). J. Zool. 215, 171–185 (1989).Article 

Google Scholar 
Mundy, C. A., Aleen-Williams, L. J., Underwood, N. & Warrington, S. Prey selection and foraging behavior by Pterostichus cupreus L. (Col., Carabidae) under laboratory conditions. J. Appl. Entomol. 124, 349–358 (2000).Article 

Google Scholar 
Kielty, J. P., Allen-Williams, L. J., Underwood, N. & Eastwood, E. A. Behavioral responses of three species of ground beetles (Carabidae: Coloeptera) to olfactory cues associated with prey and habitat. J. Insect. Behav. 9, 237–249 (1996).Article 

Google Scholar 
Tréfás, H., Canning, H., McKinlay, R. G., Armstrong, G. & Bujaki, G. Preliminary experiments on the olfactory responses of Pterostichus melanarius Illiger (Coleoptera:Carabidae) to intact plants. Agric. Entomol. 3, 71–76 (2001).Article 

Google Scholar 
McKemey, A. R., Symondson, W. O. C. & Glen, D. M. Predation and prey size choice by the carabid Pterostichus melanarius (Coleoptera: Carabidae): the dangers of extrapolating from laboratory to field. Bull. Entomol. Res. 93, 227–234 (2003).CAS 
PubMed 
Article 

Google Scholar 
Thomas, R. S., Glen, D. M. & Symondson, W. O. C. Prey detection through olfaction by the soil-dwelling larvae of the carabid predator Pterostichus melanarius. Soil Biol. Biochem. 40, 207–216 (2008).CAS 
Article 

Google Scholar 
Talarico, F. et al. Electrophysiological and behavioral analyses on prey selecting in the myrmecophagous carabid beetle Siagona europaea Dejean 1826 (Coleoptera: Carabidae). Etho. Ecol. Evol. 22, 375–384 (2010).Article 

Google Scholar 
Dessaint, F., Chadoeuf, R. & Barrales, G. Spatial pattern analysis of weed seeds in the cultivated soil seed bank. J. Appl. Ecol. 28, 721–730 (1991).Article 

Google Scholar 
Oster, M., Smith, L., Beck, J. J., Howard, A. & Field, C. B. Orientational behavior of predaceous ground beetle species in response to volatile emissions identified from yellow starthistle damaged by an invasive slug. Arthropod-Plant. Inte. 8, 429–437 (2014).Article 

Google Scholar 
Srinivasan, M. V., Poteser, M. & Karl, K. Motion detection in insect orientation and navigation. Vis. Res. 39, 2749–2766 (1999).CAS 
PubMed 
Article 

Google Scholar 
Sato, K. & Touhara, K. Insect olfaction: receptors, signal transduction, and behavior. Cell 47, 121–138 (2009).CAS 

Google Scholar 
Leal, W. S. Odorant reception in insects: roles of receptors, binding proteins, and degrading enzymes. Ann. Rev. Entomol. 58, 373–391 (2013).CAS 
Article 

Google Scholar 
Schmidt, H. R. & Benton, R. Molecular mechanisms of olfactory detection in insects: beyond receptors. Open Biol. 10, 200252 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Prokopy, R. J. & Owens, E. D. Visual detection of plants by herbivorous insects. Ann. Rev. Entomol. 28, 337–364 (1983).Article 

Google Scholar 
Ploomi, A. et al. Antennal sensilla in ground beetles (Coleoptera: Carabidae). Agron. Res. 1, 221–228 (2003).
Google Scholar 
Merivee, E. et al. Electrophysiological responses from neurons of antennal taste sensilla in the polyphagous predatory ground beetle Pterostichus oblongopunctatus (Fabricius 1787) to plant sugars and amin acids. J. Insect. Physiol. 54, 1213–1219 (2008).CAS 
PubMed 
Article 

Google Scholar 
Merivee, E., Ploomi, A., Luik, A., Rahi, M. & Smmelselg, V. Antennal sensilla of the ground beetle Platynus dorsalis (Pontoppidan, 1763) (Coleoptera: Carabidae). Micros. Res. Tech. 55, 339–349 (2001).CAS 
Article 

Google Scholar 
Merivee, E. et al. Antennal sensilla of the ground beetle Bembidion properans Steph. (Coleoptera: Carabidae). Micron 33, 429–440 (2002).PubMed 
Article 

Google Scholar 
Giglio, A., Perotta, E., Talarico, F., Brandmayr, T. E. & Ferrera, E. A. Sensilla on the maxillary and labial palps in a helicophagous ground beetle larva (Coleoptera: Carabidae). Acta Zool. 200, 1463–6393 (2013).
Google Scholar 
Van Naters, W. V. D. G. & Carlson, J. R. J. R. Receptors and neurons for fly odors in Drosophila. Curr. Biol. 17, 606–612 (2007).Article 
CAS 

Google Scholar 
Amrein, H. & Throne, N. Gustatory perception and behavior in Dropsophila melanogaster. Curr. Biol. 15, R673–R684 (2005).CAS 
PubMed 
Article 

Google Scholar 
Su, C. Y., Menuz, K. & Carlson, J. R. Olfactory perception: receptors, cells, and circuits. Cell 139, 45–59 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Krieger, J. & Breer, H. Olfactory receptors in invertebrates. Science 286, 720–723 (1999).CAS 
PubMed 
Article 

Google Scholar 
Chapman, R. F. The Insects: Structure and Function 4th edn, 1–584 (Cambridge University Press, 1998).Bhandari, S. R., Jo, J. S. & Lee, J. G. Comparisons of glucosinolate profiles in different tissues of nine Brassica crops. Molecules 20, 15827–15841 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Reifenrath, K., Riederer, M. & Muller, M. Leaf surface wax layers of Brassicaceae lack feeding stimulants for Phaedon cochleariae. Entomol. Exp. Appl. 115, 41–50 (2005).CAS 
Article 

Google Scholar 
Stadler, E. & Reifenrath, K. Glucosinolates on the leaf surface perceived by insect herbivores: review of ambiguous results and new investigations. Phytoch. Rev. 8, 207–225 (2009).Article 
CAS 

Google Scholar 
Sharma, A., Sandhi, R. K. & Reddy, G. V. P. A review of interactions between insect biological control agents and semiochemicals. Insects 10, 439 (2019).PubMed Central 
Article 

Google Scholar 
Warwick, S. I., Francis, A. & Susko, D. J. The biology of Canadian weeds. 9. Thlaspi arvense L. (updated). Can. J. Plant. Sci. 82, 803–823 (2002).Article 

Google Scholar 
Moyna, P. & Garcia, M. Chemical composition of oat seed epicuticular lipids. J. Sci. Food Agric. 34, 209–211 (1983).CAS 
PubMed 
Article 

Google Scholar 
Kunst, L. & Samuels, A. L. Biosynthesis and secretion of plant cuticular wax. Prog. Lipid Res. 42, 51–80 (2003).CAS 
PubMed 
Article 

Google Scholar 
Eigenbrode, S. D. & Espelie, K. E. Effects of plants epicuticular lipids on insect herbivores. Annu. Rev. Entomol. 40, 171–194 (1995).Article 

Google Scholar 
Finch, S. Volatile plant chemicals and their effect on host plant by the cabbage root fly (Delia brassicae). Entomol. Exp. Appl. 24, 350–359 (1978).CAS 
Article 

Google Scholar 
Udayagiri, S. & Mason, C. E. Epicuticular wax chemicals in Zea mays influence oviposition in Ostrinia nubilalis. J. Chem. Ecol. 23, 1675–1687 (1997).CAS 
Article 

Google Scholar 
Adati, T. & Matsuda, K. The effect of leaf surface wax on feeding of the strawberry leaf beetle, Galerucella vittaticollis, with reference to host plant preference. Tohoku. J. Agric. Res. 50, 57–61 (2000).
Google Scholar 
Damon, S. J., Groves, R. L. & Harvey, M. J. Variation for epicuticular waxes on onion foliage and impacts on numbers of onion thrips. J. Am. Soc. Hortic. Sci. 139, 495–501 (2014).CAS 
Article 

Google Scholar 
Braccini, C. L., Vega, A. S., Chludil, H. D., Leicach, S. R. & Fernandez, P. C. Host selection, oviposition behavior and leaf traits in a specialist willow sawfly on species of Salix (Salicaceae). Ecol. Entomol. 38, 617–626 (2013).Article 

Google Scholar 
Wojcicka, A. Effects of epicuticular waxes from triticale on the feeding behaviour and mortality of the grain aphid, Sitobion avenae (Fabricius) (Hemiptera: Aphididae). J. Plant. Prot. Res. 56, 39–44 (2016).CAS 
Article 

Google Scholar 
Medina, E. et al. Taxonomic significance of the epicuticular wax composition in species of genus Clusia from Panama. Biochem. Syst. Ecol. 34, 319–326 (2006).CAS 
Article 

Google Scholar 
Schulz-Bohm, K., Martin-Sanchez, L. & Garbeva, P. Microbial volatiles: small molecules with an inter-kingdom interactions. Front. Microbiol. 8, 2484 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Ali, K. A. Mechanisms of Seed Discrimination and Selective Seed Foraging in Carabid Weed Seed Predators. https://harvest.usask.ca/bitstream/handle/10388/13815/ALI-DISSERTATION-2022.pdf?sequence=1&isAllowed=y (2022).Webster, B., Qvarfordt, E., Olsson, U. & Glinwood, R. Different roles for innate and learnt behavioral responses to odors in insect host location. Behav. Ecol. 24, 366–372 (2013).Article 

Google Scholar 
Luff, M. L. Adult and larval feeding habits of Pterostichus madidus (F.) (Carabidae: Coleoptera). J. Nat. Hist. 8, 403–409 (1974).Article 

Google Scholar 
Blubaugh, C. K. & Kaplan, I. Invertebrate seed predators reduce weed emergence following seed rain. Weed Sci. 64, 80–86 (2016).Article 

Google Scholar 
Blubaugh, C. K., Hagler, J. R., Machtley, S. A. & Kaplan, I. Cover crops increase foraging activity of omnivorous predators in seed patches and facilitate weed biological control. Agric. Ecosyst. Environ. 231, 264–270 (2016).Article 

Google Scholar 
Foffova, H. et al. Which seed properties determine the preferences of carabid beetles seed predators? Insects 11, 757 (2020).Petit, S., Boursault, A. & Bohan, D. A. Weed seed choice by carabid beetles (Coleoptera: Carabidae): linking field measurements and laboratory diet assessments. Eur. J. Entomol. 111, 615–620 (2014).Article 

Google Scholar 
Carbonne, B. et al. Direct and indirect effects of landscape and field management intensity on carabids through trophic resources and weeds. J. Appl. Ecol. 59, 176–187 (2022).Article 

Google Scholar 
Foffova, H., Bohan, D. A. & Saska, P. Do properties and species of weed seeds affect their consumption by carabid beetles? Acta Zool. Acad. Sci. Hung. 66, 37–48 (2020b).Article 

Google Scholar 
De Heij, S. E. & Willenborg, C. J. Connected carabids: network interactions and their impact on biocontrol by carabid beetles. Bioscience 70, 90–500 (2020).Article 

Google Scholar 
Honek, A., Martinkova, Z., Saska, P. & Pekar, S. Size and taxonomic constraints determine seed preference of Carabidae (Coleoptera). Basic Appl. Ecol. 8, 343–353 (2007).Article 

Google Scholar 
Spence, J. R. & Niemela, J. K. Sampling carabid assemblages with pitfall traps: the madness and the method. Can. Entomol. 126, 881–884 (1994).Article 

Google Scholar 
Lindroth, C. H. The Ground Beetles (Carabidae, excluding Cicindelinae) of Canada and Alaska. Supplement 20, 24, 29, 33, 34, 35. Part I, pages I–XLVIII, 1969. Part II, pages 1–200, 1961. Part III, pages 201–408, 1963. Part IV, pages 409–648, 1966. Part V, pages 649–944, 1968. Part VI, pages 945–1192 (Opusca Entomology, 1961–1969).White, S. S., Renner, K. A., Menalled, F. D. & Landis, D. A. Feeding preferences of weed seed predators and effect on weed emergence. Weed. Sci. 55, 606–612 (2007).CAS 
Article 

Google Scholar 
Glinwood, R., Ahmed, E., Ovarfordt, E. & Ninkovic, V. Olfactory learning of plant genotypes by a polyphagous predator. Oecologia 166, 637–647 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Sablon, L., Dickens, J. C., Haubruge, E. H. & Verhggen., F. J. Chemical ecology of the Colorado potato beetle, Leptinotarsa decemlineata (Say) (Coleoptera: Chrysomelidae), and potential for alternative control methods. Insects 4, 31–54 (2013).Article 

Google Scholar 
Zhang, L., Li, H. & Zhang, L. Two olfactory pathways to detect aldehydes on locust mouthpart. Int. J. Biol. Sci. 13, 759–771 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pekar, S. & Hruskova, M. M. How granivorous Coreus marginatus (Hemiptera: Cereidae) recognizes its food. Acta Ethol. 9, 26–30 (2006).Article 

Google Scholar 
Ardenghi, N., Mulch, A., Pross, J. & Niedermeyer, E. M. Leaf wax n-alkane extraction: an optimized procedure. Org. Geochem. 113, 283–292 (2017).CAS 
Article 

Google Scholar 
Takahashi, S. & Gassa, A. Roles of cuticular hydrocarbons in intra- and interspecific recognition behavior of two Rhinotermitidae species. J. Chem. Ecol. 21, 1837–1845 (1995).CAS 
PubMed 
Article 

Google Scholar 
Bates, D., Machler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 
CAS 

Google Scholar 
Nobre, J. S. & Singer, J. D. M. Residual analysis for linear mixed models. Biom. J. 49, 863–875 (2007).PubMed 
Article 

Google Scholar 
Schielzeth, H. et al. Robustness of linear mixed-effects models to violations of distributional assumptions. Methods Ecol. Evol. 11, 1141–1152 (2020).Article 

Google Scholar  More

Field-data collectionFieldwork was conducted in DRC between January 2018 and March 2020. Ten transects (4–11 km long) were installed, identical to the approach in ref. 9, in locations that were highly likely to be peatland. These were selected to help test hypotheses about the role of vegetation, surface wetness, nutrient status and topography in peat accumulation (Fig. 1a and Supplementary Table 1). A further eight transects (0.5–3 km long) were installed to assess our peat mapping capabilities (Fig. 1a and Supplementary Table 1).Every 250 m along each transect, land cover was classified as one of six classes: water, savannah, terra firme forest, non-peat-forming seasonally inundated forest, hardwood-dominated peat swamp forest or palm-dominated peat swamp forest. Peat swamp forest was classified as palm dominated when >50% of the canopy, estimated by eye, was palms (commonly Raphia laurentii or Raphia sese). In addition, several ground-truth points were collected at locations in the vicinity of each transect from the clearly identifiable land-cover classes water, savannah and terra firme forest.Peat presence/absence was recorded every 250 m along all transects, and peat thickness (if present) was measured by inserting metal poles into the ground until the poles were prevented from going any further by the underlying mineral layer, identical to the pole method of ref. 9. In addition, a core of the full peat profile was extracted every kilometre along the ten hypothesis-testing transects, if peat was present, with a Russian-type corer (52 mm stainless steel Eijkelkamp model); these 63 cores were sealed in plastic for laboratory analysis.Peat-thickness laboratory measurementsPeat was defined as having an organic matter (OM) content of ≥65% and a thickness of ≥0.3 m (sensu ref. 9). Therefore, down-core OM content of all 63 cores was analysed to measure peat thickness. The organic matter content of each 0.1-m-thick peat sample was estimated via loss on ignition (LOI), whereby samples were heated at 550 °C for 4 h. The mass fraction lost after heating was used as an estimate of total OM content (% of mass). Peat thickness was defined as the deepest 0.1 m with OM ≥ 65%, after which there is a transition to mineral soil. Samples below this depth were excluded from further analysis. Rare mineral intrusions into the peat layer above this depth, where OM 4× the mean Cook’s distance were excluded as influential outliers. Mean pole-method offset was significantly higher along the DRC transects (0.94 m) than along those in ROC (0.48 m; P  More

Study sites and swarm characterizationThe survey was conducted in 10 villages in south-western Burkina Faso especially around the district of Bobo-Dioulasso, Santitougou (N11° 17′ 16″, W4° 13′ 04″), Kimidougou (N11° 17′ 53″; W4° 14′ 11″), Nastenga (N10.96871; W003.23477), Zeyama (N10.87638; W 003.26145), Mogobasso (N11° 25′ 31″, W4° 06′ 08″), Synbekuy (N11° 53′ 28″, W3° 44′ 02″), Ramatoulaye (N11° 33′ 39″, W3° 57′ 05″) Syndombokuy (N11° 53′ 06″, W3° 43′ 19″), Lampa (N11.16464; W 003.6374) et Syndounkuy (N11.14541; W 003.05141) (Fig. 1). All villages are located north of Bobo-Dioulasso, on the national road 10 (N10), ranged from 20 and 90 km. The region is characterised by wooded savannah located in south-western Burkina Faso, and the mean annual rainfall is about 1200 mm. The rainy season extends from May to October and the dry season from November to April. Malaria transmission in the area extends from June to November. However, residual transmission may occur beyond this period in specific locations. An. gambiae is the major malaria vector following by An. coluzzii and An. Arabiensis. Villages were chosen to represent similar ecological and entomological settings, they are middle sized and relatively isolated from one another.Figure 1Localization of the study sites in south-western Burkina Faso. This map was created under QGIS version 2.18 Las Palmas. link: https://changelog.qgis.org/en/qgis/version/2.18.0/Full size imageSpray Application Against Mosquito Swarms (SAMS) consisted of spraying diluted insecticide (Actellic 50: tap water with 1:20 concentration) at dusk by trained volunteer teams. They used the innovative technology of targeted swarm spraying with handheld sprayers and conventional broadcast space spray with backpack sprayers to achieve maximum effect. The spraying activities were conducted in eight of the ten villages. The target swarm spray was used in the four villages Kimidougou, Nastenga, Ramatoulaye and Syndombokuy. The broadcast space spray was applied in four other villages, Zeyama, Mogobasso, Lampa and Syndounkuy. The two remaining villages, Santidougou and Synbekuy were chosen as controls (Fig. 1). In each village, the potential swarm markers and the positive swarm sites were identified and geo-referenced using GPS. All concessions also were geo-referenced and labelled using paint.Procedure of the interventionTargeted swam spraying using handheld sprayersTargeted swarm spraying was carried out in four villages. Members of each team and volunteers from the selected villages were trained to target the swarms and apply an appropriate amount of spray each time. After the pre-intervention phase, all swarm sites scattered through the villages were repaired and swarm characteristics recorded. At 30 min before dusk (the estimated swarming time), a volunteer was placed in each compound with a sprayer. The objective of each volunteer was to destroy any swarm in the compound by applying insecticide with the handheld sprayer (Fig. 2A,B). Screening of the compound was continued for about 30 min until it was dark and no mosquitoes were visible. A single operator was able to effectively target 5 to 10 swarms per spray evening, depending on the distribution of swarms across the village. Spraying was carried out for 10 successive days throughout each village. The period of spraying approximately covered the period of pre-imaginal mosquito stages and was renewed after 45 days. The quantity of insecticide used was measured daily, in order to determine with precision the total quantity of insecticide used during targeted spraying.Figure 2Volunteer spraying swarms using handheld sprayers (A,B). Backpack spraying activities (C,D).Full size imageConventional broadcast spraying using Backpack sprayersThe broadcast spraying was also carried out in 4 villages but, unlike the targeted spraying, there was no direct targeting of swarms. At swarming time (estimated around 30 min at dusk) two volunteers with backpack sprayers ran through the entire village along paths between the compounds while spraying insecticide (Fig. 2C,D). As with the targeted spraying procedure, the broadcast spraying was carried out for 10 successive days in all 4 villages simultaneously, and spraying recommenced after 45 days. The quantity of insecticide used was measured daily, in order to determine with precision the total quantity of insecticide used during targeted spraying.Evaluation of the interventionA year prior to the intervention, baseline entomological data was collected in both villages to estimate mosquito density, human biting rate, female insemination rate, age structure of females and entomological inoculation rate29. The same parameters were evaluated immediately before and after intervention. The pre- and post-intervention evaluation of the abovementioned parameters were carried in both control and intervention villages at the same time. In both pre-intervention and post-intervention phases, two methods of mosquito collection were performed in each village, the human landing catch (HLC), indoor and outdoor in 4 houses for 4 successive nights, the pyrethroid spray catch (PSC) in the same10 houses and 10 randomly selected houses. To identify these, all houses in each village were coded and these codes were used to randomly select those to be sampled. All sampled sites were mapped using a global positioning system (GPS). Collected anopheline mosquitoes were sorted by taxonomic status, physiological status, and sex. Approximately, the ovaries of 200 females/month/village (100 females indoor and 100 females outdoor) were dissected to determine the physiological age, and parous females were subsequently subjected to ELISA assays to determine Plasmodium sporozoite rates. Data produced from indoor and outdoor mosquito collections were then used to estimate mosquito densities, their spatial distribution, produce a map identifying hotspots where the highest mosquito densities and biting occurred within the village, female age structure and quantify the intensity of malaria transmission. The impact of the spray was measured to see how it affected each of these parameters in the intervention villages compared to the controls.Statistical analysisThe resting mosquito abundance was assessed as the number of mosquitoes per house, the human biting rate assessed as the number of bites per person per night, the parity rate assessed as the percentage of parous females, and the insemination rate assessed as the percentage of the inseminated females. The list above defined the key entomological parameters to determine the dynamic of An. gambie s.l. populations and malaria transmission. The generalized estimating equation (GEE) method was used to estimate population averaged effect of intervention on various outcome measurements. As the GEE models do not require distributional assumptions but only specification of the mean and variance structure, they are more robust against misspecification of higher-order features of the data, and are useful when the main interest is in population averaged effects of an intervention or treatment. However, because they do not use a full likelihood model, they cannot be used for individual-specific inference30,31. Despite this shortcoming, their robustness to different types of correlation structures in the data (due to temporal ordering of measurements, or other hierarchical structure in data) makes them attractive for analyses of this type. GEE models were run in R version 3.6.232, using the package “geepack”33 for three datasets on insemination and parity rate, number of bites per person per night (NBPN), and density of adult male and female mosquitoes. To clean and plot the data the “tidyverse” family of R packages34 were used.Ethical considerationsThis study did not involve human patients. The full protocol of the study was submitted to the Institutional Ethics Committee of the “Institut de Recherche en Sciences de la Sante” for review and approval (A17-2016/CEIRES). In accordance with the approval, presentations of the project were given to the study site villagers and requests for their participation were made. During these visits the objectives, protocol and expected results were explained and discussed, as well as the implications for the households willing to take part in this study. A written consent form was signed or marked with fingerprint by the head of the households before any activity could take place in his compound. Insecticides used in this study are approved for use by the Burkina Faso insecticide regulation authority. More

Descamps, S. et al. Diverging phenological responses of Arctic seabirds to an earlier spring. Glob. Change Biol. 25, 4081–4091 (2019).ADS 
Article 

Google Scholar 
Ramp, C., Delarue, J., Palsbøll, P. J., Sears, R. & Hammond, P. S. Adapting to a warmer ocean—seasonal shift of baleen whale movements over three decades. PLoS ONE 10, e0121374 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Insley, S. J., Halliday, W. D., Mouy, X. & Diogou, N. Bowhead whales overwinter in the Amundsen Gulf and Eastern Beaufort Sea. R. Soc. Open Sci. 8, 1 (2021).Article 

Google Scholar 
Heide-Jørgensen, M. P., Laidre, K. L., Quakenbush, L. T. & Citta, J. J. The Northwest Passage opens for bowhead whales. Biol. Lett. 8, 270–273 (2012).PubMed 
Article 

Google Scholar 
Durant, J., Hjermann, D., Ottersen, G. & Stenseth, N. Climate and the match or mismatch between predator requirements and resource availability. Clim. Res. 33, 271–283 (2007).Article 

Google Scholar 
Staudinger, M. D. et al. It’s about time: A synthesis of changing phenology in the Gulf of Maine ecosystem. Fish. Oceanogr. 28, 532–566 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Miller-Rushing, A. J., Høye, T. T., Inouye, D. W. & Post, E. The effects of phenological mismatches on demography. Philos. Trans. R. Soc. B Biol. Sci. 365, 3177–3186 (2010).Article 

Google Scholar 
Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430, 881–884 (2004).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Record, N. et al. Rapid climate-driven circulation changes threaten conservation of endangered North Atlantic right whales. Oceanography 32, 1 (2019).Article 

Google Scholar 
MacLeod, C. Global climate change, range changes and potential implications for the conservation of marine cetaceans: a review and synthesis. Endanger. Species Res. 7, 125–136 (2009).Article 

Google Scholar 
Learmonth, J. A. et al. Potential effects of climate change on marine mammals. Oceanogr. Mar. Biol. Annu. Rev. 44, 431–464 (2006).
Google Scholar 
Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Pershing, A. J. et al. Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery. Science 350, 809–812 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Gulf of Maine Research Institute. Gulf of Maine Warming Update: 2021 the Hottest Year on Record. (2022).Saba, V. S. et al. Enhanced warming of the Northwest Atlantic Ocean under climate change. J. Geophys. Res. Oceans 121, 118–132 (2016).ADS 
Article 

Google Scholar 
Friedland, K. D. et al. Trends and change points in surface and bottom thermal environments of the US Northeast Continental Shelf Ecosystem. Fish. Oceanogr. 29, 396–414 (2020).Article 

Google Scholar 
Nye, J., Link, J., Hare, J. & Overholtz, W. Changing spatial distribution of fish stocks in relation to climate and population size on the Northeast United States continental shelf. Mar. Ecol. Prog. Ser. 393, 111–129 (2009).ADS 
Article 

Google Scholar 
Kress, S. W., Shannon, P. & O’Neal, C. Recent changes in the diet and survival of Atlantic puffin chicks in the face of climate change and commercial fishing in midcoast Maine, USA. FACETS 1, 27–43 (2017).Article 

Google Scholar 
Davis, G. E. et al. Exploring movement patterns and changing distributions of baleen whales in the western North Atlantic using a decade of passive acoustic data. Glob. Change Biol. 26, 4812–4840 (2020).ADS 
Article 

Google Scholar 
Pace, R. M., Corkeron, P. J. & Kraus, S. D. State-space mark-recapture estimates reveal a recent decline in abundance of North Atlantic right whales. Ecol. Evol. 7, 8730–8741 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Meyer-Gutbrod, E. L. & Greene, C. H. Uncertain recovery of the North Atlantic right whale in a changing ocean. Glob. Change Biol. 24, 455–464 (2018).ADS 
Article 

Google Scholar 
Sorochan, K. A. et al. North Atlantic right whale (Eubalaena glacialis) and its food: (II) interannual variations in biomass of Calanus spp. on western North Atlantic shelves. J. Plankton Res. 41, 687–708 (2019).Article 

Google Scholar 
Friedland, K. D. et al. Spring bloom dynamics and zooplankton biomass response on the US Northeast Continental Shelf. Cont. Shelf Res. 102, 47–61 (2015).ADS 
Article 

Google Scholar 
Meyer-Gutbrod, E., Greene, C., Davies, K. & Johns, D. Ocean regime shift is driving collapse of the North Atlantic right whale population. Oceanography 34, 22–31 (2021).Article 

Google Scholar 
Knowlton, A., Hamilton, P., Marx, M., Pettis, H. & Kraus, S. Monitoring North Atlantic right whale Eubalaena glacialis entanglement rates: A 30 yr retrospective. Mar. Ecol. Prog. Ser. 466, 293–302 (2012).ADS 
Article 

Google Scholar 
Davies, K. T. A. & Brillant, S. W. Mass human-caused mortality spurs federal action to protect endangered North Atlantic right whales in Canada. Mar. Policy 104, 157–162 (2019).Article 

Google Scholar 
Kraus, S. D. & Rolland, R. M. Right whales in the urban ocean. in The urban whale: North Atlantic right whales at the crossroads 1–38 (Harvard University Press, 2010). https://doi.org/10.2307/j.ctv1pnc1q9.Winn, H. E., Price, C. A. & Sorensen, P. W. The distributional biology of the right whale (Eubalaena glacialis) in the western North Atlantic. Rep. Int. Whal. Comm. Spec. 10, 129–138 (1986).
Google Scholar 
Mayo, C. A. & Marx, M. K. Surface foraging behaviour of the North Atlantic right whale, Eubalaena glacialis, and associated zooplankton characteristics. Can. J. Zool. 68, 2214–2220 (1990).Article 

Google Scholar 
Mayo, C. A. et al. Distribution, demography, and behavior of North Atlantic right whales (Eubalaena glacialis) in Cape Cod Bay, Massachusetts, 1998–2013. Mar. Mammal Sci. 34, 979–996 (2018).Article 

Google Scholar 
Pendleton, D. E. et al. Regional-scale mean copepod concentration indicates relative abundance of North Atlantic right whales. Mar. Ecol. Prog. Ser. 378, 211–225 (2009).ADS 
Article 

Google Scholar 
Kenney, R. D., Winn, H. E. & Macaulay, M. C. Cetaceans in the Great South Channel, 1979–1989: right whale (Eubalaena glacialis). Cont. Shelf Res. 15, 385–414 (1995).ADS 
Article 

Google Scholar 
Brown, M. W. et al. Recovery strategy for the North Atlantic right whale (Eubalaena glacialis) in Atlantic Canadian waters. in Species at risk act recovery strategy series (Fisheries and Oceans Canada, 2009).Weinrich, M. T., Kenney, R. D. & Hamilton, P. K. Right whales (Eubalaena glacialis) on Jeffreys Ledge: a habitat of unrecognized importance?. Mar. Mammal Sci. 16, 326–337 (2000).Article 

Google Scholar 
Cole, T. et al. Evidence of a North Atlantic right whale Eubalaena glacialis mating ground. Endanger. Species Res. 21, 55–64 (2013).Article 

Google Scholar 
Ganley, L., Brault, S. & Mayo, C. What we see is not what there is: estimating North Atlantic right whale Eubalaena glacialis local abundance. Endanger. Species Res. 38, 101–113 (2019).Article 

Google Scholar 
Simard, Y., Roy, N., Giard, S. & Aulanier, F. North Atlantic right whale shift to the Gulf of St. Lawrence in 2015, revealed by long-term passive acoustics. Endanger. Species Res. 40, 271–284 (2019).Article 

Google Scholar 
Leiter, S. et al. North Atlantic right whale Eubalaena glacialis occurrence in offshore wind energy areas near Massachusetts and Rhode Island, USA. Endanger. Species Res. 34, 45–59 (2017).Article 

Google Scholar 
Stone, K. M. et al. Distribution and abundance of cetaceans in a wind energy development area offshore of Massachusetts and Rhode Island. J. Coast. Conserv. 21, 527–543 (2017).Article 

Google Scholar 
Vanderlaan, A., Taggart, C., Serdynska, A., Kenney, R. & Brown, M. Reducing the risk of lethal encounters: Vessels and right whales in the Bay of Fundy and on the Scotian Shelf. Endanger. Species Res. 4, 283–297 (2008).Article 

Google Scholar 
National Marine Fisheries Service. Endangered and threatened species; critical habitat for endangered North Atlantic right whale. Fed. Regist. 80, 9314–9345 (2015).
Google Scholar 
National Marine Fisheries Service. Taking of marine mammals incidental to commercial fishing operations; Atlantic large whale take reduction plan regulations; Atlantic coastal fisheries cooperative management act provisions; American lobster fishery. Fed. Regist. 85, 86878–86900 (2020).
Google Scholar 
Reeves, R. R., Breiwick, J. M. & Mitchell, E. D. History of whaling and estimated kill of right whales, Balaena glacialis, in the Northeastern United States, 1620–1924. Mar. Fish. Rev. 36, 1 (1999).
Google Scholar 
Allen, G. M. The whalebone whales of New England. Mem. Boston Soc. Nat. Hist. 8, 107–322 (1915).ADS 

Google Scholar 
CETAP (Cetacean and Turtle Assessment Program). A characterization of marine mammals and turtles in the mid- and North- Atlantic areas of the U.S. Outer Continental Shelf, final report. (1982).Kenney, R. D. & Vigness-Raposa, K. J. Marine mammals and sea turtles of Narragansett Bay, Block Island Sound, Rhode Island Sound, and nearby waters: An analysis of existing data for the Rhode Island Ocean Special Area Management Plan. in Rhode Island Ocean Special Area Management Plan; Volume 2 Appendix A: Technical Reports for the Rhode Island Ocean Special Area Management Plan. 701–1037 (Rhode Island Coastal Resources Management Council, Wakefield, RI, 2010).Pendleton, D. et al. Weekly predictions of North Atlantic right whale Eubalaena glacialis habitat reveal influence of prey abundance and seasonality of habitat preferences. Endanger. Species Res. 18, 147–161 (2012).MathSciNet 
Article 

Google Scholar 
Kraus, S. D., Kenney, R. D. & Thomas, L. A framework for studying the effects of offshore wind development on marine mammals and turtles. (2019). Report prepared for the Massachusetts Clean Energy Center, Boston, MA, and the Bureau of Ocean Energy Management, Office of Renewable Energy Programs, Sterling, VA. Anderson Cabot Center for Ocean Life, New England Aquarium, Boston, MA. 48 pp.Quintana-Rizzo, E. et al. Residency, demographics, and movement patterns of North Atlantic right whales Eubalaena glacialis in an offshore wind energy development area in southern New England, USA. Endanger. Species Res. 45, 251–268 (2021).Article 

Google Scholar 
Taylor, J. K. D., Kenney, R. D., LeRoi, D. J. & Kraus, S. D. Automated vertical photography for detecting pelagic species in multitaxon aerial surveys. Mar. Technol. Soc. J. 48, 36–48 (2014).Article 

Google Scholar 
Hamilton, P. K., Knowlton, A. R. & Marx, M. K. Right whales tell their own stories: the photo-identification catalog. in The urban whale: North Atlantic right whales at the crossroads 75–104 (Harvard University Press, 2010).Buckland, S. T., Anderson, D. R., Burnham, K. P. & Laake, J. L. Distance sampling: Estimating abundance of biological populations Vol. 50 (Chapman and Hall, 1993).MATH 
Book 

Google Scholar 
R: The R Project for Statistical Computing. https://www.r-project.org/.Miller, D. L., Rexstad, E., Thomas, L., Marshall, L. & Laake, J. L. Distance Sampling in R. J. Stat. Softw. 89, 1–28 (2019).Article 

Google Scholar 
Eberhardt, L. L., Chapman, D. G. & Gilbert, J. R. A review of marine mammal census methods. Wildl. Monogr. 1, 3–46 (1979).
Google Scholar 
Durant, S. M. et al. Long-term trends in carnivore abundance using distance sampling in Serengeti National Park, Tanzania: Serengeti carnivore trends. J. Appl. Ecol. 48, 1490–1500 (2011).Article 

Google Scholar 
Reeves, R. R. & Mitchell, E. The Long Island, New York, right whale fishery: 1650–1924. Rep. Int. Whal. Comm. 10, 201–220 (1986).
Google Scholar 
Davis, G. E. et al. Long-term passive acoustic recordings track the changing distribution of North Atlantic right whales (Eubalaena glacialis) from 2004 to 2014. Sci. Rep. 7, 13460 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Jackson, J. et al. Have whales returned to a historical hotspot of industrial whaling? The pattern of southern right whale Eubalaena australis recovery at South Georgia. Endanger. Species Res. 43, 323–339 (2020).Article 

Google Scholar 
Carroll, E. L. et al. Reestablishment of former wintering grounds by New Zealand southern right whales. Mar. Mammal Sci. 30, 206–220 (2014).Article 

Google Scholar 
Charlton, C. et al. Southern right whales (Eubalaena australis) return to a former wintering calving ground: Fowlers Bay, South Australia. Mar. Mammal Sci. 35, 1438–1462 (2019).Article 

Google Scholar 
Garrigue, C. et al. Searching for humpback whales in a historical whaling hotspot of the Coral Sea, South Pacific. Endanger. Species Res. 42, 67–82 (2020).Article 

Google Scholar 
Clapham, P. J., Aguilar, A. & Hatch, L. T. Determining spatial and temporal scales for management: lessons from whaling. Mar. Mammal Sci. 24, 183–201 (2008).Article 

Google Scholar 
Watkins, W. A. & Schevill, W. E. Right whale feeding and baleen rattle. J. Mammal. 57, 58–66 (1976).Article 

Google Scholar 
Beardsley, R. C. et al. Spatial variability in zooplankton abundance near feeding right whales in the Great South Channel.. Deep Sea Res Part II Top. Stud. Oceanogr. 43, 1601–1625 (1996).ADS 
Article 

Google Scholar 
Wishner, K. F. et al. Copepod patches and right whales in the Great South Channel off New England. Bull. Mar. Sci. 43, 825–844 (1988).ADS 

Google Scholar 
Baumgartner, M., Cole, T., Clapham, P. & Mate, B. North Atlantic right whale habitat in the lower Bay of Fundy and on the SW Scotian Shelf during 1999–2001. Mar. Ecol. Prog. Ser. 264, 137–154 (2003).ADS 
Article 

Google Scholar 
Moore, M. J. & van der Hoop, J. M. The painful side of trap and fixed net fisheries: Chronic entanglement of large whales. J. Mar. Biol. 2012, 1–4 (2012).Article 

Google Scholar  More

Xie, Y., Fang, M. & Shauman, K. STEM education. Annu. Rev. Sociol. 41, 331–357 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, X. STEM attrition: College students’ paths into and out of STEM fields. National Center for Education Statistics. Retrieved from http://ies.ed.gov/pubsearch/pubsinfo.asp?pubid=2014001rev. Accessed 22 September 2021.Huang G, Taddese N, Walter E (2000) Entry and persistence of women and minorities in college science and engineering education. National Center for Education Statistics. Retrieved from https://eric.ed.gov/?id=ED566411. Accessed 22 September 2021.Hurtado, S., Eagan, K., & Chang, M. Degrees of Success: Bachelor’s Degree Completion Rates among Initial STEM Majors (Higher Education Research Institute, Los Angeles, CA) (2010).National Science Foundation, Broadening Participation Working Group (2014) Pathways to broadening participation in response to the CEOSE 2011–2012 recommendation. National Science Foundation. Retrieved from https://www.nsf.gov/pubs/2015/nsf15037/nsf15037.pdf. Accessed 22 Sep 2021.James, S. M. & Singer, S. R. From the NSF: The National Science Foundation’s investments in broadening participation in science, technology, engineering, and mathematics education through research and capacity building. CBE Life Sci. Educ. 15(3), 1–8 (2016).Article 

Google Scholar 
Smith, B. L., MacGregor, J., Matthews, R. & Gabelnick, F. Learning communities: Reforming undergraduate education (Jossey-Bass, 2004).
Google Scholar 
Andrade, M. S. Learning communities: Examining positive outcomes. J. Coll. Stud. Ret. 9(1), 1–20 (2007).Article 

Google Scholar 
Maton, K. I., Pollard, S. A., McDougall Weise, T. V. & Hrabowski, F. A. Meyerhoff Scholars Program: A strengths-based, institution-wide approach to increasing diversity in science, technology, engineering, and mathematics. Mt Sinai J. Med. 79(5), 610–623 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Dagley, M., Georgiopoulos, M., Reece, A. & Young, C. Increasing retention and graduation rates through a STEM learning community. J. Coll. Stud. Ret. 18(2), 167–182 (2016).Article 

Google Scholar 
National Survey of Student Engagement (2015) Engagement Insights: Survey Findings on the Quality of Undergraduate Education—Annual Results 2015 (Bloomington, IN).Tinto, V. Leaving college: Rethinking the causes and cures of student attrition (University of Chicago Press, 1987).
Google Scholar 
Tinto, V. Learning better together: The impact of learning communities on student success. Higher Educ. Monogr. Ser. 1(8), 1–8 (2003).
Google Scholar 
Otto, S., Evins, M. A., Boyer-Pennington, M. & Brinthaupt, T. M. Learning communities in higher education: Best practices. Journal of Student Success and Retention 2(1), 1–20 (2015).
Google Scholar 
Boda, Z., Elmer, T., Vörös, A. & Stadtfeld, C. Short-term and long-term effects of a social network intervention on friendships among university students. Sci. Rep. 10(1), 1–2 (2020).Article 
CAS 

Google Scholar 
Hotchkiss, J. L., Moore, R. E. & Pitts, M. M. Freshman learning communities, college performance, and retention. Educ. Econ. 14(2), 197–210 (2006).Article 

Google Scholar 
Whalen, D. F. & Shelley, M. C. Academic success for STEM and non-STEM majors. J. STEM Educ. 11(1), 45–60 (2010).
Google Scholar 
Xu, D., Solanki, S., McPartlan, P. & Sato, B. EASEing students into college: The impact of multidimensional support for underprepared students. Educ. Res. 47(7), 435–450 (2018).Article 

Google Scholar 
Jaffee, D., Carle, A., Phillips, R. & Paltoo, L. Intended and unintended consequences of first-year learning communities: An initial investigation. J. First-Year Exp. Stud. Trans. 20(1), 53–70 (2008).
Google Scholar 
Tinto, V. & Goodsell, A. Freshman interest groups and the first-year experience: Constructing student communities in a large university. J. First Year Exp. Stud. Trans. 6(1), 7–28 (1994).
Google Scholar 
Domizi, D. Student perceptions about their informal learning experiences in a first-year residential learning community. J. First Year Exp. Stud. Transit. 20(1), 97–110 (2008).
Google Scholar 
Lee, D. S. & Lemieux, T. Regression discontinuity designs in economics. J. Econ. Lit. 2, 281–355 (2010).Article 

Google Scholar 
Jacob, R., Zhu, P., Somers, M.A., & Bloom, H. A Practical Guide to Regression Discontinuity (MDRC, New York, NY, 2012).Hays, R. B. & Oxley, D. Social network development and functioning during a life transition. J. Pers. Soc. Psychol. 50(2), 305–313 (1986).CAS 
PubMed 
Article 

Google Scholar 
Freeman, T. M., Anderman, L. H. & Jensen, J. M. Sense of belonging in college freshmen at the classroom and campus levels. J. Exp. Educ. 75(3), 203–220 (2007).Article 

Google Scholar 
Zumbrunn, S., McKim, C., Buhs, E. & Hawley, L. R. Support, belonging, motivation, and engagement in the college classroom: A mixed method study. Instr. Sci. 42(5), 661–684 (2014).Article 

Google Scholar 
Hasan, S. & Bagde, S. The mechanics of social capital and academic performance in an Indian college. Am. Sociol. Rev. 78(6), 1009–1032 (2013).Article 

Google Scholar 
Stadtfeld, C., Vörös, A., Elmer, T., Boda, Z. & Raabe, I. J. Integration in emerging social networks explains academic failure and success. Proc. Natl. Acad. Sci. USA 116(3), 792–797 (2019).CAS 
PubMed 
Article 

Google Scholar 
Kraemer, B. A. The academic and social integration of Hispanic students into college. Rev. High Educ. 20(2), 163–179 (1997).Article 

Google Scholar 
Nora, A. Two-year colleges and minority students’ educational aspirations: Help or hindrance. Higher Educ. Handb. Theory Res. 9(3), 212–247 (1993).
Google Scholar 
McCabe, J.M. Connecting in College: How Friendship Networks Matter for Academic and Social Success (University of Chicago Press, Chicago, IL, 2016).Felten, P., & Lambert, L. M. Relationship-rich Education: How Human Connections Drive Success in College (Johns Hopkins University Press, Baltimore, MD, 2020).Hallinan, M. T. The peer influence process. Stud. Educ. Eval. 7(3), 285–306 (1981).Article 

Google Scholar 
Thomas, S. L. Ties that bind: A social network approach to understanding student integration and persistence. J. Higher Educ. 71(5), 591–615 (2000).
Google Scholar 
Turetsky, K. M., Purdie-Greenaway, V., Cook, J. E., Curley, J. P. & Cohen, G. L. A psychological intervention strengthens students’ peer social networks and promotes persistence in STEM. Sci. Adv. 6(45), 1–10 (2020).Article 

Google Scholar 
Dokuka, S., Valeeva, D. & Yudkevich, M. How academic achievement spreads: The role of distinct social networks in academic performance diffusion. PLoS ONE 15(7), 1–16 (2020).Article 
CAS 

Google Scholar 
Epstein, J. L. & Karweit, N. (eds) Friends in school: Patterns of selection and influence in secondary schools (Academic Press, 1983).
Google Scholar 
Feld, S. L. The focused organization of social ties. AJS 86(5), 1015–1035 (1981).
Google Scholar 
Rivera, M. T., Soderstrom, S. B. & Uzzi, B. Dynamics of dyads in social networks: Assortative, relational, and proximity mechanisms. Annu. Rev. Sociol. 36, 91–115 (2010).Article 

Google Scholar 
Mollenhorst, G., Volker, B. & Flap, H. Changes in personal relationships: How social contexts affect the emergence and discontinuation of relationships. Soc. Netw. 37, 65–80 (2014).Article 

Google Scholar 
Thomas, R. J. Sources of friendship and structurally induced homophily across the life course. Sociol Perspect 62(6), 822–843 (2019).Article 

Google Scholar 
Kubitschek, W. N. & Hallinan, M. T. Tracking and students’ friendships. Soc. Psychol. Q 46, 1–5 (1998).Article 

Google Scholar 
Frank, K. A., Muller, C. & Mueller, A. S. The embeddedness of adolescent friendship nominations: The formation of social capital in emergent network structures. AJS 119(1), 216–253 (2013).PubMed 
PubMed Central 

Google Scholar 
Kossinets, G. & Watts, D. J. Origins of homophily in an evolving social network. AJS 115(2), 405–450 (2009).
Google Scholar 
Wimmer, A. & Lewis, K. Beyond and below racial homophily: ERG models of a friendship network documented on Facebook. AJS 116(2), 583–642 (2010).PubMed 

Google Scholar 
Hallinan, M. T. & Sørensen, A. B. Ability grouping and student friendships. Am. Educ. Res. J. 51, 485–499 (1985).Article 

Google Scholar 
Leszczensky, L. & Pink, S. Ethnic segregation of friendship networks in school: Testing a rational-choice argument of differences in ethnic homophily between classroom-and grade-level networks. Soc. Netw. 42, 18–26 (2015).Article 

Google Scholar 
DiMaggio, P. & Garip, F. Network effects and social inequality. Annu. Rev. Sociol. 54, 93–118 (2012).Article 

Google Scholar 
Johnson, A. M. ‘“I can turn it on when i need to”’: Pre-college Integration, culture, and peer academic engagement among black and Latino/a engineering Students. Sociol. Educ. 56, 1–20 (2019).Article 

Google Scholar 
Perry, B. L., Pescosolido, B. A. & Borgatti, S. P. Egocentric network analysis: Foundations, methods, and models (Cambridge University Press, 2018).Book 

Google Scholar 
Wasserman, S. & Faust, K. Social network analysis: Methods and applications (Cambridge University Press, 1994).MATH 
Book 

Google Scholar 
Hartup, W. W. & Stevens, N. Friendships and adaptation in the life course. Psychol. Bull. 121(3), 355 (1997).Article 

Google Scholar 
Vaquera, E. & Kao, G. Do you like me as much as I like you? Friendship reciprocity and its effects on school outcomes among adolescents. Soc. Sci. Res. 37(1), 55–72 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
Imbens, G. W. & Lemieux, T. Regression discontinuity designs: A guide to practice. J. Econom. 142(2), 615–635 (2008).MathSciNet 
MATH 
Article 

Google Scholar 
Imbens, G. W. & Angrist, J. D. Identification and estimation of local average treatment effects. Econometrica 62(2), 467–475 (1994).MATH 
Article 

Google Scholar 
Robins, G., Pattison, P., Kalish, Y. & Lusher, D. An introduction to exponential random graph (p*) models for social networks. Soc. Netw. 29(2), 173–191 (2007).Article 

Google Scholar 
Handcock, M. S., Hunter, D. R., Butts, C. T., Goodreau, S. M. & Morris, M. Statnet: Software tools for the representation, visualization, analysis and simulation of network data. J. Stat. Softw. 24(1), 1548–7660 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
Calonico, S., Cattaneo, M. D. & Titiunik, R. Optimal data-driven regression discontinuity plots. J. Am. Stat. Assoc. 110(512), 1753–1769 (2015).MathSciNet 
CAS 
MATH 
Article 

Google Scholar 
Duxbury, S. W. The problem of scaling in exponential random graph models. Sociol. Methods Res. https://doi.org/10.1177/0049124120986178:1-39 (2021).MathSciNet 
Article 

Google Scholar 
McPherson, M., Smith-Lovin, L. & Cook, J. M. Birds of a feather: Homophily in social networks. Annu. Rev. Sociol. 27(1), 415–444 (2001).Article 

Google Scholar 
Kadushin, C. Understanding social networks: Theories, concepts, and findings (Oxford University Press, 2012).
Google Scholar 
Flashman, J. Academic achievement and its impact on friend dynamics. Sociol. Educ. 85(1), 61–80 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Carrell, S. E., Sacerdote, B. I. & West, J. E. From natural variation to optimal policy? The importance of endogenous peer group formation. Econometrica 81(3), 855–882 (2013).MathSciNet 
MATH 
Article 

Google Scholar 
Cox, A. B. Cohorts, ‘“siblings”,’ and mentors: Organizational structures and the creation of social capital. Sociol. Educ. 90(1), 47–63 (2017).Article 

Google Scholar 
Valente, T. W. Network interventions. Science 337(6090), 49–53 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Nunn, L. M. College belonging: How first-year and first-generation students navigate campus life (Rutgers University Press, 2021).Book 

Google Scholar 
Garlick, R. Academic peer effects with different group assignment policies: Residential tracking versus random assignment. Am. Econ. J. Appl. Econ. 10(3), 345–369 (2018).Article 

Google Scholar 
Carrell, S. E., Fullerton, R. L. & West, J. E. Does your cohort matter? Measuring peer effects in college achievement. J. Labor. Econ. 27(3), 439–464 (2009).Article 

Google Scholar 
Lomi, A., Snijders, T. A., Steglich, C. E. & Torló, V. J. Why are some more peer than others? Evidence from a longitudinal study of social networks and individual academic performance. Soc. Sci. Res. 40(6), 1506–1520 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Poldin, O., Valeeva, D. & Yudkevich, M. Which peers matter: How social ties affect peer-group effects. Res. High Educ. 57(4), 448–468 (2016).Article 

Google Scholar 
Raabe, I. J., Boda, Z. & Stadtfeld, C. The social pipeline: How friend influence and peer exposure widen the STEM gender gap. Sociol. Educ. 92(2), 105–123 (2019).Article 

Google Scholar 
Burt, R. S. Structural holes and good ideas. AJS 110(2), 349–399 (2004).
Google Scholar 
Oakes, J. Keeping track: How schools structure inequality (Yale University Press, 2005).
Google Scholar 
Park JJ et al. (2021) Who are you studying with? The role of diverse friendships in STEM and corresponding inequality. Res. High Educ. https://doi.org/10.1007/s11162-021-09638-8.Marsden, P. V. & Campbell, K. E. Measuring tie strength. Soc. Forces 63(2), 482–501 (1984).Article 

Google Scholar 
Mattie, H., Engø-Monsen, K., Ling, R. & Onnela, J. P. Understanding tie strength in social networks using a local “bow tie” framework. Sci. Rep. 8(1), 1–9 (2018).CAS 
Article 

Google Scholar 
Sørensen, A. B. Organizational differentiation of students and educational opportunity. Sociol. Educ. 43(4), 355–376 (1970).Article 

Google Scholar  More

Predictors of microbial carbon stocksWe used a machine learning modeling approach to predict soil microbial carbon from a set of environmental covariates. To account for stochastic variability, we ran a set of models to assess the importance of environmental factors, which showed that the contribution of each variable to the model fit differed between runs, with some overlap between a number of them (Fig. 2b). Mean annual temperature was always the most important variable, with soil organic carbon and soil pH following. Clay content, precipitation, land-cover type, nitrogen content, and sand content contributed roughly equally to explaining variations in microbial carbon. Finally, NDVI and elevation had the lowest variable importance. Coniferous forests had the highest and most variable predicted values of microbial carbon (Supplementary Figs. 1, 2), which can be explained by high soil organic matter and a thick litter layer26. Tropical forests also had fairly high values of microbial carbon, while shrublands and croplands had the lowest values26. We used partial prediction response curves to evaluate the direction and range of effect of the predictor variables (Supplementary Figs. 1, 2). In agreement with the variable importance measure, variables that scored high often showed strong effects on the predicted microbial carbon values, while variables with a low variable importance score (e.g., elevation, NDVI, and sand content) only showed smaller responses. The only exception was for precipitation, which had a relatively high variable importance, although the response curves only showed a weak effect of precipitation for forests and grasslands, with limited effect on other land-cover types (Supplementary Fig. 2). The importance of precipitation might also indicate that this relationship involves interactions with other variables7,28. Overall, the differences in microbial carbon between land-cover types showed mostly similar patterns across the range of variables. Soil organic carbon and nitrogen content had a positive and mostly linear effect on microbial carbon (Supplementary Fig. 1). In contrast, clay content, soil pH, and mean temperature had non-linear relationships, with high microbial carbon in the low range of these variables and a rapid decrease that reached an asymptote at low microbial carbon values for the higher portion of the range. Soil pH patterns showed a decrease in microbial carbon for values between 4.1 and 5.8, and a constant pattern between 5.8 and 8.6. Contrary to our expectations, we did not find a parabolic effect of soil pH on microbial carbon26. Instead, our model predicted higher values in very acidic soils with a pH below 5.2, which are rare globally and almost only found in central Amazonia. Similarly, locations with a clay content lower than 16.9% had higher values in microbial carbon, and then stabilized until 51.0%.Fig. 2: Microbial carbon stock spatial predictions and temporal trends.a Microbial carbon stock predictions for 2013. b Variable importance from 100 random forest model runs, calculated by the mean decrease in accuracy after variable permutation. Variables were ordered by the median variable importance. SOC soil organic carbon, NDVI normalized difference vegetation index. Center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; points, outliers. c Relative microbial carbon stocks rate of change in percentage per year.Full size imageMean temperature showed an interesting shift with much higher microbial carbon values with a mean annual temperature below zero, but had otherwise a limited effect on microbial carbon values in the rest of the range above zero up to 28.9 °C. Based on partial predictions (Supplementary Figs. 1–2), microbial carbon decreased monotonically with an increase in temperature (with all other variables fixed to their median), with the relationship being mostly stable for parts of the range. We observed an especially sharp decrease at around 0°C, which is in agreement with the patterns observed in the data. The reason for sites with a mean annual temperature below the freezing point to have higher microbial carbon stocks is not fully understood. This could be due to a regime shift in which microbial communities are in a semi-dormant state for a major part of the year35. Moreover, it could also be in part explained by the soil organic carbon content that follows a similar trend and accumulates in higher latitude soils9, thus promoting higher microbial carbon stocks. Within these cold, high organic carbon soils, large microbial populations can be maintained, due to the low temperature that reduces metabolic requirements35. In contrast, at higher temperatures, metabolic activity increases and requires more resources and nutrients to maintain microorganisms alive. Experimental evidence is divided about the effects of warming on microbial carbon18,36, highlighting the strong context-dependency of this relationship, although global observations show a clear pattern, where low-temperature sites have higher soil microbial carbon stocks. Despite this uncertainty, there is a strong indication that a warming soil would tend to lose organic carbon17,37, and subsequent patterns in microbial carbon can also be expected, because of the dependency on organic substrate9,26,38. These dynamics were observed in Melillo et al.39, where the warming of sites in a mid-latitude forest ecosystem led to a decrease in soil carbon, followed by a decrease in microbial carbon12.Even with predictions being made for each grid location separately, microbial carbon values showed distinctive patterns and transitions over the globe (Fig. 2a). While temporal changes took place, broad spatial patterns were relatively constant over the range of years studied (Supplementary Movie 1). The highest microbial carbon stock values ranging from 1.50 to 7.00 t ha−1 were found at high latitudes in the Northern Hemisphere in areas of coniferous forest. Tropical humid regions also showed high microbial carbon values between 0.50 and 1.50 t ha−1 in the Amazon Rainforest and Central Africa. The main regions with low microbial carbon below 0.30 t ha−1 were in Eastern South America, areas directly south of the Sahara Desert, East Africa, and most of Australia, all of which mostly correspond to shrublands. Cropland areas as seen in India were also predicted with low microbial carbon values ranging from 0.06 to 0.38 t ha−1. A strong latitudinal gradient was visible for North America and Eurasia, with the highest microbial carbon stocks at high latitude, medium values in temperate ecosystems, and decreasing values towards the Equator. Positive coastal effects can also be observed, mostly on the Eastern South American and Australian coasts. In total, we estimated that there is 4.34 Gt of microbial carbon in the 5 to 15 cm layer for the predicted areas. Using the coefficient of variation calculated from the variability assessment set of models, we found that predictions made for the Amazon Basin, Northern Canada, and South-East Russia were more variable than for other regions (Supplementary Fig. 3a). Especially Western Europe, Central North America, and South-East Asia, however, showed high stability in the predictions between model runs.Drivers of changeThe analysis of the rate of change of microbial carbon stocks over time revealed that large regions of the globe experienced important changes in soil microbial carbon stocks between 1992 and 2013, with contrasting patterns across areas, and overall larger regions showed a decrease rather than an increase in microbial carbon stocks (Fig. 2c and Supplementary Fig. 3b). To account for spatial differences in microbial carbon stocks, we calculated the relative rate of change in percentage for each location (Fig. 2c). When considering all predictable regions together, microbial carbon stocks in the 5–15 cm layer showed a decrease of 7.09 Mt per year, summing to 148.80 Mt between 1992 and 2013, or 3.4% of the global microbial carbon pool predicted (Supplementary Fig. 4a; p = 0.038). The main regions with a microbial carbon loss higher than 0.7 kg ha−1 y−1 were in Northern Canada and a large continuous region in North-Eastern Europe. These northern regions accounted for an important part of the global loss in microbial carbon stocks, with large areas that had both a high soil microbial carbon stock and a fast decrease (Figs. 3 and 4). Other areas of high loss were in the Amazon basin, Western Argentina, the USA East Coast, Southern South Africa, and South-East Russia. The main continuous region of microbial carbon increase above 0.7 kg ha−1 y−1 was in central Russia, with smaller regions present in India, Europe, Central North America, and parts of Africa. Besides these general patterns, predictions vary at the local scale, and they consider the effects of parameters including soil properties, elevation, and land-cover type, which change between neighbor locations and affect the observed patterns. This is especially visible in the Americas, where both increases and decreases happen side-by-side.Fig. 3: Status of microbial carbon stocks between 1992 and 2013.Bivariate plot comparing the relative microbial carbon stock rate of change (% per year) with the amount of microbial carbon stock. The status groups were allocated using quantile distributions.Full size imageFig. 4: Distribution and classification of point values from the locations in Fig. 3.The assignment of points into the 9 groups was performed using quantile distributions. Areas in dark red are especially vulnerable to climate and land-cover change.Full size imagePatterns in the relative rate of change have a lot in common with that of absolute change, with a few notable differences (Fig. 2c and Supplementary Fig. 3b). Both positive and negative stock changes in tropical and subtropical regions are more prominent in relative terms, as these regions typically have low microbial carbon stocks. Similarly, regions in Central Russia with high microbial carbon stocks show less decrease in relative terms. To assess how stable these trends are over time, we show the p values of the rate of change for the 22 years (Supplementary Fig. 3c). The largest region with low p values is associated with more significant trends in Western Russia, and corresponds to an area with a fast loss of microbial carbon. India and Central Russia show high p values, and are informative of high variability compared to the strength of the signal. Considering that only up to 22 data points are available for each grid location and that especially climatic conditions vary considerably from year to year, p values are only provided as a complementary assessment. We can summarize the global situation by combining the two maps of microbial carbon stocks and relative rate of change to categorize and define vulnerable locations that experienced a high loss of microbial carbon (Figs. 3 and 4), and where the provision of soil functions is potentially at risk.It is informative to look at regional trends, by grouping grid locations using the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) sub-regions, and assessing regional-scale changes in microbial carbon stocks (Fig. 5, Supplementary Table 1). The main regions that contributed to microbial carbon loss were North America with a decrease of 62.49 Mt of microbial carbon and Eastern Europe with 60.88 Mt over the studied period, although both trends had high yearly variability and were non-significant. The region with the highest increase was North-East Asia with a gain of 4.49 Mt, but this change was also non-significant. The Caribbean was the only region to show a significant increase in soil microbial carbon stocks over time (+2.1% over 22 y, p = 0.017), while significant decreases in stocks were found in North Africa (−4.1%, p  More

Sunquist, M. E. & Sunquist, F. C. Family Felidae. In Handbook of the Mammals of the World Vol. 1 (eds Wilson, D. E. & Mittermeier, R. A.) 54–170 (Lynx Editions, 2009).
Google Scholar 
Breitenmoser, U. et al. Action plan for the conservation of the Eurasian Lynx (Lynx lynx) in Europe. Nat. Environ. 112, 1–70 (2000).
Google Scholar 
Linnell, J. D. C., Breitenmoser, U., Breitenmoser-Würsten, C., Odden, J. & von Arx, M. Recovery of Eurasian lynx in Europe: What part has reintroduction played? In Reintroduction of Top-Order Predators (eds Hayward, M. W. & Somers, M. J.) 72–91 (Blackwell Publishing, 2009).Chapter 

Google Scholar 
Schmidt, K., Ratkiewicz, M. & Konopiński, M. K. The importance of genetic variability and population differentiation in the Eurasian lynx Lynx lynx for conservation, in the context of habitat and climate change. Mammal Rev. 41, 112–124 (2011).Article 

Google Scholar 
von Arx, M. et al. Status and conservation of the Eurasian lynx (Lynx lynx) in Europe in 2001. KORA Bericht 19, 1–330 (2004).
Google Scholar 
Kaczensky, P. et al. Status, management and distribution of large carnivores—Bear, lynx, wolf and wolverine in Europe. Part 1 – Europe summaries. Report: 1–72. A Large Carnivore Initiative for Europe Report prepared for the European Commission (2013).Chapron, G. et al. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science 346, 1517–1519 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Franz, K. W. & Romanowski, J. Revisiting the reintroduced Eurasian lynx population in Kampinos National Park Poland. Eur. Zool. J. 88, 966–979. https://doi.org/10.1080/24750263.2021.1968046 (2021).Article 

Google Scholar 
Bieniek, M., Wolsan, M. & Okarma, H. Historical biogeography of the lynx in Poland. Acta Zool. Cracov. 41, 143–167 (1998).
Google Scholar 
Jędrzejewski, W., Nowak, S., Schmidt, K. & Jędrzejewska, B. Wilk i ryś w Polsce: Wyniki inwentaryzacji w 2001 roku. Kosmos 51, 491–499 (2002).
Google Scholar 
Mysłajek, R., Kwiatkowska, I., Diserens, T., Haidt, A. & Nowak, S. Occurrence of Eurasian lynx in western Poland after two decades of strict protection. CATnews 69, 12–13 (2019).
Google Scholar 
Schmidt, K. Program ochrony rysia Lynx lynx w Polsce – Project. Strategia ochrony Rysia Warunkująca Trwałość Populacji Gatunku w Polsce (Warsaw University of Life Sciences, 2011).
Google Scholar 
Kaczensky, P. et al. Status, management and distribution of large carnivores—Bear, lynx, wolf and wolverine in Europe. Part 2: Country Species Summaries. Report: 1–200. A Large Carnivore Initiative for Europe Report prepared for the European Commission (2013).Breitenmoser, U. et al. Lynx lynx (errata version published in 2017). The IUCN Red List of Threatened Species 2015: e.T12519A121707666. Accessed 30 Oct 2021 (2015).Vandel, J.-M., Stahl, P., Herrenschmidt, V. & Marboutin, E. Reintroduction of the lynx into the Vosges mountain massif: From animal survival and movements to population development. Biol. Conserv. 131, 370–385. https://doi.org/10.1016/j.biocon.2006.02.012 (2006).Article 

Google Scholar 
Zimmermann, F., Breitenmoser-Würsten, C. & Breitenmoser, U. Importance of dispersal for the expansion of a Eurasian lynx Lynx lynx population in a fragmented landscape. Oryx 41, 358–368. https://doi.org/10.1017/s0030605307000712 (2007).Article 

Google Scholar 
Schmidt, K., Kowalczyk, R., Ozolins, J., Mannil, P. & Fickel, J. Genetic structure of the Eurasian lynx population in north-eastern Poland and the Baltic states. Conserv. Genet. 10, 497–501. https://doi.org/10.1007/s10592-008-9795-7 (2009).Article 

Google Scholar 
Ratkiewicz, M. et al. Long-range gene flow and the effects of climatic and ecological factors on genetic structuring in a large, solitary carnivore: The Eurasian Lynx. PLoS ONE 9, e115160. https://doi.org/10.1371/journal.pone.0115160 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Port, M. et al. Rise and fall of a Eurasian lynx (Lynx lynx) stepping-stone population in central Germany. Mammal Res. https://doi.org/10.1007/s13364-020-00527-6 (2020).Article 

Google Scholar 
Drouilly, M. & O’Riain, J. M. Rewilding the world’s large carnivores without neglecting the human dimension: A response to reintroducing the Eurasian lynx to southern Scotland, England and Wales. Biodivers. Conserv. 30, 917–923. https://doi.org/10.1007/s10531-021-02112-y (2021).Article 

Google Scholar 
Böer, M., Smielowski, J. & Tyrala, P. Reintroduction of the European lynx (Lynx lynx) to the Kampinoski National Park/Poland field experiment with zooborn individuals. Part I: Selection, adaptation and training. Der Zool. Garten 70, 304–312 (1994).
Google Scholar 
Jakimiuk, S. (ed.). Aktywna ochrona populacji nizinnej rysia w Polsce. 1–144 (WWF, Poland, 2015).Huck, M. et al. Habitat suitability, corridors and dispersal barriers for large carnivores in Poland. Acta Theriol. 55, 177–192 (2010).Article 

Google Scholar 
Niedziałkowska, M. et al. Environmental correlates of Eurasian lynx occurrence in Poland: Large scale census and GIS mapping. Biol. Conserv. 133, 63–69. https://doi.org/10.1016/j.biocon.2006.05.022 (2006).Article 

Google Scholar 
Schmidt, K., Kowalczyk, R., Ozolins, J., Männil, P. & Fickel, J. Genetic structure of the Eurasian lynx population in north-eastern Poland and the Baltic states. Conserv. Genet. 10, 497–501. https://doi.org/10.1007/s10592-008-9795-7 (2009).Article 

Google Scholar 
Tracz, M. et al. The return of lynx to northwestern Poland. CATnews 14, 43–44 (2021).
Google Scholar 
The Return of Lynx to north-west Poland. http://www.rysie.org/en/rysie-strona-glowna. Accessed on 31 Oct 2021.IUCN/SSC. Guidelines for Reintroductions and Other Conservation Translocations. Version 1.0. 1–57 (IUCN Species Survival Commission, 2013).Rueda, C., Jiménez, J., Palacios, M. J. & Margalida, A. Exploratory and territorial behavior in a reintroduced population of Iberian lynx. Sci. Rep. 11, 14148. https://doi.org/10.1038/s41598-021-93673-z (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Gusset, M. A framework for evaluating reintroduction success in carnivores: Lessons from African wild dogs. In Reintroduction of Top-Order Predators (eds Hayward, M. W. & Somers, M. J.) 307–320 (Blackwell Publishing, 2009).Chapter 

Google Scholar 
Breitenmoser, U. & Haller, H. Patterns of predation by reintroduced European Lynx in the Swiss Alps. J. Wildl. Manage. 57, 135–144 (1993).Article 

Google Scholar 
Drouilly, M. & O’Riain, M. J. Rewilding the world’s large carnivores without neglecting the human dimension. Biodivers. Conserv. 30, 917–923 (2021).Article 

Google Scholar 
Jędrzejewski, W. et al. Population dynamics (1869–1994), demography, and home ranges of the Lynx in Białowieza Primeval Forest (Poland and Belarus). Ecography 19, 122–138 (1996).Article 

Google Scholar 
Palmero, S. et al. Demography of a Eurasian lynx (Lynx lynx) population within a strictly protected area in Central Europe. Sci. Rep. 11, 19868. https://doi.org/10.1038/s41598-021-99337-2 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Maran, T., Põdra, M., Põlma, M. & Macdonald, D. The survival of captive-born animals in restoration programmes: Case study of the endangered European mink Mustela lutreola. Biol. Conserv. 142, 1685–1692 (2009).Article 

Google Scholar 
Moehrenschlager, A. & Macdonald, D. W. Movement and survival parameters of translocated and resident swift foxes Vulpes velox. Anim. Conserv. 6, 199–206 (2003).Article 

Google Scholar 
Böer, M., Reklewski, J., Śmiełowski, J. & Tyrała, P. Reintroduction of the European Lynx to the Kampinoski Nationalpark/Poland: A field experiment with zooborn individuals. Part III: Demographic development of the population from December 1993 until January 2000. Der Zool. Garten 70, 304–312 (2000).
Google Scholar 
Jule, K. R., Leaver, L. A. & Lea, E. G. L. The effects of captive experience on reintroduction survival in carnivores: A review and analysis. Biol. Conserv. 141, 355–363 (2008).Article 

Google Scholar 
Hellstedt, P. & Kallio, E. R. Survival and behaviour of captive-born weasels (Mustela nivalis nivalis) released in nature. J. Zool. 266, 37–44 (2005).Article 

Google Scholar 
Devineau, O. et al. Evaluating the Canada lynx reintroduction programme in Colorado: Patterns in mortality. J. Appl. Ecol. 47, 524–531 (2010).Article 

Google Scholar 
Lengger, J., Breitenmoser, U. & Sliwa, A. EAZA breeding programmes as sources for lynx reintroductions. CATnews 14, 76–77 (2021).
Google Scholar 
Reading, P. R. & Clark, T. W. Carnivore introductions: An interdisciplinary Examination. In Carnivore Behavior, Ecology and Evolution (ed. Gittleman, J. L.) 296–336 (Cornell University Press, 1996).
Google Scholar 
McCarthy, M. A., Armstrong, D. P. & Runge, M. C. Adaptive management of reintroduction. In Reintroduction Biology: Integrating Science and Management (eds Ewen, J. G. et al.) 256–289 (Wiley-Blackwell, 2012).Chapter 

Google Scholar 
Bremner-Harrison, S., Prodohl, P. A. & Elwood, R. W. Behavioural trait assessment as a release criterion: Boldness predicts early death in a reintroduction programme of captive-bred swift fox (Vulpes velox). Anim. Conserv. 7, 313–320 (2004).Article 

Google Scholar 
Harrington, L., Põdra, M., Macdonald, D. & Maran, T. Post-release movements of captive-born European mink Mustela lutreola. Endanger. Species Res. 24, 137–148 (2014).Article 

Google Scholar 
Andrén, H. et al. Survival rates and causes of mortality in Eurasian lynx (Lynx lynx) in multi-use landscapes. Biol. Conserv. 131, 23–32 (2006).Article 

Google Scholar 
Heurich, M. et al. Illegal hunting as a major driver of the source-sink dynamics of a reintroduced lynx population in Central Europe. Biol. Conserv. 224, 355–365 (2018).Article 

Google Scholar 
Schmidt-Posthaus, H., Breitenmoser, Ch., Posthaus, H., Bacciarini, L. & Breitenmoser, U. Causes of mortality in reintroduced Eurasian lynx in Switzerland. J. Wildl. Dis. 38, 84–92 (2002).PubMed 
Article 

Google Scholar 
Kołodziej-Sobocińska, M., Zalewski, A. & Kowalczyk, R. Sarcoptic mange vulnerability in carnivores of the Białowieża Primeval Forest, Poland: underlying determinant factors. Ecol. Res. 29, 237–244 (2014).Article 

Google Scholar 
Holt, G. & Berg, C. Sarcoptic mange in red fox and other wild carnivores in Norway. Nor Veterinaertidsskr 102, 427–432 (1990).
Google Scholar 
Mörner, T. Sarcoptic mange in Swedish wildlife. Rev. Sci. Tech. Off. Int. Epiz. 11, 1115–1121 (1992).Article 

Google Scholar 
Ryser-Degiorgis, M. P. et al. Notoedric and sarcoptic mange in free-ranging lynx from Switzerland. J. Wildl. Dis. 38, 228–232 (2002).PubMed 
Article 

Google Scholar 
Soulsbury, C. D. et al. The impact of sarcoptic mange Sarcoptes scabiei on the British fox Vulpes vulpes population. Mam. Rev. 37, 278–296 (2007).
Google Scholar 
Garrote, G., Fernández-López, J., López, G., Ruiz, G. & Simón, M. A. Prediction of Iberian lynx road–mortality in southern Spain: A new approach using the MaxEnt algorithm. Anim. Biodivers. Conserv. 41, 217–225 (2018).Article 

Google Scholar 
Bencin, H., Prange, S., Rose, Ch. & Popescu, V. Roadkill and space use data predict vehicle-strike hotspots and mortality rates in a recovering bobcat (Lynx rufus) population. Sci. Rep. 9, 15391 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bouyer, Y. et al. Tolerance to anthropogenic disturbance by a large carnivore: The case of Eurasian lynx in south-eastern Norway. Anim. Conserv. https://doi.org/10.1111/acv.12168 (2014).Article 

Google Scholar 
López-Bao, J. V. et al. Eurasian lynx fitness shows little variation across Scandinavian human-dominated landscapes. Sci. Rep. 9, 8903 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Cimatti, M. et al. Large carnivore expansion in Europe is associated with human population density and land cover changes. Divers. Distrib. 27, 602–617 (2021).Article 

Google Scholar 
Wegner, M. (ed.). Statistical Yearbook of Zachodniopomorskie Voivodship. 1–213 (Statistical Office in Szczecin, 2020).Górny, M., Schmidt, K. & Kowalczyk, R. Analiza przydatności środowiska dla reintrodukcji rysia w północno-zachodniej Polsce oraz prognoza i perspektywy funkcjonowania populacji. Expert study under the project POIS.02.04.00–0143/16 “Return of the lynx to northwestern Poland”. 1–25.Woodford, M. H., Keet, D. F. & Bengis, R. G. Post-mortem Procedures for Wildlife Veterinarians and Field Biologists. 1–55 (IUCN Species Survival Commission (SSC) & Veterinary Specialist Group, Care for the Wild International, World Organisation for Animal Health (OIE), 2000).Fain, A. Ѐtude de la variabilitѐ de Sarcoptes scabiei avec une rѐvision des Sarcoptidae. Acta Zool. Pathol. Antverp 47, 1–196 (1968).
Google Scholar 
Kaplan, E. L. & Meier, P. Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53, 457–481 (2012).MathSciNet 
MATH 
Article 

Google Scholar 
Therneau, M., Lumley, T., Atkinson, E. & Crowson, C. Survival Analysis. R Package Version 3.2-13. http://CRAN.R-project.org/package=survival (2021).Kassambara, A., Kosinski, M., Biecek, P. & Scheipl, F. survminer. Drawing Survival Curves using ‘ggplot2’. R package version 0.4.9. http://CRAN.R-project.org/package=survminer (2021).Dardis, C. survMisc. Miscellaneous Functions for Survival Data. R package version 0.5.5. http://CRAN.R-project.org/package=survMisc (2018).R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing). https://www.R-project.org (2021).Snedecor, G. W. & Cochran, W. G. Statistical Methods 7th edn. (Iowa State University Press, 1980).MATH 

Google Scholar 
Cox, D. R. Regression models and life tables (with discussion). J. R. Stat. Soc. B. 34, 187–220 (1972).MATH 

Google Scholar 
Bradburn, M. J., Clark, T. G., Love, S. B. & Altman, D. G. Survival Analysis Part II: Multivariate data analysis: An introduction to concepts and methods. Br. J. Cancer. 89, 431–436 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wald, A. Tests of statistical hypothesis concerning several parameters when the number of observations is large. Trans. Am. Math. Soc. 54, 426–482 (1943).MATH 
Article 

Google Scholar 
Aitchison, J. & Silvey, S. D. Maximum likelihood estimation of parameters subject to restraints. Ann. Math. Stat. 29, 813–828 (1958).MathSciNet 
MATH 
Article 

Google Scholar 
Mantel, N. Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemother. Rep. 50, 163–170 (1966).CAS 
PubMed 

Google Scholar  More