Ecology

1.Krause, J. & Ruxton, G. D. Living in Groups (Oxford University Press, Oxford, 2002).
Google Scholar 
2.Ward, A. & Webster, M. Sociality: The Behaviour of Group-Living Animals (Springer, Berlin, 2016).
Google Scholar 
3.Costa, J. T. & Ross, K. G. Fitness effects of group merging in a social insect. Proc. R. Soc. B 270, 1697–1702 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
4.Nicieza, A. G. Interacting effects of predation risk and food availability on larval anuran behaviour and development. Oecologia 123, 497–505 (2000).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Dugatkin, L. A. Animal cooperation among unrelated individuals. Naturwissenschaften 89, 533–541 (2002).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Clutton-Brock, T. Breeding together: Kin selection and mutualism in cooperative vertebrates. Science 69, 69–72 (2002).ADS 
Article 

Google Scholar 
7.Haney, B. R. & Fewell, J. H. Ecological drivers and reproductive consequences of non-kin cooperation by ant queens. Oecologia 187, 643–655 (2018).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Tschinkel, W. R. Brood raiding and the population dynamics of founding and incipient colonies of the fire ant, Solenopsis invicta. Ecol. Entomol. 17, 179–188 (1992).Article 

Google Scholar 
9.Clark, R. M. & Fewell, J. H. Transitioning from unstable to stable colony growth in the desert leafcutter ant Acromyrmex versicolor. Behav. Ecol. Sociobiol. https://doi.org/10.1007/s00265-013-1632-4 (2013).Article 

Google Scholar 
10.Cole, B. The ecological setting of social evolution: the demography of ant populations. In Organization of Insect Societies: From Genome to Sociocomplexity (eds Gadau, J. & Fewell, J.) 74–104 (Harvard University Press, Cambridge, 2009).
Google Scholar 
11.Kang, Y., Clark, R., Makiyama, M. & Fewell, J. Mathematical modeling on obligate mutualism: Interactions between leaf-cutter ants and their fungus garden. J. Theor. Biol. 289, 116–127 (2011).MathSciNet 
PubMed 
MATH 
Article 
PubMed Central 

Google Scholar 
12.Karsai, I. & Wenzel, J. Productivity, individual-level and colony-level flexibility, and organization of work as consequences of colony size. J. Theor. Biol. 289, 116–127 (1998).
Google Scholar 
13.Tibbetts, E. A. & Reeve, H. K. Benefits of foundress associations in the paper wasp Polistes dominulus : increased productivity and survival, but no assurance of fitness returns. Behav. Ecol. 14, 510–514 (2003).Article 

Google Scholar 
14.Cahan, S. & Julian, G. E. Fitness consequences of cooperative colony founding in the desert leaf-cutter ant Acromyrmex versicolor. Behav. Ecol. 10, 585–591 (1999).Article 

Google Scholar 
15.Tschinkel, W. R. Colony growth and the ontogeny of worker polymorphism in the fire ant, Solenopsis invicta. Behav. Ecol. Sociobiol. 22, 103–115 (1988).Article 

Google Scholar 
16.Choe, J. & Perlman, D. Social conflict and cooperation among founding queens in ants (Hymenoptera: Formicidae). In Social Behavior in Insects and Arachnids 392–406 (Cambridge University Press, Cambridge, 1997).
Google Scholar 
17.Bernasconi, G. & Strassmann, J. E. Cooperation among unrelated individuals: The ant foundress case. Trends Ecol. Evol. 14, 477–482 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Hartke, T. R. & Rosengaus, R. B. Costs of pleometrosis in a polygamous termite. Proc. R. Soc. B 280, 20122563 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Gamboa, G. J. Intraspecific defense: Advantage of social cooperation among paper wasp foundresses. Science 199, 1463–1466 (1978).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Kolmer, K. & Heinze, J. Rank orders and division of labour among unrelated cofounding ant queens. Proc. R. Soc. B 267, 1729–1734 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Clark, R. M. & Fewell, J. H. Social dynamics drive selection in cooperative associations of ant queens. Behav. Ecol. 25, 117–123 (2014).Article 

Google Scholar 
22.Johnson, R. A. Colony founding by pleometrosis in the semiclaustral seed-harvester ant Pogonomyrmex californicus (Hymenoptera: Formicidae ). Anim. Behav. 68, 1189–1200 (2004).Article 

Google Scholar 
23.Tschinkel, W. R. & Howard, D. F. Colony founding by pleometrosis in the fire ant Solenopsis invicta. Behav. Ecol. Sociobiol. 12, 103–113 (1983).Article 

Google Scholar 
24.Rissing, S. W. & Pollock, G. B. Queen aggression, pleometrotic advantage and brood raiding in the ant Veromessor pergandei (Hymenoptera: Formicidae). Anim. Behav. 35, 975–981 (1987).Article 

Google Scholar 
25.Deslippe, R. J. & Savolainen, R. Colony Foundation and Polygyny in the Ant Formica podzolic. Behav. Ecol. Sociobiol. 37, 1–6 (1995).Article 

Google Scholar 
26.Bourke, A. F. G. & Franks, N. R. Social Evolution in Ants (Princeton University Press, Princeton, 1995).
Google Scholar 
27.Hölldobler, B. & Wilson, E. The Ants (Harvard University Press, Cambridge, 1990).
Google Scholar 
28.Mintzer, A. Primary polygyny in the ant Atta texana: number and weight of females nad colony foundation success in the laboratory. Insect Soc 34, 108–117 (1987).Article 

Google Scholar 
29.Heinze, J. & Hölldobler, B. Ants in the cold. Memorab. Zool. 48, 99–108 (1994).
Google Scholar 
30.Helms Cahan, S. Cooperation and conflict in ant foundress associations: Insights from geographical variation. Anim. Behav. 61, 819–825 (2001).Article 

Google Scholar 
31.Heinze, J. & Rüppel, O. The frequency of multi-queen colonies increases in a Nearctic ant. Ecol Entomol 39, 527–529 (2014).Article 

Google Scholar 
32.Brown, M. Semi-claustral founding and worker behaviour in gynes of Messor andrei. Insect Soc. 46, 194–195 (1999).Article 

Google Scholar 
33.Oster, G. & Wilson, E. Caste and Ecology in the Social Insects (Princeton University Press, Princeton, 1978).
Google Scholar 
34.Hölldobler, B. & Wilson, E. O. The Superorganism: The Beauty, Elegance, and Strangeness of Insect Societies (W.W. Norton & Company, New York, 2009).
Google Scholar 
35.Holbrook, C. T., Eriksson, T. H., Overson, R. P., Gadau, J. & Fewell, J. H. Colony-size effects on task organization in the harvester ant Pogonomyrmex californicus. Insectes Soc. 60, 191–201 (2013).Article 

Google Scholar 
36.Thomas, M. L. & Elgar, M. A. Colony size affects division of labour in the ponerine ant Rhytidoponera metallica. Naturwissenschaften 90, 88–92 (2003).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Jeanson, R., Fewell, J. H., Gorelick, R. & Bertram, S. M. Emergence of increased division of labor as a function of group size. Behav. Ecol. Sociobiol. 62, 289–298 (2007).Article 

Google Scholar 
38.Holbrook, C. T., Barden, P. M. & Fewell, J. H. Division of labor increases with colony size in the harvester ant Pogonomyrmex californicus. Behav. Ecol. 22, 960–966 (2011).Article 

Google Scholar 
39.Dornhaus, A., Powell, S. & Bengston, S. Group size and its effects on collective organization. Ann. Rev. Entomol. 57, 123–141 (2012).CAS 
Article 

Google Scholar 
40.Wilson, E. Colony ontogeny of Atta cephalotes. Behav. Ecol. Sociobiol. 7, 143–156 (1983).Article 

Google Scholar 
41.Jeanne, R. Social complexity in the Hymenoptera, with special attention to the wasps. In Genes, Behaviors, and Evolution of Social Insects (eds Kikuchi, T. et al.) 81–130 (Hokkaido University Press, Sapporo, 2003).
Google Scholar 
42.Gordon, D. M. The organization of work in social insect colonies. Nature 380, 121–124 (1996).ADS 
CAS 
Article 

Google Scholar 
43.Mailleux, A., Deneubourg, J. & Detrain, C. How does colony growth influence communication in ants?. Insectes Soc. 50, 24–31 (2003).Article 

Google Scholar 
44.Overson, R., Fewell, J. & Gadau, J. Distribution and origin of intraspecific social variation in the California harvester ant Pogonomyrmex californicus. Insectes Soc. 63, 531–541 (2016).Article 

Google Scholar 
45.Haney, B. R. et al. Ecological Drivers and Reproductive Consequences of Queen Cooperation in the California Harvester Ant Pogonomyrmex Californicus (Arizona State University, Tempe, 2017).
Google Scholar 
46.Bhatkar, A. & Whitcomb, W. H. Artificial diet for rearing various species of ants. Fla. Entomol. 53, 229–232 (1970).Article 

Google Scholar 
47.Cahan, S. H. & Fewell, J. H. Division of labor and the evolution of task sharing in queen associations of the harvester ant Pogonomyrmex californicus. Behav. Ecol. Sociobiol. 56, 9–17 (2004).Article 

Google Scholar 
48.Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, Thousand Oaks, 2019).
Google Scholar 
49.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
50.Therneau, T. A Package for Survival Analysis in R. R Package Version 3.2-7. (2020).51.Therneau, T. coxme: Mixed Effects Cox Models. R Package Version 2.2-16.52.Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).MathSciNet 
PubMed 
MATH 
Article 
PubMed Central 

Google Scholar 
53.Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.17. (2020).54.Riehl, C. & Riehl, C. Evolutionary routes to non-kin cooperative breeding in birds. Proc. R. Soc. B 278, 20132242 (2013).
Google Scholar 
55.Clutton-Brock, T. Cooperation between non-kin in animal societies. Nature 461, 51–57 (2009).ADS 
Article 
CAS 

Google Scholar 
56.Emlen, S. T. The evolution of helping. I. An Ecological Constraints Model. Am. Nat. 119, 29–39 (1982).Article 

Google Scholar 
57.Heg, D., Bachar, Z., Brouwer, L. & Taborsky, M. Predation risk is an ecological constraint for helper dispersal in a cooperatively breeding cichlid. Proc. R. Soc. B 271, 2367–2374 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
58.Kennedy, P., Higginson, A. D., Radford, A. N. & Sumner, S. Altruism in a volatile world. Nature 555, 359–362 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Gasperin, O. D., Blacher, P., Grasso, G. & Chapuisat, M. Winter is coming: Harsh environments limit independent reproduction of cooperative-breeding queens in a socially polymorphic ant. Biol. Lett. 16, 20190730 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
60.Lukas, D. & Clutton-Brock, T. Climate and the distribution of cooperative breeding in mammals. R. Soc. Open Sci. 4, 160897 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Jetz, W. & Rubenstein, D. R. Environmental uncertainty and the global biogeography of cooperative breeding in birds. Curr. Biol. 21, 72–78 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Cornwallis, C. K. et al. Cooperation facilitates the colonization of harsh environments. Nat. Ecol. Evol. 1, 1–10 (2017).Article 

Google Scholar 
63.Heinze, J. Queen-queen interactions in polygynous ants. In Queen Number and Sociality in Insects (ed. Keller, L.) 262–293 (Oxford University Press, Oxford, 1993).
Google Scholar 
64.Schmid-Hempel, P. & Crozier, R. H. Polyandry versus polygyny versus parasites. Phil. Trans. R. Soc. B 354, 507–515 (1999).Article 

Google Scholar 
65.Hughes, W. O. H. & Boomsma, J. J. Genetic diversity and disease resistance in leaf-cutting ant societies. Evolution 58, 1251–1260 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Mattila, H. R. & Seeley, T. D. Genetic diversity in honey productivity and fitness. Science 317, 362–365 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Seeley, T. D. & Tarpy, D. R. Queen promiscuity lowers disease within honeybee colonies. Proc. R. Soc. B 274, 67–72 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
68.Whitehorn, P. R., Tinsley, M. C., Brown, M. J. F., Darvill, B. & Goulson, D. Genetic diversity, parasite prevalence and immunity in wild bumblebees. Proc. R. Soc. B 278, 1195–1202. https://doi.org/10.1098/rspb.2010.1550 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
69.Johnson, R. A. Water loss in desert ants: Caste variation and the effect of cuticle abrasion. Physiol. Entomol. 25, 48–53 (2000).Article 

Google Scholar 
70.Reber, A., Purcell, J., Buechel, S. D., Buri, P. & Chapuisat, M. The expression and impact of antifungal grooming in ants. J. Evol. Biol. 24, 954–964 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Wilson, S. N. et al. How emergent social patterns in allogrooming combat parasitic infections. Front. Ecol. Evol. 8, 54 (2020).Article 

Google Scholar 
72.Hutchins, M. & Barash, D. Grooming in primates: Implications for its utilitarian function. Primates 17, 145–150 (1976).Article 

Google Scholar 
73.Lobo, J., Bettencourt, L. M. A., Strumsky, D. & West, G. B. Urban scaling and the production function for cities. PLoS ONE 8, e58407–e58407 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
74.Bettencourt, L., Lobo, J., Helbing, D., Kuhnert, C. & West, G. Growth, innovation, scaling and the pace of life in cities. PNAS 104, 7301–7306 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
75.Bondi, A. Characteristics of scalability and their impact on performance. 195–203 (2000).76.Duboc, L., Rosenblum, D. & Wicks, T. A framework for characterization and analysis of software system scalability. Proceedings of the European Software Engineering Conference 375–384 (2007).77.Johnson, R. A. Semi-claustral colony founding in the seed-harvester ant Pogonomyrmex californicus: A comparative analysis of colony founding strategies. Oecologia 132, 60–67 (2002).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
78.Wilson, E. The Insect Societies (Harvard University Press, Cambridge, 1971).
Google Scholar 
79.Seid, M. A. & Traniello, J. F. A. Age-related repertoire expansion and division of labor in Pheidole dentata (Hymenoptera: Formicidae): A new perspective on temporal polyethism and behavioral plasticity in ants. Behav. Ecol. Sociobiol. 60, 631–644 (2006).Article 

Google Scholar 
80.Seeley, T. Adaptive significance of the age polyethism schedule in honey bee colonies. Behav. Ecol. Sociobiol. 11, 287–293 (1982).Article 

Google Scholar  More

Based on the composition of the dated samples, two calibration procedures must be undertaken to transform the radiocarbon (14C) dates into accurate calendar dates. The 14C dates of the wood, newspaper and textile fragments were calibrated using the IntCal20 atmospheric calibration curve22 (Table 2, Fig. 4). All results are statistically consistent and give calibrated dates between 1646 and 1950 AD. The combination of the three dates provides the interval 1667–1950 AD. The elongated distribution is due to the flat shape of the calibration curve for this period23. Nevertheless, the results show that all the wood, newspaper and textile samples found inside the statue definitively date after 1650.Figure 4Calibrated 14C dates for wood, textile and paper samples taken from the Flora bust (in grey). The statistical combination of the three dates in green gives the interval of 1667–1950 AD. The χ2 test value of T = 4.1 (5% 6.0) shows their consistency.Full size imageTo calibrate the 14C dates obtained from the wax samples, the composition of the material has to be carefully considered. The Flora bust and “Leda and the swan” relief waxes are principally composed of spermaceti from a sperm whale that lives in the ocean, mixed with minor amounts of beeswax and other organic compounds extracted from terrestrial animals. The wax is thus primarily composed of marine material with some of terrestrial origin. The 14C source of terrestrial animals is in equilibrium with the atmosphere whereas that of whales 14C source is subject to the Marine Reservoir Effect (MRE)24. The MRE affects 14C dates since carbon consumed by organisms in the ocean is older than that consumed on land. Because the wax used for the sculptures is composed of carbon from different sources, other than just atmospheric carbon, the 14C measurements produce apparent old uncalibrated radiocarbon ages from 340 to 420 BP (Table 3) and a correction is needed to compensate this effect in calibration calculations.The mixture of marine and terrestrial sources in the wax requires the use of a combination of two calibration curves: IntCal20 atmospheric22 and Marine20 marine25, both weighted by the proportion of terrestrial and marine materials. In the case of the Flora bust, the determination of the exact ratio of spermaceti wax and terrestrial wax was not feasible because only a few samples of wax were available for analysis.To further complicate the procedure, the location of the marine source must be known to accurately calibrate marine material. Whales travel long distances, integrating the reservoir ages of the different water masses along their paths making that the determination of the marine reservoir age (MRA) for whale material 14C dates difficult. The global-average (MRA) of surface waters is c. 500 years25 but values range from about 400 years in subtropical oceans to over 1000 years in the poles. According to our knowledge no MRA has been reported for sperm whale (Physeter Macrocephalus L.) bone or for spermaceti except the estimation of 300 ± 200 years made by Freundlich5. Various values can be found for other cetacean materials in literature. One of the more complete studies, which is based on the analysis of 21 whales caught in Norway during the 19th c., proposed an average marine reservoir age (MRA) of 370 ± 30 years for various whales from the North Atlantic26. Previous publications recommended to use a c. 200 years marine reservoir correction for bowhead whales from Canadian Artic27, or determined a mean value correction of 320 ± 35 years for marine mammals, including whales, living near Sweden28 or c. 350 years correction for a 17th c. Finnback whale bone collected in Spitsbergen29. Additionally, based on an exhaustive compilation of published marine mammal radiocarbon dates, both live-harvested materials and subfossils, from the Canadian Arctic Archipelago, Furze et al.30 provided reservoir offset values for beluga (D. leucas) and bowhead (B. mysticetus) corresponding to a MRA of 570 ± 95 years for the latter.Calibration of the 14C dates of the 19th c. wax objects made by Richard Cockle LucasSince the spermaceti MRA value and the spermaceti wax content cannot be determined precisely, another approach was developed to calibrate the 14C dates of the Flora bust. This approach is based on the well-dated wax relief, “Leda and the Swan”. This relief was created by R. C. Lucas in 1850 and the chemical analysis has shown that its composition is similar to that of the Flora bust (Figs. 2, 3). The “Leda and the Swan” relief was used as reference to determine the appropriate combination of the IntCal20 and Marine20 calibration curves to be applied to the Flora wax material. The percentage of each curve was established by adjusting the calibrated date distribution of the Leda relief on both sides of the year 1850. To obtain this result, a combination of 15% atmospheric/85% marine curves was selected with an uncertainty of 10% to reflect material variability. The resulting distribution of dates is from 1704 to 1950 AD (Table 3, lower part of Fig. 5) which is not very precise, but this method has the advantage to take into account uncertainties on spermaceti MRA and on the spermaceti/beeswax content ratio. Figure 5 also shows that the results calibrated with the IntCal20 atmospheric curve are inconsistent with the known date of creation of the “Leda and the Swan” relief, which confirms the presence of marine material in the wax.Figure 5Calibrated 14C dates for the wax samples of the Leda and the Swan relief using atmospheric curve only, in light grey and light green, give dates out of range of the known date of creation of this artwork made by Lucas in 1850. A calibration of the same samples with a combination of 15% atmospheric/85% marine (± 10%) calibration curves, in dark grey, gives dates in the time frame of the relief’s creation in 1850. The statistical combination of the three dates, in blue, gives the interval of 1704–1950 AD.Full size imageCalibration of the 14C dates of the Flora bustThe same combination of atmospheric and marine calibration curves was applied to calibrate the 14C dates obtained for wax samples taken from six different locations at the surface and inside of the Flora bust because the composition of the Flora is similar to that of the Lucas wax objects. The results are presented in Fig. 6 and Table 3. All the dates are after 1704 AD, with a statistical combination on the six dates of 1712–1950. Uncertainty on the calibration curves lead to a broad interval for the dates of the Flora wax with about two centuries precision. Calibrated dates obtained on the wax samples, when the MRE is taken into account, agree with those of the wood, paper and textile samples, which confirms the strength and validity of our approach. All of the analysed constituents of the Flora bust are dated after 1700 AD, precluding the bust from being created in the Renaissance period.Figure 6Calibrated 14C dates for the wax samples of the Flora bust using a combination of 15% atmospheric/85% marine (± 10%) calibration curves (in dark grey). The statistical combination of the three dates in blue gives the interval of 1707–1950 AD.Full size imageChemical analyses and absolute dating were performed on different materials and several wax samples taken from the surface and inner parts from the Flora bust as well as on two dated wax reliefs made by the British 19th c. sculptor Richard Cockle Lucas, who some claim is the author of the Flora bust. The Lucas object “Leda and the swan” dated at 1850 could only be accurately dated using 14C measurements when a mixed terrestrial and marine calibration was taken into consideration because the wax is primarily made from spermaceti with minor amount of beeswax. Because the spermaceti was extracted from sperm whales living in deep and shallow seawaters, 14C dating must to consider the MRE. The Flora bust was shown to have an extremely similar composition to the Lucas object. Thus the same calibration correction procedure was applied to the uncalibrated 14C dates of the Flora bust. This new procedure involved calibrating of the 14C dates by considering a combination of 85% marine/15% atmospheric curves. The result dates the Flora materials to the 18-19th c., which proves that the bust was not produced during the Renaissance, and therefore cannot be attributed to Leonardo. This study also illustrates that 14C dating must take into account the heterogeneity and diversity of art objects, some of which may contain uncommon materials such as spermaceti wax.While it is somewhat disappointing to learn that the bust cannot be attributed to Leonardo, this information does provide useful insight into history. The sperm whale population suffered a serious decline in the 1740s when sperm whaling started on an industrial scale. The use of spermaceti in art objects shows how widespread the use of sperm whale products was and highlights the whaling industry’s importance during the industrial revolution. Other culturally significant objects may also be composed of materials that show the importance of certain industries or materials. There is clearly a need for art historical research to integrate natural science investigations in order to provide information allowing an improved attribution of art works and allowing to give another dimension to the historical value of such objects. More

1.Biard, C., Monceau, K., Motreuil, S. & Moreau, J. Interpreting immunological indices: the importance of taking parasite community into account. An example in blackbirds Turdus merula. Methods Ecol. Evol. 6, 960–972. https://doi.org/10.1111/2041-210x.12371 (2015).Article 

Google Scholar 
2.Boughton, R. K., Joop, G. & Armitage, S. A. O. Outdoor immunology: methodological considerations for ecologists. Funct. Ecol. 25, 81–100. https://doi.org/10.1111/j.1365-2435.2010.01817.x (2011).Article 

Google Scholar 
3.Maizels, R. M. & Nussey, D. H. Into the wild: digging at immunology’s evolutionary roots. Nat. Immunol. 14, 879–883. https://doi.org/10.1038/ni.2643 (2013).CAS 
Article 
PubMed 

Google Scholar 
4.Martin, L. B., Weil, Z. M. & Nelson, R. J. Refining approaches and diversifying directions in ecoimmunology. Integr. Comp. Biol. 46, 1030–1039. https://doi.org/10.1093/icb/icl039 (2006).CAS 
Article 
PubMed 

Google Scholar 
5.Johnson, W. et al. Pathogenic and humoral immune responses to porcine reproductive and respiratory syndrome virus (PRRSV) are related to viral load in acute infection. Vet. Immunol. Immunopathol. 102, 233–247. https://doi.org/10.1016/j.vetimm.2004.09.010 (2004).CAS 
Article 
PubMed 

Google Scholar 
6.Ortiz, R. H. et al. Differences in virulence and immune response induced in a murine model by isolates of Mycobacterium ulcerans from different geographic areas. Clin. Exp. Immunol. 157, 271–281. https://doi.org/10.1111/j.1365-2249.2009.03941.x (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
7.Sela, U., Euler, C. W., da Rosa, J. C. & Fischetti, V. A. Strains of bacterial species induce a greatly varied acute adaptive immune response: the contribution of the accessory genome. PLoS Pathog. https://doi.org/10.1371/journal.ppat.1006726 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
8.Skjesol, A. et al. IPNV with high and low virulence: host immune responses and viral mutations during infection. Virol. J. https://doi.org/10.1186/1743-422x-8-396 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
9.Hornef, M. W., Wick, M. J., Rhen, M. & Normark, S. Bacterial strategies for overcoming host innate and adaptive immune responses. Nat. Immunol. 3, 1033–1040. https://doi.org/10.1038/ni1102-1033 (2002).CAS 
Article 
PubMed 

Google Scholar 
10.Fassbinder-Orth, C. A. et al. Immunoglobulin detection inwild birds: effectiveness of three secondary anti-avian IgY antibodies in direct ELISAs in 41 avian species. Methods Ecol. Evol. 7, 1174–1181. https://doi.org/10.1111/2041-210x.12583 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
11.Janeway, C. Immunobiology: The Immune System in Health and Disease (Garland Science, 2005).
Google Scholar 
12.Coltman, D. W., Pilkington, J., Kruuk, L. E. B., Wilson, K. & Pemberton, J. M. Positive genetic correlation between parasite resistance and body size in a free-living ungulate population. Evolution 55, 2116–2125 (2001).CAS 
Article 

Google Scholar 
13.Hayward, A. D. et al. Natural selection on individual variation in tolerance of gastrointestinal nematode infection. PLoS. Biol. https://doi.org/10.1371/journal.pbio.1001917 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
14.Johnson, J. S. et al. Antibodies to Pseudogymnoascus destructans are not sufficient for protection against white-nose syndrome. Ecol. Evol. 5, 2203–2214. https://doi.org/10.1002/ece3.1502 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
15.Fischer, J. R., Stallknecht, D. E., Luttrell, M. P., Dhondt, A. A. & Converse, K. A. Mycoplasmal conjunctivitis in wild songbirds: the spread of a new contagious disease in a mobile host population. Emerg. Infect. Dis. 3, 69–72. https://doi.org/10.3201/eid0301.970110 (1997).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
16.Luttrell, M. P., Fischer, J. R., Stallknecht, D. E. & Kleven, S. H. Field investigation of Mycoplasma gallisepticum infections in house finches (Carpodacus mexicanus) from Maryland and Georgia. Avian Dis. 40, 335–341 (1996).CAS 
Article 

Google Scholar 
17.Delaney, N. F. et al. Ultrafast evolution and loss of CRISPRs following a host shift in a novel wildlife pathogen, Mycoplasma gallisepticum. Plos Genet. https://doi.org/10.1371/journal.pgen.1002511 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
18.Dhondt, A. A., Tessaglia, D. L. & Slothower, R. L. Epidemic mycoplasmal conjunctivitis in house finches from Eastern North America. J. Wildl. Dis. 34, 265–280 (1998).CAS 
Article 

Google Scholar 
19.Nolan, P. M., Hill, G. E. & Stoehr, A. M. Sex, size, and plumage redness predict house finch survival in an epidemic. Proc. R. Soc. Lond. Ser. B Biol. Sci. 265, 961–965 (1998).Article 

Google Scholar 
20.Bonneaud, C. et al. Rapid evolution of disease resistance is accompanied by functional changes in gene expression in a wild bird. Proc. Natl. Acad. Sci. U.S.A. 108, 7866–7871 (2011).ADS 
CAS 
Article 

Google Scholar 
21.Bonneaud, C. et al. Rapid antagonistic coevolution in an emerging pathogen and its vertebrate host. Curr. Biol. 28, 2978–2983 (2018).CAS 
Article 

Google Scholar 
22.Staley, M., Bonneaud, C., McGraw, K. J., Vleck, C. M. & Hill, G. E. Detection of Mycoplasma gallisepticum in House Finches (Haemorhous mexicanus) from Arizona. Avian Dis. 62, 14–17 (2018).Article 

Google Scholar 
23.Tardy, L., Giraudeau, M., Hill, G. E., McGraw, K. J. & Bonneaud, C. Contrasting evolution of virulence and replication rate in an emerging bacterial pathogen. Proc. Natl. Acad. Sci. U.S.A. 116, 16927–16932. https://doi.org/10.1073/pnas.1901556116 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
24.Grodio, J. L., Buckles, E. L. & Schat, K. A. Production of house finch (Carpodacus mexicanus) IgA specific anti-sera and its application in immunohistochemistry and in ELISA for detection of Mycoplasma gallisepticum-specific IgA. Vet. Immunol. Immunopathol. 132, 288–294 (2009).CAS 
Article 

Google Scholar 
25.Warr, G. W., Magor, K. E. & Higgins, D. A. IgY—clues to the origins of modern antibodies. Immunol. Today 16, 392–398. https://doi.org/10.1016/0167-5699(95)80008-5 (1995).CAS 
Article 
PubMed 

Google Scholar 
26.Diebolder, C. A. et al. Complement is activated by IgG hexamers assembled at the cell surface. Science 343, 1260–1263. https://doi.org/10.1126/science.1248943 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
27.Bonneaud, C. et al. Evolution of both host resistance and tolerance to an emerging bacterial pathogen. Evol. Lett. 3, 544–554. https://doi.org/10.1002/evl3.133 (2019).Article 

Google Scholar 
28.Staley, M., Hill, G. E., Josefson, C. C., Armbruster, J. W. & Bonneaud, C. Bacterial pathogen emergence requires more than direct contact with a novel passerine host. Infect. Immun. 86, 9. https://doi.org/10.1128/iai.00863-17 (2018).CAS 
Article 

Google Scholar 
29.Grodio, J. L. et al. Pathogenicity and immunogenicity of three Mycoplasma gallisepticum isolates in house finches (Carpodacus mexicanus). Vet. Microbiol. 155, 53–61. https://doi.org/10.1016/j.vetmic.2011.08.003 (2012).CAS 
Article 
PubMed 

Google Scholar 
30.Javed, M. A. et al. Correlates of immune protection in chickens vaccinated with Mycoplasma gallisepticum strain GT5 following challenge with pathogenic M-gallisepticum strain R-low. Infect. Immun. 73, 5410–5419 (2005).CAS 
Article 

Google Scholar 
31.Dumke, R. & Jacobs, E. Antibody response to Mycoplasma pneumoniae: Protection of host and influence on outbreaks?. Front. Microbiol. 7, 7. https://doi.org/10.3389/fmicb.2016.00039 (2016).Article 

Google Scholar 
32.Avakian, A. P. & Ley, D. H. Protective immune-response to Mycoplasma-gallisepticum demonstrated in respiratory-tract washings from M-gallisepticum-infected chickens. Avian Dis. 37, 697–705. https://doi.org/10.2307/1592017 (1993).CAS 
Article 
PubMed 

Google Scholar 
33.Yagihashi, T. & Tajima, M. Antibody-responses in sera and respiratory secretions from chickens infected with Mycoplasma gallisepticum. Avian Dis. 30, 543–550. https://doi.org/10.2307/1590419 (1986).CAS 
Article 
PubMed 

Google Scholar 
34.Glatman-Freedman, A. & Casadevall, A. Serum therapy for tuberculosis revisited: reappraisal of the role of antibody-mediated immunity against Mycobacterium tuberculosis. Clin. Microbiol. Rev. 11, 514. https://doi.org/10.1128/cmr.11.3.514 (1998).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Vogl, G. et al. Mycoplasma gallisepticum invades chicken erythrocytes during infection. Infect. Immun. 76, 71–77. https://doi.org/10.1128/iai.00871-07 (2008).CAS 
Article 
PubMed 

Google Scholar 
36.Dowling, A. J., Hill, G. E. & Bonneaud, C. Multiple differences in pathogen-host cell interactions following a bacterial host shift. Sci. Rep. 10, 6779 (2020).ADS 
CAS 
Article 

Google Scholar 
37.Arfi, Y. et al. MIB-MIP is a mycoplasma system that captures and cleaves immunoglobulin G. Proc. Natl. Acad. Sci. U.S.A. 113, 5406–5411. https://doi.org/10.1073/pnas.1600546113 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
38.Staley, M., Bonneaud, C., McGraw, K. J., Vleck, C. M. & Hill, G. E. Detection of Mycoplasma gallisepticum in House Finches (Haemorhous mexicanus) from Arizona. Avian Dis. https://doi.org/10.1637/11610-021317-RegR (2018).Article 
PubMed 

Google Scholar 
39.Roberts, S. R., Nolan, P. M., Lauerman, L. H., Li, L. Q. & Hill, G. E. Characterization of the mycoplasmal conjunctivitis epizootic in a house finch population in the southeastern USA. J. Wildl. Dis. 37, 82–88 (2001).CAS 
Article 

Google Scholar 
40.Papazisi, L. et al. GapA and CrmA coexpression is essential for Mycoplasma gallisepticum cytadherence and virulence. Infect. Immun. 70, 6839–6845 (2002).CAS 
Article 

Google Scholar 
41.Ruijter, J. M. et al. Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data. Nucleic Acids Res. 37, 12. https://doi.org/10.1093/nar/gkp045 (2009).CAS 
Article 

Google Scholar 
42.Tuomi, J. M., Voorbraak, F., Jones, D. L. & Ruijter, J. M. Bias in the C-q value observed with hydrolysis probe based quantitative PCR can be corrected with the estimated PCR efficiency value. Methods 50, 313–322. https://doi.org/10.1016/j.ymeth.2010.02.003 (2010).CAS 
Article 
PubMed 

Google Scholar 
43.Ruijter, J., Villalba, A., Hellemans, J., Untergasser, A. & van den Hoff, M. Removal of between-run variation in a multi-plate qPCR experiment. Biomol. Detect. Quantif. 5, 10–14 (2015).CAS 
Article 

Google Scholar 
44.Grodio, J. L., Dhondt, K. V., O’Connell, P. H. & Schat, K. A. Detection and quantification of Mycoplasma gallisepticum genome load in conjunctival samples of experimentally infected house finches (Carpodacus mexicanus) using real-time polymerase chain reaction. Avian Pathol. 37, 385–391. https://doi.org/10.1080/03079450802216629 (2008).CAS 
Article 
PubMed 

Google Scholar 
45.R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, 2016).46.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
47.ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).48.Repeatability Estimation for Gaussian and Non-Gaussian Data v. 0.9.21 (2018). More

Fossils, such as this skeleton of a T. rex on display in the Netherlands, may be even rarer than scientists realised. Credit: Marten van Dijl/AFP via Getty

Ever wondered how many Tyrannosaurus rex ever roamed the Earth? The answer is 2.5 billion over the two million or so years for which the species existed, according to a calculation published today in Science1. The figure has allowed researchers to estimate just how exceedingly rare it is for animals to fossilize.Palaeontologists led by Charles Marshall at the University of California, Berkeley, used a method employed by ecologists studying contemporary creatures to estimate the population density of T. rex during the late Cretaceous period. “You hold a fossil in your hand and you know it’s rare. The question is, how rare?” says Marshall. “To know that, you need to know how many of them existed.”To do that, he and his co-authors turned to a method used to estimate the population density of living animals from their body mass and the geographic ranges that they occupy. Damuth’s Law stipulates that the average population density of a species decreases in a predictable way as body mass increases; for example, there are fewer elephants than mice in a given area.Chances of being fossilized vanishingly smallThe team used their estimates of the total range of T. rex across modern North America, combined with their estimates of the dinosaur’s body mass, to calculate that, at any one time, around 20,000 T. rex would have been alive on the planet. That translates to around 3,800 T. rex in an area the size of California, or just two T. rex patrolling Washington DC. Calculating that T. rex survived for about 127,000 generations before becoming extinct, the researchers came up with a figure of 2.5 billion individuals over the species’ entire existence. Only 32 adult T. rex have been discovered as fossils, so the fossil record accounts for just 1 in about every 80 million T. rex. This means that the chances of being fossilized — even for one of the largest-ever carnivores — were vanishingly small.These numbers suggest that fossils in general are exceedingly rare, and hint that many species that were much less widespread than T. rex were probably never preserved, says Marshall, who adds: “The fossil record is our only direct knowledge of these completely unimaginable past histories of our planet.”Thomas Holtz, a vertebrate palaeontologist at the University of Maryland in College Park, calls the calculation an “interesting speculation”, adding that “we always knew that the chance of any individual becoming a fossil was exceedingly rare, but we lacked the calculation to figure out how rare”.But he says it would be good “to see someone ground-truth these kinds of estimations against living species to get a better sense of accuracy”. He’d also like to see comparable studies made on extinct species with more abundant fossils, such as woolly mammoths, Neanderthals and dire wolves, which might allow us to better understand historic ecosystems. More

1.McPhaden, M. J. & Busalacchi, A. J. The tropical ocean-global atmosphere observing system: A Decade of progress research. Oceans. https://doi.org/10.1029/97JC02906 (1998).2.Osland, M. J. et al. Climatic controls on the global distribution, abundance, and species richness of mangrove forests. Ecol. Monogr. 87(2), 341–359 (2017).Article 

Google Scholar 
3.Adame, M. F. et al. Mangroves in arid regions: Ecology, threats, and opportunities. Estuar. Coast. Shelf Sci. 1, 106796 (2020).
Google Scholar 
4.Asbridge, E. F. et al. Assessing the distribution and drivers of mangrove dieback in Kakadu National Park, Northern Australia. Estuar. Coast. Shelf Sci. 228, 106353 (2019).Article 

Google Scholar 
5.Lovelock, C. E. et al. Assessing the risk of carbon dioxide emissions from blue carbon ecosystems. Front. Ecol. Environ. 15(5), 257–265 (2017).Article 

Google Scholar 
6.Spalding, M. D. et al. The role of ecosystems in coastal protection: adapting to climate change and coastal hazards. Ocean. Coast. Manag. 90, 50–57 (2014).7.Sippo, J. Z., Lovelock, C. E., Santos, I. R., Sanders, C. J. & Maher, D. T. Mangrove mortality in a changing climate: An overview. Estuar. Coast. Shelf Sci. 215, 241–249 (2018).ADS 
Article 

Google Scholar 
8.Mafi-Gholami, D., Zenner, E. K., & Jaafari, A. Mangrove regional feedback to sea level rise and drought intensity at the end of the 21st century. Ecol. Indic. 110, 105972 (2020).Article 

Google Scholar 
9.Jump, A. S., & Penuelas J. Running to stand still: adaptation and the response of plants to rapid climate change. Ecol. Lett. 8(9), 1010–1020 (2005).Article 

Google Scholar 
10.Jimenez, J. A., Lugo, A. E. & Cintron, G. Tree mortality in mangrove forests. Biotropica 17, 177–185 (1985).Article 

Google Scholar 
11.Xie, S.-P. et al. Indo-western pacific ocean capacitor and coherent climate anomalies in post-ENSO summer: A review. Adv. Atmos. Sci. 33(4), 411–432 (2016).Article 

Google Scholar 
12.Hamlington, B. D. et al. An ongoing shift in Pacific Ocean sea level. J. Geophys/ Res. Oceans 121, 5084–5097 (2016).ADS 
Article 

Google Scholar 
13.Merrifield, M. A., Thompson, P. R. & Lander, M. Multidecadal sea level anomalies and trends in the western tropical Pacific. Geophys. Res. Lett. 39, 2–6 (2012).Article 

Google Scholar 
14.Godfrey, J. S. & Ridgway, K. R. The large-scale environment of the poleward-flowing Leeuwin Current, Western Australia: Longshore steric height gradients, wind stresses and geostrophic flow. J. Phys. Oceanogr. 15, 481–495 (1985).ADS 
Article 

Google Scholar 
15.Drexler, J. Z. & Ewel, K. C. Wetland complex linked references are available on JSTOR for this article: Effect of the 1997–1998 ENSO-related drought on hydrology and salinity in a Micronesian wetland complex. Estuaries 24, 347–356 (2001).Article 

Google Scholar 
16.Duke, N. C. et al. Large-scale dieback of mangroves in Australia’s Gulf of Carpentaria: A severe ecosystem response, coincidental with an unusually extreme weather event. Mar. Freshw. Res. 68(10), 1816–1829 (2017).Article 

Google Scholar 
17.Cai, W. et al. More extreme swings of the South Pacific convergence zone due to greenhouse warming. Nature 488, 365–369 (2012).ADS 
CAS 
Article 

Google Scholar 
18.Wilson, S. G., Taylor, J. G., & Pearce, A. F. The Seasonal Aggregation of Whale Sharks at Ningaloo Reef, Western Australia: Currents, Migrations and the El Niño/Southern Oscillation. Environmental Biology of Fishes. https://idp.springer.com/authorize/casa?redirect_uri=https://link.springer.com/article/10.1023/A:1011069914753&casa_token=55v4NHJmcDcAAAAA:owpASeBazqNzQzH7Z9xJI0BOtHzNZMvjTiJHRjLGIFCWzhyiWwMvYJUU8cloH46JDWCSZ7XOhu_CZuzZ0w. (2001).19.Lovelock, C. E., Feller, I. C., Reef, R., Hickey, S. & Ball, M. C. Mangrove dieback during fluctuating sea levels. Sci. Rep. 1, 1–8 (2017).
Google Scholar 
20.Giri, C. Observation and monitoring of mangrove forests using remote sensing: Opportunities and challenges. Remot. Sens. 8, 783 (2016).ADS 
Article 

Google Scholar 
21.Fatoyinbo, T. E., Simard, M., Washington-Allen, R. A. & Shugart, H. H. Landscape-scale extent, height, biomass, and carbon estimation of Mozambique’s mangrove, forests with Landsat ETM+ and Shuttle Radar Topography Mission elevation data. J. Geophys. Res. Biogeosci. 113, 1–13 (2008).Article 

Google Scholar 
22.Rodriguez, W., Feller, I. C. & Cavanaugh, K. C. Spatio-temporal changes of a mangrove saltmarsh ecotone in the northeastern coast of Florida, USA. Glob. Ecol. Conserv. 7, 245–261 (2016).Article 

Google Scholar 
23.Bureau of Meteorology. Record-Breaking La Niña Events. Australian Government. http://www.bom.gov.au/climate/enso/history/La-Nina-2010-12.pdf (2012).24.Jensen, J. R. et al. The measurement of mangrove characteristics in southwest Florida using spot multispectral data. Geocarto Int. 6, 13–21 (1991).Article 

Google Scholar 
25.Eslami-Andargoli, L., Dale, P., Sipe, N. & Chaseling, J. Mangrove expansion and rainfall patterns in Moreton Bay, Southeast Queensland, Australia. Estuar. Coast. Shelf Sci. 85, 292–298 (2009).ADS 
Article 

Google Scholar 
26.Hicks, W., Fitzpatrick, R. W., & Bowman, G. (2003) Managing coastal acid sulfate soils: the East Trinity example. in Advances in regolith: Proceedings of the CRC LEME regional regolith symposia. CRC LEME, Bentley 174–177.27.Harris, N. L. et al. Using spatial statistics to identify emerging hot spots of forest loss. Environ. Res. Lett. 12, 024012 (2017).ADS 
Article 

Google Scholar 
28.Bryan-Brown, D. N. et al. Global trends in mangrove forest fragmentation. Sci. Rep. 10(1), 7117 (2020).ADS 
CAS 
Article 

Google Scholar 
29.Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the anthropocene. Science 359(6371), 80–83 (2018).30.Wang, H. J., Zhang, R. H., Cole, J. & Chavez, F. El Niño and the related phenomenon southern oscillation (ENSO): The largest signal in interannual climate variation. Proc. Natl. Acad. Sci. USA. 96(20), 11071–11072 (1999).ADS 
CAS 
Article 

Google Scholar 
31.Berg, A. et al. Land-atmosphere feedbacks amplify aridity increase over land under global warming. Nat. Clim. Chang. 6, 869–874 (2016).ADS 
Article 

Google Scholar 
32.Perry, S. J., McGregor, S., Gupta, A. S. & England, M. H. Future changes to El Niño-southern oscillation temperature and precipitation teleconnections. Geophys. Res. Lett. 44(20), 10608–10616 (2017).ADS 
Article 

Google Scholar 
33.Osland, M. J. et al. Beyond just sea-level rise: Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change. Glob. Change Biol. 22, 1–11 (2016).ADS 
Article 

Google Scholar 
34.Jentsch, A. & Beierkuhnlein, C. Research frontiers in climate change: Effects of extreme meteorological events on ecosystems. C.R. Geosci. 340, 621–628 (2008).ADS 
Article 

Google Scholar 
35.Chander, G., Markham, B. L. & Helder, D. L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 113, 893–903 (2009).ADS 
Article 

Google Scholar 
36.Landsat 7 (L7) Data Users Handbook. USGS. https://prd-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/atoms/files/LSDS-1927_L7_Data_Users_Handbook-v2.pdf. (2009).37.Landsat 8 (L8) Data Users Handbook. USGS. https://prd-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/atoms/files/LSDS-1574_L8_Data_Users_Handbook-v5.0.pdf. (2009).38.Story, M. & Congalton, R. G. Accuracy assessment: A user’s perspective. Photogramm. Eng. Remote. Sens. 52, 397–399 (1986).
Google Scholar 
39.Moore, C. et al. Improving essential fish habitat designation to support sustainable ecosystem-based fisheries management. Mar. Policy 69, 32–41 (2016).40.Burnham, K. P., & Anderson, R. A practical information-theoretic approach. in Model Selection and Multimodel Inference 2. http://sutlib2.sut.ac.th/sut_contents/H79182.pdf.41.Burnham, K. P., & Anderson, D. R. Practical use of the information-theoretic approach. in Model Selection and Inference: A Practical Information-Theoretic Approach (eds. Burnham K. P. & Anderson D. R.) 75–117 (New York, NY, Springer, 1998).42.Cornforth, W. A., Fatoyinbo, T. E., Freemantle, T. P. & Pettorelli, N. Advanced land observing satellite phased array type L-Band SAR (ALOS PALSAR) to inform the conservation of mangroves: Sundarbans as a case study. Remot. Sens. 5, 224–237 (2013).ADS 
Article 

Google Scholar 
43.Giri, C., Pengra, B., Zhu, Z., Singh, A. & Tieszen, L. L. Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuar. Coast. Shelf Sci. 73, 91–100 (2007).ADS 
Article 

Google Scholar 
44.Long, J., Giri, C., Primavera, J. & Trivedi, M. Damage and recovery assessment of the Philippines ’ mangroves following Super Typhoon Haiyan. MPB 109, 734–743 (2016).CAS 

Google Scholar 
45.Satyanarayana, B., Mohamad, K. A., Idris, I. F., Husain, M.-L. & Dahdouh-Guebas, F. Assessment of mangrove vegetation based on remote sensing and ground-truth measurements at Tumpat, Kelantan Delta, East Coast of Peninsular Malaysia. Int. J. Remot. Sens. 32, 1635–1650 (2011).Article 

Google Scholar 
46.Almahasheer, H., Aljowair, A., Duarte, C. M. & Irigoien, X. Decadal stability of red sea mangroves. Estuar. Coast. Shelf Sci. 169, 164–172 (2016).ADS 
Article 

Google Scholar 
47.Pettorelli, N. et al. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol. Evol. 20, 503–510 (2005).Article 

Google Scholar  More

1.Agovino, M., Casaccia, M., Ciommi, M., Ferrara, M. & Marchesano, K. Agriculture, climate change and sustainability: The case of EU-28. Ecol. Ind. 105, 525–543 (2019).Article 

Google Scholar 
2.Vegro, C. L. R. & de Almeida, L. F. in Coffee Consumption and Industry Strategies in Brazil 3–19 (Elsevier, 2020).3.Bunn, C., Läderach, P., Jimenez, J. G. P., Montagnon, C. & Schilling, T. Multiclass classification of agro-ecological zones for Arabica coffee: An improved understanding of the impacts of climate change. PLoS ONE 10, e0140490 (2015).Article 

Google Scholar 
4.Bunn, C., Läderach, P., Rivera, O. O. & Kirschke, D. A bitter cup: climate change profile of global production of Arabica and Robusta coffee. Clim. Change 129, 89–101 (2015).ADS 
Article 

Google Scholar 
5.Pham, Y., Reardon-Smith, K., Mushtaq, S. & Cockfield, G. The impact of climate change and variability on coffee production: A systematic review. Clim. Change 156, 609–630 (2019).ADS 
CAS 
Article 

Google Scholar 
6.Chemura, A., Kutywayo, D., Chidoko, P. & Mahoya, C. Bioclimatic modelling of current and projected climatic suitability of coffee (Coffea arabica) production in Zimbabwe. Reg. Environ. Change 16, 473–485 (2016).Article 

Google Scholar 
7.Laderach, P. et al. in The economic, social and political elements of climate change 703–723 (Springer, 2011).8.Baker, P. & Haggar, J. Global warming: Effects on global coffee (SCAA Conference Handout, Long Beach, 2007).9.Craparo, A., Van Asten, P. J., Läderach, P., Jassogne, L. T. & Grab, S. Coffea arabica yields decline in Tanzania due to climate change: Global implications. Agric. For. Meteorol. 207, 1–10 (2015).ADS 
Article 

Google Scholar 
10.Alves, M. C., Carvalho, L. G., Pozza, E. A., Sanches, L. & Maia, J. Ecological zoning of soybean rust, coffee rust and banana sigatoka based on Brazilian climate changes. Earth Syst. Sci. Global Change Clim. People 6, 35–46. https://doi.org/10.1016/j.proenv.2011.05.005 (2011).Article 

Google Scholar 
11.Jaramillo, J., Muchugu, E., Vega, F. E., Davis, A. & Borgemesister, C. Some like it hot: The influence and implications of climate change on coffee berry borer (Hypothenemus hampei) and coffee production in East Africa. PLoS ONE 6, e24528. https://doi.org/10.1371/journal.pone.0024528 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
12.Kutywayo, D., Chemura, A., Kusena, W., Chidoko, P. & Mahoya, C. The impact of climate change on the potential distribution of agricultural pests: The case of the coffee white stem borer (Monochamus leuconotus P.) in Zimbabwe. Plos One 8, e73432. https://doi.org/10.1371/journal.pone.0073432 (2013).13.Läderach, P. et al. Climate change adaptation of coffee production in space and time. Clim. Change 141, 47–62 (2017).Article 

Google Scholar 
14.Scholz, M. B. d. S., Kitzberger, C. S. G., Prudencio, S. H. & Silva, R. S. d. S. F. d. The typicity of coffees from different terroirs determined by groups of physico-chemical and sensory variables and multiple factor analysis. Food Res. Int. 114, 72–80. https://doi.org/10.1016/j.foodres.2018.07.058 (2018).15.Bertrand, B. et al. Comparison of the effectiveness of fatty acids, chlorogenic acids, and elements for the chemometric discrimination of coffee (Coffea arabica L.) varieties and growing origins. J. Agric. Food Chem. 56, 2273–2280 (2008).16.Cheng, B., Furtado, A., Smyth, H. E. & Henry, R. J. Influence of genotype and environment on coffee quality. Trends Food Sci. Technol. 57, 20–30 (2016).CAS 
Article 

Google Scholar 
17.Bote, A. D. & Vos, J. Tree management and environmental conditions affect coffee (Coffea arabica L.) bean quality. NJAS-Wageningen J. Life Sci. 83, 39–46 (2017).18.de Carvalho, A. M. et al. Relationship between the sensory attributes and the quality of coffee in different environments. Afr. J. Agric. Res. 11, 3607–3614 (2016).Article 

Google Scholar 
19.Sberveglieri, V. et al. in AIP Conference Proceedings. 86–87 (American Institute of Physics).20.Bertrand, B. et al. Climatic factors directly impact the volatile organic compound fingerprint in green Arabica coffee bean as well as coffee beverage quality. Food Chem. 135, 2575–2583 (2012).CAS 
Article 

Google Scholar 
21.International Trade Centre. The Coffee Exporter’s Guide (World Trade Organization and the United Nations, 2011).
Google Scholar 
22.Lambot, C. et al. in The Craft and Science of Coffee (ed Britta Folmer) 17–49 (Academic Press, 2017).23.Ahmed, S. & Stepp, J. R. Beyond yields: Climate effects on specialty crop quality and agroecological management. Element. Sci. Anthropocene 4, 92 (2016).24.Purba, P., Sukartiko, A. & Ainuri, M. in IOP Conference Series: Earth and Environmental Science. 012021 (IOP Publishing).25.Traore, T. M., Wilson, N. L. & Fields, D. What explains specialty coffee quality scores and prices: A case study from the cup of excellence program. J. Agric. Appl. Econ. 50, 349–368 (2018).Article 

Google Scholar 
26.Barjolle, D., Quiñones-Ruiz, X. F., Bagal, M. & Comoé, H. The role of the state for geographical indications of coffee: Case studies from Colombia and Kenya. World Dev. 98, 105–119 (2017).Article 

Google Scholar 
27.Oguamanam, C. & Dagne, T. Geographical indication (GI) options for Ethiopian coffee and Ghanaian cocoa. Innovation and intellectual property: Collaborative dynamics in Africa, 77–108 (2014).28.Boaventura, P. S. M., Abdalla, C. C., Araujo, C. L. & Arakelian, J. S. Value co-creation in the specialty coffee value chain: The third-wave coffee movement. Revista de Administração de Empresas 58, 254–266 (2018).Article 

Google Scholar 
29.Lannigan, J. Making a space for taste: Context and discourse in the specialty coffee scene. Int. J. Inf. Manage. 51, 101987 (2020).Article 

Google Scholar 
30.Masters, G., Baker, P. & Flood, J. Climate change and agricultural commodities. CABI Work. Pap. 2, 1–38 (2010).
Google Scholar 
31.Rahman, S., Gross, M., Battiste, M. & Gacioch, M. Specialty Coffee Farmers’ Climate Change Concern and Perceived Ability to Adapt. (2016).32.Srinivasan, R., Giannikas, V., Kumar, M., Guyot, R. & McFarlane, D. Modelling food sourcing decisions under climate change: A data-driven approach. Comput. Ind. Eng. 128, 911–919 (2019).Article 

Google Scholar 
33.Chemura, A., Schauberger, B. & Gornott, C. Impacts of climate change on agro-climatic suitability of major food crops in Ghana. PLoS ONE 15, e0229881 (2020).CAS 
Article 

Google Scholar 
34.FAO. (Food Agriculture Organization of the United Nations, Roma, 2012).35.Hirons, M. et al. Pursuing climate resilient coffee in Ethiopia: A critical review. Geoforum 91, 108–116 (2018).Article 

Google Scholar 
36.Central Statistical Agency (CSA). Agricultural Sample Survey 2018/19. (2019).37.Murken, L. et al. Climate Risk Analysis for Identifying and Weighing Adaptation Strategies in Ethiopia’s Agricultural Sector. (2020).38.Ridley, F. The past and future climatic suitability of arabica coffee (Coffea arabica L.) in East Africa, Durham University, (2011).39.Putri, S. P., Irifune, T. & Fukusaki, E. GC/MS based metabolite profiling of Indonesian specialty coffee from different species and geographical origin. Metabolomics 15, 126 (2019).Article 

Google Scholar 
40.Mengistie, G. in Extending the Protection of Geographical Indications: Case studies of Agricultural Products of Africa Vol. 15 (eds M Blakeney, T Coulet, Getachew Mengistie, & M.T Mahop) 150 (Routledge, 2011).41.Kufa, T., Ayano, A., Yilma, A., Kumela, T. & Tefera, W. The contribution of coffee research for coffee seed development in Ethiopia. J. Agric. Res. Dev. 1, 009–016 (2011).
Google Scholar 
42.Moat, J. et al. Resilience potential of the Ethiopian coffee sector under climate change. Nat. Plants 3, 17081 (2017).Article 

Google Scholar 
43.Moat, J., Gole, T. W. & Davis, A. P. Least Concern to Endangered: Applying climate change projections profoundly influences the extinction risk assessment for wild Arabica coffee. Glob. Change Biol. 25, 390–403 (2019).ADS 
Article 

Google Scholar 
44.Davis, A. P., Gole, T. W., Baena, S. & Moat, J. The impact of climate change on indigenous arabica coffee (Coffea arabica): Predicting future trends and identifying priorities. PLoS ONE 7, e47981. https://doi.org/10.1371/journal.pone.0047981 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.CIAT. Future Climate Scenarios for Tanzania’s Arabica Coffee Growing Areas. 27 (International Center for Tropical Agriculture, Cali, Colombia: , 2012).46.Laderach, P., Jarvis, A. & Ramirez, J. The impact of climate change in coffee-growing regions: The case of 10 municipalities in Nicaragua. 4 (CafeDirect/GTZ, 2006).47.Gomes, L. C. et al. Agroforestry systems can mitigate the impacts of climate change on coffee production: A spatially explicit assessment in Brazil. Agr. Ecosyst. Environ. 294, 106858. https://doi.org/10.1016/j.agee.2020.106858 (2020).Article 

Google Scholar 
48.Brown, N. in Daily Coffee News (Roast Magazine, 2018).49.Labouisse, J.-P., Bellachew, B., Kotecha, S. & Bertrand, B. Current status of coffee (Coffea arabica L.) genetic resources in Ethiopia: implications for conservation. Genet. Resour. Crop Evol. 55, 1079 (2008).50.MFA. Coffee production in Ethiopia. The 4th World Coffee Conference in Addis Ababa, Ministry of Foreign Affairs of Ethiopia, Addis Ababa, Ethiopia (2016).51.Tolessa, K., D’heer, J., Duchateau, L. & Boeckx, P. Influence of growing altitude, shade and harvest period on quality and biochemical composition of Ethiopian specialty coffee. J. Sci. Food Agric. 97, 2849–2857 (2017).52.Chemura, A., Mahoya, C., Chidoko, P. & Kutywayo, D. Effect of soil moisture deficit stress on biomass accumulation of four coffee (Coffea arabica) varieties in Zimbabwe. ISRN Agron. 1–10, 2014. https://doi.org/10.1155/2014/767312 (2014).Article 

Google Scholar 
53.Hannah, L. et al. Climate change, wine, and conservation. Proc. Natl. Acad. Sci. 110, 6907–6912 (2013).ADS 
CAS 
Article 

Google Scholar 
54.Impact on variety and origin chemometric determination. Villarreal, D. et al. Genotypic and environmental effects on coffee (Coffea arabica L.) bean fatty acid profile. J. Agric. Food Chem. 57, 11321–11327 (2009).Article 

Google Scholar 
55.Sisay, B. T. in Sustainable agriculture reviews 33 99–113 (Springer, 2018).56.DaMatta, F. b. M., Avila, R. T., Cardoso, A. A., Martins, S. C. & Ramalho, J. C. Physiological and agronomic performance of the coffee crop in the context of climate change and global warming: A review. J. Agric. Food Chem. 66, 5264–5274 (2018).57.CABI. (2015).58.Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many?. Methods Ecol. Evol. 3, 327–338 (2012).Article 

Google Scholar 
59.Liu, C., Newell, G. & White, M. The effect of sample size on the accuracy of species distribution models: Considering both presences and pseudo-absences or background sites. Ecography 42, 535–548 (2019).Article 

Google Scholar 
60.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978. https://doi.org/10.1002/joc.1276 (2005).Article 

Google Scholar 
61.R Core Team. R: A language and environment for statistical computing. (2019).62.Hengl, T. et al. SoilGrids1km—global soil information based on automated mapping. PloS one 9 (2014).63.Nair, K. P. P. The Agronomy and Economy of Important Tree Crops of the Developing World. 368 (Elservier, 2010).64.Coste, J. Coffee: The plant and the product. (Longman, 1992).65.Lin, F.-J. Solving multicollinearity in the process of fitting regression model using the nested estimate procedure. Qual. Quant. 42, 417–426 (2008).Article 

Google Scholar 
66.Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).Article 

Google Scholar 
67.Breiman, L. Random forests machine learning. 45: 5–32. View Article PubMed/NCBI Google Scholar (2001).68.Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).CAS 
Article 

Google Scholar 
69.Li, X. & Wang, Y. Applying various algorithms for species distribution modelling. Integr. Zool. 8, 124–135 (2013).Article 

Google Scholar 
70.Gobeyn, S. et al. Evolutionary algorithms for species distribution modelling: A review in the context of machine learning. Ecol. Model. 392, 179–195 (2019).Article 

Google Scholar 
71.Vapnik, V. The nature of statistical learning theory. (Springer science & business media, 2013).72.Choubin, B., Darabi, H., Rahmati, O., Sajedi-Hosseini, F. & Kløve, B. River suspended sediment modelling using the CART model: A comparative study of machine learning techniques. Sci. Total Environ. 615, 272–281 (2018).ADS 
CAS 
Article 

Google Scholar 
73.Pourghasemi, H. R., Yousefi, S., Kornejady, A. & Cerdà, A. Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Sci. Total Environ. 609, 764–775 (2017).ADS 
CAS 
Article 

Google Scholar 
74.Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).Article 

Google Scholar 
75.Chang, Y. & Bourque, C.P.-A. Relating modelled habitat suitability for Abies balsamea to on-the-ground species structural characteristics in naturally growing forests. Ecol. Ind. 111, 105981 (2020).Article 

Google Scholar 
76.Naimi, B. & Araújo, M. B. sdm: A reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375 (2016).Article 

Google Scholar 
77.Zurell, D. et al. A standard protocol for reporting species distribution models. Ecography (2020).78.ArcGIS Desktop v. 10.2 (Environmental Systems Research Institute, Redlands, CA, Redlands, 2012).79.WorldClim. Global climate and weather data. https://www.worldclim.org/data/cmip6/cmip6_clim2.5m.html ( 2020).80.Navarro-Racines, C., Tarapues, J., Thornton, P., Jarvis, A. & Ramirez-Villegas, J. High-resolution and bias-corrected CMIP5 projections for climate change impact assessments. Sci. Data 7, 1–14 (2020).Article 

Google Scholar 
81.van Vuuren, D. P. et al. A new scenario framework for Climate Change Research: scenario matrix architecture. Clim. Change 122, 373–386. https://doi.org/10.1007/s10584-013-0906-1 (2014).Article 

Google Scholar 
82.Popp, A. et al. Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang. 42, 331–345. https://doi.org/10.1016/j.gloenvcha.2016.10.002 (2017).Article 

Google Scholar 
83.O’Neill, B. C. et al. The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob. Environ. Chang. 42, 169–180 (2017).Article 

Google Scholar 
84.Doelman, J. C. et al. Exploring SSP land-use dynamics using the IMAGE model: Regional and gridded scenarios of land-use change and land-based climate change mitigation. Glob. Environ. Chang. 48, 119–135 (2018).Article 

Google Scholar  More

1.Jones, C. G., Lawton, J. H., & Shachak, M. Organisms as ecosystem engineers. In Ecosystem Management 130–147 (Springer, 1994).2.Alvarez-Uria, P. & Körner, C. Low temperature limits of root growth in deciduous and evergreen temperate tree species. Funct. Ecol. 21, 211–218 (2007).Article 

Google Scholar 
3.Rossi, S. et al. Pattern of xylem phenology in conifers of cold ecosystems at the Northern Hemisphere. Glob. Chang. Biol. 22, 3804–3813 (2016).PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
4.Körner, C. & Paulsen, J. A world-wide study of high altitude treeline temperatures. J. Biogeogr. 31, 713–732 (2004).Article 

Google Scholar 
5.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
6.Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

Google Scholar 
7.Albrich, K., Rammer, W. & Seidl, R. Climate change causes critical transitions and irreversible alterations of mountain forests. Glob. Change Biol. 26, 4013–4027 (2020).Article 
ADS 

Google Scholar 
8.De Frenne, P. et al. Microclimate moderates plant responses to macroclimate warming. PNAS 110, 18561–18565 (2013).PubMed 
Article 
ADS 
CAS 
PubMed Central 

Google Scholar 
9.Maclean, I. M. D. et al. Microclimates buffer the responses of plant communities to climate change. Glob. Ecol. Biogeogr. 24, 1340–1350 (2015).Article 

Google Scholar 
10.Bertrand, R. et al. Changes in plant community composition lag behind climate warming in lowland forests. Nature 479, 517–520 (2011).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
11.Weigel, R., Gilles, J., Klisz, M., Manthey, M. & Kreyling, J. Forest understory vegetation is more related to soil than to climate towards the cold distribution margin of European beech. J. Veg Sci. 30, 746–755 (2019).Article 

Google Scholar 
12.Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
13.Dozier, J. & Outcalt, S. I. An approach toward energy balance simulation over rugged terrain. Geogr. Anal. 11, 65–85 (1979).Article 

Google Scholar 
14.Rorison, I. H., Sutton, F. & Hunt, R. Local climate, topography and plant growth in Lathkill Dale NNR. I. A twelve-year summary of solar radiation and temperature. Plant Cell Environ. 9, 49–56 (1986).
Google Scholar 
15.Ackerly, D. D. et al. The geography of climate change: Implications for conservation biogeography. Divers. Distrib. 16, 476–487 (2010).Article 

Google Scholar 
16.Baldocchi, D. D. & Xu, L. What limits evaporation from Mediterranean oak woodlands—The supply of moisture in the soil, physiological control by plants or the demand by the atmosphere?. Adv. Water Resour. 30, 2113–2122 (2007).Article 
ADS 

Google Scholar 
17.Komatsu, H. Forest categorization according to dry-canopy evaporation rates in the growing season: Comparison of the Priestley-Taylor coefficient values from various observation sites. Hydrol. Process. 19, 3873–3896 (2005).Article 
ADS 

Google Scholar 
18.Lenoir, J. et al. Local temperatures inferred from plant communities suggest strong spatial buffering of climate warming across Northern Europe. Glob. Chang. Biol. 19, 1470–1481 (2013).PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
19.Aussenac, G. Interactions between forest stands and microclimate: Ecophysiological aspects and consequences for silviculture. Ann. For. Sci. 57, 287–301 (2000).Article 

Google Scholar 
20.von Arx, G., Dobbertin, M. & Rebetez, M. Spatio-temporal effects of forest canopy on understory microclimate in a long-term experiment in Switzerland. Agric. For. Meteorol. 166, 144–155 (2012).Article 
ADS 

Google Scholar 
21.Gaudio, N. et al. Impact of tree canopy on thermal and radiative microclimates in a mixed temperate forest: A new statistical method to analyse hourly temporal dynamics. Agric. For. Meteorol. 237, 71–79 (2017).Article 
ADS 

Google Scholar 
22.Niinemets, Ü. A review of light interception in plant stands from leaf to canopy in different plant functional types and in species with varying shade tolerance. Ecol. Res. 25, 693–714 (2010).Article 

Google Scholar 
23.Breshears, D. D., Myers, O. B. & Barnes, F. J. Horizontal heterogeneity in the frequency of plant-available water with woodland intercanopy-canopy vegetation patch type rivals that occurring vertically by soil depth. Ecohydrology 2, 503–519 (2009).Article 

Google Scholar 
24.Zou, C. B., Barron-Gafford, G. A. & Breshears, D. D. Effects of topography and woody plant canopy cover on near-ground solar radiation: Relevant energy inputs for ecohydrology and hydropedology. Geophys. Res. Lett. 34, L24S21 (2007).Article 

Google Scholar 
25.Renaud, V., Innes, J. L., Dobbertin, M. & Rebetez, M. Comparison between open-site and below-canopy climatic conditions in Switzerland for different types of forests over 10 years (1998–2007). Theor. Appl. Climatol. 105, 119–127 (2011).Article 
ADS 

Google Scholar 
26.De Frenne, P. et al. Global buffering of temperatures under forest canopies. Nat. Ecol. Evol. 3, 744–749 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
27.Harsch, M. A. & Bader, M. Y. Treeline form—A potential key to understanding treeline dynamics. Glob. Ecol. Biogeogr. 20, 582–596 (2011).Article 

Google Scholar 
28.Körner, C. et al. Where, why and how? Explaining the low-temperature range limits of temperate tree species. J. Ecol. 104, 1076–1088 (2016).Article 
CAS 

Google Scholar 
29.Lenoir, J., Hattab, T. & Pierre, G. Climatic microrefugia under anthropogenic climate change: Implications for species redistribution. Ecography 40, 253–266 (2017).Article 

Google Scholar 
30.Bonanomi, G. et al. Anthropogenic and environmental factors affect the tree line position of Fagus sylvatica along the Apennines (Italy). J. Biogeogr. 45, 2595–2608 (2018).Article 

Google Scholar 
31.Bonanomi, G. et al. Climatic and anthropogenic factors explain the variability of Fagus sylvatica treeline elevation in fifteen mountain groups across the Apennines. For. Ecosyst. 7, 5 (2020).Article 

Google Scholar 
32.Driessen, P., Deckers, J., Spaargaren, O. & Nachtergaele, F. (Eds.). Lecture notes on the major soils of the world. In World Soil Resources Report; No. 94. (Food and Agricultural Organization of the United Nations, 2001).33.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
34.R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (R Foundation for Statistical Computing, Vienna, 2019).35.Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (CRC Press, 2017).
Google Scholar 
36.Davis, K. T., Dobrowski, S. Z., Holden, Z. A., Higuera, P. E. & Abatzoglou, J. T. Microclimatic buffering in forests of the future: The role of local water balance. Ecography 42, 1–11 (2019).Article 

Google Scholar 
37.Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.15. https://CRAN.R-project.org/package=MuMIn (2019).38.Geiger, R., Aron, R. H. & Todhunter, P. The Climate near the Ground (Rowman & Littlefield Publishers, 2003).
Google Scholar 
39.Bader, M., Rietkerk, M. & Bregt, A. Vegetation structure and temperature regimes of tropical alpine treelines. Arct. Antarct. Alp. Res. 39, 353–364 (2007).Article 

Google Scholar 
40.Potter, B. E., Teclaw, R. M. & Zasada, J. C. The impact of forest structure on near-ground temperatures during two years of contrasting temperature extremes. Agric. For. Meteorol. 106, 331–336 (2001).Article 
ADS 

Google Scholar 
41.von Arx, G., Pannatier, E. G., Thimonier, A. & Rebetez, M. Microclimate in forests with varying leaf area index and soil moisture: Potential implications for seedling establishment in a changing climate. J. Ecol. 101, 1201–1213 (2013).Article 

Google Scholar 
42.Frey, B. R. et al. An analysis of sucker regeneration of trembling aspen. Can. J. For. Res. 33, 1169–1179 (2003).Article 

Google Scholar 
43.Lenz, A., Hoch, G. & Vitasse, Y. Fast acclimation of freezing resistance suggests no influence of winter minimum temperature on the range limit of European beech. Tree Physiol. 36, 490–501 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Keitel, C. et al. Carbon and oxygen isotope composition of organic compounds in the phloem sap provides a short-term measure for stomatal conductance of European beech (Fagus sylvatica L.). Plant Cell Environ. 26, 1157–1168 (2003).CAS 
Article 

Google Scholar 
45.van der Maaten, E., Bouriaud, O., van der Maaten-Theunissen, M., Mayer, H. & Spiecker, H. Meteorological forcing of day-to-day stem radius variations of beech is highly synchronic on opposing aspects of a valley. Agric. For. Meteorol. 181, 85–93 (2013).Article 
ADS 

Google Scholar 
46.Smith, D. L. & Johnson, L. Vegetation-mediated changes in microclimate reduce soil respiration as woodlands expand into grasslands. Ecology 85, 3348–3361 (2004).Article 

Google Scholar 
47.Wu, Z., Dijkstra, P., Koch, G. W., Peñuelas, J. & Hungate, B. A. Responses of terrestrial ecosystems to temperature and precipitation change: A meta-analysis of experimental manipulation. Glob. Change Biol. 17, 927–942 (2011).Article 
ADS 

Google Scholar 
48.Gehlhausen, S. M., Schwartz, M. W. & Augspurger, C. K. Vegetation and microclimatic edge effects in two mixed-mesophytic forest fragments. Plant Ecol. 147, 21–35 (2000).Article 

Google Scholar 
49.Hofmeister, J. et al. Microclimate edge effect in small fragments of temperate forests in the context of climate change. For. Ecol. Manag. 448, 48–56 (2019).Article 

Google Scholar 
50.Treml, V. & Banaš, M. The effect of exposure on alpine treeline position: A case study from the High Sudetes, Czech Republic. Arct. Antarct. Alp. Res. 40, 751–760 (2008).Article 

Google Scholar 
51.Zellweger, F. et al. Seasonal drivers of understorey temperature buffering in temperate deciduous forests across Europe. Glob. Ecol. Biogeogr. 28, 1774–1786 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
52.Frey, S. J. et al. Spatial models reveal the microclimatic buffering capacity of old-growth forests. Sci. Adv. 2, e1501392 (2016).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
53.Ashcroft, M. B. & Gollan, J. R. Moisture, thermal inertia, and the spatial distributions of near-surface soil and air temperatures: Understanding factors that promote microrefugia. Agric. For. Meteorol. 176, 77–89 (2013).Article 
ADS 

Google Scholar 
54.Holden, Z. A., Klene, A. E., Keefe, R. F. & Moisen, G. G. Design and evaluation of an inexpensive radiation shield for monitoring surface air temperatures. Agric. For. Meteorol. 180, 281–286 (2013).Article 
ADS 

Google Scholar 
55.Maher, E. L., Germino, M. J. & Hasselquist, N. J. Interactive effects of tree and herb cover on survivorship, physiology, and microclimate of conifer seedlings at the alpine tree-line ecotone. Can. J. For. Res. 35, 567–574 (2005).Article 

Google Scholar 
56.Maher, E. L. & Germino, M. J. Microsite differentiation among conifer species during seedling establishment at alpine treeline. Ecoscience 13, 334–341 (2006).Article 

Google Scholar 
57.Mayor, J. R. et al. Elevation alters ecosystem properties across temperate treelines globally. Nature 542, 91–95 (2017).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
58.Allevato, E. et al. Canopy damage by spring frost in European beech along the Apennines: Effect of latitude, altitude and aspect. Remote Sens. Environ. 225, 431–440 (2019).Article 
ADS 

Google Scholar 
59.Nolè, A., Rita, A., Ferrara, A. M. S. & Borghetti, M. Effects of a large-scale late spring frost on a beech (Fagus sylvatica L.) dominated Mediterranean mountain forest derived from the spatio-temporal variations of NDVI. Ann. For. Sci. 75, 83 (2018).Article 

Google Scholar 
60.Müller, M. et al. Soil temperature and soil moisture patterns in a Himalayan alpine treeline ecotone. Arct. Antarct. Alp. Res. 48, 501–521 (2016).Article 

Google Scholar 
61.Liechty, H. O., Holmes, M. J., Reed, D. D. & Mroz, G. D. Changes in microclimate after stand conversion in two northern hardwood stands. For. Ecol. Manag. 50, 253–264 (1992).Article 

Google Scholar 
62.Peterson, D. W. & Peterson, D. L. Mountain hemlock growth responds to climatic variability at annual and decadal time scales. Ecology 82, 3330–3345 (2001).Article 

Google Scholar 
63.Jarvis, P. et al. Drying and wetting of Mediterranean soils stimulates decomposition and carbon dioxide emission: The “Birch effect”. Tree Physiol. 27, 929–940 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Binkley, D. & Fisher, R. F. Ecology and Management of Forest Soils (Wiley-Blackwell, 2013).
Google Scholar  More

1.Hope, P. R. & Jones, G. Warming up for dinner: Torpor and arousal in hibernating Natterer’s bats (Myotis nattereri) studied by radio telemetry. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 182, 569–578. https://doi.org/10.1007/s00360-011-0631-x (2012).Article 

Google Scholar 
2.Czenze, Z. J., Jonasson, K. A. & Willis, C. K. R. Thrifty females, frisky males: Winter energetics of hibernating bats from a cold climate. Physiol. Biochem. Zool. 90, 502–511. https://doi.org/10.1086/692623 (2017).Article 
PubMed 

Google Scholar 
3.Reynolds, D. S., Shoemaker, K., von Oettingen, S. & Najjar, S. High rates of winter activity and arousals in two New England bat species: Implications for a reduced white-nose syndrome impact?. Northeast. Nat. 24, B188–B208 (2017).Article 

Google Scholar 
4.Kunz, T. H. & Martin, R. A. Plecotus townsendii. Mamm. Species 175, 1–6 (1982).
Google Scholar 
5.Twente, J. W. Aspects of a population study of cavern-dwelling bats. J. Mamm. 36, 379–390 (1955).Article 

Google Scholar 
6.Humphrey, S. R. & Kunz, T. H. Ecology of a Pleistocene relict, the western big-eared bat (Plecotus townsendii), in the southern Great Plains. J. Mamm. 57, 470–494. https://doi.org/10.2307/1379297 (1976).Article 

Google Scholar 
7.Czenze, Z. J., Park, A. D. & Willis, C. K. R. Staying cold through dinner: Cold-climate bats rewarm with conspecifics but not sunset during hibernation. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 183, 859–866. https://doi.org/10.1007/s00360-013-0753-4 (2013).Article 

Google Scholar 
8.Pearson, O. P., Koford, M. R. & Pearson, A. K. Reproduction of the lump-nosed bat (Corynorhinus rafinesquei) in California. J. Mamm. 33, 273–320 (1952).Article 

Google Scholar 
9.Johnson, J. S., Lacki, M. J., Thomas, S. C. & Grider, J. F. Frequent arousals from winter torpor in Rafinesque’s big-eared bat (Corynorhinus rafinesquii). PLoS ONE 7, e49754. https://doi.org/10.1371/journal.pone.0049754 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Lausen, C. L. & Barclay, R. M. R. Winter bat activity in the Canadian prairies. Can. J. Zool.-Rev. Can. Zool. 84, 1079–1086. https://doi.org/10.1139/z06-093 (2006).Article 

Google Scholar 
11.Thomas, D. W. & Cloutier, D. Evaporative water-loss by hibernating little brown bats, Myotis lucifugus. Physiol. Zool. 65, 443–456 (1992).Article 

Google Scholar 
12.Ben-Hamo, M., Munoz-Garcia, A., Williams, J. B., Korine, C. & Pinshow, B. Waking to drink: Rates of evaporative water loss determine arousal frequency in hibernating bats. J. Exp. Biol. 216, 573–577. https://doi.org/10.1242/jeb.078790 (2013).Article 
PubMed 

Google Scholar 
13.Czenze, Z. J. & Willis, C. K. R. Warming up and shipping out: Arousal and emergence timing in hibernating little brown bats (Myotis lucifugus). J. Comp. Physiol. B-Biochem. Syst. Environ. Physiol. 185, 575–586. https://doi.org/10.1007/s00360-015-0900-1 (2015).Article 

Google Scholar 
14.Choate, J. R. & Anderson, J. M. Bats of jewel cave national monument, South Dakota. Prairie Nat. 29, 39–47 (1997).
Google Scholar 
15.Klüg-Baerwald, B. J., Gower, L. E., Lausen, C. L. & Brigham, R. M. Environmental correlates and energetics of winter flight by bats in southern Alberta, Canada. Can. J. Zool. 94, 829–836. https://doi.org/10.1139/cjz-2016-0055 (2016).Article 

Google Scholar 
16.Johnson, J. S. et al. Migratory and winter activity of bats in Yellowstone National Park. J. Mamm. 98, 211–221. https://doi.org/10.1093/jmammal/gyw175 (2017).Article 

Google Scholar 
17.Norquay, K. & Willis, C. Hibernation phenology of Myotis lucifugus. J. Zool. 294, 85–92 (2014).Article 

Google Scholar 
18.Barclay, R. M. et al. Variation in the reproductive rate of bats. Can. J. Zool. 82, 688–693 (2004).Article 

Google Scholar 
19.Jonasson, K. A. & Willis, C. K. Changes in body condition of hibernating bats support the thrifty female hypothesis and predict consequences for populations with white-nose syndrome. PLoS ONE 6, e21061. https://doi.org/10.1371/journal.pone.0021061 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
20.Speakman, J. R., Webb, P. I. & Racey, P. A. Effects of disturbance on the energy expenditure of hibernating bats. J. Appl. Ecol. 28, 1087–1104. https://doi.org/10.2307/2404227 (1991).Article 

Google Scholar 
21.Reeder, D. M., Field, K. A. & Slater, M. H. Balancing the costs of wildlife research with the benefits of understanding a panzootic disease, white-nose syndrome. ILAR J. 56, 275–282. https://doi.org/10.1093/ilar/ilv035 (2015).CAS 
Article 

Google Scholar 
22.Boyles, J. G. Benefits of knowing the costs of disturbance to hibernating bats. Wildl. Soc. Bull. 41, 388–392. https://doi.org/10.1002/wsb.755 (2017).Article 

Google Scholar 
23.Thomas, D. W. Hibernating bats are sensitive to nontactile human disturbance. J. Mamm. 76, 940–946. https://doi.org/10.2307/1382764 (1995).Article 

Google Scholar 
24.Furey, N. M. & Racey, P. A. Bats in the Anthropocene: Conservation of Bats in a Changing World 463–500 (Springer, 2016).
Google Scholar 
25.Sheffield, S. R., Shaw, J. H., Heidt, G. A. & McClenaghan, L. R. Guidelines for the protection of bat roosts. J. Mamm. 73, 707–710 (1992).
Google Scholar 
26.Jones, G., Jacobs, D. S., Kunz, T. H., Willig, M. R. & Racey, P. A. Carpe noctem: The importance of bats as bioindicators. Endang. Species Res. 8, 93–115 (2009).Article 

Google Scholar 
27.Blehert, D. S. et al. Bat white-nose syndrome: An emerging fungal pathogen?. Science 323, 227. https://doi.org/10.1126/science.1163874 (2009).CAS 
Article 
PubMed 

Google Scholar 
28.Foley, J., Clifford, D., Castle, K., Cryan, P. & Ostfeld, R. S. Investigating and managing the rapid emergence of white-nose syndrome, a novel, fatal, infectious disease of hibernating bats. Conserv. Biol. 25, 223–231. https://doi.org/10.1111/j.1523-1739.2010.01638.x (2011).Article 
PubMed 

Google Scholar 
29.Ingersoll, T. E., Sewall, B. J. & Amelon, S. K. Effects of white-nose syndrome on regional population patterns of 3 hibernating bat species. Conserv. Biol. 30, 1048–1059. https://doi.org/10.1111/cobi.12690 (2016).Article 
PubMed 

Google Scholar 
30.Minnis, A. M. & Lindner, D. L. Phylogenetic evaluation of Geomyces and allies reveals no close relatives of Pseudogymnoascus destructans, comb. nov., in bat hibernacula of eastern North America. Fungal Biol. 117, 638–649. https://doi.org/10.1016/j.funbio.2013.07.001 (2013).Article 
PubMed 

Google Scholar 
31.Lorch, J. M. et al. Experimental infection of bats with Geomyces destructans causes white-nose syndrome. Nature 480, 376 (2011).ADS 
CAS 
Article 

Google Scholar 
32.Verant, M. L. et al. White-nose syndrome initiates a cascade of physiologic disturbances in the hibernating bat host. BMC Physiol. 14, 10 (2014).Article 

Google Scholar 
33.Warnecke, L. et al. Inoculation of bats with European Geomyces destructans supports the novel pathogen hypothesis for the origin of white-nose syndrome. Proc. Natl. Acad. Sci. U.S.A. 109, 6999–7003. https://doi.org/10.1073/pnas.1200374109 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
34.Lilley, T. M. et al. White-nose syndrome survivors do not exhibit frequent arousals associated with Pseudogymnoascus destructans infection. Front. Zool. https://doi.org/10.1186/s12983-016-0143-3 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
35.McGuire, L. P., Mayberry, H. W. & Willis, C. K. R. White-nose syndrome increases torpid metabolic rate and evaporative water loss in hibernating bats. Am. J. Physiol.-Regulat. Integr. Compar. Physiol. 313, R680–R686. https://doi.org/10.1152/ajpregu.00058.2017 (2017).CAS 
Article 

Google Scholar 
36.Knudsen, G. R., Dixon, R. D. & Amelon, S. K. Potential spread of white-nose syndrome of bats to the Northwest: Epidemiological considerations. Northwest Sci. 87, 292–306. https://doi.org/10.3955/046.087.0401 (2013).Article 

Google Scholar 
37.Bernard, R. F. & McCracken, G. F. Winter behavior of bats and the progression of white-nose syndrome in the southeastern United States. Ecol. Evol. 7, 1487–1496. https://doi.org/10.1002/ece3.2772 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
38.Cheng, T. L. et al. Higher fat stores contribute to persistence of little brown bat populations with white-nose syndrome. J. Anim. Ecol. 88, 591–600 (2019).Article 

Google Scholar 
39.Turner, J. M. et al. Conspecific disturbance contributes to altered hibernation patterns in bats with white-nose syndrome. Physiol. Behav. 140, 71–78 (2015).CAS 
Article 

Google Scholar 
40.Blazek, J. et al. Numerous cold arousals and rare arousal cascades as a hibernation strategy in European Myotis bats. J. Therm. Biol 82, 150–156. https://doi.org/10.1016/j.jtherbio.2019.04.002 (2019).Article 
PubMed 

Google Scholar 
41.Lorch, J. M. et al. First detection of bat white-nose syndrome in Western North America. mSphere 1(4), e00148. https://doi.org/10.1128/mSphere.00148-16 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
42.Weller, T. J. et al. A review of bat hibernacula across the western United States: Implications for white-nose syndrome surveillance and management. PLoS ONE https://doi.org/10.1371/journal.pone.0205647 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
43.Whiting, J. C. et al. Bat hibernacula in caves of southern Idaho: Implications for monitoring and management. West. N. Am. Nat. 78, 165–173 (2018).Article 

Google Scholar 
44.Whiting, J. C. et al. Long-term bat abundance in sagebrush steppe. Sci. Rep. 8, 12288 (2018).ADS 
Article 

Google Scholar 
45.Call, R. S. et al. Maternity roosts of Townsend’s big-eared bats in lava tube caves of southern Idaho. Northwest Sci. 92, 158–165 (2018).ADS 
Article 

Google Scholar 
46.Clark, B. S., Clark, B. K. & Leslie, D. M. Seasonal variation in activity patterns of the endangered Ozark big-eared bat (Corynorhinus townsendii ingens). J. Mamm. 83, 590–598. https://doi.org/10.1644/1545-1542(2002)083%3c0590:sviapo%3e2.0.co;2 (2002).Article 

Google Scholar 
47.French, A. R. The patterns of mammalian hibernation. Am. Sci. 76, 568–575 (1988).ADS 

Google Scholar 
48.Reynolds, T. D., Connelly, J. W., Halford, D. K. & Arthur, W. J. Vertebrate fauna of the Idaho National Environmental Research Park. Gt. Basin Nat. 46, 513–527 (1986).
Google Scholar 
49.Genter, D. L. Wintering bats of the upper Snake River Plain: Occurrence in lava-tube caves. Gt. Basin Nat. 46, 241–244 (1986).
Google Scholar 
50.Gillies, K. E., Murphy, P. J. & Matocq, M. D. Hibernacula characteristics of Townsend’s big-eared bats in southeastern Idaho. Nat. Areas J. 34, 24–30 (2014).Article 

Google Scholar 
51.Sikes, R. S. et al. Guidelines of the American Society of Mammalogists for the use of wild mammals in research and education. J. Mamm. 97(663–688), 2016. https://doi.org/10.1093/jmammal/gyw078 (2016).Article 

Google Scholar 
52.Schwab, N. A. & Mabee, T. J. Winter acoustic activity of bats in Montana. Northwest. Nat. 95, 13–27 (2014).Article 

Google Scholar 
53.Britzke, E. R., Slack, B. A., Armstrong, M. P. & Loeb, S. C. Effects of orientation and weatherproofing on the detection of bat echolocation calls. J. Fish Wildl. Manage. 1, 136–141. https://doi.org/10.3996/072010-jfwm-025 (2010).Article 

Google Scholar 
54.Skalak, S. L., Sherwin, R. E. & Brigham, R. M. Sampling period, size and duration influence measures of bat species richness from acoustic surveys. Methods Ecol. Evol. 3, 490–502. https://doi.org/10.1111/j.2041-210X.2011.00177.x (2012).Article 

Google Scholar 
55.Miller, B. W. A method for determining relative activity of free flying bats using a new activity index for acoustic monitoring. Acta Chiropt. 3, 93–105 (2001).
Google Scholar 
56.Nocera, T., Ford, W. M., Silvis, A. & Dobony, C. A. Patterns of acoustical activity of bats prior to and 10 years after WNS on Fort drum army installation, New York. Glob. Ecol. Conserv. https://doi.org/10.1016/j.gecco.2019.e00633 (2019).Article 

Google Scholar 
57.Britzke, E. R., Gillam, E. H. & Murray, K. L. Current state of understanding of ultrasonic detectors for the study of bat ecology. Acta Theriol. 58, 109–117. https://doi.org/10.1007/s13364-013-0131-3 (2013).Article 

Google Scholar 
58.O’Farrell, M. J., Miller, B. W. & Gannon, W. L. Qualitative identification of free-flying bats using the Anabat detector. J. Mamm. 80, 11–23. https://doi.org/10.2307/1383203 (1999).Article 

Google Scholar 
59.Whiting, J. C., Doering, B. & Pennock, D. Acoustic surveys for local, free-flying bats in zoos: An engaging approach for bat education and conservation. J. Bat Res. Conserv. 12, 94–99. https://doi.org/10.14709/BarbJ.12.1.2019.12 (2019).Article 

Google Scholar 
60.O’Farrell, M. J. & Gannon, W. L. A comparison of acoustic versus capture techniques for the inventory of bats. J. Mamm. 80, 24–30. https://doi.org/10.2307/1383204 (1999).Article 

Google Scholar 
61.Stahlschmidt, P. & Bruhl, C. A. Bats as bioindicators—The need of a standardized method for acoustic bat activity surveys. Methods Ecol. Evol. 3, 503–508. https://doi.org/10.1111/j.2041-210X.2012.00188.x (2012).Article 

Google Scholar 
62.Avery, M. I. Winter activity of pipistrelle bats. J. Anim. Ecol. 54, 721–738. https://doi.org/10.2307/4374 (1985).Article 

Google Scholar 
63.McCulloch, C. E. & Neuhaus, J. M. Generalized linear mixed models. In Encyclopedia of Biostatistics (eds Armitage, P. & Colton, T.) (Wiley, 2005).
Google Scholar 
64.Nelder, J. A. & Wedderburn, R. W. Generalized linear models. J. R. Stat. Soc. Ser. A (Gen.) 135, 370–384 (1972).Article 

Google Scholar 
65.Hardin, J. W. & Hilbe, J. M. Generalized Linear Models and Extensions (Stata Press, 2007).
Google Scholar 
66.Consul, P. & Famoye, F. Generalized Poisson regression model. Commun. Stat. Theory Methods 21, 89–109 (1992).Article 

Google Scholar 
67.Aho, K. A. Foundational and Applied Statistics for Biologists using R (CRC Press, 2013).
Google Scholar 
68.Akaike, H. Selected Papers of Hirotugu Akaike 199–213 (Springer, 1998).
Google Scholar 
69.Burnham, K. P. & Anderson, D. A. Model Selection and Multimodel Inference: A practical Information-Theoretic Approach 2nd edn. (Springer, 2002).
Google Scholar 
70.RCoreTeam. R: A Language and Environment for Statistical Computing (2020).71.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S-PLUS (Springer, 2013).
Google Scholar 
72.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
73.Perkins, J. M., Barss, J. M. & Peterson, J. Winter records of bats in Oregon and Washington. Northwest. Nat. 71, 59–62. https://doi.org/10.2307/3536594 (1990).Article 

Google Scholar 
74.Nagorsen, D. W. et al. Winter bat records for British Columbia. Northwest Nat. 74, 61–66 (1993).Article 

Google Scholar 
75.Hayman, D. T., Cryan, P. M., Fricker, P. D. & Dannemiller, N. G. Long-term video surveillance and automated analyses reveal arousal patterns in groups of hibernating bats. Methods Ecol. Evol. 8, 1813–1821 (2017).Article 

Google Scholar 
76.Boyles, J. G., Dunbar, M. B. & Whitaker, J. O. Activity following arousal in winter in North American vespertilionid bats. Mamm. Rev. 36, 267–280. https://doi.org/10.1111/j.1365-2907.2006.00095.x (2006).Article 

Google Scholar 
77.Speakman, J. R. & Racey, P. A. Hibernal ecology of the pipistrelle bat: Energy expenditure, water requirements and mass-loss, implications for survial and the function of winter emergence flights. J. Anim. Ecol. 58, 797–813. https://doi.org/10.2307/5125 (1989).Article 

Google Scholar 
78.Lawrence, B. D. & Simmons, J. A. Measurements of atmospheric attenuation at ultrasonic frequencies and the significance for echolocation by bats. J. Acoust. Soc. Am. 71, 585–590 (1982).ADS 
CAS 
Article 

Google Scholar 
79.Dunbar, M. B. & Tomasi, T. E. Arousal patterns, metabolic rate, and an energy budget of eastern red bats (Lasiurus borealis) in winter. J. Mamm. 87, 1096–1102. https://doi.org/10.1644/05-mamm-a-254r3.1 (2006).Article 

Google Scholar 
80.Ford, W. M., Britzke, E. R., Dobony, C. A., Rodrigue, J. L. & Johnson, J. B. Patterns of acoustical activity of bats prior to and following white-nose syndrome occurrence. J. Fish Wildl. Manage. 2, 125–134. https://doi.org/10.3996/042011-jfwm-027 (2011).Article 

Google Scholar 
81.Bernard, R. F., Foster, J. T., Willcox, E. V., Parise, K. L. & McCracken, G. F. Molecular detection of the causative agent of white-nose syndrome on Rafinesque’s big-eared bats (Corynorhinus rafinesquii) and two species of migratory bats in the southeastern USA. J. Wildl. Dis. 51, 519–522. https://doi.org/10.7589/2014-08-202 (2015).Article 
PubMed 

Google Scholar 
82.Dzal, Y., McGuire, L. P., Veselka, N. & Fenton, M. B. Going, going, gone: the impact of white-nose syndrome on the summer activity of the little brown bat (Myotis lucifugus). Biol. Lett. 7, 392–394 (2010).Article 

Google Scholar 
83.Brooks, R. T. Declines in summer bat activity in central New England 4 years following the initial detection of white-nose syndrome. Biodivers. Conserv. 20, 2537–2541. https://doi.org/10.1007/s10531-011-9996-0 (2011).Article 

Google Scholar 
84.Holloway, G. L. & Barclay, R. M. R. Myotis ciliolabrum. Mamm. Species 670, 1–5. https://doi.org/10.1644/1545-1410(2001)670%3c0001:mc%3e2.0.co;2 (2001).Article 

Google Scholar 
85.Halsall, A. L., Boyles, J. G. & Whitaker, J. O. Jr. Body temperature patterns of big brown bats during winter in a building hibernaculum. J. Mamm. 93, 497–503 (2012).Article 

Google Scholar 
86.Paige, K. N. Bats and barometric pressure: conserving limited energy and tracking insects from the roost. Funct. Ecol. 9, 463–467 (1995).Article 

Google Scholar 
87.Frick, W. F. Acoustic monitoring of bats, considerations of options for long-term monitoring. Therya 4, 69–78 (2013).ADS 
Article 

Google Scholar 
88.Whitaker, J. O. & Rissler, L. J. Winter activity of bats at a mine entrance in Vermillion County, Indiana. Am. Midl. Nat. 127, 52–59. https://doi.org/10.2307/2426321 (1992).Article 

Google Scholar  More