Philippa Kaur

1.
Endler, J. A., Westcott, D. A., Madden, J. R. & Robson, T. Animal visual systems and the evolution of color patterns: sensory processing illuminates signal evolution. Evolution 59, 1795–1818 (2005).
PubMed  Article  Google Scholar 
2.
Norris, K. S. & Lowe, C. H. An analysis of background color-matching in amphibians and reptiles. Ecology 45, 565–580 (1964).
Article  Google Scholar 

3.
Allen, J. J., Mäthger, L. M., Barbosa, A. & Hanlon, R. T. Cuttlefish use visual cues to control three-dimensional skin papillae for camouflage. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 195, 547–555 (2009).
PubMed  Article  Google Scholar 

4.
Cuthill, I. C. et al. The biology of color. Science https://doi.org/10.1126/science.aan0221 (2017).

5.
Seehausen, O., Van Alphen, J. J. M. & Lande, R. Color polymorphism and sex ratio distortion in a cichlid fish as an incipient stage in sympatric speciation by sexual selection. Ecol. Lett. 2, 367–378 (1999).
Article  Google Scholar 

6.
Pérez-Rodríguez, L., Jovani, R. & Stevens, M. Shape matters: animal colour patterns as signals of individual quality. Proc. R. Soc. Lond. Ser. B Biol. Sci. 284, 20162446 (2017).
Google Scholar 

7.
Tanaka, K. Thermal biology of a colour-dimorphic snake, Elaphe quadrivirgata, in a montane forest: Do melanistic snakes enjoy thermal advantages? Biol. J. Linn. Soc. 92, 309–322 (2007).
Article  Google Scholar 

8.
Smith, K. R. et al. Colour change on different body regions provides thermal and signalling advantages in bearded dragon lizards. Proc. R. Soc. Lond. Ser. B Biol. Sci. 283, 20160626 (2016).
Google Scholar 

9.
Christian, K. A. & Tracy, C. R. The effect of the thermal environment on the ability of hatchling galapagos land iguanas to avoid predation during dispersal. Oecologia 49, 218–223 (1981).
PubMed  Article  Google Scholar 

10.
Clusella-Trullas, S., van Wyk, J. H. & Spotila, J. R. Thermal melanism in ectotherms. J. Therm. Biol. 32, 235–245 (2007).
Article  Google Scholar 

11.
Moreno Azócar, D. L. et al. Effect of body mass and melanism on heat balance in Liolaemus lizards of the goetschi clade. J. Exp. Biol. 219, 1162–1171 (2016).
PubMed  Article  PubMed Central  Google Scholar 

12.
Farouki, O. T. Thermal properties of soils. U.S. Army Corps of Engineers, Cold Regions Research and Engineering Laboratory. https://doi.org/10.4236/ojss.2011.13011 (1981).

13.
Porter, W. P. & Gates, D. M. Thermodynamic equilibria of animals with environment. Ecol. Monogr. 39, 227–244 (1969).
Article  Google Scholar 

14.
Miller, G. E. in Introduction to Biomedical Engineering (3rd edn.) (eds. Enderle, J., & Bronzino, J.) pp. 937–993 (Academic press, 2012).

15.
Prota, G. Melanins and Melanogenesis (Academic Press, New York, 1992).

16.
Meredith, P. et al. Towards structure–property–function relationships for eumelanin. Soft Matter 2, 37–44 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Geen, M. R. S. & Johnston, G. R. Coloration affects heating and cooling in three color morphs of the Australian Bluetongue Lizard, Tiliqua scincoides. J. Therm. Biol. 43, 54–60 (2014).
PubMed  Article  PubMed Central  Google Scholar 

18.
Cordero, R. J. & Casadevall, A. Melanin. Curr. Biol. 30, R142–R143 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

19.
Jastrzebska, M. M., Isotalo, H., Paloheimo, J. & Stubb, H. Electrical conductivity of synthetic DOPA-melanin polymer for different hydration states and temperatures. J. Biomater. Sci. Polym. Ed. 7, 577–586 (1996).
Article  Google Scholar 

20.
Mostert, A. B. et al. Role of semiconductivity and ion transport in the electrical conduction of melanin. Proc. Natl Acad. Sci. USA 109, 8943–8947 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
Mostert, A. B. et al. Understanding melanin: a nano-based material for the future. In Nanomaterials: Science and Applications (eds. D. M. Kane, A. Micolich & P. Roger) 175–202 (New York: Jenny Stanford Publishing, 2016).

22.
Kellicker, J., DiMarzio, C. A. & Kowalski, G. J. Computational model of heterogeneous heating in melanin. Optical Interact. Tissue Cells XXVI 9321, 93210H (2015).
Google Scholar 

23.
Jastrzebska, M. M., Isotalo, H., Paloheimo, J. & Stubb, H. Electrical conductivity of synthetic dopa-melanin polymer for different hydration states and temperatures. J. Biomater. Sci. 7, 577–586 (1995).
CAS  Article  Google Scholar 

24.
Wünsche, J. et al. Protonic and electronic transport in hydrated thin films of the pigment eumelanin. Chem. Mater. 27, 436–442 (2015).
Article  CAS  Google Scholar 

25.
Rienecker, S. B., Mostert, A. B., Schenk, G., Hanson, G. R. & Meredith, P. Heavy water as a probe of the free radical nature and electrical conductivity of melanin. J. Phys. Chem. B 119, 14994–15000 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

26.
Migliaccio, L. et al. Evidence of unprecedented high electronic conductivity in mammalian pigment based eumelanin thin films after thermal annealing in vacuum. Front. Chem. 7, 162 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Rosenblum, E. B., Hoekstra, H. E. & Nachman, M. Adaptive reptile color variation and the evolution of the Mc1r gene. Evolution 58, 1794–1808 (2004).
CAS  PubMed  PubMed Central  Google Scholar 

28.
Jackson, J. F., Iii, W. I. & Campbell, H. W. The dorsal pigmentation pattern of snakes as an antipredator strategy: a multivariate approach. Am. Naturalist 110, 1029 (1976).
Article  Google Scholar 

29.
Wüster, W. et al. Do aposematism and Batesian mimicry require bright colours? A test, using European viper markings. Proc. R. Soc. Lond. Ser. B Biol. Sci. 271, 2495–2499 (2004).
Article  Google Scholar 

30.
Allen, W. L., Baddeley, R., Scott-Samuel, N. E. & Cuthill, I. C. The evolution and function of pattern diversity in snakes. Behav. Ecol. 24, 1237–1250 (2013).
Article  Google Scholar 

31.
Clause, A. G. & Becker, R. N. Temperature shock as a mechanism for color pattern aberrancy in snakes. Herpetol. Notes 8, 331–334 (2015).
Google Scholar 

32.
Ressel, S. & Schall, J. J. Parasites and showy males: malarial infection and color variation in fence lizards. Oecologia 78, 158–164 (1989).
CAS  PubMed  Article  Google Scholar 

33.
Morrison, R. L., Rand, M. S. & Frost-Mason, S. K. Cellular basis of color differences in three morphs of the lizard Sceloporus undulatus erythrocheilus. Copeia 1995, 397–408 (1995).

34.
Stuart-Fox, D. M. & Ord, T. J. Sexual selection, natural selection and the evolution of dimorphic coloration and ornamentation in agamid lizards. Proc. R. Soc. Lond. Ser. B Biol. Sci. 271, 2249–2255 (2004).
Article  Google Scholar 

35.
Langkilde, T. & Boronow, K. E. Hot boys are blue: temperature-dependent color change in male eastern fence lizards. J. Herpetol. 46, 461–465 (2012).

36.
Moreno Azócar, D. L. et al. Variation in body size and degree of melanism within a lizards clade: is it driven by latitudinal and climatic gradients? J. Zool. 295, 243–253 (2014).
Article  Google Scholar 

37.
Pearson, O. P. The effect of substrate and of skin color on thermoregulation of a lizard. Comp. Biochem. Physiol. Part A Physiol. 58, 353–358 (1977).
Article  Google Scholar 

38.
Hutchinson, V. H. & Larimer, J. L. Reflectivity of the integuments of some lizards from different habitats. Ecology 41, 199–209 (1960).
Article  Google Scholar 

39.
Norris, K. S. in Lizard Ecology: A Symposium (ed. W. W. Milstead) 162–229 (University of Missouri Press, 1967).

40.
Barry, R. G., & Chorley, R. J. Atmosphere, Weather and Climate (Routledge, 2003).

41.
Olalla‐Tarraga, M. Á. & Rodríguez, M. Á. Energy and interspecific body size patterns of amphibian faunas in Europe and North America: anurans follow Bergmann’s rule, urodeles its converse. Glob. Ecol. Biogeogr. 16, 606–617 (2007).
Article  Google Scholar 

42.
Uetz, P., Freed, P. & Hošek, J. (eds.). The Reptile Database. http://www.reptile-database.org (2020).

43.
Ohta, Y. I., Kanade, T. & Sakai, T. Color information for region segmentation. Comput. Graph. Image Process. 13, 222–241 (1980).
Article  Google Scholar 

44.
Gueymard, C. A., Myers, D. & Emery, K. Proposed reference irradiance spectra for solar energy systems testing. Sol. Energy 73, 443–467 (2002).
Article  Google Scholar 

45.
Shawkey, M. D. et al. Beyond colour: consistent variation in near infrared and solar reflectivity in sunbirds (Nectariniidae). Sci. Nat. (Naturwissenschaften) 104, 78 (2017).
Article  CAS  Google Scholar 

46.
Shine, R. & Kearney, M. Field studies of reptile thermoregulation: how well do physical models predict operative temperatures? Funct. Ecol. 15, 282–288 (2001).
Article  Google Scholar 

47.
Reguera, S., Zamora-Camacho, F. J. & Moreno-Rueda, G. The lizard Psammodromus algirus (Squamata: Lacertidae) is darker at high altitudes. Biol. J. Linn. Soc. 112, 132–141 (2014).
Article  Google Scholar 

48.
Martínez-Freiría, F., Toyama, K. S., Freitas, I. & Kaliontzopoulou, A. Thermal melanism explains macroevolutionary variation of dorsal pigmentation in Eurasian vipers. Sci. Rep. 10, 1–10 (2020).
Article  CAS  Google Scholar 

49.
Pizzigalli, C. et al. Eco-geographical determinants of ornamentation in vipers. Biol. J. Linnean Soc. 130, 1–14 (2020).

50.
Kurschner, W. M., Kvacek, Z. & Dilcher, D. L. The impact of Miocene atmospheric carbon dioxide fluctuations on climate and the evolution of terrestrial ecosystems. Proc. Natl Acad. Sci. USA 105, 449–453 (2008).
PubMed  Article  PubMed Central  Google Scholar 

51.
Schraft, H. A., Goodman, C. & Clark, R. W. Do free-ranging rattlesnakes use thermal cues to evaluate prey? J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 204, 295–303 (2018).
PubMed  Article  PubMed Central  Google Scholar 

52.
Alencar, L. R. V. et al. Diversification in vipers: phylogenetic relationships, time of divergence and shifts in speciation rates. Mol. Phylogenet. Evol. 105, 50–62 (2016).
PubMed  Article  PubMed Central  Google Scholar 

53.
Zhang, Z. et al. Aridification of the Sahara desert caused by Tethys Sea shrinkage during the Late Miocene. Nature 513, 401–404 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Pokorny, L. et al. Living on the edge: timing of Rand Flora disjunctions congruent with ongoing aridification in Africa. Front. Genet. 6, 154 (2015).
PubMed  PubMed Central  Article  Google Scholar 

55.
Barlow, A. et al. Ancient habitat shifts and organismal diversification are decoupled in the African viper genus Bitis (Serpentes: Viperidae). J. Biogeogr. 46, 1234–1248 (2019).
Article  Google Scholar 

56.
Senut, B., Pickford, M. & Ségalen, L. Neogene desertification of Africa. C. R. Geosci. 341, 591–602 (2009).
CAS  Article  Google Scholar 

57.
Douglas, M. E., Douglas, M. R., Schuett, G. W. & Porras, L. W. Evolution of rattlesnakes (Viperidae; Crotalus) in the warm deserts of western North America shaped by Neogene vicariance and Quaternary climate change. Mol. Ecol. 15, 3353–3374 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

58.
Zhisheng, A., Kutzbach, J. E., Prell, W. L. & Porter, S. C. Evolution of Asian monsoons and phased uplift of the Himalaya–Tibetan plateau since Late Miocene times. Nature 411, 62 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

59.
Janis, C. M., Damuth, J. & Theodor, J. M. The species richness of Miocene browsers, and implications for habitat type and primary productivity in the North American grassland biome. Palaeogeogr. Palaeoclimatol. Palaeoecol. 207, 371–398 (2004).
Article  Google Scholar 

60.
Walters, K. A., & Roberts, M. S. The structure and function of Skin. https://doi.org/10.1002/yea (2002).

61.
Wüster, W., Peppin, L., Pook, C. E. & Walker, D. E. A nesting of vipers: phylogeny and historical biogeography of the Viperidae (Squamata: Serpentes). Mol. Phylogenet. Evol. 49, 445–459 (2008).
PubMed  Article  PubMed Central  Google Scholar 

62.
Shine, R. & Li-Xin, S. Arboreal ambush site selection by pit-vipers Gloydius shedaoensis. Anim. Behav. 63, 565–576 (2002).
Article  Google Scholar 

63.
Ursenbacher, S. et al. Postglacial recolonization in a cold climate specialist in western europe: patterns of genetic diversity in the adder (Vipera berus) support the central-marginal hypothesis. Mol. Ecol. 24, 3639–3651 (2015).
PubMed  Article  PubMed Central  Google Scholar 

64.
Blumthaler, M., Ambach, W. & Ellinger, R. Increase in solar UV radiation with altitude. J. Photochem. Photobiol. B Biol. 39, 130–134 (1997).
CAS  Article  Google Scholar 

65.
Gaston, K. J. Global patterns in biodiversity. Nature 405, 220–227 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

66.
Körner, C. et al. in Ecosystems and Human Well-being, Chapter 24, vol. 1. (Island Press, 2005).

67.
Tuniyev, B. et al. Gloydius halys. The IUCN Red List of Threatened Species 2009: e.T157282A5069394. https://www.iucnredlist.org/species/157282/5069394 (2009).

68.
Salter, C., Hobbs, J., Wheeler, J., Kostbade, J. T. Essentials of World Regional Geography 2nd edn. (Harcourt Brace, New York, 2005) pp. 464–465.

69.
Couplan, F., & Ligeon, J. C. Fleurs des Alpes: balade d’un botaniste, des plaines aux sommets (Nathan, 2005).

70.
Solórzano, A., Porras, L. W., Chaves, G., Bonilla, F. & Batista, A. Atropoides picadoi. The IUCN Red List of Threatened Species 2014: e.T203657A2769424. https://doi.org/10.2305/IUCN.UK.2014-1.RLTS.T203657A2769424.en. (2014).

71.
Canseco-Márquez, L. & Muñoz-Alonso, A. Bothriechis rowleyi. The IUCN Red List of Threatened Species 2007: e.T64304A12761506. https://doi.org/10.2305/IUCN.UK.2007.RLTS.T64304A12761506.en. (2020).

72.
Feldman, A., Sabath, N., Pyron, R. A., Mayrose, I. & Meiri, S. Body sizes and diversification rates of lizards, snakes, amphisbaenians and the tuatara. Glob. Ecol. Biogeogr. 25, 187–197 (2016).
Article  Google Scholar 

73.
Hill, N. Description of cranial elements and ontogenetic change within Tropidolaemus wagleri (Serpentes: Crotalinae). PLoS ONE 14, e0206023 (2019).

74.
Savage, J. M. The Amphibians and Reptiles of Costa Rica: A Herpetofauna between two Continents, between two Seas. (University of Chicago Press, Chicago, 2002).

75.
Fathinia, B., Rastegar-Pouyani, N., Rastegar-Pouyani, E., Todehdehghan, F. & Amiri, F. Avian deception using an elaborate caudal lure in Pseudocerastes urarachnoides (Serpentes: Viperidae). Amphib. Reptilia 36, 223–231 (2015).
Article  Google Scholar 

76.
Menegon, M., Davenport, T. R. & Howell, K. M. Description of a new and critically endangered species of Atheris (Serpentes: Viperidae) from the Southern Highlands of Tanzania, with an overview of the country’s tree viper fauna. Zootaxa 3120, 43–54 (2011).
Article  Google Scholar 

77.
Goldenberg, J., D’Alba, L. Bisschop, K., Vanthournout, B., Shawkey, M. “Replication Data for: Substrate thermal properties influence ventral brightness evolution in ectotherms”; MacroBright v.0.1, https://doi.org/10.34894/FZ66NU, DataverseNL, V2. (2020).

78.
R-Core-Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/ (2019)

79.
Pennell, M. W. et al. geiger v2.0: an expanded suite of methods for fitting macroevolutionary models to phylogenetic trees. Bioinformatics 15, 2216–2218 (2014).
Article  CAS  Google Scholar 

80.
Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evolution 3, 217–223 (2012).
Article  Google Scholar 

81.
Stayton, C. T. convevol: Analysis of Convergent Evolution. R package version 1.3. https://CRAN.R-project.org/package=convevol (2018).

82.
Stayton, C. T. The definition, recognition, and interpretation of convergent evolution, and two new measures for quantifying and assessing the significance of convergence. Evolution 69, 2140–2153 (2015).
PubMed  Article  PubMed Central  Google Scholar 

83.
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R Package. J. Stat. Softw. 33, 1–22 (2010).
Article  Google Scholar 

84.
Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.15. https://CRAN.R-project.org/package=MuMIn (2019).

85.
Marchetti, M. P., Light, T., Moyle, P. B. & Viers, J. H. Fish invasions in California watersheds: testing hypotheses using landscape patterns. Ecol. Appl. 14, 1507–1525 (2004).
Article  Google Scholar 

86.
Buxton, A. S., Groombridge, J. J., Zakaria, N. B. & Griffiths, R. A. Seasonal variation in environmental DNA in relation to population size and environmental factors. Sci. Rep. 7, 1–9 (2017).
Article  CAS  Google Scholar 

87.
Hadfield, J. MCMC Course Notes. https://cran.r-project.org/web/packages/MCMCglmm/vignettes/CourseNotes.pdf (2018).

88.
Gelman, A. & Rubin, B. D. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–511 (1992).
Article  Google Scholar 

89.
Porter, W. P., Mitchell, J. W., Beckman, W. A. & DeWitt, C. B. Behavioral implications of mechanistic ecology – Thermal and behavioral modeling of desert ectotherms and their microenvironment. Oecologia 13, 1–54 (1973).
CAS  PubMed  Article  PubMed Central  Google Scholar 

90.
Orlov, N. L., Sundukov, Y. N. & Kropachev, I. I. Distribution of pitvipers of “Gloydius blomhoffii” complex in Russia with the first records of Gloydius blomhoffii blomhoffii at Kunashir island (Kuril archipelago, Russian far east). Russ. J. Herpetol. 21, 169–178 (2014). More

Biological experiments
Cell culture
We obtained unlabelled parental (sensitive) and radiation-resistant populations from two prostate cell lines, PC3 and DU145, from the Liu laboratory (University of Toronto, Canada). Radioresistant cell populations comprised pooled cells from the parental line that survived a clinically-relevant course of radiotherapy23,24. To produce stable, fluorescent cell lines, we transduced cells using lentiviral particles containing the vectors, pCDH1-CMV-GFP-EF1-Hygro or pCHD1-CMV-DsRed-EF1-Hygro (Systems Biosciences), collected the top 30% of brightest cells by flow cytometry, and used hygromycin B (50 mg/mL, Gibco) for selection (200 µg/mL for PC3 and 250 µg/mL for DU145). Cell lines were cultured in DMEM medium (low glucose, pyruvate, GlutaMAX, Gibco) supplemented with 25 mM HEPES (Gibco), 10% foetal bovine serum (Sigma or Pan-Biotech), and 1% penicillin/streptomycin. Authentication was performed using STR profiling (Promega PowerPlex 21 PCR kit, Eurofins), and mycoplasma checks were performed routinely using MycoAlert Mycoplasma Detection Kit (Lonza). All cells were maintained in an incubator (37 °C, 5% CO2). Unlabelled cells were cultured for up to 10 passages (~6 weeks) for transduction; labelled cells were cultured for up to 10 passages (~6 weeks). No additional courses of radiation were used to maintain resistance. We measured clonogenic survival at 2 Gy (SF2) to verify that the labelled cells maintained the resistance phenotype for up to 12 passages (~8 weeks) in culture.
Monolayer growth experiments
Single cells (1 × 103 cells/well, 200 μL medium) were seeded in triplicate in flat-bottom 96-well plates, allowed to attach overnight, irradiated (0, 2, or 6 Gy), and imaged daily using brightfield (Incucyte Live Cell Imaging System, Sartorius). The medium was changed every 2 days. Cell confluence was determined using Incucyte Base Software (Sartorius).
Clonogenic assays
Survival of cell lines after radiation was measured using a clonogenic assay36. Briefly, cells were seeded in triplicate in six-well plates and irradiated using a Cs-137 (dose rate of 0.89 Gy/min) or an X-ray irradiator (195 kV, 10 mA). Surviving colonies were stained after 10 days with crystal violet and counted. The surviving fraction was calculated as (number of colonies/number of seeded cells) × plating efficiency.
Response to cisplatin
To check whether the RR cells had altered DNA damage response, we measured the cell viability of each population in response to a range of concentrations of cisplatin using a modified cytotoxicity assay37. Briefly, single cells (2 × 103 cells in 100 μL/well) were seeded as monolayers in triplicate in a 96-well plate and allowed to attach for 36 h before treatment. Increasing concentrations of cisplatin (made in 100 μL/well) were added to each well resulting in a final volume of 200 μL/well; cisplatin (Sigma) was prepared fresh for each treatment by dissolving the powder in 0.9% sterile-filtered saline to a stock concentration of 3.3 mM. Treated cells were then cultured for 72 h in cisplatin before they were fixed with 10% formalin. Cell confluence was determined using Incucyte Base Software (Sartorius) and reformatted to a concentration–response curve by normalising cell confluence values to the untreated well.
Spheroid generation and culture
Homogeneous and mixed spheroids were generated in 96-well, ultra-low attachment plates (7007, Corning) by seeding different ratios of parental and radioresistant cell populations (2 × 103 total cells/well) using Matrigel (5% v/v, Corning) to promote spheroid formation38. For all spheroid experiments, after a formation phase of 3 days, spheroids were fed every 2 days by replacing 50% of the medium in each well with fresh medium (200 μL total/well). Culture medium and incubation conditions were as described under ‘Cell culture’. Spheroid volumes were calculated using SpheroidSizer39.
Unirradiated spheroid growth experiments
Spheroids were generated as described in ‘Spheroid generation and culture’ and monitored for growth by brightfield imaging (Leica DM IRBE, Hamamatsu).
Flow cytometry
The proportions, survival, and cell cycle of each population from spheroids were measured by flow cytometry. Mixed unirradiated or irradiated spheroids (seeded 1:1 parental:RR, 6–8 pooled/group) were incubated with EdU (10 µM final concentration) 12 h prior to dissociation, dissociated (100 μL Accumax, Millipore) for 20 min at 37 °C, washed with phosphate-buffered saline (PBS), centrifuged (300 × g, 5 min), and incubated with efluor-780 (1 μL/mL PBS; ThermoFisher Scientific) for 30 min on ice in the dark to distinguish live/dead cells. After washing in PBS, samples were fixed for 10 min in IC Fixation Buffer (ThermoFisher Scientific), and permeabilized and stained with Click-iT Plus EdU Alexa Fluor 647 (ThermoFisher Scientific) according to manufacturer’s instructions. Following a wash in 1× saponin, cells were incubated 30 min with FxCycle Violet Stain (1:1000, 300 µL of 1× saponin; ThermoFisher Scientific) before being run on the BD LSR Fortessa X-20 Cytometer or the Attune NxT Flow Cytometer using the 405, 488, 561, and 633 lasers. Data were analysed using FlowJo (Treestar, Inc.) as described in Supplementary Figs. 2 and 4.
Spheroid growth experiments after radiation
To determine bulk radiation response of spheroids, PC3 cells were seeded as spheroids (n = 15 per dose per group) with 4 groups as described in ‘Spheroid generation and culture’: parental, 9:1 parental:RR, 1:1 parental:RR, and RR. After formation, spheroids were irradiated (0, 2.5, 5, 7.5, 10, 15, and 20 Gy) on day 4 and imaged for up to 48 days to monitor regrowth using brightfield (Celigo Imaging Cytometer, Nexelcom). After log-transforming the volume data, we calculated the radiation-induced growth delay (days) relative to untreated spheroids as the time for each irradiated spheroid to reach a volume endpoint (2.5 times the starting volume right before irradiation); we selected the lowest endpoint that was still within the exponential growth phase of all spheroids in the experiment. The average time for untreated spheroids to reach endpoint was estimated in R by local regression using the loess function with “direct” surface estimation to allow extrapolation for the parental spheroids (R project, v. 3.6.2).
Regrowth experiments were repeated by irradiating day 3 spheroids from three groups (parental, mixed and RR) of PC3 cells (6 Gy, n = 17–18/ group) and of DU145 cells (6 Gy, n = 12/group; 10 Gy, n = 20/group). Spheroids were imaged using brightfield (Leica DM IRBE, Hamamatsu) for up to 27 days (PC3) and up to 23 days (DU145). The radiation-induced growth delay (delays) was calculated as above, but with different endpoints (3.5 times starting volume for PC3 and 4 times starting volume for DU145) to ensure the endpoint was within the exponential growth phase. Data from Fig. 2 were used to estimate average time of untreated spheroids; the ‘span’ parameter of the loess function was reduced from the default of 0.75–0.5 for the unirradiated DU145 spheroids to better estimate the average time of reaching the endpoint.
To measure changes in the radiation response of PC3 cell populations within spheroids, untreated homogeneous and mixed spheroids were grown until day 5 or 11, dissociated using Accumax, seeded as single cells for clonogenic experiments, and allowed to attach for 6 h prior to radiation. Fluorescent colonies were counted using the Celigo Cytometer.
Immunofluorescence
For immunofluorescence and H&E experiments, spheroids were treated and fixed prior to staining40. To investigate the spatial distribution of fluorescent populations, sections were hydrated in PBS, stained for 10 min with Hoechst (1 μg/mL in PBS, Sigma) to visualise nuclei, and mounted using ProLong Diamond Antifade Mountant (ThermoFisher). For hypoxia, spheroids were pre-treated with 300 μM of the hypoxia drug EF5 (gift from Dr. Cameron Koch, University of Pennsylvania) prior to fixation. They were then permeabilized (PBS containing 0.3% Tween-20, 10 min), blocked (5% goat serum in PBS containing 0.1% Tween-20, 30 min), stained using anti-EF5 antibody (75 μg/mL; from Dr. Cameron Koch) overnight at 4 °C, washed (ice-cold PBS containing 0.3% Tween-20, 2 × 45 min)40, stained for nuclei as above, and mounted. For Ki67, spheroid sections were permeabilized (PBS containing 0.3% Tween-20, 10 min), blocked (5% goat serum in PBS containing 0.1% Tween-20, 30 min), and incubated overnight at 4 °C with primary antibody (clone SP6, 1:100, Vector Laboratories). After washing in PBS, sections were incubated for 1 h with goat anti-rabbit Alexa Fluor 647 (4 μg/mL, ThermoFisher), washed, and stained with Hoechst 33342 (5 μg/mL, Sigma) for 10 min. Slides were mounted and imaged using epifluorescence microscopy (20× objective; 0.30 NA; 0.64 μm resolution; excitation lasers: 395, 470, 555, and 640; Nikon Ti-E). Sections were stained using H&E and imaged using a Bright Field Slide Scanner (Aperio CS2, Leica) to visualise necrosis. To quantify the ratio of parental to radioresistant populations in spheroid cross-sections (n = 16 spheroids from 4 batches), we measured the number of pixels from each population (i.e., signal) by applying a threshold value 5 times higher than the median value of the background (i.e., noise) (Octave 4.4.1).
Oxygen consumption measurements
OCR was measured from each population using the Seahorse assay. Cells (1.2 × 104/well) were seeded in triplicate using the normal culture medium in a Seahorse XF 96-well microplate (Agilent) and allowed to attach overnight. Prior to the assay, cells were washed with and incubated in assay medium (DMEM basal medium containing 5 mM glucose, 4 mM glutamine, 5 mM pyruvate, pH 7.4; 200 μL/well) for 2 h at 37 °C without CO2 to degas the medium. Calibrant buffer (200 μL/well) was added to wells of the probe plate and also left at 37 °C without CO2 to degas. After OCR was measured on the Seahorse XF Analyser (Agilent Biosciences), cells were fixed using 4% paraformaldehyde, stained using Hoechst 33342, and counted (Celigo Cytometer, Nexelcom).
Transwell experiments
Co-culture experiments were performed to measure whether transferred factors between cell populations enhanced survival under hypoxia. Cells were seeded in triplicate (3.0 × 104/bottom well and 1.0 × 104/insert) in 12-well plates and in Transwell inserts, and allowed to attach overnight. Once the medium was changed, the plates were placed into normoxia or hypoxia (0.1% O2) for 24 and 120 h. Cells were fixed using 4% paraformaldehyde, stained with Hoechst (5 μg/mL), and counted (Celigo Cytometer, Nexelcom).
Statistics and reproducibility
Data were evaluated for equal variance using homoscedasticity plots (absolute value of residual vs predicted value) and for normality using Q–Q plots (Prism 8.0, GraphPad). Unless otherwise indicated, statistical significance was evaluated using one-way ANOVA, two-way ANOVA, or a mixed-effects model followed by multiple testing correction (α = 0.05). For clonogenic assays, the radiation protection factor was calculated as the area under the dose–response curve (AUC) for the RR cell populations divided by that of the parentals; AUC values were analysed for significance using a Student’s t test (unpaired, one-tailed, α = 0.05). For cisplatin cytotoxicity assays, IC50 values were calculated using a normalised response, variable slope, dose–response model (Prism, 8.0, GraphPad) and evaluated for statistical significance using extra sum-of-squares F-test. For post-radiation growth experiments, survival curves were analysed using the Mantel–Cox (log-rank) test and adjusted for multiple testing using Holm’s correction; spheroids that did not reach the endpoint during the timeframe of the experiment were marked as ‘censored’ on the final day of the experiment (please see Supplementary Methods section 3 for further details). Due to heteroscedasticity, cell counts from flow cytometry experiments involving cell cycle and death, and from co-culture Transwell assays were analysed using overdispersed Poisson or binomial regression models (please see Supplementary Methods section 3 for further details). For quantification of population proportions in microscopy images, pixel numbers were analysed using a two-tailed, Wilcoxon matched-pairs signed-rank test. Adjusted P values (Padj) are reported in the main text for experiments where multiple comparisons were performed.
Data points represent biological replicates; experiments were performed using at least two separate batches of cells. We note the following data exclusions: missing data from some time points due to technical failures in imaging (Fig. 1), one excluded mixed DU145 spheroid because its growth did not resemble that of the other 35 spheroids (Fig. 2a), and one excluded plate of PC3 spheroids from survival analysis (Fig. 4) because of irregular growth that did not match the other nine plates. Sample sizes were approximated using effect sizes from pilot studies to ensure power (approximate β = 0.8); randomisation and blinding were not possible.
Mathematical experiments
Non-spatial mathematical models
We used the logistic growth model to describe the growth of homogeneous tumour spheroids26. Thus, the rate of change of spheroid volume V at time t is given by

$$frac{{dV}}{{dt}} = rV left(1 – frac{V}{K}right),$$
(1)

where r  > 0 represents the growth rate, K  > 0 is the carrying capacity (the limiting volume of the spheroid) and V(t = 0) = V0 denotes the spheroid volume at t = 0. The analytical solution to the logistic model is given by

$$Vleft( t right) = frac{{V_0Ke^{rt}}}{{K + V_0(e^{rt} – 1)}}.$$
(2)

The Lotka–Volterra model was used to describe the growth of mixtures of parental and RR cell populations

$$left. {begin{array}{*{20}{c}} {frac{{dV_P}}{{dt}} = r_PK_Pleft( {1 – frac{{V_P}}{{K_P}} – lambda _{RR}frac{{V_{RR}}}{{K_P}}} right)} \ {frac{{dV_{RR}}}{{dt}} = r_{RR}K_{RR}left( {1 – frac{{V_{RR}}}{{K_{RR}}} – lambda _Pfrac{{V_P}}{{K_{RR}}}} right)} end{array}} right},$$
(3)

with VP(t = 0) = VP0 and VRR(t = 0) = VRR0. In these equations, VP and VRR represent respectively the volumes of parental and RR populations, rP and rRR their initial growth rates, KP and KRR their carrying capacities, and VP0 and VRR0 their initial volumes. The parameters λP and λRR describe the effect that parental cells have on RR cells, and vice versa. These type of interactions, found in ecology12, may be competitive (λP  > 0 and λRR  > 0), mutualistic (λP  More

1.
Weiskittel, A. R. et al. A call to improve methods for estimating tree biomass for regional and national assessments. J. For. 113, 414–424 (2015).
Google Scholar 
2.
Huang, H., Liu, C., Wang, X., Zhou, X. & Gong, P. Integration of multi-resource remotely sensed data and allometric models for forest aboveground biomass estimation in China. Remote Sens. Environ. 221, 225–234 (2019).
Article  Google Scholar 

3.
Zianis, D. & Seura, S. Biomass and stem volume equations for tree species in Europe. Silva Fenn. Monogr. 4, 1–63 (2005).
Google Scholar 

4.
Henry, M. et al. Estimating tree biomass of sub-Saharan African forests: a review of available allometric equations. Silva Fenn. 45, 477–569 (2011).
Article  Google Scholar 

5.
Jenkins, J. C., Chojnacky, D. C., Heath, L. S. & Birdsey, R. A. Comprehensive Database of Diameter-Based Biomass Regressions for North American Tree Species (USDA Forest Service, 2003).

6.
Yuen, J. Q., Fung, T. & Ziegler, A. D. Review of allometric equations for major land covers in SE Asia: uncertainty and implications for above- and below-ground carbon estimates. For. Ecol. Manag. 360, 323–340 (2016).
Article  Google Scholar 

7.
Liu, C. et al. Separating regressions for model fitting to reduce the uncertainty in forest volume–biomass relationship. Forests 10, 658 (2019).
Article  Google Scholar 

8.
Niklas, K. J. A phyletic perspective on the allometry of plant biomass-partitioning patterns and functionally equivalent organ-categories. New Phytol. 171, 27–40 (2006).
PubMed  Article  Google Scholar 

9.
Smith, J. E., Heath, L. S. & Jenkins, J. C. Forest Volume-to-Biomass Models and Estimates of Mass for Live and Standing Dead Trees of U.S. Forests (USDA Forest Service, 2003).

10.
Jalkanen, A., Mäkipää, R., Ståhl, G., Lehtonen, A. & Petersson, H. Silviculture-driven vegetation change in a European temperate deciduous forest. Ann. For. Sci. 62, 313–323 (2005).
Article  Google Scholar 

11.
Guo, Z., Fang, J., Pan, Y. & Birdsey, R. Inventory-based estimates of forest biomass carbon stocks in China: a comparison of three methods. For. Ecol. Manag. 259, 1225–1231 (2010).
Article  Google Scholar 

12.
Chave, J. et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob. Change Biol. 20, 3177–3190 (2014).
Article  Google Scholar 

13.
Ishihara, M. I. et al. Efficacy of generic allometric equations for estimating biomass: a test in Japanese natural forests. Ecol. Appl. 25, 1433–1446 (2015).
PubMed  Article  Google Scholar 

14.
Xiang, W. et al. General allometric equations and biomass allocation of Pinus massoniana trees on regional scale in southern China. Ecol. Res. 26, 697–711 (2011).
Article  Google Scholar 

15.
Parresol, B. R. Assessing tree and stand biomass: a review with examples and critical comparisons. For. Sci. 45, 573–593 (1999).
Google Scholar 

16.
Wirth, C., Schumacher, J. & Schulze, E.-D. Generic biomass functions for Norway spruce in Central Europe—a meta-analysis approach toward prediction and uncertainty estimation. Tree Physiol. 24, 121–139 (2004).
PubMed  Article  Google Scholar 

17.
Rutishauser, E. et al. Generic allometric models including height best estimate forest biomass and carbon stocks in Indonesia. For. Ecol. Manag. 307, 219–225 (2013).
Article  Google Scholar 

18.
Chave, J. et al. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145, 87–99 (2005).
CAS  PubMed  Article  Google Scholar 

19.
Gonzalez-Benecke, C. A. et al. Local and general above-stump biomass functions for loblolly pine and slash pine trees. For. Ecol. Manag. 334, 254–276 (2014).
Article  Google Scholar 

20.
Sileshi, G. W. A critical review of forest biomass estimation models, common mistakes and corrective measures. For. Ecol. Manag. 329, 237–254 (2014).
Article  Google Scholar 

21.
Picard, N., Rutishauser, E., Ploton, P., Ngomanda, A. & Henry, M. Should tree biomass allometry be restricted to power models? For. Ecol. Manag. 353, 156–163 (2015).
Article  Google Scholar 

22.
Sheil, D. et al. Does biomass growth increase in the largest trees? Flaws, fallacies and alternative analyses. Funct. Ecol. 31, 568–581 (2017).
Article  Google Scholar 

23.
Muller-Landau, H. C. et al. Testing metabolic ecology theory for allometric scaling of tree size, growth and mortality in tropical forests. Ecol. Lett. 9, 575–588 (2006).
PubMed  Article  Google Scholar 

24.
Schafer, J. L. & Mack, M. C. Growth, biomass, and allometry of resprouting shrubs after fire in scrubby flatwoods. Am. Midl. Nat. 172, 266–284 (2014).
Article  Google Scholar 

25.
Poorter, H. et al. How does biomass distribution change with size and differ among species? An analysis for 1200 plant species from five continents. New Phytol. 208, 736–749 (2015).
PubMed  PubMed Central  Article  Google Scholar 

26.
Smith, R. J. Rethinking allometry. J. Theor. Biol. 87, 97–111 (1980).
CAS  PubMed  Article  Google Scholar 

27.
Dassot, M. et al. Terrestrial laser scanning for measuring the solid wood volume, including branches, of adult standing trees in the forest environment. Comput. Electron. Agric. 89, 86–93 (2012).
Article  Google Scholar 

28.
Disney, M. I. et al. Weighing trees with lasers: advances, challenges and opportunities. Interface Focus 8, 201700484 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

29.
Sarrus, P. F. & Rameaux, J.-F. Application des sciences accessoires et principalement des mathématiques à la physiologie générale. Bull. Acad. R. Méd. 3, 1094–1100 (1838).
Google Scholar 

30.
Huxley, J. S. & Teissier, G. Terminology of relative growth. Nature 137, 780–781 (1936).
Article  Google Scholar 

31.
Gayon, J. History of the concept of allometry. Am. Zool. 40, 748–758 (2000).
Google Scholar 

32.
Rubner, M. Über den einfluss der körpergrösse auf stoff- und kraftwechsel. Z. Biol. 19, 536–562 (1883).
Google Scholar 

33.
von Bertalanffy, L. General System Theory: Foundations, Development, Applications (George Braziller, 1973).

34.
Kleiber, M. Body size and metabolism. Hilgardia 6, 315–353 (1932).
CAS  Article  Google Scholar 

35.
West, G. B., Brown, J. H. & Enquist, B. J. A general model for the origin of allometric scaling laws in biology. Science 276, 122–126 (1997).
CAS  PubMed  Article  Google Scholar 

36.
West, G. B., Brown, J. H. & Enquist, B. J. A general model for ontogenetic growth. Nature 413, 628–631 (2001).
CAS  PubMed  Article  Google Scholar 

37.
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).
Article  Google Scholar 

38.
Bokma, F. Evidence against universal metabolic allometry. Funct. Ecol. 18, 184–187 (2004).
Article  Google Scholar 

39.
Dodds, P. S., Rothman, D. H. & Weitz, J. S. Re-examination of the “3/4-law” of metabolism. J. Theor. Biol. 209, 9–27 (2001).
CAS  PubMed  Article  Google Scholar 

40.
Kozłowski, J. & Konarzewski, M. Is West, Brown and Enquist’s model of allometric scaling mathematically correct and biologically relevant? Funct. Ecol. 18, 283–289 (2004).
Article  Google Scholar 

41.
Henry, M. et al. Wood density, phytomass variations within and among trees, and allometric equations in a tropical rainforest of Africa. For. Ecol. Manag. 260, 1375–1388 (2010).
Article  Google Scholar 

42.
Satoo, T. Notes on Kittredge’s method of estimation of amount of leaves of forest stand. Jpn. J. For. 44, 267–272 (1962).
Google Scholar 

43.
Ruark, G. A., Martin, G. L. & Bockheim, J. G. Comparison of constant and variable allometric ratios for estimating populus tremuloides biomass. For. Sci. 33, 294–300 (1987).
Google Scholar 

44.
Mori, S. et al. Mixed-power scaling of whole-plant respiration from seedlings to giant trees. Proc. Natl Acad. Sci. USA 107, 1447–1451 (2010).
CAS  PubMed  Article  Google Scholar 

45.
Tjørve, E. Shapes and functions of species-area curves (II): a review of new models and parameterizations. J. Biogeogr. 36, 1435–1445 (2009).
Article  Google Scholar 

46.
Luo, Y., Wang, X., Zhang, X. & Lu, F. Biomass and Its Allocation of Forest Ecosystems in China [in Chinese] (Chinese Forestry Publishing House, 2013).

47.
Stovall, A. E. L., Shugart, H. H., Stovall, A. E. L. & Anderson-Teixeira, K. J. Assessing terrestrial laser scanning for developing non-destructive biomass allometry. For. Ecol. Manag. 427, 217–229 (2018).
Article  Google Scholar 

48.
Packard, G. C. Is logarithmic transformation necessary in allometry? Biol. J. Linn. Soc. 109, 476–486 (2013).
Article  Google Scholar 

49.
Mascaro, J., Litton, C. M., Hughes, R. F., Uowolo, A. & Schnitzer, S. A. Is logarithmic transformation necessary in allometry? Ten, one-hundred, one-thousand-times yes. Biol. J. Linn. Soc. 111, 230–233 (2014).
Article  Google Scholar 

50.
Sprugel, D. G. Correcting for bias in log-transformed allometric equations. Ecology 64, 209–210 (1983).
Article  Google Scholar 

51.
Peichl, M. & Arain, M. A. Allometry and partitioning of above- and belowground tree biomass in an age-sequence of white pine forests. For. Ecol. Manag. 253, 68–80 (2007).
Article  Google Scholar 

52.
Wolf, A., Field, C. B. & Berry, J. A. Allometric growth and allocation in forests: a perspective from FLUXNET. Ecol. Appl. 21, 1546–1556 (2011).
PubMed  Article  Google Scholar 

53.
Litton, C. M., Raich, J. W. & Ryan, M. G. Carbon allocation in forest ecosystems. Glob. Change Biol. 13, 2089–2109 (2007).
Article  Google Scholar 

54.
Vallet, P., Dhôte, J. F., Moguédec, G. L. E., Ravart, M. & Pignard, G. Development of total aboveground volume equations for seven important forest tree species in France. For. Ecol. Manag. 229, 98–110 (2006).
Article  Google Scholar 

55.
Cannell, M. G. R. World Forest Biomass and Primary Production Data (Academic Press, 1982).

56.
Usoltsev, V. A. Forest Biomass and Primary Production Database for Eurasia (Ural State Forest Engineering Univ., 2013).

57.
West, G. B. & Brown, J. H. The origin of allometric scaling laws in biology from genomes to ecosystems: towards a quantitative unifying theory of biological structure and organization. J. Exp. Biol. 208, 1575–1592 (2005).
PubMed  Article  Google Scholar 

58.
Reich, P. B. et al. Universal scaling of respiratory metabolism, size and nitrogen in plants. Nature 439, 457–461 (2006).
CAS  PubMed  Article  Google Scholar 

59.
Li, H., Han, X. & Wu, J. Lack of evidence for 3/4 scaling of metabolism in terrestrial plants. J. Integr. Plant Biol. 47, 1173–1183 (2005).
Article  Google Scholar 

60.
Zhou, X. et al. Correcting the overestimate of forest biomass carbon on the national scale. Method Ecol. Evol. 7, 447–455 (2016).
Article  Google Scholar 

61.
Enquist, B. J., Brown, J. H. & West, G. B. Allometric scaling of plant energetics and population density. Nature 395, 163–165 (1998).
CAS  Article  Google Scholar  More

1.
Heinz, A., Deserno, L. & Reininghaus, U. Urbanicity, social adversity and psychosis. World Psychiatry 12, 187–197 (2013).
PubMed  PubMed Central  Article  Google Scholar 
2.
McDonald, R. I. et al. Research gaps in knowledge of the impact of urban growth on biodiversity. Nat. Sustain. https://doi.org/10.1038/s41893-019-0436-6 (2019).
Article  Google Scholar 

3.
Bratman, G. N. et al. Nature and mental health: An ecosystem service perspective. Sci. Adv. 5, eaax903 (2019).
ADS  Article  Google Scholar 

4.
Ramaswami, A. Unpacking the urban infrastructure nexus with environment, health, livability, well-being, and equity. One Earth 2, 120–124 (2020).
Article  Google Scholar 

5.
Endreny, T. A. Strategically growing the urban forest will improve our world. Nat. Commun. 9, 10–12 (2018).
Article  CAS  Google Scholar 

6.
Blicharska, M. et al. Biodiversity’s contributions to sustainable development. Nat. Sustain. 2, 1083–1093 (2019).
Article  Google Scholar 

7.
Wolf, L. J., Zu Ermgassen, S., Balmford, A., White, M. & Weinstein, N. Is variety the spice of life? An experimental investigation into the effects of species richness on self-reported mental well-being. PLoS ONE 12, e0170225 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

8.
Stewart, G. H. et al. Urban biotopes of aotearoa New Zealand (URBANZ) II: Floristics, biodiversity and conservation values of urban residential and public woodlands. Christchurch. Urban For. Urban Green. 8, 149–162 (2009).
Article  Google Scholar 

9.
Church, S. P. From street trees to natural areas: Retrofitting cities for human connectedness to nature. J. Environ. Plan. Manag. 61, 878–903 (2018).
Article  Google Scholar 

10.
Salmond, J. A. et al. Health and climate related ecosystem services provided by street trees in the urban environment. Environ. Heal. 15, 1–36 (2016).
Article  Google Scholar 

11.
Wolf, K. L. et al. Urban trees and human health: A scoping review. Int. J. Environ. Res. Public Health 17, 4371 (2020).
PubMed Central  Article  PubMed  Google Scholar 

12.
Elsadek, M., Liu, B., Lian, Z. & Xie, J. The influence of urban roadside trees and their physical environment on stress relief measures: A field experiment in Shanghai. Urban For. Urban Green. 42, 51–60 (2019).
Article  Google Scholar 

13.
Barnes, M. R. et al. Characterizing nature and participant experience in studies of nature exposure for positive mental health: An integrative review. Front. Psychol. 9, 66 (2019).
Article  Google Scholar 

14.
van den Berg, M. et al. Health benefits of green spaces in the living environment: A systematic review of epidemiological studies. Urban For. Urban Green. 14, 806–816 (2015).
Article  Google Scholar 

15.
Coldwell, D. F. & Evans, K. L. Visits to urban green-space and the countryside associate with different components of mental well-being and are better predictors than perceived or actual local urbanisation intensity. Landsc. Urban Plan. 175, 114–122 (2018).
Article  Google Scholar 

16.
Collins, R. M. et al. A systematic map of research exploring the effect of greenspace on mental health. Landsc. Urban Plan. 201, 103823 (2020).
Article  Google Scholar 

17.
Ekkel, E. D. & de Vries, S. Nearby green space and human health: Evaluating accessibility metrics. Landsc. Urban Plan. 157, 214–220 (2017).
Article  Google Scholar 

18.
Jiang, X., Larsen, L. & Sullivan, W. Connections–between daily greenness exposure and health outcomes. Int. J. Environ. Res. Public Health 17, 3965 (2020).
PubMed Central  Article  PubMed  Google Scholar 

19.
Marselle, M. R., Martens, D., Dallimer, M. & Irvine, K. N. Review of the mental health and wellbeing benefits of biodiversity. In Biodiversity and Health in the Face of Climate Change (eds Marselle, M. R. et al.) (Springer, Berlin, 2019).
Google Scholar 

20.
de Vries, S. & Snep, R. Biodiversity in the context of ‘biodiversity – mental health’ research. In Biodiversity and Health in the Face of Climate Change (eds Marselle, M. R. et al.) 159–173 (Springer, Berlin, 2019).
Google Scholar 

21.
Reid, C. E., Clougherty, J. E., Shmool, J. L. C. & Kubzansky, L. D. Is all urban green space the same? A comparison of the health benefits of trees and grass in New York City. Int. J. Environ. Res. Public Health 14, 1411 (2017).
PubMed Central  Article  PubMed  Google Scholar 

22.
Roberts, H., van Lissa, C., Hagedoorn, P., Kellar, I. & Helbich, M. The effect of short-term exposure to the natural environment on depressive mood: A systematic review and meta-analysis. Environ. Res. 177, 108606 (2019).
CAS  PubMed  Article  Google Scholar 

23.
Frumkin, H. et al. Nature contact and human health: A research agenda. Environ. Health Perspect. 125, 66 (2017).
Article  Google Scholar 

24.
van den Bosch, M. & Sang, Å. O. Urban natural environments as nature-based solutions for improved public health—A systematic review of reviews. Environ. Res. 158, 373–384 (2017).
PubMed  Article  CAS  Google Scholar 

25.
Cook, P. A., Howarth, M. & Wheater, C. P. Biodiversity and health in the face of climate change—Implications for public health. In Biodiversity and Health in the Face Of Climate Change (eds Marselle, M. R. et al.) (Springer, Berlin, 2019).
Google Scholar 

26.
Wendelboe-Nelson, C., Kelly, S., Kennedy, M. & Cherrie, J. W. A scoping review of mapping research on green space and associated mental health benefits. Int. J. Environ. Res. Public Health 16, 2081 (2019).
PubMed Central  Article  PubMed  Google Scholar 

27.
Gidlow, C. et al. Research note: Natural environments and prescribing in England. Landsc. Urban Plan. 151, 103–108 (2016).
Article  Google Scholar 

28.
Helbich, M., Klein, N., Roberts, H., Hagedoorn, P. & Groenewegen, P. P. More green space is related to less antidepressant prescription rates in the Netherlands: A Bayesian geoadditive quantile regression approach. Environ. Res. 166, 290–297 (2018).
CAS  PubMed  Article  Google Scholar 

29.
Triguero-Mas, M. et al. Natural outdoor environments and mental and physical health: Relationships and mechanisms. Environ. Int. 77, 41 (2015).
Article  Google Scholar 

30.
Taylor, M. S., Wheeler, B. W., White, M. P., Economou, T. & Osborne, N. J. Research note: Urban street tree density and antidepressant prescription rates—A cross-sectional study in London, UK. Landsc. Urban Plan. 136, 174–179 (2015).
Article  Google Scholar 

31.
Smith, G. et al. Characterisation of the natural environment: Quantitative indicators across Europe. Int. J. Health Geogr. 16, 1–15 (2017).
Article  Google Scholar 

32.
Egorov, A. I. et al. Vegetated land cover near residence is associated with reduced allostatic load and improved biomarkers of neuroendocrine, metabolic and immune functions. Environ. Res. 158, 508–521 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
de Vries, S., Verheij, R. A., Groenewegen, P. P. & Spreeuwenberg, P. Natural environments-healthy environments? An exploratory analysis of the relationship between greenspace and health. Environ. Plan. A 35, 1717–1732 (2003).
Article  Google Scholar 

34.
van den Berg, A. E., Maas, J., Verheij, R. A. & Groenewegen, P. P. Green space as a buffer between stressful life events and health. Soc. Sci. Med. 70, 1203–1210 (2010).
PubMed  Article  PubMed Central  Google Scholar 

35.
Kaplan, R. The nature of the view from home: Psychological benefits. Environ. Behav. 33, 507–542 (2001).
Article  Google Scholar 

36.
Markevych, I. et al. Exploring pathways linking greenspace to health: Theoretical and methodological guidance. Environ. Res. 158, 301–317 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Hartig, T., Mitchell, R., de Vries, S. & Frumkin, H. Nature and health. Annu. Rev. Public Health 35, 207–228 (2014).
PubMed  Article  PubMed Central  Google Scholar 

38.
Shanahan, D. F. et al. Health benefits of nature experiences depend on dose. Sci. Rep. 6, 66 (2016).
Google Scholar 

39.
Shanahan, D. F., Fuller, R. A., Bush, R., Lin, B. B. & Gaston, K. J. The health benefits of urban nature: How much do we need?. Bioscience 65, 476–485 (2015).
Article  Google Scholar 

40.
Luck, T. et al. The prevalence of current depressive symptoms in an urban adult population: Results of the Leipzig population-based study of adults (LIFE-ADULT-Study). Thieme 44, 148–153 (2017).
Google Scholar 

41.
Zuelke, A. E. et al. The association between unemployment and depression—Results from the population-based LIFE-adult-study. J. Affect. Disord. 235, 399–406 (2018).
PubMed  Article  Google Scholar 

42.
Techniker Krankenkasse. Depressionsatlas: Arbeitsunfähigkeit und Arzneiverordnungen Depression Atlas: Inability to Work and Medication Prescriptions. https://www.tk.de/resource/blob/2026640/c767f9b02cabbc503fd3cc6188bc76b4/tk-depressionsatlas-data.pdf (2015).

43.
Mitchell, R. & Popham, F. Effect of exposure to natural environment on health inequalities: An observational population study. Lancet 372, 1655–1660 (2008).
PubMed  Article  Google Scholar 

44.
Mitchell, R. J., Richardson, E. A., Shortt, N. K. & Pearce, J. R. Neighborhood environments and socioeconomic inequalities in mental well-being. Am. J. Prev. Med. 49, 80–84 (2015).
PubMed  Article  Google Scholar 

45.
Sarkar, C., Webster, C. & Gallacher, J. Residential greenness and prevalence of major depressive disorders: A cross-sectional, observational, associational study of 94 879 adult UK Biobank participants. Lancet Planet. Heal. 2, E162–E173 (2018).
Article  Google Scholar 

46.
OECD. Health at a Glance 2017: OECD Indicators. https://www.oecd-ilibrary.org/social-issues-migration-health/health-at-a-glance-2017_health_glance-2017-en (2017).

47.
Landry, S. M. & Chakraborty, J. Street trees and equity: Evaluating the spatial distribution of an urban amenity. Environ. Plan. A 41, 2651–2670 (2009).
Article  Google Scholar 

48.
Lin, J., Wang, Q. & Li, X. Landscape and urban planning socioeconomic and spatial inequalities of street tree abundance, species diversity, and size structure in New York City. Landsc. Urban Plan. 206, 103992 (2021).
Article  Google Scholar 

49.
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, Berlin, 2016).
Google Scholar 

50.
Stadt Leipzig. Local subdivision of Leipzig: city districts [Kommunale Gebietsgliederung Leipzig: Stadtbezirke]. https://opendata.leipzig.de/dataset/kommunale-gebietsgliederung-leipzig-stadtbezirke4987b (2020).

51.
Helbich, M. et al. Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing China. Environ. Int. 126, 107–117 (2019).
PubMed  PubMed Central  Article  Google Scholar 

52.
Thompson, C. W. et al. More green space is linked to less stress in deprived communities: Evidence from salivary cortisol patterns. Landsc. Urban Plan. 105, 221–229 (2012).
Article  Google Scholar 

53.
Maas, J. et al. Morbidity is related to a green living environment. J. Epidemiol. Community Health 63, 967 (2009).
CAS  PubMed  Article  Google Scholar 

54.
Honold, J., Lakes, T., Beyer, R. & van der Meer, E. Restoration in urban spaces: Nature views from home, greenways, and public parks. Environ. Behav. 48, 796–825 (2016).
Article  Google Scholar 

55.
Zhao, J., Wu, J. & Wang, H. Characteristics of urban streets in relation to perceived restorativeness. J. Expo. Sci. Environ. Epidemiol. 30, 309–319 (2020).
CAS  PubMed  Article  Google Scholar 

56.
Kuo, F. E. Coping with poverty—Impacts of environment and attention in the inner city. Environ. Behav. 33, 5–34 (2001).
Article  Google Scholar 

57.
Giollabhui, N. M., Olino, T. M., Nielsen, J., Abramson, L. Y. & Alloy, L. B. Is worse attention a risk factor for or a consequence of depression, or are worse attention and depression better accounted for by stress? A prospective test of three hypotheses. Clin. Psychol. Sci. 7, 93–109 (2019).
PubMed  Article  Google Scholar 

58.
Cohen, S. & Janicki-Deverts, D. Who’s stressed? Distributions of psychological stress in the United States in probability samples from 1983, 2006, and 2009. J. Appl. Soc. Psychol. 42, 1320–1334 (2012).
Article  Google Scholar 

59.
St John, A. M., Kibbe, M. & Tarullo, A. R. A systematic assessment of socioeconomic status and executive functioning in early childhood. J. Exp. Child Psychol. 178, 352–368 (2019).
PubMed  Article  Google Scholar 

60.
Dallimer, M. et al. Biodiversity and the feel-good factor: understanding associations between self-reported human well-being and species richness. Bioscience 62, 47–55 (2012).
Article  Google Scholar 

61.
Cox, D. T. C. et al. Doses of neighborhood nature: The benefits for mental health of living with nature. Bioscience 67, 147–155 (2017).
Google Scholar 

62.
Cracknell, D., White, M. P., Pahl, S. & Depledge, M. H. A preliminary investigation into the restorative potential of public aquaria exhibits: A UK student-based study. Landsc. Res. 42, 18–32 (2017).
Article  Google Scholar 

63.
Vich, G., Marquet, O. & Miralles-Guasch, C. Green streetscape and walking: Exploring active mobility patterns in dense and compact cities. J. Transp. Heal. 12, 50–59 (2019).
Article  Google Scholar 

64.
Cox, D. T. C., Hudson, H. L., Shanahan, D. F., Fuller, R. A. & Gaston, K. J. The rarity of direct experiences of nature in an urban population. Landsc. Urban Plan. 160, 79–84 (2017).
Article  Google Scholar 

65.
Cox, D. T. C. et al. Skewed contributions of individual trees to indirect nature experiences. Landsc. Urban Plan. 185, 28–34 (2019).
Article  Google Scholar 

66.
Chang, C. C. et al. Life satisfaction linked to the diversity of nature experiences and nature views from the window. Landsc. Urban Plan. 202, 103874 (2020).
Article  Google Scholar 

67.
Dzhambov, A. M. et al. Does greenery experienced indoors and outdoors provide an escape and support mental health during the COVID-19 quarantine?. Environ. Res. 110420, 66. https://doi.org/10.1016/j.envres.2020.110420 (2020).
CAS  Article  Google Scholar 

68.
Lindenmayer, D. B. & Laurance, W. F. The ecology, distribution, conservation and management of large old trees. Biol. Rev. 92, 1434–1458 (2017).
PubMed  Article  Google Scholar 

69.
Lindenmayer, D. B., Laurance, W. F. & Franklin, J. F. Ecology: Global decline in large old trees. Science 338, 1305–1306 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

70.
Simkin, J., Ojala, A. & Tyrväinen, L. Restorative effects of mature and young commercial forests, pristine old-growth forest and urban recreation forest—A field experiment. Urban For. Urban Green. 48, 126567 (2020).
Article  Google Scholar 

71.
Anguelovski, I., Cole, H., Connolly, J. & Triguero-Mas, M. Do green neighbourhoods promote urban health justice?. Lancet Public Heal. 3, 66 (2018).
Google Scholar 

72.
United Nations. Transforming Our World: The 2030 Agenda for Sustainable Development. United Nations Sustainable Knowledge Platform. Sustainable Development Goals https://sustainabledevelopment.un.org/post2015/transformingourworld (2015).

73.
Loeffler, M. et al. The LIFE-Adult-study: Objectives and design of a population-based cohort study with 10,000 deeply phenotyped adults in Germany. BMC Public Health 15, 691 (2015).
PubMed  PubMed Central  Article  Google Scholar 

74.
WHO Collborating Centre for Drug Statistics Methodology. ATC/DDD Index: N06A Antidepressants. https://www.whocc.no/atc_ddd_index/?code=N06A (2019).

75.
Stadt Leipzig. Baumkataster und Statistik. https://www.leipzig.de/umwelt-und-verkehr/umwelt-und-naturschutz/baeume-und-baumschutz/stadtbaeume/baumkataster-und-statistik/ (2018).

76.
Kessler, R. C. & Essex, M. Marital status and depression: The importance of coping resources. Soc. Forces 61, 484–507 (1982).
Article  Google Scholar 

77.
Lampert, T., Kroll, L., Müters, S. & Stolzenberg, H. Measurement of socioeconomic status in the German health interview and examination survey for adults (DEGS1). Bundesgesundheitsblatt Gesundheitsforsch. Gesundheitsschutz 56, 631–636 (2013).
CAS  Article  Google Scholar 

78.
Fergusson, D. M., Boden, J. M. & Horwood, L. J. Tests of causal links between alcohol abuse or dependence and major depression. Arch. Gen. Psychiatry 66, 260–266 (2009).
PubMed  Article  PubMed Central  Google Scholar 

79.
Covey, L. S., Glassman, A. H. & Stetner, F. Cigarette smoking and major depression. J. Addict. Dis. 17, 35–46 (1998).
CAS  PubMed  Article  PubMed Central  Google Scholar 

80.
Zhao, G. et al. Depression and anxiety among US adults: Associations with body mass index. Int. J. Obes. 33, 257–266 (2009).
CAS  Article  Google Scholar 

81.
Øverland, S. et al. Seasonality and symptoms of depression: A systematic review of the literature. Epidemiol. Psychiatr. Sci. 29, e31 (2020).
Article  Google Scholar 

82.
Glaesmer, H. et al. Psychometric properties and population-based norms of the Life Orientation Test Revised (LOT-R). Br. J. Health Psychol. 17, 432–445 (2012).
PubMed  Article  Google Scholar 

83.
Herzberg, P. Y., Glaesmer, H. & Hoyer, J. Separating optimism and pessimism: A robust psychometric analysis of the revised Life Orientation Test (LOT-R). Psychol. Assess. 18, 433–438 (2006).
PubMed  Article  Google Scholar 

84.
Matthies, S. A., Rüter, S., Prasse, R. & Schaarschmidt, F. Factors driving the vascular plant species richness in urban green spaces: Using a multivariable approach. Landsc. Urban Plan. 134, 177–187 (2015).
Article  Google Scholar  More

1.
Gaston, K. J., Jackson, S. F., Cantú-Salazar, L. & Cruz-Piñón, G. The Ecological Performance of Protected Areas. Annu. Rev. Ecol. Evol. Syst. 39, 93–113 (2008).
Article  Google Scholar 
2.
Williams, S. E. et al. Research priorities for natural ecosystems in a changing global climate. Glob. Change Biol. 26, 410–416 (2020).
ADS  Article  Google Scholar 

3.
Hoffmann, S., Irl, S. D. H. & Beierkuhnlein, C. Predicted climate shifts within terrestrial protected areas worldwide. Nat. Commun. 10, 4787 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

4.
IUCN & UNEP. The World Database on Protected Areas (WDPA). www.protectedplanet.net. (UNEP-WCMC, 2018).

5.
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A. & Ziese, M. GPCC Full Data Monthly Product Version 2018 at 0.25°: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historical Data. 10.5676/DWD_GPCC/FD_M_V2018_025; ftp://ftp.dwd.de/pub/data/gpcc/html/fulldata-monthly_v2018_doi_download.html; accessed on 26 March 2019. (2018).

6.
Schneider, U., Finger, P., Meyer-Christoffer, A., Ziese, M. & Becker, A. Global Precipitation Analysis Products of the GPCC. Deutscher Wetterdienst, Abt. Hydrometeorologie, Weltzentrum für Niederschlagsklimatologie (WZN) 17 (2018).

7.
Hofstra, N., Haylock, M., New, M. & Jones, P. D. Testing E-OBS European high-resolution gridded data set of daily precipitation and surface temperature. J. Geophys. Res. 114, D21101 (2009).
ADS  Article  Google Scholar 

8.
Prein, A. F. & Gobiet, A. Impacts of uncertainties in European gridded precipitation observations on regional climate analysis: UNCERTAINTY IN EUROPEAN PRECIPITATION. Int. J. Climatol. 37, 305–327 (2017).
PubMed  Article  PubMed Central  Google Scholar 

9.
Zandler, H., Haag, I. & Samimi, C. Evaluation needs and temporal performance differences of gridded precipitation products in peripheral mountain regions. Sci. Rep. 9, 15118 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

10.
Liu, M. et al. Evaluation of high-resolution satellite rainfall products using rain gauge data over complex terrain in southwest China. Theor. Appl. Climatol. 119, 203–219 (2015).
ADS  Article  Google Scholar 

11.
Fu, Y. et al. Assessment of multiple precipitation products over major river basins of China. Theor. Appl. Climatol. 123, 11–22 (2016).
ADS  Article  Google Scholar 

12.
Hu, Z., Hu, Q., Zhang, C., Chen, X. & Li, Q. Evaluation of reanalysis, spatially interpolated and satellite remotely sensed precipitation data sets in central Asia: Central Asia Precipitation. J. Geophys. Res. Atmos. 121, 5648–5663 (2016).
Article  Google Scholar 

13.
Hu, Z. et al. Evaluation of three global gridded precipitation data sets in central Asia based on rain gauge observations. Int. J. Climatol. 38, 3475–3493 (2018).
Article  Google Scholar 

14.
Beck, H. E. et al. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrol. Earth Syst. Sci. 21, 6201–6217 (2017).
ADS  CAS  Article  Google Scholar 

15.
Iwasaki, H. NDVI prediction over Mongolian grassland using GSMaP precipitation data and JRA-25/JCDAS temperature data. J. Arid Environ. 73, 557–562 (2009).
ADS  Article  Google Scholar 

16.
Gessner, U. et al. The relationship between precipitation anomalies and satellite-derived vegetation activity in Central Asia. Glob. Planet. Change 110, 74–87 (2013).
ADS  Article  Google Scholar 

17.
Los, S. O. Testing gridded land precipitation data and precipitation and runoff reanalyses (1982–2010) between 45° S and 45° N with normalised difference vegetation index data. Hydrol. Earth Syst. Sci. 19, 1713–1725 (2015).
ADS  Article  Google Scholar 

18.
Papagiannopoulou, C. et al. Vegetation anomalies caused by antecedent precipitation in most of the world. Environ. Res. Lett. 12, 074016 (2017).
ADS  Article  Google Scholar 

19.
Chen, Z., Wang, W. & Fu, J. Vegetation response to precipitation anomalies under different climatic and biogeographical conditions in China. Sci. Rep. 10, 830 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

20.
Eckert, S., Hüsler, F., Liniger, H. & Hodel, E. Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia. J. Arid Environ. 113, 16–28 (2015).
ADS  Article  Google Scholar 

21.
Otto, M., Höpfner, C., Curio, J., Maussion, F. & Scherer, D. Assessing vegetation response to precipitation in northwest Morocco during the last decade: an application of MODIS NDVI and high resolution reanalysis data. Theor. Appl. Climatol. 123, 23–41 (2016).
ADS  Article  Google Scholar 

22.
Formica, A. F., Burnside, R. J. & Dolman, P. M. Rainfall validates MODIS-derived NDVI as an index of spatio-temporal variation in green biomass across non-montane semi-arid and arid Central Asia. J. Arid Environ. 142, 11–21 (2017).
ADS  Article  Google Scholar 

23.
Wang, X., Wu, C., Peng, D., Gonsamo, A. & Liu, Z. Snow cover phenology affects alpine vegetation growth dynamics on the Tibetan Plateau: Satellite observed evidence, impacts of different biomes, and climate drivers. Agric. For. Meteorol. 256–257, 61–74 (2018).
ADS  Article  Google Scholar 

24.
Verbyla, D. & Kurkowski, T. A. NDVI–Climate relationships in high-latitude mountains of Alaska and Yukon Territory. Arct. Antarct. Alp. Res. 51, 397–411 (2019).
Article  Google Scholar 

25.
Breckle, S.-W. Flora and vegetation of Afghanistan. badr 1, 155–194 (2007).
Article  Google Scholar 

26.
Bedunah, D. J., Shank, C. C. & Alavi, M. A. Rangelands of Band-e-Amir National Park and Ajar Provisional Wildlife Reserve, Afghanistan. Rangelands 32, 41–52 (2010).
Article  Google Scholar 

27.
Pohl, E., Knoche, M., Gloaguen, R., Andermann, C. & Krause, P. Sensitivity analysis and implications for surface processes from a hydrological modelling approach in the Gunt catchment, high Pamir Mountains. Earth Surf. Dyn. 3, 333–362 (2015).
ADS  Article  Google Scholar 

28
Soelberg, J. & Jäger, A. K. Comparative ethnobotany of the Wakhi agropastoralist and the Kyrgyz nomads of Afghanistan. J. Ethnobiol. Ethnomed. https://doi.org/10.1186/s13002-015-0063-x (2016).
Article  PubMed  PubMed Central  Google Scholar 

29.
Didan, K. MOD13Q1 MODIS/terra vegetation indices 16-day L3 global 250m SIN Grid V006. NASA EOSDIS Land Process. DAAC https://doi.org/10.5067/MODIS/MOD13Q1.006 (2015).

30.
Dinku, T. et al. Validation of the CHIRPS satellite rainfall estimates over eastern Africa. Q. J. R. Meteorol. Soc. 144, 292–312 (2018).
ADS  Article  Google Scholar 

31.
Sun, Q. et al. A review of global precipitation data sets: data sources, estimation, and intercomparisons. Rev. Geophys. 56, 79–107 (2018).
ADS  Article  Google Scholar 

32.
Hall, D. K. & Riggs, G. A. MOD10A1 MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 6. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MOD10A1.006. Accessed on 25 March 2020. (2016).

33.
Wang, K. et al. Snow effects on alpine vegetation in the Qinghai-Tibetan Plateau. Int. J. Digit. Earth 8, 58–75 (2013).
Article  Google Scholar 

34.
Chen, X., An, S., Inouye, D. W. & Schwartz, M. D. Temperature and snowfall trigger alpine vegetation green-up on the world’s roof. Glob. Change Biol. 21, 3635–3646 (2015).
ADS  Article  Google Scholar 

35.
Asam, S. et al. Relationship between spatiotemporal variations of climate, snow cover and plant phenology over the Alps—an earth observation-based analysis. Remote Sens. 10, 1757 (2018).
ADS  Article  Google Scholar 

36.
Funk, C. C. et al. CHIRPS-2.0. A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p. http://pubs.usgs.gov/ds/832/. Accessed on 25 March 2020. (2014).

37.
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 

38.
Copernicus Climate Change Service. C3S ERA5-Land reanalysis . Copernicus Climate Change Service, https://cds.climate.copernicus.eu/cdsapp#!/home. Accessed on 25 March 2020. (2019).

39.
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A. & Ziese, M. GPCC Monitoring Product Version 6: Near Real-Time Monthly Land-Surface Precipitation from Rain-Gauges based on SYNOP and CLIMAT data. 10.5676/DWD_GPCC/MP_M_V6_100; ftp://ftp.dwd.de/pub/data/gpcc/monitoring_v6/. Accessed on 25 March 2020. (2018).

40.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J. & Jackson, T. GPM IMERG Final Precipitation L3 1 month 0.1 degree x 0.1 degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC),https://doi.org/10.5067/GPM/IMERG/3B-MONTH/06. Accessed on 25 March 2020. (2019).

41.
Global Modeling and Assimilation Office. MERRA-2 tavgM_2d_flx_Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Surface Flux Diagnostics V5.12.4; https://doi.org/10.5067/0JRLVL8YV2Y4. Accessed on 25 March 2020. (Goddard Earth Sciences Data and Information Services Center (GES DISC), 2015).

42.
Unger-Shayesteh, K. et al. What do we know about past changes in the water cycle of Central Asian headwaters? A review. Glob. Planet. Change 110, 4–25 (2013).
ADS  Article  Google Scholar 

43.
Amante, C. & Eakins, B. W. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24. National Geophysical Data Center, NOAA. https://doi.org/10.7289/V5C8276M, Accessed on 25 March 2020. (2009).

44.
Jpl, N. A. S. A. NASA shuttle radar topography mission global 1 arc second data set. NASA EOSDIS Land Process. DAAC. https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003 (2013).

45.
QGIS Development Team. GIS Geographic Information System. Version 3.12 București. Open Source Geospatial Foundation Project. http://qgis.osgeo.org/. (2020).

46.
Smallwood, P. D. & Shank, C. C. From buffer zone to national park: Afghanistan’s Wakhan National Park. In Collateral Values Vol. 25 (eds Lookingbill, T. R. & Smallwood, P. D.) 213–233 (Springer, Berlin, 2019).
Google Scholar 

47.
Vanselow, K. A. The high-mountain pastures of the Eastern Pamirs (Tajikistan): an evaluation of the ecological basis and the pasture potential. (Erlangen, Nürnberg, Univ., Diss., 2011).

48.
Breckle, S. W. & Rafiqpoor, M. D. Field Guide Afghanistan—Flora and Vegetation. (Scientia Bonnensis, 2010).

49.
Moheb, Z. & Bradfield, D. Status of the common leopard in Afghanistan. ISSN 1027–2992. Cat News 61, (2014).

50.
Mohibbi, A. A. & Cochard, R. Residents’ resource uses and nature conservation in Band-e-Amir National Park, Afghanistan. Environ. Dev. 11, 141–161 (2014).
Article  Google Scholar 

51.
Moqanaki, E. M. et al. Distribution and status of the Pallas’s cat in the south-west part of its range. ISSN 1027–2992. Cat News Special Issue 13, (2019).

52.
Gray, T. I. & Tapley, B. D. Vegetation health: Nature’s climate monitor. Adv. Space Res. 5, 371–377 (1985).
ADS  Article  Google Scholar 

53.
Sun, J. & Qin, X. Precipitation and temperature regulate the seasonal changes of NDVI across the Tibetan Plateau. Environ. Earth Sci. 75, 291 (2016).
Article  Google Scholar 

54.
Anyamba, A. & Tucker, C. J. Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981–2003. J. Arid Environ. 63, 596–614 (2005).
ADS  Article  Google Scholar 

55.
Quetin, G. R. & Swann, A. L. S. Empirically derived sensitivity of vegetation to climate across global gradients of temperature and precipitation. J. Clim. 30, 5835–5849 (2017).
ADS  Article  Google Scholar 

56
Meroni, M., Fasbender, D., Rembold, F., Atzberger, C. & Klisch, A. Near real-time vegetation anomaly detection with MODIS NDVI: timeliness vs. accuracy and effect of anomaly computation options. Remote Sens. Environ. 221, 508–521 (2019).
ADS  PubMed  PubMed Central  Article  Google Scholar 

57.
Rita, A. et al. The impact of drought spells on forests depends on site conditions: the case of 2017 summer heat wave in southern Europe. Glob. Change Biol. 26, 851–863 (2020).
ADS  Article  Google Scholar 

58.
Kandasamy, S., Baret, F., Verger, A., Neveux, P. & Weiss, M. A comparison of methods for smoothing and gap filling time series of remote sensing observations – application to MODIS LAI products. Biogeosciences 10, 4055–4071 (2013).
ADS  Article  Google Scholar 

59.
Liu, R., Shang, R., Liu, Y. & Lu, X. Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise resistance and curve stability. Remote Sens. Environ. 189, 164–179 (2017).
ADS  Article  Google Scholar 

60.
Zandler, H., Brenning, A. & Samimi, C. Quantifying dwarf shrub biomass in an arid environment: comparing empirical methods in a high dimensional setting. Remote Sens. Environ. 158, 140–155 (2015).
ADS  Article  Google Scholar 

61.
Hyndman, R. J. Discussion of ‘High-dimensional autocovariance matrices and optimal linear prediction’. Electron. J. Stat. 9, 792–796 (2015).
MathSciNet  MATH  Article  Google Scholar 

62.
Propastin, P. A., Kappas, M. & Muratova, N. R. Inter-annual changes in vegetation activities and their relationship to temperature and precipitation in Central Asia from 1982 to 2003. J. Environ. Inf. 12, 75–87 (2008).
Article  Google Scholar 

63
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
ADS  Article  Google Scholar 

64.
Parker, W. S. Reanalyses and observations: what’s the difference?. Bull. Am. Meteorol. Soc. 97, 1565–1572 (2016).
ADS  Article  Google Scholar 

65.
El Kenawy, A. M. & McCabe, M. F. A multi-decadal assessment of the performance of gauge- and model-based rainfall products over Saudi Arabia: climatology, anomalies and trends: RAINFALL PRODUCTS IN SAUDI ARABIA. Int. J. Climatol. 36, 656–674 (2016).
Article  Google Scholar 

66.
Song, S. & Bai, J. Increasing winter precipitation over arid Central Asia under global warming. Atmosphere 7, 139 (2016).
ADS  Article  Google Scholar 

67.
Ahmed, K., Shahid, S., Wang, X., Nawaz, N. & Najeebullah, K. Evaluation of gridded precipitation datasets over arid regions of Pakistan. Water 11, 210 (2019).
Article  Google Scholar 

68.
Anjum, M. N. et al. Performance evaluation of latest integrated multi-satellite retrievals for Global Precipitation Measurement (IMERG) over the northern highlands of Pakistan. Atmos. Res. 205, 134–146 (2018).
Article  Google Scholar 

69.
Gelaro, R. et al. The modern-era retrospective analysis for research and applications, Version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).
ADS  PubMed  Article  PubMed Central  Google Scholar 

70.
Reichle, R. H. et al. Land surface precipitation in MERRA-2. J. Clim. 30, 1643–1664 (2017).
ADS  Article  Google Scholar 

71.
Peng, S., Piao, S., Ciais, P., Fang, J. & Wang, X. Change in winter snow depth and its impacts on vegetation in China. Glob. Change Biol. https://doi.org/10.1111/j.1365-2486.2010.02210.x (2010).
Article  Google Scholar 

72.
Qiu, B. et al. Satellite-observed solar-induced chlorophyll fluorescence reveals higher sensitivity of alpine ecosystems to snow cover on the Tibetan Plateau. Agric. For. Meteorol. 271, 126–134 (2019).
ADS  Article  Google Scholar 

73.
Hall, D. K., Riggs, G. A., DiGirolamo, N. E. & Román, M. O. Evaluation of MODIS and VIIRS cloud-gap-filled snow-cover products for production of an Earth science data record. Hydrol. Earth Syst. Sci. 23, 5227–5241 (2019).
ADS  Article  Google Scholar 

74.
Salomonson, V. V. & Appel, I. Development of the Aqua MODIS NDSI fractional snow cover algorithm and validation results. IEEE Trans. Geosci. Remote Sens. 44, 1747–1756 (2006).
ADS  Article  Google Scholar 

75.
Riggs, G., Hall, D. & Román, M. O. VIIRS Snow Cover Algorithm Theoretical Basis Document (ATBD). 38 (2015).

76
Zhu, A.-X. Resampling Raster. In International Encyclopedia of Geography: People, the Earth, Environment and Technology (eds Richardson, D. et al.) 1–5 (Wiley, New York, 2017). https://doi.org/10.1002/9781118786352.wbieg0878.
Google Scholar 

77.
Behnke, R. et al. Evaluation of downscaled, gridded climate data for the conterminous United States. Ecol. Appl. 26, 1338–1351 (2016).
CAS  PubMed  Article  Google Scholar 

78.
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
MATH  Article  Google Scholar 

79.
Zandler, H. Wakhan Rangeland Assessment Report 2018. Unpublished report. (2018).

80.
Camberlin, P., Martiny, N., Philippon, N. & Richard, Y. Determinants of the interannual relationships between remote sensed photosynthetic activity and rainfall in tropical Africa. Remote Sens. Environ. 106, 199–216 (2007).
ADS  Article  Google Scholar 

81.
Vicente-Serrano, S. M. et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl. Acad. Sci. 110, 52–57 (2013).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

82.
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
MathSciNet  MATH  Google Scholar 

83.
Peña, M. A., Brenning, A. & Sagredo, A. Constructing satellite-derived hyperspectral indices sensitive to canopy structure variables of a Cordilleran Cypress (Austrocedrus chilensis) forest. ISPRS J. Photogram. Remote Sens. 74, 1–10 (2012).
Article  Google Scholar 

84.
Zandler, H., Brenning, A. & Samimi, C. Potential of space-borne hyperspectral data for biomass quantification in an arid environment: advantages and limitations. Remote Sens. 7, 4565–4580 (2015).
ADS  Article  Google Scholar 

85
Efron, B. & Tibshirani, R. An Introduction to the Bootstrap (Chapman & Hall, London, 1993).
Google Scholar 

86.
Banik, S. & Kibria, B. M. Confidence intervals for the population correlation coefficient ρ. Int. J. Stats. Med. Res. 5, 99–111 (2016).
Article  Google Scholar 

87.
Mudelsee, M. Estimating Pearson’s correlation coefficient with bootstrap confidence interval from serially dependent time series. Math. Geol. 35, 651–665 (2003).
MATH  Article  Google Scholar 

88.
Abdi, A. M. et al. The El Niño – La Niña cycle and recent trends in supply and demand of net primary productivity in African drylands. Clim. Change 138, 111–125 (2016).
ADS  Article  Google Scholar 

89.
Lima, E., Davies, P., Kaler, J., Lovatt, F. & Green, M. Variable selection for inferential models with relatively high-dimensional data: Between method heterogeneity and covariate stability as adjuncts to robust selection. Sci. Rep. 10, 8002 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

90.
Degenhardt, F., Seifert, S. & Szymczak, S. Evaluation of variable selection methods for random forests and omics data sets. Brief. Bioinform. 20, 492–503 (2019).
PubMed  Article  PubMed Central  Google Scholar 

91
Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta package. J. Stat. Soft. https://doi.org/10.18637/jss.v036.i11 (2010).
Article  Google Scholar 

92.
Diesing, M. Deep-sea sediments of the global ocean. https://essd.copernicus.org/preprints/essd-2020-22/ (2020) 10.5194/essd-2020-22.

93.
R Core Team. R: A Language and Environment for Statistical Computing. Version 4.0.3. https://www.R-project.org/. (R Foundation for Statistical Computing, 2020).

94.
Daham, A., Han, D., Rico-Ramirez, M. & Marsh, A. Analysis of NVDI variability in response to precipitation and air temperature in different regions of Iraq, using MODIS vegetation indices. Environ. Earth Sci. 77, 389 (2018).
Article  Google Scholar 

95.
Chen, S., Gan, T. Y., Tan, X., Shao, D. & Zhu, J. Assessment of CFSR, ERA-Interim, JRA-55, MERRA-2, NCEP-2 reanalysis data for drought analysis over China. Clim. Dyn. 53, 737–757 (2019).
Article  Google Scholar 

96
Kath, J. et al. Not so robust: robusta coffee production is highly sensitive to temperature. Glob. Change Biol. https://doi.org/10.1111/gcb.15097 (2020).
Article  Google Scholar 

97.
Mahto, S. S. & Mishra, V. Does ERA-5 outperform other reanalysis products for hydrologic applications in India?. J. Geophys. Res. Atmos. 124, 9423–9441 (2019).
ADS  Article  Google Scholar 

98.
Royé, D., Íñiguez, C. & Tobías, A. Comparison of temperature–mortality associations using observed weather station and reanalysis data in 52 Spanish cities. Environ. Res. 183, 109237 (2020).
PubMed  Article  CAS  PubMed Central  Google Scholar 

99.
Dee, D. P., Källén, E., Simmons, A. J. & Haimberger, L. Comments on “Reanalyses Suitable for Characterizing Long-Term Trends”. Bull. Am. Meteorol. Soc. 92, 65–70 (2011).
ADS  Article  Google Scholar 

100.
Rasmussen, R. et al. How well are we measuring snow: the NOAA/FAA/NCAR winter precipitation test bed. Bull. Am. Meteorol. Soc. 93, 811–829 (2012).
ADS  Article  Google Scholar 

101.
Yuan, X., Li, L. & Chen, X. Increased grass NDVI under contrasting trends of precipitation change over North China during 1982–2011. Remote Sens. Lett. 6, 69–77 (2015).
Article  Google Scholar 

102.
Wang, X., Ciais, P., Wang, Y. & Zhu, D. Divergent response of seasonally dry tropical vegetation to climatic variations in dry and wet seasons. Glob. Change Biol. 24, 4709–4717 (2018).
ADS  Article  Google Scholar 

103
Basheer, M. & Elagib, N. A. Performance of satellite-based and GPCC 7.0 rainfall products in an extremely data-scarce country in the Nile Basin. Atmos. Res. 215, 128–140 (2019).
Article  Google Scholar 

104.
Piazzi, G. et al. Cross-country assessment of H-SAF snow products by sentinel-2 imagery validated against in-situ observations and webcam photography. Geosciences 9, 129 (2019).
ADS  Article  Google Scholar 

105.
Lievens, H. et al. Snow depth variability in the Northern Hemisphere mountains observed from space. Nat. Commun. 10, 4629 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

106.
Sur, C., Park, S.-Y., Kim, T.-W. & Lee, J.-H. Remote sensing-based agricultural drought monitoring using hydrometeorological variables. KSCE J. Civ. Eng. 23, 5244–5256 (2019).
Article  Google Scholar 

107.
Geruo, A., Velicogna, I., Zhao, M., Colliander, A. & Kimball, J. S. Satellite detection of varying seasonal water supply restrictions on grassland productivity in the Missouri basin, USA. Remote Sens. Environ. 239, 111623 (2020).
ADS  Article  Google Scholar 

108.
Lu, X. et al. Correcting GPM IMERG precipitation data over the Tianshan Mountains in China. J. Hydrol. 575, 1239–1252 (2019).
ADS  Article  Google Scholar 

109.
Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).
PubMed  PubMed Central  Article  Google Scholar 

110.
Bai, L., Shi, C., Li, L., Yang, Y. & Wu, J. Accuracy of CHIRPS satellite-rainfall products over Mainland China. Remote Sens. 10, 362 (2018).
ADS  Article  Google Scholar 

111.
Berg, A. A., Famiglietti, J. S., Walker, J. P. & Houser, P. R. Impact of bias correction to reanalysis products on simulations of North American soil moisture and hydrological fluxes. J. Geophys. Res. 108, ACL2-1-ACL2-5 (2003).
Google Scholar 

112.
Sahoo, A. K., Sheffield, J., Pan, M. & Wood, E. F. Evaluation of the tropical rainfall measuring mission multi-satellite precipitation analysis (TMPA) for assessment of large-scale meteorological drought. Remote Sens. Environ. 159, 181–193 (2015).
ADS  Article  Google Scholar 

113.
Zambrano, F., Wardlow, B., Tadesse, T., Lillo-Saavedra, M. & Lagos, O. Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile. Atmos. Res. 186, 26–42 (2017).
Article  Google Scholar 

114.
Dörre, A. Local knowledge-based water management and irrigation in the western pamirs. Int. J. EI 1, 254–266 (2018).
Article  Google Scholar  More

1.
Currey, J. D. The failure of exoskeletons and endoskeletons. J. Morphol. 123, 1–16 (1967).
CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Taylor, D. & Dirks, J.-H. Shape optimization in exoskeletons and endoskeletons: a biomechanics analysis. J. R. Soc. Interface 9, 3480–3489 (2012).
PubMed  PubMed Central  Article  Google Scholar 

3.
Boßelmann, F., Romano, P., Fabritius, H., Raabe, D. & Epple, M. The composition of the exoskeleton of two crustacea: The American lobster Homarusamericanus and the edible crab Cancerpagurus. Thermochim. Acta 463, 65–68 (2007).
Article  CAS  Google Scholar 

4.
Tynyakov, J. et al. A crayfish molar tooth protein with putative mineralized exoskeletal chitinous matrix properties. J. Exp. Biol. 218, 3487–3498 (2015).
PubMed  Article  PubMed Central  Google Scholar 

5.
Sugawara, A. et al. Self-organization of oriented calcium carbonate/polymer composites: Effects of a matrix peptide isolated from the exoskeleton of a crayfish. Angew. Chemie – Int. Ed. 45, 2876–2879 (2006).
CAS  Article  Google Scholar 

6.
Inoue, H., Ozaki, N. & Nagasawa, H. Purification and structural determination of a phosphorylated peptide with anti-calcification and chitin-binding activities in the exoskeleton of the crayfish, Procambarus clarkii. Biosci. Biotechnol. Biochem. 65, 1840–1848 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Taylor, J. R. A., Hebrank, J. & Kier, W. M. Mechanical properties of the rigid and hydrostatic skeletons of molting blue crabs, Callinectes sapidus Rathbun. J. Exp. Biol. 210, 4272–4278 (2007).
PubMed  Article  PubMed Central  Google Scholar 

8.
Chen, P. Y., Lin, A. Y. M., McKittrick, J. & Meyers, M. A. Structure and mechanical properties of crab exoskeletons. Acta Biomater. 4, 587–596 (2008).
PubMed  Article  PubMed Central  Google Scholar 

9.
Cribb, B. W. et al. Structure, composition and properties of naturally occuring non-calcified crustacean cuticle. Arthropod Struct. Dev. 38, 173–178 (2009).
CAS  PubMed  Article  Google Scholar 

10.
Bentov, S., Weil, S., Glazer, L., Sagi, A. & Berman, A. Stabilization of amorphous calcium carbonate by phosphate rich organic matrix proteins and by single phosphoamino acids. J. Struct. Biol. 171, 207–215 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Halcrow, K. Cell and tissue the fine structure of the carapace integument of Daphniamagna Straus (Crustacea Branchiopoda). Cell Tissue Res. 276, 267–276 (1976).
Google Scholar 

12.
Benzie, J. A. H. Cladocera: The Genus Daphnia (Including Daphniopsis) Benzie Teil 1. Guides to the Identification of the Microinvertebrates of the Continental Waters of the World Vol. 21 (2005).

13.
Harvell, C. D. & Tollrian, R. The Ecology and Evolution of Inducible Defenses Vol. 65. (Princeton University Press, Princeton, 1999).

14.
Laforsch, C., Beccara, L. & Tollrian, R. Inducible defenses The relevance of chemical alarm cues in Daphnia. Limnol. Oceanogr. 51, 1466–1472 (2006).
ADS  Article  Google Scholar 

15.
Tollrian, R. Predator-induced morphological defenses: Costs, life history shifts, and maternal effects in Daphniapulex. Ecology 76, 1691–1705 (1995).
Article  Google Scholar 

16.
Van Der Stap, I., Vos, M. & Mooij, W. M. Inducible defenses and rotifer food chain dynamics. Hydrobiologia 593, 103–110 (2007).
Article  CAS  Google Scholar 

17.
Altwegg, R., Marchinko, K. B., Duquette, S. L. & Anholt, B. R. Dynamics of an inducible defence in the protist Euplotes. Arch. Hydrobiol. 160, 431–446 (2004).
Article  Google Scholar 

18.
Frost, S. D. W. The immune system as an inducible defense. in The Ecology and Evolution of Inducible Defenses 104–126 (1999).

19.
Grant, J. W. G. W. G. & Bayly, I. A. E. Predator induction of crests in morphs of the Daphnia carinata King complex. Limnol. Oceanogr. 26, 201–218 (1981).

20.
Laforsch, C. & Tollrian, R. Inducible defenses in multipredator environments: Cyclomorphosis in Daphniacucullata. Ecology 85, 2302–2311 (2004).
Article  Google Scholar 

21.
Ritschar, S., Rabus, M. & Laforsch, C. Predator-specific inducible morphological defenses of a water flea against two freshwater predators. J. Morphol. 281, 653–661 (2020).
PubMed  Article  Google Scholar 

22.
Laforsch, C., Ngwa, W., Grill, W. & Tollrian, R. An acoustic microscopy technique reveals hidden morphological defenses in Daphnia. PNAS 101, 15911–15914 (2004).
ADS  CAS  PubMed  Article  Google Scholar 

23.
Rabus, M., Söllradl, T., Clausen-Schaumann, H. & Laforsch, C. Uncovering ultrastructural defences in Daphniamagna. PLoS ONE 8, e67856 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
Otte, K. A., Fröhlich, T., Arnold, G. J. & Laforsch, C. Proteomic analysis of Daphniamagna hints at molecular pathways involved in defensive plastic responses. BMC Genomics 15, 306 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

25.
Otte, K. A., Schrank, I., Fröhlich, T., Arnold, G. J. & Laforsch, C. Interclonal proteomic responses to predator exposure in Daphnia magna may depend on predator composition of habitats. Mol. Ecol. 24, 3901–3917 (2015).

26.
Krafft, C. et al. Label-free molecular imaging of biological cells and tissues by linear and nonlinear Raman spectroscopic approaches. Angew. Chem. Int. Ed. 56, 4392–4430 (2017).
CAS  Article  Google Scholar 

27.
Butler, H. J. et al. Using Raman spectroscopy to characterize biological materials. Nat. Protoc. 11, 664–687 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

28.
Kuhar, N., Sil, S., Verma, T. & Umapathy, S. Challenges in application of Raman spectroscopy to biology and materials. RSC Adv. 8, 25888–25908 (2018).
CAS  Article  Google Scholar 

29.
Talari, A. C. S., Movasaghi, Z., Rehman, S. & Rehman, I. U. Raman spectroscopy of biological tissues. Appl. Spectrosc. Rev. 50, 46–111 (2015).
ADS  CAS  Article  Google Scholar 

30.
Kruppert, S. et al. Push or Pull? The light-weight architecture of the Daphniapulex carapace is adapted to withstand tension, not compression. J. Morphol. 277, 1320–1328 (2016).
PubMed  Article  PubMed Central  Google Scholar 

31.
Gierlinger, N., Reisecker, C., Hild, S. & Gamsjaeger, S. Raman microscopy: Insights into chemistry and structure of biological materials. in Materials Design Inspired by Nature: Function Through Inner Architecture. 151–179 (2013). https://doi.org/10.1039/9781849737555-00151.

32.
Rabus, M., Waterkeyn, A., Van Pottelbergh, N., Brendonck, L. & Laforsch, C. Interclonal variation, effectiveness and long-term implications of Triops-induced morphological defences in Daphniamagna Strauss. J. Plankton Res. 34, 152–160 (2012).
Article  Google Scholar 

33.
Rabus, M. & Laforsch, C. Growing large and bulky in the presence of the enemy. Funct. Ecol. 25, 1137–1143 (2011).
Article  Google Scholar 

34.
Nikolov, S. et al. Robustness and optimal use of design principles of arthropod exoskeletons studied by ab initio-based multiscale simulations. J. Mech. Behav. Biomed. Mater. 4, 129–145 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Al-Sawalmih, A., Li, C., Siegel, S., Fratzl, P. & Paris, O. On the stability of amorphous minerals in lobster cuticle. Adv. Mater. 21, 4011–4015 (2009).
CAS  Article  Google Scholar 

36.
Luquet, G. Biomineralizations: Insights and prospects from crustaceans. Zookeys 176, 103–121 (2012).
Article  Google Scholar 

37.
Becker, A., Ziegler, A. & Epple, M. The mineral phase in the cuticles of two species of crustacea consists of magnesium calcite, amorphous calcium carbonate and amorphous calcium phosphate. R. Soc. Chem. 1814–1820 (2005).

38.
Roer, R. & Dillaman, R. The structure and clacification of the crustacean cuticle. Am. Zool. 24, 893–909 (1984).
CAS  Article  Google Scholar 

39.
Gower, L. B. Biomimetic model systems for investigating the amorphous precursor pathway and its role in biomineralization. Chem. Rev. 108, 4551–4627 (2008).
CAS  PubMed  Article  Google Scholar 

40.
Bots, P., Benning, L. G., Rodriguez-Blanco, J.-D., Roncal-Herrero, T. & Shaw, S. Mechanistic insights into the crystallization of amorphous calcium carbonate (ACC). Cryst. Growth Des. 12, 3806–3814 (2012).
CAS  Article  Google Scholar 

41.
Addadi, L., Raz, S. & Weiner, S. Taking advantage of disorder: Amorphous calcium carbonate and its roles in biomineralization. Adv. Mater. 15, 959–970 (2003).
CAS  Article  Google Scholar 

42.
Kawasaki, T. et al. Crystalline calcium phosphate and magnetic mineral content of Daphnia resting eggs. Zool. Sci. 21, 63–67 (2004).
CAS  Article  Google Scholar 

43.
Gerrish, G. A. & Cáceres, C. E. Genetic versus environmental influence on pigment variation in the ephippia of Daphniapulicaria. Freshw. Biol. 48, 1971–1982 (2003).
Article  Google Scholar 

44.
Bentov, S., Abehsera, S. & Sagi, A. The mineralized exoskeletons of crustaceans. in (Cohen, E., Moussian, B. eds) Extracellular Composite Matrices in Arthropods 137–163. (Springer, Cham, 2016). https://doi.org/10.1007/978-3-319-40740-1.

45.
Weiner, S., Levi-Kalisman, Y., Raz, S. & Addadi, L. Biologically formed amorphous calcium carbonate. Connect. Tissue Res. 44, 214–218 (2003).
CAS  PubMed  Article  Google Scholar 

46.
Hessen, D. A. G. O. & Rukke, N. A. The costs of moulting in Daphnia; mineral regulation of carbon budgets. Freshw. Biol. 1, 169–178 (2000).
Article  Google Scholar 

47.
Waervagen, S. B., Rukke, N. A. & Hessen, D. O. Calcium content of crustacean zooplankton and its potential role in species distribution. Freshw. Biol. 47, 1866–1878 (2002).
CAS  Article  Google Scholar 

48.
Nikolov, S. et al. Revealing the design principles of high-performance biological composites using Ab initio and multiscale simulations: The example of lobster cuticle. Adv. Mater. 22, 519–526 (2010).
CAS  PubMed  Article  Google Scholar 

49.
Bentov, S., Aflalo, E. D., Tynyakov, J., Glazer, L. & Sagi, A. Calcium phosphate mineralization is widely applied in crustacean mandibles. Sci. Rep. 6, 1–10 (2016).
Article  CAS  Google Scholar 

50.
Raabe, D., Al-Sawalmih, A., Yi, S. B. & Fabritius, H. Preferred crystallographic texture of α-chitin as a microscopic and macroscopic design principle of the exoskeleton of the lobster Homarus americanus. Acta Biomater. 3, 882–895 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Raabe, D. et al. Discovery of a honeycomb structure in the twisted plywood patterns of fibrous biological nanocomposite tissue. J. Cryst. Growth 283, 1–7 (2005).
ADS  CAS  Article  Google Scholar 

52.
Kruppert, S. et al. Biomechanical properties of predator-induced body armour in the freshwater crustacean Daphnia. Sci. Rep. 7, 1–13 (2017).
Article  Google Scholar 

53.
Azan, S. S. E. & Arnott, S. E. The impact of calcium decline on population growth rates of crustacean zooplankton in Canadian Shield lakes. Limnol. Oceanogr. 602–616 (2017). https://doi.org/10.1002/lno.10653.

54.
Riessen, H. P. et al. Changes in water chemistry can disable plankton prey defenses. Proc. Natl. Acad. Sci. U. S. A. 109, 15377–15382 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

55.
Tan, Q.-G. & Wang, W.-X. The regulation of calcium in Daphniamagna reared in different calcium environments. Limnol. Oceanogr. 54, 746–756 (2009).
ADS  CAS  Article  Google Scholar 

56.
Elendt, B. P. Selenium deficiency in Crustacea. Protoplasma 154, 25–33 (1990).
CAS  Article  Google Scholar 

57.
Team, R. C. R: A Language and Environment for Statistical Computing. (2008).

58.
Ramoji, A. et al. Raman Spectroscopy follows time-dependent changes in T lymphocytes isolated from spleen of endotoxemic mice. ImmunoHorizons 3, 45–60 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

59.
Chaturvedi, D. et al. Different phases of breast cancer cells: Raman study of immortalized, transformed, and invasive cells. Biosensors 6 (2016).

60.
Laforsch & Tollrian. A new preparation technique of daphnids for Scanning Electron Microscopy using hexamethyldisilazane. rch. Hydrobiol. 149, 587–596 (2000).

61.
Rohlf, F. J. & Sokal, R. R. Statistical tables. (1995). More