Philippa Kaur

1.
Lüthi, D. et al. High-resolution carbon dioxide concentration record 650,000–800,000 years before present. Nature 453, 379–382. https://doi.org/10.1038/nature06949 (2008).
CAS  Article  PubMed  ADS  Google Scholar 
2.
NASA. Global Vital Signs: Vital Signs of the Planet https://climate.nasa.gov/ (2019).

3.
Pachauri, R. K. et al. Climate Change 2014: synthesis report. Fifth Assessment Report on the Intergovernmental Panel on Climate Change 151. Geneva, Switzerland. (2014)

4.
Bale, J. S. et al. Herbivory in global climate change research: direct effects of rising temperature on insect herbivores. Glob. Change Biol. 8, 1–16. https://doi.org/10.1046/j.1365-2486.2002.00451.x (2002).
Article  ADS  Google Scholar 

5.
Forrest, J. R. K. Complex responses of insect phenology to climate change. Curr. Opin. Insect Sci. 17, 49–54. https://doi.org/10.1016/j.cois.2016.07.002 (2016).
Article  PubMed  Google Scholar 

6.
Food and Agriculture Organisation of the United Nations. FAOSTAT Crops http://www.fao.org/faostat/en/#home (2019).

7.
Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620. https://doi.org/10.1126/science.1204531 (2011).
CAS  Article  PubMed  ADS  Google Scholar 

8.
Ray, D. K. et al. Climate change has likely already affected global food production. PLoS ONE 14, e0217148. https://doi.org/10.1371/journal.pone.0217148 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

9.
Ali, M. P. et al. Will climate change affect outbreak patterns of planthoppers in Bangladesh?. PLoS ONE https://doi.org/10.1371/journal.pone.0091678 (2014).
Article  PubMed  PubMed Central  Google Scholar 

10.
Ali, M. P. et al. Increased temperature induces leaffolder outbreak in rice field. J. Appl. Entomol. 143, 867–874. https://doi.org/10.1111/jen.12652 (2019).
Article  Google Scholar 

11.
Hu, G. et al. Outbreaks of the brown planthopper Nilaparvata lugens (Stål) in the Yangtze River Delta: immigration or local reproduction? PLoS ONE 9, e88973 (2014).

12.
Yukawa, J. et al. Northward range expansion by Nezara viridula (Hemiptera: Pentatomidae) in Shikoku and Chugoku Districts, Japan, possibly due to global warming. Appl. Entomol. Zool. 44, 429–437 (2009).
Article  Google Scholar 

13.
Horgan, F. G. Integrating gene deployment and crop management for improved rice resistance to Asian planthoppers. Crop Prot. 110, 21–33 (2018).
CAS  Article  Google Scholar 

14.
Ali, M. P. et al. Establishing next-generation pest control services in rice fields: eco-agriculture. Sci. Rep. 9, 1–9 (2019).
Article  Google Scholar 

15.
Horgan, F. G. et al. Effects of vegetation strips, fertilizer levels and varietal resistance on the integrated management of arthropod biodiversity in a tropical rice ecosystem. Insects 10, 328 (2019).
Article  Google Scholar 

16.
Horgan, F. G. Potential for an impact of global climate change on insect herbivory in cereal crops. In Crop Protection Under Climate Change (eds Jabran, K. et al.) 101–144 (Springer, Berlin, 2020).
Google Scholar 

17.
Fujita, D., Kohli, A. & Horgan, F. G. Rice resistance to planthoppers and leafhoppers. Crit. Rev. Plant Sci. 32, 162–191 (2013).
CAS  Article  Google Scholar 

18.
Horgan, F. G. et al. Virulence of brown planthopper (Nilaparvata lugens) populations from South and South East Asia against resistant rice varieties. Crop Prot. 78, 222–231 (2015).
Article  Google Scholar 

19.
Khush, G. S. & Virk, P. S. IR Varieties and Their Impact (International Rice Research Institute, Los Baños, Philippines, 2005).
Google Scholar 

20.
Ren, J. et al. Bph32, a novel gene encoding an unknown SCR domain-containing protein, confers resistance against the brown planthopper in rice. Sci. Rep. 6, 37645 (2016).
CAS  Article  ADS  Google Scholar 

21.
Horgan, F. G. & Ferrater, J. B. Benefits and potential trade-offs associated with yeast-like symbionts during virulence adaptation in a phloem-feeding planthopper. Entomol. Exp. Appl. 163, 112–125 (2017).
Article  Google Scholar 

22.
Horgan, F. G., Garcia, C. P. F., Haverkort, F., de Jong, P. W. & Ferrater, J. B. Changes in insecticide resistance and host range performance of planthoppers artificially selected to feed on resistant rice. Crop Prot. 127, 104963. https://doi.org/10.1016/j.cropro.2019.104963 (2020).
CAS  Article  PubMed  PubMed Central  Google Scholar 

23.
Ferreter, J. B. et al. Varied responses by yeast-like symbionts during virulence adaptation in a monophagous phloem-feeding insect. Arthropod-Plant Interact. 9, 215–224 (2015).
Article  Google Scholar 

24.
Ferrater, J. B., de Jong, P. W., Dicke, M., Chen, Y. H. & Horgan, F. G. Symbiont-mediated adaptation by planthoppers and leafhoppers to resistant rice varieties. Arthropod-Plant Interact. 7, 591–605. https://doi.org/10.1007/s11829-013-9277-9 (2013).
Article  Google Scholar 

25.
Lee, Y. H. & Hou, R. F. Physiological roles of a yeast-like symbiote in reproduction and embryonic development of the brown planthopper, Nilaparvata lugensStål. J. Insect Physiol. 33, 851–860 (1987).
Article  Google Scholar 

26.
Hongoh, Y. & Ishikawa, H. Uric acid as a nitrogen resource for the brown planthopper, Nilaparvata lugens: studies with synthetic diets and aposymbiotic insects. Zool. Sci. 14, 581–586 (1997).
CAS  Article  Google Scholar 

27.
Pan, Y. et al. Identification of brown planthopper resistance gene Bph32 in the progeny of a rice dominant genic male sterile recurrent population using genome-wide association study and RNA-seq analysis. Mol. Breed. 39, 72 (2019).
Article  Google Scholar 

28.
Stevenson, P. C., Kimmins, F. M., Grayer, R. J. & Raveendranath, S. Schaftosides from rice phloem as feeding inhibitors and resistance factors to brown planthopper, Nilaparvata lugens. Entomol. Exp. Appl. 80, 246–249 (1996).
Article  Google Scholar 

29.
Uawisetwathana, U. et al. Global metabolite profiles of rice brown planthopper-resistant traits reveal potential secondary metabolites for both constitutive and inducible defenses. Metabolomics 15, 151. https://doi.org/10.1007/s11306-019-1616-0 (2019).
CAS  Article  PubMed  Google Scholar 

30.
Saxena, R. C. & Okech, S. H. Role of plant volatiles in resistance of selected rice varieties to brown planthopper, Nilaparvata lugens (Stål)(Homoptera; Delphacidae). J. Chem. Ecol. 11, 1601–1616 (1985).
CAS  Article  Google Scholar 

31.
Kamolsukyeunyong, W. et al. Identification of spontaneous mutation for broad-spectrum brown planthopper resistance in a large, long-term fast neutron mutagenized rice population. Rice 12, 16 (2019).
Article  Google Scholar 

32
Nguyen, C. D. et al. The development and characterization of near-isogenic and pyramided lines carrying resistance genes to brown planthopper with the genetic background of japonica rice (Oryza sativa L.). Plants 8, 498 (2019).
CAS  Article  Google Scholar 

33.
Salim, M. & Saxena, R. C. Temperature stress and varietal resistance in rice: effects on whitebackedplanthopper. Crop Sci. 31, 1620–1625. https://doi.org/10.2135/cropsci1991.0011183X003100060048x (1991).
Article  Google Scholar 

34.
Wang, B.-J., Xu, H.-X., Zheng, X.-S., Fu, Q. & Lu, Z.-X. High temperature modifies resistance performances of rice varieties to brown planthopper, Nilaparvata lugens (Stål). Rice Sci. 17, 334–338. https://doi.org/10.1016/S1672-6308(09)60036-6 (2010).
CAS  Article  Google Scholar 

35.
Havko, N. E., Kapali, G., Das, M. R. & Howe, G. A. Stimulation of insect herbivory by elevated temperature outweighs protection by the jasmonate pathway. Plants 9, 172 (2020).
Article  Google Scholar 

36.
Yuan, J. S., Himanen, S. J., Holopainen, J. K., Chen, F. & Stewart, C. N. Jr. Smelling global climate change: mitigation of function from plant volatile organic compounds. Trends Ecol. Evol. 24, 323–331 (2009).
Article  Google Scholar 

37.
Horgan, F. G., Arida, A., Ardestani, G. & Almazan, M. L. P. Temperature-dependent oviposition and nymph performance reveal distinct thermal niches of coexisting planthoppers with similar thresholds for development. PLoS ONE 15, e0235506 (2020).
CAS  Article  Google Scholar 

38.
Srinivas, M., Devi, R. S., Varmaand, N. R. G. & Jagadeeshwar, R. Interactive effect of temperature and CO2 on resistance of rice genotypes to brown planthopper, Nilaparvata lugens (Stål.). J. Entomol. Zool. Stud. 8, 600–602 (2020).
Google Scholar 

39.
Zhang, L., Wu, J. & Chen, B. Influence of temperature and light on expression of resistance in rice to the brown planthopper, Nilaparvata lugens (Homoptera: Delphacidae). J. South China Agric. Univ. 11, 64–70 (1990).
Google Scholar 

40.
Romena, S. & Saxena, R. Screening for resistance to whitebacked planthopper, Sogatella furcifera (Horvath): effect of temperature on seedling damage (Pest Control Council of the Philippines, Cebu City (Philippines), 1988).
Google Scholar 

41.
Horgan, F. G. et al. Resistance and tolerance to the brown planthopper, Nilaparvata lugens (Stål), in rice infested at different growth stages across a gradient of nitrogen applications. Field Crops Res. 217, 53–65. https://doi.org/10.1016/j.fcr.2017.12.008 (2018).
Article  PubMed  PubMed Central  Google Scholar 

42.
Neven, L. G. Physiological responses of insects to heat. Postharvest Biol. Technol. 21, 103–111 (2000).
CAS  Article  Google Scholar 

43.
Bühler, A., Lanzrein, B. & Wille, H. Influence of temperature and carbon dioxide concentration on juvenile hormone titre and dependent parameters of adult worker honey bees (Apis mellifera L). J. Insect Physiol. 29, 885–893 (1983).
Article  Google Scholar 

44.
Foissac, X., Edwards, M., Du, J., Gatehouse, A. & Gatehouse, J. Putative protein digestion in a sap-sucking homopteran plant pest (rice brown plant hopper; Nilaparvata lugens: Delphacidae)—identification of trypsin-like and cathepsin B-like proteases. Insect Biochem. Mol. Biol. 32, 967–978 (2002).
CAS  Article  Google Scholar 

45.
MacMillan, H. A. & Sinclair, B. J. Mechanisms underlying insect chill-coma. J. Insect Physiol. 57, 12–20 (2011).
CAS  Article  Google Scholar 

46.
Vailla, S., Muthusamy, S., Konijeti, C., Shanker, C. & Vattikuti, J. L. Effects of elevated carbon dioxide and temperature on rice brown planthopper, Nilaparvata lugens (Stål) populations in India. Curr. Sci. 116, 988 (2019).
CAS  Article  Google Scholar 

47.
Wang, B., Xu, H., Zheng, X., Fu, Q. & Lu, X. Effect of temperature on resistance of rice to brown planthopper, Nilaparvata lugens. Chin. J. Rice Sci. 24, 443–446 (2010).
Google Scholar 

48.
Ji, R. et al. A salivary endo-β-1, 4-glucanase acts as an effector that enables the brown planthopper to feed on rice. Plant Physiol. 173, 1920–1932 (2017).
CAS  Article  Google Scholar 

49.
Venkatesh, J. & Kang, B.-C. Current views on temperature-modulated R gene-mediated plant defense responses and tradeoffs between plant growth and immunity. Curr. Opin. Plant Biol. 50, 9–17 (2019).
CAS  Article  Google Scholar 

50.
Murai, M. & Kiritani, K. Influence of parental age upon the offspring in the green rice leafhopper, Nephotettix cincticeps Uhler (Hemiptera: Deltocephalidae). Appl. Entomol. Zool. 5, 189–201 (1970).
Article  Google Scholar 

51.
Lu, K. et al. Nutritional signaling regulates vitellogenin synthesis and egg development through juvenile hormone in Nilaparvata lugens (Stål). Int. J. Mol. Sci. 17, 269 (2016).
Article  Google Scholar 

52.
Thoeun, H. C. Observed and projected changes in temperature and rainfall in Cambodia. Weather Clim. Extremes 7, 61–71 (2015).
Article  Google Scholar 

53.
PAGASA. Observed Climate Trends and Projected Climate Change in the Philippines. (Philippine Athmospheric, Geophysical and Astronomical Services Administration (PAGASA), Philippines, (2018).

54.
Yu Media Group. Weather Atlas: weather around the world – list of countries. http://www.weather-atlas.com/en/countries (2020).

55
You, L. L. et al. Driving pest insect populations: agricultural chemicals lead to an adaptive syndrome in Nilaparvata Lugens Stål (Hemiptera: Delphacidae). Sci. Rep. https://doi.org/10.1038/srep37430 (2016).
Article  PubMed  PubMed Central  Google Scholar 

56.
Ge, L. Q. et al. Molecular basis for insecticide-enhanced thermotolerance in the brown planthopper Nilaparvata lugens Stål (Hemiptera: Delphacidae). Mol. Ecol. 22, 5624–5634. https://doi.org/10.1111/mec.12502 (2013).
CAS  Article  PubMed  Google Scholar  More

1.
Ben-Dor, E., Irons, J. & Epema, G. Soil reflectance. Remote Sens. Earth Sci. Man. Remote Sens. 3, 111–188 (1999).
Google Scholar 
2.
Soriano-Disla, J. M., Janik, L. J., Viscarra Rossel, R. A., Macdonald, L. M. & McLaughlin, M. J. The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties. Appl. Spectrosc. Rev. 49, 139–186 (2014).
ADS  CAS  Article  Google Scholar 

3.
Viscarra Rossel, R. A. et al. A global spectral library to characterize the world’s soil. Earth-Sci. Rev. 155, 198–230 (2016).
Article  Google Scholar 

4.
Orgiazzi, A., Ballabio, C., Panagos, P., Jones, A. & Fernández-Ugalde, O. Lucas soil, the largest expandable soil dataset for europe: a review. Eur. J. Soil Sci. 69, 140–153 (2018).
Article  Google Scholar 

5.
Viscarra Rossel, R. A. & Webster, R. Predicting soil properties from the australian soil visible-near infrared spectroscopic database. Eur. J. Soil Sci. 63, 848–860 (2012).
Article  Google Scholar 

6.
Shi, Z., Ji, W., Viscarra Rossel, R. A., Chen, S. & Zhou, Y. Prediction of soil organic matter using a spatially constrained local partial least squares regression and the c hinese vis-nir spectral library. Eur. J. Soil Sci. 66, 679–687 (2015).
CAS  Article  Google Scholar 

7.
Wijewardane, N. K., Ge, Y., Wills, S. & Loecke, T. Prediction of soil carbon in the conterminous united states: visible and near infrared reflectance spectroscopy analysis of the rapid carbon assessment project. Soil Sci. Soc. Am. J. 80, 973–982 (2016).
ADS  CAS  Article  Google Scholar 

8.
Peng, Y. et al. Predicting soil organic carbon at field scale using a national soil spectral library. J. Near Infrared Spectrosc. 21, 213–222 (2013).
ADS  CAS  Article  Google Scholar 

9.
Terra, F. . S. ., Demattê, J. . A. . & Viscarra Rossel, R. . A. . Spectral libraries for quantitative analyses of tropical brazilian soils: comparing vis-nir and mid-ir reflectance data. Geoderma 255, 81–93 (2015).
ADS  Article  Google Scholar 

10.
Clairotte, M. et al. National calibration of soil organic carbon concentration using diffuse infrared reflectance spectroscopy. Geoderma 276, 41–52 (2016).
ADS  CAS  Article  Google Scholar 

11.
Tziolas, N., Tsakiridis, N., Ben-Dor, E., Theocharis, J. & Zalidis, G. A memory-based learning approach utilizing combined spectral sources and geographical proximity for improved vis-nir-swir soil properties estimation. Geoderma 340, 11–24 (2019).
ADS  CAS  Article  Google Scholar 

12.
Wold, Svante, Harold Martens, and Herman Wold. The multivariate calibration problem in chemistry solved by the pls method. Matrix Pencils. Springer. 286–293 (1983).

13.
Viscarra Rossel, R. A. & Behrens, T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158, 46–54 (2010).
ADS  Article  Google Scholar 

14.
Lee, S., Choi, H., Cha, K. & Chung, H. Random forest as a potential multivariate method for near-infrared (nir) spectroscopic analysis of complex mixture samples: Gasoline and naphtha. Microchem. J. 110, 739–748 (2013).
CAS  Article  Google Scholar 

15.
Devos, O., Ruckebusch, C., Durand, A., Duponchel, L. & Huvenne, J.-P. Support vector machines (svm) in near infrared (nir) spectroscopy: focus on parameters optimization and model interpretation. Chemom. Intell. Lab. Syst. 96, 27–33 (2009).
CAS  Article  Google Scholar 

16.
Daniel, K., Tripathi, N. & Honda, K. Artificial neural network analysis of laboratory and in situ spectra for the estimation of macronutrients in soils of lop buri (thailand). Soil Res. 41, 47–59 (2003).
CAS  Article  Google Scholar 

17.
Rawat, W. & Wang, Z. Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput. 29, 2352–2449 (2017).
MathSciNet  Article  Google Scholar 

18.
Ji, S., Xu, W., Yang, M. & Yu, K. 3d convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35, 221–231 (2012).
Article  Google Scholar 

19.
Wallach, I., Dzamba, M. & Heifets, A. Atomnet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-Based Drug Discovery. arXiv:1510.02855 (2015).

20.
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
ADS  CAS  Article  Google Scholar 

21.
Veres, M., Lacey, G. & Taylor, G. W. Deep learning architectures for soil property prediction. In 2015 12th Conference on Computer and Robot Vision, 8–15 (IEEE, 2015).

22.
Liu, L., Ji, M. & Buchroithner, M. Transfer learning for soil spectroscopy based on convolutional neural networks and its application in soil clay content mapping using hyperspectral imagery. Sensors 18, 3169 (2018).
Article  Google Scholar 

23.
Tsakiridis, N. L., Keramaris, K. D., Theocharis, J. B. & Zalidis, G. C. Simultaneous prediction of soil properties from vnir-swir spectra using a localized multi-channel 1-d convolutional neural network. Geoderma 367, 114208 (2020).
ADS  CAS  Article  Google Scholar 

24.
Padarian, J., Minasny, B. & McBratney, A. Using deep learning to predict soil properties from regional spectral data. Geoderma Reg. 16, e00198 (2019).
Article  Google Scholar 

25.
Padarian, J., Minasny, B. & McBratney, A. Transfer learning to localise a continental soil vis-nir calibration model. Geoderma 340, 279–288 (2019).
ADS  CAS  Article  Google Scholar 

26.
Ng, W. et al. Convolutional neural network for simultaneous prediction of several soil properties using visible/near-infrared, mid-infrared, and their combined spectra. Geoderma 352, 251–267 (2019).
ADS  CAS  Article  Google Scholar 

27.
Hutter, F., Kotthoff, L. & Vanschoren, J. Automated Machine Learning: Methods, Systems, Challenges (Springer, Berlin, 2019).
Google Scholar 

28.
Ioffe, S. & Szegedy, C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 (2015).

29.
Santurkar, S., Tsipras, D., Ilyas, A. & Madry, A. How does batch normalization help optimization? In Advances in Neural Information Processing Systems 31 (eds Bengio, S. et al.) 2483–2493 (Curran Associates, Inc., Red Hook, 2018).
Google Scholar 

30.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014).
MathSciNet  MATH  Google Scholar 

31.
Hahnloser, R. H., Sarpeshkar, R., Mahowald, M. A., Douglas, R. J. & Seung, H. S. Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405, 947–951 (2000).
ADS  CAS  Article  Google Scholar 

32.
Nair, V. & Hinton, G. E. Rectified linear units improve restricted boltzmann machines. In ICML (2010).

33.
Maas, A. L., Hannun, A. Y. & Ng, A. Y. Rectifier nonlinearities improve neural network acoustic models. In Proceedings of ICML 30, 3 (2013).

34.
Clevert, D.-A., Unterthiner, T. & Hochreiter, S. Fast and Accurate Deep Network Learning by Exponential Linear Units (elus). arXiv:1511.07289 (2015).

35.
Klambauer, G., Unterthiner, T., Mayr, A. & Hochreiter, S. Self-normalizing neural networks. Adv. Neural Inf. Process. Syst. 30, 971–980 (2017).
Google Scholar 

36.
Ramachandran, P., Zoph, B. & Le, Q. V. Searching for Activation Functions. arXiv:1710.05941 (2017).

37.
Ruder, S. An Overview of Gradient Descent Optimization Algorithms. arXiv:1609.04747 (2016).

38.
Simonyan, K. & Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556 (2014).

39.
Pascanu, R., Mikolov, T. & Bengio, Y. On the difficulty of training recurrent neural networks. In International Conference on Machine Learning 1310–1318 (2013).

40.
Mnih, V. et al. Playing Atari with Deep Reinforcement Learning. arXiv:1312.5602 (2013).

41.
Duchi, J., Hazan, E. & Singer, Y. Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12.7 (2011).

42.
Kingma, D. P. & Ba, J. Adam: A Method for Stochastic Optimization. arXiv:1412.6980 (2014).

43.
Keskar, N. S., Mudigere, D., Nocedal, J., Smelyanskiy, M. & Tang, P. T. P. On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima. arXiv:1609.04836 (2016).

44.
Hsu, C.-W., Chang, C.-C., Lin, C.-J. et al. A practical guide to support vector classification. 1396–1400 (2003).

45.
Lerman, P. Fitting segmented regression models by grid search. J. R. Stat. Soc. Ser. C (Appl. Stat.) 29, 77–84 (1980).
Google Scholar 

46.
Bergstra, J. & Bengio, Y. Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281–305 (2012).
MathSciNet  MATH  Google Scholar 

47.
Franceschi, L., Donini, M., Frasconi, P. & Pontil, M. Forward and Reverse Gradient-Based Hyperparameter Optimization. arXiv:1703.01785 (2017).

48.
Luketina, J., Berglund, M., Greff, K. & Raiko, T. Scalable gradient-based tuning of continuous regularization hyperparameters. In International Conference on Machine Learning 2952–2960 (2016).

49.
Maclaurin, D., Duvenaud, D. & Adams, R. Gradient-based hyperparameter optimization through reversible learning. In International Conference on Machine Learning 2113–2122 (2015).

50.
Bengio, Y. Gradient-based optimization of hyperparameters. Neural Comput. 12, 1889–1900 (2000).
CAS  Article  Google Scholar 

51.
Domke, J. Generic methods for optimization-based modeling. In Artificial Intelligence and Statistics 318–326 (2012).

52.
Bergstra, J. S., Bardenet, R., Bengio, Y. & Kégl, B. Algorithms for hyper-parameter optimization. Adv. Neural Inf. Process. Syst. 24, 2546–2554 (2011).

53.
Hutter, F., Hoos, H. H. & Leyton-Brown, K. Sequential model-based optimization for general algorithm configuration. In International Conference on Learning and Intelligent Optimization, 507–523 (Springer, 2011).

54.
Snoek, J., Larochelle, H. & Adams, R. P. Practical bayesian optimization of machine learning algorithms. Adv. Neural Inf. Process. Syst. 25, 2951–2959 (2012).
Google Scholar 

55.
Jamieson, K. & Talwalkar, A. Non-stochastic best arm identification and hyperparameter optimization. In Artificial Intelligence and Statistics 240–248 (2016).

56.
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A. & Talwalkar, A. Hyperband: a novel bandit-based approach to hyperparameter optimization. J. Mach. Learn. Res. 18, 6765–6816 (2017).
MathSciNet  MATH  Google Scholar 

57.
Loshchilov, I. & Hutter, F. Cma-es for Hyperparameter Optimization of Deep Neural Networks. arXiv:1604.07269 (2016).

58.
Jaderberg, M. et al. Population Based Training of Neural Networks. arXiv:1711.09846 (2017).

59.
Eggensperger, K. et al. Towards an empirical foundation for assessing bayesian optimization of hyperparameters. In NIPS Workshop on Bayesian Optimization in Theory and Practice 10, 3 (2013).

60.
Hutter, F., Hoos, H. & Leyton-Brown, K. An efficient approach for assessing hyperparameter importance. In International Conference on Machine Learning 754–762 (2014).

61.
Prechelt, L. Early Stopping-but When? In Neural Networks: Tricks of the Trade 55–69 (Springer, Berlin, 1998).
Google Scholar  More

1.
Hewitt, G. M. Some genetic consequences of ice ages. Biol. J. Lin. Soc. 58, 247–276 (1996).
Article  Google Scholar 
2.
Husemann, M., Schmitt, T., Zachos, F. E., Ulrich, W. & Habel, J. C. Palearctic biogeography revisited: evidence for the existence of a North African refugium for Western Palaearctic biota. J. Biogeogr. 41, 81–94. https://doi.org/10.1111/jbi.12180 (2014).
Article  Google Scholar 

3.
Hewitt, G. M. Postglacial recolonization of European biota. Biol. J. Lin. Soc. 68, 87–112 (1999).
Article  Google Scholar 

4.
Hewitt, G. M. Genetic consequences of climatic oscillations in the Quaternary. Philos. Trans. R. Soc. B Biol. Sci. 359, 183–195. https://doi.org/10.1098/rstb.2003.1388 (2004).
CAS  Article  Google Scholar 

5.
de Lattin, G. Grundriß der Zoogeographie (Gustav Fischer, Jena, 1967).
Google Scholar 

6.
Schmitt, T. & Varga, Z. Extra-Mediterranean refugia: The rule and not the exception?. Front. Zool. 9, 1–12. https://doi.org/10.1186/1742-9994-9-22 (2012).
Article  Google Scholar 

7.
Drees, C. et al. Molecular analyses and species distribution models indicate cryptic northern mountain refugia for a forest-dwelling ground beetle. J. Biogeogr. 43, 2223–2236. https://doi.org/10.1111/jbi.12828 (2016).
Article  Google Scholar 

8.
Juřičková, L., Horáčková, J. & Ložek, V. Direct evidence of central European forest refugia during the last glacial period based on mollusc fossils. Quat. Res. 82, 222–228. https://doi.org/10.1016/j.yqres.2014.01.015 (2014).
Article  Google Scholar 

9.
Pinceel, J., Jordaens, K., Pfenninger, M. & Backeljau, T. Rangewide phylogeography of a terrestrial slug in Europe: Evidence for Alpine refugia and rapid colonization after the Pleistocene glaciations. Mol. Ecol. 14, 1133–1150. https://doi.org/10.1111/j.1365-294X.2005.02479.x (2005).
CAS  Article  PubMed  Google Scholar 

10.
Gratton, P., Konopiński, M. K. & Sbordoni, V. Pleistocene evolutionary history of the Clouded Apollo (Parnassius mnemosyne): Genetic signatures of climate cycles and a “time-dependent” mitochondrial substitution rate. Mol. Ecol. 17, 4248–4262. https://doi.org/10.1111/j.1365-294X.2008.03901.x (2008).
CAS  Article  PubMed  Google Scholar 

11.
Hofman, S. et al. Phylogeography of the fire-bellied toads Bombina: Independent Pleistocene histories inferred from mitochondrial genomes. Mol. Ecol. 16, 2301–2316. https://doi.org/10.1111/j.1365-294X.2007.03309.x (2007).
CAS  Article  PubMed  Google Scholar 

12.
Junker, M. et al. Three in one-multiple faunal elements within an endangered european butterfly species. PLoS ONE 10, 1–24. https://doi.org/10.1371/journal.pone.0142282 (2015).
CAS  Article  Google Scholar 

13.
Ursenbacher, S., Carlsson, M., Helfer, V., Tegelström, H. & Fumagalli, L. Phylogeography and Pleistocene refugia of the adder (Vipera berus) as inferred from mitochondrial DNA sequence data. Mol. Ecol. 15, 3425–3437. https://doi.org/10.1111/j.1365-294X.2006.03031.x (2006).
CAS  Article  PubMed  Google Scholar 

14.
Magri, D. Patterns of post-glacial spread and the extent of glacial refugia of European beech (Fagus sylvatica). J. Biogeogr. 35, 450–463. https://doi.org/10.1111/j.1365-2699.2007.01803.x (2008).
Article  Google Scholar 

15.
Svenning, J.-C., Normand, S. & Kageyama, M. Glacial refugia of temperate trees in Europe: Insights from species distribution modelling. J. Ecol. 96, 1117–1127. https://doi.org/10.1111/j.1752-4598.2012.00212.x (2008).
Article  Google Scholar 

16.
Cheddadi, R. et al. Imprints of glacial refugia in the modern genetic diversity of Pinus sylvestris. Global Ecol. Biogeogr. 15, 271–282. https://doi.org/10.1111/j.1466-822x.2006.00226.x (2006).
Article  Google Scholar 

17.
Willis, K. J. & Van Andel, T. H. Trees or no trees? The environments of central and eastern Europe during the Last Glaciation. Quatern. Sci. Rev. 23, 2369–2387. https://doi.org/10.1016/j.quascirev.2004.06.002 (2004).
ADS  Article  Google Scholar 

18.
Rudner, Z. E. & Sümegi, P. Recurring Taiga forest-steppe habitats in the Carpathian Basin in the Upper Weichselian. Quatern. Int. 76, 177–189. https://doi.org/10.1016/S1040-6182(00)00101-4 (2001).
Article  Google Scholar 

19.
van Swaay, C., Warren, M. & Grégoire, L. Biotope use and trends of European butterflies. J. Insect Conserv. 10, 189–209. https://doi.org/10.1007/s10841-006-6293-4 (2006).
Article  Google Scholar 

20.
Slamova, I., Klecka, J. & Konvicka, M. Diurnal behavior and habitat preferences of Erebia aethiops, an aberrant lowland species of a mountain butterfly clade. J. Insect Behav. 24, 230–246. https://doi.org/10.1007/s10905-010-9250-8 (2011).
Article  Google Scholar 

21.
Burnaz, S. & Balazs, S. Contributions to the knowledge of diurnal Lepidoptera fauna of the North-Eastern part of Ţarcu Mountains (Southern Carpathians, Romania). Buletin de Informare Entomologica 22, 41–52 (2011).
Google Scholar 

22.
Slamova, I., Klecka, J. & Konvicka, M. Woodland and grassland mosaic from a butterfly perspective: Habitat use by Erebia aethiops (Lepidoptera: Satyridae). Insect Conserv. Divers. 6, 243–254. https://doi.org/10.1111/j.1752-4598.2012.00212.x (2013).
Article  Google Scholar 

23.
Tshikolovets, V. V. Butterflies of Europe & the Mediterranean area (Tshikolovets Publications, Pardubice, 2011).
Google Scholar 

24.
Varga, Z. Die Erebien der Balkanhalbinsel und Karpaten V. Übersicht der subspezifischen Gliederung und der Verbreitung der Erebia Dalman, 1816 -Arten (Lepidoptera, Nymphalidae, Saryrinae) in der Balkanhalbinsel und in den Karpaten (II. Teil). Entomol Roman 6, 5–39 (2001).
Google Scholar 

25.
Imbrie, J., et al. Milankovitch theory, the two shorter cycles can be explained radiation cycle (arising from of Geological Sciences, Brown 2 Institut d’ Astronomie et de Geophysique of Earth, Atmospheric, and Planetary Earth Observatory, Columbia for Climatic and Sp. Cycle 8, 699–735 (1993).

26.
Nei, M. Genetic Distance between Populations 106, 283–29 (1972).

27.
Hausdorf, B. & Hennig, C. Species delimitation using dominant and codominant multilocus. Markers 59, 491–503. https://doi.org/10.1093/sysbio/syq039 (2010).
CAS  Article  Google Scholar 

28.
Nakatani, T., Usami, S. & Itoh, T. Molecular phylogenetic analysis of the Erebia aethiops group (Lepidoptera, Nymphalidae). Lepidopterol. Soc. Jpn. 58, 387–404 (2007).
Google Scholar 

29.
Lukhtanov, V. & Lukhtanov, A. Die Tagfalter Nordwestasiens (Dr. Ulf Eitschberger, Marktleuthen, 1994).
Google Scholar 

30.
Peña, C., Witthauer, H., Klečková, I., Fric, Z. & Wahlberg, N. Adaptive radiations in butterflies: Evolutionary history of the genus Erebia (Nymphalidae: Satyrinae). Biol. J. Lin. Soc. 116(2), 449–467. https://doi.org/10.1111/bij.12597 (2015).
Article  Google Scholar 

31.
Hampe, A. & Petit, R. J. Conserving biodiversity under climate change: The rear edge matters. Ecol. Lett. 8, 461–467. https://doi.org/10.1111/j.1461-0248.2005.00739.x (2005).
Article  PubMed  Google Scholar 

32.
Schmitt, T., Hewitt, G. M. & Müller, P. Disjunct distributions during glacial and interglacial periods in mountain butterflies: Erebia epiphron as an example. J. Evol. Biol. 19, 108–113. https://doi.org/10.1111/j.1420-9101.2005.00980.x (2006).
CAS  Article  PubMed  Google Scholar 

33.
Schmitt, T. & Seitz, A. Intraspecific allozymatic differentiation reveals the glacial refugia and the postglacial expansions of European Erebia medusa (Lepidoptera: Nymphalidae). Biol. J. Linn. Soc. 74, 429–458. https://doi.org/10.1006/bijl.2001.0584 (2001).
Article  Google Scholar 

34.
van Husen, D. Geological processes during the Quaternary. Mitteilungen Der Österreichischen Geologischen Gesellschaft 92, 135–156 (2000).
Google Scholar 

35.
Schmitt, T. Biogeographical and evolutionary importance of the European high mountain systems. Front. Zool. 6, 1–10. https://doi.org/10.1186/1742-9994-6-9 (2009).
Article  Google Scholar 

36.
Albre, J., Gers, C. & Legal, L. Molecular phylogeny of the Erebia tyndarus (Lepidoptera, Rhopalocera, Nymphalidae, Satyrinae) species group combining CoxII and ND5 mitochondrial genes: A case study of a recent radiation. Mol. Phylogenet. Evol. 47, 196–210. https://doi.org/10.1016/j.ympev.2008.01.009 (2008).
CAS  Article  PubMed  Google Scholar 

37.
Fišer Pečnikar, Ž, Balant, M., Glasnović, P. & Surina, B. Seed dormancy and germination of the rare, high elevation Balkan endemic Cerastium dinaricum (Caryophyllaceae). Biologia 73, 937–943. https://doi.org/10.2478/s11756-018-0115-5 (2018).
CAS  Article  Google Scholar 

38.
Giachino, P. M. A new species of Aphaobiella Pretner, 1949 from Grintavec Mt., Slovenia (Coleoptera: Cholevidae, Leptodirinae). Fragm. Entomol. 48, 19–23. https://doi.org/10.4081/fe.2016.165 (2016).
Article  Google Scholar 

39.
Canestrelli, D., Salvi, D., Maura, M., Bologna, M. A. & Nascetti, G. One species, three Pleistocene evolutionary histories: Phylogeography of the Italian crested newt, Triturus carnifex. PLoS ONE 7, e41754. https://doi.org/10.1371/journal.pone.0041754 (2012).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

40.
Previšić, A., Walton, C., KuČiniĆ, M., Mitrikeski, P. T. & Kerovec, M. Pleistocene divergence of Dinaric Drusus endemics (Trichoptera, Limnephilidae) in multiple microrefugia within the Balkan Peninsula. Mol. Ecol. 18, 634–647. https://doi.org/10.1111/j.1365-294X.2008.04046.x (2009).
CAS  Article  PubMed  Google Scholar 

41.
Deffontaine, V. et al. Beyond the Mediterranean peninsulas: Evidence of central European glacial refugia for a temperate forest mammal species, the bank vole (Clethrionomys glareolus). Mol. Ecol. 14, 1727–1739. https://doi.org/10.1111/j.1365-294X.2005.02506.x (2005).
CAS  Article  PubMed  Google Scholar 

42.
Krebs, P., Pezzatti, G. B., Beffa, G., Tinner, W. & Conedera, M. Revising the sweet chestnut (Castanea sativa Mill.) refugia history of the last glacial period with extended pollen and macrofossil evidence. Quat. Sci. Rev. 206, 111–128. https://doi.org/10.1016/j.quascirev.2019.01.002 (2019).
ADS  Article  Google Scholar 

43.
Varga, Z. Geographische Isolation und Subspeziation bei den Hochgebirgslepidopteren der Balkanhalbinsel. Acta Entomol Jugoslaviae. (1975).

44.
Bhagwat, S. A. & Willis, K. J. Species persistence in northerly glacial refugia of Europe: A matter of chance or biogeographical traits?. J. Biogeogr. 35, 464–482. https://doi.org/10.1111/j.1365-2699.2007.01861.x (2008).
Article  Google Scholar 

45.
Filipi, K., Marková, S., Searle, J. B. & Kotlík, P. Mitogenomic phylogenetics of the bank vole Clethrionomys glareolus, a model system for studying end-glacial colonization of Europe. Mol. Phylogenet. Evol. 82, 245–257. https://doi.org/10.1016/j.ympev.2014.10.016 (2015).
Article  PubMed  Google Scholar 

46.
Hammouti, N., Schmitt, T., Seitz, A., Kosuch, J. & Veith, M. Combining mitochondrial and nuclear evidences: A refined evolutionary history of Erebia medusa (Lepidoptera: Nymphalidae: Satyrinae) in Central Europe based on the COI gene. J. Zool. Syst. Evolut. Res. 48, 115–125. https://doi.org/10.1111/j.1439-0469.2009.00544.x (2010).
Article  Google Scholar 

47.
Vila, M., Marí-Mena, N., Guerrero, A. & Schmitt, T. Some butterflies do not care much about topography: a single genetic lineage of Erebia euryale (Nymphalidae) along the northern Iberian mountains. J. Zool. Syst. Evolut. Res. 49, 119–132. https://doi.org/10.1111/j.1439-0469.2010.00587.x (2011).
Article  Google Scholar 

48.
Vodă, R., Dapporto, L., Dincă, V. & Vila, R. Cryptic matters: Overlooked species generate most butterfly beta-diversity. Ecography 38, 405–409. https://doi.org/10.1111/ecog.00762 (2015).
Article  Google Scholar 

49.
Hebert, P. D. N., Ratnasingham, S. & DeWaard, J. R. Barcoding animal life: Cytochrome c oxidase subunit 1 divergences among closely related species. Proc. R. Soc. B Biol. Sci. 270, 1–4. https://doi.org/10.1098/rsbl.2003.0025 (2003).
CAS  Article  Google Scholar 

50.
Weller, S. J., Pashley, D. P., Martin, J. A. & Constable, J. L. Phylogeny of noctuoid moths and the utility of combining independent nuclear and mitochondrial genes. Syst. Biol. 43, 194–211. https://doi.org/10.1093/sysbio/43.2.194 (1994).
Article  Google Scholar 

51.
Hebert, P. D. N., & Beaton, M. J. Methodologies for allozyme analysis using cellulose acetate electrophoresis. Zoology 32 (1993).

52.
Kearse, M. et al. Geneious basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649. https://doi.org/10.1093/bioinformatics/bts199 (2012).
Article  PubMed  PubMed Central  Google Scholar 

53.
Thompson, J. D., Higgins, D. G. & Gibson, T. J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680. https://doi.org/10.1093/nar/22.22.4673 (1994).
CAS  Article  PubMed  PubMed Central  Google Scholar 

54.
Hall, T. A. BioEdit. Nucleic Acids Symp. Ser. 41, 95–98 (1999).
CAS  Google Scholar 

55.
Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34, 3299–3302. https://doi.org/10.1093/molbev/msx248 (2017).
CAS  Article  Google Scholar 

56.
Bandelt, H. J., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48. https://doi.org/10.1093/oxfordjournals.molbev.a026036 (1999).
CAS  Article  Google Scholar 

57.
Leigh, J. W. & Bryant, D. POPART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116. https://doi.org/10.1111/2041-210X.12410 (2015).
Article  Google Scholar 

58.
Bouckaert, R. et al. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 15, e1006650. https://doi.org/10.1371/journal.pcbi.1006650 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

59.
Lanfear, R., Frandsen, P. B., Wright, A. M., Senfeld, T. & Calcott, B. Partitionfinder 2: New methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. Evol. 34, 772–773. https://doi.org/10.1093/molbev/msw260 (2017).
CAS  Article  PubMed  Google Scholar 

60.
Papadopoulou, A., Anastasiou, I. & Vogler, A. P. Revisiting the insect mitochondrial molecular clock: The mid-aegean trench calibration. Mol. Biol. Evol. 27, 1659–1672. https://doi.org/10.1093/molbev/msq051 (2010).
CAS  Article  PubMed  Google Scholar 

61.
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904. https://doi.org/10.1093/sysbio/syy032 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

62.
Drummond, A. J., & Ho, S. Y. W. A rough guide to BEAST 1.4. Edinburgh, 1–41, http://beast-mcmc.googlecode.com/files/BEAST14_Manual_17May2007.pdf (2007).

63.
Yu, Y., Harris, A. J., Blair, C. & He, X. RASP (Reconstruct Ancestral State in Phylogenies): A tool for historical biogeography. Mol. Phylogenet. Evol. 87, 46–49. https://doi.org/10.1016/j.ympev.2015.03.008 (2015).
Article  PubMed  Google Scholar 

64.
Siegismund, H. R. G-STAT, Version 3, Genetical Statistical Programs for the Analysis of Population Data. (The Arboretum, Royal Veterinary and Agricultural University, Horsholm, Denmark, 1993).

65.
Excoffier, L., Laval, G. & Schneider, S. Arlequin (version 3.0): An integrated software package for population genetics data analysis. Evol. Bioinform. 1, 117693430500100. https://doi.org/10.1177/117693430500100003 (2005).
Article  Google Scholar 

66.
Rice, W. R. The sequential Bonferroni test. Evolution 43, 235 (1989).
Article  Google Scholar 

67.
Saitou, N. & Nei, M. The neighbor-joining method: A new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 97, 407–425 (1987).
Google Scholar 

68.
Felsenstein, J. PHYLIP (Phylogeny Inference Package), Version 3.5.c. (Department of Genetics, University of Washington, Seattle, Washington, 2000).

69.
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

70.
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x (2005).
CAS  Article  PubMed  Google Scholar 

71.
Guillot, G., Estoup, A., Mortier, F., & Cosson, J. F. A spatial statistical model for landscape genetics. Genetics 170, 1261–1280. https://doi.org/10.1534/genetics.104.033803 (2005).
CAS  Article  PubMed  PubMed Central  Google Scholar 

72.
Team, Q. D. QGIS Geographic Information System. Open Source Geospatial Foundation Project (2020). http://qgis.osgeo.org. Accessed 05 Nov 2020. More

Clothianidin exposure in field experiments
The experimental bee yard was situated in a suburban area in Kirchhain (Germany) with sufficient natural pollen sources in late summer, where the honeybees were allowed to forage freely. The field experiment took place with 20 Apis mellifera carnica colonies in Kirchhainer mating nuclei (25.5 × 19.8 × 17 cm), established with sister queens (A. m. carnica breeding line, mother: 17:27:20:11) mated on an island mating station (Norderney) and 180 g of worker bees each. We tried to reduce the variance between colonies by using young sister queens from an island mating station. Furthermore, we used small hives to minimize food competition so that we were able to set up the colonies within a rather small area of 20 by 20 m. In detail, the worker bees originated from four healthy colonies of the institute, located at an apiary of the institute. In order to get a homogenous composition, worker bees of brood combs and honeycombs of two colonies were shaken into a box and mixed thoroughly, before they were uniformly distributed into 10 mating nuclei. This procedure was repeated to fill 20 mating nuclei. Subsequently, the mating boxes were placed in a cool room (12 °C) for three day, to acclimatize, before they were transported to the island mating station Norderney. The queens started to lay eggs at June 25, 2014. The clothianidin exposure started at July 28. Thus, the colonies containing freshly mated, egg-laying queens were allowed to establish and build up an intact brood nest for four weeks. Two days before clothianidin exposure (sampling day 0, S0; SFig. 1), the colony strength was assessed and the treatment groups were randomly assigned to the hives such that differences between treatment groups were minimized with respect to the strength of their colonies. Every week, from July 30 to September 10, each colony received 400 mL of Apiinvert (Südzucker AG, Mannheim, Germany) sugar syrup (39% w/v fructose, 31% w/v saccharose and 30% w/v glucose) spiked with clothianidin (1, 10 or 100 µg/L). Control colonies received sugar syrup containing the same concentration of the solvent (water) as the clothianidin-treated groups. The syrup was fed in zip-look bags placed inside the food chamber containing a climbing aid. After 1 week, the leftovers were removed and weighed to record food consumption. For all four experimental groups, the analyzed clothinidin levels were close to the target concentrations (Suppl. Table 1). Environmental data were recorded during the study period using a USB data logger (EL-USB-2, Lascar Electronics Ltd., temperature accuracy ± 0.5 °C, relative humidity accuracy ± 3%) located under the colonies.
Sampling
During weeks 3 and 7, random samples of worker bees, larvae and worker jelly were taken from each colony. In detail, 20 randomly chosen worker bees located on a brood comb and five larvae (larval stage: day 7 or 8 after egg laying) were immediately frozen for chemical analysis. To collect worker jelly, extra thick blotting paper (Protean, Xi size; Bio-Rad, Hercules, CA, USA) was cut into strips, cleaned in pure ethanol and acetone (Carl Roth, Karlsruhe, Germany) and dried in a heating cabinet at 80 °C. Each strip was inserted into five brood cells containing a small larva (developmental day 4–5) to suck up the worker jelly and the strips were immediately frozen.
To document brood development, each side of each comb was photographed within an empty hive box transformed into a photo box containing a digital camera (Canon PowerShot A1000 IS, Tokyo, Japan) and a ring-flash (Aputure Amaran AHL-C60 LED, Shenzhen, China). Brood documentation took place every week. The colonies were sampled starting with the control and then from the lowest to highest concentration of clothianidin to minimize the risk of carryover.
HPG size measurements
To obtain worker bees of a defined age, single frames of late-stage capped brood (Binder, Tuttlingen, Germany) were brought to the laboratory and incubated in the dark at 32 °C, with humidity provided by open water jars. The frames with worker brood were collected from two full-sized colonies, which were regularly inspected for symptoms of disease and tested for Chronic bee paralysis virus, Deformed wing virus, Acute bee paralysis virus, and Sac brood virus65. Newly-emerged bees (≤ 24 h) were collected, color marked, and transferred to the experimental colonies on July 28 (15 marked bees per colony). During the second week of exposure, the marked bees (Suppl. Table 2) were removed from the colonies after 12 days in the hive and immobilized on ice. The HPGs were dissected in ice-cold phosphate-buffered saline (PBS, pH 7.4). The specimens were fixed in formaldehyde (4% in PBS, Carl Roth), rinsed three times in PBS, and mounted in Aquapolymount (Polysciences, Eppelheim, Germany). Three pictures of each gland (only one gland per bee) were photographed at 400x magnification using a phase contrast/fluorescence microscope (Leica DMIL, Leica camera DFC 420C) and LAS v4.4 image-capturing software (Leica Microsystems, Wetzlar, Germany). To measure the size of each gland, the diameters of 30 acini per bee were measured using ImageJ v1.49o (http://rsb.info.nih.gov/ij/index.html).
High-performance thin-layer chromatography
HPTLC was chosen as sensitive analytical method in order to detect qualitative and semi-quantitative differences in the composition of brood food and larvae of dosed hives. Individual larvae (one per colony) differing in their weights (200–500 mg/larva) were solely macerated and extracted in 1 mL n-hexane ( > 99% pure, Rotisolv, Carl Roth) in an ultrasonic bath for 1 min and then vortexed for 1 min. For the analysis of worker jelly (from three cells per colony), adsorptive filter strips (Sugi strips, Kettenbach, Eschenburg, Germany) were cut in half and one part was dipped in brood combs until maximal absorption of the material, whereas the other strip was used as background control strip, and both strips were extracted as above. The supernatants of larvae and worker jelly were transferred to a fresh vial 1000 μL isopropylacetate/methanol (3/2, v/v). After maceration for 1 min, the mix was vortexed for 1 min and the supernatant was transferred to another vial. Between extractions, the samples were cooled on ice and stored at –20 °C.
The isopropylacetate/methanol extracts (20 µL/band for worker jelly and 7 µL/band for larvae) were sprayed onto the silica gel 60 F254 HPTLC plate (Merck, Darmstadt, Germany) using an Automatic TLC Sampler 4. The plate of worker jelly was developed with a mobile phase consisting of chloroform/methanol/water/ammonia (30/17/2/1, v/v/v/v, all Carl Roth66 and the plate of larvae with an 8-step gradient development based on methanol, chloroform, toluene and n-hexane66,67. After drying in a stream of cold air for 2 min, the plate images were documented at UV 366 nm using the TLC Visualizer. For derivatisation, the chromatogram was dipped into the primuline solution (100 mg primuline in 200 mL acetone/water, 4/1 v/v, Sigma-Aldrich, Steinheim, Germany) at an immersion speed of 2.5 cm/s and an immersion time of 1 s, dried and derivatised as before. The data were processed using winCATS version 1.4.2.8121 (all instrumentation from CAMAG).
The bacterium Aliivibrio fischeri (NRRL-B11177, strain 7151), obtained from the German Collection of Microorganisms and Cell Cultures (DSMZ, Leibniz Institute, Berlin, Germany), was used to assess a non-targeted, broad range of effective substances within the worker jelly. HPTLC plates were developed as described above, neutralized and immersed in a bacterial suspension, prepared according to DIN EN ISO 11,348–1 845 at an immersion speed of 3 cm/s and an immersion time of 2 s. The bioluminescence of the wet bioautogram was recorded in an interval of 3 min over 30 min using the BioLuminizer (CAMAG).
Brood assessment
The free extension of the open source program ImageJ, Fiji (http://fiji.sc/Fiji) was used to count all cells with eggs, larvae, or sealed brood, for every colony on every sampling day. The photos of a single comb from different sampling days were aligned in the program and all brood cells were counted. Because the colonies had no wax foundations, some cells on the edges of these naturally-built combs were at an unfavorable angle. Therefore, most but presumably not all cells with brood were visible in the photos. This uncertainty was similar in all hives. To estimate the brood survival, we first tracked the development of individual eggs, but found a high mortality rate even in control colonies. Therefore, we tracked the development of individual larvae (day 4–5 after egg laying) over 4 weeks (= sampling weeks). Brood survival was estimated for two periods during the experiment (weeks 1–4 and 3–7).
Modeling of demographic compensation
The aim of the model was to estimate the number of new larvae that must hatch each week in order to maintain a stable number of larvae up to 7 weeks of age given the survivorship schedule of larvae week by week. Let h denote the number of eggs that hatch into larvae each week, and let the probability that any individual larva dies in each successive week be mi, where i takes values in the set {1, 2, …, 7} to indicate each of seven successive weeks, after which we assume that surviving larvae pupate. We used a demographic matrix model68 to describe the state of the population of larvae each week as follows. Let lx denote the number of larvae in the colony that are aged x weeks post-hatching and let mx denote their per capita weekly mortality rate. The population of larvae is distributed into seven age classes and we also assign a class to queens, which give rise to larvae by producing eggs. Using the Lefcovitch matrix approach, the larval population of a colony can thus be viewed as a state vector nt whose week-by-week change is the product of a matrix A and the population state vector nt, as shown in Eq. (1):

$$An_{t} = left[ {begin{array}{*{20}c} 0 & 0 & 0 & ldots & 0 & {varvec{h}} \ {(1 – {varvec{m}}_{1} )} & 0 & 0 & ldots & 0 & 0 \ 0 & {left( {1 – {varvec{m}}_{2} } right)} & 0 & ldots & 0 & 0 \ vdots & vdots & vdots & ddots & vdots & vdots \ 0 & 0 & 0 & ldots & {left( {1 – {varvec{m}}_{7} } right)} & 0 \ 0 & 0 & 0 & ldots & 0 & 1 \ end{array} } right]left[ {begin{array}{*{20}c} {{varvec{l}}_{1} } \ {{varvec{l}}_{2} } \ {{varvec{l}}_{3} } \ vdots \ {{varvec{l}}_{7} } \ {varvec{Q}} \ end{array} } right]$$
(1)

The lowermost element of nt is the number of queens (Q) which is here set to Q = 1 for all models, and the total number of larvae in the colony at any time is ({varvec{L}}_{{varvec{t}}} = mathop sum nolimits_{{varvec{x}}} {varvec{l}}_{{varvec{x}}}). Given the observed mortality schedule (mx), we used the model to solve for the value of h that produces a stable value for Lt, as shown in Eq. (2):

$$An_{t} = n_{t}$$
(2)

We determined the mortality schedule by observing the survivorship of a larval cohort. For example, if a cohort of St larvae was originally marked at time t and, of these, St+1 survived until the following week, the per capita weekly mortality rate would be estimated as shown in Eq. (3):

$${varvec{m}}_{{varvec{t}}} = 1 – frac{{{varvec{S}}_{{{varvec{t}} + 1}} }}{{{varvec{S}}_{{varvec{t}}} }}$$
(3)

BEEHAVE simulations
To predict the impact of clothianidin on colony development in standard hives we used BEEHAVE, a honeybee model that simulates colony dynamics and agent-based foraging in realistic landscapes44(http://beehave-model.net/). Although BEEHAVE does not explicitly allow the incorporation of pesticides, the effect of pesticides on behavior and mortality can nevertheless be addressed59,60. BEEHAVE simulates the development of a single honeybee colony, starting with 10,000 foragers on January 1. Colony dynamics are based on a daily egg laying rate, with the developmental stages eggs, larvae, pupae, and adults (in drones) or in-hive workers and foragers (in workers). The brood needs to be tended by in-hive bees, and the larvae additionally need to be fed with nectar and pollen. Foragers can scout for new food sources or collect nectar and pollen from sources already known. Successful foragers can recruit nestmates to the food source. Mortality rates depend on the developmental stage and the time spent on foraging. The colony dies if it either runs out of honey or if the colony size falls below 4000 bees at the end of the year. Swarming may take place when the brood nest grows to more than 17,000 bees before July 18. Under default conditions, two food sources are present at distances of 500 or 1,500 m. Daily foraging conditions are based on weather data from Rothamsted, UK.
We ran two sets of simulations: (A) We first set up BEEHAVE to mimic our experimental nucleus colonies. We then determined how the protein content of the jelly produced by nurses (ProteinFactorNurses) had to be modified to replicate the larval mortality we observed in our empirical data. (B) We then set up BEEHAVE under default conditions but modified ProteinFactorNurses according to the results from the previous simulations to assess the impact of clothianidin exposure under more realistic conditions.
To mimic the experimental nucleus colonies (simulation A), the maximum honey store (MAX_HONEY_STORE_kg) was reduced to 0.77 kg and the maximum size of the brood nest (MAX_BROODCELLS) was reduced to 250. Furthermore, HoneyIdeal was set to ‘true’, so that even though the honey store was small it was filled every day, reflecting the feeding of the experimental bees. In contrast, PollenIdeal was set to ‘false’, because the experimental colonies still had to forage for pollen. On day 209 (July 28), we set the number of pupae to 100 and the number of workers to 650, similar to the experimental colony sizes. During the exposure to clothianidin between days 211 (July 30) and 253 (September 10), the protein content of the jelly fed to the larvae (ProteinFactorNurses) was modified by the new variable ProteinNursesModifier_Exposed. We tested for ProteinNursesModifier_Exposed values from 0.6 to 1 in steps of 0.01. The main output of the simulation was the survival of the brood, calculated from the brood cohort sizes aged 19 days divided by the sizes of these cohorts when they were 3 days old. We calculated the mean brood survival over 30 replicates, using the last 10 cohorts only (i.e. those reaching the age of 19 days between September 1 and 10). Those parameter values for ProteinNursesModifier_Exposed resulting in brood survival most similar to the experimental brood survival were then chosen to represent the clothianidin concentrations of 100, 10 and 1 µg/L.
To assess the impact of clothianidin on standard colonies under more realistic conditions (simulation B), we ran BEEHAVE under default settings but reduced the protein content of the jelly (ProteinFactorNurses) during times of exposure. We assumed that colonies would be exposed when rapeseed plants are flowering, defined in the model as the period between days 95 (April 5) and 130 (May 10). ProteinNursesModifier_Exposed values representing the tested concentrations of clothianidin were derived from the previous set of simulations, and for the control we set ProteinNursesModifier_Exposed to 1 (i.e. no effect of clothianidin). Swarming was either prevented or allowed, in which case the simulation followed the colony remaining in the hive.
Statistical methods
Statistical analysis was carried out using R v3.4.269, including the add-on packages lme470 for linear mixed-effects models, pbkrtest71 for testing fixed effects in mixed-effects models, parallel69 to increase computational power, RLRsim72 for testing random effects in mixed-effects model, multcomp73 for multiple comparisons, and lattice74 for various graphical displays. We used linear mixed-effects models for one- and two-factorial analysis of variance (ANOVA) or regressions as indicated and where necessary. In those models, the colonies were modeled as random effects to reflect the (longitudinal) grouping structure in the data. For the analysis of larval survival, we used a logarithmic transformation of the proportion of surviving larvae. When testing fixed effects in mixed-effects models, we used the Kenward-Roger method (and double-checked the results by comparing them with parametric bootstrap values). Where sufficient, we simplified the analysis using linear (fixed-effects) models. Model diagnostics were performed for all fitted models using qualitative tools such as normal q-q-plots for residuals and plots of residuals versus fitted values to assess the validity of model assumptions like homoscedastic normality. Dunnett’s test (or customized contrasts as appropriate) was used for multiple comparisons in post hoc analysis, and P values were appropriately adjusted for multiple testing within well-defined test families using the single-step or Westfall’s method. Model and analysis details, model diagnostic graphs, and further information are available in the supplemental statistical report. More

A unified framework model of the H/P ratio
Based on the Lotka–Volterra equations24,25, the biomass dynamics of primary producers (P) and herbivores (H) are described as follows:

$$begin{array}{l}dP/dt = gleft( P right)P – xP – fleft( P right)PH,\ dH/dt = kfleft( P right)PH – mHend{array}$$
(1)

where g(P) is biomass-specific primary production (gC gC−1 d−1) and may be a function of P (gC m−2) owing to density-dependent growth, f(P) is per capita grazing rate of herbivores (m2 gC−1 d−1) and also may be function of P depending on the functional response, x is biomass-specific loss rate of primary producers other than due to grazing loss (gC gC−1 d−1), k is the conversion efficiency of herbivores as a fraction of ingested food converted into herbivore biomass (dimensionless: 0~1), and m is per capita mortality rate of herbivores owing to predation and other factors (gC gC−1 d−1). A list of model variables is listed in Supplementary Table 1. If we assume both g(P) and f(P) are constants, Eq. (1) is basically the Lotka–Volterra model, whereas it is an expansion of the Rosenzweig–MacArthur model if we assume logistic growth for g(P) and Michaelis–Menten (Holling type II) functional responses for f(P)24. At the equilibrium state, i.e., dP/dt = 0 and dH/dt = 0, the abundance of producers (P*) and consumers (H*) can be represented as:

$$H ast /P ast = left{ {left[ {gleft( {P ast } right) – x} right]} right./left. {left. {fleft( {P ast } right)} right]} right}/left{ {{mathrm{m/}}left[ {kfleft( {P ast } right)} right]} right}$$
(2)

Thus, the relationship between H and P is not affected by the types of the functional response in herbivores (f(P)). If we set g(P) as the biomass-specific primary production rate at equilibrium, as in simple Lotka–Volterra equations (i.e., g(P*) = g), then the H/P ratio can be expressed with log transformation as:

$${mathrm{log}}left( {H ast /P ast } right) = {mathrm{log}}left( k right) + {mathrm{log}}(g – x) – {mathrm{log}}left( m right)$$
(3)

At equilibrium, (g − x)P* is the amount of primary production that herbivores consume per unit of time (f(P*)P*H*). Thus, if this amount is divided by primary production per unit of time (P*g), it corresponds to the fraction of primary production that herbivores consume (0~1). We define it as β (= 1 − x/g). Large β values imply that producers are efficiently grazed at the equilibrium state. Thus, β is a gauge of inefficiency in the producers’ defensive traits. Using these parameters, the H*/P* ratio can be expressed as:

$${mathrm{log}}left( {H ast /P ast } right) = {mathrm{log}}left( k right) + {mathrm{log}}left( beta right) + {mathrm{log}}left( g right) – {mathrm{log}}left( m right)$$
(4)

This equation implies that the H*/P* biomass ratio on a log scale is affected additively by the specific primary production rate (log(g)), the grazeable fraction of primary production (log(β)), the conversion efficiency (log(k)), and the mortality rate of herbivores (log(m)). According to this equation, communities with relatively low carnivore abundance would have a correspondingly low value of m and will exhibit high herbivore biomass relative to producer biomass (H*/P*), whereas those with low primary production (with low value of g) owing to, for example, low light supply will have a low H*/P* ratio. An increase in defended producers such as armored plants or a decrease in edible producers will decrease β by increasing the loss rate x owing to the cost of defense, and will result in a decreased H*/P* ratio. Finally, when the nutritional value of producers decreases, the conversion efficiency of herbivores (k) should be low, which in turn decreases the H*/P*biomass ratio.
The model for a test with plankton communities
To apply Eq. (4) to a natural community, some modifications are necessary. Here, we consider a plankton community composed of algae and zooplankton. A theory of ecological stoichiometry suggests that the carbon content of primary producers relative to their nutrient content such as nitrogen or phosphorus is an important property affecting growth efficiency in herbivores4. Supporting the theory, a number of studies have shown that growth rate in terms of carbon accumulation relative to ingestion rate strongly depends on the carbon contents of the food relative to nutrients26,27,28,29. Thus, k can be expressed as:

$$k = q_1 times a_{{mathrm{nut}}}^{varepsilon 1}$$
(5)

where αnut is carbon content relative to nutrient content of primary producers and q1 is the conversion factor adjusting to biomass units. In this study, we applied a power function with coefficient of ε1 as a first order approximation because effects of this factor on the H*/P* biomass ratio may not be proportionally related to plant nutrient content. For example, if ε1 is much smaller than zero, it means that negative effects of the carbon to phosphorus ratio of algal food on an herbivore’s k are more substantial when the carbon to phosphorus ratio is high compared with the case when the carbon to phosphorus ratio is low. However, if this factor does not affect the H*/P* biomass ratio, ε1 = 0 and k is constant.
As herbivorous zooplankton cannot efficiently graze on larger phytoplankton due to gape limitation30, the feeding efficiency of herbivores or the defense efficiency of the producers’ resistance traits, β, would be related to the fraction of edible algae in terms of size as follows:

$$beta = q_2 times a_{{mathrm{edi}}}^{varepsilon 2}$$
(6)

where αedi is a trait determining producer edibility, q2 is a factor for converting the traits to edible efficiency, and ε2 is how effective the trait is in defending against grazing. We expect ε2 = 0 if this factor does not matter in regulating the H*/P* biomass ratio but ε2  > 0 if it has a role. Similarly, g can be described as

$$g = q_3 times mu ^{varepsilon 3}$$
(7)

where μ is the specific growth rate of producers, q3 is a conversion factor, and ε3 is the effect of µ on growth rate. Again, we expect that ε3 ≠ 0 if g has a role in determining the H*/P* ratio. Finally, assuming a Holling type I functional response of carnivores, the mortality rate of herbivores, m, is expressed as:

$$m = q_4 times theta ^{varepsilon 4}$$
(8)

where θ is abundance of carnivores, q4 is specific predation rate, and ε4 is the effect size of carnivore abundance on m.
By inserting Eqs. (5–8) to Eq. (4), effects of factors on the H*/P* biomass ratio is formulated as:

$${!}{mathrm{log}}left( {H ast /P ast } right) {!}= varepsilon _1{mathrm{log}}(a_{{mathrm{nut}}}) + varepsilon _2{mathrm{log}}(a_{{mathrm{edi}}}) + varepsilon _3{mathrm{log}}(mu ) – varepsilon _4{mathrm{log}}(theta ) + gamma$$
(9)

where γ is log(q1) + log(q2) + log(q3) − log(q4). If differences in the H*/P* ratio among communities are regulated by growth rate (μ), edibility (αedi), and nutrient contents (αnut) of producers as well as by predation by carnivores (θ), we expect non-zero values for ε1–ε4. Thus, Eq. (9) can be used to evaluate the relative importance of the four hypothesized agents if all of them simultaneously affect the H*/P* ratio. Here we undertake this analysis using data for natural plankton communities in experimental ponds where primary production rate was manipulated with different abundance of carnivore fish.
Experimental test by plankton communities
The experiment was carried out at two ponds (pond ID 217 and 218) located at the Cornell University Experimental Ponds Facility in Ithaca, NY, USA during 4 June to 28 August 2016 (Fig. 1). Each pond has a 0.09 ha surface area (30 × 30 m) and is 1.5 m deep. To initiate the experiment, we equally divided each of the two ponds into four sections using vinyl-coated canvas curtains, and randomly assigned the four sections to either high-shade (64% shading), mid-shade (47% shading), low-shade (33% shading), or no-shade treatments (no shading). Shading in each treatment was made using opaque floating mats (6 m diameter; Solar-cell SunBlanket, Century Products, Inc., Georgia, USA)31. The floating mats were deployed silvered side up to reflect sunlight and blue side down to avoid pond heating. Sampling was performed biweekly for water chemistry and abundance of phytoplankton and zooplankton with measurements of vertical profiles of water temperature, dissolved oxygen (DO) concentration and photosynthetic active radiation (PAR).
Fig. 1: Ponds used in the experimental test.

Pond 217 (a) and Pond 218 (b) in the Cornell University Experimental Ponds Facility divided into four sections by vinyl-canvas curtains and partially shaded by floating mats to regulate primary production rate. Floating docks were placed at the center of the ponds for sampling.

Full size image

PAR in the water column was lower in the sections with larger shaded areas throughout the experiment (Fig. 2b). Water temperature varied from 18 to 25 °C during the experiment but showed no notable differences in mean values of the water columns or the vertical profiles among the four treatments of the two ponds regardless of the shading treatment (Supplementary Fig. 1a, Supplementary Fig. 2). In all treatments, pH values gradually decreased towards the end of experiment and were higher in pond 218 (Supplementary Fig. 1b). DO concentration varied among the treatments and between the ponds, but were within the range of 5–12 mg L−1 (Supplementary Fig. 1c).
Fig. 2: Relationships between zooplankton biomass and phytoplankton biomass, and between H/P mass ratio and photosynthetic active radiation.

Biplots of zooplankton biomass (μg C L−1) and phytoplankton biomass (mg C L−1) (a) and H/P mass ratio and photosynthetic active radiation (PAR, mol photon m−2 d−1) (b) in the water column during the experiment in no-shade (blue), low-shade (orange), mid-shade (red), and high-shade (gray) treatments in pond 217 (circles) and 218 (squares). In each panel, small symbols denote values at each sampling date, and large symbols denote the mean values among the sampling dates. Bars denote standard errors on the means (n = 7 sampling date in each section). Correlation coefficients (r) with p values between the mean values are inserted in each panel.

Full size image

Phytoplankton biomass (mg C L−1) correlated significantly with chlorophyll a (µg L−1) (r = 0.702, p  More

1.
USDAFS. Press Release: Survey Finds 18 Million Trees Died in California in 2018. https://www.fs.usda.gov/Internet/FSE_DOCUMENTS/FSEPRD609321.pdf (USDAFS, 2019).
2.
Griffin, D. & Anchukaitis, K. J. How unusual is the 2012-2014 California drought? Geophys. Res. Lett. 41, 9017–9023 (2014).
ADS  Article  Google Scholar 

3.
Robeson, S. M. Revisiting the recent California drought as an extreme value. Geophys. Res. Lett. 42, 6771–6779 (2015).
ADS  Article  Google Scholar 

4.
Asner, G. P. et al. Progressive forest canopy water loss during the 2012-2015 California drought. Proc. Natl Acad. Sci. USA 113, E249–E255 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

5.
Brodrick, P. G. & Asner, G. P. Remotely sensed predictors of conifer tree mortality during severe drought. Environ. Res. Lett. 12, 115013 (2017).
ADS  Article  Google Scholar 

6.
Fettig, C. J. in Managing Sierra Nevada Forests. PSW-GTR-237 Ch. 2 (USDA Forest Service, 2012).

7.
Kolb, T. E. et al. Observed and anticipated impacts of drought on forest insects and diseases in the United States. For. Ecol. Manag. 380, 321–334 (2016).
Article  Google Scholar 

8.
Waring, R. H. & Pitman, G. B. Modifying lodgepole pine stands to change susceptibility to mountain pine beetle attack. Ecology 66, 889–897 (1985).
Article  Google Scholar 

9.
Restaino, C. et al. Forest structure and climate mediate drought-induced tree mortality in forests of the Sierra Nevada, USA. Ecol. Appl. 0, e01902 (2019).
Article  Google Scholar 

10.
USDAFS. Press Release: Record 129 million dead trees in California. https://www.fs.usda.gov/Internet/FSE_DOCUMENTS/fseprd566303.pdf (USDAFS, 2017).

11.
Young, D. J. N. et al. Long-term climate and competition explain forest mortality patterns under extreme drought. Ecol. Lett. 20, 78–86 (2017).
PubMed  Article  PubMed Central  Google Scholar 

12.
Raffa, K. F. et al. Cross-scale drivers of natural disturbances prone to anthropogenic amplification: The dynamics of bark beetle eruptions. BioScience 58, 501–517 (2008).
Article  Google Scholar 

13.
Boone, C. K., Aukema, B. H., Bohlmann, J., Carroll, A. L. & Raffa, K. F. Efficacy of tree defense physiology varies with bark beetle population density: a basis for positive feedback in eruptive species. Can. J. Res. 41, 1174–1188 (2011).
Article  Google Scholar 

14.
Fettig, C. J., Mortenson, L. A., Bulaon, B. M. & Foulk, P. B. Tree mortality following drought in the central and southern Sierra Nevada, California, U.S. For. Ecol. Manag. 432, 164–178 (2019).
Article  Google Scholar 

15.
Stephenson, N. L., Das, A. J., Ampersee, N. J. & Bulaon, B. M. Which trees die during drought? The key role of insect host-tree selection. J. Ecol. 75, 2383–2401 (2019).
Article  Google Scholar 

16.
Senf, C., Campbell, E. M., Pflugmacher, D., Wulder, M. A. & Hostert, P. A multi-scale analysis of western spruce budworm outbreak dynamics. Landsc. Ecol. 32, 501–514 (2017).
Article  Google Scholar 

17.
Seidl, R. et al. Small beetle, large-scale drivers: how regional and landscape factors affect outbreaks of the European spruce bark beetle. J. Appl Ecol. 53, 530–540 (2016).
Article  Google Scholar 

18.
Fettig, C. J. in Insects and Diseases of Mediterranean Forest Systems (eds Lieutier, F. & Paine, T. D.) 499–528 (Springer International Publishing, 2016).

19.
Raffa, K. F. & Berryman, A. A. The role of host plant resistance in the colonization behavior and ecology of bark beetles (Coleoptera: Scolytidae). Ecol. Monogr. 53, 27–49 (1983).
Article  Google Scholar 

20.
Logan, J. A., White, P., Bentz, B. J. & Powell, J. A. Model analysis of spatial patterns in mountain pine beetle outbreaks. Theor. Popul. Biol. 53, 236–255 (1998).
CAS  PubMed  MATH  Article  PubMed Central  Google Scholar 

21.
Wallin, K. F. & Raffa, K. F. Feedback between individual host selection behavior and population dynamics in an eruptive herbivore. Ecol. Monogr. 74, 101–116 (2004).
Article  Google Scholar 

22.
Franceschi, V. R., Krokene, P., Christiansen, E. & Krekling, T. Anatomical and chemical defenses of conifer bark against bark beetles and other pests. N. Phytol. 167, 353–376 (2005).
CAS  Article  Google Scholar 

23.
Raffa, K. F., Grégoire, J.-C. & Staffan Lindgren, B. Natural History and Ecology of Bark Beetles 1–40 (Elsevier, 2015).

24.
Bentz, B. J. et al. Climate change and bark beetles of the western United States and Canada: direct and indirect effects. BioScience 60, 602–613 (2010).
Article  Google Scholar 

25.
DeRose, R. J. & Long, J. N. Drought-driven disturbance history characterizes a southern Rocky Mountain subalpine forest. Can. J. Res. 42, 1649–1660 (2012).
Article  Google Scholar 

26.
Hart, S. J., Veblen, T. T., Schneider, D. & Molotch, N. P. Summer and winter drought drive the initiation and spread of spruce beetle outbreak. Ecology 98, 2698–2707 (2017).
PubMed  Article  PubMed Central  Google Scholar 

27.
Netherer, S., Panassiti, B., Pennerstorfer, J. & Matthews, B. Acute drought Is an important driver of bark beetle infestation in Austrian Norway spruce stands. Front. For. Glob. Change 2, 39 (2019).

28.
Kaiser, K. E., McGlynn, B. L. & Emanuel, R. E. Ecohydrology of an outbreak: mountain pine beetle impacts trees in drier landscape positions first. Ecohydrology 6, 444–454 (2013).
Article  Google Scholar 

29.
Marini, L. et al. Climate drivers of bark beetle outbreak dynamics in Norway spruce forests. Ecography 40, 1426–1435 (2017).
Article  Google Scholar 

30.
Sambaraju, K. R., Carroll, A. L. & Aukema, B. H. Multiyear weather anomalies associated with range shifts by the mountain pine beetle preceding large epidemics. For. Ecol. Manag. 438, 86–95 (2019).
Article  Google Scholar 

31.
Hayes, C. J., Fettig, C. J. & Merrill, L. D. Evaluation of multiple funnel traps and stand characteristics for estimating western pine beetle-caused tree mortality. J. Econ. Entomol. 102, 2170–2182 (2009).
PubMed  Article  PubMed Central  Google Scholar 

32.
Thistle, H. W. et al. Surrogate pheromone plumes in three forest trunk spaces: composite statistics and case studies. For. Sci. 50, 610–625 (2004).

33.
Miller, J. M. & Keen, F. P. Biology and Control of the Western Pine Beetle: A Summary of The First Fifty Years of Research (US Department of Agriculture, 1960).

34.
Chubaty, A. M., Roitberg, B. D. & Li, C. A dynamic host selection model for mountain pine beetle, Dendroctonus ponderosae Hopkins. Ecol. Model. 220, 1241–1250 (2009).
Article  Google Scholar 

35.
Graf, M., Reid, M. L., Aukema, B. H. & Lindgren, B. S. Association of tree diameter with body size and lipid content of mountain pine beetles. Can. Entomol. 144, 467–477 (2012).
Article  Google Scholar 

36.
Geiszler, D. R. & Gara, R. I. in Theory and Practice of Mountain Pine Beetle Management in Lodgepole Pine Forests: Symposium Proceedings (eds Berryman, A. A., Amman, G. D. & Stark, R. W.) (1978).

37.
Klein, W. H., Parker, D. L. & Jensen, C. E. Attack, emergence, and stand depletion trends of the mountain pine beetle in a lodgepole pine stand during an outbreak. Environ. Entomol. 7, 732–737 (1978).
Article  Google Scholar 

38.
Mitchell, R. G. & Preisler, H. K. Analysis of spatial patterns of lodgepole pine attacked by outbreak populations of the mountain pine beetle. For. Sci. 37, 1390–1408 (1991).
Google Scholar 

39.
Preisler, H. K. Modelling spatial patterns of trees attacked by bark-beetles. Appl. Stat. 42, 501 (1993).
MATH  Article  Google Scholar 

40.
Jactel, H. & Brockerhoff, E. G. Tree diversity reduces herbivory by forest insects. Ecol. Lett. 10, 835–848 (2007).
PubMed  Article  Google Scholar 

41.
Faccoli, M. & Bernardinelli, I. Composition and elevation of spruce forests affect susceptibility to bark beetle attacks: implications for forest management. Forests 5, 88–102 (2014).
Article  Google Scholar 

42.
Berryman, A. A. in Bark Beetles in North American Conifers: A System for the Study of Evolutionary Biology 264–314 (University of Texas Press, 1982).

43.
Fettig, C. J. et al. The effectiveness of vegetation management practices for prevention and control of bark beetle infestations in coniferous forests of the western and southern United States. For. Ecol. Manag. 238, 24–53 (2007).
Article  Google Scholar 

44.
Moeck, H. A., Wood, D. L. & Lindahl, K. Q. Host selection behavior of bark beetles (Coleoptera: Scolytidae) attacking Pinus ponderosa, with special emphasis on the western pine beetle, Dendroctonus brevicomis. J. Chem. Ecol. 7, 49–83 (1981).
CAS  PubMed  Article  Google Scholar 

45.
Evenden, M. L., Whitehouse, C. M. & Sykes, J. Factors influencing flight capacity of the mountain pine beetle (Coleoptera: Curculionidae: Scolytinae). Environ. Entomol. 43, 187–196 (2014).
CAS  PubMed  Article  Google Scholar 

46.
Raffa, K. F. & Berryman, A. A. Accumulation of monoterpenes and associated volatiles following inoculation of grand fir with a fungus transmitted by the fir engraver, Scolytus ventralis (Coleoptera: Scolytidae). Can. Entomol. 114, 797–810 (1982).
CAS  Article  Google Scholar 

47.
Anderegg, W. R. L. et al. Tree mortality from drought, insects, and their interactions in a changing climate. N. Phytol. 208, 674–683 (2015).
Article  Google Scholar 

48.
Kane, V. R. et al. Assessing fire effects on forest spatial structure using a fusion of Landsat and airborne LiDAR data in Yosemite National Park. Remote Sens. Environ. 151, 89–101 (2014).
ADS  Article  Google Scholar 

49.
Larson, A. J. & Churchill, D. Tree spatial patterns in fire-frequent forests of western North America, including mechanisms of pattern formation and implications for designing fuel reduction and restoration treatments. For. Ecol. Manag. 267, 74–92 (2012).
Article  Google Scholar 

50.
Morris, J. L. et al. Managing bark beetle impacts on ecosystems and society: Priority questions to motivate future research. J. Appl. Ecol. 54, 750–760 (2017).
Article  Google Scholar 

51.
Shiklomanov, A. N. et al. Enhancing global change experiments through integration of remote-sensing techniques. Front. Ecol. Environ. 17, 215–224 (2019).

52.
Jeronimo, S. M. A. et al. Forest structure and pattern vary by climate and landform across active-fire landscapes in the montane Sierra Nevada. For. Ecol. Manag. 437, 70–86 (2019).
Article  Google Scholar 

53.
Roussel, J.-R., Auty, D., De Boissieu, F. & Meador, A. S. lidR: Airborne LiDAR Data Manipulation and Visualization for Forestry Applications (2019).

54.
McDowell, N. et al. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? N. Phytol. 178, 719–739 (2008).
Article  Google Scholar 

55.
Seybold, S. J. et al. Management of western North American bark beetles with semiochemicals. Annu. Rev. Entomol. 63, 407–432 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

56.
Fettig, C. J., McKelvey, S. R. & Huber, D. P. W. Nonhost angiosperm volatiles and verbenone disrupt response of western pine beetle, Dendroctonus brevicomis (Coleoptera: Scolytidae), to attractant-baited traps. J. Econ. Entomol. 98, 2041–2048 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

57.
Fettig, C. J., Dabney, C. P., McKelvey, S. R. & Huber, D. P. W. Nonhost angiosperm volatiles and verbenone protect individual ponderosa pines from attack by western pine beetle and red turpentine beetle (Coleoptera: Curculionidae, Scolytinae). West J. Appl. 23, 40–45 (2008).
Google Scholar 

58.
Fettig, C. J. et al. Efficacy of ‘Verbenone Plus’ for protecting ponderosa pine trees and stands from Dendroctonus brevicomis (Coleoptera: Curculionidae) attack in British Columbia and California. J. Econ. Entomol. 105, 1668–1680 (2012).
PubMed  Article  PubMed Central  Google Scholar 

59.
Oliver, W. W. Is self-thinning in ponderosa pine ruled by Dendroctonus bark beetles? In Forest Health Through Silviculture: Proceedings of the 1995 National Silviculture Workshop 6 (1995).

60.
Fettig, C. & McKelvey, S. Resiliency of an interior ponderosa pine forest to bark beetle infestations following fuel-reduction and forest-restoration treatments. Forests 5, 153–176 (2014).
Article  Google Scholar 

61.
Fettig, C. J. & Hilszczański, J. Bark Beetles 555–584. https://doi.org/10.1016/B978-0-12-417156-5.00014-9 (Elsevier, 2015).

62.
Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).
Article  Google Scholar 

63.
Fricker, G. A. et al. More than climate? Predictors of tree canopy height vary with scale in complex terrain, Sierra Nevada, CA (USA). For. Ecol. Manag. 434, 142–153 (2019).
Article  Google Scholar 

64.
Ma, S., Concilio, A., Oakley, B., North, M. & Chen, J. Spatial variability in microclimate in a mixed-conifer forest before and after thinning and burning treatments. For. Ecol. Manag. 259, 904–915 (2010).
Article  Google Scholar 

65.
Stovall, A. E. L., Shugart, H. & Yang, X. Tree height explains mortality risk during an intense drought. Nat. Commun. 10, 1–6 (2019).
Article  CAS  Google Scholar 

66.
Stephenson, N. L. & Das, A. J. Height-related changes in forest composition explain increasing tree mortality with height during an extreme drought. Nat. Commun. 11, 3402 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

67.
Stovall, A. E. L., Shugart, H. H. & Yang, X. Reply to ‘Height-related changes in forest composition explain increasing tree mortality with height during an extreme drought’. Nat. Commun. 11, 3401 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

68.
Person, H. L. Tree selection by the western pine beetle. J. For. 26, 564–578 (1928).
Google Scholar 

69.
Person, H. L. Theory in explanation of the selection of certain trees by the western pine beetle. J. For. 29, 696–699 (1931).
CAS  Google Scholar 

70.
Pile, L. S., Meyer, M. D., Rojas, R., Roe, O. & Smith, M. T. Drought impacts and compounding mortality on forest trees in the southern Sierra Nevada. Forests 10, 237 (2019).
Article  Google Scholar 

71.
Frey, J., Kovach, K., Stemmler, S. & Koch, B. UAV photogrammetry of forests as a vulnerable process. A sensitivity analysis for a structure from motion RGB-image pipeline. Remote Sens. 10, 912 (2018).
ADS  Article  Google Scholar 

72.
James, M. R. & Robson, S. Mitigating systematic error in topographic models derived from UAV and ground-based image networks. Earth Surf. Process. Landf. 39, 1413–1420 (2014).
ADS  Article  Google Scholar 

73.
Gray, P. C. et al. A convolutional neural network for detecting sea turtles in drone imagery. Methods Ecol. Evol. 10, 345–355 (2019).
Article  Google Scholar 

74.
Millar, C. I., Stephenson, N. L. & Stephens, S. L. Climate change and forests of the future: Managing in the face of uncertainty. Ecol. Appl. 17, 2145–2151 (2007).
PubMed  Article  PubMed Central  Google Scholar 

75.
Vose, J. M. et al. in Impacts, Risks, and Adaptation in the United States: The Fourth National Climate Assessment Vol II (eds Reidmiller, D. R., et al.) 232–267. https://nca2018.globalchange.gov/chapter/6/https://doi.org/10.7930/NCA4.2018.CH6 (2018).

76.
Bedard, W. D. et al. Western pine beetle: field response to its sex pheromone and a synergistic host terpene, myrcene. Science 164, 1284–1285 (1969).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

77.
Byers, J. A. & Wood, D. L. Interspecific inhibition of the response of the bark beetles, Dendroctonus brevicomis and Ips paraconfusus, to their pheromones in the field. J. Chem. Ecol. 6, 149–164 (1980).
CAS  Article  Google Scholar 

78.
Shepherd, W. P., Huber, D. P. W., Seybold, S. J. & Fettig, C. J. Antennal responses of the western pine beetle, Dendroctonus brevicomis (Coleoptera: Curculionidae), to stem volatiles of its primary host, Pinus ponderosa, and nine sympatric nonhost angiosperms and conifers. Chemoecology 17, 209–221 (2007).
CAS  Article  Google Scholar 

79.
DJI. Zenmuse X3 – Creativity Unleashed. DJI Official https://www.dji.com/zenmuse-x3/info (2015).

80.
Micasense. MicaSense. https://support.micasense.com/hc/en-us/articles/215261448-RedEdge-User-Manual-PDF-Download- (2015).

81.
DJI. DJI – The World Leader in Camera Drones/Quadcopters for Aerial Photography. DJI Official https://www.dji.com/matrice100/info (2015).

82.
Wyngaard, J. et al. Emergent challenges for science sUAS data management: fairness through community engagement and best practices development. Remote Sens. 11, 1797 (2019).
ADS  Article  Google Scholar 

83.
Rouse, W., Haas, R. H., Deering, W. & Schell, J. A. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation (Remote Sensing Center, Texas A&M Univ., 1973).

84.
DronesMadeEasy. Map Pilot for DJI on iOS. App Store https://itunes.apple.com/us/app/map-pilot-for-dji/id1014765000?mt=8 (2018).

85.
Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45, RG2004 (2007).

86.
Zhang, W. et al. An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sens. 8, 501 (2016).
ADS  Article  Google Scholar 

87.
Hijmans, R. J. et al. Raster: Geographic Data Analysis and Modeling (2019).

88.
Gitelson, A. & Merzlyak, M. N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. J. Plant Physiol. 143, 286–292 (1994).
CAS  Article  Google Scholar 

89.
Coops, N. C., Johnson, M., Wulder, M. A. & White, J. C. Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation. Remote Sens. Environ. 103, 67–80 (2006).
ADS  Article  Google Scholar 

90.
Clevers, J. G. P. W. & Gitelson, A. A. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3. Int. J. Appl. Earth Observ. Geoinf. 23, 344–351 (2013).
ADS  Article  Google Scholar 

91.
Li, W., Guo, Q., Jakubowski, M. K. & Kelly, M. A new method for segmenting individual trees from the LiDAR point cloud. Photogramm. Eng. Remote Sens. 78, 75–84 (2012).
Article  Google Scholar 

92.
Jakubowski, M. K., Li, W., Guo, Q. & Kelly, M. Delineating individual trees from LiDAR data: a comparison of vector- and raster-based segmentation approaches. Remote Sens. 5, 4163–4186 (2013).
ADS  Article  Google Scholar 

93.
Shin, P., Sankey, T., Moore, M. & Thode, A. Evaluating unmanned aerial vehicle images for estimating forest canopy fuels in a ponderosa pine stand. Remote Sens. 10, 1266 (2018).
ADS  Article  Google Scholar 

94.
Roussel, J.-R. lidRplugins: Extra Functions and Algorithms for lidR Package (2019).

95.
Eysn, L. et al. A benchmark of LiDAR-based single tree detection methods using heterogeneous forest data from the alpine space. Forests 6, 1721–1747 (2015).
Article  Google Scholar 

96.
Vega, C. et al. PTrees: a point-based approach to forest tree extraction from LiDAR data. Int. J. Appl. Earth Observ. Geoinf. 33, 98–108 (2014).
ADS  Article  Google Scholar 

97.
Plowright, A. ForestTools: Analyzing Remotely Sensed Forest Data (2018).

98.
Pau, G., Fuchs, F., Sklyar, O., Boutros, M. & Huber, W. EBImage: An R package for image processing with applications to cellular phenotypes. Bioinformatics 26, 979–981 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

99.
Meyer, F. & Beucher, S. Morphological segmentation. J. Vis. Commun. Image Represent. 1, 21–46 (1990).
Article  Google Scholar 

100.
Hunziker, P. Velox: Fast Raster Manipulation and Extraction (2017).

101.
Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).
Article  Google Scholar 

102.
Wang, Y. et al. Is field-measured tree height as reliable as believed – a comparison study of tree height estimates from field measurement, airborne laser scanning and terrestrial laser scanning in a boreal forest. ISPRS J. Photogramm. Remote Sens. 147, 132–145 (2019).
ADS  Article  Google Scholar 

103.
Weinstein, B. G., Marconi, S., Bohlman, S., Zare, A. & White, E. Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks. Remote Sens. 11, 1309 (2019).
ADS  Article  Google Scholar 

104.
dos Santos, A. A. et al. Assessment of CNN-based methods for individual tree detection on images captured by RGB cameras attached to UAVs. Sensors (Basel) 19, 3595 (2019).

105.
Stephenson, N. Actual evapotranspiration and deficit: biologically meaningful correlates of vegetation distribution across spatial scales. J. Biogeogr. 25, 855–870 (1998).
Article  Google Scholar 

106.
Flint, L. E., Flint, A. L., Thorne, J. H. & Boynton, R. Fine-scale hydrologic modeling for regional landscape applications: The California Basin Characterization Model development and performance. Ecol. Process. 2, 25 (2013).
Article  Google Scholar 

107.
Millar, C. I. et al. Forest mortality in high-elevation whitebark pine (Pinus albicaulis) forests of eastern California, USA: influence of environmental context, bark beetles, climatic water deficit, and warming. Can. J. For. Res. 42, 749–765 (2012).
Article  Google Scholar 

108.
Baldwin, B. G. et al. Species richness and endemism in the native flora of California. Am. J. Bot. 104, 487–501 (2017).
PubMed  Article  PubMed Central  Google Scholar 

109.
Bürkner, P.-C. brms: an R package for bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).
Article  Google Scholar 

110.
Hoffman, M. D. & Gelman, A. The no-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res. 15, 31 (2014).
MathSciNet  MATH  Google Scholar 

111.
Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. 76, 1–32 (2017).
Article  Google Scholar 

112.
Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434 (1998).
MathSciNet  Google Scholar 

113.
Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. & Gelman, A. Visualization in Bayesian workflow. J. R. Stat. Soc. Ser. A 182, 389–402 (2019).
MathSciNet  Article  Google Scholar 

114.
Koontz, M. J., Latimer, A. M., Mortenson, L. A., Fettig, C. J. & North, M. P. Drone-derived data supporting “Cross-scale interaction of host tree size and climatic water deficit governs bark beetle-induced tree mortality”. https://doi.org/10.17605/OSF.IO/3CWF9 (2020).

115.
Baldwin, B. G. et al. Master spatial file for native California vascular plants used by Baldwin et al. (2017 Amer. J. Bot.), Dryad, Dataset, 2017. https://doi.org/10.6078/D16K5W.

116.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).

117.
Koontz, M. J., Latimer, A. M., Mortenson, L. A., Fettig, C. J. & North, M. P. Local-structure-wpb-severity. https://doi.org/10.17605/OSF.IO/WPK5Z (2019). More

1.
Huang, S. D. et al. Autotrophic and heterotrophic soil respiration responds asymmetrically to drought in a subtropical forest in the Southeast China. Soil Biol. Biochem. 123, 242–249 (2018).
CAS  Article  Google Scholar 
2.
Peichl, M. et al. Management and climate effects on carbon dioxide and energy exchanges in a maritime grassland. Agric. Ecosyst. Environ. 158, 132–146 (2012).
Article  Google Scholar 

3.
Zhang, X. B. et al. How do environmental factors and different fertilizer strategies affect soil CO2 emission and carbon sequestration in the upland soils of southern China?. Appl. Soil Ecol. 72, 109–118 (2013).
Article  Google Scholar 

4.
Curiel, Y. J. et al. Microbial soil respiration and its dependency on carbon inputs, soil temperature and moisture. Glob. Change Biol. 13, 2018–2035 (2010).
ADS  Article  Google Scholar 

5.
Peri, P. L., Bahamonde, H. & Christiansen, R. Soil respiration in Patagonian semiarid grasslands under contrasting environmental and use conditions. J. Arid Environ. 119, 1–8 (2015).
ADS  Article  Google Scholar 

6.
Davidson, E. A. et al. Effects of soil water content on soil respiration in forests and cattle pastures of eastern Amazonia. Biogeochemistry 48, 53–69 (2000).
CAS  Article  Google Scholar 

7.
Buchmann, N. Biotic and abiotic factors controlling soil respiration rates in Piceaavies stands. Soil Biol Biochem. 32, 1625–1635 (2000).
CAS  Article  Google Scholar 

8.
Li, X. et al. Contribution of root respiration to total soil respiration in a semi-arid grassland on the Loess Plateau, China. Sci. Total Environ. 62, 1209–1217 (2018).
ADS  Google Scholar 

9.
Jia, B. & Zhou, G. Integrated diurnal soil respiration model during growing season of a typical temperate steppe: Effects of temperature, soil water content and biomass production. Soil Biol. Biochem. 41, 681–686 (2009).
CAS  Article  Google Scholar 

10.
Jin, Z., Qi, Y. C., Dong, Y. S. & Domroes, M. Seasonal patterns of soil respiration in three types of communities along grass-desert shrub transition in Inner Mongolia, China. Adv. Atmos. Sci. 26, 503–512 (2009).
CAS  Article  Google Scholar 

11.
Li, Y. C., Hou, C. C., Song, C. C. & Guo, Y. D. Seasonal changes in the contribution of root respiration to total soil respiration in a freshwater marsh in Sanjiang Plain, Northeast China. Environ. Earth Sci. 75, 1–10 (2016).
ADS  Article  Google Scholar 

12.
Zhao, C. Y., Zhao, Z. M., Yilihamu, Hong, Z. & Jun, L. Contribution of root and rhizosphere respiration of Haloxylonammodendron to seasonal variation of soil respiration in the central Asian desert. Quatern. Int. 244, 304–309 (2011).
Article  Google Scholar 

13.
Wang, X. et al. Soil respiration under climate warming: Differential response of heterotrophic and autotrophic respiration. Glob. Change Biol. 20, 3229–3237 (2014).
ADS  Article  Google Scholar 

14.
Sun, Z., Han, J. C. & Wang, H. Y. Soft rock for improving crop yield in sandy soil of Mu Us sandy land, China. Arid Land Res. Manag. 33, 136–154 (2019).
CAS  Article  Google Scholar 

15.
Dong, O. G. et al. water infiltration of covering soils with Different textures and bulk densities in gravelmulched areas. Appl. Soil Ecol. 17, 14039–14052 (2019).
Google Scholar 

16.
Lei, N., Han, J. C., Mu, X. M., Sun, Z. H. & Wang, H. Y. Effects of improved materials on reclamation of soil properties and crop yield in hollow villages in China. J. Soil Sediment 19, 2374–2380 (2019).
CAS  Article  Google Scholar 

17.
Fu, G. et al. Response of ecosystem respiration to experimental warming and clipping at daily time scale in an alpine meadow of Tibet. J. Mt. Sci.-Engl. 10, 455–463 (2013).
Article  Google Scholar 

18.
Wu, J. P. et al. Response of soil respiration and ecosystem carbon budget to vegetation removal in Eucalyptus plantations with contrasting ages. Sci. Rep.-UK 4, 6262 (2014).
Article  Google Scholar 

19.
Han, T. F., Huang, W. J., Liu, J. X., Zhou, G. Y. & Xiao, Y. Different soil respiration responses to litter manipulation in three subtropical successional forests. Sci. Rep.-UK 5, 18166 (2015).
ADS  CAS  Article  Google Scholar 

20.
Lei, N. & Han, J. C. Effect of precipitation on respiration of different reconstructed soils. Sci. Rep.-UK 10, 7328 (2020).
ADS  CAS  Article  Google Scholar 

21.
Kosugi, Y. et al. Spatial and temporal variation in soil respiration in a Southeast Asian tropical rainforest. Agric. Forest Meteorol. 147, 35–47 (2007).
ADS  Article  Google Scholar 

22.
Hu, Z. D., Liu, S. R., Shi, Z. M., Liu, X. L. & He, F. Diel variations and seasonal dynamics of soil respirations in subalpine meadow in western Sichuan Province, China. Acta Ecol. Sin. 32, 6376–6386 (2012).
Article  Google Scholar 

23.
Shi, Z. et al. Seasonal variation and temperature sensitivity of soil V respiration under different plant communities along an elevation gradient in Wuyi Mountains of China. J. Appl. Ecol. 19, 2357–2363 (2008).
Google Scholar 

24.
Wu, C. S., Sha, L. Q., Gao, J. M. & Dong, L. Y. Analysis of soil respiration in a montane evergreen broad-leaved forest and an artificial tea garden in Ailao Mountains, Yunnan province. J. Nanjing For. Univ. (Nat. Sci.) 36, 64–68 (2012) (in Chinese).
Google Scholar 

25.
Zhao, J. X. et al. Soil respiration and its affecting factors of Pinusyunnanensis in the Middle Regions of Yunnan. J. Northwest For. Univ. 30, 8–13 (2015) (in Chinese).
Google Scholar 

26.
Rayment, M. B. & Jarvis, P. G. Temporal and spatial variation of soil CO2 efflux in a Canadian boreal forest. Soil Biol. Biochem. 32, 35–45 (2000).
CAS  Article  Google Scholar 

27.
Qi, Y. & Xu, M. Separating the effects of moisture and temperature on soil CO2 efflux in a coniferous forest in the Sierra Nevada Mountains. Plant Soil 237, 15–23 (2001).
CAS  Article  Google Scholar 

28.
Dilustro, J. J., Collins, B., Duncan, L. & Crawford, C. Moisture and soil texture effects on soil CO2 efflux components in southeastern mixed pine forests. Forest Ecol. Manag. 204, 87–97 (2005).
Article  Google Scholar 

29.
Gaumont-guay, D. et al. Influence of temperature and drought on seasonal and interannual variations of soil, bole and ecosystem respiration in a boreal aspen stand. Agric Forest Meteorol. 140, 203–219 (2006).
ADS  Article  Google Scholar 

30.
Yan, J. X., Hao, Z., Jing, X. K. & Li, H. J. Variations of soil respiration and its temperature sensitivity in an orchard in Jinci Region of Taiyuan City. J. Environ. Sci.-China 37, 3625–3633 (2016).
Google Scholar 

31.
Han, G. X., Zhou, G. S. & Xu, Z. Z. Research and prospects for soil respiration of farmland ecosystems in China. J. Plant Ecol.-UK 32, 719–733 (2008).
CAS  Google Scholar 

32.
Yuste, J. C., Janssens, I. A., Carrara, A., Meiresonne, L. & Ceulemans, R. Interactive effects of temperature and precipitation on soil respiration in a temperate maritime pine forest. Tree Physiol. 23, 1263–1270 (2003).
Article  Google Scholar 

33.
Conant, R. T., Dalla-bett, A. P., Klopatek, C. C. & Klopatek, J. Controls on soil respiration in semiarid soils. Soil Biol. Biochem. 36, 945–951 (2004).
CAS  Article  Google Scholar 

34.
Giardina, C. P. & Ryan, M. G. Evidence that decomposition rates of organic carbon in mineral soil do not vary with temperature. Nature 404, 858–861 (2000).
ADS  CAS  PubMed  Article  Google Scholar 

35.
Grace, J. & Rayment, M. Respiration in the balance. Nature 404, 819–820 (2000).
CAS  PubMed  Article  Google Scholar 

36.
Andrews, R. T. et al. Successful embolization of collaterals from the ovarian artery during uterine artery embolization for fibroids: A case report. J. Vasc. Interv. Radiol. 11, 607–610 (2000).
CAS  PubMed  Article  Google Scholar 

37.
Fu, G., Shen, Z. X., Zhang, X. Z. & Zhou, Y. T. Response of soil microbial biomass to short-term experimental warming in alpine meadow on the Tibetan Plateau. Appl. Soil Ecol. 61, 158–160 (2012).
Article  Google Scholar 

38.
Chang, S. X., Shi, Z. & Thomas, B. R. Soil respiration and its temperature sensitivity in agricultural and afforested poplar plantation systems in northern Alberta. Biol. Fertil. Soils 52, 629–641 (2016).
CAS  Article  Google Scholar 

39.
Uribe, C. et al. Effect of wildfires on soil respiration in three typical Mediterranean forest ecosystems in Madrid, Spain. Plant Soil 369, 403–420 (2013).
CAS  Article  Google Scholar 

40.
Bergaust, L., Mao, Y. J., Bakken, L. R. & Frostegard, A. Denitrification response patterns during the transition to anoxic respiration and posttranscriptional effects of suboptimal pH on nitrogen oxide reductase in Paracoccusdenitrificans. Appl. Environ. Microbiol. 76, 6387–6396 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

41.
Mu, Z. et al. Linking N2O emission to soil mineral N as estimated by CO2 emission and soil C/N ratio. Soil Biol. Biochem. 41, 2593–2597 (2009).
CAS  Article  Google Scholar 

42.
Fu, G. et al. Response of soil respiration to grazing in an alpine meadow at three elevations in Tibet. Sci. World J. 2014, 1–9 (2014).
Google Scholar 

43.
Yu, C. Q., Han, F. S. & Fu, G. Effects of 7 years experimental warming on soil bacterial and fungal community structure in the Northern Tibet alpine meadow at three elevations. Sci. Total Environ. 655, 814–822 (2019).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar  More

1.
Lawrence, W. S. Dispersal: an alternative mating tactic conditional on sex ratio and body size. Behav. Ecol. Sociobiol. 21, 367–373 (1987).
Article  Google Scholar 
2.
Russell, E. M. & Rowley, I. Philopatry or dispersal: competition for territory vacancies in the splendid fairy-wren, Malurus splendens. Anim. Behav. 45, 519–539 (1993).
Article  Google Scholar 

3.
Herzig, A. L. Effects of population density on long-distance dispersal in the goldenrod beetle Trirhabda virgata. Ecology 76, 2044–2054 (1995).
Article  Google Scholar 

4.
Verhulst, S., Perrins, C. M. & Riddington, R. Natal dispersal of great tits in a patchy environment. Ecology 78, 864–872 (1997).
Article  Google Scholar 

5.
Johnson, M. L. & Gaines, M. S. Evolution of dispersal: theoretical models and empirical tests using birds and mammals. Annu. Rev. Ecol. Evol. Sys. 21, 449–480 (1990).
Article  Google Scholar 

6.
Clobert, J., Danchin, E., Dhondt, A. A. & Nichols, J. Dispersal (Oxford University Press, Oxford, 2001).
Google Scholar 

7.
Bowler, D. E. & Benton, T. G. Causes and consequences of animal dispersal strategies: relating individual behaviour to spatial dynamics. Biol. Rev. 80, 205–225 (2005).
PubMed  Article  PubMed Central  Google Scholar 

8.
Szulkin, M. & Sheldon, B. C. Dispersal as a means of inbreeding avoidance in a wild bird population. Proc. R. Soc. B 275, 703–711 (2008).
PubMed  Article  PubMed Central  Google Scholar 

9.
Cornwallis, C. K. Cooperative breeding and the evolutionary coexistence of helper and nonhelper strategies. Proc. Nat. Acad. Sc. USA 115, 1684–1686 (2018).
CAS  Article  Google Scholar 

10.
Nelson-Flower, M. J., Ridley, A. R., Wiley, E. M. & Flower, T. P. Individual dispersal delays in a cooperative breeder: ecological constraints, the benefits of philopatry and the social queue for dominance. J. Anim. Ecol. 87, 1227–1238 (2018).
PubMed  Article  PubMed Central  Google Scholar 

11.
Komdeur, J. Importance of habitat saturation and territory quality for evolution of cooperative breeding in the Seychelles warbler. Nature 358, 493 (1992).
ADS  Article  Google Scholar 

12.
Kokko, H. & Lundberg, P. Dispersal, migration, and offspring retention in saturated habitats. Am. Nat. 157, 188–202 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

13.
Kokko, H. & Ekman, J. Delayed dispersal as a route to breeding: territorial inheritance, safe havens, and ecological constraints. Am. Nat. 160, 468–484 (2002).
PubMed  Article  PubMed Central  Google Scholar 

14.
Lemel, J. Y., Belichon, S., Clobert, J. & Hochberg, M. E. The evolution of dispersal in a two-patch system: some consequences of differences between migrants and residents. Evol. Ecol. 11, 613–629 (1997).
Article  Google Scholar 

15.
Forero, M. G., Donázar, J. A. & Hiraldo, F. Causes and fitness consequences of natal dispersal in a population of black kites. Ecology 83, 858–872 (2002).
Article  Google Scholar 

16.
Kokko, H. & López-Sepulcre, A. From individual dispersal to species ranges: perspectives for a changing world. Science 313, 789–791 (2006).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Bonte, D. et al. Costs of dispersal. Biol. Rev. 87, 290–312 (2012).
PubMed  Article  PubMed Central  Google Scholar 

18.
Serrano, D. & Tella, J. L. Lifetime fitness correlates of natal dispersal distance in a colonial bird. J. Anim. Ecol. 81, 97–107 (2012).
PubMed  Article  PubMed Central  Google Scholar 

19.
Kubisch, A., Holt, R. D., Poethke, H. J. & Fronhofer, E. A. Where am I and why? Synthesizing range biology and the eco-evolutionary dynamics of dispersal. Oikos 123, 5–22 (2014).
Article  Google Scholar 

20.
Drobniak, S. M., Wagner, G., Mourocq, E. & Griesser, M. Family living: an overlooked but pivotal social system to understand the evolution of cooperative breeding. Behav. Ecol. 26, 805–811 (2015).
Article  Google Scholar 

21.
Taborsky, M. Sneakers, satellites, and helpers: parasitic and cooperative behavior in fish reproduction. Adv. Study Behav. 23, e100 (1994).
Google Scholar 

22.
Clutton-Brock, T. H. & Lukas, D. The evolution of social philopatry and dispersal in female mammals. Mol. Ecol. 21, 472–492 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

23.
Pruett-Jones, S. G. & Lewis, M. J. Sex ratio and habitat limitation promote delayed dispersal in superb fairy-wrens. Nature 348, 541–542 (1990).
ADS  Article  Google Scholar 

24.
Bergmüller, R., Heg, D. & Taborsky, M. Helpers in a cooperatively breeding cichlid stay and pay or disperse and breed, depending on ecological constraints. Proc. R. Soc. B 272, 325–331 (2005).
PubMed  Article  PubMed Central  Google Scholar 

25.
Carrete, M., Donázar, J. A., Margalida, A. & Bertran, J. Linking ecology, behaviour and conservation: does habitat saturation change the mating system of bearded vultures?. Biol. Lett. 2, 624–627 (2006).
PubMed  PubMed Central  Article  Google Scholar 

26.
Covas, R., Doutrelant, C. & du Plessis, M. A. Experimental evidence of a link between breeding conditions and the decision to breed or to help in a colonial cooperative bird. Proc. R. Soc. B 271, 827–832 (2004).
PubMed  Article  PubMed Central  Google Scholar 

27.
Baglione, V. et al. Does yearround territoriality rather than habitat saturation explain delayed natal dispersal and cooperative breeding in the carrion crow?. J. Anim. Ecol. 74, 842–851 (2005).
Article  Google Scholar 

28.
Hatchwell, B. J. & Komdeur, J. Ecological constraints, life history traits and the evolution of cooperative breeding. Anim. Behav. 59, 1079–1086 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

29.
Pen, I. & Weissing, F. J. Towards a unified theory of cooperative breeding: the role of ecology and life history re-examined. Proc. R. Soc. B 267, 2411–2418 (2000).
Article  Google Scholar 

30.
Covas, R. & Griesser, M. Life history and the evolution of family living in birds. Proc. R. Soc. B 274, 1349–1357 (2007).
PubMed  Article  PubMed Central  Google Scholar 

31.
Griesser, M. & Barnaby, J. Role of Nepotism Cooperation and Competition in the Avian Families. Birds-Evolution, Behavior and Ecology Series (Nova Science Pub Inc, New York, 2010).
Google Scholar 

32.
Baglione, V., Canestrari, D., Marcos, J. M., Griesser, M. & Ekman, J. History, environment and social behaviour: experimentally induced cooperative breeding in the carrion crow. Proc. R. Soc B 269, 1247–1251 (2002).
PubMed  Article  PubMed Central  Google Scholar 

33.
Tchabovsky, A. & Bazykin, G. Females delay dispersal and breeding in a solitary gerbil, Meriones tamariscinus. J. Mammal. 85, 105–112 (2004).
Article  Google Scholar 

34.
Ellis, E. C., Fuller, D. Q., Kaplan, J. O. & Lutters, W. G. Dating the Anthropocene: Towards an empirical global history of human transformation of the terrestrial biosphere. Elem. Sci. Anth 1, 18 (2013).
Article  Google Scholar 

35.
Bernardo-Madrid, R. et al. Human activity is altering the world’s zoogeographical regions. Ecol. Lett. 22, 1297–1305 (2019).
PubMed  PubMed Central  Google Scholar 

36.
Rodríguez-Martínez, S., Carrete, M., Roques, S., Rebolo-Ifrán, N. & Tella, J. L. High urban breeding densities do not disrupt genetic monogamy in a bird species. PLoS ONE 9, e91314 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

37.
McKinney, M. L. Urbanization as a major cause of biotic homogenization. Biol. Conserv. 127, 247–260 (2006).
Article  Google Scholar 

38.
Grimm, N. B. et al. Global change and the ecology of cities. Science 319, 756–760 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
McKinney, M. L. & Lockwood, J. L. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453 (1999).
CAS  PubMed  Article  Google Scholar 

40.
Sol, D., González-Lagos, C., Moreira, D., Maspons, J. & Lapiedra, O. Urbanisation tolerance and the loss of avian diversity. Ecol. Lett. 17, 942–950 (2014).
PubMed  Article  PubMed Central  Google Scholar 

41.
Carrete, M. & Tella, J. L. Inter-individual variability in fear of humans and relative brain size of the species are related to contemporary urban invasion in birds. PLoS ONE 6, e18859 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Díaz, M. et al. The geography of fear: a latitudinal gradient in anti-predator escape distances of birds across Europe. PLoS ONE 8, e64634 (2013).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

43.
Oro, D., Genovart, M., Tavecchia, G., Fowler, M. S. & Martínez-Abraín, A. Ecological and evolutionary implications of food subsidies from humans. Ecol. Lett. 16, 1501–1514 (2013).
PubMed  Article  PubMed Central  Google Scholar 

44.
Marzluff, J. M. Worldwide urbanization and its effects on birds. Avian ecology and conservation in an urbanizing world (pp. 19–47) (Springer, Boston, MA. 2001).

45.
Haskell, D. G., Knupp, A. M. & Schneider, M. C. Nest predator abundance and urbanization. Avian ecology and conservation in an urbanizing world (pp. 243–258). Springer, Boston, MA. (2001).

46.
Luna, Á., Romero-Vidal, P., Hiraldo, F. & Tella, J. L. Cities may save some threatened species but not their ecological functions. PeerJ 6, e4908 (2018).
PubMed  PubMed Central  Article  Google Scholar 

47.
Muhly, T. B., Semeniuk, C., Massolo, A., Hickman, L. & Musiani, M. Human activity helps prey win the predator–prey space race. PLoS ONE 6, e17050 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

48.
Rebolo-Ifrán, N., Tella, J. L. & Carrete, M. Urban conservation hotspots: predation release allows the grassland-specialist burrowing owl to perform better in the city. Sci. Rep. 7, 3527 (2017).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

49.
Tuomainen, U. & Candolin, U. Behavioural responses to human-induced environmental change. Biol. Rev. 86, 640–657 (2011).
PubMed  Article  PubMed Central  Google Scholar 

50.
Rodewald, A. D., Kearns, L. J. & Shustack, D. P. Anthropogenic resource subsidies decouple predator-prey relationships. Ecol. Appl. 21, 936–943 (2011).
PubMed  Article  PubMed Central  Google Scholar 

51.
Sih, A., Ferrari, M. C. & Harris, D. J. Evolution and behavioural responses to human-induced rapid environmental change. Evol. Appl. 4, 367–387 (2011).
PubMed  PubMed Central  Article  Google Scholar 

52.
Carrete, M. & Tella, J. L. Behavioural correlations associated with fear of humans differ between rural and urban burrowing owls. Front. Ecol. Evol. 5, 54 (2017).
Article  Google Scholar 

53.
Moore, J. A., Kamarainen, A. M., Scribner, K. T., Mykut, C. & Prince, H. H. The effects of anthropogenic alteration of nesting habitat on rates of extra-pair fertilization and intraspecific brood parasitism in Canada geese branta Canadensis. Ibis 154, 354–362 (2012).
Article  Google Scholar 

54.
Ryder, T. B., Fleischer, R. C., Shriver, W. G. & Marra, P. P. The ecological–evolutionary interplay: density-dependent sexual selection in a migratory songbird. Ecol. Evol. 2, 976–987 (2012).
PubMed  PubMed Central  Article  Google Scholar 

55.
Luna, Á., Palma, A., Sánz-Aguilar, A., Tella, J. L. & Carrete, M. Personality-dependent breeding dispersal in rural but not urban burrowing owls. Sci. Rep. 9, 2886 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

56.
Luna, Á., Palma, A., Sánz-Aguilar, A., Tella, J. L. & Carrete, M. Sex, personality and conspecific density influence natal dispersal with lifetime fitness consequences in urban and rural burrowing owls. PLoS ONE 15, e0226089 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

57.
Mueller, J. C. et al. Evolution of genomic variation in the burrowing owl in response to recent colonization of urban areas. Proc. R. Soc. B 285, 20180206 (2018).
PubMed  Article  PubMed Central  Google Scholar 

58.
Miles, L. S., Rivkin, L. R., Johnson, M. T., Munshi-South, J. & Verrelli, B. C. Gene flow and genetic drift in urban environments. Mol. Ecol. 28, 4138–4151 (2019).
PubMed  Article  PubMed Central  Google Scholar 

59.
Henger, C. S. et al. Genetic diversity and relatedness of a recently established population of eastern coyotes (Canis latrans) in New York City. Urban Ecosyst. 23, 1–12 (2019).
Google Scholar 

60.
Carrete, M. & Tella, J. L. Individual consistency in flight initiation distances in burrowing owls: a new hypothesis on disturbance-induced habitat selection. Biol. Lett. 6, 167–170 (2010).
PubMed  Article  PubMed Central  Google Scholar 

61.
Cockburn, A. Evolution of helping behavior in cooperatively breeding birds. Annu. Rev. Ecol. Syst. 29, 141–177 (1998).
Article  Google Scholar 

62.
Clutton-Brock, T. Breeding together: kin selection and mutualism in cooperative vertebrates. Science 296, 69–72 (2002).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
Browning, L. E. et al. Career provisioning rules in an obligate cooperative breeder: prey type, size and delivery rate. Behav. Ecol. Sociobiol. 66, 1639–1649 (2012).
Article  Google Scholar 

64.
Richardson, D. S., Burke, T. & Komdeur, J. Direct benefits and the evolution of female-biased cooperative breeding in Seychelles warblers. Evolution 56, 2313–2321 (2002).
PubMed  Article  PubMed Central  Google Scholar 

65.
Gamero, A., Székely, T. & Kappeler, P. M. Delayed juvenile dispersal and monogamy, but no cooperative breeding in white-breasted mesites (Mesitornis variegata). Behav. Ecol. Sociobiol. 68, 73–83 (2014).
Article  Google Scholar 

66.
Brown, J. L. Territorial behavior and population regulation in birds: a review and re-evaluation. Wilson Bull. 81, 293–329 (1969).
Google Scholar 

67.
Emlen, S. T. The evolution of helping. I. An ecological constraints model. Am. Nat. 119, 29–39 (1982).
Article  Google Scholar 

68.
Emlen, S. T. An evolutionary theory of the family. Proc. Natl. Acad. Sci. USA 92, 8092–8099 (1995).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

69.
Ekman, J., Eggers, S., Griesser, M. & Tegelström, H. Queuing for preferred territories: delayed dispersal of Siberian jays. J. Anim. Ecol. 70(2), 317–324. https://doi.org/10.1111/j.1365-2656,2001.00490.x (2001).
Article  Google Scholar 

70.
Baglione, V., Canestrari, D., Marcos, J. M., Griesser, M. & Ekman, J. History, environment and social behaviour: experimentally induced cooperative breeding in the carrion crow. Proc. R. Soc. B 269, 1247–1251 (2002).
PubMed  Article  PubMed Central  Google Scholar 

71.
Gardner, J. L., Magrath, R. D. & Kokko, H. Stepping stones of life: natal dispersal in the group-living but noncooperative speckled warbler. Anim. Behav. 66, 521–530 (2003).
Article  Google Scholar 

72.
Russell, E. M., Yom-Tov, Y. & Geffen, E. Extended parental care and delayed dispersal: northern, tropical, and southern passerines compared. Behav. Ecol. 15, 831–838 (2004).
Article  Google Scholar 

73.
Lucia, K. E. et al. Philopatry in prairie voles: an evaluation of the habitat saturation hypothesis. Behav. Ecol. 19, 774–783 (2008).
Article  Google Scholar 

74.
Clobert, J., Le Galliard, J. F., Cote, J., Meylan, S. & Massot, M. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett. 12, 197–220 (2009).
PubMed  Article  PubMed Central  Google Scholar 

75.
Bocedi, G., Heinonen, J. & Travis, J. M. Uncertainty and the role of information acquisition in the evolution of context-dependent emigration. Am. Nat. 179, 606–620 (2012).
PubMed  Article  PubMed Central  Google Scholar 

76.
Bergmüller, R., Taborsky, M., Peer, K. & Heg, D. Extended safe havens and between-group dispersal of helpers in a cooperatively breeding cichlid. Behaviour 142, 1643–1667 (2005).
Article  Google Scholar 

77.
Tanaka, H., Frommen, J. G., Engqvist, L. & Kohda, M. Task-dependent workload adjustment of female breeders in a cooperatively breeding fish. Behav. Ecol. 29, 221–229 (2018).
Article  Google Scholar 

78.
Suárez-Seoane, S., Osborne, P. E. & Alonso López, J. C. Large-scale habitat selection by agricultural steppe birds in Spain: identifying species–habitat responses using generalized additive models. J. Appl. Ecol. 39, 755–771 (2002).
Article  Google Scholar 

79.
Boyce, M. S. et al. Can habitat selection predict abundance?. J. Anim. Ecol. 85, 11–20 (2016).
PubMed  Article  PubMed Central  Google Scholar 

80.
Muller, K. L., Stamps, J. A., Krishnan, V. V. & Willits, N. H. The effects of conspecific attraction and habitat quality on habitat selection in territorial birds (Troglodytes aedon). Am. Nat. 150, 650–661 (1997).
CAS  PubMed  Article  PubMed Central  Google Scholar 

81.
Danchin, E., Boulinier, T. & Massot, M. Conspecific reproductive success and breeding habitat selection: implications for the study of coloniality. Ecology 79, 2415–2428 (1998).
Article  Google Scholar 

82.
Brown, C. R., Brown, M. B. & Danchin, E. Breeding habitat selection in cliff swallows: The effect of conspecific reproductive success on colony choice. J. Anim. Ecol. 69, 133–142 (2000).
Article  Google Scholar 

83.
Danchin, E., Giraldeau, L. A., Valone, T. J. & Wagner, R. H. Public information: from nosy neighbors to cultural evolution. Science 305, 487–491 (2004).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

84.
Serrano, D., Forero, M. G., Donázar, J. A. & Tella, J. L. Dispersal and social attraction affect colony selection and dynamics of lesser kestrels. Ecology 85, 3438–3447 (2004).
Article  Google Scholar 

85.
Farrell, S. L., Morrison, M. L., Campomizzi, A. J. & Wilkins, R. N. Conspecific cues and breeding habitat selection in an endangered woodland warbler. J. Anim. Ecol. J. Anim. Ecol. 81, 1056–1064 (2012).
PubMed  Article  PubMed Central  Google Scholar 

86.
Pärt, T. The importance of local familiarity and search costs for age-and sex-biased philopatry in the collared flycatcher. Anim. Behav. 49, 1029–1038 (1995).
Article  Google Scholar 

87.
Clarke, A. L., Sæther, B. E. & Røskaft, E. Sex biases in avian dispersal: a reappraisal. Oikos 79, 429–438 (1997).
Article  Google Scholar 

88.
Piper, W. H., Walcott, C., Mager, J. N. & Spilker, F. J. Nestsite selection by male loons leads to sex-biased site familiarity. J. Anim. Ecol. J. Anim. Ecol. 77, 205–210 (2008).
PubMed  Article  PubMed Central  Google Scholar 

89.
Stacey, P. B. & Ligon, J. D. The benefits-of-philopatry hypothesis for the evolution of cooperative breeding: variation in territory quality and group size effects. Am. Nat. 137, 831–846 (1991).
Article  Google Scholar 

90.
Goldstein, J. M., Woolfenden, G. E. & Hailman, J. P. A same-sex stepparent shortens a prebreeder’s duration on the natal territory: tests of two hypotheses in Florida scrub-jays. Behav. Ecol. Sociobiol. 44, 15–22 (1998).
Article  Google Scholar 

91.
Ekman, J. & Griesser, M. Why offspring delay dispersal: experimental evidence for a role of parental tolerance. Proc. R. Soc. B 269, 1709–1713 (2002).
PubMed  Article  PubMed Central  Google Scholar 

92.
Griesser, M. & Ekman, A. Nepotistic alarm calling in the Siberian jay, Perisoreus infaustus. Anim. Behav. 67, 933–939 (2004).
Article  Google Scholar 

93.
Griesser, M. Referential calls signal predator behavior in a group-living bird species. Curr. Biol. 18, 69–73 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

94.
Emlen, S. T. & Wrege, P. H. Breeding biology of white-fronted bee-eaters at Nakuru: the influence of helpers on breeder fitness. J. Anim. Ecol. 60, 309–326 (1991).
Article  Google Scholar 

95.
Cockburn, A. et al. Can we measure the benefits of help in cooperatively breeding birds: the case of superb fairy-wrens Malurus cyaneus?. J. Anim. Ecol. J. Anim. Ecol. 77, 430–438 (2008).
PubMed  Article  PubMed Central  Google Scholar 

96.
Kingma, S. A., Hall, M. L., Arriero, E. & Peters, A. Multiple benefits of cooperative breeding in purple-crowned fairy-wrens: a consequence of fidelity?. J. Anim. Ecol. 79, 757–768 (2010).
PubMed  PubMed Central  Google Scholar 

97.
Fitch, M. A. & Shugart, G. W. Comparative biology and behavior of monogamous pairs and one male-two female trios of Herring Gulls. Behav. Ecol. Sociobiol. 14, 1–7 (1983).
Article  Google Scholar 

98.
Del Hoyo, J., Elliot, A. & Sargatal, J. Handbook of the Birds of the World. Barn Owls to Hummingbirds. Vol. 5.Lynx Edicions, Barcelona (1999).

99.
Clayton, K. M. & Schmutz, J. K. Is the decline of Burrowing Owls Speotyto cunicularia in prairie Canada linked to changes in Great Plains ecosystems?. Bird Conserv. Int. 9, 163–185 (1999).
Article  Google Scholar 

100.
Haug, E. A., Millsap, B. A. & Martell, M. S. The burrowing owl (Speotyto cunicularia). In Poole, A. & F. Gill (editors). The birds of North America. Philadelphia: The Academy of Natural Sciences; Washington, D. C. The American Ornithologists’ Union. Washington, D. C. The American Ornithologists’ Union (1993).

101.
Carrete, M. & Tella, J. L. High individual consistency in fear of humans throughout the adult lifespan of rural and urban burrowing owls. Sci. Rep. 3, 3524 (2013).
ADS  PubMed  PubMed Central  Article  Google Scholar 

102.
Rebolo-Ifrán, N. et al. Links between fear of humans, stress and survival support a non-random distribution of birds among urban and rural habitats. Sci. Rep. 5, 13723 (2015).
ADS  PubMed  PubMed Central  Article  Google Scholar 

103.
Kalinowski, S. T., Wagner, A. P. & Taper, M. L. ML-Relate: Software for estimating relatedness and relationship from multilocus genotypes. Mol. Ecol. Notes 6, 576–579 (2006).
CAS  Article  Google Scholar 

104.
Kalinowski, S. T., Taper, M. L. & Marshall, T. C. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol. Ecol. 16, 1099–1106 (2007).
PubMed  Article  PubMed Central  Google Scholar 

105.
Grueber, C. E., Nakagawa, S., Laws, R. J. & Jamieson, I. G. Multimodel inference in ecology and evolution: challenges and solutions. J. Evol. Biol. 24, 699–711 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

106.
Oliehoek, P. A., Windig, J. J., Van Arendonk, J. A. & Bijma, P. Estimating relatedness between individuals in general populations with a focus on their use in conservation programs. Genetics 173, 483–496 (2006).
CAS  PubMed  PubMed Central  Article  Google Scholar 

107.
Laake, J. L. RMark: an R interface for analysis of capture-recapture data with MARK (2013).

108.
Zar, J. H. Statistical significance of mutation frequencies, and the power of statistical testing, using the Poisson distribution. Biometr. J. 26, 83–88 (1984).
Article  Google Scholar 

109.
Labocha, M. K. & Hayes, J. P. Morphometric indices of body condition in birds: a review. J. Ornithol. 153, 1–22 (2012).
Article  Google Scholar 

110.
Choquet, R., Lebreton, J. D., Gimenez, O., Reboulet, A. M. & Pradel, R. U-CARE: Utilities for performing goodness of fit tests and manipulating CApture–REcapture data. Ecography 32, 1071–1074 (2009).
Article  Google Scholar 

111.
Choquet, R., Rouan, L., Pradel, R. Program E-SURGE: a software application for fitting multievent models. In Modeling demographic processes in marked populations. pp. 845–865. Springer, Boston, MA, 2009.

112.
White, G. C. & Burnham, K. P. Program MARK: survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).
Article  Google Scholar 

113.
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 

114.
Mollie, E. B. et al. glmmTMB balances speed and flexibility among Packages for zeroinflated generalized linear mixed modeling. R J. 9, 378–400 (2017).
Article  Google Scholar 

115.
Hartig F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. R Package. 2018. https://www.cran.r-project.org/package=DHARMa, Version 0. 2. 0.

116.
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference. A Practical Information-Theoretic Approach (Springer, New York, 2002).
Google Scholar 

117.
Barton, K. MuMIn: Multi-Model Inference. R package version 1. 40. 0. https://CRANhttps://CRAN.R-project.org/package=MuMIn (2017). More