Ecology

Cox, P. & Jones, C. Climate change – Illuminating the modern dance of climate and CO2. Science 321, 1642–1644 (2008).CAS 
PubMed 
Article 

Google Scholar 
Gilmanov, T. G. et al. Gross primary production and light response parameters of four Southern Plains ecosystems estimated using long-term CO2-flux tower measurements. Glob. Biogeochem. Cycle 17, 1071 (2003).ADS 
Article 
CAS 

Google Scholar 
Running, S. W. Climate change – Ecosystem disturbance, carbon, and climate. Science 321, 652–653 (2008).CAS 
PubMed 
Article 

Google Scholar 
Sun, Z. et al. Spatial pattern of GPP variations in terrestrial ecosystems and its drivers: Climatic factors, CO2 concentration and land-cover change, 1982–2015. Ecol. Inform. 46, 156–165 (2018).CAS 
Article 

Google Scholar 
Running, S. W. et al. A global terrestrial monitoring network integrating tower fluxes, flask sampling, ecosystem modeling and EOS satellite data. Remote Sens. Environ. 70, 108–127 (1999).ADS 
Article 

Google Scholar 
Madani, N. et al. The Impacts of Climate and Wildfire on Ecosystem Gross Primary Productivity in Alaska. J. Geophys. Res.-Biogeosci. 126, e2020JG006078 (2021).ADS 
Article 

Google Scholar 
Morales, P. et al. Comparing and evaluating process-based ecosystem model predictions of carbon and water fluxes in major European forest biomes. Glob. Change Biol. 11, 2211–2233 (2005).ADS 
Article 

Google Scholar 
Tramontana, G., Ichii, K., Camps-Valls, G., Tomelleri, E. & Papale, D. Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data. Remote Sens. Environ. 168, 360–373 (2015).ADS 
Article 

Google Scholar 
Canadell, J. G. et al. Carbon metabolism of the terrestrial biosphere: A multitechnique approach for improved understanding. Ecosystems 3, 115–130 (2000).CAS 
Article 

Google Scholar 
Fletcher, B. J. et al. Photosynthesis and productivity in heterogeneous arctic tundra: consequences for ecosystem function of mixing vegetation types at stand edges. J. Ecol. 100, 441–451 (2012).CAS 
Article 

Google Scholar 
Liu, L., Guan, L. & Liu, X. Directly estimating diurnal changes in GPP for C3 and C4 crops using far-red sun-induced chlorophyll fluorescence. Agr. Forest Meteorol. 232, 1–9 (2017).ADS 
Article 

Google Scholar 
Xu, X. et al. Long-term trend in vegetation gross primary production, phenology and their relationships inferred from the FLUXNET data. J. Environ. Manage. 246, 605–616 (2019).PubMed 
Article 

Google Scholar 
Baldocchi, D. D. How eddy covariance flux measurements have contributed to our understanding of Global Change Biology. Glob. Change Biol. 26, 242–260 (2020).ADS 
Article 

Google Scholar 
He, L., Chen, J. M., Liu, J., Belair, S. & Luo, X. Assessment of SMAP soil moisture for global simulation of gross primary production. J. Geophys. Res.-Biogeosci. 122, 1549–1563 (2017).Article 

Google Scholar 
Wang, S., Ibrom, A., Bauer-Gottwein, P. & Garcia, M. Incorporating diffuse radiation into a light use efficiency and evapotranspiration model: An 11-year study in a high latitude deciduous forest. Agr. Forest Meteorol. 248, 479–493 (2018).ADS 
Article 

Google Scholar 
Wang, S. et al. Recent global decline of CO2 fertilization effects on vegetation photosynthesis. Science 370, 1295–1300 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Yu, G., Fu, Y., Sun, X., Wen, X. & Zhang, L. Recent progress and future directions of ChinaFLUX. Sci. China Ser. D-Earth Sci. 49, 1–23 (2006).ADS 
Article 

Google Scholar 
McCallum, I. et al. Improved light and temperature responses for light-use-efficiency-based GPP models. Biogeosciences 10, 6577–6590 (2013).ADS 
Article 

Google Scholar 
Stocker, B. D. et al. Drought impacts on terrestrial primary production underestimated by satellite monitoring. Nature Geoscience 12, 264‐+ (2019).ADS 
Article 
CAS 

Google Scholar 
Cheng, S. J. et al. Variations in the influence of diffuse light on gross primary productivity in temperate ecosystems. Agr. Forest Meteorol. 201, 98–110 (2015).ADS 
Article 

Google Scholar 
Zhang, M. et al. Effects of cloudiness change on net ecosystem exchange, light use efficiency, and water use efficiency in typical ecosystems of China. Agr. Forest Meteorol. 151, 803–816 (2011).ADS 
Article 

Google Scholar 
Oliphant, A. J. et al. The role of sky conditions on gross primary production in a mixed deciduous forest. Agr. Forest Meteorol. 151, 781–791 (2011).ADS 
Article 

Google Scholar 
Urban, O. et al. Ecophysiological controls over the net ecosystem exchange of mountain spruce stand. Comparison of the response in direct vs. diffuse solar radiation. Glob. Change Biol. 13, 157–168 (2007).ADS 
Article 

Google Scholar 
Zhou, H. et al. Large contributions of diffuse radiation to global gross primary productivity during 1981–2015. Glob. Biogeochem. Cycle 35, e2021GB006957 (2021).ADS 
CAS 
Article 

Google Scholar 
Guanter, L. et al. Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements. Remote Sens. Environ. 121, 236–251 (2012).ADS 
Article 

Google Scholar 
Guanter, L. et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl. Acad. Sci. USA 111, E1327–E1333 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu, L. & Cheng, Z. Detection of vegetation light-use efficiency based on solar-induced chlorophyll fluorescence separated from canopy radiance spectrum. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 3, 306–312 (2010).ADS 
Article 

Google Scholar 
MacBean, N. et al. Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data (vol 8, 1973, 2018). Sci. Rep. 8, 10420 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Meroni, M. et al. Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications. Remote Sens. Environ. 113, 2037–2051 (2009).ADS 
Article 

Google Scholar 
Zheng, T. & Chen, J. M. Photochemical reflectance ratio for tracking light use efficiency for sunlit leaves in two forest types. ISPRS-J. Photogramm. Remote Sens. 123, 47–61 (2017).ADS 
Article 

Google Scholar 
Damm, A. et al. Remote sensing of sun-induced fluorescence to improve modeling of diurnal courses of gross primary production (GPP). Glob. Change Biol. 16, 171–186 (2010).ADS 
Article 

Google Scholar 
Lee, J. E. et al. Simulations of chlorophyll fluorescence incorporated into the Community Land Model version 4. Glob. Change Biol. 21, 3469–3477 (2015).ADS 
Article 

Google Scholar 
Pinto, F. et al. Sun-induced chlorophyll fluorescence from high-resolution imaging spectroscopy data to quantify spatio-temporal patterns of photosynthetic function in crop canopies. Plant Cell Environ. 39, 1500–1512 (2016).CAS 
PubMed 
Article 

Google Scholar 
Porcar-Castell, A. et al. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. J. Exp. Bot. 65, 4065–4095 (2014).CAS 
PubMed 
Article 

Google Scholar 
Xie, X., Li, A., Jin, H., Yin, G. & Nan, X. Derivation of temporally continuous leaf maximum carboxylation rate (V-cmax) from the sunlit leaf gross photosynthesis productivity through combining BEPS model with light response curve at tower flux sites. Agr. Forest Meteorol. 259, 82–94 (2018).ADS 
Article 

Google Scholar 
Chen, J. M., Liu, J., Leblanc, S. G., Lacaze, R. & Roujean, J. L. Multi-angular optical remote sensing for assessing vegetation structure and carbon absorption. Remote Sens. Environ. 84, 516–525 (2003).ADS 
Article 

Google Scholar 
Chen, J. M. et al. Effects of foliage clumping on the estimation of global terrestrial gross primary productivity. Glob. Biogeochem. Cycle 26, GB1019 (2012).ADS 
Article 
CAS 

Google Scholar 
Running, S. W., Thornton, P. E., Nemani, R. & Glassy, J. M. in Methods in Ecosystem Science. Ch.3 (Springer, New York, NY. Press, 2000).Wu, C., Munger, J. W., Niu, Z. & Kuang, D. Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest. Remote Sens. Environ. 114, 2925–2939 (2010).ADS 
Article 

Google Scholar 
Makela, A. et al. Developing an empirical model of stand GPP with the LUE approach: analysis of eddy covariance data at five contrasting conifer sites in Europe. Glob. Change Biol. 14, 92–108 (2008).ADS 
Article 

Google Scholar 
McCallum, I. et al. Satellite-based terrestrial production efficiency modeling. Carbon Balanc. Manag. 4, 8–8 (2009).Article 

Google Scholar 
Wang, H. et al. Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling. Remote Sens. Environ 114, 2248–2258 (2010).ADS 
Article 

Google Scholar 
Yu, R. An improved estimation of net primary productivity of grassland in the Qinghai-Tibet region using light use efficiency with vegetation photosynthesis model. Ecol. Model. 431, 109121 (2020).Article 

Google Scholar 
Yuan, W. et al. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agr. Forest Meteorol. 143, 189–207 (2007).ADS 
Article 

Google Scholar 
Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560 (2004).Article 

Google Scholar 
Zhang, Y. et al. Development of a coupled carbon and water model for estimating global gross primary productivity and evapotranspiration based on eddy flux and remote sensing data. Agr. Forest Meteorol. 223, 116–131 (2016).ADS 
Article 

Google Scholar 
He, M. et al. Development of a two-leaf light use efficiency model for improving the calculation of terrestrial gross primary productivity. Agr. Forest Meteorol. 173, 28–39 (2013).ADS 
Article 

Google Scholar 
Zhou, Y. et al. Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites. J. Geophys. Res.-Biogeosci. 121, 1045–1072 (2016).Article 

Google Scholar 
Friedlingstein, P. et al. Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks. J. Clim. 27, 511–526 (2014).ADS 
Article 

Google Scholar 
Raich, J. W. et al. Potential net primary productivity in South-America – application of a global-model. Ecol. Appl. 1, 399–429 (1991).CAS 
PubMed 
Article 

Google Scholar 
Li, J. et al. An algorithm differentiating sunlit and shaded leaves for improving canopy conductance and vapotranspiration estimates. J. Geophys. Res.-Biogeosci. 124, 807–824 (2019).ADS 
Article 

Google Scholar 
Chen, J. M., Liu, J., Cihlar, J. & Goulden, M. L. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecol. Model. 124, 99–119 (1999).CAS 
Article 

Google Scholar 
Keenan, T. F. et al. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun. 7, 13428 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Huang, M. et al. Air temperature optima of vegetation productivity across global biomes. Nat. Ecol. Evol. 3, 772–779 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Prentice, I. C., Dong, N., Gleason, S. M., Maire, V. & Wright, I. J. Balancing the costs of carbon gain and water transport: testing a new theoretical framework for plant functional ecology. Ecol. Lett. 17, 82–91 (2014).PubMed 
Article 

Google Scholar 
Korson, L., Drosthan, W. & Millero, F. J. Viscosity of water at various temperatures. J. Phys. Chem. 73, 34–39 (1969).CAS 
Article 

Google Scholar 
Olofsson, P., Van Laake, P. E. & Eklundh, L. Estimation of absorbed PAR across Scandinavia from satellite measurements Part I: Incident PAR. Remote Sens. Environ. 110, 252–261 (2007).ADS 
Article 

Google Scholar 
González, J. A. & Calbó, J. Modelled and measured ratio of PAR to global radiation under cloudless skies. Agr. Forest Meteorol. 110, 319–325 (2002).ADS 
Article 

Google Scholar 
Zhang, X., Zhang, Y. & Zhoub, Y. Measuring and modelling photosynthetically active radiation in Tibet Plateau during April–October. Agr. Forest Meteorol. 102, 207–212 (2000).ADS 
Article 

Google Scholar 
Yang, Y., Xiao, P., Feng, X. & Li, H. Accuracy assessment of seven global land cover datasets over China. ISPRS-J. Photogramm. Remote Sens. 125, 156–173 (2017).ADS 
Article 

Google Scholar 
Liu, Y., Liu, R. & Chen, J. M. GLOBMAP global Leaf Area Index since 1981. Zenodo https://doi.org/10.5281/zenodo.4700264 (2019).Vermote, E. MOD09A1 MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC https://doi.org/10.5067/MODIS/MOD09A1.006 (2015).Deng, F., Chen, J. M., Plummer, S., Chen, M. & Pisek, J. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Trans. Geosci. Remote Sens. 44, 2219–2229 (2006).ADS 
Article 

Google Scholar 
Vermote, E. NOAA CDR Program. NOAA Climate Data Record (CDR) of AVHRR Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Version 5. LAI. NOAA National Centers for Environmental Information https://doi.org/10.7289/V5TT4P69 (2019).He, L., Chen, J. M., Pisek, J., Schaaf, C. & Strahler, A. Global clumping index map derived from the MODIS BRDF product. Remote Sens. Environ. 119, 118–130 (2012).ADS 
Article 

Google Scholar 
Liu, R. G. & Liu, Y. Generation of new cloud masks from MODIS land surface reflectance products. Remote Sens. Environ. 133, 21–37 (2013).ADS 
CAS 
Article 

Google Scholar 
Chen, J. M., Deng, F. & Chen, M. Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter. IEEE Trans. Geosci. Remote Sens. 44, 2230–2238 (2006).ADS 
Article 

Google Scholar 
Harris, I.C. CRU JRA: Collection of CRU JRA forcing datasets of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data. Centre for Environmental Data Analysis http://catalogue.ceda.ac.uk/uuid/863a47a6d8414b6982e1396c69a9efe8 (2019).Li, X., Liang, H. & Cheng, W. Evaluation and comparison of light use efficiency models for their sensitivity to the diffuse PAR fraction and aerosol loading in China. Int. J. Appl. Earth Obs. Geoinf. 95, 102269 (2021).
Google Scholar 
Duan, Q. Y., Sorooshian, S. & Gupta, V. Effective and efficient global optimization for conceptual rain full-runoff models. Water Resour. Res. 28, 1015–1031 (1992).ADS 
Article 

Google Scholar 
Gu, L. H. et al. Advantages of diffuse radiation for terrestrial ecosystem productivity. J. Geophys. Res.-Atmos. 107, 4050 (2002).ADS 

Google Scholar 
Bi, W. & Zhou, Y. A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies (1992–2020). Dryad https://doi.org/10.5061/dryad.dfn2z352k (2022).Ogutu, B. O. & Dash, J. Assessing the capacity of three production efficiency models in simulating gross carbon uptake across multiple biomes in conterminous USA. Agr. Forest Meteorol. 174, 158–169 (2013).ADS 
Article 

Google Scholar 
Cai, W. et al. Large differences in terrestrial vegetation production derived from satellite-based light use efficiency models. Remote Sens. 6, 8945–8965 (2014).ADS 
Article 

Google Scholar 
Anav, A. et al. Spatiotemporal patterns of terrestrial gross primary production: a review. Rev. Geophys. 53, 785–818 (2015).ADS 
Article 

Google Scholar 
Li, X. & Xiao, J. Mapping photosynthesis solely from solar-induced chlorophyll fluorescence: A global, fine-resolution dataset of gross primary production derived from OCO-2. Remote Sens. 11, 2563 (2019).ADS 
Article 

Google Scholar 
Alemohammad, S. H. et al. Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence. Biogeosciences 14, 4101–4124 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Joiner, J. et al. Estimation of terrestrial global gross primary production (GPP) with satellite data-driven models and eddy covariance flux data. Remote Sens. 10, 1346 (2018).ADS 
Article 

Google Scholar 
Wang, S., Zhang, Y., Ju, W., Qiu, B. & Zhang, Z. Tracking the seasonal and inter-annual variations of global gross primary production during last four decades using satellite near-infrared reflectance data. Sci. Total Environ. 755, 142569 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Zheng, Y. et al. Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017. Earth Syst. Sci. Data 12, 2725–2746 (2020).ADS 
Article 

Google Scholar 
Running, S., Mu, Q. & Zhao, M. MOD17A2H MODIS/Terra Gross Primary Productivity 8-Day L4 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC https://doi.org/10.5067/MODIS/MOD17A2H.006 (2015).Ciais, P. et al. A three-dimensional synthesis study of delta O-18 in atmospheric CO2 .1. Surface fluxes. J. Geophys. Res.-Atmos. 102, 5857–5872 (1997).ADS 
CAS 
Article 

Google Scholar 
Zhang, Y., Joiner, J., Gentine, P. & Zhou, S. Reduced solar-induced chlorophyll fluorescence from GOME-2 during Amazon drought caused by dataset artifacts. Glob. Change Biol. 24, 2229–2230 (2018).ADS 
Article 

Google Scholar 
Xie, X. et al. Assessment of five satellite-derived LAI datasets for GPP estimations through ecosystem models. Sci. Total Environ. 690, 1120–1130 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Fang, H., Wei, S., Jiang, C. & Scipal, K. Theoretical uncertainty analysis of global MODIS, CYCLOPES, and GLOBCARBON LAI products using a triple collocation method. Remote Sens. Environ. 124, 610–621 (2012).ADS 
Article 

Google Scholar 
Camacho, F., Cemicharo, J., Lacaze, R., Baret, F. & Weiss, M. GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products. Remote Sens. Environ. 137, 310–329 (2013).ADS 
Article 

Google Scholar 
Prince, S. D. & Goward, S. N. Global primary production: A remote sensing approach. J. Biogeogr. 22, 815–835 (1995).Article 

Google Scholar 
Verma, S. B. et al. Annual carbon dioxide exchange in irrigated and rainfed maize-based agroecosystems. Agr. Forest Meteorol. 131, 77–96 (2005).ADS 
Article 

Google Scholar 
Yan, H. et al. Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants. Ecol. Model. 297, 42–59 (2015).CAS 
Article 

Google Scholar 
Jiang, S. et al. Comparison of satellite-based models for estimating gross primary productivity in agroecosystems. Agr. Forest Meteorol. 297, 108253 (2021).ADS 
Article 

Google Scholar 
Yang, X. et al. Solar-induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest. Geophys. Res. Lett. 42, 2977–2987 (2015).ADS 
CAS 
Article 

Google Scholar 
Zhou, H. et al. Responses of gross primary productivity to diffuse radiation at global FLUXNET sites. Atmos. Environ. 244, 117905 (2021).CAS 
Article 

Google Scholar 
Han, J. et al. Effects of diffuse photosynthetically active radiation on gross primary productivity in a subtropical coniferous plantation vary in different timescales. Ecol. Indic. 115, 106403 (2020).Article 

Google Scholar 
Grant, I. F., Prata, A. J. & Cechet, R. P. The impact of the diurnal variation of albedo on the remote sensing of the daily mean albedo of grassland. J. Appl. Meteorol. 39, 231–244 (2000).ADS 
Article 

Google Scholar 
Singarayer, J. S., Ridgwell, A. & Irvine, P. Assessing the benefits of crop albedo bio-geoengineering. Environ. Res. Lett. 4, 045110 (2009).ADS 
Article 

Google Scholar 
Tang, S. et al. LAI inversion algorithm based on directional reflectance kernels. J. Environ. Manage. 85, 638–648 (2007).CAS 
PubMed 
Article 

Google Scholar  More

Ehrlich, P. R. & Raven, P. H. Butterflies and plants: A study in coevolution. Evolution 18, 586 (1964).Article 

Google Scholar 
Raguso, R. A. et al. The raison d’être of chemical ecology. Ecology 96, 617–630 (2015).PubMed 
Article 

Google Scholar 
Kariyat, R. R. & Portman, S. L. Plant–herbivore interactions: Thinking beyond larval growth and mortality. Am. J. Bot. 103, 789–791 (2016).PubMed 
Article 

Google Scholar 
Raubenheimer, D. & Simpson, S. J. Nutritional ecology and foraging theory. Curr. Opin. Insect Sci. 27, 38–45 (2018).PubMed 
Article 

Google Scholar 
Goehring, L. & Oberhauser, K. S. Effects of photoperiod, temperature, and host plant age on induction of reproductive diapause and development time in Danaus plexippus. Ecol. Entomol. 27, 674–685 (2002).Article 

Google Scholar 
Hahn, D. A. Larval nutrition affects lipid storage and growth, but not protein or carbohydrate storage in newly eclosed adults of the grasshopper Schistocerca americana. J. Insect Physiol. 51, 1210–1219 (2005).CAS 
PubMed 
Article 

Google Scholar 
Portman, S. L., Kariyat, R. R., Johnston, M. A., Stephenson, A. G. & Marden, J. H. Cascading effects of host plant inbreeding on the larval growth, muscle molecular composition, and flight capacity of an adult herbivorous insect. Funct. Ecol. 29, 328–337 (2015).Article 

Google Scholar 
Johnson, C. G. Physiological factors in insect migration by flight. Nature 198, 423–427 (1963).Article 

Google Scholar 
Harrison, R. G. Dispersal polymorphisms in insects. Annu. Rev. Ecol. Syst. 11, 95–118 (1980).Article 

Google Scholar 
Zera, A. J. & Denno, R. F. Physiology and ecology of dispersal polymorphism in insects. Annu. Rev. Entomol. 42, 207–231 (1997).CAS 
PubMed 
Article 

Google Scholar 
Marden, J. H. et al. Weight and nutrition affect pre-mRNA splicing of a muscle gene associated with performance, energetics and life history. J. Exp. Biol. 211, 3653–3660 (2008).CAS 
PubMed 
Article 

Google Scholar 
Raguso, R. A., Ojeda-Avila, T., Desai, S., Jurkiewicz, M. A. & Arthur Woods, H. The influence of larval diet on adult feeding behaviour in the tobacco hornworm moth, Manduca sexta. J. Insect Physiol. 53, 923–932 (2007).CAS 
PubMed 
Article 

Google Scholar 
Cease, A. J. et al. Nutritional imbalance suppresses migratory phenotypes of the Mongolian locust (Oedaleus asiaticus). R. Soc. Open Sci. 4, https://doi.org/10.1098/rsos.161039 (2017).Reichstein, T., Von Euw, J., Parsons, J. A. & Rothschild, M. Heart poisons in the monarch butterfly. Science 161, 861–866 (1968).CAS 
PubMed 
Article 

Google Scholar 
Brower, L. P., Ryerson, W. N., Coppinger, L. L. & Glazier, S. C. Ecological chemistry and the palatability spectrum. Science 161, 1349–1351 (1968).CAS 
PubMed 
Article 

Google Scholar 
Young, A. M. An evolutionary-ecological model of the evolution of migratory behavior in the Monarch Butterfly, and its absence in the Queen Butterfly. Acta Biotheor. 31, 219–237 (1982).Article 

Google Scholar 
Agrawal, A. A. Monarchs and Milkweed: A Migrating Butterfly, a Poisonous Plant, and Their Remarkable Story of Coevolution. (Princeton University Press, 2017).Batalden, R. V. & Oberhauser, K. S. Potential changes in eastern north American monarch migration in response to an introduced Milkweed, Asclepias curassavica. in Monarchs in a Changing World: Biology and Conservation of an Iconic Butterfly 215–224 (2015).Tyler Flockhart, D. T. et al. Tracking multi-generational colonization of the breeding grounds by monarch butterflies in eastern North America. Proc. R. Soc. B Biol. Sci. 280, 20131087 (2013).Saunders, S. P., Ries, L., Oberhauser, K. S., Thogmartin, W. E. & Zipkin, E. F. Local and cross-seasonal associations of climate and land use with abundance of monarch butterflies Danaus plexippus. Ecography. 41, 278–290 (2018).Article 

Google Scholar 
Pleasants, J. M. & Oberhauser, K. S. Milkweed loss in agricultural fields because of herbicide use: Effect on the monarch butterfly population. Insect Conserv. Divers. 6, 135–144 (2013).Article 

Google Scholar 
Borders, B. & Lee-Mäder, B. B. Project milkweed. in Monarchs in a Changing World: Biology and Conservation of an Iconic Butterfly. pp.190-196 (Cornell University press, 2015).Agrawal, A. A., Petschenka, G., Bingham, R. A., Weber, M. G. & Rasmann, S. Toxic cardenolides: Chemical ecology and coevolution of specialized plant-herbivore interactions. N. Phytologist 194, 28–45 (2012).CAS 
Article 

Google Scholar 
Malcolm, S. B. Milkweeds, monarch butterflies and the ecological significance of cardenolides. Chemoecology 5–6, 101–117 (1994).Article 

Google Scholar 
Pocius, V. M., Debinski, D. M., Bidne, K. G., Hellmich, R. L. & Hunter, F. K. Performance of early Instar Monarch Butterflies (Danaus plexippus L.) on nine Milkweed species native to Iowa. J. Lepid. Soc. 71, 153–161 (2017).
Google Scholar 
Ali, J. G. & Agrawal, A. A. Specialist versus generalist insect herbivores and plant defense. Trends Plant Sci. 17, 293–302 (2012).CAS 
PubMed 
Article 

Google Scholar 
Zalucki, M. P., Brower, L. P. & Alonso-M, A. Detrimental effects of latex and cardiac glycosides on survival and growth of first-instar monarch butterfly larvae Danaus plexippus feeding on the sandhill milkweed Asclepias humistrata. Ecol. Entomol. 26, 212–224 (2001).Article 

Google Scholar 
Agrawal, A. A., Hastings, A. P., Patrick, E. T. & Knight, A. C. Specificity of herbivore-induced hormonal signaling and defensive traits in five closely related milkweeds (Asclepias spp.). J. Chem. Ecol. 40, 717–729 (2014).CAS 
PubMed 
Article 

Google Scholar 
Agrawal, A. A., Ali, J. G., Rasmann, S. & Fishbein, M. Macroevolutionary trends in the defense of milkweeds against monarchs. Monarch. a Chang. World Biol. Conserv. Iconic Insect. Cornell University Press, Ithaca, NY. pp. 47–59 (2011).Pocius, V. M. et al. Milkweed matters: Monarch butterfly (Lepidoptera: Nymphalidae) survival and development on nine midwestern milkweed species. Environ. Entomol. 46, 1098–1105 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Petschenka, G. et al. Stepwise evolution of resistance to toxic cardenolides via genetic substitutions in the na+/k+-atpase of milkweed butterflies (lepidoptera: Danaini). Evolution (N. Y). 67, 2753–2761 (2013).CAS 

Google Scholar 
Agrawal, A. A. et al. Cardenolides, toxicity, and the costs of sequestration in the coevolutionary interaction between monarchs and milkweeds. Proc. Natl Acad. Sci. USA 118, e2024463118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Marden, J. H. Variability in the size, composition, and function of insect flight muscles. Annu. Rev. Physiol. 62, 157–178 (2000).CAS 
PubMed 
Article 

Google Scholar 
Bicudo, J. E. P. W., Buttemer, W. A., Chappell, M. A., Pearson, J. T. & Bech, C. Ecological and Environmental Physiology of Birds. Ecological and Environmental Physiology of Birds 3 (Oxford University Press, 2010).Bailey, E. Biochemistry of Insect Flight. in Insect Biochemistry and Function. pp. 89–176 (Springer, 1975).Dudley, R. The biomechanics of insect flight: form, function, evolution. Annals of the Entomological Society of America 93 (Princeton University Press, 2000).Solensky, M. J. Overview of monarch migration. in The Monarch Butterfly: Biology and Conservation 79–83 (2004).Urquhart, F. A. & Urquhart, N. R. Monarch butterfly (Danaus plexippus L.) overwintering population in Mexico (Lep. Danaidae). Atalanta 7, 56–61 (1976).
Google Scholar 
Brower, L. P. Understanding and misunderstanding the migration of the monarch butterfly (Nymphalidae) in North America: 1857–1995. J. – Lepid. Soc. 49, 304–385 (1995).
Google Scholar 
Fisher, K. E., Adelman, J. S. & Bradbury, S. P. Employing Very High Frequency (VHF) radio telemetry to recreate monarch butterfly flight paths. Environ. Entomol. 49, 312–323 (2020).PubMed 
Article 

Google Scholar 
Reppert, S. M. & de Roode, J. C. Demystifying monarch butterfly migration. Curr. Biol. 28, R1009–R1022 (2018).CAS 
PubMed 
Article 

Google Scholar 
Zhu, H., Gegear, R. J., Casselman, A., Kanginakudru, S. & Reppert, S. M. Defining behavioral and molecular differences between summer and migratory monarch butterflies. BMC Biol. 7, 1–14 (2009).Heinze, S. & Reppert, S. M. Anatomical basis of sun compass navigation I: The general layout of the monarch butterfly brain. J. Comp. Neurol. 520, 1599–1628 (2012).PubMed 
Article 

Google Scholar 
Zhan, S. et al. The genetics of monarch butterfly migration and warning colouration. Nature 514, 317–321 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Soule, A. J., Decker, L. E. & Hunter, M. D. Effects of diet and temperature on monarch butterfly wing morphology and flight ability. J. Insect Conserv. 24, 961–975 (2020).Article 

Google Scholar 
Decker, L. E., Soule, A. J., de Roode, J. C. & Hunter, M. D. Phytochemical changes in milkweed induced by elevated CO2 alter wing morphology but not toxin sequestration in monarch butterflies. Funct. Ecol. 33, 411–421 (2019).Article 

Google Scholar 
Heinrich, B. Temperature regulation of the sphinx moth, Manduca sexta. I. Flight energetics and body temperature during free and tethered flight. J. Exp. Biol. 54, 141–152 (1971).CAS 
PubMed 
Article 

Google Scholar 
Nicolson, S. W. & Louw, G. N. Simultaneous measurement of evaporative water loss, oxygen consumption, and thoracic temperature during flight in a carpenter bee. J. Exp. Zool. 222, 287–296 (1982).Article 

Google Scholar 
Rothe, U. & Nachtigall, W. Flight of the honey bee IV. J. Comp. Physiol. B 158, 711–718 (1989).Article 

Google Scholar 
Nachtigall, W., Hanauer-Thieser, U. & Mörz, M. Flight of the honey bee VII: Metabolic power versus flight speed relation. J. Comp. Physiol. B 165, 484–489 (1995).Article 

Google Scholar 
Niven, J. E. & Scharlemann, J. P. W. Do insect metabolic rates at rest and during flight scale with body mass? Biol. Lett. 1, 346–349 (2005).PubMed 
PubMed Central 
Article 

Google Scholar 
Zalucki, M. P., Parry, H. R. & Zalucki, J. M. Movement and egg laying in Monarchs: To move or not to move, that is the equation. Austral. Ecol. 41, 154–167 (2016).Article 

Google Scholar 
Marden, J. H. & Chai, Peng Aerial predation and butterfly design: How palatability, mimicry, and the need for evasive flight constrain mass allocation. Am. Nat. 138, 15–36 (1991).Article 

Google Scholar 
Levin, E., Lopez-Martinez, G., Fane, B. & Davidowitz, G. Hawkmoths use nectar sugar to reduce oxidative damage from flight. Science 355, 733–735 (2017).CAS 
PubMed 
Article 

Google Scholar 
Petschenka, G. & Agrawal, A. A. Milkweed butterfly resistance to plant toxins is linked to sequestration, not coping with a toxic diet. Proc. R. Soc. B Biol. Sci. 282, 20151865 (2015).Petschenka, G. & Agrawal, A. A. How herbivores coopt plant defenses: Natural selection, specialization, and sequestration. Curr. Opin. Insect Sci. 14, 17–24 (2016).PubMed 
Article 

Google Scholar 
Tan, W. H., Tao, L., Hoang, K. M., Hunter, M. D. & de Roode, J. C. The effects of milkweed induced defense on parasite resistance in monarch butterflies, Danaus plexippus. J. Chem. Ecol. 44, 1040–1044 (2018).CAS 
PubMed 
Article 

Google Scholar 
Brower, L. P. & Glazier, S. C. Localization of heart poisons in the monarch butterfly. Science 188, 19–25 (1975).CAS 
PubMed 
Article 

Google Scholar 
Zalucki, M. P. et al. It’s the first bites that count: Survival of first-instar monarchs on milkweeds. Austral. Ecol. 26, 547–555 (2001).Article 

Google Scholar 
Zalucki, M. P., Malcolm, S. B., Hanlon, C. C. & Paine, T. D. First-instar monarch larval growth and survival on milkweeds in Southern California: Effects of latex, leaf hairs and cardenolides. Chemoecology 22, 75–88 (2012).Article 

Google Scholar 
Ziegler, R. & Van Antwerpen, R. Lipid uptake by insect oocytes. Insect Biochem. Mol. Biol. 36, 264–272 (2006).Beenakkers, A. M. T., Van der Horst, D. J. & Van Marrewijk, W. J. A. Insect flight muscle metabolism. Insect Biochem. 14, 243–260 (1984).CAS 
Article 

Google Scholar 
Beall, G. The fat content of a butterfly, Danaus Plexippus Linn., as affected by migration. Ecology 29, 80–94 (1948).Article 

Google Scholar 
James, D. G. Phenology of weight, moisture and energy reserves of Australian monarch butterflies, Danaus plexippus. Ecol. Entomol. 9, 421–428 (1984).Article 

Google Scholar 
Briegel, H. Metabolic relationship between female body size, reserves, and fecundity of Aedes aegypti. J. Insect Physiol. 36, 165–172 (1990).Article 

Google Scholar 
Hines, W. J. W. & Smith, M. J. H. Some aspects of intermediary metabolism in the desert locust (Schistocerca gregaria Forskål). J. Insect Physiol. 9, 463–468 (1963).CAS 
Article 

Google Scholar 
Inagaki, S. & Yamashita, O. Metabolic shift from lipogenesis to glycogenesis in the last instar larval fat body of the silkworm, Bombyx mori. Insect Biochem. 16, 327–331 (1986).CAS 
Article 

Google Scholar 
Venkatesh, K. & Morrison, P. E. Studies of weight changes and amount of food ingested by the stable fly, stomoxys calcitrans (Diptera: Muscidae). Can. Entomol. 112, 141–149 (1980).Article 

Google Scholar 
Arrese, E. L. & Soulages, J. L. Insect fat body: Energy, metabolism, and regulation. Annu. Rev. Entomol. 55, 207–225 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mevi-Schütz, J. & Erhardt, A. Larval nutrition affects female nectar amino acid preference in the map butterfly (Araschnia levana). Ecology 84, 2788–2794 (2003).Article 

Google Scholar 
Wassenaar, L. I. & Hobson, K. A. Natal origins of migratory monarch butterflies at wintering colonies in Mexico: New isotopic evidence. Proc. Natl Acad. Sci. USA 95, 15436–15439 (1998).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Majewska, A. A. & Altizer, S. Exposure to Non-Native Tropical Milkweed Promotes Reproductive Development in Migratory Monarch Butterflies. Insects 10, 253 (2019).Howard, E., Aschen, H. & Davis, A. K. Citizen science observations of monarch butterfly overwintering in the Southern United States. Psyche: A Journal of Entomology 2010, https://doi.org/10.1155/2010/689301 (2010).Satterfield, D. A., Maerz, J. C. & Altizer, S. Loss of migratory behaviour increases infection risk for a butterfly host. Proc. R. Soc. B Biol. Sci. 282, 20141734 (2015).Petschenka, G. et al. Relative selectivity of plant cardenolides for Na+/K+-ATPases from the monarch butterfly and non-resistant insects. Front. Plant Sci. 9, 1424 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Jones, P. L., Petschenka, G., Flacht, L. & Agrawal, A. A. Cardenolide intake, sequestration, and excretion by the monarch butterfly along gradients of plant toxicity and larval ontogeny. J. Chem. Ecol. 45, 264–277 (2019).CAS 
PubMed 
Article 

Google Scholar 
Tao, L., Hoang, K. M., Hunter, M. D. & de Roode, J. C. Fitness costs of animal medication: antiparasitic plant chemicals reduce fitness of monarch butterfly hosts. J. Anim. Ecol. 85, 1246–1254 (2016).PubMed 
Article 

Google Scholar 
Lederhouse, R. C. The effect of female mating frequency on egg fertility in the black swallowtail, Papilio polyxenes asterius (Papilionidae). J. Lepid. Soc. 35, 266–277 (1981).
Google Scholar 
Jones, R. E., Hart, J. R. & Bull, G. D. Temperature, size and egg production in the Cabbage Butterfly, Pieris rapae L. Aust. J. Zool. 30, 159–168 (1982).Article 

Google Scholar 
Haukioja, E. & Neuvonen, S. The relationship between size and reproductive potential in male and female Epirrita autumnata (Lep., Geometridae). Ecol. Entomol. 10, 267–270 (1985).Article 

Google Scholar 
Altizer, S. M., Oberhauser, K. S. & Brower, L. P. Associations between host migration and the prevalence of a protozoan parasite in natural populations of adult monarch butterflies. Ecol. Entomol. 25, 125–139 (2000).Article 

Google Scholar 
Masters, A. R., Malcolm, S. B. & Brower, L. P. Monarch butterfly (Danaus plexippus) thermoregulatory behavior and adaptations for overwintering in Mexico. Ecology 69, 458–467 (1988).Article 

Google Scholar 
Kammer, A. E. Thoracic temperature, shivering, and flight in the monarch butterfly, Danaus plexippus (L.). Z. Vgl. Physiol. 68, 334–344 (1970).Article 

Google Scholar 
Pendar, H. & Socha, J. J. Estimation of instantaneous gas exchange in flow-through respirometry systems: A modern revision of bartholomew’s ztransform method. PLoS One 10, e0139508 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lighton, J. R. B. Measuring Metabolic Rates: A Manual for Scientists. (Oxford University Press, 2008).Alonso-Mejía, A., Rendon-Salinas, E., Montesinos-Patiño, E. & Brower, L. P. Use of lipid reserves by monarch butterflies overwintering in Mexico: Implications for conservation. Ecol. Appl. 7, 934–947 (1997).Article 

Google Scholar 
Diaz, R., Overholt, W. A., Hahn, D. & Samayoa, A. C. Diapause induction in Gratiana boliviana (Coleoptera: Chrysomelidae), a biological control agent of tropical soda apple in Florida. Ann. Entomol. Soc. Am. 104, 1319–1326 (2011).Article 

Google Scholar 
Tschinkel, W. R. Sociometry and sociogenesis of colonies of the fire ant Solenopsis invicta during one annual cycle. Ecol. Monogr. 63, 425–457 (1993).Article 

Google Scholar 
Fink, L. S. & Brower, L. P. Birds can overcome the cardenolide defence of monarch butterflies in Mexico. Nature 291, 67–70 (1981).CAS 
Article 

Google Scholar 
Ali, J. G. & Agrawal, A. A. Trade-offs and tritrophic consequences of host shifts in specialized root herbivores. Funct. Ecol. 31, 153–160 (2017).Article 

Google Scholar 
Woodson, R. E. The North American Species of Asclepias L. Ann. Mo. Bot. Gard. 41, 1 (1954).Article 

Google Scholar 
NRCS USDA. The PLANTS Database. National Plant Data Center. http://plants.usda.gov (2006).Agrawal, A. A., Salminen, J. P. & Fishbein, M. Phylogenetic trends in phenolic metabolism of milkweeds (Asclepias): Evidence for escalation. Evolution (N. Y). 63, 663–673 (2009).CAS 

Google Scholar 
Pocius, V. M. et al. Monarch butterflies show differential utilization of nine midwestern milkweed species. Front. Ecol. Evol. 6, 169 (2018).Pocius, V. M., Debinski, D. M., Pleasants, J. M., Bidne, K. G. & Hellmich, R. L. Monarch butterflies do not place all of their eggs in one basket: Oviposition on nine Midwestern milkweed species. Ecosphere 9, e02064 (2018).Article 

Google Scholar 
Ladner, D. T. & Altizer, S. Oviposition preference and larval performance of North American monarch butterflies on four Asclepias species. Entomol. Exp. Appl. 116, 9–20 (2005).Article 

Google Scholar 
Borders, B. A guide to the native milkweeds of Oregon. Xerces Soc. Invertebr. Conserv. www.xerces.org, 5, 12-23 (2012). More

All research methods included in this study were performed in accordance with the relevant guidelines and regulations, under ZA/LP/81996 research permit, with ethical approval from the Animal Welfare Ethical Review Board (AWERB) at Durham University. The authors confirm the study was carried out in compliance with ARRIVE guidelines.All inter-individual association data was collected between June 2018 and June 2019 on a wild habituated group of Afro-montane chacma baboons in the western Soutpansberg Mountains, South Africa (central coordinates S29.44031°, E23.02217°) (for study site description see2). The study group was habituated circa 2005 and was the focus of intermittent research attention until 2014. The study area experienced long-term anthropogenic activities (local farming, forestry, and residences) prior to 2005, as such, consistent interactions with humans have been ongoing with this population for some time. From 2007 onwards numerous researchers were able to collect expansive datasets on the study group (e.g. Refs.17,18), indicating that habituation was at a typical level found elsewhere (also validated by AA and RH, who had researched chacma baboons elsewhere). From 2014 the group received full day (dawn until dusk) follows 3–4 days a week, with occasional gaps of up to 5 weeks in duration. These gaps did not appear to effect habituation levels, likely due to the presence of other researchers at the field site who always tried to act benignly when encountering the habituated group. The follow schedule was designed so that the study group retained as much of their natural interactions with predators as possible by ensuring the baboons spent significant time without observers who may influence the frequency and nature of predator–prey interactions19.The study site was located in a private nature reserve and the study group was not hunted during observation gaps or engaged in any conflict with humans, other than occasionally being scared (chasing, yelling, throwing stones etc.) from a small plantation by local workers, usually resulting in alarm barks and fleeing responses. However, the study group appeared adept at recognising the differences between researchers and these threats20. The majority of the study group’s home-range typically overlapped with the core area of the Lajuma Research Centre, and as a result, interactions with staff living in the area, unfamiliar researchers, and tourists were frequent. However, the baboons had not engaged in ‘raiding’ residences, threatening humans, or any other potentially negative symptom of habituation before the end of this study.Sampling methodology for proximity associations30-s focal sampling was used to collect proximity associations between all group members (excluding infants). All data was collected between June and December 2018 and January and June 2019; the majority of 2018s data was collected during the wet season, whilst most of 2019s data was collected during the dry season. To account for time of day, each day was split into four time-periods that were seasonally adjusted ensuring each period accounted for 25% of the current day length. A randomly ordered list of individuals was produced for each day, the first individual identified from the top 15 (approx. 20% of group size) individuals on the list was sampled immediately. Individuals could only be sampled once per time period per day, and a maximum of twice total per day. All individuals received at least 14 focal observations per time period (56 total) across the study period (see below for how we handled uneven sampling for some individuals). A video camera was used by AA (the only observer to collect this data) to record all focal observations (Panasonic HC-W580 Camcorder). At the end of the 30-s focal observation the identities of all neighbouring conspecifics within 5 m, 2.5 m, 1 m, and touching the focal animal were recorded (audibly by AA). We chose the end of the focal observation to record this data as this was most likely to reflect the conditions during the focal, i.e., the observer had been in proximity for at least 30 s.Neighbour information was extracted from video footage and entered manually by AA and AW. Data was split into separate years to reflect an observation gap of several weeks and to understand whether there was consistency in the hypothesized effects through time and to reflect underlying differences in environmental conditions during the two study periods; during the dry season fruits and seeds are scarce and day lengths are several hours shorter than in the wet season such that day journey lengths are often shorter in the dry season and animals are much more sedentary which could impact inter-individual spacings. In 2018 each individual was sampled between 28 and 30 times; 28 focals were randomly selected from each individual to make sampling even. For 2019 there were between 25 and 27 focals per individual; 25 samples of each individual were randomly selected. Observations were undertaken at a range of distances. For both years the median end observer distance was 4.5 m; data was thus split into close focal observations of less than or equal to 4.5 m (2018: n = 918, 2019: n = 809), and observations greater than 4.5 m (2018: n = 902 2019: n = 816). See supporting information Table S1 for summary statistics of the observation distances of each individual.We did not make any attempt to record our focal data evenly across the various habitats at our field site (see Supporting information text S1 for complete habitat descriptions) as our previous research indicated there was little difference in general spatial cohesion/inter-individual proximity patterns across these habitats (see Supporting information text S2 and Table S2). As a result, we considered it unlikely that there were fundamental differences in inter-individual association patterns across habitats, or that observers struggled to reliably detect or identify neighbours in dense habitats. We do acknowledge, however, that there will always be an element of bias with such methods, as observations were avoided, aborted, or excluded if visual obstructions (e.g., cliffs, rocks, walls, buildings, very dense vegetation etc.) prohibited accurate assessments; the observations used in the current study are from occasions when these factors were not an issue.During this study the group contained between 85 and 92 individuals. Age-sex class was defined according to secondary sexual characteristics (e.g., testes descending/enlarging, sexual swelling, canine eruption) and changes in pelage throughout juvenile development (see Supporting information text S3 for full descriptions). All 65 non-infant individuals that were present during 2017 (when displacement tolerances were calculated) and still remaining in the group by the end of 2019 were used in this study (4 individuals from the prior FID study were no longer present). There were a high number of births between 2018 and 2019, but none were independent by the time either of our sampling periods begun in 2018 or 2019. There was no immigration of foreign individuals, but two individuals disappeared, both during the 2018 focal sampling period. As a result, we had a very consistent pool of individuals to sample from during this study. We removed all data associated with the two individuals who disappeared as their occurrences as neighbours would have been poorly sampled (due to missing more than half the study) relative to the rest of the group which would have led to statistical biases21.Flight initiation distance procedureIndividual displacement tolerance estimates were previously quantified in our previous research2 using a flight initiation distance (FID) procedure22 that was completed between October 2017 and April 2018, prior and independent to the commencement of proximity association focal sampling in June 2018. Individual baboons were approached by an observer, and the distance at which the animal displaced away from the observer measured (see Supporting information Table S2 for summary statistics). This procedure was repeated 24 times for each individual baboon, with approaches spread evenly across two observers differing in familiarity. At the beginning of each approach we also recorded several behavioural, social, and environmental factors that could have hypothetically influenced an individual’s FID2 including whether the animal was engaged (e.g., digging or grooming) or not engaged (e.g., resting, chewing food, being groomed), habitat type (open/closed: see Supporting text S1), whether the animal was on the ground or sat on a low branch or rock within 50 cm of the ground, the number of conspecifics within 5 m of the focal animal, and whether there had been any external events within the preceding 5 min (e.g., alarm calls, aggressions, encountering another group or predator). During the approach, we also recorded the visual orientation distance (the distance at which the focal animal directed its line of vision towards the head of the approaching observer) and whether one of the focal animal’s neighbours had displaced/fled before the focal animal. Although all but neighbour flee first and external events showed some importance for predicting looking (see Table S4), FID was found to be distinct amongst individuals and repeatable within each individual, evidence that displacement tolerance may be an individual level trait2. Full details of methods, statistical analysis, and results (including comparison to the original model) for this updated model are in Supporting information text S4, with model summary results for the previous and updated models in Tables S3 and S4.The notion of an observer approaching a habituated primate may be considered atypical or likely to result in habituation/sensitization effects or agonistic behaviours being directed towards the approaching observers. However, our previous study2 showed that almost all approaches resulted in the animal passively relocating (98.85%), a very benign response identical to the behaviours of subordinate baboons displacing away from dominant conspecifics. This suggests that in this group, observers may be considered equivalent to a high-level social threat2. Throughout observation periods on habituated animals, observers are likely to approach or displace animals either incidentally or accidentally multiple times throughout the day, especially during lengthy focal observations. As such, the approach methodology is unlikely to represent a stimulus outside of the norm for our study animals. This may explain why displacement responses were so passive and why there was no evidence of habituation or sensitization effects across the group or individually through a range of temporal periods2 or after life-threatening events20. As a result, our situation was possible without risk of causing stress or anxiety in the study subjects, eliciting agonistic behaviours towards observers, or interfering with their prior habituation levels.Statistical analysisInfluence of tolerance and observer distance on inter-individual association patternsQuantifying displacement toleranceTo quantify displacement tolerance towards observers we extracted the individual conditional modes from the updated FID model using the ranef function in brms. Conditional modes are often referred to as Best Linear Unbiased Predictors (BLUPs) and are the difference between the predicted mean population-level response for a given set of treatments (i.e., population-level effects) and the predicted responses for each individual, and therefore infer the extent to which each individual differs from the population mean. The conditional modes and their associated standard deviations can be found in supporting information Table S5.To validate that the conditional modes from the updated model were both representative of the individual’s flight responses and in line with the estimates produced from our previous study2 we performed additional tests. Firstly, we performed a Pearson’s correlation between the conditional modes from the updated model and the conditional modes from the previous article. Individual tolerance estimates were consistent (r(63) = 0.915, p  More

Barrington, D. J. & Ghadouani, A. Application of hydrogen peroxide for the removal of toxic cyanobacteria and other phytoplankton from wastewater. Environ. Sci. Technol. 42, 8916–8921. https://doi.org/10.1021/es801717y (2008).CAS 
Article 
PubMed 

Google Scholar 
Vikrant, K. et al. Engineered/designer biochar for the removal of phosphate in water and wastewater. Sci. Total Environ. 616–617, 1242–1260. https://doi.org/10.1016/j.scitotenv.2017.10.193 (2018).CAS 
Article 
PubMed 

Google Scholar 
Merel, S. et al. State of knowledge and concerns on cyanobacterial blooms and cyanotoxins. Environ. Int. 59, 303–327. https://doi.org/10.1016/j.envint.2013.06.013 (2013).CAS 
Article 
PubMed 

Google Scholar 
Paerl, H. W. & Otten, T. G. Harmful cyanobacterial blooms: Causes, consequences, and controls. Microb. Ecol. 65, 995–1010. https://doi.org/10.1007/s00248-012-0159-y (2013).CAS 
Article 
PubMed 

Google Scholar 
Monchamp, M. E. et al. Homogenization of lake cyanobacterial communities over a century of climate change and eutrophication. Nat. Ecol. Evol. 2, 317–324. https://doi.org/10.1038/s41559-017-0407-0 (2018).Article 
PubMed 

Google Scholar 
Paerl, H. W. & Fulton, R. S. Ecology of harmful cyanobacteria. In Ecology of Harmful Algae (eds Granéli, E. & Turner, J. T.) 95–109 (Springer, 2006).Chapter 

Google Scholar 
Guan, Y., Zhang, M., Yang, Z., Shi, X. & Zhao, X. Intra-annual variation and correlations of functional traits in Microcystis and Dolichospermum in Lake Chaohu. Ecol. Indic. 111, 106052. https://doi.org/10.1016/j.ecolind.2019.106052 (2020).Article 

Google Scholar 
Zhang, M. et al. Spatial and seasonal shifts in bloom-forming cyanobacteria in Lake Chaohu: Patterns and driving factors. Phycol. Res. 64, 44–55. https://doi.org/10.1111/pre.12112 (2016).Article 

Google Scholar 
Krishnamurthy, T., Carmichael, W. W. & Sarver, E. W. Toxic peptides from freshwater cyanobacteria (blue-green algae) I. Isolation, purification and characterization of peptides from Microcystis aeruginosa and Anabaena flos-aquae. Toxicon 24, 865–873. https://doi.org/10.1016/0041-0101(86)90087-5 (1986).CAS 
Article 
PubMed 

Google Scholar 
Mahmood, N. A. & Carmichael, W. W. Anatoxin-a(s), an anticholinesterase from the cyanobacterium Anabaena flos-aquae NRC 525–17. Toxicon 25, 1221–1227. https://doi.org/10.1016/0041-0101(87)90140-1 (1987).CAS 
Article 
PubMed 

Google Scholar 
Li, X., Dreher, T. W. & Li, R. An overview of diversity, occurrence, genetics and toxin production of bloom-forming Dolichospermum (Anabaena) species. Harmful Algae 54, 54–68. https://doi.org/10.1016/j.hal.2015.10.015 (2016).CAS 
Article 
PubMed 

Google Scholar 
Buratti, F. M. et al. Cyanotoxins: Producing organisms, occurrence, toxicity, mechanism of action and human health toxicological risk evaluation. Arch. Toxicol. 91, 1049–1130. https://doi.org/10.1007/s00204-016-1913-6 (2017).CAS 
Article 
PubMed 

Google Scholar 
Iredale, R. S., McDonald, A. T. & Adams, D. G. A series of experiments aimed at clarifying the mode of action of barley straw in cyanobacterial growth control. Water Res. 46, 6095–6103. https://doi.org/10.1016/j.watres.2012.08.040 (2012).CAS 
Article 
PubMed 

Google Scholar 
Zhang, S. H., Zhang, S. Y. & Li, G. Acorus calamus root extracts to control harmful cyanobacteria blooms. Ecol. Eng. 94, 95–101. https://doi.org/10.1016/j.ecoleng.2016.05.053 (2016).Article 

Google Scholar 
Mecina, G. F. et al. Effect of flavonoids isolated from Tridax procumbens on the growth and toxin production of Microcystis aeruginosa. Aquat. Toxicol. 211, 81–91. https://doi.org/10.1016/j.aquatox.2019.03.011 (2019).CAS 
Article 
PubMed 

Google Scholar 
Yuan, R. et al. The allelopathic effects of aqueous extracts from Spartina alterniflora on controlling the Microcystis aeruginosa blooms. Sci. Total Environ. 712, 13622. https://doi.org/10.1016/j.scitotenv.2019.136332 (2020).CAS 
Article 

Google Scholar 
Tan, K. et al. A review of allelopathy on microalgae. Microbiology 165, 587–592. https://doi.org/10.1099/mic.0.000776 (2019).CAS 
Article 
PubMed 

Google Scholar 
Mecina, G. F. et al. Response of Microcystis aeruginosa BCCUSP 232 to barley (Hordeum vulgare L.) straw degradation extract and fractions. Sci. Total. Environ. 599–600, 1837–1847. https://doi.org/10.1016/j.scitotenv.2017.05.156 (2017).CAS 
Article 
PubMed 

Google Scholar 
Zhao, W., Zheng, Z., Zhang, J., Roger, S. F. & Luo, X. Allelopathically inhibitory effects of eucalyptus extracts on the growth of Microcystis aeruginosa. Chemosphere 225, 424–433. https://doi.org/10.1016/j.chemosphere.2019.03.070 (2019).CAS 
Article 
PubMed 

Google Scholar 
Bottino, F. et al. Effects of macrophyte leachate on Anabaena sp. and Chlamydomonas moewusii growth in freshwater tropical ecosystems. Limnology 19, 171–176. https://doi.org/10.1007/s10201-017-0532-0 (2018).CAS 
Article 

Google Scholar 
Zhang, K., Yu, M., Xu, P., Zhang, S. & Benoit, G. Physiological and morphological response of Aphanizomenon flos-aquae to watermelon (Citrullus lanatus) peel aqueous extract. Aquat. Toxicol. 225, 105548. https://doi.org/10.1016/j.aquatox.2020.105548 (2020).CAS 
Article 
PubMed 

Google Scholar 
Lichtenthaler, H. K. & Buschmann, C. Chlorophylls and carotenoids: Measurement and characterization by UV-VIS spectroscopy. Curr. Protoc. Food Anal. Chem. 1, F4.3.1-F4.38 (2001).Article 

Google Scholar 
Ozaki, K. et al. Electron microscopic study on lysis of a cyanobacterium Microcystis. J. Health Sci. 55, 578–585. https://doi.org/10.1248/jhs.55.578 (2009).CAS 
Article 

Google Scholar 
Staats, N., De Winder, B., Stal, L. J. & Mur, L. R. Isolation and characterization of extracellular polysaccharides from the epipelic diatoms Cylindrotheca closterium and Navicula salinarum. Eur. J. Phycol. 34, 161–169. https://doi.org/10.1080/09670269910001736212 (1999).Article 

Google Scholar 
Hellebust, J. & Craigie, J. (eds) Handbook of Phycological Methods. Physiological and Biochemical Methods (Cambridge University, 1978).
Google Scholar 
Roháček, K. & Barták, M. Technique of the modulated chlorophyll fluorescence: Basic concepts, useful parameters, and some applications. Photosynthetica 37, 339–363. https://doi.org/10.1023/A:1007172424619 (1999).Article 

Google Scholar 
Zhang, T. T., He, M., Wu, A. P. & Nie, L. W. Inhibitory effects and mechanisms of Hydrilla verticillata (Linn.f.) royle extracts on freshwater algae. Bull. Environ. Contam. Toxicol. 88, 477–481. https://doi.org/10.1007/s00128-011-0500-z (2012).CAS 
Article 
PubMed 

Google Scholar 
Zhao, S., Pan, W. & Ma, C. Stimulation and inhibition effects of algae-lytic products from Bacillus cereus strain L7 on Anabaena flos-aquae. J. Appl. Phycol. 24, 1015–1021. https://doi.org/10.1007/s10811-011-9725-9 (2012).CAS 
Article 

Google Scholar 
Kaminski, A. et al. Aquatic macrophyte Lemna trisulca (L.) as a natural factor for reducing anatoxin-a concentration in the aquatic environment and biomass of cyanobacterium Anabaena flos-aquae (Lyngb.) de Bréb. Algal Res. 9, 212–217. https://doi.org/10.1016/j.algal.2015.03.014 (2015).Article 

Google Scholar 
Gumbo, J. R., Cloete, T. E., van Zyl, G. J. J. & Sommerville, J. E. M. The viability assessment of Microcystis aeruginosa cells after co-culturing with Bacillus mycoides B16 using flow cytometry. Phys. Chem. Earth. 72–75, 24–33. https://doi.org/10.1016/j.pce.2014.09.004 (2014).Article 

Google Scholar 
Fan, J., Ho, L., Hobson, P. & Brookes, J. Evaluating the effectiveness of copper sulphate, chlorine, potassium permanganate, hydrogen peroxide and ozone on cyanobacterial cell integrity. Water Res. 47, 5153–5164. https://doi.org/10.1016/j.watres.2013.05.057 (2013).CAS 
Article 
PubMed 

Google Scholar 
Lu, Z. Studies on oxidative stress and programmed cell death of Microcystis aeruginosa induced by polyphenolic allelochemicals (D). Institute of Hydrobiology, Chinese Academy of Sciences (2014).Lu, Z. et al. Polyphenolic allelochemical pyrogallic acid induces caspase-3(like)-dependent programmed cell death in the cyanobacterium Microcystis aeruginosa. Algal Res. 21, 148–155. https://doi.org/10.1016/j.algal.2016.11.007 (2017).Article 

Google Scholar 
Chen, Y. et al. Vitamin C modulates Microcystis aeruginosa death and toxin release by induced Fenton reaction. J. Hazard. Mater. 321, 888–895. https://doi.org/10.1016/j.jhazmat.2016.10.010 (2017).CAS 
Article 
PubMed 

Google Scholar 
Latifi, A., Ruiz, M. & Zhang, C. C. Oxidative stress in cyanobacteria. FEMS Microbiol. Rev. 33, 258–278. https://doi.org/10.1111/j.1574-6976.2008.00134.x (2009).CAS 
Article 
PubMed 

Google Scholar 
Shao, J. H., Wu, X. Q. & Li, R. H. Physiological responses of Microcystis aeruginosa PCC7806 to nonanoic acid stress. Environ. Toxicol. 24, 610–617. https://doi.org/10.1002/tox.20462 (2009).CAS 
Article 
PubMed 

Google Scholar 
Hua, Q. et al. Allelopathic effect of the rice straw aqueous extract on the growth of Microcystis aeruginosa. Ecotox. Environ. Safe. 148, 953–959. https://doi.org/10.1016/j.ecoenv.2017.11.049 (2018).CAS 
Article 

Google Scholar 
Chen, L., Wang, Y., Shi, L., Zhao, J. & Wang, W. Identification of allelochemicals from pomegranate peel and their effects on Microcystis aeruginosa growth. Environ. Sci. Pollut. Res. 26, 22389–22399. https://doi.org/10.1007/s11356-019-05507-1 (2019).CAS 
Article 

Google Scholar 
Zhang, S. H., Xu, P. Y. & Chang, J. J. Physiological responses of Aphanizomenon flos-aquae under the stress of Sagittaria sagittifolia extract. Bull. Environ. Contam. Toxicol. 97, 870–875. https://doi.org/10.1007/s00128-016-1948-7 (2016).CAS 
Article 
PubMed 

Google Scholar 
Li, J. et al. Growth inhibition and oxidative damage of Microcystis aeruginosa induced by crude extract of Sagittaria trifolia tubers. J. Environ. Sci. 43, 40–47. https://doi.org/10.1016/j.jes.2015.08.020 (2016).CAS 
Article 

Google Scholar 
Shao, J. et al. Inhibitory effects of sanguinarine against the cyanobacterium Microcystis aeruginosa NIES-843 and possible mechanisms of action. Aquat. Toxicol. 142–143, 257–263. https://doi.org/10.1016/j.aquatox.2013.08.019 (2013).CAS 
Article 
PubMed 

Google Scholar 
Apel, K. & Hirt, H. Reactive oxygen species: Metabolism, oxidative stress, and signal transduction. Annu. Rev. Plant. Biol. 55, 373–399. https://doi.org/10.1146/annurev.arplant.55.031903.141701 (2004).CAS 
Article 
PubMed 

Google Scholar 
Zhang, S. & Benoit, G. Comparative physiological tolerance of unicellular and colonial Microcystis aeruginosa to extract from Acorus calamus rhizome. Aquat. Toxicol. 215, 105271. https://doi.org/10.1016/j.aquatox.2019.105271 (2019).CAS 
Article 
PubMed 

Google Scholar 
Derks, A., Schaven, K. & Bruce, D. Diverse mechanisms for photoprotection in photosynthesis. Dynamic regulation of photosystem II excitation in response to rapid environmental change. BBA-Bioenergetics 1847, 468–485. https://doi.org/10.1016/j.bbabio.2015.02.008 (2015).CAS 
Article 
PubMed 

Google Scholar 
Jiang, H. & Qiu, B. Photosynthetic adaptation of a bloom-forming cyanobacterium Microcystis aeruginosa (cyanophyceae) to prolonged uv-b exposure. J. Phycol. 41, 983–992. https://doi.org/10.1111/j.1529-8817.2005.00126.x (2005).Article 

Google Scholar 
Azizullah, A., Richter, P. & Häder, D. P. Photosynthesis and photosynthetic pigments in the flagellate Euglena gracilis: As sensitive endpoints for toxicity evaluation of liquid detergents. J. Photochem. Photobiol. B Biol. 133, 18–26. https://doi.org/10.1016/j.jphotobiol.2014.02.011 (2014).CAS 
Article 

Google Scholar 
Singh, D. P., Khattar, J. I. S., Gupta, M. & Kaur, G. Evaluation of toxicological impact of cartap hydrochloride on some physiological activities of a non-heterocystous cyanobacterium Leptolyngbya foveolarum. Pestic. Biochem. Phys. 110, 63–70. https://doi.org/10.1016/j.pestbp.2014.03.002 (2014).CAS 
Article 

Google Scholar 
Movasaghi, Z., Rehman, S. & Rehman, I. U. Raman spectroscopy of biological tissues. Appl. Spectrosc. Rev. 42, 493–541. https://doi.org/10.1080/05704920701551530 (2007).CAS 
Article 

Google Scholar 
Li, K. et al. In vivo kinetics of lipids and astaxanthin evolution in Haematococcus pluvialis mutant under 15% CO2 using Raman microspectroscopy. Bioresource Technol. 244, 1439–1444. https://doi.org/10.1016/j.biortech.2017.04.116 (2017).CAS 
Article 

Google Scholar 
Beutner, S. et al. Quantitative assessment of antioxidant properties of natural colorants and phytochemicals: Carotenoids, flavonoids, phenols and indigoids. The role of beta-carotene in antioxidant functions. J. Sci. Food. Agric. 81, 559–568. https://doi.org/10.1002/jsfa.849 (2001).CAS 
Article 

Google Scholar 
Kelman, D., Ben-Amotz, A. & Berman-Frank, I. Carotenoids provide the major antioxidant defence in the globally significant N2-fixing marine cyanobacterium Trichodesmiumem. Environ. Microbiol. 11, 1897–1908. https://doi.org/10.1111/j.1462-2920.2009.01913.x (2009).CAS 
Article 
PubMed 

Google Scholar 
Zhou, T. et al. Growth suppression and apoptosis-like cell death in Microcystis aeruginosa by H2O2: A new insight into extracellular and intracellular damage pathways. Chemosphere 211, 1098–1108. https://doi.org/10.1016/j.chemosphere.2018.08.042 (2018).CAS 
Article 
PubMed 

Google Scholar 
Schreiber, U., Quayle, P., Schmidt, S., Escher, B. I. & Mueller, J. F. Methodology and evaluation of a highly sensitive algae toxicity test based on multiwell chlorophyll fluorescence imaging. Biosens. Bioelectron. 22, 2554–2563. https://doi.org/10.1016/j.bios.2006.10.018 (2007).CAS 
Article 
PubMed 

Google Scholar 
Kumar, K. S. et al. Algal photosynthetic responses to toxic metals and herbicides assessed by chlorophyll a fluorescence. Ecotox. Environ. Safe. 104, 51–71. https://doi.org/10.1016/j.ecoenv.2014.01.042 (2014).CAS 
Article 

Google Scholar 
Maxwell, K. & Johnson, G. N. Chlorophyll fluorescence: A practical guide. J Exp Bot 51, 659–668. https://doi.org/10.1093/jxb/51.345.659 (2000).CAS 
Article 
PubMed 

Google Scholar 
Lürling, M. & Roessink, I. On the way to cyanobacterial blooms: Impact of the herbicide metribuzin on the competition between a green alga (Scenedesmus) and a cyanobacterium (Microcystis). Chemosphere 65, 618–626. https://doi.org/10.1016/j.chemosphere.2006.01.073 (2006).CAS 
Article 
PubMed 

Google Scholar 
Zhu, J. Y., Liu, B. Y., Wang, J., Gao, Y. N. & Wu, Z. B. Study on the mechanism of allelopathic influence on cyanobacteria and chlorophytes by submerged macrophyte (Myriophyllum spicatum) and its secretion. Aquat. Toxicol. 98, 196–203. https://doi.org/10.1016/j.aquatox.2010.02.011 (2010).CAS 
Article 
PubMed 

Google Scholar 
Wan, J., Guo, P., Peng, X. & Wen, K. Effect of erythromycin exposure on the growth, antioxidant system and photosynthesis of Microcystis flos-aquae. J. Hazard. Mater. 283, 778–786. https://doi.org/10.1016/j.jhazmat.2014.10.026 (2015).CAS 
Article 
PubMed 

Google Scholar 
Wang, R. et al. Evaluating the effects of allelochemical ferulic acid on Microcystis aeruginosa by pulse-amplitude-modulated (PAM) fluorometry and flow cytometry. Chemosphere 147, 264–271. https://doi.org/10.1016/j.chemosphere.2015.12.109 (2016).CAS 
Article 
PubMed 

Google Scholar 
Long, M. et al. Allelochemicals from Alexandrium minutum induce rapid inhibition of metabolism and modify the membranes from Chaetoceros muelleri. Algal Res. 35, 508–518. https://doi.org/10.1016/j.algal.2018.09.023 (2018).Article 

Google Scholar 
Cosgrove, J. & Borowitzka, M. A. Chloreophyll fluorescence terminology: An introduction. In Chlorophyll a Fluorescence in Aquatic Sciences: Methods and Applications, Developments in Applied Phycology Vol. 4 (eds Sugget, D. J. et al.) 1–18 (Springer, 2010).
Google Scholar 
Kumar, K. S. & Han, T. Physiological response of Lemna species toherbicides and its probable use in toxicity testing. Toxicol. Environ. Health Sci. 2, 39–49. https://doi.org/10.1007/BF03216512 (2010).Article 

Google Scholar 
Ricart, M. et al. Primary and complex stressors in polluted mediterranean rivers: Pesticide effects on biological communities. J. Hydrol. 383, 52–61. https://doi.org/10.1016/j.jhydrol.2009.08.014 (2010).CAS 
Article 

Google Scholar 
Deng, C., Pan, X. & Zhang, D. Influence of of loxacin on photosystems I and II activities of Microcystis aeruginosa and the potential role of cyclic electron flow. J. Biosci. Bioeng. 119, 159–164. https://doi.org/10.1016/j.jbiosc.2014.07.014 (2015).CAS 
Article 
PubMed 

Google Scholar 
Pereira, S. et al. Complexity of cyanobacterial exopolysaccharides: Composition, structures, inducing factors and putative genes involved in their biosynthesis and assembly. FEMS Microbiol. Rev. 33, 917–941. https://doi.org/10.1111/j.1574-6976.2009.00183.x (2009).CAS 
Article 
PubMed 

Google Scholar 
Gao, L. et al. Extracellular polymeric substances buffer against the biocidal effect of H2O2 on the bloom-forming cyanobacterium Microcystis aeruginosa. Water Res. 69, 51–58. https://doi.org/10.1016/j.watres.2014.10.060 (2015).CAS 
Article 
PubMed 

Google Scholar 
Zhang, S. et al. Ameliorating effects of extracellular polymeric substances excreted by Thalassiosira pseudonana on algal toxicity of CdSe quantum dots. Aquat. Toxicol. 126, 214–223. https://doi.org/10.1016/j.aquatox.2012.11.012 (2013).CAS 
Article 
PubMed 

Google Scholar 
Henriques, I. D. S. & Love, N. G. The role of extracellular polymeric substances in the toxicity response of activated sludge bacteria to chemical toxins. Water Res. 41, 4177–4185. https://doi.org/10.1016/j.watres.2007.05.001 (2007).CAS 
Article 
PubMed 

Google Scholar 
Zheng, S. M. et al. Role of extracellular polymeric substances on the behavior and toxicity of silver nanoparticles and ions to green algae Chlorella vulgaris. Sci. Total Environ. 660, 1182–1190. https://doi.org/10.1016/j.scitotenv.2019.01.067 (2019).CAS 
Article 
PubMed 

Google Scholar  More

Hutchinson, G. E. An Introduction to Population Ecology (Yale University Press, 1978).MATH 

Google Scholar 
Fussman, G. F. & Heber, G. Food web complexity and chaotic population dynamics. Ecol. Lett. 5, 394–401 (1978).Article 

Google Scholar 
Maron, J. L. & Crone, E. Herbivory: effects on plant abundance, distribution, and population growth. Proc. R. Soc. B. 272, 2575–2584 (1978).
Google Scholar 
Johst, K., Berryman, A. & Lima, M. From individual interactions to population dynamics: Individual resource partitioning simulation exposes the causes of nonlinear intra-specific competition. Pop. Ecol. 50, 79–90 (2008).Article 

Google Scholar 
McIntire, K. M. & Juliano, S. A. How can mortality increase population size? A test of two hypotheses. Ecology 99, 1660–1670 (2018).PubMed 
Article 

Google Scholar 
Mylius, S. D. & Deikmann, O. On evolutionary stable life histories, optimization and the need to be specific about density dependence. Oikos 74, 218–224 (1995).Article 

Google Scholar 
Courchamp, F., Clutton-Brock, T. & Grenfell, B. Inverse density dependence and the Allee effect. Trends Ecol. Evol. 14, 405–410 (1999).CAS 
PubMed 
Article 

Google Scholar 
MacLean, R. C. & Gudelj, I. Resource competition and social conflict in experimental populations of yeast. Nature 44, 498–501 (2006).ADS 
Article 
CAS 

Google Scholar 
Khatchikian, C. E. et al. Recent and rapid population growth and range expansion of the Lyme disease tick vector, Ixodes scapularis North America. Evolution 69, 1678–1689 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Lafferty, K. D. & Holt, R. D. How should environmental stress affect the population dynamics of disease?. Ecol. Lett. 6, 654–664 (2003).Article 

Google Scholar 
Sibley, R. M., Barker, D., Denham, M. C., Hone, J. & Pagel, M. On the regulation of populations of mammals, birds, fish, and insects. Science 309, 607–610 (2005).ADS 
Article 
CAS 

Google Scholar 
Bjorndal, K., Bolten, A. B. & Chaloupka, M. Y. Green turtle somatic growth model: evidence for density-dependence. Ecol. App. 10, 269–282 (2000).
Google Scholar 
Lamb, J. S., Satgé, Y. G. & Jodice, P. G. R. Influence of density-dependent competition on foraging and migratory behavior of a subtropical colonial seabird. Ecol. Evol. 7, 6469–6481 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Kobayashi, K. Sexual selection sustains biodiversity via producing negative density-dependent population growth. J. Ecol. 107, 1433–1438 (2018).Article 

Google Scholar 
López-Sepulcre, A. & Kokko, H. Territorial defense, territory size, and population regulation. Am. Nat. 166, 317–325 (2005).PubMed 
Article 

Google Scholar 
Maag, N., Cozzi, G., Clutton-Brock, T. & Ozgul, A. Density-dependent dispersal strategies in a cooperative breeder. Ecology 99, 1932–1941 (2018).PubMed 
Article 

Google Scholar 
Bonenfant, C. et al. Empirical evidence of density- dependence in populations of large herbivores. Adv. Ecol. Res. 41, 313–357 (2009).Article 

Google Scholar 
Legros, M., Lloyd, A. L., Huang, Y. & Gould, F. Density-dependent intraspecific competition in the larval stage of Aedes aegypt (Diptera: Culicidae): Revisiting the current paradigm. J. Med. Entomol. 46, 409–419 (2009).PubMed 
Article 

Google Scholar 
Hixon, M. A. & Jones, G. P. Competition, predation, and density-dependent mortality in demersal marine fishes. Ecology 86, 2847–2859 (2006).Article 

Google Scholar 
Vonesh, J. R. & De La Cruz, O. Complex life cycles and density dependence: Assessing the contribution of egg mortality to amphibian declines. Oecologia 133, 325–333 (2002).ADS 
PubMed 
Article 

Google Scholar 
Southwood, T. R., Murdie, G., Yasuno, M., Tonn, R. J. & Reader, P. M. Studies on the life budget of Ae. aegypti in Wat Samphaya, Bangkok, Thailand. Bull. World Health Organ. 46, 211–226 (1972).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dye, C. Intraspecific competition amongst larval Aedes aegypti: food exploitation or chemical interference. Ecol. Entomol. 7, 39–46 (1982).Article 

Google Scholar 
Dye, C. Models for the population dynamics of the yellow fever mosquito, Aedes aegypti. J. Anim. Ecol. 53, 247–268 (1984).Article 

Google Scholar 
Livdahl, T. P. & Willey, M. S. Prospects for an invasion: competition between Aedes albopictus and native Aedes triseriatus. Science 253, 189–191 (1991).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Alto, B. W., Lounibos, L. P., Higgs, S. & Juliano, S. A. Larval competition differentially affects arbovirus infection in Aedes mosquito. Ecology 86, 3279–3288 (2005).PubMed 
Article 

Google Scholar 
Juliano, S. A. Population dynamics. J. Am. Mosq. Control Assoc. 23, 265–275 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
Focks, D. A., Haile, D. G., Daniels, E. & Mount, G. A. Dynamics life table model for Aedes aegypti (diptera: Culicidae): simulation results and validation. J. Med. Entomol. 30, 1018–1028 (1993).CAS 
PubMed 
Article 

Google Scholar 
Ellis, A. M., Garcia, A. J., Focks, D. A., Morrison, A. C. & Scott, T. W. Parameterization and sensitivity analysis of a complex simulation model for mosquito population dynamics, dengue transmission, and their control. Am. J. Trop. Med. Hyg. 85, 257–264 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Gilpin, M. E. & McClelland, G. A. H. Systems analysis of the yellow fever mosquito Aedes aegypti. Fortschr. Zool. 25, 355–388 (1979).CAS 
PubMed 

Google Scholar 
Juliano, S. A. Species introduction and replacement among mosquitoes: Interspecific resource competition or apparent competition?. Ecology 79, 255–268 (1998).Article 

Google Scholar 
Lord, C. C. Density dependence in larval Aedes albopictus (Diptera: Culicidae). J. Med. Entomol. 35, 825–829 (1998).CAS 
PubMed 
Article 

Google Scholar 
Agnew, P., Hide, M., Sidobre, C. & Michalakis, Y. A minimalist approach to the effects of density-dependent competition on insect life-history traits. Ecol. Entomol. 27, 396–402 (2002).Article 

Google Scholar 
Walsh, R. K., Facchinelli, L., Ramsey, J. M., Bond, J. G. & Gould, F. Assessing the impact of density dependence in field populations of Aedes aegypti. J. Vect. Ecol. 36, 300–307 (2011).CAS 
Article 

Google Scholar 
Walsh, R. K., Bradley, C., Apperson, C. S. & Gould, F. An experimental field study of delayed density dependence in natural populations of Aedes albopictus. PLoS ONE 7, e35959 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Walsh, R. K. et al. Regulation of Aedes aegypti population dynamics in field systems: Quantifying direct and delayed density dependence. Am. J. Trop. Med. Hyg. 89, 68–77 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Livdahl, T. P. & Sugihara, G. Non-linear interactions of populations and the importance of estimating per capita rates of change. J. Anim. Ecol. 53, 573–580 (1984).Article 

Google Scholar 
Getz, W. M. A hypothesis regarding the abruptness of density dependence and the growth rate of populations. Ecology 77, 2014–2026 (1996).Article 

Google Scholar 
Tenan, S., Tavecchia, G., Oro, D. & Pradel, R. Assessing the effect of density on population growth when modeling individual encounter data. Ecology 100, e02595 (2019).PubMed 
Article 

Google Scholar 
Arditi, R., Bersier, L. & Rohr, R. P. The perfect mixing paradox and the logistic equation: Verhulst vs. Lotka. Ecosphere 7, e01599 (2016).Article 

Google Scholar 
Cortés, E. Perspectives on the intrinsic rate of population growth. Meth. Ecol. Evol. 7, 1136–1145 (2016).Article 

Google Scholar 
Smith, F. E. Population dynamics in Daphnia magna and a new model for population growth. Ecology 4, 651–663 (1963).Article 

Google Scholar 
Ayala, F. J., Gilpin, M. E. & Ehrenfeld, J. G. Competition between species: Theoretical models and experimental tests. Theor. Pop. Biol. 4, 331–356 (1973).MathSciNet 
CAS 
Article 

Google Scholar 
Borlestean, A., Frost, P. C. & Murray, D. L. A mechanistic analysis of density dependence in algal population dynamics. Front. Ecol. Evol. 3, 37 (2015).Article 

Google Scholar 
Clark, F., Brook, B. W., Delean, S., Akçakaya, H. R. & Bradshaw, C. J. A. The theta-logistic is unreliable for modelling most census data. Methods Ecol. Evol. 1, 253–262 (2010).Article 

Google Scholar 
Chmielewski, M. W., Khatchikian, C. & Livdahl, T. Estimating the per capita rate of population change: How well do life-history surrogates perform?. Ann. Entomol. Soc. Am. 103, 734–741 (2010).Article 

Google Scholar 
Neale, J. T. & Juliano, S. A. Finding the sweet spot: What levels of larval mortality lead to compensation or overcompensation in adult production?. Ecosphere. 10, e02855 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Armistead, J. S., Arias, J. R., Nishimura, N. & Lounibos, L. P. Interspecific larval competition between Aedes albopictus and Aedes japonicus (Diptera: Culicidae) in northern Virginia. J. Med. Entomol. 45, 629–637 (2008).CAS 
PubMed 
Article 

Google Scholar 
Kaplan, L., Kendell, D., Robertson, D., Livdahl, T. & Khatchikian, C. Aedes aegypti and Aedes albopictus in Bermuda: Extinction, invasion, invasion and extinction. Bio. Invasions. 12, 3277–3288 (2010).Article 

Google Scholar 
Juliano, S. A. Coexistence, exclusion, or neutrality? A meta-analysis of competition between Aedes albopictus and resident mosquitoes. Isr. J. Ecol. Evol. 56, 325–351 (2010).PubMed 
Article 

Google Scholar 
Murrell, E. G. & Juliano, S. A. Competitive abilities in experimental microcosms are accurately predicted by a demographic index for R*. PLoS ONE 7, e43458 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Leisnham, P. T. & Juliano, S. A. Interpopulation differences in competitive effect and response of the mosquito Aedes aegypti and resistance to invasion of a superior competitor. Oecologia 164, 221–230 (2010).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Leisnham, P. T., Lounibos, L. P., O’Meara, G. F. & Juliano, S. A. Interpopulation divergence in competitive interactions of the mosquito Aedes albopictus. Ecology 90, 2405–2413 (2009).CAS 
PubMed 
Article 

Google Scholar 
Evans, M. V., Drake, J. M., Jones, L. & Murdock, C. C. Assessing temperature-dependent competition between two invasive mosquito species. Ecol. Appl. 31, e02334 (2021).PubMed 

Google Scholar 
Léonard, P. M. & Juliano, S. A. Effects of leaf litter and density on fitness and population performance of the hole mosquito Aedes triseriatus. Ecol. Entomol. 20, 125–136 (1995).Article 

Google Scholar 
Chandrasegaran, K. & Juliano, S. A. How do trait-mediated non-lethal effects of predation affect population-level performance of mosquitoes?. Front. Ecol. Evol. 7, 25 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Yee, D. A., Kaufman, M. G. & Juliano, S. A. The significance of ratios of detritus types and microorganism productivity to competitive interactions between aquatic insect detritivores. J. Anim. Ecol. 76, 1105–1115 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
Fader, J. E. & Juliano, S. A. An empirical test of the aggregation model of coexistence and consequences for competing container-dwelling mosquitoes. Ecology 94, 478–488 (2013).PubMed 
Article 

Google Scholar 
Murrell, E. G., Damal, K., Lounibos, L. P. & Juliano, S. A. Distributions of competing container mosquitoes depend on detritus types, nutrient ratios, and food availability. Ann. Entomol. Soc. Am. 104, 688–698 (2011).PubMed 
Article 

Google Scholar 
Tjørve, K. M. C. & Tjørve, E. The use of Gompertz models in growth analyses, and new Gompertz-model approach: An addition to the Unified-Richards family. PLoS ONE 12, e0178691 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Motulsky, H. & Christopoulos, A. Fitting Models to Biological Data using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting (Oxford University Press, 2004).MATH 

Google Scholar 
Osenberg, C. W. et al. Rethinking ecological inference: density dependence in reef fishes. Ecol. Lett. 5, 715–721 (2002).Article 

Google Scholar 
Schmitt, R. J., Holbrook, S. J. & Osenberg, C. W. Quantifying the effects of multiple processes on local abundance: A cohort approach for open populations. Ecol. Lett. 2, 294–303 (1999).CAS 
PubMed 
Article 

Google Scholar 
Fish, D. An analysis of adult size variation within natural mosquito population. In Ecology of Mosquitoes: Proceedings of a Workshop (eds Lounibos, L. P. et al.) 419–429 (Medical Entomology Laboratory, 1985).
Google Scholar 
Schneider, J. R., Chadee, D. D., Mori, A., Romero-Severson, J. & Severson, D. W. Heritability and adaptive phenotypic plasticity of adult body size in the mosquito Aedes aegypti with implications for dengue vector competence. Infect. Genet. Evol. 11, 11–16 (2011).PubMed 
Article 

Google Scholar 
Wormington, J. D. & Juliano, S. A. Sexually dimorphic body size and development time plasticity in Aedes mosquitoes (Diptera: Culicidae). Evol. Ecol. Res. 16, 1–12 (2014).
Google Scholar 
Steinwascher, K. Competition and growth among Aedes aegypti larvae: Effects of distributing food inputs over time. PLoS ONE 15, e0234676 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Barrera, R. Competition and resistance to starvation in larvae of container-inhabiting Aedes mosquitoes. Ecol. Entomol. 21, 117–127 (1996).Article 

Google Scholar 
Servanty, S. et al. Assessing whether mortality is additive using marked animals: A Bayesian state-space modeling approach. Ecology 91, 1916–1923 (2010).PubMed 
Article 

Google Scholar 
Wolfe, M. L. et al. Is anthropogenic cougar mortality compensated by changes in natural mortality in Utah? Insights from long-term studies. Biol. Conserv. 182, 187–196 (2015).Article 

Google Scholar 
Kogan, M. Integrated pest management: Historical perspectives and contemporary developments. Ann. Rev. Entomol. 43, 243–270 (1998).CAS 
Article 

Google Scholar 
Lounibos, L. P. Invasions by insect vectors of human diseases. Ann. Rev. Entomol. 47, 233–266 (2002).CAS 
Article 

Google Scholar 
Juliano, S. A. & Lounibos, L. P. Ecology of invasive mosquitoes: Effects on resident species and on human health. Ecol. Lett. 8, 558–574 (2005).PubMed 
PubMed Central 
Article 

Google Scholar  More

Benali, A. et al. Glob. Ecol. Biogeogr. 26, 799–811 (2017).Article 

Google Scholar 
Parker, C. H., Keefe, E. R., Herzog, N. M., O’Connell, J. F. & Hawkes, K. Evol. Anthropol. 25, 54–63 (2016).Article 

Google Scholar 
MacDonald, K., Scherjon, F., van Veen, E., Vaesen, K. & Roebroeks, W. Proc. Natl Acad. Sci. USA 118, e2101108118 (2021).CAS 
Article 

Google Scholar 
Ellis, E. C. et al. Proc. Natl Acad. Sci. USA 118, e2023483118 (2021).CAS 
Article 

Google Scholar 
Kelly, L. T. et al. Science 370, eabb0355 (2020).CAS 
Article 

Google Scholar 
Roos, C. I., Zedeño, M. N., Hollenback, K. L. & Erlick, M. M. H. Proc. Natl Acad. Sci. USA 115, 8143–8148 (2018).CAS 
Article 

Google Scholar 
Bliege Bird, R. & Bird, D. W. Am. J. Hum. Biol. 33, 4 (2021).Article 

Google Scholar 
Gragson, T. L., Coughlan, M. R. & Leigh, D. S. Sustainability 12, 3882 (2020).Article 

Google Scholar 
Long, J. W. et al. For. Ecol. Manage. 500, 119597 (2021).Article 

Google Scholar 
Adlam, C. et al. Soc. Nat. Resour. https://doi.org/10.1080/08941920.2021.2006385 (2021).Article 

Google Scholar 
Long, J. W. et al. Ecopsychology 12, 71–82 (2020).Article 

Google Scholar 
Sullivan, A. P. III & Olszewski, D. I. (eds) Archaeological Variability and Interpretation in Global Perspective (Univ. Press Colorado, 2016).Carter, V. A. et al. Commun. Earth. Environ. 2, 72 (2021).Article 

Google Scholar 
Roos, C. I. et al. Proc. Natl Acad. Sci. USA 118, e2018733118 (2021).CAS 
Article 

Google Scholar 
Maezumi, S. Y. et al. Nat. Plants 4, 540–547 (2018).Article 

Google Scholar 
Hoffman, K. M. et al. Proc. Natl Acad. Sci. USA 118, e2105073118 (2021).CAS 
Article 

Google Scholar 
Sullivan, A. P. III & Mink, P. B. Am. Antiq. 83, 619–638 (2018).Article 

Google Scholar 
Klimaszewski-Patterson, A. & Mensing, S. Landsc. Ecol. 35, 2659–2678 (2020).Article 

Google Scholar 
Bliege Bird, R. et al. Biol. Conserv. 219, 110–118 (2018).Article 

Google Scholar 
McCaffrey, S. et al. Int. J. Wildland Fire 22, 15 (2013).CAS 
Article 

Google Scholar 
Anderson, M. K. Tending the Wild (Univ. California Press, 2005)Marks-Block, T. et al. Fire Ecol. 17, 6 (2021).Article 

Google Scholar 
Liebmann, M. J. et al. Proc. Natl Acad. Sci. USA 113, E696–E704 (2016).CAS 
Article 

Google Scholar 
Coughlan, M. R. J. Ethnobiol. 33, 86–104 (2013).Article 

Google Scholar 
Klimaszewski-Patterson, A. & Mensing, S. A. Anthropocene 15, 37–48 (2016).Article 

Google Scholar 
Derr, K. M. Can. J. Arch. 38, 250–279 (2014).
Google Scholar 
Snitker, G. J. Archaeol. Sci. 95, 1–15 (2018).Article 

Google Scholar 
Coughlan, M. R. For. Ecol. Manage. 312, 55–66 (2014).Article 

Google Scholar 
Le Couédic, M. et al. Rapport de Prospection et Sondages, Larrau, Pyrénées-Atlantiques. Campagne 2014 (ITEM, EA 3002, Université de Pau et des Pays de l’Adour, 2014).Leigh, D. S. et al. Quat. Int. 402, 61–71 (2016).Article 

Google Scholar  More

Specifications of the study areaUrmia Lake, as the largest inland lake of Iran, is a national park and one of the largest Ramsar sites of Iran (Ramsar, 1971). The lake is formed in a natural depression within the catchment area in the northwest of Iran. The basin of the lake covers an area of 52,000 km2 and its area is about 5,700 km249. In addition, its maximum length and width are 140 and 50 km, respectively. Further, the lake catchment is a closed inland basin in which all rainwater runoff flows to the central saline lake, and evaporation from the surface of the lake is the only way out. More importantly, it is the largest saltwater lake in Iran and the second largest saltwater lake in the world.The current surface flow system to Urmia Lake consists of 10 main rivers with permanent flow potential, including Zola, Nazlu, Rozeh, Shahrchai, Baranduz, Gadar, Mahabad, Simineh, Zarrineh, and Aji. In terms of the water supply potential of Urmia Lake, Zarrineh, Simineh, Aji, and Nazlu rivers with a flow allocation of 41, 11, 10, and 6% have a key role, respectively.The rivers of this basin are originated from mountains and pass through the heights and enter the agricultural plains. The main usage in plains are for agriculture which cause the changes in natural rivers flow regime. On the other hand, the natural flow regime of the rivers should be considered as the basis for e-flow calculation. So, in the current study the obtained data from the stations situated in the upstream of the rivers and the stations before the agricultural plains are utilized to alleviate the effects of agricultural use on natural flow regime of the rivers. Also, to eliminate the effects of dam rule curve on river flow regime, stations situated in the upstream of the dams are considered as the main scale in the upstream of the dammed rivers like Zarrineh, Mahabad and Zola. Despite all the efforts made to select stations with the least human impact, the two stations related to Aji and Shahar Rivers have been affected by the structures built above them. Therefore, in order to eliminate the effects of the constructed structures at the upstream of the stations, flow naturalization methods were used only for the two stations of Venyar of Aji River and the Band Urmia station of Shahar River. There are several ways to naturalize hydrometric station data. Terrier et al.51 by studying flow naturalization methods in various researches were able to provide a comprehensive study of naturalization methods and selection criteria for each of these methods. According to their studies, the first and the most important prerequisite for stream naturalization is to identify the factors affecting the river and the quality of data in the region, which play a major role in choosing the flow naturalization method. Two factors play a major role in affecting river hydrology. The first factor is the construction of hydraulic structures along the path of rivers and the second factor is the change of land use that has occurred in the rivers basin. In the current study, the purpose of flow naturalization is to eliminate the effects of large dams built on the inlet rivers of the lake, which can affect the hydrology of the river flow. It should be noted that it is not possible to eliminate the effects of land use change due to the gradual nature of the changes, the inability to determine the exact amount and time of the changes and the lack of required data as well. Therefore, in this study, the effects of land use change at the upstream of the stations have been neglected. The most important reason that the Aji River needs to naturalize is the existence of several small dams upstream of Venyar station. To eliminate the effects of dams and flow naturalization at the upstream of this station, the spatial interpolation method introduced by Hughes and Smakhtin52 was used. In this method, Sahzab hydrometric station located at the upstream of the river was used as a base station to naturalize the flow. The next station which needs to be naturalized the flow is the Band Urmia station Shahar River. The main problem for this river has been the construction of a dam upstream of Band Urmia river station since 2004. The drainage area ratio method introduced by Hirsch53 was used to eliminate the effect of this dam on the station data. This method has been used by various researchers to naturalize river flow54,55,56 which is based on the upstream drainage area of the stations. In this method the ratio of the drainage area of the two stations is used to naturalize the flow in the affected station. For this purpose, the data of Bardehsoor station located upstream of the dam was used to naturalize the data of the Band Urmia station. So, anthropogenic effects are at the minimum level in calculations. The utilized stations to calculate the e-flow as upstream stations are illustrated in Fig. 1.Figure 1An overview of the Urmia Lake basin, the rivers, and selected gauging stations. Figure 1 was generated by ArcGIS v10.2 software50 (Environmental Systems Research Institute, Inc., USA, URL http://www.esri.com/).Full size imageAppropriate criteria for allocating the EWR of the Urmia LakeDue to the high salinity of Urmia Lake, only a small number of invertebrates make up the living organisms of this huge water body. Saltwater shrimp or Artemia is a type of aquatic crustacean which can be found in saltwater lakes or coastal lagoons worldwide. Artemia can tolerate salinity less than 10 gl−1 up to 340 gl−1 and adapt to environmental conditions. Artemia Urmiana, the most well-known species of the Urmia Lake, is considered as the main food of migratory birds that spend part of their wintering period on the lake and surrounding wetlands. The presence of this species in the Urmia Lake was first reported by Gunter (1899), and many researchers have confirmed the existence of this bisexual creature in this lake57,58,59,60,61.One of the key factors in estimating the EWR of Urmia Lake is to create an appropriate environmental condition for its dominant species. Abbaspour and Nazari Doost39 identified the EWR of the Urmia Lake by considering the living conditions of Artemia as its dominant species. In this study, Artemia Urmaina was selected as a biological indicator, along with NaCl and elevation above mean sea level (AMSL) as the indicators of water quality and quantity, respectively. The combination of these three indicators forms the ecological basis of Urmia Lake. Therefore, salinity is considered to be equal to 240 gl−1 as the tolerable limit of the biological index. Using long-term statistics in the Urmia Lake and the relationship between quantitative and qualitative water indicators, the water level of 1274.1 m (AMSL) was chosen as the ecological level of the lake so that the balance of these three indicators remained within the allowable range. The study indicated that the calculated environmental water demand of Urmia Lake was equal to 3084 Mm3 per year provided by main rivers entering the lake. Therefore, the proposed new methods should be able to deliver this volume of water to the lake and simultaneously feed the EFR of the river. To supply this water volume, government has programs in order to mitigate the water consumption especially in agriculture. The most important program is 40 percent reduction in agricultural water consumption which is accompanied with the increase of efficiency. Also the government pursues urban wastewater treatment to retrieve some of domestic water to the lake. The mentioned programs are time consuming, however, the new methods presented in this study can be useful for managers in determining the allocation patterns and consumption management. Ordinary method of flow duration curve shifting (FDCS) in estimating e-flowSince the early 1990s, various methods have been developed based on the hydrological indices62 in order to determine the e-flow by taking into account the flow variability and adaptation to the ecological conditions of rivers. One of the intended diagrams in the study of the hydrological characteristics is the flow duration curve (FDC), which is used to assess the fluctuations and variability of water flow from an environmental point of view. Given the importance of the presence of flood currents in the restoration of the river and wetland ecosystems63,64, the FDC is one of the most practical methods to show the full range of river discharge characteristics from water shortage to flood events. This diagram also demonstrates the relationship between the amount and frequency of the flow which can be prepared for daily, annual, and monthly time intervals65. The FDCS is a method in which FDC is employed to estimate the river flow. This method was introduced by Smakhtin and Anputhas66 to evaluate the e-flow in the river system. The method, which is called FDCS, provides a hydrological regime to protect the river in the desired ecological conditions.In the previous research, most of the rivers in the Urmia Lake basin have been compatible with FDCS, and due to the lack of biological data regarding these rivers, it is always one of the top priorities among the methods of estimating the e-flow in rivers leading to the Urmia Lake67,68. It is noteworthy that the characteristics of calculation steps of the ordinary method are provided as follows.This method consists of four main steps:

1.

Assessing the existing hydrological conditions (preparing the FDC for a natural river flow regime),

2.

Selecting the appropriate environmental management class;

3.

Acquiring the environmental FDC;

4.

Generating e-flow time series.

The first step is to prepare the FDC in the desired river range using monthly flow data. In this method, FDC for the natural river flow regime is prepared by 17 fixed percentage points of occurrence probabilities (0.01, 0.1, 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 99, 99.9, 99.99) where P1 = 99.99% and P17 = 0.01% represent the highest and lowest probability of occurrence, respectively. These points ensure that the entire flow range is adequately covered, as well as facilitating the continuation of the next steps.This method, which uses mean monthly flow (MMF) data, considers six environmental management classes (EMC) from A to F. The FDC of EFR (FDC-EFR) for each class in terms of EMC is determined based on the obtained natural river FDC by the MMF. The higher EMC needs more water to maintain the ecosystem. These classes are determined based on empirical relationships between the flow and ecological status of rivers, which currently have no specific criteria for identifying these limits. The selection of the appropriate class individually relies on the expert’s judgment of the river ecosystem condition.After obtaining the natural FDC, the next step is to calculate the FDC-EFR for each EMC using the lateral shifts of FDC to the left along the probabilistic axis. For EMC-A rivers, one lateral shift to the left is applied while two, three, and four lateral shifts are employed for EMC-B, EMC-C, and EMC-D rivers, respectively. It should be noted that the overall hydrological pattern of the flow will be maintained although the flow variation is lost for each shift.In the current study, global e-flow calculation (GEFC) v2.0 software69,70 has been utilized to compute the e-flow by the FDCS method. The long-term data (at least 20 years) of MMF are the required input data for this software.According to the research conducted on the rivers of the Urmia Lake basin, EMC-C is the minimum considered EMC for 10 main rivers of the lake, thus the EMC-C has been considered in this study, and all calculations for classes A, B, C have been performed accordingly.The description of new methods based on ordinary methodThe main purpose of presenting new methods is to combine the EWR of wetlands or lakes and the hydrological method of FDCS, which can be used to calculate the e-flow of rivers and meet the needs of lakes or wetlands in downstream. These methods relies on the FDCS while with the difference that the proposed method includes three fundamental changes compared to the original one.

1.

Applying monthly FDC (FDC for each month separately) instead of annual FDC,

2.

Employing daily flow data instead of MMF,

3.

Considering the downstream EWR in the amount of the lateral shift in the FDCS method.

The use of the structure of new methods lead to a dynamic process that is based on the selected EMC of the river, the amount of the natural flow, and the date of occurrence and can compute the amount of the e-flow of the river on each day of the year.River hydrology greatly varies depending on the type of the basin, the climate of the area, and the relationship between the basin and the river each exhibiting different behaviors during the months of the year. Accordingly, the proposed methods should provide sufficient comprehensiveness in estimating the e-flow by considering different flow characteristics. Due to the type and timing of precipitation in the Urmia Lake basin, the rivers are full of water from March to June and spend extremely less flows during the other times of the year. For example, Fig. 2 shows the distribution of the Nazlu River flows in the west of Urmia Lake throughout the year. According to the data, 74% of the AF crosses the river from March to June, and the highest and lowest river discharges are related to May with 29% and September and August with 2% of the AF, respectively.Figure 2Historical hydrograph at the Tapik Station, Nazlu River: (a) Daily and mean monthly distribution of flows and (b) Magnified hydrograph for a typical year (1993).Full size imageAccording to the flow distribution throughout the year, the annual FDC is an average FDC of each month of the year. However, the flow of a river during the months of the year represents significant changes. Therefore, the monthly FDC is higher than the annual FDC in the high-water months (e.g., May). Additionally, this curve is lower compared to the annual FDC in the low-water months (e.g., September). Accordingly, the use of monthly FDCs provides more details of changes in the hydrological parameters of the flow and can be a better indicator of the hydrological index of river flows.In the conventional FDCS method, the FDC is obtained using the MMF data of each station. The obtained curve represents the monthly average of river flow and does not illustrates the minimum, maximum and the effect of flow fluctuations in the estimation of e-flows (Fig. 2).In the new methods, all FDC diagrams were obtained by daily data. Both annual (EFR-Ann) and monthly (EFR-Mon) methods are separately utilized to compare the calculation of the e-flow and to choose the best method. The annual FDC is a probabilistic chart for the whole year and the monthly FDC includes 12 probability curves for each year. Due to the use of FDC in e-flow estimations, it has been attempted to perform all calculations from this diagram. Therefore, concepts related to the flow volume can be integrated with the FDCS method. Some of the applied concepts for this purpose are as follows.In the FDCS method, the FDC is defined based on 17 probabilistic percentage points. To calculate the mean AF (MAF) volume, the theorem of the mean value for a definite integral is employed in the FDC diagram. Accordingly, considering that FDC is continuous between the first and seventeenth probability points, the mean flow (Fm) is obtained from Eq. (1) as.
$$F_{m} = frac{1}{{P_{1} – P_{17} }}mathop smallint limits_{{P_{17} }}^{{p_{1} }} Fleft( p right)dp$$
(1)
Fm = Mean flow. P1, P17 = Points of FDC probability that P1 = 99.99 and P17 = 0.01.Given that the FDC consists of 17 probability points and the probability function ‘F(P)’ is unavailable for this curve as a mathematical equation, obtaining this equation for each flow curve increases the computational cost. Therefore, numerical integration methods can be used in this regard. The trapezoidal numerical solution method has been utilized for this purpose. By applying the trapezoidal method in solving Eq. (1), Eq. (2) is obtained, which is used to compute the mean flow of the FDC.$$F_{m} = frac{1}{{P_{1} – P_{17} }}mathop sum limits_{i = 1}^{17} frac{{left( {F_{i} + F_{i + 1} } right)}}{2}{*}left[ {P_{i} – P_{i + 1} } right]$$
(2)
Pi = 17 points of FDC probability that P1 = 99.99% and P17 = 0.01%. Fi = The amount of the river flow with the probability of the occurrence of Pi.To calculate the AF volume by monthly and annual FDCs, Eq. (3) can be applied for the AF volume in the EFR-Ann method, as well as employing Eqs. (4) and (5) for the monthly and AF volume in the EFR-Mon method, respectively.$${text{V}}_{{AF_{Ann} }} = frac{365*24*3600}{{P_{1} – P_{17} }}mathop sum limits_{i = 1}^{17} frac{{left( {F_{i} + F_{i + 1} } right)}}{2}{*}left[ {P_{i} – P_{i + 1} } right]$$
(3)
$${text{V}}_{Monthly } = frac{{D_{k} *24*3600}}{{P_{1} – P_{17} }}mathop sum limits_{i = 1}^{17} frac{{left( {F_{i} + F_{i + 1} } right)}}{2}{*}left[ {P_{i} – P_{i + 1} } right]$$
(4)
$${text{V}}_{{AF_{Mon} }} = mathop sum limits_{k = 1}^{12} left[ {{text{V}}_{Monthly } } right]_{k}$$
(5)

VAFAnn = AF volume using annual FDC. VMonthly = Monthly flow volume. VAFMon = AF volume using monthly FDC. Dk = Number of the days of the kth month. k = Number of each month.The required e-flow by wetlands and lakes must have two basic characteristics. The volume of EWR for maintaining their ecological level must be determined and provided by the studies of their ecosystems. In addition, fluctuations must be maintained in water levels in the lake due to hydrological conditions under the basins of the lake supplying rivers given the fact that maintaining the hydrological conditions of the river is one of the major goals of the FDCS method in estimating the e-flow of the river. On the other hand, the rehabilitation of the wetland or lake downstream of rivers requires a certain amount of water, and the new methods must be applied to combine these two goals. In this regard, the AF volume, which can be transferred to the lake (VL Mon or Ann) by these rivers, is calculated by taking into account the natural flow conditions of the rivers in the basin and without considering the consumptions,.$${text{V}}_{{L_{Ann} }} { } = mathop sum limits_{j = 1}^{{text{n}}} left[ {{text{V}}_{{AF_{Ann} }} } right]_{j}$$
(6)
$${text{V}}_{{L _{Mon} }} = mathop sum limits_{j = 1}^{{text{n}}} left[ {{text{V}}_{{AF_{Mon} }} } right]_{j}$$
(7)

n = Number of input rivers to the lake. VLAnn = AF volume, which can be transferred to the lake using annual FDC. VLMon = AF volume, which can be transferred to the lake using monthly FDC.The ratio of the EWR of the lake or wetland to the average annual volume of the basin should be determined at this stage.$$b = frac{{{text{V}}_{EWR} }}{{{text{V}}_{{L_{Ann} }} or {text{V}}_{{L _{Mon} }} }}$$
(8)
b = The ratio of the EWR of the lake or wetland to the average annual volume of the basin. VEWR = Volume of environmental water requirement of the lake or wetland.In the conventional FDCS method, which is determined using GEFC v2.0 software70 (It is then called the GEFC method), depending on the type of the river EMC, the allocation curve is obtained with one or more shifts of the FDC. Each EMC includes a certain ratio of the MAF volume of the river, and changing the flow EMC facilitates changing the flow volume. It is impossible to supply a specific and predetermined downstream water volume of the river. Therefore, in the new methods, a new process must be used to calculate the amount of the FDC shift in order to provide a certain volume of water in the shifting of the FDC. First, a new definition of the EMC was developed for the new methods. In this definition, instead of using a specific shift of the FDC, the range between the two classes was characterized as an EMC. For example, the region between the curve of EMC-A and the natural flow and the region between the EMC-A and EMC-B curves are defined as EMC-A and EMC-B areas, respectively. These regions can be defined for all EMCs (Fig. 3).Figure 3Comparison of the EFR allocated to each of the environmental management classes from this new approach (on the left) with the conventional FDCS methods (on the right).Full size imageBased on the new definition of the range of EMC, the FDC can be shifted as much as needed according to the volume of downstream EWR. The EWR can be defined as the annual percentage river flow respecting the shift of EMCs or a percentage between two specific classes. If the required flow volume is between two specific classes, Eq. (9) can be used to shift the FDC. In fact, with the new definition, any required probable shift can be applied to the FDC ِdiagram to reach a certain volume. In this case, new probable points are determined using Eq. (9), followed by performing the FDC shift similar to the FDCS method in the next step.$$P_{{i_{new} }} = P_{i} + a{*}left( {P_{i – 1} – P_{i} } right)quad i = t, ldots ,16$$
(9)
Pinew = New shifted probability point. Pi = 17 points of FDC probability that P1 = 99.99% and P17 = 0.01%. a = Coefficient of shift which defined between 0 and 1. t = Number of shifts performed on the FDC diagram numbered 1–6 for the areas of EMC A, B, C, D, E, F, respectively.The concept of numerical integration and Eqs. (9) and (3) were utilized to calculate the annual volume of different EMCs for each river, and Eqs. (10) and (12) were obtained for the new annual and monthly methods, respectively.$$begin{aligned} & {text{V}}_{{AF class_{t} Ann }} = frac{1}{{P_{1} – left[ {P_{17} + a*left( {P_{16} – P_{17} } right)} right]}} \ & quad quad quad quad quad *left[ {F_{1} *left[ {P_{1} – left[ {P_{t + 1} + a*left( {P_{t} – P_{t + 1} } right)} right]} right] + mathop sum limits_{{i = {text{t}} + 1}}^{16} frac{{left( {F_{i – t} + F_{i – t + 1} } right)}}{2}{*}left[ {P_{i} – P_{i + 1} + a{*}left( {P_{i – 1} – 2P_{i} + P_{i + 1} } right)} right]} right]*365*24*3600 \ end{aligned}$$
(10)
$$begin{aligned}&{text{V}}_{{ class_{t} Mon }} = frac{{D_{k} *24*3600}}{{P_{1} – left[ {P_{17} + a*left( {P_{16} – P_{17} } right)} right]}} \ & quad quad quad quad quad *left[ {F_{1} *left[ {P_{1} – left[ {P_{t + 1} + a*left( {P_{t} – P_{t + 1} } right)} right]} right] + mathop sum limits_{{i = {text{t}} + 1}}^{16} frac{{left( {F_{i – t} + F_{i – t + 1} } right)}}{2}{*}left[ {P_{i} – P_{i + 1} + a{*}left( {P_{i – 1} – 2P_{i} + P_{i + 1} } right)} right]} right] end{aligned}$$
(11)
$${text{V}}_{{AF class_{t} Mon}} = mathop sum limits_{K = 1}^{12} left[ {{text{V}}_{{ class_{t} Mon}} } right]_{k}$$
(12)

VAF classt Ann = AF volume for the related class of selected t for annual method. Vclasst Mon = Monthly flow volume for the related class of selected t for monthly method. VAF classt Mon = AF volume for the related class of selected t for monthly method.where t is the number of shifts performed on the FDC diagram numbered 1–6 for the areas of EMC A, B, C, D, E, F, respectively. To find the exact value of a in these equations, the scope of the EMC must be determined based on the required volume by downstream. Therefore, assuming a = 0 in these equations, the AF volume at the boundary of each class is obtained for both EFR-Mon (Eq. (10)) and EFR-Ann (Eq. (12)) methods. The nearest calculated annual volume is selected as the appropriate EMC which is smaller than the volume of downstream. Further, the corresponding t-class is used to solve the equations, representing the range of the selected EMC.At this stage, the value of the obtained ‘a’ from the FDC shift diagram equals the required volume of downstream. For this purpose, Eqs. (13) and (14) for the EFR-Ann and EFR-Mon methods are obtained from Eqs. (10) and (12), respectively.$$b{text{*V}}_{{AF_{Ann} }} = V_{{AF class_{t } Ann }}$$
(13)
$$b{text{*V}}_{{AF_{Mon} }} = V_{{AF class_{t} Mon }}$$
(14)

By solving Eqs. (13) and (14), the obtained value of a represents the annual and monthly methods, and the obtained shifted FDC stands for the required annual volume downstream.After determining the appropriate FDC, it is used to calculate the daily e-flow needs of the river using the spatial interpolation algorithm52, which is also employed in the FDCS method. To this end, the probability of the river flow occurrence from the annual or monthly FDCs (according to the selected method) is determined and then the required river flow in the specified probability of occurrence is obtained using the e-flow curve.The range of variability approach (RVA)71,72 is a complex method based on the use of e-flow for achieving the goals of river ecosystem management. This method is applied to compare the methods and select the best one based on the least hydrological change compared to the natural flow of the river. Furthermore, it is based on the importance of the hydrological feature impact of the river on the life, biodiversity of native aquatic species, and the natural ecosystem of the river and aims to provide complete statistical characteristics of the flow regime.In the RVA method, the indicators of hydrologic alteration (IHA) parameters related to the natural river flow are considered as a basis, and changes in the IHA parameters of different EMCs are evaluated accordingly. Richter et al.72 suggested that the distribution of the annual values of IHA parameters for maintaining river environmental conditions must be kept as close as possible to natural flow condition parameters. In several studies, this method was used to investigate changes in the hydrological parameters of a river over time37.Moreover, the total data related to the natural flow of the river for each IHA parameter are classified into three categories in the RVA method. In this study, this classification is based on Default software, and the 17% distance from the median is introduced as the boundary of the classes. By this definition, three classes of the same size are created, in which the middle category is between 34 and 67, and the lower and higher ranges are called the lowest and highest categories, respectively.Using the current change factor obtained from Eq. (15), the RVA method can quantify the change amount in the values of the 33 IHA parameters compared to the natural flow conditions.$$HA = left( {O_{f} – E_{f} } right)/E_{f}$$
(15)
HA = Hydrological alteration index. Of = Number of flows occurring within a certain category of the IHA parameter under changed flow conditions. Ef = Number of flows occurring in the same category specified by the parameter under natural flow conditions.In this case, for each IHA parameter, three HA factors are obtained, which can be separately examined for river flows in these three categories. In the analysis of parameters, the positive HA means that the number of occurrences of the phenomenon has increased in a certain IHA category compared to the natural conditions of the river flow. Negative values imply a decrease in the number of occurrences of the same phenomenon. To compare the number of changes in IHA parameters, the HA factor of the RVA method and IHA software (Version 7.1)73 was employed to allocate e-flows in different methods. The obtained results using RVA method calculates and represents HA of each 33 parameters. However, making decision to choose the best method, all parameters need to be assessed and presented as a total index. Due to calculate total HA index based on studies of Xue et al.74 Eq. (16) can be used.$$HA_{o} = sqrt {frac{{mathop sum nolimits_{i = 1}^{33} HA_{i}^{2} }}{33}} *100$$
(16)
HAo = Total hydrological alteration index. HAi = Hydrological alteration of each of 33 parameters.Determination of EFR for different EMCs for all methodsInitially, the MMF for each available statistical month was obtained by daily data from stations located in the upstream of the basin rivers of Urmia Lake (Fig. 1). The FDC for the natural flow and various EMCs were obtained using MMF values and GEFC software. Next, to perform the calculations in the EFR-Ann method, the FDC of a natural flow and different EMCs during the year were plotted by daily data. Finally, for the EFR-Mon method, the daily data of each month of the year were examined and the FDC of the natural flow and EMCs were separately plotted for each month.Based on the presented method in this research, Fig. 4 illustrates a step-by-step diagram for determining the e-flows of rivers in the Urmia Lake basin.Figure 4Step-by-step flowchart for determining the environmental flows of rivers in the Urmia Lake basin.Full size image More

Swank, W. T., Waide, J. B., Crossley, D. A. & Todd, R. L. Insect defoliation enhances nitrate export from forest ecosystems. Oecologia 51, 297–299 (1981).CAS 
PubMed 
Article 

Google Scholar 
Hunter, M. D. Insect population dynamics meets ecosystem ecology: effects of herbivory on soil nutrient dynamics. Agric. For. Entomol. 3, 77–84 (2001).Article 

Google Scholar 
Metcalfe, D. B. et al. Herbivory makes major contributions to ecosystem carbon and nutrient cycling in tropical forests. Ecol. Lett. 17, 324–332 (2014).PubMed 
Article 

Google Scholar 
Metcalfe, D. B., Crutsinger, G. M., Kumordzi, B. B. & Wardle, D. A. Nutrient fluxes from insect herbivory increase during ecosystem retrogression in boreal forest. Ecology 97, 124–132 (2016).PubMed 
Article 

Google Scholar 
Lovett, G. M. et al. Insect defoliation and nitrogen cycling in forests. Bioscience 52, 335 (2002).Article 

Google Scholar 
Frost, C. J. & Hunter, M. D. Recycling of nitrogen in herbivore feces: Plant recovery, herbivore assimilation, soil retention, and leaching losses. Oecologia 151, 42–53 (2007).PubMed 
Article 

Google Scholar 
Le Mellec, A. & Michalzik, B. Impact of a pine lappet (Dendrolimus pini) mass outbreak on C and N fluxes to the forest floor and soil microbial properties in a Scots pine forest in Germany. Can. J. Res. 38, 1829–1841 (2008).Article 
CAS 

Google Scholar 
Grüning, M. M., Simon, J., Rennenberg, H. & L-M-Arnold, A. Defoliating insect mass outbreak affects soil N fluxes and tree N nutrition in scots pine forests. Front. Plant Sci. 8, 954 (2017).Mikola, J., Yeates, G. W., Barker, G. M., Wardle, D. A. & Bonner, K. I. Effects of defoliation intensity on soil food-web properties in an experimental grassland community. Oikos 92, 333–343 (2001).Article 

Google Scholar 
Chapman, S. K., Hart, S. C., Cobb, N. S., Whitham, T. G. & Koch, G. W. Insect herbivory increases litter quality and decomposition: an extension of the acceleration hypothesis. Ecology 84, 2867–2876 (2003).Article 

Google Scholar 
Pitman, R. M., Vanguelova, E. I. & Benham, S. E. The effects of phytophagous insects on water and soil nutrient concentrations and fluxes through forest stands of the Level II monitoring network in the UK. Sci. Total Environ. 409, 169–181 (2010).CAS 
PubMed 
Article 

Google Scholar 
Kaukonen, M. et al. Moth herbivory enhances resource turnover in subarctic mountain birch forests? Ecology 94, 267–272 (2013).PubMed 
Article 

Google Scholar 
Weintraub, M. Biological phosphorus cycling in arctic and alpine soils. In Phosphorus in Action (eds. Bünemann E., Oberson, A. & Frossard, E.) Vol. 26, p. 295–316 (Springer, 2011).Högberg, P., Näsholm, T., Franklin, O. & Högberg, M. N. Tamm review: on the nature of the nitrogen limitation to plant growth in fennoscandian boreal forests. Ecol. Manag. 403, 161–185 (2017).Article 

Google Scholar 
Maynard, D. G. et al. How do natural disturbances and human activities affect soils and tree nutrition and growth in the Canadian boreal forest? Environ. Rev. 22, 161–178 (2014).CAS 
Article 

Google Scholar 
Wan, S., Hui, D. & Luo, Y. Fire effects on nitrogen pools and dynamics in terrestrial ecosystems: a Meta-Analysis. Ecol. Appl. 11, 1349–1365 (2001).Article 

Google Scholar 
Hart, S. A. & Chen, H. Y. H. Understory vegetation dynamics of North American boreal forests. CRC Crit. Rev. Plant Sci. 25, 381–397 (2006).Article 

Google Scholar 
Martineau, C., Beguin, J., Séguin, A. & Paré, D. Cumulative effects of disturbances on soil nutrients: predominance of antagonistic short-term responses to the salvage logging of insect-killed stands. Ecosystems 23, 812–827 (2020).CAS 
Article 

Google Scholar 
Coulombe, D., Sirois, L. & Paré, D. Effect of harvest gap formation and thinning on soil nitrogen cycling at the boreal–temperate interface. Can. J. Res. 47, 308–318 (2017).CAS 
Article 

Google Scholar 
Grenon, F., Bradley, R. L. & Titus, B. D. Temperature sensitivity of mineral N transformation rates, and heterotrophic nitrification: Possible factors controlling the post-disturbance mineral N flush in forest floors. Soil Biol. Biochem. 36, 1465–1474 (2004).CAS 
Article 

Google Scholar 
Guntiñas, M. E., Leirós, M. C., Trasar-Cepeda, C. & Gil-Sotres, F. Effects of moisture and temperature on net soil nitrogen mineralization: A laboratory study. Eur. J. Soil Biol. 48, 73–80 (2012).Article 
CAS 

Google Scholar 
Houle, D., Duchesne, L. & Boutin, R. Effects of a spruce budworm outbreak on element export below the rooting zone: a case study for a balsam fir forest. Ann. Sci. 66, 707–707 (2009).Article 
CAS 

Google Scholar 
Griffin, J. M. & Turner, M. G. Changes to the N cycle following bark beetle outbreaks in two contrasting conifer forest types. Oecologia 170, 551–565 (2012).PubMed 
Article 

Google Scholar 
Orwig, D. A., Cobb, R. C., D’Amato, A. W., Kizlinski, M. L. & Foster, D. R. Multi-year ecosystem response to hemlock woolly adelgid infestation in southern New England forests. Can. J. Res. 38, 834–843 (2008).Article 

Google Scholar 
McMillin, J. D. & Wagner, M. R. Chronic defoliation impacts pine sawfly (Hymenoptera: Diprionidae) performance and host plant quality. Oikos 79, 357 (1997).Article 

Google Scholar 
Pureswaran, D. S., Johns, R., Heard, S. B. & Quiring, D. Paradigms in eastern spruce budworm (Lepidoptera: Tortricidae) population ecology: a century of debate. Environ. Entomol. 45, 1333–1342 (2016).PubMed 
Article 

Google Scholar 
Vidal, M. C. & Murphy, S. M. Bottom-up vs. top-down effects on terrestrial insect herbivores: a meta-analysis. Ecol. Lett. 21, 138–150 (2018).PubMed 
Article 

Google Scholar 
White, T. C. R. The abundance of invertebrate herbivores in relation to the availability of nitrogen in stressed food plants. Oecologia 63, 90–105 (1984).CAS 
PubMed 
Article 

Google Scholar 
White, T. C. R. An alternative hypothesis explains outbreaks of conifer-feeding budworms of the genus Choristoneura (Lepidoptera: Tortricidae) in Canada. J. Appl. Entomol. 142, 725–730 (2018).Article 

Google Scholar 
Bouchard, M., Régnière, J. & Therrien, P. Bottom-up factors contribute to large-scale synchrony in spruce budworm populations1. Can. J. Res. 48, 277–284 (2018).CAS 
Article 

Google Scholar 
I-M-Arnold, A. et al. Forest defoliator pests alter carbon and nitrogen cycles. R. Soc. Open Sci. 3, 1–7 (2016).
Google Scholar 
Pureswaran, D. S. et al. Climate-induced changes in host tree–insect phenology may drive ecological state-shift in boreal forests. Ecology 96, 1480–1491 (2015).Article 

Google Scholar 
MFFP (Ministère des Forêts de la Faune et des Parcs). Aires infestées par la tordeuse des bourgeons de l’épinette au Québec en 2019 – Version 1.1. (2019).Forkner, R. E. & Hunter, M. D. What goes up must come down? Nutrient addition and predation pressure on oak herbivores. Ecology 81, 1588–1600 (2000).Article 

Google Scholar 
Schlesinger, W. H. Some thoughts on the biogeochemical cycling of potassium in terrestrial ecosystems. Biogeochemistry 154, 427–432 (2021).Article 

Google Scholar 
Kristensen, J. A., Metcalfe, D. B. & Rousk, J. The biogeochemical consequences of litter transformation by insect herbivory in the Subarctic: a microcosm simulation experiment. Biogeochemistry 138, 323–336 (2018).CAS 
Article 

Google Scholar 
Kagata, H. & Ohgushi, T. Ecosystem consequences of selective feeding of an insect herbivore: Palatability-decomposability relationship revisited. Ecol. Entomol. 36, 768–775 (2011).Article 

Google Scholar 
Kagata, H. & Ohgushi, T. Positive and negative impacts of insect frass quality on soil nitrogen availability and plant growth. Popul. Ecol. 54, 75–82 (2012).Article 

Google Scholar 
Weihrauch, D. & O’Donnell, M. J. Mechanisms of nitrogen excretion in insects. Curr. Opin. Insect Sci. 47, 25–30 (2021).PubMed 
Article 

Google Scholar 
Choudhury, D. Herbivore induced changes in leaf-litter resource quality: a neglected aspect of herbivory in ecosystem nutrient dynamics. Oikos 51, 389–393 (1988).Article 

Google Scholar 
Régnière, J. & You, M. A simulation model of spruce budworm (Lepidoptera: Tortricidae) feeding on balsam fir and white spruce. Ecol. Modell. 54, 277–297 (1991).Article 

Google Scholar 
Balducci, L. et al. The paradox of defoliation: declining tree water status with increasing soil water content. Agric. Meteorol. 290, 108025 (2020).Article 

Google Scholar 
Conant, R. T. et al. Temperature and soil organic matter decomposition rates – synthesis of current knowledge and a way forward. Glob. Change Biol. 17, 3392–3404 (2011).Article 

Google Scholar 
Doran, O., MacLean, D. A. & Kershaw, J. A. Needle longevity of balsam fir is increased by defoliation by spruce budworm. Trees – Struct. Funct. 31, 1933–1944 (2017).Article 

Google Scholar 
Wu, Y., Maclean, D. A., Hennigar, C. & Taylor, A. R. Interactions among defoliation level, species, and soil richness determine foliage production during and after simulated spruce budworm attack. Can. J. Res. 50, 565–580 (2020).Article 

Google Scholar 
Fierravanti, A., Rossi, S., Kneeshaw, D., De Grandpré, L. & Deslauriers, A. Low non-structural carbon accumulation in spring reduces growth and increases mortality in conifers defoliated by spruce budworm. Front. Glob. Change 2, 1–13 (2019).Article 

Google Scholar 
Hennigar, C. R., MacLean, D. A., Quiring, D. T. & Kershaw, J. A. Differences in spruce budworm defoliation among balsam fir and white, red, and black spruce. For. Sci. 54, 158–166 (2008).
Google Scholar 
Bognounou, F., De Grandpré, L., Pureswaran, D. S. & Kneeshaw, D. Temporal variation in plant neighborhood effects on the defoliation of primary and secondary hosts by an insect pest. Ecosphere 8, e01759 (2017).Article 

Google Scholar 
Li, F. et al. Responses of tree and insect herbivores to elevated nitrogen inputs: a meta-analysis. Acta Oecologica 77, 160–167 (2016).Article 

Google Scholar 
Shaw, G. G., Little, C. H. A. & Durzan, D. J. Effect of fertilization of balsam fir trees on spruce budworm nutrition and development. Can. J. Res. 8, 364–374 (1978).CAS 
Article 

Google Scholar 
Mattson, W. J., Haack, R. A., Lawrence, R. K. & Slocum, S. S. Considering the nutritional ecology of the spruce budworm in its management. Ecol. Manag. 39, 183–210 (1991).Article 

Google Scholar 
Metcalfe, D. B. et al. Ecological stoichiometry and nutrient partitioning in two insect herbivores responsible for large-scale forest disturbance in the Fennoscandian subarctic. Ecol. Entomol. 44, 118–128 (2019).Article 

Google Scholar 
Kaitaniemi, P., Ruohomäki, K., Ossipov, V., Haukioja, E. & Pihlaja, K. Delayed induced changes in the biochemical composition of host plant leaves during an insect outbreak. Oecologia 116, 182–190 (1998).PubMed 
Article 

Google Scholar 
Fuentealba, A. & Bauce, É. Interspecific variation in resistance of two host tree species to spruce budworm. Acta Oecol. 70, 10–20 (2016).Article 

Google Scholar 
Nealis, V. G. & Régnière, J. Insect – host relationships influencing disturbance by the spruce budworm in a boreal mixedwood forest. Can. J. Res. 1882, 1870–1882 (2004).Article 

Google Scholar 
Greenbank, D. O. Staminate flowers and the spruce budworm. Mem. Entomol. Soc. Can. 95, 202–218 (1963).Article 

Google Scholar 
Sturtevant, B. R., Cooke, B. J., Kneeshaw, D. D. & MacLean, D. A. Modeling insect disturbance across forested landscapes: insights from the spruce budworm. in Simulation Modeling Of Forest Landscape Disturbances. 93–134 (Springer, 2015).Zalucki, M. P., Clarke, A. R. & Malcolm, S. B. Ecology and behavior of first instar larval Lepidoptera. Annu. Rev. Entomol. 47, 361–393 (2002).CAS 
PubMed 
Article 

Google Scholar 
Despland, E. Effects of phenological synchronization on caterpillar early-instar survival under a changing climate1. Can. J. Res. 48, 247–254 (2018).Article 

Google Scholar 
Mattson, W. J. Herbivory in relation to plant nitrogen content. Annu. Rev. Ecol. Syst. 11, 119–161 (1980).Article 

Google Scholar 
Greenbank, D. O. The role of climate and dispersal in the initiation of the spruce budworm outbreak in New Brunswick: II. The role of dispersal. Can. J. Zool. 35, 385–403 (1957).Article 

Google Scholar 
Boulanger, Y. et al. The use of weather surveillance radar and high-resolution three dimensional weather data to monitor a spruce budworm mass exodus flight. Agric. Meteorol. 234–235, 127–135 (2017).Article 

Google Scholar 
Landry, J. S. & Parrott, L. Could the lateral transfer of nutrients by outbreaking insects lead to consequential landscape-scale effects? Ecosphere 7, e01265 (2016).Article 

Google Scholar 
Andersen, T., Elser, J. J. & Hessen, D. O. Stoichiometry and population dynamics. Ecol. Lett. 7, 884–900 (2004).Article 

Google Scholar 
Environment Canada. Canadian climate normals: 1981-2010 Climate normals and averages. (2015). Available at: http://climate.weather.gc.ca/climate_normals/index_e.html. (Accessed: 5 April 2016).De Grandpré, L., Morissette, J. & Gauthier, S. Long-term post-fire changes in the northeastern boreal forest of Quebec. J. Veg. Sci. 11, 791–800 (2000).Gauthier, S., Boucher, D., Morissette, J. & De Grandpré, L. Fifty-seven years of composition change in the eastern boreal forest of Canada. J. Veg. Sci. 21, 772–785 (2010).
Google Scholar 
Bouchard, M. & Pothier, D. Spatiotemporal variability in tree and stand mortality caused by spruce budworm outbreaks in eastern Quebec. Can. J. Res. 40, 86–94 (2010).Article 

Google Scholar 
Fettes, J. J. Investigations of sampling techniques for population studies of the spruce budworm on balsam fir in Ontario (Forest Insect Laboratory, 1950).Miller, R. O. High-Temperature oxidation: dry ashing. In Handbook of Reference Methods for Plant Analysis (ed. Karla, Y. P.) 53–56 (CRC Press, Taylor & Francis Group, 1998).Trottier-Picard, A. et al. Amounts of logging residues affect planting microsites: a manipulative study across northern forest ecosystems. Ecol. Manag. 312, 203–215 (2014).Article 

Google Scholar 
RCoreTeam. R.: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2021).Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & RCoreTeam. _nlme: Linear and Nonlinear Mixed Effects Models_. (2020).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Lenth, R. V. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.6.0. (2021). More