Ecology

Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).CAS 

Google Scholar 
Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).CAS 

Google Scholar 
Choy, C., Wabnitz, C., Weijerman, M., Woodworth-Jefcoats, P. & Polovina, J. Finding the way to the top: how the composition of oceanic mid-trophic micronekton groups determines apex predator biomass in the central North Pacific. Mar. Ecol. Prog. Ser. 549, 9–25 (2016).
Google Scholar 
Pauly, D. & Christensen, V. Primary production required to sustain global fisheries. Nature 374, 255–257 (1995).Bertrand, A. et al. Broad impacts of fine-scale dynamics on seascape structure from zooplankton to seabirds. Nat. Commun. 5, 5239 (2014).CAS 

Google Scholar 
Brierley, A. S. Diel vertical migration. Curr. Biol. 24, R1074–R1076 (2014).CAS 

Google Scholar 
Behrenfeld, M. J. et al. Global satellite-observed daily vertical migrations of ocean animals. Nature 576, 257–261 (2019).CAS 

Google Scholar 
Angel, M. V. & de C. Baker, A. Vertical distribution of the standing crop of plankton and micronekton at three stations in the northeast Atlantic. Biol. Oceanogr. 2, 1–30 (1982).
Google Scholar 
Cook, A. B., Sutton, T. T., Galbraith, J. K. & Vecchione, M. Deep-pelagic (0–3000 m) fish assemblage structure over the Mid-Atlantic Ridge in the area of the Charlie-Gibbs Fracture Zone. Deep Sea Res. 2 98, 279–291 (2013).
Google Scholar 
Hidaka, K., Kawaguchi, K., Murakami, M. & Takahashi, M. Downward transport of organic carbon by diel migratory micronekton in the western equatorial Pacific: its quantitative and qualitative importance. Deep Sea Res. 1 48, 1923–1939 (2001).Ariza, A., Garijo, J. C., Landeira, J. M., Bordes, F. & Hernández-León, S. Migrant biomass and respiratory carbon flux by zooplankton and micronekton in the subtropical northeast Atlantic Ocean (Canary Islands). Prog. Oceanogr. 134, 330–342 (2015).
Google Scholar 
Saba, G. K. et al. Toward a better understanding of fish-based contribution to ocean carbon flux. Limnol. Oceanogr. 66, 1639–1664 (2021).CAS 

Google Scholar 
Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).
Google Scholar 
Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).CAS 

Google Scholar 
Tittensor, D. P. et al. A protocol for the intercomparison of marine fishery and ecosystem models: Fish-MIP v1.0. Geosci. Model Dev. 11, 1421–1442 (2018).
Google Scholar 
Bryndum-Buchholz, A. et al. Twenty-first-century climate change impacts on marine animal biomass and ecosystem structure across ocean basins. Glob. Change Biol. 25, 459–472 (2019).
Google Scholar 
Kwiatkowski, L., Aumont, O. & Bopp, L. Consistent trophic amplification of marine biomass declines under climate change. Glob. Change Biol. 25, 218–229 (2019).
Google Scholar 
Lotze, H. K. et al. Global ensemble projections reveal trophic amplification of ocean biomass declines with climate change. Proc. Natl Acad. Sci. USA 116, 12907–12912 (2019).CAS 

Google Scholar 
Tittensor, D. P. et al. Next-generation ensemble projections reveal higher climate risks for marine ecosystems. Nat. Clim. Change 11, 973–981 (2021).
Google Scholar 
Heneghan, R. F. et al. Disentangling diverse responses to climate change among global marine ecosystem models. Prog. Oceanogr. 198, 102659 (2021).
Google Scholar 
Reid, S. B., Hirota, J., Young, R. E. & Hallacher, L. E. Mesopelagic-boundary community in Hawaii: micronekton at the interface between neritic and oceanic ecosystems. Mar. Biol. 109, 427–440 (1991).
Google Scholar 
Ben Mustapha, Z., Alvain, S., Jamet, C., Loisel, H. & Dessailly, D. Automatic classification of water-leaving radiance anomalies from global SeaWiFS imagery: application to the detection of phytoplankton groups in open ocean waters. Remote Sens. Environ. 146, 97–112 (2014).
Google Scholar 
Pakhomov, E. & Yamamura, O. Report of the Advisory Panel on Micronekton Sampling Inter-calibration Experiment. PICES Scientific Report 38 (North Pacific Marine Science Organization, 2010).Kaartvedt, S., Staby, A. & Aksnes, D. Efficient trawl avoidance by mesopelagic fishes causes large underestimation of their biomass. Mar. Ecol. Prog. Ser. 456, 1–6 (2012).
Google Scholar 
Gjøsaeter, J. & Kawaguchi, K. A Review of the World Resources of Mesopelagic Fish Fisheries Technical Paper 193 (FAO, 1980).Catul, V., Gauns, M. & Karuppasamy, P. K. A review on mesopelagic fishes belonging to family Myctophidae. Rev. Fish Biol. Fish. 21, 339–354 (2011).
Google Scholar 
Benoit-Bird, K. J. & Lawson, G. L. Ecological insights from pelagic habitats acquired using active acoustic techniques. Annu. Rev. Mar. Sci. 8, 463–490 (2016).
Google Scholar 
Annasawmy, P. et al. Micronekton diel migration, community composition and trophic position within two biogeochemical provinces of the south west Indian Ocean: insight from acoustics and stable isotopes. Deep Sea Res. 1 138, 85–97 (2018).CAS 

Google Scholar 
Haris, K. et al. Sounding out life in the deep using acoustic data from ships of opportunity. Sci. Data 8, 23 (2021).CAS 

Google Scholar 
Irigoien, X. et al. The Simrad EK60 echosounder dataset from the Malaspina circumnavigation. Sci. Data 8, 259 (2021).
Google Scholar 
Irigoien, X. et al. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Nat. Commun. 5, 3271 (2014).
Google Scholar 
Klevjer, T. A. et al. Large scale patterns in vertical distribution and behaviour of mesopelagic scattering layers. Sci. Rep. 6, 19873 (2016).CAS 

Google Scholar 
Proud, R., Cox, M., Le Guen, C. & Brierley, A. Fine-scale depth structure of pelagic communities throughout the global ocean based on acoustic sound scattering layers. Mar. Ecol. Prog. Ser. 598, 35–48 (2018).
Google Scholar 
Proud, R., Cox, M. J. & Brierley, A. S. Biogeography of the global ocean’s mesopelagic zone. Curr. Biol. 27, 113–119 (2017).CAS 

Google Scholar 
Ramsay, J. O. & Silverman, B. W. Functional Data Analysis (Springer, 2005).Moriarty, R. & O’Brien, T. D. Distribution of mesozooplankton biomass in the global ocean. Earth Syst. Sci. Data 5, 45–55 (2013).
Google Scholar 
Aksnes, D. L. et al. Light penetration structures the deep acoustic scattering layers in the global ocean. Sci. Adv. 3, e1602468 (2017).
Google Scholar 
Bertrand, A., Ballón, M. & Chaigneau, A. Acoustic observation of living organisms reveals the upper limit of the oxygen minimum zone. PLoS ONE 5, e10330 (2010).
Google Scholar 
Bianchi, D., Galbraith, E. D., Carozza, D. A., Mislan, K. A. S. & Stock, C. A. Intensification of open-ocean oxygen depletion by vertically migrating animals. Nat. Geosci. 6, 545–548 (2013).CAS 

Google Scholar 
Godø, O. R., Patel, R. & Pedersen, G. Diel migration and swimbladder resonance of small fish: some implications for analyses of multifrequency echo data. ICES J. Mar. Sci. 66, 1143–1148 (2009).
Google Scholar 
Agersted, M. D. et al. Mass estimates of individual gas-bearing mesopelagic fish from in situ wideband acoustic measurements ground-truthed by biological net sampling. ICES J. Mar. Sci. 78, 3658–3673 (2021).
Google Scholar 
Backus, R. & Craddock, J. in Oceanic Sound Scattering Prediction (eds Anderson, N. R. & Zahuranec, B. J.) 529–547 (Springer, 1977).Longhurst, A. Ecological Geography of the Sea (Elsevier, 2010).Spalding, M. D., Agostini, V. N., Rice, J. & Grant, S. M. Pelagic provinces of the world: A biogeographic classification of the world’s surface pelagic waters. Ocean Coast. Manage. 60, 19–30 (2012).
Google Scholar 
Sutton, T. T. et al. A global biogeographic classification of the mesopelagic zone. Deep Sea Res. 1 126, 85–102 (2017).
Google Scholar 
IPCC Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Kooijman, B. & Kooijman, S. A. L. M. Dynamic Energy Budget Theory for Metabolic Organisation (Cambridge Univ. Press, 2010).Cheung, W. W. L., Watson, R. & Pauly, D. Signature of ocean warming in global fisheries catch. Nature 497, 365–368 (2013).CAS 

Google Scholar 
Fossheim, M. et al. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat. Clim. Change 5, 673–677 (2015).
Google Scholar 
Proud, R., Handegard, N. O., Kloser, R. J., Cox, M. J. & Brierley, A. S. From siphonophores to deep scattering layers: uncertainty ranges for the estimation of global mesopelagic fish biomass. ICES J. Mar. Sci. 76, 718–733 (2019).
Google Scholar 
Chapman, R. P., Bluy, O. Z., Adlington, R. H. & Robison, A. E. Deep scattering layer spectra in the Atlantic and Pacific oceans and adjacent seas. J. Acoust. Soc. Am. 56, 1722–1734 (1974).
Google Scholar 
Dornan, T., Fielding, S., Saunders, R. A. & Genner, M. J. Swimbladder morphology masks Southern Ocean mesopelagic fish biomass. Proc. R. Soc. B 286, 20190353 (2019).
Google Scholar 
Escobar-Flores, P. C., O’Driscoll, R. L., Montgomery, J. C., Ladroit, Y. & Jendersie, S. Estimates of density of mesopelagic fish in the Southern Ocean derived from bulk acoustic data collected by ships of opportunity. Polar Biol. 43, 43–61 (2020).
Google Scholar 
Dornan, T., Fielding, S., Saunders, R. A. & Genner, M. J. Large mesopelagic fish biomass in the Southern Ocean resolved by acoustic properties. Proc. R. Soc. B 289, 20211781 (2022).
Google Scholar 
Reygondeau, G. et al. Climate change-induced emergence of novel biogeochemical provinces. Front. Mar. Sci. 7, 657 (2020).
Google Scholar 
Blanchard, J. L. et al. Linked sustainability challenges and trade-offs among fisheries, aquaculture and agriculture. Nat. Ecol. Evol. 1, 1240–1249 (2017).
Google Scholar 
Bianchi, D., Carozza, D. A., Galbraith, E. D., Guiet, J. & DeVries, T. Estimating global biomass and biogeochemical cycling of marine fish with and without fishing. Sci. Adv. 7, eabd7554 (2021).
Google Scholar 
Grimaldo, E. et al. Investigating the potential for a commercial fishery in the northeast Atlantic utilizing mesopelagic species. ICES J. Mar. Sci. 77, 2541–2556 (2020).
Google Scholar 
Olsen, R. E. et al. Can mesopelagic mixed layers be used as feed sources for salmon aquaculture? Deep Sea Res. 2 180, 104722 (2020).CAS 

Google Scholar 
De Robertis, A. & Higginbottom, I. A post-processing technique to estimate the signal-to-noise ratio and remove echosounder background noise. ICES J. Mar. Sci. 64, 1282–1291 (2007).
Google Scholar 
Ryan, T. E., Downie, R. A., Kloser, R. J. & Keith, G. Reducing bias due to noise and attenuation in open-ocean echo integration data. ICES J. Mar. Sci. 72, 2482–2493 (2015).
Google Scholar 
Perrot, Y. et al. Matecho: an open-source tool for processing fisheries acoustics data. Acoust. Aust. 46, 241–248 (2018).
Google Scholar 
Stanton, T. Review and recommendations for the modelling of acoustic scattering by fluid-like elongated zooplankton: euphausiids and copepods. ICES J. Mar. Sci. 57, 793–807 (2000).
Google Scholar 
GEBCO: A Continuous Terrain Model of the Global Oceans and Land (British Oceanographic Data Centre, 2019).EchoPY v.1.1: Fisheries Acoustic Data Processing in Python (Python, 2020); https://pypi.org/project/echopyde Boor, C. A Practical Guide to Splines (Springer, 1978).Clustering (SciKit Learn, 2021); https://scikit-learn.org/stable/modules/clusteringEyring, V. et al. Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).
Google Scholar 
Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).
Google Scholar 
Sonnewald, M., Dutkiewicz, S., Hill, C. & Forget, G. Elucidating ecological complexity: unsupervised learning determines global marine eco-provinces. Sci. Adv. 6, eaay4740 (2020).
Google Scholar 
Sonnewald, M. & Lguensat, R. Revealing the impact of global heating on North Atlantic circulation using transparent machine learning. J. Adv. Model. Earth Syst. 13, e2021MS002496 (2021).
Google Scholar 
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Google Scholar 
Locarnini, R. et al. World Ocean Atlas 2018, Volume 1: Temperature NOAA Atlas NESDIS 81 (NOAA, 2018).García, H. et al. World Ocean Atlas 2018, Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation NOAA Atlas NESDIS 83 (NOAA, 2018).Sathyendranath, S. et al. ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Version 5.0 Data. NERC EDS Centre for Environmental Data Analysis, 19 May 2021; http://www.esa-oceancolour-cci.org More

The overall magnitude of changes in SOC, TN, and C:N in response to chemical nitrogen fertilizers reductionThe results showed that chemical nitrogen fertilizers reduction significantly decreased SOC and TN by 2.76% and 4.19% respectively, while increased C:N by 6.11% across all database (Fig. 1). SOC mainly derives from crop residues and secretions which closely related to crops growths, and crops growths were affected by fertilization, especially nitrogen fertilization20,21. The reduction of chemical nitrogen fertilizer led to poor crop growth, which reduced the amount of crop residues return, and then decreased SOC. Similarly, TN from crops was reduced due to poor crop growth. In addition, the reduction of chemical nitrogen fertilizers directly reduced the input of soil nitrogen. The increase of C:N was the result of the decrease of TN being greater than that of SOC. The responses of C:N to chemical nitrogen fertilizers reduction enhanced the comprehension of the couple relationship between SOC and TN, which was beneficial to the evolution of the C-N coupling models. Moreover, the accuracy of C-N coupling models depends on the precise quantification of the responses of SOC and TN to nitrogen fertilization. And our results accurately quantified the difference responses of SOC and TN to different nitrogen fertilization regimes, thus optimizing the C-N coupling models.Figure 1The weighted response ratio (RR++) for the responses to chemical nitrogen fertilizers of soil organic carbon (SOC, a), total nitrogen (TN, b), and their ratios (C:N, c). Bars denote the overall mean response ratio RR++ and 95% confidence intervals (CI). The star (*) indicates significance when the 95% CI that do not go across the zero line. The vertical lines are drawn at lnRR = 0. The value represents independent sample size.Full size imageResponses of SOC, TN and C:N to chemical nitrogen fertilizers reduction magnitudeWhen grouped by chemical nitrogen fertilizers reduction magnitude, SOC showed a significant increase by 6.9% in medium magnitude, while SOC was significantly decreased by 3.10% and 7.26% in high and total magnitude respectively (Fig. 1a). Liu and Greaver22 also stated the reduction of medium nitrogen fertilizer increased the average microbial biomass from 15 to 20%, thereby increasing the SOC content. Previous studies had reported that there were strong positive correlations between soil organic matter and soil microbial biomass in both the agricultural ecosystem and natural ecosystem23,24. Numerous researchers have demonstrated the significance of nitrogen availability in soil for the plant biomass across most ecosystems25,26. Moreover, nitrogen deficient would inhibit the activity of extracellular enzymes and root activities27. Generally, soil degradation caused by continuous rising chemical nitrogen fertilizers application may inhibit the growth of crops and ultimately reduce the SOC28.TN showed no significant difference in low and medium chemical nitrogen fertilizers reduction magnitude (p  > 0.05), while TN in high magnitude and total chemical nitrogen fertilizers reduction magnitude exhibited a decrease with 3.10% and 9.37% respectively (Fig. 1b). Numerous studies described that the amount of nitrogen fertilizers used in China was higher than the demand of N for crop, which caused serious N leaching and runoff29,30. Chemical nitrogen fertilizers in low and medium magnitude would not decrease the TN of soil by reducing N leaching and runoff. However, the residual nitrogen in soil cannot meet the requirement for the sustainable growth of plant with litter or without exogenous nitrogen supplement, which resulted in the decrease of TN in high and total chemical nitrogen fertilizers magnitude. Consequently, optimal nitrogen fertilizers application rates will take into account crops yield and environment friendliness.Additionally, C:N had a significant increase with ranging from 1.82% to 8.98% under the four chemical nitrogen fertilizers reduction magnitude (Fig. 1c), suggesting the relative increase of SOC compared to TN. Previous studies have revealed that C:N had significantly influence on the soil bacterial community structures31. And there were also considerable studies indicated that chemical nitrogen fertilizers have impact on the soil bacterial communities32,33. We may speculate that the change of C:N would bring about the variations of soil bacteria communities under the chemical nitrogen fertilizers regimes.Responses of SOC, TN, and C:N to chemical nitrogen fertilizers reduction durationNegative response of SOC to short-term chemical nitrogen fertilizers reduction was observed in our study, which was consistent with the study of Gong, et al.34 that chemical nitrogen fertilizers reduction decreased SOC by reducing crop-derived carbon by one year. However, SOC was significantly increased by 1.06% and 4.65% at mid-term and long-term chemical nitrogen fertilizers reduction respectively, which was similar with the findings of Ning, et al.11 that SOC was significantly increased under more than 5 years of chemical nitrogen fertilizers reduction treatment. TN was significantly decreased by 1.96% at short-term chemical nitrogen fertilizers reduction duration, while the results converted at mid-term chemical nitrogen fertilizers reduction duration. The effect of long-term chemical nitrogen fertilizers reduction on TN was not significant (p  > 0.05). The divergent response of TN to different chemical nitrogen fertilizers duration was mainly caused by the various treatments. In terms of C:N, a greater positive response was observed at short-term chemical nitrogen fertilizers duration (9.06%) than mid-term and long-term duration (1.99%). Moreover, with the prolongation of the chemical reduction time of nitrogen, the response ratio tends to zero, suggesting that the effect of chemical fertilizers gradually decrease. This may be ascribed to the buffer capacity of soil to resist the changes from external environment, including nutrients, pollutants, and redox substances35.Responses of SOC, TN, and C:N to different chemical nitrogen fertilizers reduction patternsUnder the pattern of chemical nitrogen fertilizers reduction without organic fertilizers supplement, SOC and TN significantly decreased by 3.83% and 11.46% respectively, however, chemical nitrogen fertilizers reduction with organic fertilizers supplement significantly increased SOC and TN by 4.92% and 8.33% respectively. Moreover, C:N significantly increased under the two chemical nitrogen fertilizers patterns (p  0.05), but there was a negative effect on SOC in high and total magnitude (p  0.05). The no significant decrease at mid-term duration might result from the limited information reported in original studies of this meta-analysis36. TN showed no significant response to chemical nitrogen fertilizers without organic fertilizers supplement in the low and medium magnitude (p  > 0.05). However, TN was significantly decreased by 8.62% and 16.7% respectively in the high and total magnitude. When regarding to chemical nitrogen fertilizers reduction duration, TN was significantly reduced at all of the categories, ranging from 3.13% to 13.4% (Fig. 2c). In the pattern of chemical nitrogen fertilizers reduction with organic fertilizers supplement, chemical nitrogen fertilizers reduction at medium, high, and total magnitudes significantly increased SOC by 13.85%, 13.03%, and 5.46%respectively, however, the response of SOC in the low chemical nitrogen fertilizers magnitude was not significant. Chemical nitrogen fertilizers reduction duration significantly increased SOC by 7.01%, 1.71%, and 22.02% in the short-term, mid-term, and long-term respectively. Comparatively, TN showed a significantly increase in most chemical nitrogen fertilizers categories expect for the long-term chemical nitrogen fertilizers duration, with an increasing from 4.90% to 14.69% (Fig. 2d).Figure 2The weighted response ratio (RR++) for the responses to chemical nitrogen fertilizers of soil organic carbon (SOC, a), total nitrogen (TN, b), and their ratios (C:N, c) under the two patterns (with organic fertilizers ; without organic fertilizers). Bars denote the overall mean response ratio RR++ and 95% confidence intervals (CI). The star (*) indicates significance when the 95% CI that do not go across the zero line. The vertical lines are drawn at lnRR = 0. The values represent independent sample size.Full size imageOrganic fertilizers were mainly derived from animal manure or crops straws, which contained large amount of organic matter and nitrogen elements37,38. The application of organic fertilizers increased the input of SOC and TN directly. Moreover, organic fertilizer could promote the growth of crops by releasing phenols, vitamins, enzymes, auxins and other substances during the decomposition process, thus the SOC derived from crops would be increased37,39. In addition, organic fertilizers provide various nutrients for microbial reproduction, which increase the microbial population and organic carbon and total nitrogen content37. More importantly, the application of organic fertilizers could improve organic carbon sequestration and maintain its stability in aggregates, thereby reducing losses of SOC and TN40.C:N showed an increase under all of the chemical nitrogen fertilizers reduction with organic fertilizer supplement. The positive response of C:N to organic fertilizer supplement may be related to the higher C:N of organic fertilizer than soil. The average values of C:N of the commonly used organic fertilizers including animal manure, crop straws and biochar were 14, 60 and 30 respectively, while the C:N of soil was lower than 10 in average according to extensive literature researches41. Therefore, organic fertilizers would be a favorable alternative of chemical fertilizers for the sustainable development of agriculture.The correlation between the response of SOC, TN, and C:N and environmental variablesThe analysis of linear regression was conducted to analyze the environmental variables including mean annual temperature (MAT), mean annual precipitation (MAP), accumulated temperature above 10 °C (MATA), which may exert influence on SOC, TN and C:N. No significant correlation among the lnRR of SOC, TN, C:N and environmental variables were observed among the whole database (p  > 0.05; Fig. S1). Rule out the interference of organic fertilizers supplement, we analyzed the relationship between lnRR of SOC, TN, C:N and environmental variables as the Figures showed in Figs. 3 and 4 respectively. Under chemical nitrogen fertilizers without organic fertilizers supplement, there was a significant negative correlation between lnRR of SOC and MAT (p  More

Irigoien, X. et al. Nat. Commun. 5, 3271 (2014).Article 

Google Scholar 
Ariza, A. et al. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01479-2 (2022).Article 

Google Scholar 
Klevjer, T. A. et al. Sci. Rep. 6, 19873 (2016).CAS 
Article 

Google Scholar 
Braun, C. D. et al. Annu. Rev. Mar. Sci. 14, 129–159 (2022).Article 

Google Scholar 
Heneghan, R. F. et al. Prog. Oceanogr. 198, 102659 (2021).Article 

Google Scholar 
Polovina, J. J., Dunne, J. P., Woodworth, P. A. & Howell, E. A. ICES J. Mar. Sci. 68, 986–995 (2011).Article 

Google Scholar 
Cheung, W. W. L. et al. Fish Fish. 10, 235–251 (2009).Article 

Google Scholar 
Hazen, E. L. et al. Nat. Clim. Change 3, 234–238 (2013).Article 

Google Scholar 
Powers, R. P. & Jetz, W. Nat. Clim. Change 9, 323–329 (2019).Article 

Google Scholar 
Purves, D. et al. Nature 493, 295–297 (2013).CAS 
Article 

Google Scholar 
Hobday, A. J., Spillman, C. M., Paige Eveson, J. & Hartog, J. R. Fish. Oceanogr. 25, 45–56 (2016).Article 

Google Scholar 
Pons, M. et al. Proc. Natl Acad. Sci. USA 119, e2114508119 (2022).Article 

Google Scholar  More

ChemicalsErbium (III) oxide nanoparticles (Er2O3-NPs) were purchased from Sigma-Aldrich Chemical Company (Saint Louis, USA) with pink appearance and product number (203,238). Powders of Er2O3-NPs with 99.9 trace metals basis were suspended in deionized distilled water to prepare the required concentrations and ultra-sonicated prior use.Cell lineHuman hepatocellular carcinoma (Hep-G2) cells were obtained from Nawah Scientific Inc., (Mokatam, Cairo Egypt). Cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM) media supplemented with streptomycin (100 mg/mL), penicillin (100 units/mL) and heat-inactivated fetal bovine serum (10) in humidified, 5% (v/v) CO2 atmosphere at 37 °C.Characterization of Er2O3-NPsThe purchased powders of Er2O3-NPs were characterized using a charge coupled device diffractometer (XPERT-PRO, PANalytical, Netherlands) to determine its X-ray diffraction (XRD) pattern. Zeta potential and particles’ size distribution of Er2O3-NPs were also detected using Malvern Instrument Zeta sizer Nano Series (Malvern Instruments, Westborough, MA) equipped with a He–Ne laser (λ = 633 nm, max 5mW). Moreover, transmission electron microscopy (TEM) imaging was done to detect the shape and average particles’ size of Er2O3-NPs suspension.Sulforhodamine B (SRB) cytotoxicity assaySulforhodamine B (SRB) assay was conducted to assess the influence of Er2O3-NPs on the proliferation of cancerous Hep-G2 cells12. Aliquots of 100 µl of Hep-G2 cells suspension containing 5 × 103 cells were separately cultured in 96-well plates and incubated for 24 h in complete media. Hep-G2 Cells were then treated with five different concentrations of Er2O3-NPs (0.01, 0.1, 1, 10 and 100 µg/ml) incubated for 24 h or (0.1, 1, 10, 100 and 1000 µg/ml) incubated for 72 h. After 24 or 72 h of Er2O3-NPs exposure, cultured cells were fixed by replacing media with 10% trichloroacetic acid (TCA) and incubated for one hour at 4 °C. Cells were then washed five times with distilled water, SRB solution (0.4% w/v) was added and incubated cells in a dark place at room temperature for 10 min. All plates were washed three times with 1% acetic acid and allowed to air-dry overnight. Then, protein-bound SRB stain was dissolved by adding TRIS (10 mM) and the absorbance was measured at 540 nm using a BMG LABTECH-FLUO star Omega microplate reader (Ortenberg, Germany).Cells treatmentCancerous Hep-G2 cells were cultured at the appropriate conditions and dived into control and treated cells. The control cells were treated with an equal volume of the vehicle (DMSO; final concentration, ≤ 0.1%), while the treated cells were treated with the IC50 of Er2O3-NPs. All cells were left for 72 h after nanoparticles treatment and were harvested by brief trypsinization and centrifugation. Each treatment was conducted in triplicate. Cells were washed twice with ice-cold PBS and used for different molecular assays.Estimation of genomic DNA integrityThe impact of Er2O3-NPs exposure on the integrity of genomic DNA in cancerous Hep-G2 cells was estimated using alkaline Comet assay13,14. Treated and control cells were mixed with low melting agarose and spread on clean slides pre-coated with normal melting agarose. After drying, slides were incubated in cold lysis buffer for 24 h in dark and then electrophoresed in alkaline electrophoresis buffer. Electrophoresed DNA was neutralized in Tris buffer and fixed in cold absolute ethanol. For analysis slides were stained with ethidium bromide, examined using epi-fluorescent microscope at magnification 200× and fifty comet nuclei were analyzed per sample using Comet Score software.Estimation of intracellular ROS generationThe effect of Er2O3-NPs exposure on intracellular ROS production in cancer Hep-G2 cells was studied using 2,7-dichlorofluorescein diacetate dye15. Cultured cells were washed with phosphate buffered saline (PBS) and then 2,7-dichlorofluorescein diacetate dye was added. Mixed cells and dye were left for 30 min in dark and spread on clean slides. The resultant fluorescent dichlorofluorescein complex from interaction of intracellular ROS with dichlorofluorescein diacetate dye was examined under epi-fluorescent at 20× magnification.Measuring the expression levels of apoptotic and anti-apoptotic genesQuantitative real time Polymerase chain reaction (RT-PCR) was conducted to measure the mRNA expression levels of apoptotic (p53 and Bax) and anti-apoptotic (Bcl2) genes in control and treated Hep-G2 cells. Whole cellular RNA was extracted according to the instructions listed by the GeneJET RNA Purification Kit (Thermo scientific, USA) (Thermo scientific, USA) and using Nanodrop device purity and concentration of the extracted RNAs were determined. These RNAs were then reverse transcribed into complementary DNA (cDNA) using the instructions of the Revert Aid First Strand cDNA Synthesis Kit (Thermo scientific, USA). For amplification, RT-PCR was performed using the previously designed primers shown in Table 116,17 by the 7500 Fast system (Applied Biosystem 7500, Clinilab, Egypt). A comparative Ct (DDCt) method was conducted to measure the expression levels of amplified genes and GAPDH gene was used as a housekeeping gene. Results were expressed as mean ± S.D.Table 1 Sequences of the used primers in qRT-PCR.Full size tableAnalysis of cell cycle distributionDistribution of cell cycle was analyzed using flow cytometry. Control and treated cancer Hep-G2 cells with IC50 of Er2O3-NPs for 72 h were harvested, washed with PBS and re-suspended in 1 mL of PBS containing RNAase A (50 µg/mL) and propidium iodide (10 µg/mL) (PI). Cells were incubated for 20 min in dark at 37 C and analyzed for DNA contents using FL2 (λex/em 535/617 nm) signal detector (ACEA Novocyte flow cytometer, ACEA Biosciences Inc., San Diego, CA, USA). For each sample, 12,000 events are acquired and cell cycle distribution is calculated using ACEA NovoExpress software (ACEA Biosciences Inc., San Diego, CA, USA).Estimation of apoptosis inductionApoptotic and necrotic cell populations were determined using Annexin V- Fluorescein isothiocyanate (FITC) apoptosis detection kit (Abcam Inc., Cambridge Science Park Cambridge, UK) coupled with two fluorescent channels flow cytometry. After treatment with Er2O3-NPs for 72 h and doxorubicin as a positive control, Hep-G2 cells were collected by trypsinization and washed twice with ice-cold PBS (pH 7.4). Harvested cells are incubated in dark with Annexin V-FITC/ propidium iodide (PI) solution for 30 min at room temperature, then injected via ACEA Novocyte flowcytometer (ACEA Biosciences Inc., San Diego, CA, USA) and analyzed for FITC and PI fluorescent signals using FL1 and FL2 signal detector, respectively (λex/em 488/530 nm for FITC and λex/em 535/617 nm for PI). For each sample, 12,000 events were acquired and positive FITC and/or PI cells are quantified by quadrant analysis and calculated using ACEA NovoExpress software (ACEA Biosciences Inc., San Diego, CA, USA).Statistical analysisResults of the current study are expressed as mean ± Standard Deviation (S.D) and were analyzed using the Statistical Package for the Social Sciences (SPSS) (version 20) at the significance level p  More

General description of the experimental areaThe experiment was performed for two years at the National Key Irrigation Experimental Station located on the Songnen Plain in Heping town, Qing’an County, Suihua, Heilongjiang, China, with a geographical location of 45° 63′ N and 125° 44′ E at an elevation of 450 m above sea level (Fig. 1). This region consists of plain topography and has a semiarid cold temperate continental monsoon climate, i.e., a typical cold region with a black soil distribution area. The average annual temperature is 2.5 °C, the average annual precipitation is 550 mm, the precipitation is concentrated from June to September of each year, and the average annual surface evaporation is 750 mm. The growth period of crops is 156–171 days, and there is a frost-free period of approximately 128 days year−122. The soil at the study site is albic paddy soil with a mean bulk density of 1.01 g/cm3 and a porosity of 61.8% prevails. The basic physicochemical properties of the soil were as follows: the mass ratio of organic matter was 41.8 g/kg, pH value was 6.45, total nitrogen mass ratio was 15.06 g/kg, total phosphorus mass ratio was 15.23 g/kg, total potassium mass ratio was 20.11 g/kg, mass ratio of alkaline hydrolysis nitrogen was 198.29 mg/kg, available phosphorus mass ratio was 36.22 mg/kg and available potassium mass ratio was 112.06 mg/kg.Figure 1Location of the study area. The map and inset map in this image were drawn by the authors using ArcGIS software. The software version used was ArcGIS software v.10.2, and its URL is http://www.esri.com/.Full size imageHumic acid fertilizerHumic acid fertilizer was produced by Yunnan Kunming Grey Environmental Protection Engineering Co., Ltd., China (Fig. 2). The organic matter was ≥ 61.4%, and the total nutrients (nitrogen, phosphorus and potassium) were ≥ 18.23%, of which N ≥ 3.63%, P2O5 ≥ 2.03%, and K2O ≥ 12.57%. The moisture content was ≤ 2.51%, the pH value was 5.7, the worm egg mortality rate was ≥ 95%, and the amount of faecal colibacillosis was ≤ 3%. The fertilizer contained numerous elements necessary for plants. The contents of harmful elements, including arsenic, mercury, lead, cadmium and chromium, were ≤ 2.8%, 0.01%, 7.6%, 0.1% and 4.7%, respectively; these were lower than the test standard.Figure 2Humic acid fertilizer in powder form.Full size imageExperimental design and observation methodsIrrigationIn this experiment, three irrigation practices, namely, control irrigation (C), wet irrigation (W) and flood irrigation (F), were designed (Table 1).Table 1 Different irrigation methods.Full size tableControl irrigation (C) of rice had no water layer in the rest of the growing stages, except for the shallow water layer at the regreen stage of rice, which was maintained at 0–30 mm, and the natural dryness in the yellow stage. The irrigation time and irrigation quota were determined by the root soil moisture content as the control index. The upper limit of irrigation was the saturated moisture content of the soil, the lower limit of soil moisture at each growth stage was the percentage of saturated moisture content, and the TPIME-PICO64/32 soil moisture analyser was used to determine the soil moisture content at 7:00 a.m. and 18:00 p.m., respectively. When the soil moisture content was close to or lower than the lower limit of irrigation, artificial irrigation occurred until the upper irrigation limit was reached. The soil moisture content was maintained between the upper irrigation limit and the lower irrigation limit of the corresponding fertility stage. Under the wet irrigation (W) and flood irrigation (F) conditions, it was necessary to read the depth of the water layer through bricks and a vertical ruler embedded in the field before and after 8:00 am every day to determine if irrigation was needed. If irrigation was needed, then the water metre was recorded before and after each irrigation. The difference between before and after was the amount of irrigation23.FertilizationIn our research, five fertilization methods were applied, as shown in Table 2. In this experiment, the rice cultivar “Suijing No. 18” was selected. Urea and humic acid fertilizer were applied according to the proportion of base fertilizer:tillering fertilizer:heading fertilizer (5:3:2). The amounts of phosphorus and potassium fertilizers were the same for all treatments, and P2O5 (45 kg ha−1) and K2O (80 kg ha−1) were used. Phosphorus was applied once as a basal application. Potassium fertilizer was applied twice: once as a basal fertilizer and at 8.5 leaf age (panicle primordium differentiation stage) at a 1:1 ratio22.Table 2 The fertilizer methods.Full size tableThis study was performed with a randomized complete block design with three replications. Three irrigation practices and five fertilizer methods were applied, for a total of 15 treatments as follows: CT1, CT2, CT3, CT4, CT5; WT1, WT2, WT3, WT4, WT5; FT1, FT2, FT3, FT4, and FT5 (C, W, and F represent control irrigation, wet irrigation, and flood irrigation; T represents fertilizer treatment).Measurements of the samplesA soil temperature sensor (HZTJ1-1) was buried in each experimental plot to monitor the temperature of each soil layer (5 cm, 10 cm, 15 cm, 20 cm and 25 cm depth). The transmission of photosynthetically active radiation was measured from 11:00 to 13:00 by using a SunScan Canopy Analysis System (Delta T Devices, Ltd., Cambridge, UK), and data during the crop-growing season were recorded every day24.Plant measurements were taken during the periods of tillering to ripening on days with no wind and good light. The fluorescence parameters were measured by a portable fluorescence measurement system (Li-6400XT, America). The detection light intensity was 1500 μmol m−2 s−1, and the saturated pulsed light intensity was 7200 μmolm−2 s−1. The functional leaves were dark adapted for 30 min, and then the maximum photosynthetic efficiency of PSII (Fv/Fm) was measured. Photochemical quenching (QP) and nonphotochemical quenching (NPQ) were measured with natural light. Simultaneously, the leaf chlorophyll relative content (SPAD) was monitored using SPAD 502 (Konica Minolta, Inc., Tokyo, Japan). For plant agronomic characteristics, the distance from the stem base to the stem tip was measured with a straight ruler to quantify plant height24.Statistical analysisExperimental data obtained for different parameters were analysed statistically using the analysis of variance technique as applicable to randomized complete block design. Duncan’s multiple range test was employed to assess differences between the treatment means at a 5% probability level. All statistical analyses were performed using SPSS 22.0 for Windows24.
Ethics approvalExperimental research and field studies on plants, including the collection of plant material, comply with relevant institutional, national, and international guidelines and legislation. We had appropriate permissions/licences to perform the experiment in the study area. More

The bee health modelThe conceptual model of the interactions of various stressors with honey bee health is described by the following system of ordinary differential equations (ODEs)$${{tau }_{{HB}}dot{x}}_{{HB}}= {-{delta }_{{HB}}x}_{{HB}}+{g}_{{TC}}left({x}_{{TC}}right)+{g}_{{VA}}left({x}_{{VA}}right)+{g}_{{VI}}left({x}_{{VI}}right) \ +{bar{f}}_{S,C}left({u}_{S},{u}_{C},{x}_{{TC}},{x}_{{VA}}right)+{bar{f}}_{P}left({u}_{P},{x}_{{TC}}right)+{underline{f}}_{{HB}}left({u}_{T}right)$$
(1)
$${{tau }_{{TC}}dot{x}}_{{TC}}={-{delta }_{{TC}}x}_{{TC}}+{g}_{{HB}}left({x}_{{HB}}right)$$
(2)
$${{tau }_{{VA}}dot{x}}_{{VA}}={-{delta }_{{VA}}x}_{{VA}}+{h}_{{VA}}left({{x}_{{HB}},x}_{{TC}},varepsilon {x}_{{VI}}right)+{underline{f}}_{{VA}}left({u}_{T}right)$$
(3)
$${{tau }_{{VI}}dot{x}}_{{VI}}={-{delta }_{{VI}}x}_{{VI}}+{h}_{{VI}}left({{x}_{{HB}},x}_{{TC}},{varepsilon x}_{{VI}}right)$$
(4)
for the state variables ({x}_{{HB}}) representing honey bee health, ({x}_{{TC}}) the stress due to toxic compounds (e.g., neonicotinoid insecticides), ({x}_{{VA}}) the stress due to parasites (e.g., V. destructor) and ({x}_{{VI}}) the stress due to pathogens (e.g., DWV). The system includes the effects of external inputs as sugar ({u}_{S}), pollen ({u}_{P}), absolute deviation from desired temperature ({u}_{T}) and sub-optimal temperature ({u}_{C}). All the inputs and possible parameters are non-negative; the coefficients (tau) denote the time constants; the coefficients (delta) denote the self-regulation parameters; (varepsilon) in the last two equations allows to account for pathogens that can ((varepsilon , > , 0)) or cannot ((varepsilon=0)) impair the immune system (through link m in Fig. 1). We assume that the functions (g) are smooth, bounded, positive, convex and decreasing to 0; the functions (bar{f}) are smooth, bounded, non-negative, concave and increasing with respect to (w.r.t.) (u) arguments (vanishing only when the first (u) argument vanishes) while convex and decreasing to 0 w.r.t. (x) arguments; the functions ({underline{f}}) are smooth, bounded, non-positive and decreasing (vanishing only when (u=0)); the functions (h) are smooth, bounded, positive, convex and decreasing to 0 w.r.t. the first argument while concave and increasing w.r.t. all the other arguments. For a detailed description of the various functions, together with a summary of the biological effects they account for and a reference to the conceptual model in Fig. 1, see Supplementary Table 3.Structural analysis of the bee health modelWe describe here the structural considerations and computations that yield the structural influence matrix for the honey bee health system.The structural influence matrix (M) is defined as follows. (M) is a symbolic matrix with entries ({M}_{{ij}}) chosen among: +,−,0,?, according to the criteria described below. Consider an equilibrium point (bar{x}) and a constant perturbation (u) applied on the (j)-th system variable (small enough not to compromise the stability of the equilibrium). The equilibrium value will be modified as (bar{x}+delta bar{x}). Consider the sign of the perturbation of the (i)-th variable, (delta bar{{x}_{i}}). Then ({M}_{{ij}}) = + if (delta bar{{x}_{i}}) always has the same sign as (u); ({M}_{{ij}}=) − if (delta bar{{x}_{i}}) always has the opposite sign as (u); ({M}_{{ij}}) = 0 if always (delta bar{{x}_{i}}=0); regardless of the system parameters. Conversely, if the sign does depend on the system parameters, we set ({M}_{{ij}}) = ?.In this section we prove that the influence matrix of the honey bee health system is structurally determined, i.e., there are no “?”‘ entries in (M).We start with the following proposition.
Proposition 1
Assume that a matrix
(J)
is Hurwitz stable (i.e., all its eigenvalues have negative real part) and has the sign pattern
$${sign}left(Jright)=left[begin{array}{cccc}- & – & – & -\ – & – & 0 & 0\ – &+& – &+\ – &+& 0 & -end{array}right]$$
Then, the sign pattern of
({adj}left(-Jright))
, the adjoint of
(-J)
, is
$${sign}left({adj}left(-Jright)right)=left[begin{array}{cccc}+& – & – & -\ – &+&+&+\ – &+&+&+\ – &+&+&+end{array}right]$$
Proof To prove the statement, we just change the sign of the first variable, hence we change sign to the first row and column of matrix (J). The resulting matrix (M) is such that$${sign}left(Mright)=left[begin{array}{cccc}- &+&+&+\+& – & 0 & 0\+&+& – &+\+&+& 0 & -end{array}right]$$We observe that (M) is a Metzler matrix, namely, all its off-diagonal entries are non-negative. Moreover, the matrix is Hurwitz stable. Then, we can proceed as in the proof of Proposition 4 in a previous report16. Given a Metzler matrix that is Hurwitz stable, its inverse has non-positive entries; hence, the inverse of (-M) has non-negative entries: ({left(-Mright)}^{-1}ge 0) elementwise. Moreover, we observe that(,M) is an irreducible matrix, i.e., there is no variable permutation that brings the matrix in a block (either upper or lower) triangular form. This implies that the inverse of (-M) has strictly positive entries: ({left(-Mright)}^{-1} , > , 0) elementwise. Also, stability implies that the determinant of (-M) is positive: ({det }left(-Mright) , > , 0). Then, ({adj}left(-Mright)={left(-Mright)}^{-1}{det }left(-Mright) > 0), hence the adjoint of (-M) is also positive elementwise. To consider again the original sign of the variables, we change sign to the first row and column of ({adj}left(-Mright)), and we get the signature above for ({adj}left(-Jright)).The next step is the characterization of the structural influence matrix, which corresponds to the sign pattern of the adjoint of the negative Jacobian matrix in Proposition 1.To this aim, we first consider the linearized system and write it in a matrix-vector form$$dot{x}left(tright)={Jx}left(tright)+{e}_{j}u$$where (dot{x}left(tright)) is the time derivative of the four-dimensional vector (xleft(tright)) and ({e}_{k}), (k={{{{mathrm{1,2,3,4}}}}}), is an input vector, constant in time, with a single non-zero component, the (k)-th, equal to 1, while the scalar (u , > , 0) is the magnitude of the input. We wish to assess the (i)-th component of (xleft(tright)), ({x}_{i}left(tright)={e}_{i}^{T}xleft(tright)). If (J) is Hurwitz, as assumed, the steady-state value of variable ({x}_{i}left(tright)) due to the input perturbation ({e}_{k}) applied to the equation of variable ({x}_{k}left(tright)) is achieved for$$0=Jbar{x}+{e}_{k}u,$$namely$${x}_{i}=-{e}_{i}^{T}{J}^{-1}{e}_{k}u,$$which implies that the sign of the steady-state value ({bar{x}}_{i}) of variable ({x}_{i}) due to a persistent positive input acting on the (k)-th equation has the same sign as ({(-{J}^{-1})}_{{ik}}), the (left(i,kright)) entry of matrix ({left(-Jright)}^{-1}). Since we assume Hurwitz stability, we have that ({det }left(-Jright)) is positive, hence the sign pattern of the inverse ({left(-Jright)}^{-1}) corresponds to the sign pattern of the adjoint, ({adj}left(-Jright)). In fact, ({adj}left(-Jright)={left(-Jright)}^{-1}{det }left(-Jright)).We next consider the nonlinear system under investigation, which we write in the form$$dot{x}left(tright)=fleft(xleft(tright)right)$$and without restriction we assume that the zero vector is an equilibrium point: (0=fleft(0right)). This condition can be always achieved, without loss of generality, by a translation of coordinates. We also consider a stable equilibrium: we assume that the linearized system at the equilibrium is asymptotically stable, namely its Jacobian (J), which has the sign pattern considered in Proposition 1 above, is Hurwitz. We also assume that a constant input perturbation of magnitude (u) is applied to the system, affecting the (k)-th equation, i.e.,$$dot{x}left(tright)=fleft(xleft(tright)right)+{e}_{k}u,$$and that the perturbation is small enough to keep the state in the domain of attraction of the considered equilibrium. Due to this perturbation, a new steady state (bar{x}left(uright)) is reached that satisfies the condition$$0=fleft(bar{x}left(uright)right)+{e}_{k}u$$To determine the sign of the new equilibrium components (bar{x}left(uright)), we consider this new equilibrium vector as a function of (u) in a small interval (left[0,{x}_{{MAX}}right]). Adopting the implicit function theorem yields$$frac{d}{{dx}}bar{x}left(uright)=-J{left(uright)}^{-1}{e}_{k}u,$$where we have denoted by (Jleft(uright)) the Jacobian matrix computed at the perturbed equilibrium (bar{x}left(uright)). Hence, for (u) small enough, the sign of the derivatives of the entries of the new, perturbed equilibrium are, structurally, the same as those in the (k)-th column of matrix (-{J}^{-1}). Since, by construction, (xleft(0right)=0), this is also the sign of the elements of vector (bar{x}left(uright)), for (u) in the interval (left[0,{x}_{{MAX}}right]).We have therefore proved that the original nonlinear system describing honey bee health admits the following structural influence matrix:$$left[begin{array}{cccc}+& – & – & -\ – &+&+&+\ – &+&+&+\ – &+&+&+end{array}right]$$System equilibriaThe results concerning the system equilibria were obtained through a standard analytical treatment of the nonlinear equations describing the equilibrium conditions of the system of differential Eqs. (1), (2), (3), (4). A detailed description of methods is reported in Supplementary Methods.Laboratory experiments using honey beesTo confirm the bistability of the system representing honey bee health as affected by multiple stressors, we used data from several survival experiments, carried out in a laboratory environment according to the same standardized method, over a 6-year period (Source data file).All experiments involved Apis mellifera worker bees, sampled at the larval stage or before eclosion, from the hives of the experimental apiary of the University of Udine (46°04′54.2″N, 13°12′34.2″E). Previous studies indicated that the local bee population consists of hybrids between A. mellifera ligustica and A.m. carnica62,63. Ethical approval was not required for this study.We considered experiments on the effect of the following stressors: infection with 1000 DWV genome copies administered through the diet before pupation, feeding with a 50 ppm nicotine in a sugar solution at the adult stage, exposition to a sub-optimal temperature of 32 °C at the adult stage. All experiments were replicated 3 to 13 times, using, in total, the number of bees reported in Table 1.For the artificial infection with DWV, we collected with soft forceps individual L4 larvae from the brood cells of several combs. Groups of 20–30 of such larvae were placed in Petri dishes with an artificial diet made of 50% royal jelly, 37% distilled water, 6% glucose, 6% fructose, and 1% yeast. 25 DWV copies per mg of diet were added or not to the diet according to the experimental group (note that a bee larva at this stage consumes about 40 mg of larval food per day, thus the viral infection per bee was 1000 viral copies). After 24 h larvae were transferred onto a piece of filter paper to remove the residues of the diet and then into a clean Petri dish, where they were maintained until eclosion. At the day of emergence, bees were transferred to plastic cages in a thermostatic cabinet, where they were kept until death. The DWV extract was prepared according to previously described protocols64 and quantified according to standard methods.For the treatment with nicotine, 10 µL of pure nicotine were added to 200 g of the sugar solution used for the feeding of the caged bees, to reach the concentration of 50 ppm.Finally, to expose bees to a 32 °C temperature, the plastic cages with the adult bees were kept in a thermostatic cabinet whose temperature was set accordingly.To monitor the survival of the adult bees treated as above, they were maintained from eclosion until death in plastic cages in a dark incubator at 34.5 °C (or 32 °C, according to the experiment), 75% R.H.; two syringes were used to supply a sugar solution made of 2.4 mol/L of glucose and fructose (61% and 31%, respectively) and water, respectively; dead bees were counted daily.All the results of these experiments are reported in Source data file.All experiments were carried out during the summer months, from June to September for 6 consecutive years. Previous data indicated that, in this region, virus prevalence increases along the active season starting from very low levels in spring and reaching 100% of virus-infected honey bees by the end of the summer; virus abundance in infected honey bees follows a similar trend28. For this reason, it can be assumed that bees sampled early in the season are either uninfected or they bear only a very low viral infection level, whereas bees sampled later in the season are likely to be virus-infected, bearing moderate to high viral infections. To confirm this assumption and identify a method for filtering our data according to viral infection, we assessed viral infection in a sample of bees from the untreated control group of each experiment, by means of qRT-PCR. According to standard practice, we assumed that Ct values below 30 are indicative of an effective viral infection, whereas Ct above that threshold are more likely in virus negative bees. As expected, we found that virus prevalence increases from June to September (Supplementary Figure 1a), in such a way that up to mid July only the minority of bees can be considered as viral infected (Supplementary Figure 1b). Therefore, we classified as “early” all the samples collected up to mid July and assumed that viral infection in those samples was low; on the other hand, samples collected from mid July till September were classified as “late” and we assumed that viral infection in those samples was high.qRT-PCR analysis of viral infection was carried out as follows. At the beginning of every experiment (i.e., at day 0), two to five bees for each replication were sampled in liquid nitrogen and transferred in a −80 °C refrigerator. After defrosting of samples in RNA later, the gut of each honey bee was eliminated to avoid the clogging of the mini spin column used after. The whole body of sampled bees was homogenized using a TissueLyser (Qiagen®, Germany). Total RNA was extracted from each bee according to the procedure provided with the RNeasy Plus mini kit (Qiagen®, Germany). The amount of RNA in each sample was quantified with a NanoDrop® spectrophotomer (ThermoFisher™, USA). cDNA was synthetized starting from 500 ng of RNA following the manufacturer specifications (PROMEGA, Italy). Additional negative control samples containing no RT enzyme were included. DWV presence was verified by qRT-PCR considering as positive all samples with a Ct value lower than 30. The following primers were adopted: DWV (F: GGTAAGCGATGGTTGTTTG, R: CCGTGAATATAGTGTGAGG65). 10 ng of cDNA from each sample were analyzed using SYBR®green dye (Ambion®) according to the manufacturer specifications, on a BioRad CFX96 Touch™ Real time PCR Detector. Primer efficiency was calculated according to the formula (E={10}^{left(-1/{{{{{{rm{slope}}}}}}}-1right)*100}). The following thermal cycling profiles were adopted: one cycle at 95 °C for 10 min, 40 cycles at 95 °C for 15 s and 60 °C for 1 min, and one cycle at 68 °C for 7 min.Individual survival and colony stabilityTo investigate how the death rate of forager bees affects colony growth, a compartment model of honey bee colony population dynamics was proposed50. This model showed that death rates over a critical threshold led to colony failure. Here we modified this model to include premature death of bees at younger age, as predicted by our model of individual bee health in the presence of an immuno-suppressive virus. We show that the critical threshold found in the previously published model50 becomes a decreasing function of the death rate of the younger individuals, so that premature death (and, in turn, immune-suppression) favors colony collapse.In more details, we first summarize the results of the previously published model50 where two populations (F) (forager) and (H) (hive) of bees are considered and where conditions are provided on the mortality (m) of (F) under which the whole population collapses: namely, mathematically stated, the system admits the zero equilibrium only. Here we extend the model partitioning (H) in two categories, (Y) (younger hive bees) and (O) (older hive bees), asintroducing an early mortality factor (n) for the young population, showing how such a factor worsens the collapsing condition.The previously published model50 concerns the interaction between hive bees (H) and forager bees (F) and is described by the ODEs$$dot{H}=Lfrac{H+F}{w+H+F}-Hleft(alpha -sigma frac{F}{H+F}right)$$$$dot{F}=Hleft(alpha -sigma frac{F}{H+F}right)-{mF}.$$Above, (L) is the queen’s eggs laying rate, (w) is the rate at which (L) is reached as the total population (H+F) gets large, (alpha) is the maximum rate at which hive bees become forager bees in the absence of the latter, (sigma) measures the reduction of recruitment of hive bees in the presence of forager bees and, finally, (m) is the death rate of forager bees (while the death rate of hive bees is assumed to be negligible).We first summarize the main results in terms of a threshold value for (m) in view of colony collapse, as our further analysis will follow a similar approach. All the parameters are assumed to be positive.The search for the equilibria of the above ODEs leads to the unique nontrivial equilibrium (beyond the trivial one)$$bar{H}=frac{L}{{mJ}}-frac{w}{1+J}$$$$bar{F}=Jbar{H}$$for$$J=Jleft(mright):=frac{alpha -sigma -m+sqrt{{left(alpha -sigma -mright)}^{2}+4malpha }}{2m}.$$Note that (J) is alway positive (and, moreover, it is independent of (L) and (w)). It follows that (bar{F}) and (bar{H}) have the same sign, so that the existence of the nontrivial equilibrium is equivalent to (bar{F}+bar{H} , > , 0). It is not difficult to recover that$$bar{F}+bar{H}=frac{w}{m}left(lfrac{1+J}{J}-mright)$$where (l:=L/w) is introduced for brevity. Then if (alpha le l) we get$$bar{F}+bar{H}=frac{w}{m}left(lfrac{1+J}{J}-mright)ge frac{w}{m}left(alpha frac{1+J}{J}-mright)=frac{w}{m}left(sigma+{mJ}right) , > , 0,$$with the last equality following from$$alpha -sigma frac{J}{1+J}-{mJ}=0,$$which in turn comes from annihilating the right-hand side of the second ODE and from using (J=bar{F}/bar{H}) while searching for equilibria. We conclude that, independently of (m), the colony never collapses if the recruitment rate (alpha) of forager bees is sufficiently low.Hence, we assume (alpha , > , l). Observe that$$bar{F}+bar{H}iff l , > , Jleft(m-lright)$$guarantees existence whenever (m) is sufficiently small, viz. (mle l). Assume then (m , > , l), so that the above condition reads$$J , < , frac{l}{m-l}$$leading to the threshold condition$$m , < , bar{m}:=frac{l}{2}frac{alpha+sigma+sqrt{{left(alpha -sigma right)}^{2}+4sigma l}}{alpha -l}$$by using the definition of (J), see Eq. (2) the previously published model50.A standard stability analysis shows that, assuming (alpha,m , > , l), the nontrivial equilibrium is (globally) asymptotically stable whenever it exists (positive), i.e., whenever (m , < , bar{m}). Otherwise, the only (globally) attracting equilibrium is the trivial one, corresponding to colony collapse (see Fig. 5 for the previously published model50 or Fig. 4 for (n=0)). In the mathematical jargon, the disappearance of the positive equilibrium, for (m) exceeding (bar{m}), is referred to as a transcritical bifurcation43.Now, in view of the outcome of the analysis of our model of individual bee health, we introduce a mortality term for the younger bees. As forager bees are recruited from adult hive bees, we divide the class of hive bees (H) in younger (Y) and older (O), assuming that the former die at a rate (n), while the death rate of the latter remains negligible according to the previously published model50. Obviously, (H=Y+O). The original ODEs are consequently modified as$$dot{Y}=Lfrac{H+F}{w+H+F}-Y$$$$dot{O}=left(1-nright)Y-Hleft(alpha -sigma frac{F}{H+F}right)$$$$dot{F}=Hleft(alpha -sigma frac{F}{H+F}right)-{mF}.$$Note that the sum of the first two equations above gives$$dot{H}=Lfrac{H+F}{w+H+F}-Hleft(alpha -sigma frac{F}{H+F}right)-{nY}.$$The new negative mortality term for younger hive bees, (-{nY}), models the fact that only the younger hive bees die prematurely while the rest of the dynamics is unchanged with respect to the original model.The search for equilibria soon gives$$bar{Y}=Lfrac{bar{H}+bar{F}}{w+bar{H}+bar{F}}$$from the first ODE above, so that the remaining two equilibrium conditions lead to$$bar{H}=frac{{L}_{n}}{{mJ}}-frac{w}{1+J}$$$$bar{F}=Jbar{H}$$for the same (J) originally defined and ({L}_{n}:=Lleft(1-nright)) (note that (nin left({{{{mathrm{0,1}}}}}right)), and the case (n=0) brings us back to the original model). From this point on the analysis is the same as that previously summarized for the original model, but for replacing (L) with ({L}_{n}) and (l) with (l:=lleft(1-nright)). Consequently, by assuming (alpha,m , > , {l}_{n}) (which is less restrictive when (n , > , 0)), the threshold condition (m < bar{m}) becomes$$m , < , bar{m}left(nright):=frac{{l}_{n}}{2}frac{alpha+sigma+sqrt{{left(alpha -sigma right)}^{2}+4sigma {l}_{n}}}{alpha -{l}_{n}},$$which clearly returns the original threshold condition when (n=0). Since$$frac{dbar{m}}{{dn}}left(nright) , < , 0$$as it can be immediately verified, it follows that the critical value for (m), (bar{m}left(nright)), beyond which the colony system admits only the zero equilibrium, i.e., the transcritical bifurcation value, decreases with (n) (Fig. 4). We thus conclude that colony collapse is favored by the premature death of younger hive bees, possibly caused by a virus impairing the immune system as shown by the analysis of our model of individual bee health.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Cumer T, Machado AP, Dumont G, Bontzorlos VA, Ceccherelli R, Charter M, Dichmann K, Martens H-D, Kassinis N, Lourenço R, Manzia F, Ovari K, Prévost L, Rakovic M, Siverio F, Roulin A, and Goudet J (2021) Population genomics of barn owls in the Western Parlearctic; NCBI bio project PRJNA727977; https://doi.org/10.1093/molbev/msab343Machado AP, Cumer T, Iseli C, Beaudoing E, Dupasquier M, Guex N, Dichmann K, Lourenço R, Lusby J, Martens H-D, Prévost L, Ramsden D, Roulin A, and Goudet J (2021) Population genomics of barn owls in the British Isles; NCBI bio project PRJNA700797; https://doi.org/10.1111/mec.16250Anguita F and Hernán F (2000) The Canary Islands origin: A unifying model. J Volcanol Geotherm Res 103:1–26. Elsevier B.VAstle WJ, Elding H, Jiang T, Allen D, Ruklisa D, Mann AL, Mead D, Bouman H, Riveros-Mckay F, Kostadima MA, Lambourne JJ, Sivapalaratnam S, Downes K, Kundu K, Bomba L, Berentsen K, Bradley JR, Daugherty LC, Delaneau O, Freson K, Garner SF, Grassi L, Guerrero J, Haimel M, Janssen-Megens EM, Kaan A, Kamat M, Kim B, Mandoli A, Marchini J, Martens JHA, Meacham S, Megy K, O’Connell J, Petersen R, Sharifi N, Sheard SM, Staley JR, Tuna S, van der Ent M, Walter K, Wang SY, Wheeler E, Wilder SP, Iotchkova V, Moore C, Sambrook J, Stunnenberg HG, Di Angelantonio E, Kaptoge S, Kuijpers TW, Carrillo-de-Santa-Pau E, Juan D, Rico D, Valencia A, Chen L, Ge B, Vasquez L, Kwan T, Garrido-Martín D, Watt S, Yang Y, Guigo R, Beck S, Paul DS, Pastinen T, Bujold D, Bourque G, Frontini M, Danesh J, Roberts DJ, Ouwehand WH, Butterworth AS, Soranzo N (2016) The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease. Cell 167:1415–1429.e19. Cell PressCAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Balloux F (2004) Heterozygote excess in small populations and the heterozygote-excess effective population size. Evolution 58:1891–1900. Society for the Study of EvolutionPubMed 
Article 

Google Scholar 
Bannerman DA (1963) Birds of the Atlantic Islands. Vol. 1. A history of the birds of the Canary Islands and of the Salvages. Oliver & BoydBirdLife International (2019) The IUCN Red List of Threatened Species. Version 6.2Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120. Oxford University PressCAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Burri R, Antoniazza S, Gaigher A, Ducrest A-L, Simon C, Fumagalli L, Goudet J, Roulin A (2016) The genetic basis of color-related local adaptation in a ring-like colonization around the Mediterranean. Evolution 70:140–153PubMed 
Article 

Google Scholar 
Carine MA, Humphries CJ, Guma IR, Reyes-Betancort JA, Santos Guerra A (2009) Areas and algorithms: evaluating numerical approaches for the delimitation of areas of endemism in the Canary Islands archipelago. J Biogeogr 36:593–611Article 

Google Scholar 
Chen MH, Raffield LM, Mousas A, Sakaue S, Huffman JE, Moscati A, Trivedi B, Jiang T, Akbari P, Vuckovic D, Bao EL, Zhong X, Manansala R, Laplante V, Chen M, Lo KS, Qian H, Lareau CA, Beaudoin M, Hunt KA, Akiyama M, Bartz TM, Ben-Shlomo Y, Beswick A, Bork-Jensen J, Bottinger EP, Brody JA, van Rooij FJ, Chitrala K, Cho K, Choquet H, Correa A, Danesh J, Di Angelantonio E, Dimou N, Ding J, Elliott P, Esko T, Evans MK, Floyd JS, Broer L, Grarup N, Guo MH, Greinacher A, Haessler J, Hansen T, Howson JM, Huang QQ, Huang W, Jorgenson E, Kacprowski T, Kähönen M, Kamatani Y, Kanai M, Karthikeyan S, Koskeridis F, Lange LA, Lehtimäki T, Lerch MM, Linneberg A, Liu Y, Lyytikäinen LP, Manichaikul A, Martin HC, Matsuda K, Mohlke KL, Mononen N, Murakami Y, Nadkarni GN, Nauck M, Nikus K, Ouwehand WH, Pankratz N, Pedersen O, Preuss M, Psaty BM, Raitakari OT, Roberts DJ, Rich SS, Rodriguez BAT, Rosen JD, Rotter JI, Schubert P, Spracklen CN, Surendran P, Tang H, Tardif JC, Trembath RC, Ghanbari M, Völker U, Völzke H, Watkins NA, Zonderman AB, Wilson PWF, Li Y, Butterworth AS, Gauchat JF, Chiang CWK, Li B, Loos RJF, Astle WJ, Evangelou E, van Heel DA, Sankaran VG, Okada Y, Soranzo N, Johnson AD, Reiner AP, Auer PL, Lettre G (2020) Trans-ethnic and ancestry-specific blood-cell genetics in 746,667 individuals from 5 global populations. Cell 182:1198–1213.e14CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Clements JF, Schulenberg TS, Iliff MJ, Billerman SM, Fredericks TA, Sullivan BL, and Wood CL (2019) The eBird/clements checklist of birds of the world: v2019Cruickshank TE, Hahn MW (2014) Reanalysis suggests that genomic islands of speciation are due to reduced diversity, not reduced gene flow. Mol Ecol 23:3133–3157PubMed 
Article 

Google Scholar 
Cumer T, Machado AP, Dumont G, Bontzorlos VA, Ceccherelli R, Charter M, Dichmann K, Martens H-D, Kassinis N, Lourenço R, Manzia F, Ovari K, Prévost L, Rakovic M, Siverio F, Roulin A, and Goudet J (2021) Landscape and climatic variations of the Quaternary shaped multiple secondary contacts among barn owls (Tyto alba) of the Western Palearctic. Mol Biol Evol, msab343, https://doi.org/10.1093/molbev/msab343.Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G, Durbin R (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Darre MJ, Harrison PC (1987) Heart rate, blood pressure, cardiac output, and total peripheral resistance of single comb White Leghorn hens during an acute exposure to 35 C ambient temperature. Poult Sci 66:541–547CAS 
PubMed 
Article 

Google Scholar 
Dolédec S, Chessel D, Gimaret-Carpentier C (2000) Niche separation in community analysis: a new method. Ecology 81:2914–2927. John Wiley & Sons, LtdArticle 

Google Scholar 
Ehret GB, Ferreira T, Chasman DI, Jackson AU, Schmidt EM, Johnson T, Thorleifsson G, Luan J, Donnelly LA, Kanoni S, Petersen AK, Pihur V, Strawbridge RJ, Shungin D, Hughes MF, Meirelles O, Kaakinen M, Bouatia-Naji N, Kristiansson K, Shah S, Kleber ME, Guo X, Lyytikäinen LP, Fava C, Eriksson N, Nolte IM, Magnusson PK, Salfati EL, Rallidis LS, Theusch E, Smith AJP, Folkersen L, Witkowska K, Pers TH, Joehanes R, Kim SK, Lataniotis L, Jansen R, Johnson AD, Warren H, Kim YJ, Zhao W, Wu Y, Tayo BO, Bochud M, Absher D, Adair LS, Amin N, Arking DE, Axelsson T, Baldassarre D, Balkau B, Bandinelli S, Barnes MR, Barroso I, Bevan S, Bis JC, Bjornsdottir G, Boehnke M, Boerwinkle E, Bonnycastle LL, Boomsma DI, Bornstein SR, Brown MJ, Burnier M, Cabrera CP, Chambers JC, Chang IS, Cheng CY, Chines PS, Chung RH, Collins FS, Connell JM, Döring A, Dallongeville J, Danesh J, De Faire U, Delgado G, Dominiczak AF, Doney ASF, Drenos F, Edkins S, Eicher JD, Elosua R, Enroth S, Erdmann J, Eriksson P, Esko T, Evangelou E, Evans A, Fall T, Farrall M, Felix JF, Ferrières J, Ferrucci L, Fornage M, Forrester T, Franceschini N, Franco OH, Franco-Cereceda A, Fraser RM, Ganesh SK, Gao H, Gertow K, Gianfagna F, Gigante B, Giulianini F, Goel A, Goodall AH, Goodarzi MO, Gorski M, Gräßler J, Groves CJ, Gudnason V, Gyllensten U, Hallmans G, Hartikainen AL, Hassinen M, Havulinna AS, Hayward C, Hercberg S, Herzig KH, Hicks AA, Hingorani AD, Hirschhorn JN, Hofman A, Holmen J, Holmen OL, Hottenga JJ, Howard P, Hsiung CA, Hunt SC, Ikram MA, Illig T, Iribarren C, Jensen RA, Kähönen M, Kang HM, Kathiresan S, Keating BJ, Khaw KT, Kim YK, Kim E, Kivimaki M, Klopp N, Kolovou G, Komulainen P, Kooner JS, Kosova G, Krauss RM, Kuh D, Kutalik Z, Kuusisto J, Kvaløy K, Lakka TA, Lee NR, Te Lee I, Lee WJ, Levy D, Li X, Liang KW, Lin H, Lin L, Lindström J, Lobbens S, Männistö S, Müller G, Müller-Nurasyid M, Mach F, Markus HS, Marouli E, McCarthy MI, McKenzie CA, Meneton P, Menni C, Metspalu A, Mijatovic V, Moilanen L, Montasser ME, Morris AD, Morrison AC, Mulas A, Nagaraja R, Narisu N, Nikus K, O’Donnell CJ, O’Reilly PF, Ong KK, Paccaud F, Palmer CD, Parsa A, Pedersen NL, Penninx BW, Perola M, Peters A, Poulter N, Pramstaller PP, Psaty BM, Quertermous T, Rao DC, Rasheed A, Rayner NW, Renström F, Rettig R, Rice KM, Roberts R, Rose LM, Rossouw J, Samani NJ, Sanna S, Saramies J, Schunkert H, Sebert S, Sheu WHH, Shin YA, Sim X, Smit JH, Smith AV, Sosa MX, Spector TD, Stančáková A, Stanton AV, Stirrups KE, Stringham HM, Sundstrom J, Swift AJ, Syvänen AC, Tai ES, Tanaka T, Tarasov KV, Teumer A, Thorsteinsdottir U, Tobin MD, Tremoli E, Uitterlinden AG, Uusitupa M, Vaez A, Vaidya D, Van Duijn CM, Van Iperen EPA, Vasan RS, Verwoert GC, Virtamo J, Vitart V, Voight BF, Vollenweider P, Wagner A, Wain LV, Wareham NJ, Watkins H, Weder AB, Westra HJ, Wilks R, Wilsgaard T, Wilson JF, Wong TY, Yang TP, Yao J, Yengo L, Zhang W, Zhao JH, Zhu X, Bovet P, Cooper RS, Mohlke KL, Saleheen D, Lee JY, Elliott P, Gierman HJ, Willer CJ, Franke L, Hovingh GK, Taylor KD, Dedoussis G, Sever P, Wong A, Lind L, Assimes TL, Njølstad I, Schwarz PEH, Langenberg C, Snieder H, Caulfield MJ, Melander O, Laakso M, Saltevo J, Rauramaa R, Tuomilehto J, Ingelsson E, Lehtimäki T, Hveem K, Palmas W, März W, Kumari M, Salomaa V, Chen YDI, Rotter JI, Froguel P, Jarvelin MR, Lakatta EG, Kuulasmaa K, Franks PW, Hamsten A, Wichmann HE, Palmer CNA, Stefansson K, Ridker PM, Loos RJF, Chakravarti A, Deloukas P, Morris AP, Newton-Cheh C, Munroe PB (2016) The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals. Nat Genet 48:1171–1184. Nature Publishing GroupCAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Exposito-Alonso M (2017) rbioclim: Improved getData function from the raster R package to interact with past, present and future climate data from worldclim.orgFirmat C, Gomes Rodrigues H, Renaud S, Claude J, Hutterer R, Garcia-Talavera F, Michaux J (2010) Mandible morphology, dental microwear, and diet of the extinct giant rats Canariomys (Rodentia: Murinae) of the Canary Islands (Spain). Biol J Linn Soc 101:28–40. Blackwell Publishing LtdArticle 

Google Scholar 
Foll M, Gaggiotti O (2008) A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics 180:977–993. Oxford AcademicPubMed 
PubMed Central 
Article 

Google Scholar 
Frankham R (1997) Do island populations have less genetic variation than mainland populations? Heredity 78:311–327PubMed 
Article 

Google Scholar 
Frichot E, Mathieu F, Trouillon T, Bouchard G, François O (2014) Fast and efficient estimation of individual ancestry coefficients. Genetics 196:973–983PubMed 
PubMed Central 
Article 

Google Scholar 
GBIF.org (2021) GBIF Occurrence Download https://doi.org/10.15468/dl.5pd26sGe SX, Jung D, Jung D, Yao R (2020) ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics 36:2628–2629. Oxford University PressCAS 
PubMed 
Article 

Google Scholar 
German CA, Sinsheimer JS, Klimentidis YC, Zhou H, Zhou JJ (2020) Ordered multinomial regression for genetic association analysis of ordinal phenotypes at Biobank scale. Genet Epidemiol 44:248–260. Wiley-Liss IncPubMed 
Article 

Google Scholar 
Gillespie R (2004) Community assembly through adaptive radiation in Hawaiian spiders. Science 303:356–359CAS 
PubMed 
Article 

Google Scholar 
Gillespie R, Croom H, Hasty G (1997) Phylogenetic relationships and adaptive shifts among major clades of tetragnatha spiders (Araneae: Tetragnathidae) in Hawai’i. Pac Sci 51:380–394CAS 

Google Scholar 
Goudet J (2005) HIERFSTAT, a package for R to compute and test hierarchical F -statistics. Mol Ecol Notes 5:184–186Article 

Google Scholar 
Goudet J, Kay T, Weir BS (2018) How to estimate kinship. Mol Ecol 27:4121–4135. Blackwell Publishing LtdPubMed 
PubMed Central 
Article 

Google Scholar 
Graffelman J (2015) Exploring diallelic genetic markers: the {HardyWeinberg. } Package J Stat Softw 64:1–23
Google Scholar 
Graffelman J, Morales-Camarena J (2008) Graphical tests for Hardy-Weinberg Equilibrium based on the ternary plot. Hum Hered 65:77–84PubMed 
Article 

Google Scholar 
Grant PR (1999) Ecology and Evolution of Darwin’s Finches. Princeton University PressGrant PR (1998) Evolution on Islands. Oxford University Press, Oxford, UK
Google Scholar 
Gu J, Liang Q, Liu C, Li S (2020) Genomic analyses reveal adaptation to hot arid and harsh environments in native chickens of China. Front Genet 11:582355CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Halonen JI, Zanobetti A, Sparrow D, Vokonas PS, Schwartz J (2011) Relationship between outdoor temperature and blood pressure. Occup Environ Med 68:296–301PubMed 
Article 

Google Scholar 
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. John Wiley & Sons, LtdArticle 

Google Scholar 
Hoffmann TJ, Ehret GB, Nandakumar P, Ranatunga D, Schaefer C, Kwok PY, Iribarren C, Chakravarti A, Risch N (2017) Genome-wide association analyses using electronic health records identify new loci influencing blood pressure variation. Nat Genet 49:54–64CAS 
PubMed 
Article 

Google Scholar 
Hutterer R, Lopez-Jurado LF, Vogel P (1987) The shrews of the eastern Canary Islands: a new species (mammalia: Soricidae). J Nat Hist 21:1347–1357Article 

Google Scholar 
Illera JC, Spurgin LG, Rodriguez-Exposito E, Nogales M, Rando JC (2016) What are we learning about speciation and extinction from the Canary Islands? Ardeola 63:15–33Article 

Google Scholar 
Irwin DE, Alcaide M, Delmore KE, Irwin JH, Owens GL (2016) Recurrent selection explains parallel evolution of genomic regions of high relative but low absolute differentiation in a ring species. Mol Ecol 25:4488–4507PubMed 
Article 

Google Scholar 
Irwin DE, Milá B, Toews DPL, Brelsford A, Kenyon HL, Porter AN, Grossen C, Delmore KE, Alcaide M, Irwin JH (2018) A comparison of genomic islands of differentiation across three young avian species pairs. Mol Ecol 27:4839–4855CAS 
PubMed 
Article 

Google Scholar 
Juan C, Emerson BC, Oromí P, and Hewitt GM (2000) Colonization and diversification: towards a phylogeographic synthesis for the Canary Islands. Elsevier Ltd.Keller LF, Waller DM (2002) Inbreeding effects in wild populations. Trends Ecol Evol 17:230–241Article 

Google Scholar 
Kichaev G, Bhatia G, Loh PR, Gazal S, Burch K, Freund MK, Schoech A, Pasaniuc B, Price AL (2019) Leveraging polygenic functional enrichment to improve GWAS power. Am J Hum Genet 104:65–75. Cell PressCAS 
PubMed 
Article 

Google Scholar 
Korneliussen TS, Albrechtsen A, Nielsen R (2014) ANGSD: analysis of next generation sequencing data. BMC Bioinforma 15:1–13. BioMed Central LtdArticle 

Google Scholar 
Kulminski AM, Huang J, Loika Y, Arbeev KG, Bagley O, Yashkin A, Duan M, Culminskaya I (2018) Strong impact of natural-selection-free heterogeneity in genetics of age-related phenotypes. Aging 10:492–514. Impact Journals LLCPubMed 
PubMed Central 
Article 

Google Scholar 
Lamichhaney S, Berglund J, Almén MS, Maqbool K, Grabherr M, Martinez-Barrio A, Promerová M, Rubin CJ, Wang C, Zamani N, Grant BR, Grant PR, Webster MT, Andersson L (2015) Evolution of Darwin’s finches and their beaks revealed by genome sequencing. Nature 518:371–375CAS 
PubMed 
Article 

Google Scholar 
Lenormand T (2002) Gene flow and the limits to natural selection. Trends Ecol Evol 17:183–189. Elsevier LtdArticle 

Google Scholar 
Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25:1754–1760. Oxford University PressCAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li H, Durbin R (2011) Inference of human population history from individual whole-genome sequences. Nature 475:493–496CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lifjeld JT, Anmarkrud JA, Calabuig P, Cooper JEJ, Johannessen LE, Johnsen A, Kearns AM, Lachlan RF, Laskemoen T, Marthinsen G, Stensrud E, García-Del-Rey E (2016) Species-level divergences in multiple functional traits between the two endemic subspecies of Blue Chaffinches Fringilla teydea in Canary Islands. BMC Zool 1:1–19. BioMed Central LtdArticle 

Google Scholar 
Liu C, Kraja AT, Smith JA, Brody JA, Franceschini N, Bis JC, Rice K, Morrison AC, Lu Y, Weiss S, Guo X, Palmas W, Martin LW, Chen YDI, Surendran P, Drenos F, Cook JP, Auer PL, Chu AY, Giri A, Zhao W, Jakobsdottir J, Lin LA, Stafford JM, Amin N, Mei H, Yao J, Voorman A, Larson MG, Grove ML, Smith AV, Hwang SJ, Chen H, Huan T, Kosova G, Stitziel NO, Kathiresan S, Samani N, Schunkert H, Deloukas P, Li M, Fuchsberger C, Pattaro C, Gorski M, Kooperberg C, Papanicolaou GJ, Rossouw JE, Faul JD, Kardia SLR, Bouchard C, Raffel LJ, Uitterlinden AG, Franco OH, Vasan RS, O’Donnell CJ, Taylor KD, Liu K, Bottinger EP, Gottesman O, Daw EW, Giulianini F, Ganesh S, Salfati E, Harris TB, Launer LJ, Dörr M, Felix SB, Rettig R, Völzke H, Kim E, Lee WJ, Te Lee I, Sheu WHH, Tsosie KS, Edwards DRV, Liu Y, Correa A, Weir DR, Völker U, Ridker PM, Boerwinkle E, Gudnason V, Reiner AP, Van Duijn CM, Borecki IB, Edwards TL, Chakravarti A, Rotter JI, Psaty BM, Loos RJF, Fornage M, Ehret GB, Newton-Cheh C, Levy D, Chasman DI (2016) Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci. Nat Genet 48:1162–1170CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Losos JB, Jackman TR, Larson A, De Queiroz K, Rodríguez-Schettino L (1998) Contingency and determinism in replicated adaptive radiations of island lizards. Science 279:2115–2118CAS 
PubMed 
Article 

Google Scholar 
Losos JB, Ricklefs RE (2009) Adaptation and diversification on islands. Nature 457:830–6. Nature Publishing GroupCAS 
PubMed 
Article 

Google Scholar 
MacArthur RH, Wilson EO (1963) An equilibrium theory of insular zoogeography. Evolution 17:373–387Article 

Google Scholar 
MacArthur RH and Wilson EO (1967) The theory of island biogeography. Princeton University PressMachado AP, Clément L, Uva V, Goudet J, Roulin A (2018) The Rocky Mountains as a dispersal barrier between barn owl (Tyto alba) populations in North America. J Biogeogr 45:1288–1300Article 

Google Scholar 
Machado AP, Cumer T, Iseli C, Beaudoing E, Dupasquier M, Guex N, Dichmann K, Lourenço R, Lusby J, Martens H-D, Prévost L, Ramsden D, Roulin A, Goudet J (2021) Unexpected post-glacial colonisation route explains the white colour of barn owls (Tyto alba) from the British Isles. Mol Ecol 1–16. https://doi.org/10.1111/mec.16250Machado AP, Topaloudis A, Cumer T, Lavanchy E, Bontzorlos VA, Ceccherelli R, Charter M, Kassinis N, Lymberakis P, Manzia F, Ducrest AL, Dupasquier M, Guex N, Roulin A, Goudet J (2022) Genomic consequences of colonisation, migration and genetic drift in barn owl insular populations of the eastern Mediterranean. Mol Ecol 31:1375–1388Malinsky M, Challis RJ, Tyers AM, Schiffels S, Terai Y, Ngatunga BP, Miska EA, Durbin R, Genner MJ, Turner GF (2015) Genomic islands of speciation separate cichlid ecomorphs in an East African crater lake. Science 350:1493–1498CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Martín A, Lorenzo JA (2001) Aves del archipiélago canario. Editor, Francisco Lemus
Google Scholar 
Martin SH, Van Belleghem SM (2017) Exploring evolutionary relationships across the genome using topology weighting. Genetics 206:429–438. Genetics Society of AmericaPubMed 
PubMed Central 
Article 

Google Scholar 
Masseti M (2010) Mammals of the Macaronesian islands (the Azores, Madeira, the Canary and Cape Verde islands): redefinition of the ecological equilibrium. Mammalia 74:3–34Article 

Google Scholar 
Mateo JA, Crochet PA, Afonso OM (2011) The species diversity of the genus Gallotia (Sauria: Lacertidae) during the Holocene on La Gomera (Canary Islands) and the Latin names of Gomeran giant lizards. Zootaxa 2755:66–68Article 

Google Scholar 
Molina-Borja M (2003) Sexual dimorphism of Gallotia atlantica atlantica and Gallotia atlantica mahoratae (Lacertidae) from the Eastern Canary Islands. J Herpetol 37:769–772Article 

Google Scholar 
Nadachowska-Brzyska K, Li C, Smeds L, Zhang G, Ellegren H (2015) Temporal dynamics of avian populations during pleistocene revealed by whole-genome sequences. Curr Biol 25:1375–1380. https://doi.org/10.1016/j.cub.2015.03.047CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Newton-Cheh C, Johnson T, Gateva V, Tobin MD, Bochud M, Coin L, Najjar SS, Zhao JH, Heath SC, Eyheramendy S, Papadakis K, Voight BF, Scott LJ, Zhang F, Farrall M, Tanaka T, Wallace C, Chambers JC, Khaw KT, Nilsson P, Van Der Harst P, Polidoro S, Grobbee DE, Onland-Moret NC, Bots ML, Wain LV, Elliot KS, Teumer A, Luan J, Lucas G, Kuusisto J, Burton PR, Hadley D, McArdle WL, Brown M, Dominiczak A, Newhouse SJ, Samani NJ, Webster J, Zeggini E, Beckmann JS, Bergmann S, Lim N, Song K, Vollenweider P, Waeber G, Waterworth DM, Yuan X, Groop L, Orho-Melander M, Allione A, Di Gregorio A, Guarrera S, Panico S, Ricceri F, Romanazzi V, Sacerdote C, Vineis P, Barroso I, Sandhu MS, Luben RN, Crawford GJ, Jousilahti P, Perola M, Boehnke M, Bonnycastle LL, Collins FS, Jackson AU, Mohlke KL, Stringham HM, Valle TT, Willer CJ, Bergman RN, Morken MA, Döring A, Gieger C, Illig T, Meitinger T, Org E, Pfeufer A, Wichmann HE, Kathiresan S, Marrugat J, O’Donnell CJ, Schwartz SM, Siscovick DS, Subirana I, Freimer NB, Hartikainen AL, McCarthy MI, O’Reilly PF, Peltonen L, Pouta A, De Jong PE, Snieder H, Van Gilst WH, Clarke R, Goel A, Hamsten A, Altshuler D, Jarvelin MR, Elliott P, Lakatta EG, Forouhi N, Wareham NJ, Loos RJF, Deloukas P, Lathrop GM, Zelenika D, Strachan DP, Soranzo N, Williams FM, Zhai G, Spector TD, Peden JF, Watkins H, Ferrucci L, Caulfield M, Munroe PB, Berglund G, Melander O, Matullo G, Uiterwaal CS, van der Schouw YT, Numans ME, Ernst F, Homuth G, Völker U, Elosua R, Laakso M, Connell JM, Mooser V, Salomaa V, Tuomilehto J, Laan M, Navis G, Seedorf U, Syvänen AC, Tognoni G, Sanna S, Uda M, Scheet P, Schlessinger D, Scuteri A, Dörr M, Felix SB, Reffelmann T, Lorbeer R, Völzke H, Rettig R, Galan P, Hercberg S, Bingham SA, Kooner JS, Bandinelli S, Meneton P, Abecasis G, Thompson JR, Braga Marcano CA, Barke B, Dobson R, Gungadoo J, Lee KL, Onipinla A, Wallace I, Xue M, Clayton DG, Leung HT, Nutland S, Walker NM, Todd JA, Stevens HE, Dunger DB, Widmer B, Downes K, Cardon LR, Kwiatkowski DP, Barrett JC, Evans D, Morris AP, Lindgren CM, Rayner NW, Timpson NJ, Lyons E, Vannberg F, Hill AVS, Teo YY, Rockett KA, Craddock N, Attwood AP, Bryan C, Bumpstead SJ, Chaney A, Ghori J, William RG, Hunt SE, Inouye M, Keniry E, King E, McGinnis R, Potter S, Ravindrarajan R, Whittaker P, Withers D, Bentley D, Groves CJ, Duncanson A, Ouwehand WH, Boorman JP, Cant B, Jolley JD, Knight AS, Koch K, Taylor NC, Watkins NA, Winzer T, Braund PS, Dixon RJ, Mangino M, Stevens S, Donnely P, Davidson D, Marchini JL, Spencer ICA, Cardin NJ, Ferreira T, Pereira-Gale J, Hallgrimsdottir IB, Howie BN, Su Z, Vukcevic D, Easton D, Everson U, Hussey JM, Meech E, Prowse CV, Walters GR, Jones RW, Ring SM, Prembey M, Breen G, St. Clair D, Ceasar S, Gordon-Smith K, Fraser C, Green EK, Grozeva D, Hamshere ML, Holmans PA, Jones IR, Kirov G, Moskovina V, Nikolov I, O’Donovan MC, Owen MJ, Craddock N, Collier DA, Elkin A, Farmer A, Williamson R, McGruffin P, Young AH, Ferrier IN, Ball SG, Balmforth AJ, Barrett JH, Bishop DT, Iles MM, Maqbool A, Yuldasheva N, Hall AS, Bredin F, Tremelling M, Parkes M, Drummond H, Lees CW, Nimmo ER, Satsangi J, Fisher SA, Lewis CM, Onnie CM, Prescott NJ, Mathew CG, Forbes A, Sanderson J, Mathew C, Barbour J, Mohiuddin MK, Todhunter CE, Mansfield JC, Ahmad T, Cummings FR, Jewell DP, Barton A, Bruce IN, Donovan H, Eyre S, Gilbert PD, Hider SL, Hinks AM, John SL, Potter C, Silman AJ, Symmons DPM, Thomson W, Worthington J, Frayling TM, Freathy RM, Lango H, Perry JRB, Weedon MN, Hattersley AT, Shields BM, Hitman GA, Walker M, Newport M, Sirugo G, Conway D, Jallow M, Bradbury LA, Pointon JL, Brown MA, Farrar C, Wordsworth P, Franklyn JA, Heward JM, Simmonds MJ, Cough SCL, Seal S, Stratton MR, Ban M, Goris A, Sawcer SJ, Compston A (2009) Genome-wide association study identifies eight loci associated with blood pressure. Nat Genet 41:666–676CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nogales M, De León L, Gómez R (1998) On the presence of the endemic skink Chalcides simonyi Steind. 1891 in Lanzarote (Canary Islands). Amphib-Reptilia 19:427–430Article 

Google Scholar 
Nogales M, Rando JC, Valido A, Martín A (2001) Discovery of a living giant lizard, genus Gallotia (Reptilia: Lacertidae), from La Gomera, Canary Islands. Herpetologica 57:169–179
Google Scholar 
Norder SJ, Proios K, Whittaker RJ, Alonso MR, Borges PAV, Borregaard MK, Cowie RH, Florens FBV, de Frias Martins AM, Ibáñez M, Kissling WD, de Nascimento L, Otto R, Parent CE, Rigal F, Warren BH, Fernández-Palacios JM, van Loon EE, Triantis KA, Rijsdijk KF (2019) Beyond the Last Glacial Maximum: Island endemism is best explained by long-lasting archipelago configurations. Glob Ecol Biogeogr 28:184–197. Blackwell Publishing LtdArticle 

Google Scholar 
O’Brien KA, Simonson TS, and Murray AJ (2020) Metabolic adaptation to high altitude. Elsevier Ltd.Oskarsson GR, Oddsson A, Magnusson MK, Kristjansson RP, Halldorsson GH, Ferkingstad E, Zink F, Helgadottir A, Ivarsdottir EV, Arnadottir GA, Jensson BO, Katrinardottir H, Sveinbjornsson G, Kristinsdottir AM, Lee AL, Saemundsdottir J, Stefansdottir L, Sigurdsson JK, Davidsson OB, Benonisdottir S, Jonasdottir A, Jonasdottir A, Jonsson S, Gudmundsson RL, Asselbergs FW, Tragante V, Gunnarsson B, Masson G, Thorleifsson G, Rafnar T, Holm H, Olafsson I, Onundarson PT, Gudbjartsson DF, Norddahl GL, Thorsteinsdottir U, Sulem P, Stefansson K (2020) Predicted loss and gain of function mutations in ACO1 are associated with erythropoiesis. Commun Biol 3:1–10. Nature ResearchArticle 

Google Scholar 
Palacios CJ (2004) Current status and distribution of birds of prey in the Canary Islands. Bird Conserv Int 14:203–213Article 

Google Scholar 
Pestano J, Brown RP, Suárez NM, Benzal J, Fajardo S (2003) Intraspecific evolution of Canary Island Plecotine bats, based on mtDNA sequences. Heredity 90:302–307. Nature Publishing GroupCAS 
PubMed 
Article 

Google Scholar 
Pickrell J and Pritchard J (2012) Inference of population splits and mixtures from genome-wide allele frequency data. Nat Preced, https://doi.org/10.1038/npre.2012.6956.1. Springer Science and Business Media LLCPurcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ, Sham PC (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
R Development Core Team (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria
Google Scholar 
Rodríguez B, Rodríguez A, Siverio F, Siverio M (2018) Factors affecting the spatial distribution and breeding habitat of an insular cliff-nesting raptor community. Curr Zool 64:173–181PubMed 
Article 

Google Scholar 
Rodríguez A, Rodríguez B, Montelongo T, Garcia‐Porta J, Pipa T, Carty M, Danielsen J, Nunes J, Silva C, Geraldes P, Medina FM, and Illera JC (2020) Cryptic differentiation in the Manx Shearwater hinders the identification of a new endemic subspecies. J Avian Biol https://doi.org/10.1111/jav.02633Romano A, Séchaud R, Roulin A (2020) Geographical variation in bill size provides evidence for Allen’s rule in a cosmopolitan raptor. Glob Ecol Biogeogr 29:65–75Article 

Google Scholar 
Romano A, Séchaud R, Roulin A (2021) Evolution of wing length and melanin-based coloration in insular populations of a cosmopolitan raptor. J Biogeogr 48:961–973. Blackwell Publishing LtdArticle 

Google Scholar 
Senfeld T, Shannon TJ, van Grouw H, Paijmans DM, Tavares ES, Baker AJ, Lees AC, Collinson JM (2020) Taxonomic status of the extinct Canary Islands Oystercatcher Haematopus meadewaldoi. Ibis 162:1068–1074. Blackwell Publishing LtdArticle 

Google Scholar 
Siverio F (1998) Distribución y estatus de Tyto alba (Scopoli, 1769) en Tenerife, islas Canarias (Aves, Tytonidae). Vieraea 26:121–131
Google Scholar 
Siverio F (2007) Lechuza común, Tyto alba. In: Lorenzo JA (Ed.) Atlas de las aves nidificantes en el archipiélago canario (1997–2003). Dirección General de Conservación de la Naturaleza-Sociedad Española de Ornitología, Madrid, p 304–310
Google Scholar 
Stamatakis A (2014) RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30:1312–1313. Oxford University PressCAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Steinbauer MJ, Field R, Grytnes JA, Trigas P, Ah-Peng C, Attorre F, Birks HJB, Borges PAV, Cardoso P, Chou CH, De Sanctis M, de Sequeira MM, Duarte MC, Elias RB, Fernández-Palacios JM, Gabriel R, Gereau RE, Gillespie RG, Greimler J, Harter DEV, Huang TJ, Irl SDH, Jeanmonod D, Jentsch A, Jump AS, Kueffer C, Nogué S, Otto R, Price J, Romeiras MM, Strasberg D, Stuessy T, Svenning JC, Vetaas OR, Beierkuhnlein C (2016) Topography-driven isolation, speciation and a global increase of endemism with elevation. Glob Ecol Biogeogr 25:1097–1107. Blackwell Publishing LtdArticle 

Google Scholar 
Surendran P, Drenos F, Young R, Warren H, Cook JP, Manning AK, Grarup N, Sim X, Barnes DR, Witkowska K, Staley JR, Tragante V, Tukiainen T, Yaghootkar H, Masca N, Freitag DF, Ferreira T, Giannakopoulou O, Tinker A, Harakalova M, Mihailov E, Liu C, Kraja AT, Nielsen SF, Rasheed A, Samuel M, Zhao W, Bonnycastle LL, Jackson AU, Narisu N, Swift AJ, Southam L, Marten J, Huyghe JR, Stančáková A, Fava C, Ohlsson T, Matchan A, Stirrups KE, Bork-Jensen J, Gjesing AP, Kontto J, Perola M, Shaw-Hawkins S, Havulinna AS, Zhang H, Donnelly LA, Groves CJ, Rayner NW, Neville MJ, Robertson NR, Yiorkas AM, Herzig KH, Kajantie E, Zhang W, Willems SM, Lannfelt L, Malerba G, Soranzo N, Trabetti E, Verweij N, Evangelou E, Moayyeri A, Vergnaud AC, Nelson CP, Poveda A, Varga TV, Caslake M, De Craen AJM, Trompet S, Luan J, Scott RA, Harris SE, Liewald DCM, Marioni R, Menni C, Farmaki AE, Hallmans G, Renström F, Huffman JE, Hassinen M, Burgess S, Vasan RS, Felix JF, Uria-Nickelsen M, Malarstig A, Reilly DF, Hoek M, Vogt TF, Lin H, Lieb W, Traylor M, Markus HS, Highland HM, Justice AE, Marouli E, Lindström J, Uusitupa M, Komulainen P, Lakka TA, Rauramaa R, Polasek O, Rudan I, Rolandsson O, Franks PW, Dedoussis G, Spector TD, Jousilahti P, Männistö S, Deary IJ, Starr JM, Langenberg C, Wareham NJ, Brown MJ, Dominiczak AF, Connell JM, Jukema JW, Sattar N, Ford I, Packard CJ, Esko T, Mägi R, Metspalu A, De Boer RA, Van Der Meer P, Van Der Harst P, Gambaro G, Ingelsson E, Lind L, De Bakker PIW, Numans ME, Brandslund I, Christensen C, Petersen ERB, Korpi-Hyövälti E, Oksa H, Chambers JC, Kooner JS, Blakemore AIF, Franks S, Jarvelin MR, Husemoen LL, Linneberg A, Skaaby T, Thuesen B, Karpe F, Tuomilehto J, Doney ASF, Morris AD, Palmer CNA, Holmen OL, Hveem K, Willer CJ, Tuomi T, Groop L, Käräjämäki A, Palotie A, Ripatti S, Salomaa V, Alam DS, Majumder AAS, Di Angelantonio E, Chowdhury R, McCarthy MI, Poulter N, Stanton AV, Sever P, Amouyel P, Arveiler D, Blankenberg S, Ferrières J, Kee F, Kuulasmaa K, Müller-Nurasyid M, Veronesi G, Virtamo J, Deloukas P, Elliott P, Zeggini E, Kathiresan S, Melander O, Kuusisto J, Laakso M, Padmanabhan S, Porteous DJ, Hayward C, Scotland G, Collins FS, Mohlke KL, Hansen T, Pedersen O, Boehnke M, Stringham HM, Frossard P, Newton-Cheh C, Tobin MD, Nordestgaard BG, Caulfield MJ, Mahajan A, Morris AP, Tomaszewski M, Samani NJ, Saleheen D, Asselbergs FW, Lindgren CM, Danesh J, Wain LV, Butterworth AS, Howson JMM, Munroe PB (2016) Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension. Nat Genet 48:1151–1161. Nature Publishing GroupCAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Thorpe RS, Baez M (1993) Geographic variation in scalation of the lizard Gallotia stehlini within the island of Gran Canaria. Biol J Linn Soc 48:75–87. John Wiley & Sons, LtdArticle 

Google Scholar 
Tigano A, Friesen VL (2016) Genomics of local adaptation with gene flow. Mol Ecol 25:2144–2164PubMed 
Article 

Google Scholar 
Turcot V, Lu Y, Highland HM, Schurmann C, Justice AE, Fine RS, Bradfield JP, Esko T, Giri A, Graff M, Guo X, Hendricks AE, Karaderi T, Lempradl A, Locke AE, Mahajan A, Marouli E, Sivapalaratnam S, Young KL, Alfred T, Feitosa MF, Masca NGD, Manning AK, Medina-Gomez C, Mudgal P, Ng MCY, Reiner AP, Vedantam S, Willems SM, Winkler TW, Abecasis G, Aben KK, Alam DS, Alharthi SE, Allison M, Amouyel P, Asselbergs FW, Auer PL, Balkau B, Bang LE, Barroso I, Bastarache L, Benn M, Bergmann S, Bielak LF, Blüher M, Boehnke M, Boeing H, Boerwinkle E, Böger CA, Bork-Jensen J, Bots ML, Bottinger EP, Bowden DW, Brandslund I, Breen G, Brilliant MH, Broer L, Brumat M, Burt AA, Butterworth AS, Campbell PT, Cappellani S, Carey DJ, Catamo E, Caulfield MJ, Chambers JC, Chasman DI, Chen YDI, Chowdhury R, Christensen C, Chu AY, Cocca M, Cook JP, Corley J, Corominas Galbany J, Cox AJ, Crosslin DS, Cuellar-Partida G, D’Eustacchio A, Danesh J, Davies G, Bakker PIW, Groot MCH, Mutsert R, Deary IJ, Dedoussis G, Demerath EW, Heijer M, Hollander AI, Ruijter HM, Dennis JG, Denny JC, Angelantonio E, Drenos F, Du M, Dubé MP, Dunning AM, Easton DF, Edwards TL, Ellinghaus D, Ellinor PT, Elliott P, Evangelou E, Farmaki AE, Farooqi IS, Faul JD, Fauser S, Feng S, Ferrannini E, Ferrieres J, Florez JC, Ford I, Fornage M, Franco OH, Franke A, Franks PW, Friedrich N, Frikke-Schmidt R, Galesloot TE, Gan W, Gandin I, Gasparini P, Gibson J, Giedraitis V, Gjesing AP, Gordon-Larsen P, Gorski M, Grabe HJ, Grant SFA, Grarup N, Griffiths HL, Grove ML, Gudnason V, Gustafsson S, Haessler J, Hakonarson H, Hammerschlag AR, Hansen T, Harris KM, Harris TB, Hattersley AT, Have CT, Hayward C, He L, Heard-Costa NL, Heath AC, Heid IM, Helgeland Ø, Hernesniemi J, Hewitt AW, Holmen OL, Hovingh GK, Howson JMM, Hu Y, Huang PL, Huffman JE, Ikram MA, Ingelsson E, Jackson AU, Jansson JH, Jarvik GP, Jensen GB, Jia Y, Johansson S, Jørgensen ME, Jørgensen T, Jukema JW, Kahali B, Kahn RS, Kähönen M, Kamstrup PR, Kanoni S, Kaprio J, Karaleftheri M, Kardia SLR, Karpe F, Kathiresan S, Kee F, Kiemeney LA, Kim E, Kitajima H, Komulainen P, Kooner JS, Kooperberg C, Korhonen T, Kovacs P, Kuivaniemi H, Kutalik Z, Kuulasmaa K, Kuusisto J, Laakso M, Lakka TA, Lamparter D, Lange EM, Lange LA, Langenberg C, Larson EB, Lee NR, Lehtimäki T, Lewis CE, Li H, Li J, Li-Gao R, Lin H, Lin KH, Lin LA, Lin X, Lind L, Lindström J, Linneberg A, Liu CT, Liu DJ, Liu Y, Lo KS, Lophatananon A, Lotery AJ, Loukola A, Luan J, Lubitz SA, Lyytikäinen LP, Männistö S, Marenne G, Mazul AL, McCarthy MI, McKean-Cowdin R, Medland SE, Meidtner K, Milani L, Mistry V, Mitchell P, Mohlke KL, Moilanen L, Moitry M, Montgomery GW, Mook-Kanamori DO, Moore C, Mori TA, Morris AD, Morris AP, Müller-Nurasyid M, Munroe PB, Nalls MA, Narisu N, Nelson CP, Neville M, Nielsen SF, Nikus K, Njølstad PR, Nordestgaard BG, Nyholt DR, O’Connel JR, O’Donoghue ML, Olde Loohuis LM, Ophoff RA, Owen KR, Packard CJ, Padmanabhan S, Palmer CNA, Palmer ND, Pasterkamp G, Patel AP, Pattie A, Pedersen O, Peissig PL, Peloso GM, Pennell CE, Perola M, Perry JA, Perry JRB, Pers TH, Person TN, Peters A, Petersen ERB, Peyser PA, Pirie A, Polasek O, Polderman TJ, Puolijoki H, Raitakari OT, Rasheed A, Rauramaa R, Reilly DF, Renström F, Rheinberger M, Ridker PM, Rioux JD, Rivas MA, Roberts DJ, Robertson NR, Robino A, Rolandsson O, Rudan I, Ruth KS, Saleheen D, Salomaa V, Samani NJ, Sapkota Y, Sattar N, Schoen RE, Schreiner PJ, Schulze MB, Scott RA, Segura-Lepe MP, Shah SH, Sheu WHH, Sim X, Slater AJ, Small KS, Smith AV, Southam L, Spector TD, Speliotes EK, Starr JM, Stefansson K, Steinthorsdottir V, Stirrups KE, Strauch K, Stringham HM, Stumvoll M, Sun L, Surendran P, Swift AJ, Tada H, Tansey KE, Tardif JC, Taylor KD, Teumer A, Thompson DJ, Thorleifsson G, Thorsteinsdottir U, Thuesen BH, Tönjes A, Tromp G, Trompet S, Tsafantakis E, Tuomilehto J, Tybjaerg-Hansen A, Tyrer JP, Uher R, Uitterlinden AG, Uusitupa M, Laan SW, Duijn CM, Leeuwen N, Van Setten J, Vanhala M, Varbo A, Varga TV, Varma R, Velez Edwards DR, Vermeulen SH, Veronesi G, Vestergaard H, Vitart V, Vogt TF, Völker U, Vuckovic D, Wagenknecht LE, Walker M, Wallentin L, Wang F, Wang CA, Wang S, Wang Y, Ware EB, Wareham NJ, Warren HR, Waterworth DM, Wessel J, White HD, Willer CJ, Wilson JG, Witte DR, Wood AR, Wu Y, Yaghootkar H, Yao J, Yao P, Yerges-Armstrong LM, Young R, Zeggini E, Zhan X, Zhang W, Zhao JH, Zhao W, Zhou W, Zondervan KT, Rotter JI, Pospisilik JA, Rivadeneira F, Borecki IB, Deloukas P, Frayling TM, Lettre G, North KE, Lindgren CM, Hirschhorn JN, Loos RJF (2018) Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity. Nat Genet 50:26–35. Nature Publishing GroupCAS 
PubMed 
Article 

Google Scholar 
Uva V, Päckert M, Cibois A, Fumagalli L, Roulin A (2018) Comprehensive molecular phylogeny of barn owls and relatives (Family: Tytonidae), and their six major Pleistocene radiations. Mol Phylogenet Evol 125:127–137. Academic PressPubMed 
Article 

Google Scholar 
van der Auwera GA, Carneiro MO, Hartl C, Poplin R, del Angel G, Levy-Moonshine A, Jordan T, Shakir K, Roazen D, Thibault J, Banks E, Garimella KV, Altshuler D, Gabriel S, DePristo MA (2013) From FastQ data to high-confidence variant calls: the genome analysis toolkit best practices pipeline. Curr Protoc Bioinform 43:11.10.1–11.10.33. John Wiley & Sons, Inc., Hoboken, NJ, USAArticle 

Google Scholar 
Vuckovic D, Bao EL, Akbari P, Lareau CA, Mousas A, Jiang T, Chen MH, Raffield LM, Tardaguila M, Huffman JE, Ritchie SC, Megy K, Ponstingl H, Penkett CJ, Albers PK, Wigdor EM, Sakaue S, Moscati A, Manansala R, Lo KS, Qian H, Akiyama M, Bartz TM, Ben-Shlomo Y, Beswick A, Bork-Jensen J, Bottinger EP, Brody JA, van Rooij FJA, Chitrala KN, Wilson PWF, Choquet H, Danesh J, Di Angelantonio E, Dimou N, Ding J, Elliott P, Esko T, Evans MK, Felix SB, Floyd JS, Broer L, Grarup N, Guo MH, Guo Q, Greinacher A, Haessler J, Hansen T, Howson JMM, Huang W, Jorgenson E, Kacprowski T, Kähönen M, Kamatani Y, Kanai M, Karthikeyan S, Koskeridis F, Lange LA, Lehtimäki T, Linneberg A, Liu Y, Lyytikäinen LP, Manichaikul A, Matsuda K, Mohlke KL, Mononen N, Murakami Y, Nadkarni GN, Nikus K, Pankratz N, Pedersen O, Preuss M, Psaty BM, Raitakari OT, Rich SS, Rodriguez BAT, Rosen JD, Rotter JI, Schubert P, Spracklen CN, Surendran P, Tang H, Tardif JC, Ghanbari M, Völker U, Völzke H, Watkins NA, Weiss S, Cai N, Kundu K, Watt SB, Walter K, Zonderman AB, Cho K, Li Y, Loos RJF, Knight JC, Georges M, Stegle O, Evangelou E, Okada Y, Roberts DJ, Inouye M, Johnson AD, Auer PL, Astle WJ, Reiner AP, Butterworth AS, Ouwehand WH, Lettre G, Sankaran VG, Soranzo N (2020) The polygenic and monogenic basis of blood traits and diseases. Cell 182:1214–1231.e11. Cell PressCAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wain LV, Vaez A, Jansen R, Joehanes R, Van Der Most PJ, Erzurumluoglu AM, O’Reilly PF, Cabrera CP, Warren HR, Rose LM, Verwoert GC, Hottenga JJ, Strawbridge RJ, Esko T, Arking DE, Hwang SJ, Guo X, Kutalik Z, Trompet S, Shrine N, Teumer A, Ried JS, Bis JC, Smith AV, Amin N, Nolte IM, Lyytikäinen LP, Mahajan A, Wareham NJ, Hofer E, Joshi PK, Kristiansson K, Traglia M, Havulinna AS, Goel A, Nalls MA, Sõber S, Vuckovic D, Luan J, Del Greco FM, Ayers KL, Marrugat J, Ruggiero D, Lopez LM, Niiranen T, Enroth S, Jackson AU, Nelson CP, Huffman JE, Zhang W, Marten J, Gandin I, Harris SE, Zemunik T, Lu Y, Evangelou E, Shah N, De Borst MH, Mangino M, Prins BP, Campbell A, Li-Gao R, Chauhan G, Oldmeadow C, Abecasis G, Abedi M, Barbieri CM, Barnes MR, Batini C, Beilby J, Blake T, Boehnke M, Bottinger EP, Braund PS, Brown M, Brumat M, Campbell H, Chambers JC, Cocca M, Collins F, Connell J, Cordell HJ, Damman JJ, Davies G, De Geus EJ, De Mutsert R, Deelen J, Demirkale Y, Doney ASF, Dörr M, Farrall M, Ferreira T, Frånberg M, Gao H, Giedraitis V, Gieger C, Giulianini F, Gow AJ, Hamsten A, Harris TB, Hofman A, Holliday EG, Hui J, Jarvelin MR, Johansson Å, Johnson AD, Jousilahti P, Jula A, Kähönen M, Kathiresan S, Khaw KT, Kolcic I, Koskinen S, Langenberg C, Larson M, Launer LJ, Lehne B, Liewald DCM, Lin L, Lind L, Mach F, Mamasoula C, Menni C, Mifsud B, Milaneschi Y, Morgan A, Morris AD, Morrison AC, Munson PJ, Nandakumar P, Nguyen QT, Nutile T, Oldehinkel AJ, Oostra BA, Org E, Padmanabhan S, Palotie A, Paré G, Pattie A, Penninx BWJH, Poulter N, Pramstaller PP, Raitakari OT, Ren M, Rice K, Ridker PM, Riese H, Ripatti S, Robino A, Rotter JI, Rudan I, Saba Y, Saint Pierre A, Sala CF, Sarin AP, Schmidt R, Scott R, Seelen MA, Shields DC, Siscovick D, Sorice R, Stanton A, Stott DJ, Sundström J, Swertz M, Taylor KD, Thom S, Tzoulaki I, Tzourio C, Uitterlinden AG, Völker U, Vollenweider P, Wild S, Willemsen G, Wright AF, Yao J, Thériault S, Conen D, Attia J, Sever P, Debette S, Mook-Kanamori DO, Zeggini E, Spector TD, Van Der Harst P, Palmer CNA, Vergnaud AC, Loos RJF, Polasek O, Starr JM, Girotto G, Hayward C, Kooner JS, Lindgren CM, Vitart V, Samani NJ, Tuomilehto J, Gyllensten U, Knekt P, Deary IJ, Ciullo M, Elosua R, Keavney BD, Hicks AA, Scott RA, Gasparini P, Laan M, Liu Y, Watkins H, Hartman CA, Salomaa V, Toniolo D, Perola M, Wilson JF, Schmidt H, Zhao JH, Lehtimäki T, Van Duijn CM, Gudnason V, Psaty BM, Peters A, Rettig R, James A, Jukema JW, Strachan DP, Palmas W, Metspalu A, Ingelsson E, Boomsma DI, Franco OH, Bochud M, Newton-Cheh C, Munroe PB, Elliott P, Chasman DI, Chakravarti A, Knight J, Morris AP, Levy D, Tobin MD, Snieder H, Caulfield MJ, Ehret GB (2017) Novel blood pressure locus and gene discovery using genome-wide association study and expression data sets from blood and the kidney. Hypertension 70:e4–e19. Lippincott Williams and WilkinsCAS 
Article 

Google Scholar 
Warren BH, Simberloff D, Ricklefs RE, Aguilée R, Condamine FL, Gravel D, Morlon H, Mouquet N, Rosindell J, Casquet J, Conti E, Cornuault J, Fernández-Palacios JM, Hengl T, Norder SJ, Rijsdijk KF, Sanmartín I, Strasberg D, Triantis KA, Valente LM, Whittaker RJ, Gillespie RG, Emerson BC, and Thébaud C (2015) Islands as model systems in ecology and evolution: prospects fifty years after MacArthur-WilsonWeir BS, Cardon LR, Anderson AD, Nielsen DM, Hill WG (2005) Measures of human population structure show heterogeneity among genomic regions. Genome Res 15:1468–1476. Cold Spring Harbor Laboratory PressCAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Weir BS, Goudet J (2017) A unified characterization of population structure and relatedness. Genetics 206:2085–2103PubMed 
PubMed Central 
Article 

Google Scholar 
Witt KE, Huerta-Sánchez E (2019) Convergent evolution in human and domesticate adaptation to high-altitude environments. Phil. Trans. R. Soc. B 374:20180235Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS (2012) A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28:3326–3328CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhu Z, Guo Y, Shi H, Liu CL, Panganiban RA, Chung W, O’Connor LJ, Himes BE, Gazal S, Hasegawa K, Camargo CA, Qi L, Moffatt MF, Hu FB, Lu Q, Cookson WOC, Liang L (2020) Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank. J Allergy Clin Immunol 145:537–549. Mosby IncCAS 
PubMed 
Article 

Google Scholar  More

Clutton-Brock, T. & Sheldon, B. C. Individuals and populations: The role of long-term, individual-based studies of animals in ecology and evolutionary biology. Trends Ecol. Evol. 25, 562–573 (2010).PubMed 
Article 

Google Scholar 
Keuling, O., Lauterbach, K., Stier, N. & Roth, M. Hunter feedback of individually marked wild boar Sus scrofa L.: Dispersal and efficiency of hunting in northeastern Germany. Eur. J. Wildl. Res. 56, 159–167 (2010).Article 

Google Scholar 
Trondrud, L. M. et al. Fat storage influences fasting endurance more than body size in an ungulate. Funct. Ecol. 35, 1470–1480 (2021).CAS 
Article 

Google Scholar 
Wilmers, C. C. et al. The golden age of bio-logging: How animal-borne sensors are advancing the frontiers of ecology. Ecology 96, 1741–1753 (2015).PubMed 
Article 

Google Scholar 
Kukalová, M., Gazárková, A. & Adamík, P. Should i stay or should i go? The influence of handling by researchers on den use in an arboreal nocturnal rodent. Ethology 119, 848–859 (2013).Article 

Google Scholar 
Holt, R. D. et al. Estimating duration of short-term acute effects of capture handling and radiomarking. J. Wildl. Manag. 73, 989–995 (2009).Article 

Google Scholar 
Marco, I., Viñas, L., Velarde, R., Pastor, J. & Lavin, S. Effects of capture and transport on blood parameters in free-ranging mouflon (Ovis ammon). J. Zoo Wildl. Med. 28, 428–433 (1997).CAS 
PubMed 

Google Scholar 
Cattet, M., Boulanger, J., Stenhouse, G., Powell, R. A. & Reynolds-Hogland, M. J. An evaluation of long-term capture effects in ursids: Implications for wildlife welfare and research. J. Mammal. 89, 973–990 (2008).Article 

Google Scholar 
Mortensen, R. M. & Rosell, F. Long-term capture and handling effects on body condition, reproduction and survival in a semi-aquatic mammal. Sci. Rep. 10, 1–16 (2020).Article 

Google Scholar 
Soulsbury, C. D. et al. The welfare and ethics of research involving wild animals: A primer. Methods Ecol. Evol. 11, 1164–1181 (2020).Article 

Google Scholar 
Herman, J. P. et al. Regulation of the hypothalamic-pituitary- adrenocortical stress response. Compr. Physiol. 6, 603–621 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Sapolsky, R. M., Romero, L. M. & Munck, A. U. How do glucocorticoids influence stress responses? Integrating permissive, suppressive, stimulatory, and preparative actions. Endocr. Rev. 21, 55–89 (2000).CAS 
PubMed 

Google Scholar 
Sjaastad, V. Ø., Hove, K. & Sand, O. Physiology of Domestic Animals (Scandinavian Veterinary Press, 2016).
Google Scholar 
Omsjø, E. H. et al. Evaluating capture stress and its effects on reproductive success in Svalbard reindeer. Can. J. Zool. 87, 73–85 (2009).Article 

Google Scholar 
Marco, I., Viñas, L., Velarde, R., Pastor, J. & Lavin, S. The stress response to repeated capture in mouflon (Ovis ammon): Physiological, haematological and biochemical parameters. J. Vet. Med. Ser. A Physiol. Pathol. Clin. Med. 45, 243–253 (1998).CAS 
Article 

Google Scholar 
Hattingh, J., Pitts, N. I. & Ganhao, M. F. Immediate response to repeated capture and handling of wild impala. J. Exp. Zool. 248, 109–112 (1988).CAS 
PubMed 
Article 

Google Scholar 
Ortega, A. C. et al. Effectiveness of partial sedation to reduce stress in captured mule deer. J. Wildl. Manag. 84, 1445–1456 (2020).Article 

Google Scholar 
Arnemo, J. M. & Caulkett, N. Stress. In Zoo Animal and Wildlife Anesthesia and Immobilization (eds West, G. et al.) 103–109 (Blackwell Publications, 2007).
Google Scholar 
Sinclair, M. D. A review of the physiological effects of α2-agonists related to the clinical use of medetomidine in small animal practice. Can. Vet. J. 44, 885–897 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ranheim, B. et al. The effects of medetomidine and its reversal with atipamezole on plasma glucose, cortisol and noradrenaline in cattle and sheep. J. Vet. Pharmacol. Ther. 23, 379–387 (2000).CAS 
PubMed 
Article 

Google Scholar 
Carroll, G. L. et al. Effect of medetomidine and its antagonism with atipamezole on stress-related hormones, metabolites, physiologic responses, sedation, and mechanical threshold in goats. Vet. Anaesth. Analg. 32, 147–157 (2005).CAS 
PubMed 
Article 

Google Scholar 
Rode, K. D. et al. Effects of capturing and collaring on polar bears: finDings from long-term research on the southern Beaufort Sea population. Wildl. Res. 41, 311–322 (2014).Article 

Google Scholar 
Sakamoto, H., Misumi, K., Nakama, M. & Aoki, Y. The effects of xylazine on intrauterine pressure, uterine blood flow, maternal and fetal cardiovascular and pulmonary function in pregnant goats. J. Vet. Med. Sci. 58, 211–217 (1996).CAS 
PubMed 
Article 

Google Scholar 
Katila, T. & Oijala, M. The effect of detomidine (Domosedan) on the maintenance of equine pregnancy and foetal development: ten cases. Equine Vet. J. 20, 323–326 (1988).CAS 
PubMed 
Article 

Google Scholar 
Larsen, D. G. & Gauthier, D. A. Effects of capturing pregnant moose and calves on calf survivorship. J. Wildl. Manag. 53, 564 (1989).Article 

Google Scholar 
Côté, S. D., Festa-Bianchet, M. & Fournier, F. Life-history effects of chemical immobilization and radiocollars on mountain goats. J. Wildl. Manage. 62, 745–752 (1998).Article 

Google Scholar 
DelGiudice, G. D., Mech, L. D., Paul, W. J. & Karns, P. D. Effects on fawn survival of multiple immobilizations of captive pregnant white-tailed deer. J. Wildl. Dis. 22, 245–248 (1986).CAS 
PubMed 
Article 

Google Scholar 
Brivio, F., Grignolio, S., Sica, N., Cerise, S. & Bassano, B. Assessing the impact of capture on wild animals: The case study of chemical immobilisation on alpine ibex. PLoS ONE 10, e0130957 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Wingfield, J. C. et al. Ecological bases of hormone-behavior interactions: The ‘emergency life history stage’. Am. Zool. 38, 191–206 (1998).CAS 
Article 

Google Scholar 
Huber, S., Palme, R. & Arnold, W. Effects of season, sex, and sample collection on concentrations of fecal cortisol metabolites in red deer (Cervus elaphus). Gen. Comp. Endocrinol. 130, 48–54 (2003).CAS 
PubMed 
Article 

Google Scholar 
Morellet, N. et al. The effect of capture on ranging behaviour and activity of the European roe deer Capreolus capreolus. Wildlife Biol. 15, 278–287 (2009).Article 

Google Scholar 
Tarlow, E. M. & Blumstein, D. T. Evaluating methods to quantify anthropogenic stressors on wild animals. Appl. Anim. Behav. Sci. 102, 429–451 (2007).Article 

Google Scholar 
Hik, D. S. Does risk of predation influence the cyclic decline of snowshoe hares. Wildl. Res. 22, 115–129 (1995).Article 

Google Scholar 
Ordiz, A. et al. Lasting behavioural responses of brown bears to experimental encounters with humans. J. Appl. Ecol. 50, 306–314 (2013).Article 

Google Scholar 
Dechen Quinn, A. C., Williams, D. M. & Porter, W. F. Postcapture movement rates can inform data-censoring protocols for GPS-collared animals. J. Mammal. 93, 456–463 (2012).Article 

Google Scholar 
Cattet, M. R. L. Falling through the cracks: Shortcomings in the collaboration between biologists and veterinarians and their consequences for wildlife. ILAR J. 54, 33–40 (2013).CAS 
PubMed 
Article 

Google Scholar 
Albon, S. D. et al. Contrasting effects of summer and winter warming on body mass explain population dynamics in a food-limited Arctic herbivore. Glob. Change Biol. 23, 1374–1389 (2017).ADS 
Article 

Google Scholar 
Ovejero, R. et al. Do cortisol and corticosterone play the same role in coping with stressors? Measuring glucocorticoid serum in free-ranging guanacos (Lama guanicoe). J. Exp. Zool. Part A Ecol. Genet. Physiol. 319, 539–547 (2013).CAS 
Article 

Google Scholar 
Bonacic, C., Feber, R. E. & Macdonald, D. W. Capture of the vicuña (Vicugna vicugna) for sustainable use: Animal welfare implications. Biol. Conserv. 129, 543–550 (2006).Article 

Google Scholar 
Romero, L. M. & Beattie, U. K. Common myths of glucocorticoid function in ecology and conservation. J. Exp. Zool. Part A Ecol. Integr. Physiol. 337, 7–14 (2022).CAS 
Article 

Google Scholar 
Sire, J. E. et al. The effect of blood sampling on plasma cortisol in female reindeer (Rangifer tarandus tarandus L). Acta Vet. Scand. 36, 583–587 (1995).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Harlow, H. J., Thorne, E. T., Williams, E. S., Belden, E. L. & Gern, W. A. Adrenal responsiveness in domestic sheep ( Ovis aries ) to acute and chronic stressors as predicted by remote monitoring of cardiac frequency. Can. J. Zool. 65, 2021–2027 (1987).Article 

Google Scholar 
Pottinger, T. G. & Moran, T. A. Differences in plasma cortisol and cortisone dynamics during stress in two strains of rainbow trout (Oncorhynchus mykiss). J. Fish Biol. 43, 121–130 (1993).CAS 
Article 

Google Scholar 
Arnemo, J. M. & Ranheim, B. Effects of medetomidine and atipamezole on serum glucose and cortisol levels in captive reindeer (Rangifer tarandus tarandus). Rangifer 19, 85–89 (1999).Article 

Google Scholar 
Mentaberre, G. et al. Effects of azaperone and haloperidol on the stress response of drive-net captured Iberian ibexes (Capra pyrenaica). Eur. J. Wildl. Res. 56, 757–764 (2010).Article 

Google Scholar 
Northrup, J. M., Anderson, C. R. & Wittemyer, G. Effects of helicopter capture and handling on movement behavior of mule deer. J. Wildl. Manag. 78, 731–738 (2014).Article 

Google Scholar 
Jung, T. S. et al. Short-term effect of helicopter-based capture on movements of a social ungulate. J. Wildl. Manag. 83, 830–837 (2019).Article 

Google Scholar 
Nurmi, H., Laaksonen, S., Raekallio, M. & Hänninen, L. Wintertime pharmacokinetics of intravenously and orally administered meloxicam in semi-domesticated reindeer (Rangifer tarandus tarandus). Vet. Anaesth. Analg. 49, 423–428 (2022).CAS 
PubMed 
Article 

Google Scholar 
Chapple, R. S., English, A. W., Mulley, R. C. & Lepherd, E. E. Haematology and serum biochemistry of captive unsedated chital deer (Axis axis) in Australia. J. Wildl. Dis. 27, 396–406 (1991).CAS 
PubMed 
Article 

Google Scholar 
Brosh, A. Heart rate measurements as an index of energy expenditure and energy balance in ruminants: A review1. J. Anim. Sci. 85, 1213–1227 (2007).CAS 
PubMed 
Article 

Google Scholar 
Suazo, A. A., Delong, A. T., Bard, A. A. & Oddy, D. M. Repeated capture of beach mice (Peromyscus polionotus phasma and P. P. niveiventris) reduces body mass. J. Mammal. 86, 520–523 (2005).Article 

Google Scholar 
Hoyle, S. D., Horsup, A. B., Johnson, C. N., Crossman, D. G. & McCallum, H. Live-trapping of the northern hairy-nosed wombat (Lasiorhinus krefftii): Population-size estimates and effects on individuals. Wildl. Res. 22, 741–755 (1995).Article 

Google Scholar 
Estruelas, N. F. Short- and long-term physiological effects of capture and handling on free-ranging brown bears (Ursus arctos). PhD Thesis. (Inland Norway University of Applied Sciences, 2017).Veiberg, V. et al. Maternal winter body mass and not spring phenology determine annual calf production in an Arctic herbivore. Oikos 126, 980–987 (2017).Article 

Google Scholar 
Loe, L. E. et al. The neglected season: Warmer autumns counteract harsher winters and promote population growth in Arctic reindeer. Glob. Change Biol. 27, 993–1002 (2021).ADS 
CAS 
Article 

Google Scholar 
Larsen, T. S., Nilsson, N. & Blix, A. S. Seasonal changes in lipogenesis and lipolysis in isolated adipocytes from Svalbard and Norwegian reindeer. Acta Physiol. Scand. 123, 97–104 (1985).CAS 
PubMed 
Article 

Google Scholar 
Colman, J. E., Jacobsen, B. W. & Reimers, E. Summer response distances of Svalbard reindeer (Rangifer tarandus platyrhynchus) to provocation by humans on foot. Wildlife Biol. 7, 275–283 (2001).Article 

Google Scholar 
Trondrud, L. M. et al. Determinants of heart rate in Svalbard reindeer reveal mechanisms of seasonal energy management. Philos. Trans. R. Soc. B Biol. Sci. 376, 20200215 (2021).Article 

Google Scholar 
Pigeon, G. et al. Context-dependent fitness costs of reproduction despite stable body mass costs in an Arctic herbivore. J. Anim. Ecol. 91, 61–73 (2022).PubMed 
Article 

Google Scholar 
Peeters, B., Pedersen, Å., Veiberg, V. & Hansen, B. Hunting quotas, selectivity and stochastic population dynamics challenge the management of wild reindeer. Clim. Res. https://doi.org/10.3354/cr01668 (2021).Article 

Google Scholar 
Loe, L. E. et al. Activity pattern of arctic reindeer in a predator-free environment: No need to keep a daily rhythm. Oecologia 152, 617–624 (2007).ADS 
PubMed 
Article 

Google Scholar 
Dahl, S. R. et al. Assay of steroids by liquid chromatography–tandem mass spectrometry in monitoring 21-hydroxylase deficiency. Endocr. Connect. 7, 1542–1550 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Loe, L. E. et al. Testing five hypotheses of sexual segregation in an arctic ungulate. J. Anim. Ecol. 75, 485–496 (2006).PubMed 
Article 

Google Scholar 
Reimers, E., Lund, S. & Ergon, T. Vigilance and fright behaviour in the insular Svalbard reindeer (Rangifer tarandus platyrhynchus). Can. J. Zool. 89, 753–764 (2011).Article 

Google Scholar 
The R Core Team. R: A language and environment for statistical computing (2021).Burnham, K. P. & Anderson, D. R. in Model selection and multimodel inference. A Practical Information-Theoretic Approach. Ecological Modelling (Springer, 2002).Blanchet, F. G., Tikhonov, G. & Norberg, A. HMSC: Hierarchical modelling of species community. R package version 2.2-0 (2019).Ovaskainen, O. et al. How to make more out of community data? A conceptual framework and its implementation as models and software. Ecol. Lett. 20, 561–576 (2017).PubMed 
Article 

Google Scholar 
Legendre, P. & Legendre, L. Numerical Ecology (Elsevier Science BV, 2012).MATH 

Google Scholar 
Diggle, P. J., Heagerty, P., Liang, K.-Y. & Zeger, S. L. Analysis of Longitudinal Data (Oxford University Press, 2013).MATH 

Google Scholar  More