Ecology

Bi, X. et al. Response of grassland productivity to climate change and anthropogenic activities in arid regions of Central Asia. Peer J. 8, e9797–e9797 (2020).PubMed 
PubMed Central 

Google Scholar 
Zhou, W. et al. Grassland degradation remote sensing monitoring and driving factors quantitative assessment in China from 1982 to 2010. Ecol. Indic. 83, 303–313 (2017).
Google Scholar 
Liu, Y. Y. et al. Assessing the effects of climate variation and human activities on grassland degradation and restoration across the globe. Ecol. Indic. 106, 105504–105504 (2019).
Google Scholar 
Zhang, Y. et al. Vegetation dynamics and its driving forces from climate change and human activities in the Three-River Source Region, China from 1982 to 2012. Sci. Total Environ. 563–564, 210–220 (2016).ADS 
PubMed 

Google Scholar 
Wang, Z. et al. Quantitative assess the driving forces on the grassland degradation in the Qinghai-Tibet Plateau, China. Ecol. Inf. 33, 32–44 (2016).CAS 

Google Scholar 
He, C. Y., Tian, J., Gao, B. & Zhao, Y. Y. Differentiating climate- and human-induced drivers of grassland degradation in the Liao River Basin, China. Environ. Monit. Assess. 187(1), 4199 (2015).PubMed 

Google Scholar 
Liu, Y. Y. et al. Grassland dynamics in responses to climate variation and human activities in China from 2000 to 2013. Sci. Total Environ. 690, 27–39 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Jiang, L. L., Jiapaer, G., Bao, A. M., Guo, H. & Ndayisaba, F. Vegetation dynamics and responses to climate change and human activities in Central Asia. Sci. Total Environ. 599–600, 967–980 (2017).ADS 
PubMed 

Google Scholar 
Chen, T. et al. Disentangling the relative impacts of climate change and human activities on arid and semiarid grasslands in Central Asia during 1982–2015. Sci. Total Environ. 653, 1311–1325 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Gang, C. et al. The impacts of land conversion and management measures on the grassland net primary productivity over the Loess Plateau, Northern China. Sci. Total Environ. 645, 827–836 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Guo, D. & Wang, H. Simulation of permafrost and seasonally frozen ground conditions on the Tibetan Plateau, 1981–2010. J. Geophys. Res. Atmos. 118, 5216–5230 (2013).ADS 

Google Scholar 
Yang, Y. et al. Comparative assessment of grassland degradation dynamics in response to climate variation and human activities in China, Mongolia, Pakistan and Uzbekistan from 2000 to 2013. J. Arid Environ. 135, 164–172 (2016).ADS 

Google Scholar 
Li, C. X., Jong, R., Schmid, B., Wulf, H. & Michael, E. S. Changes in grassland cover and in its spatial heterogeneity indicate degradation on the Qinghai-Tibetan Plateau. Ecol. Indic. 119, 106641 (2020).
Google Scholar 
Li, F., Chen, W., Zeng, Y., Zhao, Q. J. & Wu, B. F. Improving estimates of grassland fractional vegetation cover based on a pixel dichotomy model: A case study in Inner Mongolia, China. Remote Sens. 6, 4705–4722 (2014).ADS 

Google Scholar 
Wang, J., Brown, D. G. & Chen, J. Q. Drivers of the dynamics in net primary productivity across ecological zones on the Mongolian plateau. Landsc. Ecol. 28(4), 725–739 (2014).
Google Scholar 
Han, D. M. et al. Evaluation of semiarid grassland degradation in north China from multiple perspectives. Ecol. Eng. 112, 41–50 (2018).
Google Scholar 
Liu, H. X. et al. Response of vegetation productivity to climate change and human activities in the Shaanxi–Gansu–Ningxia region, China. J. Indian Soc. Remote Sens. 46(7), 1081–1092 (2018).
Google Scholar 
Zheng, K. et al. Impacts of climate change and human activities on grassland vegetation variation in the Chinese Loess Plateau. Sci. Total Environ. 660, 236–244 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Yan, Y. C., Liu, X. P., Wen, Y. Y. & Ou, J. P. Quantitative analysis of the contributions of climatic and human factors to grassland productivity in northern China. Ecol. Indic. 103, 542–553 (2019).
Google Scholar 
Wang, H. et al. Impacts of climate change on net primary productivity in arid and semiarid regions of China. Chin. Geogra. Sci. 26, 35–47 (2016).CAS 

Google Scholar 
Thomas, M. et al. Human land-use practices lead to global long-term increases in photosynthetic capacity. Remote Sens. 6(6), 5717–5731 (2014).
Google Scholar 
Becerril-Pina, R., Mastachi-Loza, C. A., Gonzalez-Sosa, E., Diaz-Delgado, C. & Ba, K. M. Assessing desertification risk in the semi-arid highlands of central Mexico. J. Arid Environ. 120, 4–13 (2015).ADS 

Google Scholar 
Evans, J. & Geerken, R. Discrimination between climate and human-induced dryland degradation. J. Arid Environ. 57(4), 535–554 (2004).ADS 

Google Scholar 
Meng, M. et al. Vegetation change in response to climate factors and human activities on the Mongolian Plateau. Peer J. 7, e7735 (2019).PubMed 
PubMed Central 

Google Scholar 
Burrell, A. L., Evans, J. P. & Liu, Y. Detecting dryland degradation using time series segmentation and residual trend analysis (TSS-RESTREND). Remote Sens Environ. 197, 43–57 (2017).ADS 

Google Scholar 
Gedefaw, M. G., Geli, H. M. E. & Abera, T. A. Assessment of rangeland degradation in New Mexico using time series segmentation and residual trend analysis (TSS-RESTREND). Remote Sens. 13(9), 1618–1618 (2021).ADS 

Google Scholar 
Zhang, F. Changes of Grassland Net Primary Productivity in the Qinghai Tibet Plateau During the Past 34 Years and Analysis of Its Local Degradation Characteristics (Lanzhou University, 2021).
Google Scholar 
Li, L. H. et al. Current challenges in distinguishing climatic and anthropogenic contributions to alpine grassland variation on the Tibetan Plateau. Ecol. Evol. 8(11), 5949–5963 (2018).PubMed 
PubMed Central 

Google Scholar 
Zhu, Z. C. et al. Greening of the earth and its drivers. Nat. Clim. Change. 6, 791–795 (2016).ADS 
CAS 

Google Scholar 
Song, L. C., Ma, W. W., Li, G., Liu, S. N. & Lu, G. Effect of temperature changes on nitrogen mineralization in soils with different degradation gradients in Gahai Wetland. Acta Pratacul. Sin. 30(09), 27–37 (2021).
Google Scholar 
Dai, L. C. et al. Effect of grazing management strategies on alpine grassland on the northeastern Qinghai-Tibet Plateau. Ecol. Eng. 173, 106418 (2021).
Google Scholar 
Liu, Y. Y. et al. Evaluating the dynamics of grassland net primary productivity in response to climate change in China. Glob. Ecol. Conserv. 28, e01574 (2021).
Google Scholar 
Bestelmeyer, B. T., Duniway, M. C., James, D. K., Burkett, L. M. & Havstad, K. M. A test of critical thresholds and their indicators in a desertification-prone ecosystem: More resilience than we thought. Ecol. Lett. 16, 339–345 (2013).PubMed 

Google Scholar 
Kéfi, S. et al. Early warning signals of ecological transitions: Methods for spatial patterns. PLoS ONE 9(3), e92097 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Li, J. Z. et al. IKONOS image-based extraction of the distribution area of Stellera chamaejasme L. in Qilian County of Qinghai Province, China. Remote Sens. 8(2), 148 (2016).ADS 

Google Scholar 
Liu, Y. Q. & Lu, C. H. Quantifying grass coverage trends to identify the hot plots of grassland degradation in the Tibetan Plateau during 2000–2019. Int. J. Environ. Res. Public Health. 18(2), 416 (2021).MathSciNet 
PubMed Central 

Google Scholar 
Kendall, M. G. Rank Correlation Methods (Griffin, 1948).MATH 

Google Scholar 
Mann, H. B. Nonparametric tests against trend. Econometrica 13, 245–259 (1945).MathSciNet 
MATH 

Google Scholar 
Zhang, Z. M. & Lu, C. H. Clustering analysis of soybean production to understand its spatiotemporal dynamics in the North China Plain. Sustainability. 12(15), 6178 (2020).
Google Scholar 
Pei, T. T. et al. The sensitivity of vegetation phenology to extreme climate indices in the Loess Plateau, China. Sustainability. 13(14), 7623–7623 (2021).
Google Scholar 
Lu, B. B., Charlton, M., Harris, P. & Fotheringham, A. S. Geographically weighted regression with a non-Euclidean distance metric: A case study using hedonic house price data. Int. J. Geogr. Inf. Sci. 28(4), 660–681 (2014).
Google Scholar 
Sun, L. Q., Zhang, F. H., Yang, S. W., Qiu, A. G. & Zhang, X. L. The method of selecting geographically and temporally weight regression variable based on stepwise regression. Sci. Surv. Mapp. 44(01), 73–78+97 (2019).
Google Scholar 
Jiang, W. G. et al. Spatio-temporal analysis of vegetation variation in the Yellow River basin. Ecol. Indic. 51, 117–126 (2015).
Google Scholar 
Ndayisaba, F. et al. Understanding the spatial temporal vegetation dynamics in Rwanda. Remote Sens. 8(2), 129 (2016).ADS 

Google Scholar 
Kéfi, S. et al. Spatial vegetation patterns and imminent desertification in Mediterranean arid ecosystems. Nature 449(7159), 213–217 (2007).ADS 
PubMed 

Google Scholar 
Chen, J. J., Yi, S. H. & Qin, Y. The contribution of plateau pika disturbance and erosion on patchy alpine grassland soil on the Qinghai-Tibetan Plateau: Implications for grassland restoration. Geoderma 297, 1–9 (2017).ADS 
CAS 

Google Scholar 
Cai, H. Y., Yang, X. H. & Xu, X. L. Human-induced grassland degradation/restoration in the central Tibetan Plateau: The effects of ecological protection and restoration projects. Ecol. Eng. 83, 112–119 (2015).
Google Scholar 
Wang, P., Lassoie, J. P., Morreale, S. J. & Dong, S. K. A critical review of socioeconomic and natural factors in ecological degradation on the Qinghai-Tibetan Plateau. China. Rangel. J. 37(1), 1–9 (2015).
Google Scholar 
Lu, C. B. & Hou, L. F. Cause analysis and Control Countermeasures of grassland degradation in Qilian County, Qinghai Province. Today Anim. Husb. Vet. Med. 34(02), 62 (2018).
Google Scholar 
Guo, X. W. et al. Light grazing significantly reduces soil water storage in Alpine Grasslands on the Qinghai-Tibet Plateau. Sustainability. 12(6), 2523–2523 (2020).
Google Scholar 
Bai, Y. F. et al. Climate warming benefits alpine vegetation growth in Three-River Headwater Region, China. Sci. Total Environ. 742, 140574–140574 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Chen, T. et al. Unraveling the relative impacts of climate change and human activities on grassland productivity in Central Asia over last three decades. Sci. Total Environ. 743, 140649 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Li, A., Wu, J. G. & Huang, J. H. Distinguishing between human-induced and climate-driven vegetation changes: A critical application of RESTREND in inner Mongolia. Landsc. Ecol. 27(7), 969–982 (2012).CAS 

Google Scholar 
Wu, J. S. et al. Disentangling climatic and anthropogenic contributions to nonlinear dynamics of alpine grassland productivity on the Qinghai-Tibetan Plateau. J. Environ. Manag. 281, 111875–111875 (2020).
Google Scholar 
Gang, C. et al. Comparative assessment of grassland NPP dynamics in response to climate change in China, North America, Europe and Australia from 1981 to 2010. J. Agron. Crop Sci. 201(1), 57–68 (2015).
Google Scholar 
Gang, C. C. et al. Quantitative assessment of the contributions of climate change and human activities on global grassland degradation. Environ. Earth Sci. 72(11), 4273–4282 (2014).
Google Scholar 
Chen, Y. Z. et al. Grassland carbon sequestration ability in China: A new perspective from terrestrial aridity zones. Rangeland Ecol. Manag. 69(1), 84–94 (2016).
Google Scholar 
Mowll, W. et al. Climatic controls of aboveground net primary production in semi-arid grasslands along a latitudinal gradient portend low sensitivity to warming. Oecologia 177(4), 959–969 (2015).ADS 
PubMed 

Google Scholar 
Zhou, Y. et al. Climate contributions to vegetation variations in central Asian Drylands: Pre- and post-USSR collapse. Remote Sens. 7(3), 2449–2470 (2015).ADS 

Google Scholar 
Ji, Y. et al. Variation of net primary productivity and its drivers in China’s forests during 2000–2018. For. Ecosyst. 7(1), 1–11 (2020).CAS 

Google Scholar 
Zeng, B. & Yang, T. B. Impacts of climate warming on vegetation in Qaidam Area from 1990 to 2003. Environ. Monit. Assess. 144(1–3), 403–417 (2008).PubMed 

Google Scholar 
Duan, A. M. & Xiao, Z. X. Does the climate warming hiatus exist over the Tibetan Plateau?. Sci. Rep. 5(1), 13711 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
Fang, J. Y. et al. Precipitation patterns alter growth of temperate vegetation. Geophys. Res. Lett. 32(21), L21411 (2005).ADS 

Google Scholar 
Zhao, X., Tan, K., Zhao, S. & Fang, J. Changing climate affects vegetation growth in the arid region of the northwestern China. J. Arid Environ. 75(10), 946–952 (2011).ADS 

Google Scholar 
Ukkola, A. M. et al. Reduced streamflow in water-stressed climates consistent with CO2 effects on vegetation. Nat. Clim. Change. 6(1), 75–78 (2016).ADS 

Google Scholar 
Dong, S. K., Shang, Z. H., Gao, J. X. & Boone, R. B. Enhancing sustainability of grassland ecosystems through ecological restoration and grazing management in an era of climate change on Qinghai-Tibetan Plateau. Agric. Ecosyst. Environ. 287(C), 106684 (2019).
Google Scholar 
Xu, H. P. et al. Responses of plant productivity and soil nutrient concentrations to different alpine grassland degradation levels. Environ Monit Assess. 191(11), 678 (2019).CAS 
PubMed 

Google Scholar 
Wen, W. Y. et al. Research on soil net nitrogen mineralization in Stipa grandis grassland with different stages of degradation. Geosci J. 20(4), 485–494 (2016).ADS 
CAS 

Google Scholar 
She, Y. et al. Vegetation attributes and soil properties of alpine grassland in different degradation stages on the Qinghai-Tibet Plateau, China: A meta-analysis. Arab J Geosci. 15, 193 (2022).
Google Scholar 
Xu, G. P. Study on the Change of Vegetation and Soil Nutrients of Alpine Meadow Under Different Degradation Degrees in Eastern Qilian Mountains (Gansu Agricultural University, 2006).
Google Scholar 
Anderson, K. et al. Vegetation expansion in the subnival Hindu Kush Himalaya. Glob. Chang. Biol. 26(3), 1608–1625 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Chen, B. X. et al. The impact of climate change and anthropogenic activities on alpine grassland over the Qinghai-Tibet Plateau. Agric. For. Meteorol. 189–190, 11–18 (2014).ADS 

Google Scholar 
Zhang, X. W., Li, G., Dong, K. H. & Zhao, X. Effects of grazing and enclosure on community characteristics and biodiversity in Leymus chinensis grassland. J. Grassl. Forage Sci. 4, 22–27 (2019).
Google Scholar 
Huang, K. et al. The influences of climate change and human activities on vegetation dynamics in the Qinghai-Tibet Plateau. Remote Sens. 8(10), 876 (2016).ADS 

Google Scholar 
Duan, Q. T., Luo, L. H., Zhao, W. Z., Zhuang, Y. L. & Liu, F. Mapping and evaluating human pressure changes in the Qilian mountains. Remote Sens. 13(12), 2400–2400 (2021).ADS 

Google Scholar 
Wang, Y. et al. Performance and obstacle tracking to natural forest resource protection project: A rangers’ case of Qilian mountain, China. Int. J. Environ. Res. Public Health. 17(16), 5672 (2020).PubMed Central 

Google Scholar 
Li, Z. Y. et al. Changes in nutrient balance, environmental effects, and green development after returning farmland to forests: A case study in Ningxia, China. Sci. Total Environ. 735, 139370 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Li, C. X., de Jong, R., Schmid, B., Wulf, H. & Schaepman, M. E. Spatial variation of human influences on grassland biomass on the Qinghai-Tibetan plateau. Sci. Total Environ. 665, 678–689 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Li, X. L. et al. Rangeland degradation on the Qinghai-Tibet Plateau: Implications for rehabilitation. Land Degrad. Dev. 24, 72–80 (2011).
Google Scholar 
Li, C. B. et al. Regional vegetation dynamics and its response to climate change—a case study in the Tao River Basin in Northwestern China. Environ. Res. Lett. 9(12), 125003–125003 (2014).ADS 

Google Scholar 
Liu, Y. Y. et al. Untangling the effects of management measures, climate and land use cover change on grassland dynamics in the Qinghai-Tibet Plateau, China. Land Degrad. Dev. 32(17), 4974–4987 (2021).
Google Scholar 
Hou, X. Chinese Grassland Science (Science Press, 2013) (In Chinese).
Google Scholar  More

Bonan, G. B. Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449. https://doi.org/10.1126/science.1155121 (2008).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Le Quere, C. et al. Global carbon budget 2016. Earth Syst. Sci. Data 8, 605–649. https://doi.org/10.5194/essd-8-605-2016 (2016).ADS 
Article 

Google Scholar 
DavidMorales-Hidalgo, D., Oswalt, S. N. & Somanathan, E. Status and trends in global primary forest, protected areas, and areas designated for conservation of biodiversity from the Global Forest Resources Assessment 2015. Forest Ecol. Manag. 352, 68–77. https://doi.org/10.1016/j.foreco.2015.06.011 (2015).Article 

Google Scholar 
Kauppi, P. E., Sandstrom, V. & Lipponen, A. Forest resources of nations in relation to human well-being. PLoS One 13, e0196248. https://doi.org/10.1371/journal.pone.0196248 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Anderegg, W. R. L. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, 1327. https://doi.org/10.1126/science.aaz7005 (2020).CAS 
Article 

Google Scholar 
Wilson, M. C. et al. Habitat fragmentation and biodiversity conservation: Key findings and future challenges. Landsc. Ecol. 31, 219–227. https://doi.org/10.1007/s10980-015-0312-3 (2016).Article 

Google Scholar 
Haddad, N. M. et al. Habitat fragmentation and its lasting impact on earth’s ecosystems. Sci. Adv. 1, e1500052. https://doi.org/10.1126/sciadv.1500052 (2015).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Holl, K. D. Restoring tropical forests from the bottom up. Science 355, 455–456. https://doi.org/10.1126/science.aam5432 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Audino, L. D., Murphy, S. J., Zambaldi, L., Louzada, J. & Comita, L. S. Drivers of community assembly in tropical forest restoration sites: Role of local environment, landscape, and space. Ecol. Appl. 27, 1731–1745. https://doi.org/10.1002/eap.1562 (2017).Article 
PubMed 

Google Scholar 
Temperton, V. M., Hobbs, R. J., Nuttle, T. & Halle, S. in Assembly Rules and Restoration Ecology: Bridging the Gap Between Theory and Practice [Science and Practice of Ecological Restoration]. i–xv, 1–439 (2004).Young, T. P., Chase, J. M. & Huddleston, R. T. Community succession and assembly: Comparing, contrasting and combining paradigms in the context of ecological restoration. Ecol. Restor. 19, 5–18 (2001).Article 

Google Scholar 
Vellend, M. The Theory of Ecological Communities (Princeton University Press, 2016).
Google Scholar 
HilleRisLambers, J., Adler, P. B., Harpole, W. S., Levine, J. M. & Mayfield, M. M. Rethinking community assembly through the lens of coexistence theory. Annu. Rev. Ecol. Evol. Syst. 43(43), 227–248. https://doi.org/10.1146/annurev-ecolsys-110411-160411 (2012).Article 

Google Scholar 
Connell, J. H. On the role of natural enemies in preventing competitive exclusion in some marine animals and in rain forest trees. In Dynamics of Populations (eds Den Boer, P. J. & Gradwell, G. R.) (Centre for Agricultural Publishing and Documentation, 1971).
Google Scholar 
Janzen, D. H. Herbivores and the number of tree species in tropical forests. Am. Nat. 104, 501. https://doi.org/10.1086/282687 (1970).Article 

Google Scholar 
Schmid, J. S., Taubert, F., Wiegand, T., Sun, I. F. & Huth, A. Network science applied to forest megaplots: Tropical tree species coexist in small-world networks. Sci. Rep. https://doi.org/10.1038/s41598-020-70052-8 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, H. X. et al. Prevalence of inter-tree competition and its role in shaping the community structure of a natural Mongolian scots pine (Pinus sylvestris var. mongolica) forest. Forests https://doi.org/10.3390/f8030084 (2017).Article 

Google Scholar 
Hubbell, S. P. et al. Light-gap disturbances, recruitment limitation, and tree diversity in a neotropical forest. Science 283, 554–557. https://doi.org/10.1126/science.283.5401.554 (1999).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Janik, D. et al. Breaking through beech: A three-decade rise of sycamore in old-growth European forest. Forest Ecol. Manag. 366, 106–117. https://doi.org/10.1016/j.foreco.2016.02.003 (2016).Article 

Google Scholar 
Svatek, M., Rejzek, M., Kvasnica, J., Repka, R. & Matula, R. Frequent fires control tree spatial pattern, mortality and regeneration in argentine open woodlands. Forest Ecol. Manag. 408, 129–136. https://doi.org/10.1016/j.foreco.2017.10.048 (2018).Article 

Google Scholar 
Giammarchi, F. et al. Effects of the lack of forest management on spatiotemporal dynamics of a subalpine Pinus cembra forest. Scand. J. Forest Res. 32, 142–153. https://doi.org/10.1080/02827581.2016.1207802 (2017).Article 

Google Scholar 
Janik, D. et al. Patterns of Fraxinus angustifolia in an alluvial old-growth forest after declines in flooding events. Eur. J. Forest Res. 135, 215–228. https://doi.org/10.1007/s10342-015-0925-8 (2016).Article 

Google Scholar 
Bagchi, R. et al. Defaunation increases the spatial clustering of lowland western amazonian tree communities. J. Ecol. 106, 1470–1482. https://doi.org/10.1111/1365-2745.12929 (2018).Article 

Google Scholar 
Zhang, L. Y., Dong, L. B., Liu, Q. & Liu, Z. G. Spatial patterns and interspecific associations during natural regeneration in three types of secondary forest in the central part of the greater Khingan mountains, Heilongjiang province, China. Forests https://doi.org/10.3390/f11020152 (2020).Article 

Google Scholar 
Obiang, N. L. E. et al. Determinants of spatial patterns of canopy tree species in a tropical evergreen forest in Gabon. J. Veg. Sci. 30, 929–939. https://doi.org/10.1111/jvs.12778 (2019).Article 

Google Scholar 
Wiegand, T. et al. Spatially explicit metrics of species diversity, functional diversity, and phylogenetic diversity: Insights into plant community assembly processes. Annu. Rev. Ecol. Evol. Syst. 48(48), 329–351. https://doi.org/10.1146/annurev-ecolsys-110316-022936 (2017).Article 

Google Scholar 
Gabriel, E. Spatial point patterns: Methodology and applications with R. Math. Geosci. 49, 815–817. https://doi.org/10.1007/s11004-016-9670-x (2017).CAS 
Article 
MATH 

Google Scholar 
Baddeley, A., Rubak, R. & Turner, R. Spatial Point Patterns, Methodology and Applications with R (CRC Press, 2016).MATH 

Google Scholar 
Wiegand, T. & Moloney, K. A. Rings, circles, and null-models for point pattern analysis in ecology. Oikos 104, 209–229. https://doi.org/10.1111/j.0030-1299.2004.12497.x (2004).Article 

Google Scholar 
Plotkin, J. B., Chave, J. M. & Ashton, P. S. Cluster analysis of spatial patterns in Malaysian tree species. Am. Nat. 160, 629–644. https://doi.org/10.1086/342823 (2002).Article 
PubMed 

Google Scholar 
Ripley, B. D. Modeling spatial patterns. J. R. Stat. Soc. B 39, 172–212 (1977).
Google Scholar 
He, F. L. & Gaston, K. J. Estimating species abundance from occurrence. Am. Nat. 156, 553–559. https://doi.org/10.1086/303403 (2000).Article 
PubMed 

Google Scholar 
Diggle, P. Statistical Analysis of Spatial Point Patterns (Academic Press, 1983).MATH 

Google Scholar 
Pielou, E. C. The use of point-to-plant distances in the study of the pattern of plant-populations. J. Ecol. 47, 607–613. https://doi.org/10.2307/2257293 (1959).Article 

Google Scholar 
Losapio, G., Montesinos-Navarro, A. & Saiz, H. Perspectives for ecological networks in plant ecology. Plant Ecol. Divers. 12, 87–102. https://doi.org/10.1080/17550874.2019.1626509 (2019).Article 

Google Scholar 
Fuller, M. M., Wagner, A. & Enquist, B. J. Using network analysis to characterize forest structure. Nat. Resour. Model. 21, 225–247. https://doi.org/10.1111/j.1939-7445.2008.00004.x (2008).MathSciNet 
Article 

Google Scholar 
Montoya, J. M., Pimm, S. L. & Sole, R. V. Ecological networks and their fragility. Nature 442, 259–264. https://doi.org/10.1038/nature04927 (2006).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Proulx, S. R., Promislow, D. E. L. & Phillips, P. C. Network thinking in ecology and evolution. Trends Ecol. Evol. 20, 345–353. https://doi.org/10.1016/j.tree.2005.04.004 (2005).Article 
PubMed 

Google Scholar 
Nakagawa, Y., Yokozaw, M. & Hara, T. Complex network analysis reveals novel essential properties of competition among individuals in an even-aged plant population. Ecol. Complex 26, 95–116. https://doi.org/10.1016/j.ecocom.2016.03.005 (2016).Article 

Google Scholar 
Wiegand, T. & Moloney, K. A. Handbook of Spatial Point Pattern Analysis in Ecology (CRC Press, 2013).Book 

Google Scholar 
Barthelemy, M. Spatial networks. Phys. Rep. Rev. Sect. Phys. Lett. 499, 1–101. https://doi.org/10.1016/j.physrep.2010.11.002 (2011).MathSciNet 
CAS 
Article 

Google Scholar 
Keren, S. Modeling tree species count data in the understory and canopy layer of two mixed old-growth forests in the Dinaric region. Forests https://doi.org/10.3390/f11050531 (2020).Article 

Google Scholar 
Podlaski, R. Models of the fine-scale spatial distributions of trees in managed and unmanaged forest patches with Abies alba Mill. and Fagus sylvatica L. Forest Ecol. Manag. 439, 1–8 (2019).Article 

Google Scholar 
Levin, S. A. Theoretical ecology—Principles and applications, 3rd edition. Science 316, 1699–1700. https://doi.org/10.1126/science.1141870 (2007).CAS 
Article 

Google Scholar 
Martinez-Lopez, V., Garcia, C., Zapata, V., Robledano, F. & De la Rua, P. Intercontinental long-distance seed dispersal across the Mediterranean basin explains population genetic structure of a bird-dispersed shrub. Mol. Ecol. 29, 1408–1420. https://doi.org/10.1111/mec.15413 (2020).Article 
PubMed 

Google Scholar 
Dale, M. R. T. & Fortin, M. J. From graphs to spatial graphs. Annu. Rev. Ecol. Evol. Syst. 41, 21–38. https://doi.org/10.1146/annurev-ecolsys-102209-144718 (2010).Article 

Google Scholar 
Silva, C. A. et al. Treetop: A shiny-based application and R package for extracting forest information from LiDAR data for ecologists and conservationists. Methods Ecol. Evol. 13, 1164–1176. https://doi.org/10.1111/2041-210x.13830 (2022).Article 

Google Scholar 
Tatsumi, S., Yamaguchi, K. & Furuya, N. Forestscanner: A mobile application for measuring and mapping trees with LiDAR-equipped iPhone and iPad. Methods Ecol. Evol. https://doi.org/10.1111/2041-210x.13900 (2022).Article 

Google Scholar 
Ferraz, A., Saatchi, S. S., Longo, M. & Clark, D. B. Tropical tree size-frequency distributions from airborne LiDAR. Ecol. Appl. 30, e02154. https://doi.org/10.1002/eap.2154 (2020).Article 
PubMed 

Google Scholar 
Bianchi, E., Bugmann, H., Hobi, M. L. & Bigler, C. Spatial patterns of living and dead small trees in subalpine Norway spruce forest reserves in Switzerland. Forest Ecol. Manag. 494, 119315. https://doi.org/10.1016/j.foreco.2021.119315 (2021).Article 

Google Scholar 
Tatsumi, S., Owari, T., Yin, M. F. & Ning, L. Z. Neighborhood analysis of underplanted Korean pine demography in larch plantations: Implications for uneven-aged management in northeast china. Forest Ecol. Manag. 322, 10–18. https://doi.org/10.1016/j.foreco.2014.03.022 (2014).Article 

Google Scholar 
Cornett, M. W., Reich, P. B. & Puettmann, K. J. Canopy feedbacks and microtopography regulate conifer seedling distribution in two Minnesota conifer-deciduous forests. Ecoscience 4, 353–364. https://doi.org/10.1080/11956860.1997.11682414 (1997).Article 

Google Scholar 
Wang, X. F., Zheng, G., Yun, Z. X. & Moskal, L. M. Characterizing tree spatial distribution patterns using discrete aerial LiDAR data. Remote Sens. Basel 12, 712. https://doi.org/10.3390/rs12040712 (2020).ADS 
Article 

Google Scholar 
Matérn, B. Spatial variation: Stochastic models and their application to some problems in forest surveys and other sampling investigations. Meddelanden från Statens Skogsforskningsinstitut 49, 1–144 (1960).MathSciNet 

Google Scholar 
Matérn, B. Spatial Variation. Lecture Notes in Statistics Vol. 36 (Springer, 1986).Book 

Google Scholar 
Thomas, M. A generalisation of Poisson’s binomial limit for use in ecology. Biometrika 36, 18–25 (1949).MathSciNet 
CAS 
Article 

Google Scholar 
Lotwick, H. W. Simulation of some spatial hard core models, and the complete packing problem. J. Stat. Comput. Simul. 15, 295–314 (1982).MathSciNet 
Article 

Google Scholar 
Strauss, D. J. A model for clustering. Biometrika 62, 467–475 (1975).MathSciNet 
Article 

Google Scholar 
Cressie Noel, A. C. Statistics for Spatial Data (Wiley-Interscience, 1993).Book 

Google Scholar 
Besag, J. E. Contribution to the discussion of the paper by Ripley. J. R. Stat. Soc. 39, 193–195 (1977).MathSciNet 

Google Scholar  More

Klein, A. M. et al. Importance of pollinators in changing landscapes for world crops. P. R. Soc. B 274, 303–313 (2007).
Google Scholar 
Potts, S. G. et al. Global pollinator declines: trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).PubMed 

Google Scholar 
Gallai, N., Salles, J. M., Settele, J. & Vaissiere, B. E. Economic valuation of the vulnerability of world agriculture confronted with pollinator decline. Ecol. Econ. 68, 810–821 (2009).
Google Scholar 
Ollerton, J. Pollinator diversity: Distribution, ecological function, and conservation. Annu. Rev. Ecol. Evol. Syst. 48, 353–376 (2017).
Google Scholar 
Biesmeijer, J. C. et al. Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313, 351–354 (2006).ADS 
CAS 
PubMed 

Google Scholar 
Kosior, A. et al. The decline of the bumble bees and cuckoo bees (Hymenoptera : Apidae : Bombini) of Western and Central Europe. Oryx 41, 79–88 (2007).
Google Scholar 
Cameron, S. A. et al. Patterns of widespread decline in North American bumble bees. Proc. Natl. Acad. Sci. U. S. A. 108, 662–667 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cameron, S. A., Lim, H. C., Lozier, J. D., Duennes, M. A. & Thorp, R. Test of the invasive pathogen hypothesis of bumble bee decline in North America. Proc. Natl. Acad. Sci. U. S. A. 113, 4386–4391 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gill, R. J., Ramos-Rodriguez, O. & Raine, N. E. Combined pesticide exposure severely affects individual- and colony-level traits in bees. Nature 491, 105–108 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Whitehorn, P. R., O’Connor, S., Wackers, F. L. & Goulson, D. Neonicotinoid pesticide reduces bumble bee colony growth and queen production. Science 336, 351–352 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Stanley, D. A. et al. Neonicotinoid pesticide exposure impairs crop pollination services provided by bumblebees. Nature 528, 548–550 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Baron, G. L., Raine, N. E. & Brown, M. J. F. General and species-specific impacts of a neonicotinoid insecticide on the ovary development and feeding of wild bumblebee queens. P. R. Soc. B 284, 20170123 (2017).
Google Scholar 
Siviter, H., Folly, A. J., Brown, M. J. F. & Leadbeater, E. Individual and combined impacts of sulfoxaflor and Nosema bombi on bumblebee (Bombus terrestris) larval growth. Proc. Biol. Sci. 287, 20200935 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Blacquière, T., Smagghe, G., van Gestel, C. A. M. & Mommaerts, V. Neonicotinoids in bees: a review on concentrations, side-effects and risk assessment. Ecotoxicology 21, 973–992 (2012).PubMed 
PubMed Central 

Google Scholar 
Richardson, L. L. et al. Secondary metabolites in floral nectar reduce parasite infections in bumblebees. Proc. Biol. Sci. 282, 20142471 (2015).PubMed 
PubMed Central 

Google Scholar 
McAulay, M. K. & Forrest, J. R. K. How do sunflower pollen mixtures affect survival of queenless microcolonies of bumblebees (Bombus impatiens)?. Arthropod Plant Interact. 13, 517–529 (2019).
Google Scholar 
European Food Safety Authority. Guidance on the risk assessment of plant protection products on bees (Apis mellifera, Bombus spp. and solitary bees). EFSA J. 11, 3295 (2013).Besard, L. et al. Compatibility of traditional and novel acaricides with bumblebees (Bombus terrestris): a first laboratory assessment of toxicity and sublethal effects. Pest Manag. Sci. 66, 786–793 (2010).CAS 
PubMed 

Google Scholar 
Elston, C., Thompson, H. M. & Walters, K. F. A. Sub-lethal effects of thiamethoxam, a neonicotinoid pesticide, and propiconazole, a DMI fungicide, on colony initiation in bumblebee (Bombus terrestris) micro-colonies. Apidologie 44, 563–574 (2013).CAS 

Google Scholar 
Barbosa, W. F., De Meyer, L., Guedes, R. N. C. & Smagghe, G. Lethal and sublethal effects of azadirachtin on the bumblebee Bombus terrestris (Hymenoptera: Apidae). Ecotoxicology 24, 130–142 (2015).CAS 
PubMed 

Google Scholar 
Dance, C., Botías, C. & Goulson, D. The combined effects of a monotonous diet and exposure to thiamethoxam on the performance of bumblebee micro-colonies. Ecotoxicol. Environ. Saf. 139, 194–201 (2017).CAS 
PubMed 

Google Scholar 
Schmehl, D. R., Tome, H. V. V., Mortensen, A. N., Martins, G. F. & Ellis, J. D. Protocol for the in vitro rearing of honey bee (Apis mellifera L.) workers. J. Apic. Res. 55, 113–129 (2016).Pereboom, J. J. M., Velthuis, H. H. W. & Duchateau, M. J. The organisation of larval feeding in bumblebees (Hymenoptera, Apidae) and its significance to caste differentiation. Insectes Soc. 50, 127–133 (2003).
Google Scholar 
Dorigo, A. S., Rosa-Fontana, A. D., Soares-Lima, H. M., Galaschi-Teixeira, J. S., Nocelli, R. C. F. & Malaspina, O. In Vitro larval rearing protocol for the stingless bee species Melipona scutellaris for toxicological studies. PLoS One 14. https://doi.org/10.1371/journal.pone.0213109 (2019).Botina, L. L. et al. Toxicological assessments of agrochemical effects on stingless bees (Apidae, Meliponini). MethodsX 7, 100906 (2020).PubMed 
PubMed Central 

Google Scholar 
Black, B. C., Hollingworth, R. M., Ahammadsahib, K. I., Kukel, C. D. & Donovan, S. Insecticidal Action and Mitochondrial Uncoupling Activity of AC-303,630 and Related Halogenated Pyrroles. Pestic. Biochem. Physiol. 50, 115–128 (1994).CAS 

Google Scholar 
Wakita, T. et al. The discovery of dinotefuran: a novel neonicotinoid. Pest Manag. Sci. 59, 1016–1022 (2003).CAS 
PubMed 

Google Scholar 
Shafiei, M., Moczek, A. P. & Nijhout, H. F. Food availability controls the onset of metamorphosis in the dung beetle Onthophagus taurus (Coleoptera: Scarabaeidae). Physiol. Entomol. 26, 173–180 (2001).
Google Scholar 
Stieper, B. C., Kupershtok, M., Driscoll, M. V. & Shingleton, A. W. Imaginal discs regulate developmental timing in Drosophila melanogaster. Dev. Biol. 321, 18–26 (2008).CAS 
PubMed 

Google Scholar 
Nijhout, H. F. & Williams, C. Control of moulting and metamorphosis in the tobacco hornworm, Manduca sexta (L.): growth of the last-instar larva and the decision to pupate. J. Exp. Biol. 61, 481–491 (1974).Cnaani, J., Robinson, G. E. & Hefetz, A. The critical period for caste determination in Bombus terrestris and its juvenile hormone correlates. J. Comp. Physiol. A 186, 1089–1094 (2000).CAS 
PubMed 

Google Scholar 
Goulson, D. et al. Can alloethism in workers of the bumblebee, Bombus terrestris, be explained in terms of foraging efficiency?. Anim. Behav. 64, 123–130 (2002).
Google Scholar 
Syromyatnikov, M., Nesterova, E., Smirnova, T. & Popov, V. Methylene blue can act as an antidote to pesticide poisoning of bumble bee mitochondria. Sci. Rep. 11, 14710 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Raghavendra, K. et al. Chlorfenapyr: a new insecticide with novel mode of action can control pyrethroid resistant malaria vectors. Malar. J. 10, 16 (2011).PubMed 
PubMed Central 

Google Scholar 
Cao, Y. et al. HPLC/UV analysis of chlorfenapyr residues in cabbage and soil to study the dynamics of different formulations. Sci. Total Environ. 350, 38–46 (2005).ADS 
CAS 
PubMed 

Google Scholar 
Costa, E. M. et al. Toxicity of insecticides used in the Brazilian melon crop to the honey bee Apis mellifera under laboratory conditions. Apidologie 45, 34–44 (2014).CAS 

Google Scholar 
Cresswell, J. E., Robert, F.-X.L., Florance, H. & Smirnoff, N. Clearance of ingested neonicotinoid pesticide (imidacloprid) in honey bees (Apis mellifera) and bumblebees (Bombus terrestris). Pest Manag. Sci. 70, 332–337 (2014).CAS 
PubMed 

Google Scholar 
Czerwinski, M. A. & Sadd, B. M. Detrimental interactions of neonicotinoid pesticide exposure and bumblebee immunity. J Exp Zool A Ecol Integr Physiol 327, 273–283 (2017).CAS 
PubMed 

Google Scholar 
Mobley, M. W. & Gegear, R. J. One size does not fit all: Caste and sex differences in the response of bumblebees (Bombus impatiens) to chronic oral neonicotinoid exposure. PLoS ONE 13, e0200041 (2018).PubMed 
PubMed Central 

Google Scholar 
Simmons, W. R. & Angelini, D. R. Chronic exposure to a neonicotinoid increases expression of antimicrobial peptide genes in the bumblebee Bombus impatiens. Sci. Rep. 7, 44773 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Doublet, V., Labarussias, M., de Miranda, J. R., Moritz, R. F. A. & Paxton, R. J. Bees under stress: sublethal doses of a neonicotinoid pesticide and pathogens interact to elevate honey bee mortality across the life cycle. Environ. Microbiol. 17, 969–983 (2015).CAS 
PubMed 

Google Scholar 
Eiri, D. M., Suwannapong, G., Endler, M. & Nieh, J. C. Nosema ceranae can infect honey bee larvae and reduces subsequent adult longevity. PLoS One 10, (2015).Dai, P., Jack, C. J., Mortensen, A. N. & Ellis, J. D. Acute toxicity of five pesticides to Apis mellifera larvae reared in vitro. Pest Manag. Sci. 73, 2282–2286 (2017).CAS 
PubMed 

Google Scholar 
du Rand, E. E. et al. Proteomic and metabolomic analysis reveals rapid and extensive nicotine detoxification ability in honey bee larvae. Insect Biochem. Mol. Biol. 82, 41–51 (2017).PubMed 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2021). R Core Team (2021). URL https://www.R-project.org/. More

Stigall, A. L., Edwards, C. T., Freeman, R. L. & Rasmussen, C. M. Ø. Coordinated biotic and abiotic change during the Great Ordovician Biodiversification Event: Darriwilian assembly of early Paleozoic building blocks. Palaeogeogr. Palaeoclimatol. Palaeoecol. 530, 249–270 (2019).
Google Scholar 
Alroy, J. Colloquium paper: dynamics of origination and extinction in the marine fossil record. Proc. Natl. Acad. Sci. USA 105, 11536–11542 (2008). Suppl 1.CAS 

Google Scholar 
Servais, T., Cascales-Miñana, B. & Harper, D. A. T. The Great Ordovician Biodiversification Event (GOBE) is not a single event. Paleontological Res. 25, 315–328 (2021).Miller, A. I. & Foote, M. Calibrating the Ordovician Radiation of marine life: implications for Phanerozoic diversity trends. Paleobiology 22, 304–309 (1996).CAS 

Google Scholar 
Sepkoski, J. J. A compendium of marine fossil genera. vol. 2002 (Paleontological Research Institution, 2002).Zhan, R. & Harper, D. A. T. Biotic diachroneity during the Ordovician Radiation: evidence from South China. Lethaia 39, 211–226 (2006).
Google Scholar 
Fan, J. et al. A high-resolution summary of Cambrian to Early Triassic marine invertebrate biodiversity. Science 367, 272–277 (2020).CAS 

Google Scholar 
Deng, Y. et al. Timing and patterns of the Great Ordovician Biodiversification Event and Late Ordovician mass extinction: Perspectives from South China. Earth-Sci. Rev. 220, 103743 (2021).
Google Scholar 
Kröger, B., Franeck, F. & Rasmussen, C. M. Ø. The evolutionary dynamics of the early Palaeozoic marine biodiversity accumulation. Proc. R. Soc. B: Biol. Sci. 286, 3–8 (2019).
Google Scholar 
Rasmussen, C. M. Ø., Kröger, B., Nielsen, M. L. & Colmenar, J. Cascading trend of Early Paleozoic marine radiations paused by Late Ordovician extinctions. Proc. Natl. Acad. Sci. USA 116, 7207–7213 (2019).CAS 

Google Scholar 
Sepkoski, J. J. A factor analytic description of the phanerozoic marine fossil record. Paleobiology 7, 36–53 (1981).
Google Scholar 
Sepkoski, J. J. & Sheehan, P. M. Diversification, Faunal Change, and Community Replacement during the Ordovician Radiations. in Biotic interactions in recent and fossil benthic communities (eds. Tevesz, M. J. S. & McCall, P. L.) 673–717 (Plenum Press, 1983).Harper, D. A. T. The Ordovician biodiversification: Setting an agenda for marine life. Palaeogeogr. Palaeoclimatol. Palaeoecol. 232, 148–166 (2006).
Google Scholar 
Stigall, A. L., Bauer, J. E., Lam, A. R. & Wright, D. F. Biotic immigration events, speciation, and the accumulation of biodiversity in the fossil record. Global Planet. Change 148, 242–257 (2017).
Google Scholar 
Copper, P. Coral Reefs Reports Ancient reef ecosystem expansion and collapse. Coral Reefs 13, 3–11 (1994).
Google Scholar 
Trotter, J. A., Williams, I. S., Barnes, C. R., Lécuyer, C. & Nicoll, R. S. Did Cooling Oceans Trigger Ordovician Biodiversification? Evidence from Conodont Thermometry. Science 321, 550–554 (2008).CAS 

Google Scholar 
Lindström, M. The Ordovician climate based on the study of carbonate rocks. in Aspects of the Ordovician System, Paleontological Contribution of the University of Oslo (ed. Bruton, D. L.) vol. 295 81–88 (Universitetsforlaget, 1984).Rasmussen, C. M. Ø., Nielsen, A. T. & Harper, D. A. T. Ecostratigraphical interpretation of lower Middle Ordovician East Baltic sections based on brachiopods. Geological Mag. 146, 717–731 (2009).
Google Scholar 
Dabard, M. P. et al. Sea-level curve for the Middle to early Late Ordovician in the Armorican Massif (western France): Icehouse third-order glacio-eustatic cycles. Palaeogeogr. Palaeoclimatol. Palaeoecol. 436, 96–111 (2015).
Google Scholar 
Rasmussen, C. M. Ø. et al. Onset of main Phanerozoic marine radiation sparked by emerging Mid Ordovician icehouse. Sci. Rep. 6, 1–9 (2016).
Google Scholar 
Deutsch, C., Ferrel, A., Seibel, B., Pörtner, H.-O. & Huey, R. B. Climate change tightens a metabolic constraint on marine habitats. Science 348, 1132–1135 (2015).CAS 

Google Scholar 
Penn, J. L., Deutsch, C., Payne, J. L. & Sperling, E. A. Temperature-dependent hypoxia explains biogeography and severity of end-Permian marine mass extinction. Science 362, eaat1327 (2018).
Google Scholar 
Saltzman, M. R., Edwards, C. T., Adrain, J. M. & Westrop, S. R. Persistent oceanic anoxia and elevated extinction rates separate the Cambrian and Ordovician radiations. Geology 43, 807–811 (2015).CAS 

Google Scholar 
Edwards, C. T., Saltzman, M. R., Royer, D. L. & Fike, D. A. Oxygenation as a driver of the Great Ordovician Biodiversification Event. Nature Geoscience 10, 925–929 (2017).CAS 

Google Scholar 
Sperling, E. A., Knoll, A. H. & Girguis, P. R. The ecological physiology of earth’s second oxygen revolution. Ann. Rev. Ecol. Evolut. Syst. 46, 215–235 (2015).
Google Scholar 
Dahl, T. W. et al. Reorganisation of Earth’s biogeochemical cycles briefly oxygenated the oceans 520 Myr ago. Geochem. Perspect. Lett. 210–220 (2017).Dahl, T. W. et al. Atmosphere-ocean oxygen and productivity dynamics during early animal radiations. Proc. Natl. Acad. Sci. 116, 19352–19361 (2019).CAS 

Google Scholar 
Nursall, J. R. Oxygen as a prerequisite to the origin of the metazoa. Nature 183, 1170–1172 (1959).Knoll, A. H. Biological and Biogeochemical Preludes to the Ediacaran Radiation. In Origin and Early Evolution of the Metazoa (eds. Lipps, J. H. & Signor, P. W.) 53–84 (Springer US, 1992).Brennecka, G. A., Herrmann, A. D., Algeo, T. J. & Anbar, A. D. Rapid expansion of oceanic anoxia immediately before the end-Permian mass extinction. Proc. Natl. Acad. Sci. 108, 17631–17634 (2011).CAS 

Google Scholar 
Lau, K. V. et al. Marine anoxia and delayed Earth system recovery after the end-Permian extinction. Proc. Natl. Acad. Sci. USA 113, 2360–2365 (2016).CAS 

Google Scholar 
Zhang, F. et al. Congruent Permian-Triassic δ238U records at Panthalassic and Tethyan sites: Confirmation of global-oceanic anoxia and validation of the U-isotope paleoredox proxy. Geology 46, 327–330 (2018).CAS 

Google Scholar 
Andersen, M. B., Stirling, C. H. & Weyer, S. Uranium isotope fractionation. Rev. Mineral. Geochem. 82, 799–850 (2017).CAS 

Google Scholar 
Chen, X. et al. Diagenetic effects on uranium isotope fractionation in carbonate sediments from the Bahamas. Geochimica et Cosmochimica Acta 237, 294–311 (2018).CAS 

Google Scholar 
Zhang, F. et al. Uranium isotopes in marine carbonates as a global ocean paleoredox proxy: A critical review. Geochimica et Cosmochimica Acta 287, 27–49 (2020).CAS 

Google Scholar 
Stylo, M. et al. Uranium isotopes fingerprint biotic reduction. Proc. Natl. Acad. Sci. 112, 5619–5624 (2015).CAS 

Google Scholar 
Basu, A. et al. Microbial U isotope fractionation depends on the U(VI) reduction rate. Environ. Sci. Technol. 54, 2295–2303 (2020).CAS 

Google Scholar 
Dunk, R. M., Mills, R. A. & Jenkins, W. J. A reevaluation of the oceanic uranium budget for the Holocene. Chem. Geol. 190, 45–67 (2002).CAS 

Google Scholar 
Dahl, T. W. et al. Uranium isotopes distinguish two geochemically distinct stages during the later Cambrian SPICE event. Earth Planet. Sci. Lett. 401, 313–326 (2014).CAS 

Google Scholar 
Tissot, F. L. H. & Dauphas, N. Uranium isotopic compositions of the crust and ocean: Age corrections, U budget and global extent of modern anoxia. Geochimica et Cosmochimica Acta 167, 113–143 (2015).CAS 

Google Scholar 
Romaniello, S. J., Herrmann, A. D. & Anbar, A. D. Uranium concentrations and 238U/235U isotope ratios in modern carbonates from the Bahamas: Assessing a novel paleoredox proxy. Chem. Geology 362, 305–316 (2013).CAS 

Google Scholar 
Chen, X., Romaniello, S. J., Herrmann, A. D., Samankassou, E. & Anbar, A. D. Biological effects on uranium isotope fractionation (238U/235U) in primary biogenic carbonates. Geochimica et Cosmochimica Acta 240, 1–10 (2018).CAS 

Google Scholar 
Tissot, F. L. H. et al. Controls of eustasy and diagenesis on the 238U/235U of carbonates and evolution of the seawater (234U/238U) during the last 1.4 Myr. Geochimica et Cosmochimica Acta 242, 233–265 (2018).CAS 

Google Scholar 
Lindskog, A. & Eriksson, M. E. Megascopic processes reflected in the microscopic realm: sedimentary and biotic dynamics of the Middle Ordovician “orthoceratite limestone” at Kinnekulle, Sweden. Gff 139, 163–183 (2017).
Google Scholar 
Jaanusson, V. Aspects of carbonate sedimentation in the Ordovician of Baltoscandia. Lethaia 6, 11–34 (1973).
Google Scholar 
Bergström, S. M., Chen, X., Gutiérrez-marco, J. C. & Dronov, A. The new chronostratigraphic classification of the Ordovician System and its relations to major regional series and stages and to δ13C chemostratigraphy. Lethaia 42, 97–107 (2008).
Google Scholar 
Lindskog, A., Lindskog, A. M., Johansson, J. V., Ahlberg, P. & Eriksson, M. E. The Cambrian–Ordovician succession at Lanna, Sweden: stratigraphy and depositional environments. Estonian J. Earth Sci 67, 133 (2018).
Google Scholar 
Bábek, O. et al. Redox geochemistry of the red ‘orthoceratite limestone’ of Baltoscandia: Possible linkage to mid-Ordovician palaeoceanographic changes. Sedimentary Geology 420, 105934 (2021).
Google Scholar 
Azmy, K. et al. Carbon-isotope stratigraphy of the Lower Ordovician succession in Northeast Greenland: Implications for correlations with St. George Group in western Newfoundland (Canada) and beyond. Sedimentary Geology 225, 67–81 (2010).CAS 

Google Scholar 
Bartlett, R. et al. Abrupt global-ocean anoxia during the Late Ordovician–early Silurian detected using uranium isotopes of marine carbonates. Proc Natl Acad Sci USA 115, 5896–5901 (2018).CAS 

Google Scholar 
Dahl, T. W., Hammarlund, E. U., Rasmussen, C. M. Ø., Bond, D. P. G. & Canfield, D. E. Sulfidic anoxia in the oceans during the Late Ordovician mass extinctions – insights from molybdenum and uranium isotopic global redox proxies. Earth-Sci. Rev. 220, 103748 (2021).CAS 

Google Scholar 
Del Rey, Á., Havsteen, J., Bizzarro, M., Connelly, J. & Dahl, T. W. Untangling the diagenetic history of Uranium isotopes in marine carbonates: a case study tracing d238U of late Silurian oceans using calcitic brachiopod shells. Geochimica et Cosmochimica Acta 2020, 93–110.Rasmussen, J. A., Thibault, N. & Mac Ørum Rasmussen, C. Middle Ordovician astrochronology decouples asteroid breakup from glacially-induced biotic radiations.Nat Commun12, 6430 (2021).CAS 

Google Scholar 
Ainsaar, L. et al. Middle and Upper Ordovician carbon isotope chemostratigraphy in Baltoscandia: A correlation standard and clues to environmental history. Palaeogeogr. Palaeoclimatol. Palaeoecol. 294, 189–201 (2010).
Google Scholar 
Wu, R., Calner, M. & Lehnert, O. Integrated conodont biostratigraphy and carbon isotope chemostratigraphy in the Lower-Middle Ordovician of southern Sweden reveals a complete record of the MDICE. Geological Mag. 154, 334–353 (2017).CAS 

Google Scholar 
Lindskog, A., Eriksson, M. E., Bergström, S. M. & Young, S. A. Lower–Middle Ordovician carbon and oxygen isotope chemostratigraphy at Hällekis, Sweden: implications for regional to global correlation and palaeoenvironmental development. Lethaia 52, 204–219 (2019).
Google Scholar 
Rasmussen, C. M. Ø., Hansen, J. & Harper, D. A. T. Baltica: A mid Ordovivian diversity hotspot. Historical Biology 19, 255–261 (2007).
Google Scholar 
Zhang, J. Lithofacies and stratigraphy of the Ordovician Guniutan Formation in its type area, China. Geol. J. 31, 201–215 (1996).
Google Scholar 
Eriksson, M. E. et al. Biotic dynamics and carbonate microfacies of the conspicuous Darriwilian (Middle Ordovician) ‘Täljsten’ interval, south-central Sweden. Palaeogeogr. Palaeoclimatol. Palaeoecol. 367–368, 89–103 (2012).
Google Scholar 
Lindström, M., Jun-Yuan, C. & Jun-Ming, Z. Section at Daping reveals Sino-Baltoscandian parallelism of facies in the Ordovician. Geologiska Föreningen i Stockholm Förhandlingar 113, 189–205 (1991).
Google Scholar 
Edward, O. et al. A Baltic perspective on the early to early late ordovician δ13 C and δ18 O Records and its paleoenvironmental significance. Paleoceanog and Paleoclimatol 37, e2021PA004309 (2022).Pörtner, H. Climate change and temperature-dependent biogeography: oxygen limitation of thermal tolerance in animals. Naturwissenschaften 88, 137–146 (2001).
Google Scholar 
Gueguen, B. et al. The chromium isotope composition of reducing and oxic marine sediments. Geochimica et Cosmochimica Acta 184, 1–19 (2016).CAS 

Google Scholar 
Weyer, S. et al. Natural fractionation of 238U/235U. Geochimica et Cosmochimica Acta 72, 345–359 (2008).CAS 

Google Scholar 
Condon, D. J., McLean, N., Noble, S. R. & Bowring, S. A. Isotopic composition (238U/235U) of some commonly used uranium reference materials. Geochimica et Cosmochimica Acta 74, 7127–7143 (2010).CAS 

Google Scholar 
Wang, X., Planavsky, N. J., Reinhard, C. T., Hein, J. R. & Johnson, T. M. A cenozoic seawater redox record derived from 238U/235U in ferromanganese crusts. Am. J. Sci. 315, 64–83 (2016).
Google Scholar 
Trotter, J. A., Williams, I. S., Barnes, C. R., Männik, P. & Simpson, A. New conodont δ18O records of Silurian climate change: Implications for environmental and biological events. Palaeogeogr. Palaeoclimatol. Palaeoecol. 443, 34–48 (2016).
Google Scholar 
Scotese, C. R. Atlas of Silurian and Middle-Late Ordovician Paleogeographic Maps (Mollweide Projection), Maps 73-80, Volumes 5, The Early Paleozoic, PALEOMAP Atlas for ArcGIS, PALEOMAP Project, Evanston, IL. (2014). More

Correction to: Nature Ecology & Evolution https://doi.org/10.1038/s41559-019-0970-7, published online 2 September 2019.The Journal would like to note that the authors first made contact in September 2019 to raise the concerns that follow, and the Journal apologizes both for the delay in relaying these corrections publicly and for the changed instances that prevent making corrections to the original article itself. What follows is the Author correction.In the version of this article initially published, we made several errors in our R analysis code, and in the text and figures. First, the number of species with negative net dispersal velocity (net DV) were incorrectly calculated, resulting in slight changes in Fig. 2 and Supplementary Fig. 6, and in the text. The amended figures are provided below (Figs. 1–7). Changes to the text under the ‘DVs’ subsection of Results are: “When considering the mainstem pathway, we estimate that the mobile subpopulations of 134 (old version: 124) and 185 (old version: 174) (RCP 4.5 and 8.5, respectively) species will experience dispersal deficits in at least 50% of their southern Appalachian range, whereas these estimates increase to 229 (old version: 226) and 231 (old version: 232) species for the stationary subpopulation. Slow-climate-velocity tributaries reduce the number of species experiencing dispersal deficits by 99.3% (old version: 99.2%) and 90.3% (old version: 16.9%) (RCP 4.5 and 8.5, respectively) for the mobile component and 17.9% (old version: 90.8%) and 12.1% (old version: 12.9%) for the stationary component (Fig. 2a,b).” The two large discrepancies in dispersal deficit values (90.3% vs. 16.9%; 17.9% vs. 90.8%) were solely consequences of original text errors (16.9% and 90.8% values were erroneously switched), and not differences in calculations; therefore, the results did not change.Fig. 1Figure 2, original and corrected.Full size imageFig. 2Figure 3c,d, original and corrected.Full size imageFig. 3Figure 4, original and corrected.Full size imageFig. 4Figure 5, original and corrected.Full size imageFig. 5Supplementary Figure 6, original.Full size imageFig. 5Supplementary Figure 6, corrected.Full size imageFig. 6Supplementary Figure 8, original.Full size imageFig. 6Supplementary Figure 8, corrected.Full size imageFig. 7Supplementary Figure 9, original.Full size imageFig. 7Supplementary Figure 9, corrected.Full size imageSecond, we made errors when plotting Fig. 3c,d. Boxplots of mean change in habitat suitability were plotted instead of median change as specified in the caption; further, whiskers did not include the entire range of values. The amended figure is provided below. We would like to correct associated errors in text; specific changes are: “Our ENMs estimate a median 15.1% (old version: mean 14.1%) reduction (range −42.5% to +16.6% [old version: −51.6% to +2.4%] across 233 species) in habitat suitability associated with the tributary pathway compared with only a 3.8% (old version: 1.6%) reduction (−11.0% to +12.3% [old version: −7.9% to +1.8%]) for the mainstem pathway due to differing non-temperature habitat conditions (Fig. 3a,b).”Third, we made errors when plotting Fig. 4 and Supplementary Fig. 8. Specifically, net DV values were incorrectly rescaled; one extinct species was erroneously included in the plot; and the number of species in each quadrant was counted incorrectly. The amended figures are shown below. We would like to add a sentence (“The y-axes are inverse hyperbolic sine (asinh)-transformed”) to the caption of Fig. 4 to describe the y-axis scaling in the amended figures. There were two other text errors in the caption. The phrase “Mean net DV” should have been “Median net DV,” whereas the phrase “mean habitat suitability” should have read “median change in habitat suitability.” Therefore, the corrected Fig. 4 caption should read: “Species-level mismatch between net DV and upstream habitat suitability. a–d, Median net DV of mobile (a,b) and stationary subpopulations under the RCP 8.5 scenario plotted as a function of median change in habitat suitability for mainstem (a,c) and tributary (b,d) dispersal pathways. Each point represents a species and is computed as the median response across all projected occupied reaches. Red and blue numbers correspond to the number of species in each of the four quadrants. The y-axes are inverse hyperbolic sine (asinh)-transformed. e–h, Four species highlighting the diversity in dispersal-based and habitat suitability-based vulnerability: streamline chub (e); brook trout (f); flathead catfish (g); blacknose dace (h). Credit: David Neely (e–h)”. These corrections did not change our inferences.Fourth, there were errors in rescaling and plotting net DV values and in the calculation of quadrant percentages in Fig. 5 and Supplementary Fig. 9. The amended figures are shown below. We would like to add two sentences at the end of Fig. 5 caption to provide greater detail on plotting methods: “The y-axes of the scatterplots are inverse hyperbolic sine (asinh)-transformed. For clarity, the scatterplots show net DV values ≥ −13,000 and ≤ 130, and change in habitat suitability values ≤ 100, representing >99.5% of all observations.” There was one other text error in the caption: the phrase “mean habitat suitability” should have read “mean change in habitat suitability.” Therefore, the corrected Fig. 4 caption should read: “Community-level mismatch between net DV and upstream habitat suitability. a–d, Mean net DV of mobile (a,b) and stationary (c,d) subpopulations under the RCP 8.5 scenario plotted as a function of mean change in habitat suitability for mainstem (a,c) and tributary (b,d) dispersal pathways. Each point (scatterplot) and reach (map) is computed as the mean response for all species projected to occur within the reach. Quadrant numbers represent percentage of reaches in the quadrant. Colours associated with the upper-right quadrant correspond to ‘safe’ reaches where community members can keep pace with ISVs and habitat suitability increases. Colours associated with the lower-left quadrant correspond to ‘vulnerable’ reaches where community members cannot keep pace with ISVs and habitat suitability declines. The y-axes of the scatterplots are inverse hyperbolic sine (asinh)-transformed. For clarity, the scatterplots show net DV values ≥ −13,000 and ≤ 130, and change in habitat suitability values ≤ 100, representing >99.5% of all observations.” These corrections did not change our inferences.Fifth, there was an error in the last sentence of the “Calculating net DVs” subsection in Methods: “Last, we calculated the mean net DV for each species (species-specific DV) by averaging net DVs at all occupied reaches, as well as the community-wide net DV at each stream reach (reach-specific DV) by averaging the net DVs of all species at each reach.” This sentence should have read “Last, we calculated the median net DV for each species (species-specific DV) across all occupied reaches, as well as the mean community-wide net DV at each stream reach (reach-specific DV) by averaging the net DVs of all species at each reach.”Corrections of calculation errors yielded results that were similar to those in the original analysis whereas corrections of plotting and text errors did not affect our original inferences. Therefore, these errors did not change the overall results and conclusions of the article. We sincerely apologize for any misunderstanding and inconvenience caused by these errors. More

Little, C. M., Chapman, T. W. & Hillier, N. K. Plasticity is key to success of Drosophila suzukii (Diptera: Drosophilidae) invasion. J. Insect Sci. 20, 5. https://doi.org/10.1093/jisesa/ieaa034 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Tait, G. et al. Drosophila suzukii (Diptera: Drosophilidae): A decade of research towards a sustainable integrated pest management program. J. Economic Entomol. 114, 1950–1974. https://doi.org/10.1093/jee/toab158 (2021).Article 

Google Scholar 
Walsh, D. B. et al. Drosophila suzukii (Diptera: Drosophilidae): Invasive pest of ripening soft fruit expanding its geographic range and damage potential. J. Integ. Pest Manag. 2, G1–G7. https://doi.org/10.1603/IPM10010 (2011).Article 

Google Scholar 
Hamby, K. A. et al. Biotic and abiotic factors impacting development, behavior, phenology, and reproductive biology of Drosophila suzukii. J. Pest Sci. 89, 605–619. https://doi.org/10.1007/s10340-016-0756-5 (2016).Article 

Google Scholar 
Stewart, T. J., Wang, X. G., Molinar, A. & Daane, K. M. Factors limiting peach as a potential host for Drosophila suzukii (Diptera: Drosophilidae). J. Economic Entomol. 107, 1771–1779. https://doi.org/10.1603/EC14197 (2014).Article 

Google Scholar 
Keesey, I. W., Knaden, M. & Hansson, B. S. Olfactory specialization in Drosophila suzukii supports an ecological shift in host preference from rotten to fresh fruit. J. Chem. Ecol. 41, 121–128. https://doi.org/10.1007/s10886-015-0544-3 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Poyet, M. et al. The wide potential trophic niche of the Asiatic fruit fly Drosophila suzukii: The key of its invasion success in temperate Europe?. PLoS ONE 10, e0152785. https://doi.org/10.1371/journal.pone.0142785 (2015).CAS 
Article 

Google Scholar 
Lee, J. C. et al. Characterization and manipulation of fruit susceptibility to Drosophila suzukii. J. Pest Sci. 89, 771–780. https://doi.org/10.1007/s10340-015-0692-9 (2016).Article 

Google Scholar 
Entling, W., Anslinger, S., Jarausch, B., Michl, G. & Hoffmann, C. Berry skin resistance explains oviposition preferences of Drosophila suzukii at the level of grape cultivars and single berries. J. Pest Sci. 92, 477–484. https://doi.org/10.1007/s10340-018-1040-7 (2019).Article 

Google Scholar 
Guo, L. et al. Identification of potential mechanosensitive ion channels involved in texture discrimination during Drosophila suzukii egg-laying behavior. Insect Mol. Biol. 29, 444–451. https://doi.org/10.1111/imb.12654 (2020).CAS 
Article 
PubMed 

Google Scholar 
Kidera, H. & Takahashi, K. H. Chemical cues from competitors change the oviposition preference of Drosophila suzukii. Entomol. Exp. Appl. 168, 304–310. https://doi.org/10.1111/eea.12889 (2020).CAS 
Article 

Google Scholar 
Little, C. M., Dixon, P. L., Chapman, T. W. & Hillier, N. K. Role of fruit characters and colour on host selection of boreal fruits and berries by Drosophila suzukii (Diptera: Drosophilidae). Can. Entomol. 152, 546–562. https://doi.org/10.4039/tce.2020.1 (2020).Article 

Google Scholar 
Tait, G. et al. Reproductive site selection: evidence of an oviposition cue in a highly adaptive Dipteran, Drosophila suzukii (Diptera: Drosophilidae). Environ. Entomol. 49, 355–363. https://doi.org/10.1093/ee/nvaa005 (2020).CAS 
Article 
PubMed 

Google Scholar 
Tonina, L. et al. Texture features explain the susceptibility of grapevine cultivars to Drosophila suzukii (Diptera: Drosophilidae) infestation in ripening and drying grapes. Sci. rep. 10, 10245. https://doi.org/10.1038/s41598-020-66567-9 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wernicke, M., Lethmayer, C. & Bluemel, S. Laboratory trials to investigate potential repellent/oviposition deterrent effects of selected substances on Drosophila suzukii adults. Bull. Insectol 73, 249–255 (2020).
Google Scholar 
Durkin, S. M. et al. Behavioral and genomic sensory adaptation underlying the pest activity of Drosophila suzukii. Mol. Biol. Evol. 38, 2532–2546. https://doi.org/10.1093/molbev/msab048 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Dweck, H. K. M., Talross, G. J. S., Wang, W. & Carlson, J. R. Evolutionary shifts in taste coding in the fruit pest Drosophila suzukii. Elife 10, e64317. https://doi.org/10.7554/eLife.64317 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Elsensohn, J. E., Aly, M. F. K., Schal, C. & Burrack, H. J. Social signals mediate oviposition site selection in Drosophila suzukii. Sci. Rep. 11, 3796. https://doi.org/10.1038/s41598-021-83354-2 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kienzle, R. & Rohlfs, M. Mind the wound!—fruit injury ranks higher than, and interacts with, heterospecific cues for Drosophila suzukii oviposition. Insects 12, 424. https://doi.org/10.3390/insects12050424 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Sato, A., Tanaka, K. M., Yew, J. Y. & Takahashi, A. Drosophila suzukii avoidance of microbes in oviposition choice. R. Soc. Open Sci. 8, 201601. https://doi.org/10.1098/rsos.201601 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Stockton, D. G., Cha, D. H. & Loeb, G. M. Does habituation affect the efficacy of semiochemical oviposition repellents developed against Drosophila suzukii?. Environ. Entomol. 50, 1322–1331. https://doi.org/10.1093/ee/nvab099 (2021).CAS 
Article 
PubMed 

Google Scholar 
Wöhner, T. et al. Insights into the susceptibility of raspberries to Drosophila suzukii oviposition. J. Appl Entomol. 145, 182–190. https://doi.org/10.1111/jen.12839 (2021).CAS 
Article 

Google Scholar 
Baena, R. et al. Ripening stages and volatile compounds present in strawberry fruits are involved in the oviposition choice of Drosophila suzukii (Diptera: Drosophilidae). Crop Prot. 153, 105883. https://doi.org/10.1016/j.cropro.2021.105883 (2022).CAS 
Article 

Google Scholar 
R Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2022).Broström, G. & Holmberg, H. Generalized linear models with clustered data: Fixed and random effects models. Comput. Stat. Data Anal. 55, 3123–3134. https://doi.org/10.1016/j.csda.2011.06.011 (2011).MathSciNet 
Article 
MATH 

Google Scholar 
Asplen, M. K. et al. Invasion biology of spotted wing Drosophila (Drosophila suzukii): A global perspective and future priorities. J. Pest Sci. 88, 469–494. https://doi.org/10.1007/s10340-015-0681-z (2015).Article 

Google Scholar 
Knapp, L., Mazzi, D. & Finger, R. The economic impact of Drosophila suzukii: Perceived costs and revenue losses of Swiss cherry, plum and grape growers. Pest Management Sci. 77, 978–1000. https://doi.org/10.1002/ps.6110 (2020).CAS 
Article 

Google Scholar 
Ishii, S. Studies on the host preference of the cowpea weevil (Callosobruchus chinensis L.). Bull. Natl. Inst. Agric. Sci. Ser. C 1, 185–156 (1952).
Google Scholar 
Katsoyannos, B. I. & Pittara, I. S. Effect of size of artificial oviposition substrates and presence of natural host fruits on the selection of oviposition site by Dacus oleae. Entmol. Exp. Appl. 34, 326–332 (1983).Article 

Google Scholar 
McDonald, P. T. & McInnis, D. O. Ceratitis capitata: Effect of host fruit size on the number of eggs per clutch. Entomol. Exp. Appl. 37, 207–211 (1985).Article 

Google Scholar 
Pittara, I. S. & Katsoyannos, B. I. Effect of shape, size and color on selection of oviposition sites by Chaetorellia australis. Entomol. Exp. Appl. 63, 105–113 (1992).Article 

Google Scholar 
Greenberg, S. M., Sappington, T. W., Sétamou, M. & Coleman, R. J. Influence of different cotton fruit sizes on boll weevil (Coleoptera: Curculionidae) oviposition and survival to adulthood. Environ. Entomol. 33, 443–449. https://doi.org/10.1603/0046-225X-33.2.443 (2004).Article 

Google Scholar 
Showler, A. T. Relationship of different cotton square sizes to boll weevil (Coleoptera: Curculionidae) feeding and oviposition in field conditions. J. Econ. Entomol. 98, 1572–1579. https://doi.org/10.1603/0022-0493-98.5.1572 (2005).Article 
PubMed 

Google Scholar 
Charnov, E. L., Los-den Hartogh, R. L., Jones, W. T. & van den Assem, J. Sex ratio evolution in a variable environment. Nature 289, 27–33 (1981).ADS 
CAS 
Article 

Google Scholar 
Avidov, Z., Berlinger, M. J. & Applebaum, S. W. Physiological aspects of host specificity in the Bruchidae: III. Effect of curvature and surface area on oviposition of Callosobruchus chinensis L.. Anim. Behav. 13, 178–180 (1965).Article 

Google Scholar 
Sambaraju, K. R. & Phillips, T. W. Effects of physical and chemical factors on oviposition by Plodia interpunctella (Lepidoptera: Pyralidae). Ann. Entomol. Soc. Am. 101, 955–963 (2008).Article 

Google Scholar 
Schmidt, J. M. & Smith, J. J. B. Correlations between body angles and substrate curvature in the parasitoid wasp Trichogramma minutum: A possible mechanism of host radius measurement. J. Exp. Biol. 125, 271–285 (1986).Article 

Google Scholar 
Jois, S. et al. Sexually dimorphic peripheral sensory neurons regulate copulation duration and persistence in male Drosophila. Sci. Rep. 12, 1–12. https://doi.org/10.1038/s41598-022-10247-3 (2022).CAS 
Article 

Google Scholar 
Crava, C. M. et al. Structural and transcriptional evidence of mechanotransduction in the Drosophila suzukii ovipositor. J. Insect Physiol. 125, 104088. https://doi.org/10.1016/j.jinsphys.2020.104088 (2020).CAS 
Article 
PubMed 

Google Scholar 
Sampson, B. J. et al. Novel aspects of Drosophila suzukii (Diptera: Drosophilidae) biology and an improved method for culturing this invasive species with a modified D. melanogaster diet. Florida Entomol. 99, 774–780. https://doi.org/10.1653/024.099.0433 (2016).Article 

Google Scholar  More

Gupta, S. Chaos, persistence, and evolution of strain structure in antigenically diverse infectious agents. Science 280, 912–915 (1998).CAS 
PubMed 
Article 

Google Scholar 
Kucharski, A. J., Andreasen, V. & Gog, J. R. Capturing the dynamics of pathogens with many strains. J. Math. Biol. 72, 1–24 (2016).PubMed 
Article 

Google Scholar 
Lourenço, J. & Recker, M. Natural, persistent oscillations in a spatial multi-strain disease system with application to dengue. PLoS Comput. Biol. 9, e1003308 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Gog, J. R. & Grenfell, B. T. Dynamics and selection of many-strain pathogens. Proc. Natl Acad. Sci. USA 99, 17209–17214 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Recker, M., Pybus, O. G., Nee, S. & Gupta, S. The generation of influenza outbreaks by a network of host immune responses against a limited set of antigenic types. Proc. Natl Acad. Sci. USA 104, 7711–7716 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jang, Y., Seo, T. & Seo, S. H. Higher virulence of swine H1N2 influenza viruses containing avian-origin HA and 2009 pandemic PA and NP in pigs and mice. Arch. Virol. 165, 1141–1150 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Salvesen, H. A. & Whitelaw, C. B. A. Current and prospective control strategies of influenza A virus in swine. Porcine Health Manage. 7, 23 (2021).Article 

Google Scholar 
Ma, W., Kahn, R. E. & Richt, J. A. The pig as a mixing vessel for influenza viruses: human and veterinary implications. J. Mol. Genet. Med. 03, 158–166 (2009).CAS 
Article 

Google Scholar 
Mancera Gracia, J. C., Pearce, D. S., Masic, A. & Balasch, M. Influenza A virus in swine: epidemiology, challenges and vaccination strategies. Front. Vet. Sci. 7, 647 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Van Regenmortel, M. H. V. Virus species and virus identification: past and current controversies. Infect. Genet. Evol. 7, 133–144 (2007).PubMed 
Article 
CAS 

Google Scholar 
Lazebnik, T. & Bunimovich-Mendrazitsky, S. Generic approach for mathematical model of multi-strain pandemics. PLoS ONE 17, e0260683 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wikramaratna, P. S., Sandeman, M., Recker, M. & Gupta, S. The antigenic evolution of influenza: drift or thrift? Phil. Trans. R. Soc. B 368, 20120200 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Pitzer, V. E. et al. Modeling rotavirus strain dynamics in developed countries to understand the potential impact of vaccination on genotype distributions. Proc. Natl Acad. Sci. USA 108, 19353–19358 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Grenfell, B. T. et al. Unifying the epidemiological and evolutionary dynamics of pathogens. Science 303, 327–332 (2004).CAS 
PubMed 
Article 

Google Scholar 
Paploski, I. A. D. et al. Temporal dynamics of co-circulating lineages of porcine reproductive and respiratory syndrome virus. Front. Microbiol. 10, 2486 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Ferguson, N. M., Galvani, A. P. & Bush, R. M. Ecological and immunological determinants of influenza evolution. Nature 422, 428–433 (2003).CAS 
PubMed 
Article 

Google Scholar 
Bishop, S. C., Axford, R. F. E., Nicholas, F. W. & Owen, J. B. Breeding for Disease Resistance in Farm Animals 3rd edn (CABI, 2010); https://doi.org/10.1079/9781845935559.0000Domingo, E. & Schuster, P. in Quasispecies: From Theory to Experimental Systems (eds Domingo, E. & Schuster, P.) 1–22 (Springer, 2015); https://doi.org/10.1007/82_2015_453Lythgoe, K. A., Gardner, A., Pybus, O. G. & Grove, J. Short-sighted virus evolution and a germline hypothesis for chronic viral infections. Trends Microbiol. 25, 336–348 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, N., Trible, B. R., Kerrigan, M. A., Tian, K. & Rowland, R. R. R. ORF5 of porcine reproductive and respiratory syndrome virus (PRRSV) is a target of diversifying selection as infection progresses from acute infection to virus rebound. Infect. Genet. Evol. 40, 167–175 (2016).CAS 
PubMed 
Article 

Google Scholar 
Carpenter, S. Identification of Genetic Mutations that Confer Escape from Innate or Adaptive Host Immune Responses During PRRSV Infection In Vivo NPB #12-173 (National Pork Board, 2014).Dimitrov, D. S. Virus entry: molecular mechanisms and biomedical applications. Nat. Rev. Microbiol. 2, 109–122 (2004).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Dou, D., Revol, R., Östbye, H., Wang, H. & Daniels, R. Influenza A virus cell entry, replication, virion assembly and movement. Front. Immunol. 9, 1581 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hamilton, B. S., Whittaker, G. R. & Daniel, S. Influenza virus-mediated membrane fusion: determinants of hemagglutinin fusogenic activity and experimental approaches for assessing virus fusion. Viruses 4, 1144–1168 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, K. et al. Virus–host interactions in foot-and-mouth disease virus infection. Front. Immunol. 12, 571509 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Millet, J. K., Jaimes, J. A. & Whittaker, G. R. Molecular diversity of coronavirus host cell entry receptors. FEMS Microbiol. Rev. https://doi.org/10.1093/femsre/fuaa057 (2020).Wang, G., Wang, Y., Shang, Y., Zhang, Z. & Liu, X. How foot-and-mouth disease virus receptor mediates foot-and-mouth disease virus infection. Virol. J. 12, 9 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sokol, C. L. & Luster, A. D. The chemokine system in innate immunity. Cold Spring Harb. Perspect. Biol. 7, a016303 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Takeuchi, O. & Akira, S. Innate immunity to virus infection. Immunol. Rev. 227, 75–86 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Theofilopoulos, A., Baccala, R., Beutler, B. & Kono, D. Type I interferons (alpha/beta) in immunity and autoimmunity. Annu. Rev. Immunol. 23, 307–336 (2005).CAS 
PubMed 
Article 

Google Scholar 
Mueller, S. N. & Rouse, B. T. in Clinical Immunology (eds Rich, R. R. et al.) 421–431 (Elsevier, 2008); https://doi.org/10.1016/B978-0-323-04404-2.10027-2Chen, X. et al. Host immune response to influenza A virus infection. Front. Immunol. 9, 320 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Agrawal, B. Heterologous immunity: role in natural and vaccine-induced resistance to infections. Front. Immunol. 10, 2631 (2019)iCAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sharma, S. & Thomas, P. G. The two faces of heterologous immunity: protection or immunopathology. J. Leukoc. Biol. 95, 405–416 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Spackman, E. & Sitaras, I. Animal Influenza Virus (Springer, 2020).Anderson, C. S., McCall, P. R., Stern, H. A., Yang, H. & Topham, D. J. Antigenic cartography of H1N1 influenza viruses using sequence-based antigenic distance calculation. BMC Bioinformatics 19, 51 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Cai, Z., Zhang, T. & Wan, X.-F. Concepts and applications for influenza antigenic cartography. Influenza Other Respi. Viruses 5, 204–207 (2011).PubMed Central 

Google Scholar 
Wang, P. et al. Predicting influenza antigenicity by matrix completion with antigen and antiserum similarity. Front. Microbiol. 9, 2500 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Hirst, G. K. Studies of antigenic differences among strains of influenza by means of red cell agglutination. J. Exp. Med. 78, 407–423 (1943).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kendra, J. A., Tohma, K., Ford-Siltz, L. A., Lepore, C. J. & Parra, G. I. Antigenic cartography reveals complexities of genetic determinants that lead to antigenic differences among pandemic GII.4 noroviruses. Proc. Natl Acad. Sci. USA 118, e2015874118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bell, S. M., Katzelnick, L. & Bedford, T. Dengue genetic divergence generates within-serotype antigenic variation, but serotypes dominate evolutionary dynamics. Elife 8, e42496 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Yao, Y. et al. Predicting influenza antigenicity from Hemagglutintin sequence data based on a joint random forest method. Sci. Rep. 7, 1545 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zeller, M. A. et al. Machine learning prediction and experimental validation of antigenic drift in h3 influenza A viruses in swine. mSphere 6, e00920–e00920 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wikramaratna, P. S. et al. Five challenges in modelling interacting strain dynamics. Epidemics 10, 31–34 (2015).PubMed 
Article 

Google Scholar 
Elliott, P. et al. Exponential growth, high prevalence of SARS-CoV-2, and vaccine effectiveness associated with the Delta variant. Science 374, eabl9551 (2021).CAS 
PubMed 
Article 

Google Scholar 
Bianco, S., Shaw, L. B. & Schwartz, I. B. Epidemics with multistrain interactions: the interplay between cross immunity and antibody-dependent enhancement. Chaos 19, 043123 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
Nickbakhsh, S. et al. Virus–virus interactions impact the population dynamics of influenza and the common cold. Proc. Natl Acad. Sci. USA 116, 27142–27150 (2019).CAS 
PubMed Central 
Article 

Google Scholar 
Poon, A. F. Y. et al. Mapping the shapes of phylogenetic trees from human and zoonotic RNA viruses. PLoS ONE 8, e78122 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lemey, P. et al. Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2. PLoS Pathog. 10, e1003932 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lemey, P., Rambaut, A., Drummond, A. J. & Suchard, M. A. Bayesian phylogeography finds its roots. PLoS Comput. Biol. 5, e1000520 (2009).PubMed 
PubMed Central 
Article 
CAS 

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

Google Scholar 
Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Gill, M. S. et al. Improving bayesian population dynamics inference: A coalescent-based model for multiple loci. Mol. Biol. Evol. 30, 713–724 (2013).CAS 
PubMed 
Article 

Google Scholar 
Kingman, J. F. C. On the genealogy of large populations. J. Appl. Probab. 19, 27–43 (1982).Article 

Google Scholar 
Griffiths, R. C. & Tavare, S. Ancestral inference in population genetics. Stat. Sci. 9, 307–319 (1994).Article 

Google Scholar 
Magee, D., Suchard, M. A. & Scotch, M. Bayesian phylogeography of influenza A/H3N2 for the 2014–15 season in the United States using three frameworks of ancestral state reconstruction. PLoS Comput. Biol. 13, e1005389 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Müller, N. F., Rasmussen, D. & Stadler, T. MASCOT: parameter and state inference under the marginal structured coalescent approximation. Bioinformatics 34, 3843–3848 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kühnert, D., Stadler, T., Vaughan, T. G. & Drummond, A. J. Phylodynamics with migration: a computational framework to quantify population structure from genomic data. Mol. Biol. Evol. 33, 2102–2116 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Yan, L., Neher, R. A. & Shraiman, B. I. Phylodynamic theory of persistence, extinction and speciation of rapidly adapting pathogens. Elife 8, e44205 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Paploski, I. A. D. et al. Phylogenetic structure and sequential dominance of sub-lineages of PRRSV type-2 lineage 1 in the United States. Vaccines 9, 608 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kistler, K. E. & Bedford, T. Evidence for adaptive evolution in the receptor-binding domain of seasonal coronaviruses OC43 and 229E. Elife 10, e64509 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bedford, T. et al. Integrating influenza antigenic dynamics with molecular evolution. Elife 2014, e01914 (2014).Article 

Google Scholar 
de Carvalho Ferreira, H. C. et al. An integrative analysis of foot-and-mouth disease virus carriers in Vietnam achieved through targeted surveillance and molecular epidemiology. Transbound. Emerg. Dis. 64, 547–563 (2017).PubMed 
Article 
CAS 

Google Scholar 
Huang, J. H. et al. Molecular characterization and phylogenetic analysis of dengue viruses imported into Taiwan during 2008–2010. Am. J. Trop. Med. Hyg. 87, 349–358 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Höckerstedt, L. M., Siren, J. P. & Laine, A.-L. Effect of spatial connectivity on host resistance in a highly fragmented natural pathosystem. J. Evol. Biol. 31, 844–852 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Papaïx, J., Burdon, J. J., Lannou, C. & Thrall, P. H. Evolution of pathogen specialisation in a host metapopulation: joint effects of host and pathogen dispersal. PLoS Comput. Biol. 10, e1003633 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Tack, A. J. M., Hakala, J., Petäjä, T., Kulmala, M. & Laine, A.-L. Genotype and spatial structure shape pathogen dispersal and disease dynamics at small spatial scales. Ecology 95, 703–714 (2014).PubMed 
Article 

Google Scholar 
Smith, D. J. et al. Mapping the antigenic and genetic evolution of influenza virus. Science 305, 371–376 (2004).CAS 
PubMed 
Article 

Google Scholar 
Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585–595 (1989).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Korneliussen, T. S., Moltke, I., Albrechtsen, A. & Nielsen, R. Calculation of Tajima’s D and other neutrality test statistics from low depth next-generation sequencing data. BMC Bioinformatics 14, 289 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Wargo, A. R. & Kurath, G. Viral fitness: definitions, measurement, and current insights. Curr. Opin. Virol. 2, 538–545 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dayarian, A. & Shraiman, B. I. How to infer relative fitness from a sample of genomic sequences. Genetics 197, 913–923 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Neher, R. A., Russell, C. A. & Shraiman, B. I. Predicting evolution from the shape of genealogical trees. Elife 3, e03568 (2014).PubMed Central 
Article 

Google Scholar 
Doumayrou, J., Thébaud, G., Vuillaume, F., Peterschmitt, M. & Urbino, C. Mapping genetic determinants of viral traits with FST and quantitative trait locus (QTL) approaches. Virology 484, 346–353 (2015).CAS 
PubMed 
Article 

Google Scholar 
Nagylaki, T. Fixation indices in subdivided populations. Genetics 148, 1325–1332 (1998).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nei, M. & Chesser, R. K. Estimation of fixation indices and gene diversities. Ann. Hum. Genet. 47, 253–259 (1983).CAS 
PubMed 
Article 

Google Scholar 
Yang, Z. & Nielsen, R. Estimating synonymous and nonsynonymous substitution rates under realistic evolutionary models. Mol. Biol. Evol. 17, 32–43 (2000).CAS 
PubMed 
Article 

Google Scholar 
Tubiana, L., Božič, A. L., Micheletti, C. & Podgornik, R. Synonymous mutations reduce genome compactness in icosahedral ssRNA viruses. Biophys. J. 108, 194–202 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jordan-Paiz, A., Franco, S. & Martínez, M. A. Impact of synonymous genome recoding on the HIV life cycle. Front. Microbiol. https://doi.org/10.3389/fmicb.2021.606087 (2021).Cuevas, J. M., Domingo-Calap, P. & Sanjuán, R. The fitness effects of synonymous mutations in DNA and RNA viruses. Mol. Biol. Evol. 29, 17–20 (2012).CAS 
PubMed 
Article 

Google Scholar 
Kryazhimskiy, S. & Plotkin, J. B. The population genetics of dN/dS. PLoS Genet. 4, e1000304 (2008).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kosakovsky Pond, S. L. & Frost, S. D. W. Not so different after all: a comparison of methods for detecting amino acid sites under selection. Mol. Biol. Evol. 22, 1208–1222 (2005).PubMed 
Article 
CAS 

Google Scholar 
Su, Y. C. F. et al. Phylodynamics of H1N1/2009 influenza reveals the transition from host adaptation to immune-driven selection. Nat. Commun. 6, 7952 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kryazhimskiy, S., Dieckmann, U., Levin, S. A. & Dushoff, J. On state-space reduction in multi-strain pathogen models, with an application to antigenic drift in influenza A. PLoS Comput. Biol. 3, e159 (2007).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Peralta, R., Vargas-De-León, C., Cabrera, A. & Miramontes, P. Dynamics of high-risk nonvaccine human papillomavirus types after actual vaccination scheme. Comput. Math. Methods Med. 2014, 542923 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Ranjeva, S. L. et al. Recurring infection with ecologically distinct HPV types can explain high prevalence and diversity. Proc. Natl Acad. Sci. USA 114, 13573–13578 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Aguiar, M., Stollenwerk, N. & Kooi, B. W. The stochastic multi-strain dengue model: analysis of the dynamics. AIP Conf. Proc. 1389, 1224 (2011).Blower, S. M., Aschenbach, A. N., Gershengorn, H. B. & Kahn, J. O. Predicting the unpredictable: transmission of drug-resistant HIV. Nat. Med. 7, 1016–1020 (2001).CAS 
PubMed 
Article 

Google Scholar 
Sharomi, O. & Gumel, A. B. Dynamical analysis of a multi-strain model of HIV in the presence of anti-retroviral drugs. J. Biol. Dyn. 2, 323–345 (2008).CAS 
PubMed 
Article 

Google Scholar 
Roche, B., Drake, J. M. & Rohani, P. An agent-based model to study the epidemiological and evolutionary dynamics of influenza viruses. BMC Bioinformatics 12, 87 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Sofonea, M. T., Alizon, S. & Michalakis, Y. From within-host interactions to epidemiological competition: a general model for multiple infections. Phil. Trans. R. Soc. B 370, 20140303 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
VanderWaal, K. L. & Ezenwa, V. O. Heterogeneity in pathogen transmission: mechanisms and methodology. Funct. Ecol. 30, 1606–1622 (2016).Article 

Google Scholar 
Cobey, S. & Pascual, M. Consequences of host heterogeneity, epitope immunodominance, and immune breadth for strain competition. J. Theor. Biol. 270, 80–87 (2011).PubMed 
Article 

Google Scholar 
Aguiar, M., Ballesteros, S., Kooi, B. W. & Stollenwerk, N. The role of seasonality and import in a minimalistic multi-strain dengue model capturing differences between primary and secondary infections: complex dynamics and its implications for data analysis. J. Theor. Biol. 289, 181–196 (2011).PubMed 
Article 

Google Scholar 
Breban, R., Drake, J. M. & Rohani, P. A general multi-strain model with environmental transmission: invasion conditions for the disease-free and endemic states. J. Theor. Biol. 264, 729–736 (2010).PubMed 
Article 

Google Scholar 
Kamo, M. & Sasaki, A. The effect of cross-immunity and seasonal forcing in a multi-strain epidemic model. Physica D 165, 228–241 (2002).Martcheva, M. A non-autonomous multi-strain SIS epidemic model. J. Biol. Dyn. 3, 235–251 (2009).PubMed 
Article 

Google Scholar 
Pugliese, A. On the evolutionary coexistence of parasite strains. Math. Biosci. 177–178, 355–375 (2002).PubMed 
Article 

Google Scholar 
Roche, B. & Rohani, P. Environmental transmission scrambles coexistence patterns of avian influenza viruses. Epidemics 2, 92–98 (2010).PubMed 
Article 

Google Scholar 
Korobeinikov, A. & Dempsey, C. A continuous phenotype space model of RNA virus evolution within a host. Math. Biosci. Eng. 11, 919–927 (2014).Article 

Google Scholar 
Castillo-Chavez, C., Hethcote, H. W., Andreasen, V., Levin, S. A. & Liu, W. M. Epidemiological models with age structure, proportionate mixing, and cross-immunity. J. Math. Biol. 27, 233–258 (1989).CAS 
PubMed 
Article 

Google Scholar 
Gupta, S., Swinton, J. & Anderson, R. M. Theoretical studies of the effects of heterogeneity in the parasite population on the transmission dynamics of malaria. Proc. R. Soc. B 256, 231–238 (1994).CAS 
PubMed 
Article 

Google Scholar 
Koelle, K., Khatri, P., Kamradt, M. & Kepler, T. B. A two-tiered model for simulating the ecological and evolutionary dynamics of rapidly evolving viruses, with an application to influenza. J. R. Soc. Interface 7, 1257–1274 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Lion, S. & Gandon, S. Spatial evolutionary epidemiology of spreading epidemics. Proc. R. Soc. B 283, 20161170 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Lange, A. & Ferguson, N. M. Antigenic diversity, transmission mechanisms, and the evolution of pathogens. PLoS Comput. Biol. 5, e1000536 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Pilosof, S. et al. Competition for hosts modulates vast antigenic diversity to generate persistent strain structure in Plasmodium falciparum. PLoS Biol. 17, e3000336 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lipsitch, M., Colijn, C., Cohen, T., Hanage, W. P. & Fraser, C. No coexistence for free: neutral null models for multistrain pathogens. Epidemics 1, 2–13 (2009).PubMed 
Article 

Google Scholar 
Read, J. M. & Keeling, M. J. Disease evolution on networks: the role of contact structure. Proc. R. Soc. Lond. B 270, 699–708 (2003).Article 

Google Scholar 
Eshelman, C. M. et al. Unrestricted migration favours virulent pathogens in experimental metapopulations: evolutionary genetics of a rapacious life history. Phil. Trans. R. Soc. B 365, 2503–2513 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Adam, D. C. et al. Clustering and superspreading potential of SARS-CoV-2 infections in Hong Kong. Nat. Med. 26, 1714–1719 (2020).CAS 
PubMed 
Article 

Google Scholar 
Makau, D. N. et al. Integrating animal movements with phylogeography to model the spread of PRRS virus in the US. Virus Evol. https://doi.org/10.1093/ve/veab060 (2021).Kistler, K. E., Huddleston, J. & Bedford, T. Rapid and parallel adaptive mutations in spike S1 drive clade success in SARS-CoV-2. Cell Host Microbe 30, 545–555 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, H. & Roossinck, M. J. Genetic bottlenecks reduce population variation in an experimental RNA virus population. J. Virol. 78, 10582–10587 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McCrone, J. T. et al. Stochastic processes constrain the within and between host evolution of influenza virus. Elife 7, e35962 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Nelson, M. I. et al. Stochastic processes are key determinants of short-term evolution in influenza A virus. PLoS Pathog. 2, e125 (2006).PubMed 
PubMed Central 
Article 

Google Scholar 
Deng, X. et al. Genomic surveillance reveals multiple introductions of SARS-CoV-2 into Northern California. Science 369, 582–587 (2020).CAS 
PubMed 
Article 

Google Scholar 
da Silva Filipe, A. et al. Genomic epidemiology reveals multiple introductions of SARS-CoV-2 from mainland Europe into Scotland. Nat. Microbiol. 6, 112–122 (2021).PubMed 
Article 
CAS 

Google Scholar 
Tayoun, A. A. et al. Multiple early introductions of SARS-CoV-2 into a global travel hub in the Middle East. Sci. Rep. 10, 17720 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Obermeyer, F. et al. Analysis of 2.1 million SARS-CoV-2 genomes identifies mutations associated with transmissibility. Science 376, 1327–1332 (2022).CAS 
PubMed 
Article 

Google Scholar 
Wikramaratna, P. S., Pybus, O. G. & Gupta, S. Contact between bird species of different lifespans can promote the emergence of highly pathogenic avian influenza strains. Proc. Natl Acad. Sci. USA 111, 10767–10772 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Volz, E. M., Koelle, K. & Bedford, T. Viral phylodynamics. PLoS Comput. Biol. 9, e1002947 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Alizon, S., Hurford, A., Mideo, N. & Van Baalen, M. Virulence evolution and the trade-off hypothesis: history, current state of affairs and the future. J. Evolut. Biol. 22, 245–259 (2009).CAS 
Article 

Google Scholar 
Clay, P. A. & Rudolf, V. H. W. How parasite interaction strategies alter virulence evolution in multi‐parasite communities. Evolution 73, 2189–2203 (2019).PubMed 
Article 

Google Scholar 
Bishop, S. C., Doeschl-Wilson, A. B. & Woolliams, J. A. Uses and implications of field disease data for livestock genomic and genetics studies. Front. Genet. 3, 114 (2012).PubMed 
PubMed Central 

Google Scholar 
Rodríguez‐Nevado, C., Lam, T. T. Y., Holmes, E. C. & Pagán, I. The impact of host genetic diversity on virus evolution and emergence. Ecol. Lett. 21, 253–263 (2018).PubMed 
Article 

Google Scholar 
Schulte, R. D., Makus, C. & Schulenburg, H. Host–parasite coevolution favours parasite genetic diversity and horizontal gene transfer. J. Evol. Biol. 26, 1836–1840 (2013).CAS 
PubMed 
Article 

Google Scholar 
Duxbury, E. M. L. et al. Host–pathogen coevolution increases genetic variation in susceptibility to infection. Elife 8, e46440 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Chabas, H. et al. Evolutionary emergence of infectious diseases in heterogeneous host populations. PLoS Biol. 16, e2006738 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Ganusov, V. V., Bergstrom, C. T. & Antia, R. Within‐host population dynamics and the evolution of microparasites in a heterogeneous host population. Evolution 56, 213–223 (2002).PubMed 
Article 

Google Scholar 
González, R., Butković, A. & Elena, S. F. Role of host genetic diversity for susceptibility-to-infection in the evolution of virulence of a plant virus†. Virus Evol. 5, vez024 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Regoes, R. R., Nowak, M. A. & Bonhoeffer, S. Evolution of virulence in a heterogeneous host population. Evolution 54, 64–71 (2000).CAS 
PubMed 
Article 

Google Scholar 
Yates, A., Antia, R. & Regoes, R. R. How do pathogen evolution and host heterogeneity interact in disease emergence? Proc. R. Soc. B 273, 3075–3083 (2006).PubMed 
PubMed Central 
Article 

Google Scholar 
Lloyd-Smith, J. O., Schreiber, S. J., Kopp, P. E. & Getz, W. M. Superspreading and the effect of individual variation on disease emergence. Nature 438, 355–359 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rochman, N. D. et al. Ongoing global and regional adaptive evolution of SARS-CoV-2. Proc. Natl Acad. Sci. USA 118, e2104241118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Volz, E. et al. Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England. Nature 593, 266–269 (2021).CAS 
PubMed 
Article 

Google Scholar 
Choi, B. et al. Persistence and evolution of SARS-CoV-2 in an immunocompromised host. N. Engl. J. Med. 383, 2291–2293 (2020).PubMed 
Article 

Google Scholar 
Gidari, A. et al. Cross-neutralization of SARS-CoV-2 B.1.1.7 and P.1 variants in vaccinated, convalescent and P.1 infected. J. Infect. 83, 467–472 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Changrob, S. et al. Cross-neutralization of emerging SARS-CoV-2 variants of concern by antibodies targeting distinct epitopes on spike. MBio https://doi.org/10.1128/mBio.02975-21 (2021).Vidal, S. J. et al. Correlates of neutralization against SARS-CoV-2 variants of concern by early pandemic sera. J. Virol. 95, e0040421 (2021).PubMed 
Article 

Google Scholar 
Muik, A. et al. Neutralization of SARS-CoV-2 lineage B.1.1.7 pseudovirus by BNT162b2 vaccine-elicited human sera. Science 371, 1152–1153 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bushman, M., Kahn, R., Taylor, B. P., Lipsitch, M. & Hanage, W. P. Population impact of SARS-CoV-2 variants with enhanced transmissibility and/or partial immune escape. Cell 184, 6229–6242 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Koopman, J. S., Simon, C. P., Getz, W. M. & Salter, R. Modeling the population effects of escape mutations in SARS-CoV-2 to guide vaccination strategies. Epidemics 36, 100484 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Van Valen, L. Morphological variation and width of ecological niche. Am. Nat. 99, 377–390 (1965).Article 

Google Scholar 
Bolnick, D. I., Svanbäck, R., Araújo, M. S. & Persson, L. Comparative support for the niche variation hypothesis that more generalized populations also are more heterogeneous. PNAS 104, 10075–10079 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bearhop, S., Adams, C. E., Waldron, S., Fuller, R. A. & Macleod, H. Determining trophic niche width: A novel approach using stable isotope analysis. J. Anim. Ecol. 73, 1007–1012 (2004).Article 

Google Scholar 
Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: A consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).Article 

Google Scholar 
Roederer, J. G. & Malone, T. F. (eds) Resilience of Ecosystems: Local Surprise and Global Change 228–269 (Cambridge University Press, 1985).
Google Scholar 
Duffy, J. E. et al. The functional role of biodiversity in ecosystems: Incorporating trophic complexity. Ecol. Lett. 10, 522–538 (2007).PubMed 
Article 

Google Scholar 
Lafferty, D. J. R., Belant, J. L. & Phillips, D. L. Testing the niche variation hypothesis with a measure of body condition. Oikos 124, 732–740 (2015).Article 

Google Scholar 
Mangipane, L. S. et al. Dietary plasticity in a nutrient-rich system does not influence brown bear (Ursus arctos) body condition or denning. Polar Biol. 41, 763–772 (2018).Article 

Google Scholar 
Mangipane, L. S. et al. Dietary plasticity and the importance of salmon to brown bear (Ursus arctos) body size and condition in a low Arctic ecosystem. Polar Biol. 43, 825–833 (2020).Article 

Google Scholar 
Stumpf, R. M. et al. Microbiomes, metagenomics, and primate conservation: New strategies, tools, and applications. Biol. Conserv. 199, 56–66 (2016).Article 

Google Scholar 
McKenney, E. A., Koelle, K., Dunn, R. R. & Yoder, A. D. The ecosystem services of animal microbiomes. Mol. Ecol. 27, 2164–2172 (2018).CAS 
PubMed 
Article 

Google Scholar 
Kau, A. L., Ahern, P. P., Griffin, N. W., Goodman, A. L. & Gordon, J. I. Human nutrition, the gut microbiome and the immune system. Nature 474, 327–336 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Martin, A. M., Sun, E. W., Rogers, G. B. & Keating, D. J. The influence of the gut microbiome on host metabolism through the regulation of gut hormone release. Front. Physiol. 10, 428 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Amato, K. R. et al. The gut microbiota appears to compensate for seasonal diet variation in the wild black howler monkey (Alouatta pigra). Microb. Ecol. 69, 434–443 (2015).CAS 
PubMed 
Article 

Google Scholar 
Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006).ADS 
PubMed 
Article 

Google Scholar 
Cani, P. D. & Delzenne, N. M. Interplay between obesity and associated metabolic disorders: New insights into the gut microbiota. Curr. Opin. Pharmacol. 9, 737–743 (2009).CAS 
PubMed 
Article 

Google Scholar 
Arinell, K. et al. Brown bears (Ursus arctos) seem resistant to atherosclerosis­despite highly elevated plasma lipids during hibernation and active state. Clin. Transl. Sci. 5, 269–272 (2012).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Nelson, R. A. Protein and fat metabolism in hibernating bears. Fed. Proc. 39, 2955–2958 (1980).CAS 
PubMed 

Google Scholar 
Ley, R. E. et al. Evolution of mammals and their gut microbes. Science 320, 1647–1651 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McKenney, E. A., Maslanka, M., Rodrigo, A. & Yoder, A. D. Bamboo specialists from two mammalian orders (primates, carnivora) share a high number of low-abundance gut microbes. Microb. Ecol. 76, 272–284 (2018).PubMed 
Article 

Google Scholar 
Edwards, M. A., Derocher, A. E., Hobson, K. A., Branigan, M. & Nagy, J. A. Fast carnivores and slow herbivores: Differential foraging strategies among grizzly bears in the Canadian Arctic. Oecologia 165, 877–889 (2011).ADS 
PubMed 
Article 

Google Scholar 
Levi, T. et al. Community ecology and conservation of bear-salmon ecosystems. Front. Ecol. Evol. 8, 513304 (2020).Article 

Google Scholar 
Milakovic, B. & Parker, K. L. Quantifying carnivory by grizzly bears in a multi-ungulate system. J. Wildl. Manage. 77, 39–47 (2013).Article 

Google Scholar 
Krajmalnik-Brown, R., Ilhan, Z.-E., Kang, D.-W. & DiBaise, J. K. Effects of gut microbes on nutrient absorption and energy regulation. Nutr. Clin. Pract. 27, 201–214 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Flint, H. J., Scott, K. P., Duncan, S. H., Louis, P. & Forano, E. Microbial degradation of complex carbohydrates in the gut. Gut Microbes 3, 289–306 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Hashimoto, T., Hussien, R. & Brooks, G. A. Colocalization of MCT1, CD147, and LDH in mitochondrial inner membrane of L6 muscle cells: Evidence of a mitochondrial lactate oxidation complex. Am. J. Physiol.-Endocrinol. Metab. 290, E1237–E1244 (2006).CAS 
PubMed 
Article 

Google Scholar 
Baker, S. & The, H. C. Recent insights into Shigella: A major contributor to the global diarrhoeal disease burden. Curr. Opin. Infect. Dis. 31, 449–454 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Lee, K.-E. et al. The extracellular vesicle of gut microbial Paenalcaligenes hominis is a risk factor for vagus nerve-mediated cognitive impairment. Microbiome 8, 107 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Waites, K. B., Schelonka, R. L., Xiao, L., Grigsby, P. L. & Novy, M. J. Congenital and opportunistic infections: Ureaplasma species and Mycoplasma hominis. Semin. Fetal Neonatal. Med. 14, 190–199 (2009).PubMed 
Article 

Google Scholar 
Barboza, P. S., Farley, S. D. & Robbins, C. T. Whole-body urea cycling and protein turnover during hyperphagia and dormancy in growing bears (Ursus americanus and U. arctos). Can. J. Zool. 75, 2129. https://doi.org/10.1139/z97-848 (2011).Article 

Google Scholar 
Johanne Hansen, M. et al. Ursidibacter maritimus gen. nov., sp. nov. and Ursidibacter arcticus sp. nov., two new members of the family Pasteurellaceae isolated from the oral cavity of bears. Int. J. Syst. Evol. Microbiol. 65, 3683–3689 (2015).PubMed 
Article 
CAS 

Google Scholar 
Waldram, A. et al. Top-down systems biology modeling of host metabotype-microbiome associations in obese rodents. J. Proteome Res. 8, 2361–2375 (2009).CAS 
PubMed 
Article 

Google Scholar 
Hardie, J. M. & Whiley, R. A. The genus Streptococcus. In The Genera of Lactic Acid Bacteria (eds Wood, B. J. B. & Holzapfel, W. H.) 55–124 (Springer, 1995).Chapter 

Google Scholar 
Li, F., Wang, M., Wang, J., Li, R. & Zhang, Y. Alterations to the gut microbiota and their correlation with inflammatory factors in chronic kidney disease. Front. Cell. Infect. Microbiol. 9, 206 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fox, J. G. & Lee, A. The role of Helicobacter species in newly recognized gastrointestinal tract diseases of animals. Lab. Anim. Sci. 47, 222–255 (1997).CAS 
PubMed 

Google Scholar 
McKenney, E. A., Rodrigo, A. & Yoder, A. D. Patterns of gut bacterial colonization in three primate species. PLoS ONE 10, e0124618 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Stevens, C. E. & Hume, I. D. Contributions of microbes in vertebrate gastrointestinal tract to production and conservation of nutrients. Physiol. Rev. 78, 393–427 (1998).CAS 
PubMed 
Article 

Google Scholar 
Hilderbrand, G. V. et al. Plasticity in physiological condition of female brown bears across diverse ecosystems. Polar Biol. 41, 773–780 (2018).Article 

Google Scholar 
Ley, R. E. et al. Obesity alters gut microbial ecology. PNAS 102, 11070–11075 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sommer, F. et al. The gut microbiota modulates energy metabolism in the hibernating brown bear ursus arctos. Cell Rep. 14, 1655–1661 (2016).CAS 
PubMed 
Article 

Google Scholar 
Magne, F. et al. The firmicutes/bacteroidetes ratio: A relevant marker of gut dysbiosis in obese patients? Nutrients 12, 1474 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Paine, R. T. A note on trophic complexity and community stability. Am. Nat. 103, 91–93 (1969).Article 

Google Scholar 
Amato, K. R. et al. Evolutionary trends in host physiology outweigh dietary niche in structuring primate gut microbiomes. ISME J. 13, 576–587 (2019).CAS 
PubMed 
Article 

Google Scholar 
Trujillo, S. M. et al. Intrinsic and extrinsic factors influence on an omnivore’s gut microbiome. PLoS ONE 17, e0266698 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hilderbrand, G. V. et al. Body size and lean mass of brown bears across and within four diverse ecosystems. J. Zool. 305, 53–62 (2018).Article 

Google Scholar 
Wilson, R. R., Gustine, D. D. & Joly, K. Evaluating potential effects of an industrial road on winter habitat of caribou in North-Central Alaska. Arctic 67, 472–482 (2014).Article 

Google Scholar 
Gasaway, W. C. et al. The role of predation in limiting moose at low densities in Alaska and Yukon and implications for conservation. Wildl. Monogr. 12, 3–59 (1992).
Google Scholar 
Taylor, W. P., Reynolds, H. V. & Ballard, W. B. Immobilization of grizzly bears with tiletamine hydrochloride and zolazepam hydrochloride. J. Wildl. Manage. 53, 978–981 (1989).Article 

Google Scholar 
Farley, S. D. & Robbins, C. T. Development of two methods to estimate body composition of bears. Can. J. Zool. 72, 220–226 (1994).Article 

Google Scholar 
Hilderbrand, G. V., Robbins, C. T. & Farley, S. D. Response: Use of stable isotopes to determine diets of living and extinct bears. Can. J. Zool. 76, 2301–2303 (1998).Article 

Google Scholar 
McKenney, E. A., Greene, L. K., Drea, C. M. & Yoder, A. D. Down for the count: Cryptosporidium infection depletes the gut microbiome in Coquerel’s sifakas. Microb. Ecol. Health Dis. 28, 1335165 (2017).PubMed 
PubMed Central 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
PubMed 
Article 

Google Scholar 
Willis, A. D. Rarefaction, alpha diversity, and statistics. Front. Microbiol. 10, 2407 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Beule, L. & Karlovsky, P. Improved normalization of species count data in ecology by scaling with ranked subsampling (SRS): Application to microbial communities. PeerJ 8, e9593 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Galand, P. E., Casamayor, E. O., Kirchman, D. L. & Lovejoy, C. Ecology of the rare microbial biosphere of the Arctic Ocean. Proc. Natl. Acad. Sci. U.S.A. 106, 22427–22432 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Liu, L., Yang, J., Yu, Z. & Wilkinson, D. M. The biogeography of abundant and rare bacterioplankton in the lakes and reservoirs of China. ISME J. 9, 2068–2077 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Hill, M. O. Diversity and evenness: A unifying notation and its consequences. Ecology 54, 427–432 (1973).Article 

Google Scholar 
Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. 61, 1–10 (1992).Article 

Google Scholar 
Simpson, E. H. Measurement of diversity. Nature 163, 688–688 (1949).ADS 
MATH 
Article 

Google Scholar 
Lozupone, C. & Knight, R. UniFrac: A new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2006).ADS 
Article 
CAS 

Google Scholar 
Hamidi, B., Wallace, K., Vasu, C. & Alekseyenko, A. V. Wd∗$Wd*-test: Robust distance-based multivariate analysis of variance. Microbiome 7, 51 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Alekseyenko, A. V. Multivariate Welch t-test on distances. Bioinformatics 32, 3552–3558 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar  More