Philippa Kaur

Nazli, M. H., Halim, R. A., Abdullah, A. M., Hussin, G. & Samsudin, A. A. Potential of four corn varieties at different harvest stages for silage production in Malaysia. Asian-Australas. J. Anim. Sci. 32, 224–232 (2019).PubMed 
Article 

Google Scholar 
Department of Veterinary Services Malaysia. Perangkaan Ternakan Livestock Statistics (Department of Veterinary Services Malaysia, 2021).
Google Scholar 
Halim, R. A., Shampazurini, S. & Idris, A. B. Yield and nutritive quality of nine Napier grass varieties in Malaysia. Malays. J. Anim. Sci. 16, 37–44 (2013).
Google Scholar 
Ortega-Gãmez, R. et al. Nutritive quality of ten grasses during the rainy season in a hot-humid climate and ultisol soil. Trop. Subtrop. Agroecosyst. 13, 481 (2011).
Google Scholar 
Kung, L., Shaver, R. D., Grant, R. J. & Schmidt, R. J. Silage review: Interpretation of chemical, microbial, and organoleptic components of silages. J. Dairy Sci. 101, 4020–4033 (2018).CAS 
PubMed 
Article 

Google Scholar 
Bernardes, T. F. et al. Silage review: Unique challenges of silages made in hot and cold regions. J. Dairy Sci. 101, 4001–4019 (2018).CAS 
PubMed 
Article 

Google Scholar 
Koc, F., Ozduven, M., Coskuntuna, L. & Polant, C. The effects of inoculant lactic acid bacteria on the fermentation and aerobic stability of sunflower silage. Poljoprivreda 15, 47–52 (2009).
Google Scholar 
Kim, S. C. & Adesogan, A. T. Influence of ensiling temperature, simulated rainfall, and delayed sealing on fermentation characteristics and aerobic stability of corn silage. J. Dairy Sci. 89, 3122–3132 (2006).CAS 
PubMed 
Article 

Google Scholar 
Daniel, J. L. P. et al. Effects of homolactic bacterial inoculant on the performance of lactating dairy cows. J. Dairy Sci. 101, 5145–5152 (2018).CAS 
PubMed 
Article 

Google Scholar 
Pahlow, G. et al. Microbiology of ensiling. In Silage Science and Technology (eds Buxton, D. R. et al.) 31–93 (America Society of Agronomy, 2003).
Google Scholar 
Li, D., Ni, K., Zhang, Y., Lin, Y. & Yang, F. Fermentation characteristics, chemical composition and microbial community of tropical forage silage under different temperatures. Asian-Australas. J. Anim. Sci. 32, 665–674 (2019).CAS 
PubMed 
Article 

Google Scholar 
Xu, D. et al. Modulation of metabolome and bacterial community in whole crop corn silage by inoculating homofermentative Lactobacillus plantarum and heterofermentative Lactobacillus buchneri. Front. Microbiol. 9, 3299 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Guan, H. et al. Microbial communities and natural fermentation of corn silages prepared with farm bunker-silo in Southwest China. Bioresour. Technol. 265, 282–290 (2018).CAS 
PubMed 
Article 

Google Scholar 
Guan, H. et al. Screening of natural lactic acid bacteria with potential effect on silage fermentation, aerobic stability and aflatoxin B1 in hot and humid area. J. Appl. Microbiol. 128, 1301–1311 (2020).CAS 
PubMed 
Article 

Google Scholar 
Xu, Z., He, H., Zhang, S. & Kong, J. Effects of inoculants Lactobacillus brevis and Lactobacillus parafarraginis on the fermentation characteristics and microbial communities of corn stover silage. Sci. Rep. 7, 1–9 (2017).ADS 
Article 
CAS 

Google Scholar 
Muck, R. E. et al. Silage review: Recent advances and future uses of silage additives. J. Dairy Sci. 101, 3980–4000 (2018).CAS 
PubMed 
Article 

Google Scholar 
Kanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 28, 1947–1951 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kanehisa, M., Furumichi, M., Sato, Y., Ishiguro-Watanabe, M. & Tanabe, M. KEGG: Integrating viruses and cellular organisms. Nucleic Acids Res. 49, D545–D551 (2021).CAS 
PubMed 
Article 

Google Scholar 
Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McDonald, P., Henderson, A. R. & Heron, S. J. E. The Biochemistry of Silage (Chalcombe Publications, 1991).
Google Scholar 
Nkosi, B. D. et al. The influence of ensiling potato hash waste with enzyme/bacterial inoculant mixtures on the fermentation characteristics, aerobic stability and nutrient digestion of the resultant silages by rams. Small Rumin. Res. 127, 28–35 (2015).Article 

Google Scholar 
Muck, R. E. Microbiologia da silagem e seu controle com aditivos. Rev. Bras. Zootec. 39, 183–191 (2010).Article 

Google Scholar 
Yan, Y. et al. Microbial community and fermentation characteristic of Italian ryegrass silage prepared with corn stover and lactic acid bacteria. Bioresour. Technol. 279, 166–173 (2019).CAS 
PubMed 
Article 

Google Scholar 
Jiang, F. G. et al. Treatment of whole-plant corn silage with lactic acid bacteria and organic acid enhances quality by elevating acid content, reducing pH, and inhibiting undesirable microorganisms. Front. Microbiol. 11, 3104 (2020).
Google Scholar 
Ni, K., Wang, Y., Li, D., Cai, Y. & Pang, H. Characterization, identification and application of lactic acid bacteria isolated from forage paddy rice silage. PLoS ONE 10, e0121967 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Li, J. et al. Characterization of Enterococcus faecalis JF85 and Enterococcus faecium Y83 isolated from Tibetan yak (Bos grunniens) for ensiling Pennisetum sinese. Bioresour. Technol. 257, 76–83 (2018).CAS 
PubMed 
Article 

Google Scholar 
Ning, P., Peng, Y. & Fritschi, F. B. Carbohydrate dynamics in maize leaves and developing ears in response to nitrogen application. Agronomy 8, 302 (2018).CAS 
Article 

Google Scholar 
Ni, K. et al. Comparative microbiota assessment of wilted Italian ryegrass, whole crop corn, and wilted alfalfa silage using denaturing gradient gel electrophoresis and next-generation sequencing. Appl. Microbiol. Biotechnol. 101, 1385–1394 (2017).CAS 
PubMed 
Article 

Google Scholar 
Nishino, N. & Touno, E. Ensiling characteristics and aerobic stability of direct-cut and wilted grass silages inoculated with Lactobacillus casei or Lactobacillus buchneri. J. Sci. Food Agric. 85, 1882–1888 (2005).CAS 
Article 

Google Scholar 
Li, L. et al. Effect of microalgae supplementation on the silage quality and anaerobic digestion performance of Manyflower silvergrass. Bioresour. Technol. 189, 334–340 (2015).CAS 
PubMed 
Article 

Google Scholar 
McEniry, J., O’Kiely, P., Clipson, N. J. W., Forristal, P. D. & Doyle, E. M. Assessing the impact of various ensilage factors on the fermentation of grass silage using conventional culture and bacterial community analysis techniques. J. Appl. Microbiol. 108, 1584–1593 (2010).CAS 
PubMed 
Article 

Google Scholar 
Cai, Y. Identification and characterization of Enterococcus species isolated from forage crops and their influence on silage fermentation. J. Dairy Sci. 82, 2466–2471 (1999).CAS 
PubMed 
Article 

Google Scholar 
Ben-Dov, E., Shapiro, O. H., Siboni, N. & Kushmaro, A. Advantage of using inosine at the 3′ termini of 16S rRNA gene universal primers for the study of microbial diversity. Appl. Environ. Microbiol. 72, 6902–6906 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ni, K. et al. Effects of lactic acid bacteria and molasses additives on the microbial community and fermentation quality of soybean silage. Bioresour. Technol. 238, 706–715 (2017).CAS 
PubMed 
Article 

Google Scholar 
Wang, Y. et al. Effects of wilting and Lactobacillus plantarum addition on the fermentation quality and microbial community of moringa oleifera leaf silage. Front. Microbiol. 9, 1817 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Eikmeyer, F. G. et al. Metagenome analyses reveal the influence of the inoculant Lactobacillus buchneri CD034 on the microbial community involved in grass ensiling. J. Biotechnol. 167, 334–343 (2013).CAS 
PubMed 
Article 

Google Scholar 
Gagnon, M., Ouamba, A. J. K., LaPointe, G., Chouinard, P. Y. & Roy, D. Prevalence and abundance of lactic acid bacteria in raw milk associated with forage types in dairy cow feeding. J. Dairy Sci. 103, 5931–5946 (2020).CAS 
PubMed 
Article 

Google Scholar 
Li, R. et al. Microbial community dynamics during alfalfa silage with or without clostridial fermentation. Sci. Rep. 10, 1–14 (2020).ADS 
Article 
CAS 

Google Scholar 
Rooke, J. & Hatfield, R. Biochemistry of ensiling. Publ. from USDA-ARS/UNL Fac. (2003).Muck, R. E. Recent advances in silage microbiology. Agric. Food Sci. 22, 3–15 (2013).CAS 
Article 

Google Scholar 
Gharechahi, J. et al. The dynamics of the bacterial communities developed in maize silage. Microb. Biotechnol. 10, 1663–1676 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Farhana, A. & Lappin, S. L. Biochemistry, Lactate Dehydrogenase (StatPearls, 2021).
Google Scholar 
Mandhania, M. H. et al. Diversity and succession of microbiota during fermentation of the traditional Indian food idli. Appl. Environ. Microbiol. 85, e00368 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
De Mandal, S. et al. Metagenomic analysis and the functional profiles of traditional fermented pork fat ‘sa-um’ of Northeast India. AMB Express 8, 1–11 (2018).Article 
CAS 

Google Scholar 
Varki, A. & Lowe, J. B. Biological roles of glycans. Essentials Glycobiol. https://www.ncbi.nlm.nih.gov/books/NBK1897/ (2009).Ganesan, A. Natural products as a hunting ground for combinatorial chemistry. Curr. Opin. Biotechnol. 15, 584–590 (2004).CAS 
PubMed 
Article 

Google Scholar 
Dubois, M., Gilles, K. A., Hamilton, J. K., Rebers, P. A., & Smith, F. Colorimetric Method for Determination of Sugars and Related Substances. Anal. Chem., 28(3), 350–356 (1956).CAS 
Article 

Google Scholar 
Heberle, H., Meirelles, V. G., da Silva, F. R., Telles, G. P. & Minghim, R. InteractiVenn: A web-based tool for the analysis of sets through Venn diagrams. BMC Bioinform. 16, 169 (2015).Article 

Google Scholar  More

Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Duane, A., Castellnou, M. & Brotons, L. Towards a comprehensive look at global drivers of novel extreme wildfire events. Clim. Change 165, 43 (2021).ADS 
Article 

Google Scholar 
Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 259, 660–684 (2010). Adaptation of Forests and Forest Management to Changing Climate.Article 

Google Scholar 
Franklin, J. F., Mitchell, R. J. & Palik, B. J. Natural disturbance and stand development principles for ecological forestry. General Technical Report. NRS-19. Newtown Square, PA: US Department of Agriculture, Forest Service, Northern Research Station. 44. p. 19 (2007).Westoby, M., Jurado, E. & Leishman, M. Comparative evolutionary ecology of seed size. Trends Ecol. Evol. 7, 368–372 (1992).CAS 
PubMed 
Article 

Google Scholar 
Smith, C. C. & Fretwell, S. D. The optimal balance between size and number of offspring. Am. Nat. 108, 499–506 (1974).Article 

Google Scholar 
Lord, J., Westoby, M. & Leishman, M. Seed size and phylogeny in six temperate floras: Constraints, niche conservatism, and adaptation. Am. Nat. 146, 349–364 (1995).Article 

Google Scholar 
Moles, A. T. et al. Global patterns in seed size. Glob. Ecol. Biogeogr. 16, 109–116 (2007).Article 

Google Scholar 
Tautenhahn, S. et al. On the biogeography of seed mass in germany – distribution patterns and environmental correlates. Ecography 31, 457–468 (2008).Article 

Google Scholar 
Lidgard, S. & Crane, P. R. Quantitative analyses of the early angiosperm radiation. Nature 331, 344–346 (1988).ADS 
Article 

Google Scholar 
Crisp, M. D. & Cook, L. G. Cenozoic extinctions account for the low diversity of extant gymnosperms compared with angiosperms. New Phytol. 192, 997–1009 (2011).CAS 
PubMed 
Article 

Google Scholar 
Stearns, S. C. Life-history tactics: a review of the ideas. Quart. Rev. Biol. 51, 3–47 (1976).CAS 
PubMed 
Article 

Google Scholar 
Grubb, P. J. The maintenance of species-richness in plant communities: the importance of the regeneration niche. Biol. Rev. 52, 107–145 (1977).Article 

Google Scholar 
Clark, J. S., LaDeau, S. & Ibanez, I. Fecundity of trees and the colonization-competition hypothesis. Ecol. Monogr. 74, 415–442 (2004).Article 

Google Scholar 
Salguero-Gómez, R. et al. Fast-slow continuum and reproductive strategies structure plant life-history variation worldwide. Proc. Natl Acad. Sci. USA 113, 230–235 (2016).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Thomas, S. C. Age-Related Changes in Tree Growth and Functional Biology: The Role of Reproduction, p. 33-64 (Springer Netherlands, 2011).Wenk, E. H. & Falster, D. S. Quantifying and understanding reproductive allocation schedules in plants. Ecol. Evol. 5, 5521–5538 (2015).PubMed 
PubMed Central 
Article 

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

Google Scholar 
Turnbull, L. A., Rees, M. & Crawley, M. J. Seed mass and the competition/colonization trade-off: a sowing experiment. J. Ecol. 87, 899–912 (1999).Article 

Google Scholar 
Moles, A., Falster, D., Leishman, M. & Westoby, M. Small-seeded species produce more seeds per square metre of canopy per year, but not per individual per lifetime. J. Ecol. 92, 384–396 (2004).Article 

Google Scholar 
Qiu, T. et al. Is there tree senescence? the fecundity evidence. Proc. Natl Acad. Sci. USA 118, e2106130118 (2021).Westoby, M., Falster, D. S., Moles, A. T., Vesk, P. A. & Wright, I. J. Plant ecological strategies: Some leading dimensions of variation between species. Annu. Rev. Ecol. Syst. 33, 125–159 (2002).Article 

Google Scholar 
Henery, M. L. & Westoby, M. Seed mass and seed nutrient content as predictors of seed output variation between species. Oikos 92, 479–490 (2001).Article 

Google Scholar 
Turnbull, L. A., Coomes, D., Hector, A. & Rees, M. Seed mass and the competition/colonization trade-off: competitive interactions and spatial patterns in a guild of annual plants. J. Ecol. 92, 97–109 (2004).Article 

Google Scholar 
Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009).PubMed 
Article 

Google Scholar 
Poorter, L. et al. The importance of wood traits and hydraulic conductance for the performance and life history strategies of 42 rainforest tree species. New Phytol. 185, 481–492 (2010).PubMed 
Article 

Google Scholar 
Hanley, M. E., Cook, B. I. & Fenner, M. Climate variation, reproductive frequency and acorn yield in english oaks. J. Plant Ecol. 12, 542–549 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Kattge, J. et al. Try plant trait database – enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).ADS 
Article 

Google Scholar 
Ran, E., Arnon, D., Alon, B.-G., Amnon, S. & Uri, Y. Flowering and fruit set of olive trees in response to nitrogen, phosphorus, and potassium. J. Am. Soc. Hortic. Sci. Am. Soc. Hortic. Sci. 133, 639–647 (2008).Article 

Google Scholar 
Fernández-Martínez, M., Vicca, S., Janssens, I. A., Espelta, J. M. & Peñuelas, J. The role of nutrients, productivity and climate in determining tree fruit production in european forests. New Phytol. 213, 669–679 (2017).PubMed 
Article 
CAS 

Google Scholar 
Fortier, R. & Wright, S. J. Nutrient limitation of plant reproduction in a tropical moist forest. Ecology 102, e03469 (2021).Canham, C. D., Ruscoe, W. A., Wright, E. F. & Wilson, D. J. Spatial and temporal variation in tree seed production and dispersal in a new zealand temperate rainforest. Ecosphere 5, art49 (2014).Article 

Google Scholar 
Pérez-Ramos, I. M., Aponte, C., García, L. V., Padilla-Díaz, C. M. & Marañón, T. Why is seed production so variable among individuals? a ten-year study with oaks reveals the importance of soil environment. PLoS ONE 9, e115371 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sitch, S. et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Change Biol. 9, 161–185 (2003).ADS 
Article 

Google Scholar 
Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob. Biogeochem. Cycles 19, 1–33 (2005).Article 
CAS 

Google Scholar 
Fisher, R. A. et al. Vegetation demographics in earth system models: a review of progress and priorities. Glob. Change Biol. 24, 35–54 (2018).ADS 
Article 

Google Scholar 
Hanbury-Brown, A., Ward, R. & Kueppers, L. M. Future forests within earth system models: regeneration processes critical to prediction. New Phytol. in press https://doi.org/10.1111/nph.18131 (2022).Stiles, W. C. & Reid, W. S. Orchard nutrition management. Inf. Bull. (1991). https://ecommons.cornell.edu/bitstream/handle/1813/3305/Orchard%20Nutrition%20Management.pdf?sequence=2&isAllowed=y.Schlesinger, W. H. Some thoughts on the biogeochemical cycling of potassium in terrestrial ecosystems. Biogeochemistry 154, 427–432 (2021).Article 

Google Scholar 
Neilsen, D. & Neilsen, G. Efficient use of nitrogen and water in high-density apple orchards. HortTechnology 12, 19 (2002).Article 

Google Scholar 
Rubio Ames, Z., Brecht, J. K. & Olmstead, M. A. Nitrogen fertilization rates in a subtropical peach orchard: effects on tree vigor and fruit quality. J. Sci. Food Agric. 100, 527–539 (2020).CAS 
PubMed 
Article 

Google Scholar 
Elser, J. J. et al. Growth rate-stoichiometry couplings in diverse biota. Ecol. Lett. 6, 936–943 (2003).Article 

Google Scholar 
Seyednasrollah, B. & Clark, J. S. Where resource-acquisitive species are located: the role of habitat heterogeneity. Geophys. Res. Lett. 47, e2020GL087626 (2020).Rosecrance, R. C., Weinbaum, S. A. & Brown, P. H. Alternate bearing affects nitrogen, phosphorus, potassium and starch storage pools in mature pistachio trees. Ann. Bot. 82, 463–470 (1998).Article 

Google Scholar 
Sala, A., Hopping, K., McIntire, E. J. B., Delzon, S. & Crone, E. E. Masting in whitebark pine (pinus albicaulis) depletes stored nutrients. New Phytol. 196, 189–199 (2012).CAS 
PubMed 
Article 

Google Scholar 
LaDeau, S. L. & Clark, J. S. Rising co2 levels and the fecundity of forest trees. Science 292, 95–8 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Callahan, H. S., Del Fierro, K., Patterson, A. E. & Zafar, H. Impacts of elevated nitrogen inputs on oak reproductive and seed ecology. Glob. Change Biol. 14, 285–293 (2008).ADS 
Article 

Google Scholar 
Lambers, H. & Poorter, H. Inherent Variation in Growth Rate Between Higher Plants: A Search for Physiological Causes and Ecological Consequences, vol. 23, 187-261 (Academic Press, 1992).Hengl, T. et al. Soilgrids250m: global gridded soil information based on machine learning. PLoS ONE 12, 1–40 (2017).Article 
CAS 

Google Scholar 
Sharma, A., Weindorf, D. C., Wang, D. D. & Chakraborty, S. Characterizing soils via portable x-ray fluorescence spectrometer: 4. cation exchange capacity (cec). Geoderma 239, 130–134 (2015).ADS 
Article 
CAS 

Google Scholar 
Hazelton, P. & Murphy, B. Interpreting Soil Test Results: What Do All The Numbers Mean? (CSIRO publishing, 2016).Chowdhury, S. et al. Chapter Two – Role Of Cultural And Nutrient Management Practices In Carbon Sequestration In Agricultural Soil, vol. 166, 131-196 (Academic Press, 2021).Clark, J. S., Nuñez, C. L. & Tomasek, B. Foodwebs based on unreliable foundations: spatiotemporal masting merged with consumer movement, storage, and diet. Ecol. Monogr. 89, e01381 (2019).Article 

Google Scholar 
Burns, R. M. Silvics Of North America (US Department of Agriculture, Forest Service, 1990).Koenig, W. D. & Knops, J. M. H. Seed-crop size and eruptions of north american boreal seed-eating birds. J. Anim. Ecol. 70, 609–620 (2001).Article 

Google Scholar 
Greene, D. F. & Johnson, E. A. Estimating the mean annual seed production of trees. Ecology 75, 642–647 (1994).Article 

Google Scholar 
Lord, J. M. & Westoby, M. Accessory costs of seed production and the evolution of angiosperms. Evol. Int. J. Org. Evol. 66, 200–210 (2012).Article 

Google Scholar 
Hulme, P. & Benkman, C. Granivory. vol. 23, 132-154 (Oxford: Blackwell, 2002).Bond, W. J. The tortoise and the hare: ecology of angiosperm dominance and gymnosperm persistence. Biol. J. Linn. Soc. 36, 227–249 (1989).Article 

Google Scholar 
Brodribb, T. J. & Feild, T. S. Leaf hydraulic evolution led a surge in leaf photosynthetic capacity during early angiosperm diversification. Ecol. Lett. 13, 175–183 (2010).PubMed 
Article 

Google Scholar 
Davies, T. J. et al. Darwin’s abominable mystery: Insights from a supertree of the angiosperms. Proc. Natl Acad. Sci. USA 101, 1904–1909 (2004).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Berendse, F. & Scheffer, M. The angiosperm radiation revisited, an ecological explanation for darwin’s ‘abominable mystery’. Ecol. Lett. 12, 865–872 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
Barrett, S. C. H. Influences of clonality on plant sexual reproduction. Proc. Natl Acad. Sci. USA 112, 8859–8866 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Condamine, F. L., Silvestro, D., Koppelhus, E. B. & Antonelli, A. The rise of angiosperms pushed conifers to decline during global cooling. Proc. Natl Acad. Sci. USA 117, 28867–28875 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oren, R. et al. Soil fertility limits carbon sequestration by forest ecosystems in a co2-enriched atmosphere. Nature 411, 469–472 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Reich, P. B. et al. Nitrogen limitation constrains sustainability of ecosystem response to co2. Nature 440, 922–925 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Firn, J. et al. Leaf nutrients, not specific leaf area, are consistent indicators of elevated nutrient inputs. Nat. Ecol. Evol. 3, 400–406 (2019).PubMed 
Article 

Google Scholar 
Elser, J. et al. Biological stoichiometry from genes to ecosystems. Ecol. Lett. 3, 540–550 (2000).Article 

Google Scholar 
Niklas, K. J., Owens, T., Reich, P. B. & Cobb, E. D. Nitrogen/phosphorus leaf stoichiometry and the scaling of plant growth. Ecol. Lett. 8, 636–642 (2005).Article 

Google Scholar 
Kerkhoff, A. J., Fagan, W. F., Elser, J. J. & Enquist, B. J. Phylogenetic and growth form variation in the scaling of nitrogen and phosphorus in the seed plants. Am. Nat. 168, E103–E122 (2006).PubMed 
Article 

Google Scholar 
Weinbaum, S. A., Johnson, R. S. & DeJong, T. M. Causes and consequences of overfertilization in orchards. HortTechnology 2, 112b (1992).Article 

Google Scholar 
Fernandez-Escobar, R. et al. Olive oil quality decreases with nitrogen over-fertilization. HortScience 41, 215 (2006).CAS 
Article 

Google Scholar 
Han, Q., Kabeya, D., Iio, A. & Kakubari, Y. Masting in fagus crenata and its influence on the nitrogen content and dry mass of winter buds. Tree Physiol. 28, 1269–1276 (2008).PubMed 
Article 

Google Scholar 
Pettigrew, W. T. Potassium influences on yield and quality production for maize, wheat, soybean and cotton. Physiol. Plant. 133, 670–681 (2008).CAS 
PubMed 
Article 

Google Scholar 
Leeper, A. C., Lawrence, B. A. & LaMontagne, J. M. Plant-available soil nutrients have a limited influence on cone production patterns of individual white spruce trees. Oecologia 194, 101–111 (2020).ADS 
PubMed 
Article 

Google Scholar 
Chapin, F. S., Autumn, K. & Pugnaire, F. Evolution of suites of traits in response to environmental stress. Am. Nat. 142, S78–S92 (1993).Article 

Google Scholar 
Westoby, M. & Wright, I. J. Land-plant ecology on the basis of functional traits. Trends Ecol. Evol. 21, 261–268 (2006).PubMed 
Article 

Google Scholar 
Brodribb, T. J., Pittermann, J. & Coomes, D. A. Elegance versus speed: Examining the competition between conifer and angiosperm trees. Int. J. Plant Sci. 173, 673–694 (2012).Article 

Google Scholar 
Clark, J. S., Macklin, E. & Wood, L. Stages and spatial scales of recruitment limitation in southern appalachian forests. Ecol. Monogr. 68, 213–235 (1998).Article 

Google Scholar 
McEuen, A. B. & Curran, L. M. Seed dispersal and recruitment limitation across spatial scales in temperate forest fragments. Ecology 85, 507–518 (2004).Article 

Google Scholar 
Emsweller, L. N., Gorchov, D. L., Zhang, Q., Driscoll, A. G. & Hughes, M. R. Seed rain and disturbance impact recruitment of invasive plants in upland forest. Invasive Plant Sci. Manag. 11, 69–81 (2018).Article 

Google Scholar 
Lindgren, s, Eriksson, O. & Moen, J. The impact of disturbance and seed availability on germination of alpine vegetation in the scandinavian mountains. Arct. Antarct. Alp. Res. 39, 449–454 (2007).Article 

Google Scholar 
Cai, W. H., Liu, Z., Yang, Y. Z. & Yang, J. Does environment filtering or seed limitation determine post-fire forest recovery patterns in boreal larch forests? Front. Plant Sci. 9, 1318 (2018).Darwin, C. On the Origin of Species (John Murray, 1859).Black, M. Darwin and seeds. Seed Sci. Res. 19, 193–199 (2009).Article 

Google Scholar 
FAO. Global forest resources assessment 2020-key findings. un food and agriculture organization. Report (2020).Payn, T. et al. Changes in planted forests and future global implications. For. Ecol. Manag. 352, 57–67 (2015).Article 

Google Scholar 
Clark, J. S. et al. The impacts of increasing drought on forest dynamics, structure, and biodiversity in the united states. Glob. Change Biol. 22, 2329–2352 (2016).ADS 
Article 

Google Scholar 
Gazol, A., Camarero, J. J., Anderegg, W. R. L. & Vicente-Serrano, S. M. Impacts of droughts on the growth resilience of northern hemisphere forests. Glob. Ecol. Biogeogr. 26, 166–176 (2017).Article 

Google Scholar 
Stephens, S. L. et al. Managing forests and fire in changing climates. Science 342, 41–42 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
North, M. P. et al. Tamm review: reforestation for resilience in dry western u.s. forests. For. Ecol. Manag. 432, 209–224 (2019).Article 

Google Scholar 
Seidl, R., Rammer, W. & Spies, T. A. Disturbance legacies increase the resilience of forest ecosystem structure, composition, and functioning. Ecol. Appl. 24, 2063–2077 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Serra-Diaz, J. M. et al. Averaged 30 year climate change projections mask opportunities for species establishment. Ecography 39, 844–845 (2016).Article 

Google Scholar 
Davis, F. W. et al. Shrinking windows of opportunity for oak seedling establishment in southern california mountains. Ecosphere 7, e01573 (2016).
Google Scholar 
LeBauer, D. S. & Treseder, K. K. Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed. Ecology 89, 371–379 (2008).PubMed 
Article 

Google Scholar 
Clark, J. S. et al. Continent-wide tree fecundity driven by indirect climate effects. Nat. Commun. 12, 1242 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brady, N. C., Weil, R. R. & Weil, R. R. The Nature And Properties Of Soils, vol. 13 (Prentice Hall Upper Saddle River, 2008).Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45, RG2004 (2007). https://doi.org/10.1029/2005RG000183.Clark, J. S. Landscape interactions among nitrogen mineralization, species composition, and long-term fire frequency. Biogeochemistry 11, 1–22 (1990).Article 

Google Scholar 
Clark, J. S., Bell, D. M., Kwit, M. C. & Zhu, K. Competition-interaction landscapes for the joint response of forests to climate change. Glob. Change Biol. 20, 1979–1991 (2014).ADS 
Article 

Google Scholar 
Begueria, S., Vicente-Serrano, S. M., Reig, F. & Latorre, B. Standardized precipitation evapotranspiration index (spei) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 34, 3001–3023 (2014).Article 

Google Scholar 
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Sci. Data 5, 170191 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Schneider, R., Calama, R. & Martin-Ducup, O. Understanding tree-to-tree variations in stone pine (pinus pinea l.) cone production using terrestrial laser scanner. Remote Sens. 12, 173 (2020).Article 

Google Scholar 
Gavranović, A., Bogdan, S., Lanšćak, M., Čehulić, I. & Ivanković, M. Seed yield and morphological variations of beechnuts in four european beech (fagus sylvatica l.) populations in croatia. South-East Eur. For. 9, 17–27 (2018).Article 

Google Scholar 
Maitner, B. S. et al. The bien r package: a tool to access the botanical information and ecology network (bien) database. Methods Ecol. Evol. 9, 373–379 (2018).Article 

Google Scholar 
Clark, J. S., Silman, M., Kern, R., Macklin, E. & HilleRisLambers, J. Seed dispersal near and far: patterns across temperate and tropical forests. Ecology 80, 1475–1494 (1999).Article 

Google Scholar 
LePage, P. T., Canham, C. D., Coates, K. D. & Bartemucci, P. Seed abundance versus substrate limitation of seedling recruitment in northern temperate forests of british columbia. Can. J. For. Res. 30, 415–427 (2000).Article 

Google Scholar 
Clark, J. S., LaDeau, S. & Ibanez, I. Fecundity of trees and the colonization-competition hypothesis. Ecol. Monogr. 74, 415–442 (2004).Article 

Google Scholar 
Muller-Landau, H. C., Wright, S. J., Calderon, O., Condit, R. & Hubbell, S. P. Interspecific variation in primary seed dispersal in a tropical forest. J. Ecol. 96, 653–667 (2008).Article 

Google Scholar 
Jones, F. A. & Muller-Landau, H. C. Measuring long-distance seed dispersal in complex natural environments: an evaluation and integration of classical and genetic methods. J. Ecol. 96, 642–652 (2008).Article 

Google Scholar 
Clark, J. S. Individuals and the variation needed for high species diversity in forest trees. Science 327, 1129–1132 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Clark, J. S. et al. High-dimensional coexistence based on individual variation: a synthesis of evidence. Ecol. Monogr. 80, 569–608 (2010).Article 

Google Scholar 
Clark, J. S., Bell, D. M., Kwit, M. C. & Zhu, K. Competition-interaction landscapes for the joint response of forests to climate change. Glob. Change Biol. 20, 1979–91 (2014).ADS 
Article 

Google Scholar 
Minor, D. M. & Kobe, R. K. Fruit production is influenced by tree size and size-asymmetric crowding in a wet tropical forest. Ecol. Evol. 9, 1458–1472 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 506, 89–92 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Revell, L. J. phytools: an r package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

Google Scholar 
Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125, 1–15 (1985).Article 

Google Scholar 
Martins, E. P. & Hansen, T. F. Phylogenies and the comparative method: A general approach to incorporating phylogenetic information into the analysis of interspecific data. Am. Nat. 149, 646–667 (1997).Article 

Google Scholar 
Tung Ho, L. S. & Ané, C. A linear-time algorithm for gaussian and non-gaussian trait evolution models. Syst. Biol. 63, 397–408 (2014).Article 

Google Scholar 
Clark, J. S. Data from: continent-wide tree fecundity driven by indirect climate effects https://doi.org/10.7924/r4348ph5t (2020). More

Outcome of DNA sequencing, assembly, and validationIn this study, initially total DNA was isolated from the finely chopped, full-grown pupa of Blepharipa sp. The NanoDrop spectrophotometer (1294 ng/μl) and the Qubit fluorometer (732.8 ng/μl) both found that the concentration of total DNA in the sample at an optimum level for mitochondrial DNA enrichment. The Tape Station profile showed that the size of the fragments of the mitogenomic library were in the range of 250 to 550 bp. The complete insert size distribution ranged from 130 to 430 bp, with the combined adapter size being ~ 120 bp with mitogenome fragments. The appropriate distribution of fragments and their concentrations (~ 27.1 ng/μl) were also found to be suitable for sequencing. Sequencing through Illumina NextSeq500 yielded 4,402,752 raw reads, of which around 3,663,404 high-quality reads were retained after post-quality filtering. The final scaffolding and assembly of contigs generated a 15,080 bp single scaffold MtDNA in Blepharipa sp. (N50 = 15,080).The sequencing outcome was validated by performing PCR amplification of one of the protein-coding genes, in this case, nad6. Where PCR amplification resulted in a single band of expected amplicon size (shown in Supplementary Method Online). Sanger sequencing and subsequent alignment of these amplicons showed almost 92% sequence similarity to our assembled Blepharipa sp. nad6 gene (see Supplementary Method Online). This provided strong evidence that our mitogenome assembly is reliable and can be used for general applications of mitochondrial genes, e.g., as a biomarker. The second mitogenomic region, the control region (CR) was suggested by the reviewer. We have discussed that CRs constitute repetitive A + T regions (“AT richness of Control Region and role of sequencing method” and “Impact of repeats on different sequencing technologies and assembly method” section). One or more repetitive regions within the CR identified in certain species (e.g. fish, human) have shown undesirable effects on PCR amplification and sequencing125,126. Many organisms have segmental duplications in CR induced by the appearance of pseudogenes that PCR can co-amplify127,128,129,130,131. Due to these associated problems, researchers generally rely on protein or ribosomal RNA genes for phylogenetics instead of CRs132,133,134. In this case, we also faced problems validating the CR. The PCR and gel electrophoresis using external PCR primers did not show a desirable single band as seen for nad6. As an alternative strategy, we used two pairs of primers, CR int_fwd and CR int_rev, internal primers, with CR15fwd and CR08rev primers, to perform a two-way sequencing of each amplicon, which generated multiple bands (see Supplementary Method Online, Figs. S1, S2). The most prominent bands were subjected to sequencing and yielded two mixed sequences, the best of which exhibited nearly 54% sequence resemblance with the Blepharipa sp. control region (see Method in Supplementary Note). Further mapping of the Illumina reads with the assembly revealed that the depth of coverage across the CR was not as deep as that of protein-coding genes such as cox2, and it was also not inflated only over a repeated section of the CR. The depth over 1–112 varied from 5 to 20×, and that for the 15,025–15,080 bp was around 30×. We did observe that our reads didn’t cover a 10 bp stretch of CR around 15,030–15,040 bp (see Method in Supplementary Note and Figs. S3–S6). We believe that our sequencing and assembly experiment was able to cover the majority of CR successfully with reasonable coverage barring that 10 bp stretch. Our results corroborate with the difficulties of CR sequencing seen with other species, and while this doesn’t reflect on the quality of our whole mitogenome assembly, researchers using mitogenomic CR regions for any kind of phylogenetic inference should proceed with caution.Size and organization of mitogenome
Blepharipa sp. mitogenome organization and structureThe newly sequenced mitochondrial genome of Blepharipa sp. is closed circular and has a size of 15,080 bp, which falls within the typical insect mitogenome size (14 to 20 kb)135,136,137. Similar to other sequenced bilaterian mitogenomes, the Blepharipa sp. mitogenome has conventional gene content, a total of 37 genes (viz. 13 PCGs, 22 tRNAs, 2 rRNAs) and an AT-rich control region (CR) (Fig. 2A)138,139,140,141. Among these, 23 genes are present on the major strand (J strand or +ve strand), while the remaining 14 genes are present in the minor strand (N strand or –ve strand). The intron-less 13 PCGs are also separately encoded by these two strands, 9 PCGs (nad2, cox1, cox2, atp8, atp6, cox3, nad3, nad6, cytb) from the J strand and 4 PCGs (nad5, nad4, nad4l, nad1) from N strand covering 6899 bp and 4300 bp respectively constituting around 74.31% of the entire mitogenome (Fig. 2). The largest PCG present in this organism is nad5 (1716 bp), and the smallest one is the atp8 (165 bp). Excluding stop codons, the J strand has 2237 codons, and the N strand has 1430 codons. Apart from cox1 (TCG) and nad1 (TTG), 11 PCGs follow the canonical “ATN” start codon. Ten PCGs of this mitogenome have “TAA or TAG” as their stop codon except for cox1, cox2, and nad4, where they end with an incomplete stop codon, a single T (Fig. 2)142. A total of 22 tRNAs are interspersed all over the entire mitogenome, ranging from 63 bp (trnT) to 72 bp (trnV) in size. The J and N strands have 14 tRNAs and 8 tRNAs, respectively, with 928 bp and 528 bp of nucleotides. Typical clover-leaf shaped secondary structures of tRNAs have been observed with a few exceptions where trnC, trnF, trnP, and trnN lack a stable TΨC loop see Supplementary Fig. S7 online). Two N-strand rRNAs with nucleotides of 1360 bp and 783 bp are transcribed individually for rrnL and rrnS (Fig. 2B).Figure 2Complete mitochondrial genome structure of Blepharipa sp.; (A) Circular Map (B) Annotation and genome organization of mitogenome. tRNAs are represented as trn followed by the IUPAC-IUB single letter amino acid codes e.g., trnI denote tRNA-Ile.Full size imageThis mitogenome has 10 gene boundaries where genes overlap with adjacent genes, varying from 1 to 8 bp in length, for a total of 35 bp. The longest overlapping sequence of 8 bp is present over the trnW and trnC genes. Likewise, the total length of all intergenic spacer sequences (excluding the control region) is 139 bp, present at 15 gene boundaries. The length of each intergenic spacer varies between 1 and 40 bp, and the longest one is located between the trnE and trnF genes. In this organism, eleven pairs of genes are located discreetly but adjacent to each other and any PCG adjacent to tRNA, ending with an incomplete stop codon (cox1-trnL2, cox2-trnK). The control region’s length of this dipteran fly is 168 bp, and the nature of this region is highly biased towards A + T content (Fig. 2).Size comparison of Oestroidea mitogenome and their genesTo better understand the mitogenome of Blepharipa sp., it has been compared with the flies of the Oestroidea superfamily (blowflies, bot flies, flesh flies, uzi flies, and relatives). Various features have been taken into account for this comparison: mitogenome size, gene sizes, gene content, and how genes are placed in each mitogenome.The mitogenome of eukaryotic organisms shows that there are significant size differences across mammals, fungi, and plants. The typical size of an animal mitogenome is near about 16 kb, a fungal mitogenome is 19–176 kb, and a plant mitogenome is far larger, with a size range of 200 to 2500 kb143. We have shown that the Blepharipa sp. whole mitogenome size (15,080 bp) is 416 bp smaller than the average Oestroidea flies mitogenome. As for the Oestroidea superfamily, D. hominis (human bot fly), an Oestridae fly has the longest mitogenome of all (16,360 bp), and A. grahami, a Calliphoridae fly, has the shortest mitogenome of all (14,903 bp). Tachinid flies have a smaller average mitogenome size (~ 15,076 bp) than the other flies in this superfamily, and the Oestridae flies have a relatively larger mitogenome (~ 16,031 bp). We observed that the size of the total PCGs, tRNAs, and rRNAs are well-maintained across this superfamily, with an average length of 11,145 bp, 1482 bp, and 2113 bp, respectively (Fig. 1A, green, yellow, and blue line, Table 1).The difference in mitogenome size in insects can be attributed to variations in the length of non-coding regions, especially the control region that differs in length as well as the pattern of sequences (Fig. 1B)104,144. In addition, based on mtDNA sequence similarity among all the Oestroidea flies, Blepharipa sp. has high similitude with the Tachinid Fly E. flavipalpis (87.83%), followed by the two hairy maggot blowflies, Chrysomya albiceps (85.51%) and C. rufifacies (85.44%). Another well-studied uzi fly, E. sorbilans has an 84.82% sequence similarity with Blepharipa sp., while Gasterophilus horse botfly has the lowest sequence similarity (~ 77%) with Blepharipa sp. (Supplementary Data 3A).Gene content and arrangementWe found that the Oestroidea mitogenome represents the reserved gene arrangement of Ecdysozoan, for which it can be easily distinguishable from other bilaterians (Lophotrochozoa and Deuterostomia)140. The mitogenome of Blepharipa sp. and other Oestroidea have three core tRNA clusters, including (1) trnI-trnQ-trnM, (2) trnW-trnC-trnY and (3) trnA-trnR-trnN-trnS1-trnE-trnF, as depicted in Figs. 1C and 2. A comparative study revealed that the Oestroidea superfamily has 4 different kinds of mitogenome arrangements (Fig. 1C). The majority of the Oestroidea flies (25 out of 36) in this study have ancestral (A) dipteran type mitogenome sequences (Table 1)145. However, there are some minor inconsistencies exist in the Calliphoridae family (blowflies), such as the insertion of extra tRNAs (trnI in the genus Chrysomya and trnV in D. hominis) or the translocation of tRNA (trnS1 in C. chinghaiensis) (Fig. 1C)21,24. Barring this, all organisms, including Blepharipa sp., follow a standard dipteran gene arrangement and have 37 genes in their respective mitogenomes (insertion of tRNA into the genus Chrysomya and D. hominis raises gene count) (Fig. 1C (i)(ii), Table 1). In the case of dipterans other than the Oestroidea superfamily, species like gall midge (Cecidomyiidae), mosquitos (Culicidae), and crane flies (Tipulidae) exhibit various rearrangements in mitochondrial tRNAs, such as the absence, inversion, translocation, and extreme truncation of certain genes (Supplementary Data 1A)146,147.Non-coding regionsControl region (CR) of Blepharipa sp. and comparison with OestroideaThis region in the metazoan mitogenome is a single sizeable non-coding sequence containing essential regulatory elements for transcription and replication initiation; it is therefore named the control region148,149. Similar to other Diptera, the CR of Blepharipa sp. is also flanked by rrnS and the trnI-trnQ-trnM gene cluster (Fig. 2). Sequence similarity with other Oestroidea superfamily species indicates that this segment is variable due to the lack of coding constraints150. The CR sequence of Blepharipa sp. 75.49% similar to another tachinid fly Elodia flavipalpis, followed by Chrysomya bezziana (71.15%) (Supplementary Data 3B). Despite its overall high variation in nucleotides, this region harbors multiple different types of repeats (e.g., tandem repeats, inverted repeats)42,151 and conserved structures namely Poly-T stretch (15 bp), [TA(A)]n-like, G(A)nT-like stretches, and poly A tail (15 bp)152,153,154(Fig. 3A). Another conserved motif, “ATTGTAAATT” we found in the CR of Blepharipa sp. and E. flavipalpis (Fig. 3A). Such conserved structures are thought to play role in the regulatory process of transcription or replication. After binding with RNA polymerase,  they keep the initiating mode of transcription or replication by preventing the transition to elongation mode without affecting its open-complex structure155,156.Figure 3Conserved non-coding regions; (A) AT rich control region Alignment of Blepharipa sp. with other two Tachinidae species. (B) Three alignments of the common overlap region between trnW-trnC, atp8-atp6 and nad4-nad4l. (C) Three alignment of the consensus gap region between trnS2-nad1 (TACTAAAHHHHAWWMH), trnE-trnF (ACTAAHWWWAATTMHHWA), nad5-trnH (WGAYADATWYTTCAY) genes of all 36 Oestroidea mitogenome (where, W = A/T, H = A/T/C, Y = T/C, D = G/T/A, M = A/C).Full size imageThe CR is also known as the AT-rich region for having the maximum proportion of A/T nucleotides (91.4% for Blepharipa sp.) than other regions of the entire mitogenome. We observed that the Tachinidae family has higher A + T content than other groups, with the highest levels in the Mulberry uzi fly, E. sorbillans (98.10%), and AT poor CR regions identified in G. intestinalis (80.80%) and G. pecorum (80.82%) (Oestridae)42 (Supplementary Data 2A). In this study, the CR of thirteen species have above 90% A + T content, and the top 3 are the tachinid flies, led by A. grahami, D. hominis and Blepharipa sp. consecutively. The CR is prone to high mutation, yet the substitution rate is low due to high A + T content and directional mutation pressure144,154. This part of the mitogenome differs significantly in length among insects, ranging from 70 bp to 13 kb, and it accounts for most of the variation in mitogenome size153. We noted that the CR size of 36 Oestroidea flies ranges from 89 to 1750 bp, of which 16, 12, and 8 species can be categorized as large ( > 800 bp), medium (200–800 bp), and small ( 5 to  0.025 to  0.005 to  More

Emberts, Z., Escalante, I. & Bateman, P. W. The ecology and evolution of autotomy. Biol. Rev. 94, 1881–1896. https://doi.org/10.1111/brv.12539 (2019).Article 
PubMed 

Google Scholar 
Dunoyer, L. A., Seifert, A. W. & Van Cleve, J. Evolutionary bedfellows: Reconstructing the ancestral state of autotomy and regeneration. J. Exp. Zool. Part B Mol. Dev. Evol. 336, 94–115. https://doi.org/10.1002/jez.b.22974 (2021).Article 

Google Scholar 
Dial, B. E. & Fitzpatrick, L. C. Lizard tail autotomy: function and energetics of postautotomy tail movement in Scincella lateralis. Science https://doi.org/10.1126/science.219.4583.391 (1983).Article 
PubMed 

Google Scholar 
Arnold, E. Caudal autotomy as a defense. Biol. Reptil. 16, 235–273 (1988).
Google Scholar 
Bateman, P. W. & Fleming, P. A. To cut a long tail short: A review of lizard caudal autotomy studies carried out over the last 20 years. J. Zool. (Lond.) 277, 1–14 (2009).Article 

Google Scholar 
Woodland, W. Memoirs: Some observations on caudal autotomy and regeneration in the gecko (Hemidactylus flaviviridis, Rüppel), with notes on the tails of Sphenodon and Pygopus. J. Cell Sci. 2, 63–100 (1920).Article 

Google Scholar 
Alibardi, L. Morphological and Cellular Aspects of Tail and Limb Regeneration in Lizards: A Model System with Implications for Tissue Regeneration in Mammals (Springer, 2010).Book 

Google Scholar 
Maginnis, T. L. The costs of autotomy and regeneration in animals: A review and framework for future research. Behav. Ecol. 17, 857–872. https://doi.org/10.1093/beheco/arl010 (2006).Article 

Google Scholar 
Dial, B. E. & Fitzpatrick, L. C. The energetic costs of tail autotomy to reproduction in the lizard Coleonyx brevis (Sauria: Gekkonidae). Oecologia 51, 310–317. https://doi.org/10.1007/bf00540899 (1981).ADS 
Article 
PubMed 

Google Scholar 
Vitt, L. J., Congdon, J. D. & Dickson, N. A. Adaptive strategies and energetics of tail autotomy in Lizards. Ecology 58, 326–337. https://doi.org/10.2307/1935607 (1977).Article 

Google Scholar 
Clause, A. R. & Capaldi, E. A. Caudal autotomy and regeneration in lizards. J. Exp. Zool. 305, 965–973 (2006).Article 

Google Scholar 
Barr, J. I., Boisvert, C. A. & Bateman, P. W. At what cost? Trade-offs and influences on energetic investment in tail regeneration in lizards following autotomy. J. Dev. Biol. 9, 53 (2021).Article 

Google Scholar 
Etheridge, R. Lizard caudal vertebrae. Copeia, 699–721 (1967).Arnold, E. Evolutionary aspects of tail shedding in lizards and their relatives. J. Nat. Hist. 18, 127–169 (1984).Article 

Google Scholar 
Zani, P. A. Patterns of caudal-autotomy evolution in lizards. J. Zool. (Lond.) 240, 201–220 (1996).Article 

Google Scholar 
Russell, A. & Bauer, A. The m. caudifemoralis longus and its relationship to caudal autotomy and locomotion in lizards (Reptilia: Sauria). J. Zool. (Lond.) 227, 127–143. https://doi.org/10.1111/j.1469-7998.1992.tb04349.x (1992).Article 

Google Scholar 
Arnold, E. Investigating the evolutionary effects of one feature on another: Does muscle spread suppress caudal autotomy in lizards?. J. Zool. (Lond.) 232, 505–523. https://doi.org/10.1111/j.1469-7998.1994.tb01591.x (1994).Article 

Google Scholar 
Bellairs, A. & Bryant, S. Autotomy and regeneration in reptiles. Biol. Reptil. 15, 301–410 (1985).
Google Scholar 
Hoffstetter, R. & Gasc, J. P. Vertebrae and ribs of modern reptiles. Biol. Reptil. 1, 201–310 (1969).
Google Scholar 
Cooper, W. E. Jr. & Frederick, W. G. Predator lethality, optimal escape behavior, and autotomy. Behav. Ecol. 21, 91–96. https://doi.org/10.1093/beheco/arp151 (2009).Article 

Google Scholar 
Fleming, P. A., Valentine, L. E. & Bateman, P. W. Telling tails: Selective pressures acting on investment in lizard tails. Physiol. Biochem. Zool. 86, 645–658 (2013).Article 

Google Scholar 
Bateman, P. W., Fleming, P. A. & Rolek, B. Bite me: Blue tails as a ‘risky-decoy’defense tactic for lizards. Curr. Zool. 60, 333–337 (2014).Article 

Google Scholar 
Hawlena, D., Boochnik, R., Abramsky, Z. & Bouskila, A. Blue tail and striped body: Why do lizards change their infant costume when growing up?. Behav. Ecol. 17, 889–896. https://doi.org/10.1093/beheco/arl023 (2006).Article 

Google Scholar 
Barr, J. I., Somaweera, R., Godfrey, S. S. & Bateman, P. W. Increased tail length in the King’s skink, Egernia kingii (Reptilia: Scincidae): An anti-predation tactic for juveniles?. Biol. J. Linn. Soc. 126, 268–275 (2019).Article 

Google Scholar 
Pafilis, P. & Valakos, E. D. Loss of caudal autotomy during ontogeny of Balkan Green Lizard, Lacerta trilineata. J. Nat. Hist. 42, 409–419 (2008).Article 

Google Scholar 
Masters, C. & Shine, R. Sociality in lizards: family structure in free-living King’s Skinks Egernia kingii from southwestern Australia. Aust. Zool. 32, 377–380 (2003).Article 

Google Scholar 
Cury de Barros, F., Eduardo de Carvalho, J., Abe, A. S. & Kohlsdorf, T. Fight versus flight: The interaction of temperature and body size determines antipredator behaviour in tegu lizards. Anim. Behav. 79, 83–88. https://doi.org/10.1016/j.anbehav.2009.10.006 (2010).Article 

Google Scholar 
Storr, G. The genus Egernia (Lacertilia, Scincidae) in Western Australia. Rec. West. Aust. Mus. 6, 147–187 (1978).
Google Scholar 
Cogger, H. G. Reptiles and Amphibians of Australia. 7th edn, (CSIRO Publishing, 2014).Arena, P. C. & Wooller, R. D. The reproduction and diet of Egernia kingii (Reptilia : Scincidae) on Penguin Island, Western Australia. Aust. J. Zool. 51, 495–504. https://doi.org/10.1071/ZO02040 (2003).Article 

Google Scholar 
Dilly, M. L. Factors Affecting the Distribution and Variation in Abundance of the King’s Skink (Egernia kingii) (Gray) in Western Australia, Murdoch University (2000).Pearson, D., Shine, R. & How, R. Sex-specific niche partitioning and sexual size dimorphism in Australian pythons (Morelia spilota imbricata). Biol. J. Linn. Soc. 77, 113–125 (2002).Article 

Google Scholar 
Chapple, D. G. Ecology, life-history, and behaviour in the Australian scincid genus Egernia, with comments on the evolution of complex sociality in lizards. Herpetol. Monogr. 17, 145–180. https://doi.org/10.1655/0733-1347(2003)017[0145:ELABIT]2.0.CO;2 (2003).Article 

Google Scholar 
Itescu, Y., Schwarz, R., Meiri, S., Pafilis, P. & Clegg, S. Intraspecific competition, not predation, drives lizard tail loss on islands. J. Anim. Ecol. 86, 66–74. https://doi.org/10.1111/1365-2656.12591 (2017).Article 
PubMed 

Google Scholar 
Siliceo-Cantero, H., Zúñiga-Vega, J., Renton, K. & Garcia, A. Assessing the relative importance of intraspecific and interspecific interactions on the ecology of Anolis nebulosus lizards from an island vs. a mainland population. Herpetol. Conserv. Biol. 12, 673–682 (2017).
Google Scholar 
Langkilde, T. & Shine, R. Interspecific conflict in lizards: Social dominance depends upon an individual’s species not its body size. Austral Ecol. 32, 869–877 (2007).Article 

Google Scholar 
Pafilis, P., Pérez-Mellado, V. & Valakos, E. Postautotomy tail activity in the Balearic lizard, Podarcis lilfordi. Naturwissenschaften 95, 217–221 (2008).ADS 
CAS 
Article 

Google Scholar 
Browne, C. King’s Skinks (Egernia kingii) Abundance and Juvenile Survival Unaffected by Temporal Change or Presence of Invasive BLACK Rats (Rattus rattus) on Penguin Island, Western Australia, The University of Western Australia (2014).Langton, J. Population Biology of the King’s Skink (Egernia kingii) (Gray) on Penguin Island, Western Australia, Murdoch University (2000).Arena, P. Aspects of the Biology of the King’s Skink Egernia kingii (Gray), Murdoch University (1986).Pafilis, P., Meiri, S., Foufopoulos, J. & Valakos, E. Intraspecific competition and high food availability are associated with insular gigantism in a lizard. Naturwissenschaften 96, 1107–1113. https://doi.org/10.1007/s00114-009-0564-3 (2009).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Martín, J. & Salvador, A. Tail loss reduces mating success in the Iberian rock-lizard, Lacerta monticola. Behav. Ecol. Sociobiol. 32, 185–189 (1993).Article 

Google Scholar 
Salvador, A., Martin, J. & López, P. Tail loss reduces home range size and access to females in male lizards, Psammodromus algirus. Behav. Ecol. 6, 382–387. https://doi.org/10.1093/beheco/6.4.382 (1995).Article 

Google Scholar 
Smyth, M. Changes in the fat scores of the skinks Morethia boulengeri and Hemiergis peronii (Lacertilia). Aust. J. Zool. 22, 135–145. https://doi.org/10.1071/ZO9740135 (1974).Article 

Google Scholar 
Wilson, R. S. & Booth, D. Effect of tail loss on reproductive output and its ecological significance in the skink Eulamprus quoyii. J. Herpetol. 32, 128–131 (1998).Article 

Google Scholar 
Fox, S. F. & McCoy, J. K. The effects of tail loss on survival, growth, reproduction, and sex ratio of offspring in the lizard Uta stansburiana in the field. Oecologia 122, 327–334. https://doi.org/10.1007/s004420050038 (2000).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Dial, B. E. & Fitzpatrick, L. C. Predator escape success in tailed versus tailless Scinella lateralis (Sauria: Scincidae). Anim. Behav. 32, 301–302 (1984).Article 

Google Scholar 
Downes, S. & Shine, R. Why does tail loss increase a lizard’s later vulnerability to snake predators?. Ecology 82, 1293–1303 (2001).Article 

Google Scholar 
Bernardo, J. & Agosta, S. J. Evolutionary implications of hierarchical impacts of nonlethal injury on reproduction, including maternal effects. Biol. J. Linn. Soc. 86, 309–331 (2005).Article 

Google Scholar 
Stankowich, T. & Blumstein, D. T. Fear in animals: A meta-analysis and review of risk assessment. Proc. R. Soc. Biol. Sci. Ser. B 272, 2627–2634. https://doi.org/10.1098/rspb.2005.3251 (2005).Article 

Google Scholar 
Steindler, L. A., Blumstein, D. T., West, R., Moseby, K. E. & Letnic, M. Exposure to a novel predator induces visual predator recognition by naïve prey. Behav. Ecol. Sociobiol. 74, 102. https://doi.org/10.1007/s00265-020-02884-3 (2020).Article 

Google Scholar 
Blumstein, D. T. Moving to suburbia: Ontogenetic and evolutionary consequences of life on predator-free islands. J. Biogeogr. 29, 685–692. https://doi.org/10.1046/j.1365-2699.2002.00717.x (2002).Article 

Google Scholar 
Sih, A. et al. Predator–prey naïveté, antipredator behavior, and the ecology of predator invasions. Oikos 119, 610–621 (2010).Article 

Google Scholar 
Cooper, J. W. E.; Blumstein, D. T. Escaping From Predators: An Integrative View of Escape Decisions. (Cambridge University Press, 2015).Cox, J. G. & Lima, S. L. Naiveté and an aquatic–terrestrial dichotomy in the effects of introduced predators. Trends Ecol. Evol. 21, 674–680 (2006).Article 

Google Scholar 
Blumstein, D. T. & Daniel, J. C. The loss of anti-predator behaviour following isolation on islands. Proc. R. Soc. Biol. Sci. Ser. B 272, 1663–1668 (2005).Article 

Google Scholar 
Blumstein, D. T., Daniel, J. C. & Springett, B. P. A test of the multi-predator hypothesis: Rapid loss of antipredator behavior after 130 years of isolation. Ethology 110, 919–934 (2004).Article 

Google Scholar 
Jolly, C. J., Webb, J. K. & Phillips, B. L. The perils of paradise: An endangered species conserved on an island loses antipredator behaviours within 13 generations. Biol. Lett. 14, 20180222 (2018).Article 

Google Scholar 
Cooper, W. E., Pérez-Mellado, V. & Vitt, L. J. Ease and effectiveness of costly autotomy vary with predation intensity among lizard populations. J. Zool. 262, 243–255 (2004).Article 

Google Scholar 
Elwood, C., Pelsinski, J. & Bateman, B. Anolis sagrei (Brown Anole). Voluntary autotomy. Herpetol. Rev. 43, 642–642 (2012).
Google Scholar 
Slotopolsky, B. Beiträge zur Kenntnis der Verstümmelungs-und Regenerationsvorgänge am Lacertilierschwanze. Zool. Jahrb. Abt. Anat. Ontog. Tiere 43, 39–48 (1922).
Google Scholar  More

D’Amen, M., Zimmermann, N. E. & Pearman, P. B. Conservation of phylogeographic lineages under climate change. Glob. Ecol. Biogeogr. 22, 93–104. https://doi.org/10.1111/j.1466-8238.2012.00774.x (2013).Article 

Google Scholar 
Espíndola, A. et al. Predicting present and future intra-specific genetic structure through niche hindcasting across 24 millennia. Ecol. Lett. 15, 649–657. https://doi.org/10.1111/j.1461-0248.2012.01779.x (2012).Article 
PubMed 

Google Scholar 
Manel, S., Schwartz, M. K., Luikart, G. & Taberlet, P. Landscape genetics: combining landscape ecology and population genetics. Tr. Ecol. Evolut. 18, 189–197. https://doi.org/10.1016/S0169-5347(03)00008-9 (2003).Article 

Google Scholar 
Fontaine, C., Lovett, P., Sanou, H., Maley, J. & Bouvet, J. M. Genetic diversity of the shea tree (Vitellaria paradoxa CF Gaertn), detected by RAPD and chloroplast microsatellite markers. Heredity 93, 639 (2004).CAS 
Article 

Google Scholar 
Hampe, A., El Masri, L. & Petit, R. J. Origin of spatial genetic structure in an expanding oak population. Mol. Ecol. 19, 459–471. https://doi.org/10.1111/j.1365-294X.2009.04492.x (2010).Article 
PubMed 

Google Scholar 
Omondi, S. F., Odee, D. W., Ongamo, G. O., Kanya, J. I. & Khasa, D. P. Genetic consequences of anthropogenic disturbances and population fragmentation in Acacia senegal. Conserv. Genet. 17, 1235–1244. https://doi.org/10.1007/s10592-016-0854-1 (2016).Article 

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

Google Scholar 
Donkpegan, A. S. L. et al. Population genomics of the widespread African savannah trees Afzelia africana and Afzelia quanzensis reveals no significant past fragmentation of their distribution ranges. Am. J. Bot. 107, 498–509. https://doi.org/10.1002/ajb2.1449 (2020).Article 
PubMed 

Google Scholar 
Etterson, J. R. & Shaw, R. G. Constraint to adaptive evolution in response to global warming. Science 294, 151–154. https://doi.org/10.1126/science.1063656 (2001).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Holderegger, R. & Wagner, H. Landscape genetics. Bioscience 58, 199–207. https://doi.org/10.1641/B580306 (2008).Article 

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

Google Scholar 
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42. https://doi.org/10.1038/nature01286 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Pauls, S. U., Nowak, C., Bálint, M. & Pfenninger, M. The impact of global climate change on genetic diversity within populations and species. Mol. Ecol. 22, 925–946. https://doi.org/10.1111/mec.12152 (2013).Article 
PubMed 

Google Scholar 
Arnell, N. W. & Lloyd-Hughes, B. The global-scale impacts of climate change on water resources and flooding under new climate and socio-economic scenarios. Climatic Ch. 122, 127–140. https://doi.org/10.1007/s10584-013-0948-4 (2014).ADS 
Article 

Google Scholar 
Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747 (2010).ADS 
CAS 
Article 

Google Scholar 
van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic Ch. 109, 5–31. https://doi.org/10.1007/s10584-011-0148-z (2011).ADS 
Article 

Google Scholar 
Prather, M. et al. Annex II: climate system scenario tables. Climate Ch. 1395–1445 (2013).Pachauri, R. K. et al. Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. Synthesis report (Intergovernmental Panel on Climate Change, Geneva, Switzerland, 2014).Müller, C. Climate change impact on Sub-Saharan Africa. An overview and analysis of scenarios and models (Dt. Inst. für Entwicklungspolitik, Bonn, 2009).Serdeczny, O. et al. Climate change impacts in Sub-Saharan Africa: From physical changes to their social repercussions. Reg. Environ. Ch. 17, 1585–1600. https://doi.org/10.1007/s10113-015-0910-2 (2016).Article 

Google Scholar 
Linder, H. P. et al. The partitioning of Africa: Statistically defined biogeographical regions in sub-Saharan Africa. J. Biogeogr. 39, 1189–1205. https://doi.org/10.1111/j.1365-2699.2012.02728.x (2012).Article 

Google Scholar 
Sexton, G. J. et al. Influence of putative forest refugia and biogeographic barriers on the level and distribution of genetic variation in an African savannah tree, Khaya senegalensis (Desr.) A. Juss. Tree Genet. Genomes https://doi.org/10.1007/s11295-015-0933-3 (2015).Article 

Google Scholar 
Linder, H. P. et al. Numerical re-evaluation of the sub-Saharan phytopchoria of mainland Africa. Biologiske Skrifter 55, 229–252 (2005).ADS 

Google Scholar 
Ruiz Guajardo, J. C. et al. Landscape genetics of the key African acacia species Senegalia mellifera (Vahl)- the importance of the Kenyan Rift Valley. Mol. Ecol. 19, 5126–5139. https://doi.org/10.1111/j.1365-294X.2010.04833.x (2010).CAS 
Article 
PubMed 

Google Scholar 
Kebede, M., Enrich, D., Taberlet, P., Nemomissa, S. & Brochmann, C. Phylogeography and conservation genetics of a giant lobelia (Lobelia giberroa) in Ethiopian and Tropical East African mountains. Mol. Ecol. 16, 1233–1243. https://doi.org/10.1111/j.1365-294x.2007.03232.x (2007).CAS 
Article 
PubMed 

Google Scholar 
Kadu, C. et al. Phylogeography of the Afromontane Prunus africana reveals a former migration corridor between East and West African highlands. Mol. Ecol. 20, 165–178. https://doi.org/10.1111/j.1365-294X.2010.04931.x (2011).CAS 
Article 
PubMed 

Google Scholar 
Lyam, P. T., Duque-Lazo, J., Schnitzler, J., Hauenschild, F. & Müllner-Riehl, A. N. Testing the forest refuge hypothesis in sub-Saharan Africa using species distribution modeling for a key savannah tree species, Senegalia senegal (L.) Britton. Front. Biogeogr. https://doi.org/10.21425/F5FBG48689 (2020).Article 

Google Scholar 
Logossa, Z. A. et al. Molecular data reveal isolation by distance and past population expansion for the shea tree (Vitellaria paradoxa C.F. Gaertn) in West Africa. Mol. Ecol. 20, 4009–4027. https://doi.org/10.1111/j.1365-294X.2011.05249.x (2011).Article 
PubMed 

Google Scholar 
Lompo, D., Vinceti, B., Konrad, H., Gaisberger, H. & Geburek, T. Phylogeography of African locust bean (Parkia biglobosa) reveals genetic divergence and spatially structured populations in west and central Africa. J. Heredity 109, 811–824. https://doi.org/10.1093/jhered/esy047 (2018).Article 

Google Scholar 
Leong Pock Tsy, J.-M. et al. Chloroplast DNA phylogeography suggests a West African centre of origin for the baobab, Adansonia digitata L. (Bombacoideae, Malvaceae). Mol. Ecol. 18, 1707–1715. https://doi.org/10.1111/j.1365-294X.2009.04144.x (2009).CAS 
Article 
PubMed 

Google Scholar 
Allal, F. et al. Past climate changes explain the phylogeography of Vitellaria paradoxa over Africa. Heredity 107, 174–186. https://doi.org/10.1038/hdy.2011.5 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fagg, C. W. & Allison, G. E. Acacia Senegal and the gum arabic trade: monograph and annotated bibliography (University of Oxford, United Kingdom, 2004).
Google Scholar 
Lézine, A. M. Late Quaternary vegetation and climate of the Sahel. Quatern. Res. 32, 317–334 (1989).ADS 
Article 

Google Scholar 
Steele, T. Vertebrate records: Late Pleistocene of Africa. In Encyclopedia of Quaternary Science, edited by S. Elias. (Elsevier, Oxford, 2007), 3139–3150.Raddad, E., Salih, A., Fadl, M., Kaarakka, V. & Luukkanen, O. Symbiotic nitrogen fixation in eight Acacia senegal provenances in dryland clays of the Blue Nile Sudan estimated by the 15N natural abundance method. Plant Soil 275, 261–269. https://doi.org/10.1007/s11104-005-2152-4 (2005).CAS 
Article 

Google Scholar 
Gray, A. et al. Does geographic origin dictate ecological strategies in Acacia senegal (L.) Willd? Evidence from carbon and nitrogen stable isotopes. Plant Soil 369, 479–496. https://doi.org/10.1007/s11104-013-1593-4 (2013).CAS 
Article 

Google Scholar 
Ross, J. H. A conspectus of African acacia species (1979).Odee, D. W., Telford, A., Wilson, J., Gaye, A. & Cavers, S. Plio-Pleistocene history and phylogeography of Acacia senegal in dry woodlands and savannahs of sub-Saharan tropical Africa: evidence of early colonisation and recent range expansion. Heredity 109, 372–382. https://doi.org/10.1038/hdy.2012.52 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lyam, P. et al. Genetic diversity and distribution of Senegalia senegal (L.) Britton under climate change scenarios in West Africa. PLoS ONE 13, e0194726 (2018).Article 

Google Scholar 
Nicotra, A. B. et al. Plant phenotypic plasticity in a changing climate. Trends in Plant Science 15, 684–692; https://doi.org/10.1016/j.tplants.2010.09.008 (2010).Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978. https://doi.org/10.1002/joc.1276 (2005).Article 

Google Scholar 
ESRI. ArcGIS Desktop: Release 10.5. Redlands, CA: Environmental Systems Research Institute (2020).Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Res. 15, 1179–1191. https://doi.org/10.1111/1755-0998.12387 (2015).CAS 
Article 

Google Scholar 
Elhadji, S. D. et al. Exploring genetic diversity and structure of Acacia senegal (L.) Willd to improve its conservation in Niger. African J. Biotechnol. 16, 1650–1659 (2017).Article 

Google Scholar 
Muriira, N. G., Muchugi, A., Yu, A., Xu, J. & Liu, A. Genetic Diversity Analysis Reveals Genetic Differentiation and Strong Population Structure in Calotropis Plants. Sci. Rep. 8, 7832 (2018).ADS 
Article 

Google Scholar 
Conord, C., Gurevitch, J. & Fady, B. Large-scale longitudinal gradients of genetic diversity: a meta-analysis across six phyla in the Mediterranean basin. Ecol. Evol. 2, 2600–2614. https://doi.org/10.1002/ece3.350 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Omondi, S. F. et al. Genetic diversity and population structure of Acacia senegal (L) Willd Kenya. Trop. Plant Biol. 3, 59–70 (2010).Article 

Google Scholar 
Marko, P. B. & Hart, M. W. The complex analytical landscape of gene flow inference. Trends Ecol. Evol. 26, 448–456. https://doi.org/10.1016/j.tree.2011.05.007 (2011).Article 
PubMed 

Google Scholar 
Goncalves, A. L., García, M. V., Heuertz, M. & González-Martínez, S. C. Demographic history and spatial genetic structure in a remnant population of the subtropical tree Anadenanthera colubrina var cebil (Griseb.) Altschul (Fabaceae). Ann. Forest Sci. https://doi.org/10.1007/s13595-019-0797-z (2019).Article 

Google Scholar 
Rosenzweig, M. L. Species diversity in space and time (Cambridge university press, 1995).Vellend, M. & Geber, M. A. Connections between species diversity and genetic diversity. Ecol. Lett. 8, 767–781. https://doi.org/10.1111/j.1461-0248.2005.00775.x (2005).Article 

Google Scholar 
Ackerly, D. D. et al. The geography of climate change: implications for conservation biogeography. Divers. Distrib. 16, 476–487. https://doi.org/10.1111/j.1472-4642.2010.00654.x (2010).Article 

Google Scholar 
Waldvogel, A.-M. et al. Evolutionary genomics can improve prediction of species’ responses to climate change. Evol. Lett. 4, 4–18. https://doi.org/10.1002/evl3.154 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Hutchison, D. W. & Templeton, A. R. Correlation of pairwise genetic and geographic distance measures: inferring the relative influences of gene flow and drift on the distribution of genetic variability. Evol.; Int. J. Org. Evol. 53, 1898–1914 (1999).Article 

Google Scholar 
Shi, M. M., Michalski, S. G., Welk, E., Chen, X. Y. & Durka, W. Phylogeography of a widespread Asian subtropical tree: genetic east-west differentiation and climate envelope modelling suggest multiple glacial refugia. J. Biogeogr. 41, 1710–1720. https://doi.org/10.1111/jbi.12322 (2014).Article 

Google Scholar 
Voss, N., Eckstein, R. L. & Durka, W. Range expansion of a selfing polyploid plant despite widespread genetic uniformity. Ann. Botany 110, 585–593. https://doi.org/10.1093/aob/mcs117 (2012).Article 

Google Scholar 
Fiorini, C. F. et al. Phylogeography of the specialist plant Mandirola hirsuta (Gesneriaceae) suggests ancient habitat fragmentation due to savanna expansion. Flora 262, 151522 (2020).Article 

Google Scholar 
Sexton, J. P., Hangartner, S. B. & Hoffmann, A. A. Genetic isolation by environment or distance: which pattern of gene flow is most common?. Evolution 68, 1–15. https://doi.org/10.1111/evo.12258 (2014).CAS 
Article 
PubMed 

Google Scholar 
Wang, I. J. & Bradburd, G. S. Isolation by environment. Mol. Ecol. 23, 5649–5662. https://doi.org/10.1111/mec.12938 (2014).Article 
PubMed 

Google Scholar 
Nosil, P., Vines, T. H. & Funk, D. J. Reproductive isolation caused by natural selection against immigrants from divergent habitats. Evol.; Int. J. Org. Evol. 59, 705–719 (2005).
Google Scholar 
Wang, I. J. & Summers, K. Genetic structure is correlated with phenotypic divergence rather than geographic isolation in the highly polymorphic strawberry poison-dart frog. Mol. Ecol. 19, 447–458. https://doi.org/10.1111/j.1365-294X.2009.04465.x (2010).Article 
PubMed 

Google Scholar 
Xu, B. et al. Population genetic structure is shaped by historical, geographic, and environmental factors in the leguminous shrub Caragana microphylla on the Inner Mongolia Plateau of China. BMC Plant Biol. 17, 200 (2017).Article 

Google Scholar 
Hendry, A. P. & Day, T. Population structure attributable to reproductive time: isolation by time and adaptation by time. Mol. Ecol. 14, 901–916. https://doi.org/10.1111/j.1365-294X.2005.02480.x (2005).CAS 
Article 
PubMed 

Google Scholar 
Solomon, S., Manning, M., Marquis, M. & Qin, D. Climate change 2007-the physical science basis: Working group I contribution to the fourth assessment report of the IPCC (Cambridge university press, 2007).Thuiller, W. Climate change and the ecologist. Nature 448, 550–552 (2007).ADS 
CAS 
Article 

Google Scholar 
Osland, M. J. et al. Tropicalization of temperate ecosystems in North America: The northward range expansion of tropical organisms in response to warming winter temperatures. Global Ch. Biol. 27, 3009–3034 (2021).Article 

Google Scholar 
Higgins, S. I., Lavorel, S. & Revilla, E. Estimating plant migration rates under habitat loss and fragmentation. Oikos 101, 354–366 (2003).Article 

Google Scholar 
Jump, A. S. & Penuelas, J. Running to stand still: adaptation and the response of plants to rapid climate change. Ecol. Lett. 8, 1010–1020. https://doi.org/10.1111/j.1461-0248.2005.00796.x (2005).Article 
PubMed 

Google Scholar 
Jump, A. S., Marchant, R. & Peñuelas, J. Environmental change and the option value of genetic diversity. Trends Plant Sci. 14, 51–58. https://doi.org/10.1016/j.tplants.2008.10.002 (2009).CAS 
Article 
PubMed 

Google Scholar 
Kirk, H. & Freeland, J. R. Applications and implications of neutral versus non-neutral markers in molecular ecology. Int. J. Mol. Sci. 12, 3966–3988. https://doi.org/10.3390/ijms12063966 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Bucharova, A. et al. Mix and match: regional admixture provenancing strikes a balance among different seed-sourcing strategies for ecological restoration. Conserv. Genet. 20, 7–17. https://doi.org/10.1007/s10592-018-1067-6 (2019).Article 

Google Scholar 
Tong, Y. et al. Ex situ conservation of Pinus koraiensis can preserve genetic diversity but homogenizes population structure. Forest Ecol. Manag. 465, 117820 (2020).Article 

Google Scholar 
Vessella, F., Simeone, M. C. & Schirone, B. Quercus suber range dynamics by ecological niche modelling: from the Last Interglacial to present time. Quat. Sci. Rev. 119, 85–93. https://doi.org/10.1016/j.quascirev.2015.04.018 (2015).ADS 
Article 

Google Scholar 
Lovejoy, T. E. Climate change and biodiversity (TERI Press, India, 2006).
Google Scholar 
Poczai, P., Varga, I., Bell N.E. & Hyvonen, J. The molecular basis of plant genetic diversity. In Genomics meets biodiversity: advances in molecular marker development and their applications in plant genetic diversity assessment. The molecular basis of plant genetic diversity, edited by M. Caliskan (InTech Open Access Publisher2012), 3–31.Botermans, M., Sosef, M. S. M., Chatrou, L. W. & Couvreur, T. L. P. Revision of the African Genus Hexalobus (Annonaceae). Syst. Bot. 36, 33–48. https://doi.org/10.1600/036364411X553108 (2011).Article 

Google Scholar 
Sosef, M. et al. Exploring the floristic diversity of tropical Africa. BMC Biol. 15, 15 (2017).Article 

Google Scholar 
Chapuis, M.-P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631. https://doi.org/10.1093/molbev/msl191 (2007).CAS 
Article 
PubMed 

Google Scholar 
Escoffier, L. & Lische, H. ARLEQUIN suite ver. 3.5. A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Res. 10, 564–567 (2010).Article 

Google Scholar 
Lewis, P. O. & Zaykin, D. Genetic data analysis: computer program for the analysis of allelic data. Mol. Ecol. 11, 1157–1164 (2002).Article 

Google Scholar 
AComputer Program to Calculate F-Statistics. Goudet, J. FSTAT (Version 1.2). J. Hered. 6, 245–246 (1995).
Google Scholar 
El Mousadik, A. & Petit, R. J. High level of genetic differentiation for allelic richness among populations of the argan tree [Argania spinosa (L.) Skeels] endemic to Morocco. Theor. Appl. Genet. 92, 832–839 (1996).Article 

Google Scholar 
Raymond, M. & Rousset, F. GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J. Heredity 86, 248–249 (1995).Article 

Google Scholar 
Pritchard, J., Stephens, M. & Donelly, P. Inference of Population Structure Using Multilocus Genotype Data, 945–959 (2000).Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164, 1567–1587 (2003).CAS 
Article 

Google Scholar 
Earl, D. A. & von Holdt, B. M. STRUCTURE HARVESTER A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361. https://doi.org/10.1007/s12686-011-9548-7 (2012).Article 

Google Scholar 
Pritchard, J. K., Wen, W. & Falush, D. Documentation for STRUCTURE software: Version 2.3. University of Chicago, Chicago, IL, 1–37 (2010).Eliades, N. G. & Eliades, D. G. HAPLOTYPE ANALYSIS: software for analysis of haplotype data. Forest Goettingen (Germany): Genetics and Forest Tree Breeding, Georg-August University Goettingen (2009).Leigh, J. W. & Bryant, D. POPART: full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).Article 

Google Scholar 
Peakall, R. & Smouse, P. E. Genalex 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295. https://doi.org/10.1111/j.1471-8286.2005.01155.x (2006).Article 

Google Scholar 
Title, P. O. & Bemmels, J. B. ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography 41, 291–307. https://doi.org/10.1111/ecog.02880 (2018).Article 

Google Scholar 
Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).Article 

Google Scholar 
Wang, I. J. Examining the full effects of landscape heterogeneity on spatial genetic variation: a multiple matrix regression approach for quantifying geographic and ecological isolation. Evolution 67, 3403–3411. https://doi.org/10.1111/evo.12134 (2013).Article 
PubMed 

Google Scholar  More

Fu, C. H. et al. Relationships among fisheries exploitation, environmental conditions, and ecological indicators across a series of marine ecosystems. J. Mar. Syst. 148, 101–111 (2015).Article 

Google Scholar 
Too, C. C., Ong, K. S., Yule, C. M. & Keller, A. Putative roles of bacteria in the carbon and nitrogen cycles in a tropical peat swamp fores. Basic Appl. Ecol. 52, 109–123 (2020).Article 

Google Scholar 
Hou, R. J. et al. Effects of biochar and straw on greenhouse gas emission and its response mechanism in seasonally frozen farmland ecosystems. Catena 194, 104735 (2020).CAS 
Article 

Google Scholar 
Wang, X., Lu, P., Yang, P. L. & Ren, S. M. Effects of fertilizer and biochar applications on the relationship among soil moisture, temperature, and N2O emissions in farmland. PeerJ 9, e11674–e11674 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Tang, Z. M., Liu, X. R., Zhang, Q. W. & Li, G. C. Effects of biochar and straw on soil N2O emission from a wheat maize rotation system. Huan Jing Ke Xue 42(3), 1569–1580 (2021).PubMed 

Google Scholar 
Kong, Q., Wang, Z. B., Niu, P. F. & Miao, M. S. Greenhouse gas emission and microbial community dynamics during simultaneous nitrification and denitrification process. Biores. Technol. 210, 94–100 (2016).CAS 
Article 

Google Scholar 
Han, Z. M. et al. Spatial-temporal dynamics of agricultural drought in the Loess Plateau under a changing environment: Characteristics and potential influencing factors. Agric. Water Manag. 244, 106540 (2021).Article 

Google Scholar 
Clemens, S. et al. Nitrification inhibitors can increase post-harvest nitrous oxide emissions in an intensive vegetable production system. Sci. Rep. 7(1), 1–9 (2017).Article 
CAS 

Google Scholar 
Zhang, D. J. et al. Effects of tillage and fertility on soil nitrogen balance and greenhouse gas emissions of wheat-maize rotation system in Central Henan Province, China. J. Appl. Ecol. 32(5), 1753–1760 (2021).
Google Scholar 
Liu, X. C. et al. Response of soil N2O emissions to precipitation pulses under different nitrogen availabilities in a semiarid temperate steppe of Inner Mongolia, China. J. Arid Land 6(04), 410–422 (2014).Article 

Google Scholar 
Hu, Q. Y. et al. Combined effects of straw returning and chemical n fertilization on greenhouse gas emissions and yield from paddy fields in northwest Hubei Province, China. J. Soil Sci. Plant Nutr. 20(2), 392–406 (2019).Article 
CAS 

Google Scholar 
Sun, Z. C. et al. Effects of straw returning and feeding on greenhouse gas emissions from integrated rice-crayfish farming in Jianghan Plain, China. Environ. Sci. Pollut. Res. 26(12), 11710–11718 (2019).CAS 
Article 

Google Scholar 
Mei, K. et al. Stimulation of N2O emission by conservation tillage management in agricultural lands: A meta-analysis. Soil Tillage Res. 182, 86–93 (2018).Article 

Google Scholar 
Wang, H. Y., Wu, J. Q., Li, G. & Yan, L. J. Changes in soil carbon fractions and enzyme activities under different vegetation types of the northern Loess Plateau. Ecol. Evol. 10(21), 12211–12223 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Sadiq, M., Li, G., Rahim, N. & Tahir, M. M. Sustainable conservation tillage technique for improving soil health by enhancing soil physicochemical quality indicators under wheat mono-cropping system conditions. Sustainability 13(15), 8177–8177 (2021).CAS 
Article 

Google Scholar 
Nie, Z. G. et al. Evaluating the effects of different sowing dates and tillage methods on dry-land wheat grain dry matter accumulation based on the APSIM model. J. Appl. Ecol. 32(3), 913–920 (2021).
Google Scholar 
Alhassan, A. M., Yang, C. J., Ma, W. W. & Li, G. Influence of conservation tillage on Greenhouse gas fluxes and crop productivity in spring-wheat agroecosystems on the Loess Plateau of China. PeerJ 9, e11064–e11064 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Mou, L. M. et al. Breeding report of a new dryland spring wheat variety Dingxi 42. Gansu Agric. Sci. Technol. 01, 1–3 (2015).ADS 

Google Scholar 
Ma, W. W., Li, G., Wu, J. H., Xu, G. R. & Wu, J. Q. Respiration and CH4 fluxes in Tibetan peatlands are influenced by vegetation degradation. CATENA 195, 104789 (2020).CAS 
Article 

Google Scholar 
Wu, J. Q. et al. Vegetation degradation impacts soil nutrients and enzyme activities in wet meadow on the Qinghai-Tibet Plateau. Sci. Rep. 10(1), 21271–21271 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Défossez, P. et al. Impact of soil water content on the overturning resistance of young Pinus Pinaster in sandy soil. For. Ecol. Manag. 480, 118614 (2021).Article 

Google Scholar 
Mao, J., Nierop, K. G., Rietkerk, M., Damsté, J. S. S. & Te Dekker, S. C. infuence of vegetation on soil water repellency-markers and soil hydrophobicity. Sci. Total Environ. 566, 608–620 (2016).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Lu, Y., Si, B., Li, H. & Biswas, A. Elucidating controls of the variability of deep soil bulk density. Geoderma 348, 146–157 (2019).ADS 
Article 

Google Scholar 
Huang, T. T., Yang, N., Lu, C., Qin, X. L. & Siddique, K. Soil organic carbon, total nitrogen, available nutrients, and yield under different straw returning methods. Soil Tillage Res. 214, 105171 (2021).Article 

Google Scholar 
Yang, J. M., Zhang, Z. Q. & Cao, G. J. Soil nitrate and nitrite content determined by Skalar SAN++. Soil Fertil. Sci. China 02, 101–105 (2014).
Google Scholar 
Chen, N. et al. Effect of biodegradable film mulching on crop yield, soil microbial and enzymatic activities, and optimal levels of irrigation and nitrogen fertilizer for the Zea mays crops in arid region. Sci. Total Environ. 776, 145970–145970 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Akhtar, K. et al. Straw mulching with inorganic nitrogen fertilizer reduces soil CO2 and N2O emissions and improves wheat yield. Sci. Tot. Environ. 741, 140488 (2020).CAS 
Article 

Google Scholar 
Ma, E. et al. Effects of rice straw returning methods on N2O emission during wheat-growing season. Nutr. Cycl. Agroecosyst. 88(3), 463–469 (2009).Article 
CAS 

Google Scholar 
Yeboah, S. et al. Greenhouse gas emissions in a spring wheat–field pea sequence under different tillage practices in semi-arid Northwest China. Nutr. Cycl. Agroecosyst. 106(1), 77–91 (2016).CAS 
Article 

Google Scholar 
Zahid, A., Ali, S., Ahmed, M. & Iqbal, N. Improvement of soil health through residue management and conservation tillage in rice-wheat cropping system of Punjab, Pakistan. Agronomy 10(12), 1844–1844 (2020).CAS 
Article 

Google Scholar 
Dharmendra, S. et al. Effect of reversal of conservation tillage on soil nutrient availability and crop nutrient uptake in soybean in the vertisols of central India. Sustainability. 12(16), 6608 (2020).Article 
CAS 

Google Scholar 
Orzech, K., Wanic, M. & Załuski, D. The effects of soil compaction and different tillage systems on the bulk density and moisture content of soil and the yields of winter oilseed rape and cereals. Agriculture 11(7), 666–666 (2021).CAS 
Article 

Google Scholar 
Fan, B. Q. & Liu, Q. L. Effect of conservation tillage and straw application on the soil microorganism and P-dissolving characteristics. Chin. J. Eco-Agric. 03, 130–132 (2005).
Google Scholar 
Liu, X. et al. Dynamic contribution of microbial residues to soil organic matter accumulation influenced by maize straw mulching. Geoderma 333, 35–42 (2019).ADS 
CAS 
Article 

Google Scholar 
Wang, W. Y. et al. Conservation tillage enhances crop productivity and decreases soil nitrogen losses in a rainfed agroecosystem of the Loess Plateau, China. J. Clean. Prod. 274, 122854 (2020).CAS 
Article 

Google Scholar 
Zhang, Y., Xie, D. T., Ni, J. P. & Zeng, X. B. Conservation tillage practices reduce nitrogen losses in the sloping upland of the Three Gorges Reservoir area: No-till is better than mulch-till. Agric. Ecosyst. Environ. 300, 107003 (2020).CAS 
Article 

Google Scholar 
Andrea, F. et al. May conservation tillage enhance soil C and N accumulation without decreasing yield in intensive irrigated croplands? Results from an eight-year maize monoculture. Agric. Ecosyst. Environ. 296, 106926 (2020).Article 
CAS 

Google Scholar 
Wu, J. et al. Effects of different tillage and straw retention practices on soil aggregates and carbon and nitrogen sequestration in soils of the northwestern China. J. Arid. Land 11(04), 567–578 (2019).Article 

Google Scholar 
Niu, Y. N., Shen, Y. Y., Nan, Z. B., Yang, J. & Yang, Z. W. College of Pastoral Agriculture Science & Technology, Lanzhou University, China. Influence of different cultivation managements on organic carbon and nitrate nitrogen of top soil in the Loess Plateau, northwestern China. Proceedings of the XXI International Grassland Congress and the VIII International Rangeland Congress (volume II) (2008).Wang, Q., Li, F. R., Zhang, E. H., Li, G. & Vance, M. The effects of irrigation and nitrogen application rates on yield of spring wheat (longfu-920), and water use efficiency and nitrate nitrogen accumulation in soil. Aust. J. Crop Sci. 6(4), 662–672 (2012).
Google Scholar 
Pisani, O. et al. Soil nitrogen dynamics and leaching under conservation tillage in the Atlantic Coastal Plain, Georgia, United States. J. Soil Water Conserv. 72(5), 519–529 (2017).Article 

Google Scholar 
Cao, W. C. et al. Key production processes and influencing factors of nitrous oxide emissions from agricultural soils. J. Nutr. Fertil. 25(10), 1781–1798 (2019).
Google Scholar 
Liu, B., Huang, G. B., Gao, Y. Q., Li, Q. P. & Huang, T. Effects of no-tillage on daily dynamics of CO2 and N2O emission from spring wheat field during mature stage. J. Gansu Agric. Univ. 45(01), 82–87 (2010).
Google Scholar 
Akhtar, K. et al. Straw mulching with inorganic nitrogen fertilizer reduces soil CO2 and N2O emissions and improves wheat yield. Sci. Total Environ. 741, 140488 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Sina, B., Youngsun, K., Janine, K. & Gerhard, G. Plastic mulching in agriculture: Friend or foe of N2O emissions. Agric. Ecosyst. Environ. 167, 43–51 (2013).Article 
CAS 

Google Scholar 
Seiichi, N., Michio, K., Masako, T., Seiichiro, Y. & Naoto, K. Nitrous oxide evolved from soil covered with plastic mulch film in horticultural field. Biol. Fertil. Soils 48(7), 787–795 (2012).Article 
CAS 

Google Scholar 
Wang, J., Cai, L. Q., Zhang, R. Z., Wang, Y. L. & Dong, W. J. Effects of Tillage Measures on soil greenhouse gas (CO2, CH4, N2O) flux in temperate semi-arid area. Chin. J. Eco-Agric. 19(06), 1295–1300 (2011).CAS 
Article 

Google Scholar 
Chen, G. H. et al. Can conservation tillage reduce N2O emissions on cropland transitioning to organic vegetable production?. Sci. Total Environ. 618, 927–940 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Narendra, K. L. & Rattan, L. Soil aggregation and greenhouse gas flux after 15 years of wheat straw and fertilizer management in a no-till system. Soil Tillage Res. 126, 78–89 (2013).Article 

Google Scholar 
Liang, W., Shi, Y., Zhang, H., Yue, J. & Huang, G. H. Greenhouse gas emissions from Northeast china rice fields in fallow season. Pedosphere 17(5), 630–638 (2007).CAS 
Article 

Google Scholar 
Bremner, J. M., Robbins, S. G. & Blackmer, A. M. Seasonal variability in emission of nitrous oxide from soil. Geophys. Res. Lett. 7(9), 641–644 (1980).ADS 
CAS 
Article 

Google Scholar 
Maag, M. & Vinther, F. P. Nitrous oxide emission by nitrification and denitrification in the different soil types and at different soil moisture contents and temperature. Appl. Soil. Ecol. 4(1), 5–14 (1996).Article 

Google Scholar 
Castaldi, S. Responses of nitrous oxide, dinitrogen and carbon dioxide production and oxygen consumption to temperature in forest and agricultural light-textured soils determined by model experiment. Biol. Fertil. Soils 32(1), 67–72 (2000).MathSciNet 
CAS 
Article 

Google Scholar 
Braker, G., Schwarz, J. & Conrad, R. Influence of temperature on the composition and activity of denitrifying soil communities. FEMS Microbiol. Ecol. 73(1), 134–148 (2010).CAS 
PubMed 

Google Scholar 
Hu, H. W., Chen, D. & He, J. Z. Microbial regulation of terrestrial nitrous oxide formation: Understanding the biological pathways for prediction of emission rates. Narnia 39(5), 729–749 (2015).CAS 

Google Scholar 
Pokharel, P. & Chang, S. X. Biochar decreases the efficacy of the nitrification inhibitor nitrapyrin in mitigating nitrous oxide emissions at different soil moisture levels. J. Environ. Manage. 295, 113080–113080 (2021).CAS 
PubMed 
Article 

Google Scholar 
Shu, X. X. et al. Response of soil N2O emission and nitrogen utilization to organic matter in the wheat and maize rotation system. Sci. Rep. 11(1), 4396–4396 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bergaust, L., Mao, Y. J., Bakken, L. R. & Frostegård, A. Denitrification response patterns during the transition to anoxic respiration and posttranscriptional effects of suboptimal pH on nitrous [corrected] oxide reductase in Paracoccus denitrificans. Appl. Environ. Microbiol. 76(19), 6387–6396 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Clark, C. T. et al. Heavy with child? Pregnancy status and stable isotope ratios as determined from biopsies of humpback whales. Conserv. Physiol. 4, 1–13 (2016).PubMed 
Article 
CAS 

Google Scholar 
Wasser, S. K. et al. Population growth is limited by nutritional impacts on pregnancy success in endangered Southern Resident killer whales (Orcinus orca). PLoS One 12, e0179824. https://doi.org/10.1371/journal.pone.0179824 (2017).Boeuf, B. J., Perez-Cortes, H., Urbán, J., Mate, B. R. & Ollervides, F. High gray whale mortality and low recruitment in 1999: Potential causes and implications. J. Cetacean Res. Manag. 2, 85–99 (1999).
Google Scholar 
Perryman, W. L. & Lynn, M. S. Evaluation of nutritive condition and reproductive status of migrating gray whales (Eschrichtius robustus) based on analysis of photogrammetric data. J. Cetacean Res. Manag. 4, 155–164 (2002).
Google Scholar 
Moore, S. E., Grebmeier, J. M. & Davies, J. R. Gray whale distribution relative to forage habitat in the northern Bering Sea: Current conditions and retrospective summary. Can. J. Zool. 81, 734–742 (2003).Article 

Google Scholar 
Christiansen, F. et al. Poor body condition associated with an unusual mortality event in gray whales. Mar. Ecol. Prog. Ser. 658, 237–252 (2021).ADS 
Article 

Google Scholar 
Martìnez-Aguilar, S. et al. Gray Whale (Eschrichtius robustus) stranding records in Mexico during the winter breeding season in 2019. In IWC (2019).Villegas-Amtmann, S., Schwarz, L. K., Sumich, J. L. & Costa, D. P. A bioenergetics model to evaluate demographic consequences of disturbance in marine mammals applied to gray whales. Ecosphere 6, art183 (2015).Article 

Google Scholar 
Urbán, R. J., Jiménez-López, E., Guzmán, H. M. & Viloria-Gómora, L. Migratory Behavior of an Eastern North Pacific Gray Whale From Baja California Sur to Chirikov Basin, Alaska. Front. Mar. Sci. 8, 1–7 (2021).Article 

Google Scholar 
Kim, L. & Oliver, J. S. Swarming benthic crustaceans in the Bering and Chukchi seas and their relation to geographic patterns in gray whale feeding. Can. J. Zool. 67, 1531–1542 (1989).Article 

Google Scholar 
Perryman, W. L., Joyce, T., Weller, D. W. & Durban, J. W. Environmental factors influencing eastern North Pacific gray whale calf production 1994–2016. Mar. Mammal Sci. 37, 448–462 (2020).Article 

Google Scholar 
Caraveo-Patiño, J. & Soto, L. A. Stable carbon isotope ratios for the gray whale (Eschrichtius robustus) in the breeding grounds of Baja California Sur, Mexico. Hydrobiologia 539, 99–107 (2005).Article 

Google Scholar 
Pyenson, N. D. & Lindberg, D. R. What happened to gray whales during the pleistocene? The ecological impact of sea-level change on benthic feeding areas in the north pacific ocean. PLoS One 6, e21295. https://doi.org/10.1371/journal.pone.0021295 (2011).Alter, S. E., Newsome, S. D. & Palumbi, S. R. Pre-whaling genetic diversity and population ecology in eastern pacific gray whales: Insights from ancient DNA and stable isotopes. PLoS One 7, e35039 (2012).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Dunham, J. S. & Duffus, D. A. Foraging patterns of gray whales in central Clayoquot Sound, British Columbia, Canada. Mar. Ecol. Prog. Ser. 223, 299–310 (2001).ADS 
Article 

Google Scholar 
Nerini, M. A Review of Gray Whale Feeding Ecology (Academic Press, Cambridge, 1984).Book 

Google Scholar 
Jones, M. Lou & Swartz, S. L. Gray whale. In Encyclopedia of Marine Mammals, Vol. 36 1352 (Academic Press, 2009).Moore, S. E., Wynne, K. M., Kinney, J. C. & Grebmeier, J. M. Gray whale occurrence and forage southeast of Kodiak, Island, Alaska. Mar. Mammal Sci. 23, 419–428 (2007).Article 

Google Scholar 
Lagerquist, B. A. et al. Feeding home ranges of pacific coast feeding group gray whales. J. Wildl. Manag. 83, 925–937 (2019).Article 

Google Scholar 
Calambokidis, J., Laake, J. L. & Klimek, A. Updated analysis of abundance and population structure of seasonal gray whales in the Pacific, 2010 (2012).Frasier, T. R., Koroscil, S. M., White, B. N. & Darling, J. D. Assessment of population substructure in relation to summer feeding ground use in the eastern North Pacific gray whale. Endanger. Species Res. 14, 39–48 (2011).Article 

Google Scholar 
Lang, A. R. et al. Assessment of genetic structure among eastern North Pacific gray whales on their feeding grounds. Mar. Mammal Sci. 30, 1473–1493 (2014).CAS 
Article 

Google Scholar 
Burnham, R. & Duffus, D. Patterns of predator-prey dynamics between gray whales (Eschrichtius robustus) and mysid species in Clayoquot Sound. J. Cetacean Res. Manag. 19, 95–103 (2018).
Google Scholar 
Walker, T. J. Primer: With Special Attention to the California Gray Whale (Cabrillo Historical Association Pub QL737, San Diego, 1975).Walker, T. J. The California gray whale comes back (Eschrichtius robustus). Natl. Geogr. Mag. 139(3), 394–415 (1971).
Google Scholar 
Caraveo-Patiño, J. et al. Eco-physiological repercussions of dietary arachidonic acid in cell membranes of active tissues of the Gray whale. Mar. Ecol. 30, 437–447. https://doi.org/10.1111/j.1439-0485.2009.00289.x (2009).ADS 
Article 
CAS 

Google Scholar 
Pirotta, E. et al. A dynamic state model of migratory behavior and physiology to assess the consequences of environmental variation and anthropogenic disturbance on marine vertebrates. Am. Nat. 191, E40–E56. https://doi.org/10.1086/695135 (2018).Busquets-Vass, G. et al. Estimating blue whale skin isotopic incorporation rates and baleen growth rates: Implications for assessing diet and movement patterns in mysticetes. PLoS ONE 12, 1–25 (2017).Article 
CAS 

Google Scholar 
Busquets-Vass, G. et al. Isotope-based inferences of the seasonal foraging and migratory strategies of blue whales in the eastern Pacific Ocean. Mar. Environ. Res. 163, 105201. https://doi.org/10.1016/j.marenvres.2020.105201 (2021).Wild, L. A., Chenoweth, E. M., Mueter, F. J. & Straley, J. M. Evidence for dietary time series in layers of cetacean skin using stable carbon and nitrogen isotope ratios. Rapid Commun. Mass Spectrom. 32, 1425–1438 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gelippi, M., Popp, B., Gauger, M. F. W. & Caraveo-Patiño, J. Tracing gestation and lactation in free ranging gray whales using the stable isotopic composition of epidermis layers. PLoS ONE 15, 1–23. https://doi.org/10.1371/journal.pone.0240171 (2020).Article 
CAS 

Google Scholar 
Graham, B. S., Koch, P. L., Newsome, S. D., McMahon, K. W. & Aurioles, D. Using Isoscapes to Trace the Movements and Foraging Behavior of Top Predators in Oceanic Ecosystems. Isoscapes: Understanding Movement, Pattern, and Process on Earth Through Isotope Mapping. https://doi.org/10.1007/978-90-481-3354-3 (2010).Hobson, K. A. International association for ecology tracing origins and migration of wildlife using stable isotopes: A review. Source Oecol. 120, 314–326 (1999).ADS 

Google Scholar 
Ryan, C. et al. Accounting for the effects of lipids in stable isotope (δ13C and δ15N values) analysis of skin and blubber of balaenopterid whales. Rapid Commun. Mass Spectrom. 26, 2745–2754 (2012).CAS 
PubMed 
Article 

Google Scholar 
Vander Zanden, M. J. & Rasmussen, J. B. Variation in δ15N and δ13C trophic fractionation: Implications for aquatic food web studies. Limnol. Oceanogr. 46, 2061–2066 (2001).ADS 
CAS 
Article 

Google Scholar 
Newsome, S. D., Clementz, M. T. & Koch, P. L. Using stable isotope biogeochemistry to study marine mammal ecology. Mar. Mammal Sci. 26, 509–572 (2010).CAS 

Google Scholar 
Giménez, J., Ramírez, F., Almunia, J., Forero, G. M. & de Stephanis, R. From the pool to the sea: Applicable isotope turnover rates and diet to skin discrimination factors for bottlenose dolphins (Tursiops truncatus). J. Exp. Mar. Bio. Ecol. 475, 54–61 (2016).Article 
CAS 

Google Scholar 
Browning, N. E., Dold, C., I-Fan, J. & Worthy, A. J. Isotope turnover rates and diet–tissue discrimination in skin of ex situ bottlenose dolphins (Tursiops truncatus). J. Exp. Biol. 217, 214–221 (2014).CAS 
PubMed 

Google Scholar 
Borrell, A., Abad-Oliva, N., Gõmez-Campos, E., Giménez, J. & Aguilar, A. Discrimination of stable isotopes in fin whale tissues and application to diet assessment in cetaceans. Rapid Commun. Mass Spectrom. 26, 1596–1602 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Reeb, D., Best, P. B. & Kidson, S. H. Structure of the integument of southern right whales, Eubalaena australis. Anat. Rec. 290, 596–613 (2007).Article 

Google Scholar 
Morales-Guerrero, B. et al. Melanin granules melanophages and a fully-melanized epidermis are common traits of odontocete and mysticete cetaceans. Vet. Dermatol. 28, 213–e50. https://doi.org/10.1111/vde.12392 (2017).PubMed 
Article 

Google Scholar 
Ayliffe, L. K. et al. Turnover of carbon isotopes in tail hair and breath CO2 of horses fed an isotopically varied diet. Oecologia 139, 11–22 (2004).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hicks, B. D., St. Aubin, D. J., Geraci, J. R. & Brown, W. R. Epidermal growth in the bottlenose dolphin, Tursiops truncatus. J. Invest. Dermatol. 85, 60–63 (1985).CAS 
PubMed 
Article 

Google Scholar 
Aubin, D. J., St. Smith, T. G. & Geraci, J. R. Seasonal epidermal molt in beluga whales, Delphinapterus leucas. Can. J. Zool. 68, 359–367 (1990).Article 

Google Scholar 
Perryman, W. L., Donahue, M. A., Perkins, P. C. & Reilly, S. B. Gray Whale calf production 1994–2000: Are observed fluctuations related to changes in seasonal ice cover?. Mar. Mammal Sci. 18, 121–144 (2002).Article 

Google Scholar 
Urbán, R. J. et al. A review of gray whales (Eschrichtius robustus) on their wintering grounds in Mexican waters. J. Cetacean Res. Manag. 5, 281–295 (2003).
Google Scholar 
Mann, J. Behavioral sampling methods for cetaceans: A review and critique. Mar. Mammal Sci. 15, 102–122 (1999).Article 

Google Scholar 
Tyurneva, O. Y. et al. Photographic identification of the Korean-Okhotsk gray whale (Eschrichtius robustus) offshore northeast Sakhalin island and southeast Kamchatka peninsula (Russia), 2009. In SC/62/BRG9 (2014).Yakovlev, Y. M., Tyurneva, O. M., Vertyankin, V. V. & Van der Wolf, P. Photo-identification of gray whales (Eschrichtius robustus) off the northeast coast of Sakhalin Island in 2018 photo. West. Gray Whale Advis. Panel 20th meeti (2019).Reeb, D. & Best, P. B. A biopsy system for deep core sampling of the blubber of southern right whales, Eubalaena australis. Mar. Mammal Sci. 22, 206–213 (2006).Article 

Google Scholar 
Noren, D. P. & Mocklin, J. A. Review of cetacean biopsy techniques: Factors contributing to successful sample collection and physiological and behavioral impacts. Mar. Mammal Sci. 28, 154–199 (2012).Article 

Google Scholar 
Caraveo-Patiño, J. Ecología alimenticia de la ballena gris (Eschrichtius robustus, Lilljeborg, 1861): Una ventana a la dinámica interna de los ecosistemas. PhD Thesis. Centro de Investigaciones Biológicas del noroeste S.C. http://dspace.cibnor.mx:8080/handle/123456789/90 (2004).Folch, J., Lees, M. & Stanley, G. H. S. A simple method for the isolation and purification of total lipids from animal tissues. J. Biol. Chem. 226, 497–509 (1957).CAS 
PubMed 
Article 

Google Scholar 
DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of nitrogen isotopes in animals. Geochim. Cosmochim. Acta 45, 341–351 (1981).ADS 
CAS 
Article 

Google Scholar 
Iverson, S. J., Arnould, J. P. Y. & Boyd, I. L. Milk fatty acid signatures indicate both major and minor shifts in the diet of lactating Antarctic fur seals. Can. J. Zool. 75, 188–197 (1997).Article 

Google Scholar 
Newsome, S. D., Koch, P. L., Etnier, M. A. & Aurioles-Gamboa, D. Using carbon and nitrogen isotope values to investigate maternal strategies in Northeast Pacific otariids. Mar. Mammal Sci. 22, 556–572 (2006).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Moore, J. W. & Semmens, B. X. Incorporating uncertainty and prior information into stable isotope mixing models. Ecol. Lett. 11, 470–480 (2008).PubMed 
Article 

Google Scholar 
Parnell, A. C. et al. Bayesian stable isotope mixing models. Environmetrics 24, 387–399 (2013).MathSciNet 

Google Scholar 
Phillips, D. L. & Gregg, J. W. Source partitioning using stable isotopes: Coping with too many sources. Oecologia 136, 261–269 (2003).ADS 
PubMed 
Article 

Google Scholar 
Phillips, D. L. Converting isotope values to diet composition: The use of mixing models. J. Mammal. 93, 342–352 (2012).Article 

Google Scholar 
Parnell, A. C., Inger, R., Bearhop, S. & Jackson, A. L. Source partitioning using stable isotopes: Coping with too much variation. PLoS ONE 5, 1–5 (2010).
Google Scholar 
Baker, H. ASM Handbook: Alloy Phase Diagrams ASM Handbook Alloy Phase Diagrams Vol. 3 (ASM International, Materials Park, 1992).
Google Scholar 
Pereira, G. H. A. On quantile residuals in beta regression. Commun. Stat. Simul. Comput. 48, 302–316 (2019).MathSciNet 
Article 

Google Scholar 
Osterblom, H., Olsson, O., Blenckner, T. & Furness, W. Junk-food in marine ecosystems. Oikos 117, 967–977 (2008).Article 

Google Scholar 
Martínez del Rio, C. & Carleton, S. A. How fast and how faithful: The dynamics of isotopic incorporation into animal tissues. J. Mammal. 93, 353–359. https://doi.org/10.1644/11-MAMM-S-165.1 (2012).Article 

Google Scholar 
Vander Zanden, M. J., Clayton, M. K., Moody, E. K., Solomon, C. T. & Weidel, B. C. Stable isotope turnover and half-life in animal tissues: A literature synthesis. PLoS One 10, https://doi.org/10.1371/journal.pone.0116182 (2015).CAS 
Article 

Google Scholar 
Dalerum, F. & Angerbjörn, A. Resolving temporal variation in vertebrate diets using naturally occurring stable isotopes. Oecologia 144, 647–658 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Horstmann-Dehn, L., Follmann, E. H., Rosa, C., Zelensky, G. & George, C. Stable carbon and nitrogen isotope ratios in muscle and epidermis of arctic whales. Mar. Mammal Sci. 28, E173–E190. https://doi.org/10.1111/j.1748-7692.2011.00503.x (2012).Hertz, E., Trudel, M., Cox, M. K. & Mazumder, A. Effects of fasting and nutritional restriction on the isotopic ratios of nitrogen and carbon: a meta-analysis. Ecol. Evol. 5, 4829–4839 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Lian, M. et al. Assessing δ13C, δ15N and total mercury measures in epidermal biopsies from gray whales. Front. Mar. Sci. 7, 1–9 (2020).ADS 
Article 

Google Scholar 
Gulland, F. et al. Eastern North Pacific gray whale (Eschrichtius robustus) unusual mortality event, 1999–2000. U.S. Dep. Commer. NOAA Tech. Memo. NMFS-AFSC-150. 33 pp (2005).Popp, B. N. et al. Effect of phytoplankton cell geometry on carbon isotopic fractionation. Geochim. Cosmochim. Acta 62, 69–77 (1998).ADS 
CAS 
Article 

Google Scholar 
Schell, D. M. Declining carrying capacity in the Bering Sea: Isotopic evidence from whale baleen. Limnol. Oceanogr. 45, 459–462 (2000).ADS 
CAS 
Article 

Google Scholar 
Kurle, C. M. & McWhorter, J. K. Spatial and temporal variability within marine isoscapes: Implications for interpreting stable isotope data from marine systems. Mar. Ecol. Prog. Ser. 568, 31–45 (2017).ADS 
CAS 
Article 

Google Scholar 
Keeling, C. D. The Suess effect: 13Carbon –14Carbon interrelations. Environ. Int. 2, 229–300 (1979).CAS 
Article 

Google Scholar 
Grecian, W. J. et al. Contrasting migratory responses of two closely related seabirds to long-term climate change. Mar. Ecol. Prog. Ser. 559, 231–242 (2016).ADS 
CAS 
Article 

Google Scholar 
Pomerleau, C., Nelson, R. J., Hunt, B. P. V., Sastri, A. R. & Williams, W. J. Spatial patterns in zooplankton communities and stable isotope ratios (δ13C and δ15N) in relation to oceanographic conditions in the sub-Arctic Pacific and western Arctic regions during the summer of 2008. J. Plankton Res. 36, 757–775 (2014).CAS 
Article 

Google Scholar 
Lee, S. H. Use of the Beaufort Sea as feeding habitat by bowhead whales (Balaena mysticetus) as indicated by stable isotope ratios. M.S. Thesis. University of Alaska Fairbanks. http://hdl.handle.net/11122/4931 (2000).Cullen, J. T., Rosenthal, Y. & Falkowski, P. G. The effect of anthropogenic CO2 on the carbon isotope composition of marine phytoplankton. Limnol. Oceanogr. 46, 996–998 (2001).ADS 
Article 

Google Scholar 
Schell, D. M. Carbon isotope ratio variations in Bering Sea biota: The role of anthropogenic carbon dioxide. Limnol. Oceanogr. 46, 999–1000 (2001).ADS 
Article 

Google Scholar 
Eide, M., Olsen, A., Ninnemann, U. S. & Eldevik, T. A global estimate of the full oceanic 13C Suess effect since the preindustrial. Glob. Biogeochem. Cycles 31, 492–514 (2017).ADS 
CAS 
Article 

Google Scholar 
Kurle, C. M., Sinclair, E. H., Edwards, A. E. & Gudmundson, C. J. Temporal and spatial variation in the δ15N and δ13C values of fish and squid from Alaskan waters. Mar. Biol. 158, 2389–2404 (2011).Article 

Google Scholar 
Ohman, M. D., Rau, G. H. & Hull, P. M. Multi-decadal variations in stable N isotopes of California Current zooplankton. https://doi.org/10.1016/j.dsr.2011.11.003 (2011).Décima, M., Landry, M. R. & Popp, B. N. Environmental perturbation effects on baseline δ15N values and zooplankton trophic flexibility in the southern California current ecosystem. Limnol. Oceanogr. 58, 624–634 (2013).ADS 
Article 
CAS 

Google Scholar 
Caraveo-Patiño, J., Hobson, K. A. & Soto, L. A. Feeding ecology of gray whales inferred from stable-carbon and nitrogen isotopic analysis of baleen plates. Hydrobiologia 586, 17–25 (2007).Article 

Google Scholar 
Hernández-Aguierre, D. Análisis de la composición de ácidos grasos en los estratos de la capa de grasa (blubber) de la ballena gris Eschrichtius robustus (LILLJEBORG, 1861). M.S. Thesis. Centro de Investigaciones Biológicas del noroeste S.C. http://cibnor.repositorioinstitucional.mx/jspui/handle/1001/182 (2012).Ackman, R. G. Nutritional composition of fats in seafoods. Prog. Food Nutr. Sci. 13, 161–289 (1989).CAS 
PubMed 

Google Scholar 
Lahdes, E., Balogh, G., Fodor, E. & Farkas, T. Adaptation of composition and biophysical properties of phospholipids to temperature by the crustacean, Gammarus spp. Lipids 35, 1093–1098 (2000).CAS 
PubMed 
Article 

Google Scholar 
Sarur-Zanatta, J. C., Millán-Nuñez, R., Gutiérrez-Sigala, C. A. & Small Mattox-Sheahen, C. A. Variation and similarity in three zones with-different type of substrate In Laguna Ojo De Liebre, B.C.S., Mexico. Ciencias Mar. 10, 169–179 (1984).Article 

Google Scholar 
Pirotta, V., Owen, K., Donnelly, D., Brasier, M. J. & Harcourt, R. First evidence of bubble-net feeding and the formation of ‘super-groups’ by the east Australian population of humpback whales during their southward migration. Aquat. Conserv. Mar. Freshw. Ecosyst. https://doi.org/10.1002/aqc.3621 (2021).Article 

Google Scholar 
Carone, E. et al. Sex steroid hormones and behavior reveal seasonal reproduction in a resident fin whale population. Conserv. Physiol. 7, 1–13 (2019).Article 
CAS 

Google Scholar 
Prieto, R., Tobeña, M. & Silva, M. A. Habitat preferences of baleen whales in a mid-latitude habitat. Deep Res. Part II Top. Stud. Oceanogr. 141, 155–167. https://doi.org/10.1016/j.dsr2.2016.07.015 (2017).ADS 
Article 

Google Scholar 
Piatt, J. F. et al. Extreme mortality and reproductive failure of common murres resulting from the northeast Pacific marine heatwave of 2014–2016. PLoS One 15 (2020).Savage, K. Alaska and British Columbia large whale unusual mortality event summary report. NOAA Fish Report, Juneau August, 1–42 (2017).Stewart, J. D. & Weller, D. W. NOAA Technical Memorandum NMFS abundance of eastern north pacific gray whales 2019/2020 (2021).Cooke, J. G. Population assessment update for Sakhalin gray whales. West. Gray Whale Advis. Panel 13 (2020). More

May, R. M. Will a large complex system be stable? Nature 238, 413–414 (1972).CAS 
Article 
PubMed 

Google Scholar 
May, R. M. & Mac Arthur, R. H. Niche overlap as a function of environmental variability. Proc. Natl Acad. Sci. USA 69, 1109–1113 (1972).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
May, R. M. Stability and Complexity in Model Ecosystems (Princeton Univ. Press, 2019).Sinha, S. Complexity vs. stability in small-world networks. Phys. A 346, 147–153 (2005).Article 

Google Scholar 
Thébault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856 (2010).Article 
CAS 
PubMed 

Google Scholar 
Mougi, A. & Kondoh, M. Diversity of interaction types and ecological community stability. Science 337, 349–351 (2012).CAS 
Article 
PubMed 

Google Scholar 
Allesina, S. & Tang, S. Stability criteria for complex ecosystems. Nature 483, 205–208 (2012).CAS 
Article 
PubMed 

Google Scholar 
Allesina, S. & Tang, S. The stability–complexity relationship at age 40: a random matrix perspective. Popul. Ecol. 57, 63–75 (2015).Article 

Google Scholar 
Qian, J. J. & Akçay, E. The balance of interaction types determines the assembly and stability of ecological communities. Nat. Ecol. Evol. 4, 356–365 (2020).Article 
PubMed 

Google Scholar 
Landi, P., Minoarivelo, H. O., Brännström, Å., Hui, C. & Dieckmann, U. in Systems Analysis Approach for Complex Global Challenges (eds Mensah, P. et al.) 209–248 (Springer, 2018).Townsend, S. E., Haydon, D. T. & Matthews, L. On the generality of stability–complexity relationships in Lotka–Volterra ecosystems. J. Theor. Biol. 267, 243–251 (2010).Article 
PubMed 

Google Scholar 
Pimm, S. L., Lawton, J. H. & Cohen, J. E. Food web patterns and their consequences. Nature 350, 669–674 (1991).Article 

Google Scholar 
Yodzis, P. The stability of real ecosystems. Nature 289, 674–676 (1981).Article 

Google Scholar 
Winemiller, K. O. Must connectance decrease with species richness? Am. Naturalist 134, 960–968 (1989).Article 

Google Scholar 
Warren, P. H. Variation in food-web structure: the determinants of connectance. Am. Nat. 136, 689–700 (1990).Article 

Google Scholar 
de Ruiter, P. C., Neutel, A.-M. & Moore, J. C. Energetics, patterns of interaction strengths, and stability in real ecosystems. Science 269, 1257–1260 (1995).Article 
PubMed 

Google Scholar 
Schmid-Araya, J. M. et al. Connectance in stream food webs. J. Anim. Ecol. 71, 1056–1062 (2002).Article 

Google Scholar 
Neutel, A.-M. et al. Reconciling complexity with stability in naturally assembling food webs. Nature 449, 599–602 (2007).CAS 
Article 
PubMed 

Google Scholar 
James, A. et al. Constructing random matrices to represent real ecosystems. Am. Nat. 185, 680–692 (2015).Article 
PubMed 

Google Scholar 
Jacquet, C. et al. No complexity–stability relationship in empirical ecosystems. Nat. Commun. 7, 12573 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Thompson, L. R. et al. A communal catalogue reveals earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Huttenhower, C. et al. Structure, function and diversity of the healthy human microbiome. Nature 486, 207 (2012).CAS 
Article 

Google Scholar 
Fricker, A. M., Podlesny, D. & Fricke, W. F. What is new and relevant for sequencing-based microbiome research? A mini-review. J. Adv. Res. 19, 105–112 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sander, E. L., Wootton, J. T. & Allesina, S. Ecological network inference from long-term presence-absence data. Sci. Rep. 7, 7154 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Steinway, S. N., Biggs, M. B., Loughran Jr, T. P., Papin, J. A. & Albert, R. Inference of network dynamics and metabolic interactions in the gut microbiome. PLoS Comput. Biol. 11, e1004338 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bucci, V. et al. Mdsine: microbial dynamical systems inference engine for microbiome time-series analyses. Genome Biol. 17, 121 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stein, R. R. et al. Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota. PLoS Comput. Biol. 9, e1003388 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Fisher, C. K. & Mehta, P. Identifying keystone species in the human gut microbiome from metagenomic timeseries using sparse linear regression. PloS ONE 9, e102451 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gerber, G. K., Onderdonk, A. B. & Bry, L. Inferring dynamic signatures of microbes in complex host ecosystems. PLoS Comput. Biol. 8, e1002624 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Cao, H.-T., Gibson, T. E., Bashan, A. & Liu, Y.-Y. Inferring human microbial dynamics from temporal metagenomics data: pitfalls and lessons. BioEssays 39, 1600188 (2017).Article 

Google Scholar 
David, L. A. et al. Host lifestyle affects human microbiota on daily timescales. Genome Biol. 15, R89 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, R50 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Buffie, C. G. et al. Profound alterations of intestinal microbiota following a single dose of clindamycin results in sustained susceptibility to clostridium difficile-induced colitis. Infect. Immun. 80, 62–73 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Dohlman, A. B. & Shen, X. Mapping the microbial interactome: statistical and experimental approaches for microbiome network inference. Exp. Biol. Med. 244, 445–458 (2019).CAS 
Article 

Google Scholar 
Faust, K. & Raes, J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).CAS 
Article 
PubMed 

Google Scholar 
Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jiang, D. et al. Microbiome multi-omics network analysis: statistical considerations, limitations, and opportunities. Front. Genet. 10, 995 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Faust, K. Open challenges for microbial network construction and analysis. ISME J. 15, 3111–3118 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bashan, A. et al. Universality of human microbial dynamics. Nature 534, 259–262 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Vila, J. C., Liu, Y.-Y. & Sanchez, A. Dissimilarity–overlap analysis of replicate enrichment communities. ISME J. 14, 2505–2513 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Moitinho-Silva, L. et al. The sponge microbiome project. Gigascience 6, gix077 (2017).Article 
PubMed Central 

Google Scholar 
Swierts, T., Cleary, D. & de Voogd, N. Prokaryotic communities of Indo-Pacific giant barrel sponges are more strongly influenced by geography than host phylogeny. FEMS Microbiol. Ecol. 94, fiy194 (2018).CAS 
Article 
PubMed Central 

Google Scholar 
Jost, L. Entropy and diversity. Oikos 113, 363–375 (2006).Article 

Google Scholar 
Suweis, S., Grilli, J., Banavar, J. R., Allesina, S. & Maritan, A. Effect of localization on the stability of mutualistic ecological networks. Nat. Commun. 6, 10179 (2015).CAS 
Article 
PubMed 

Google Scholar 
Grilli, J., Barabás, G., Michalska-Smith, M. J. & Allesina, S. Higher-order interactions stabilize dynamics in competitive network models. Nature 548, 210–213 (2017).CAS 
Article 
PubMed 

Google Scholar 
Butler, S. & O’Dwyer, J. P. Stability criteria for complex microbial communities. Nat. Commun. 9, 2970 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Allesina, S. & Grilli, J. in Theoretical Ecology: Concepts and Applications (eds McCann, K. & Gellner, G.) Ch. 6 (Oxford Univ. Press, 2020).Jayant, P. & Shnerb, N. M. How temporal environmental stochasticity affects species richness: destabilization neutralization and the storage effect. J. Theor. Biol. 539, 111053 (2022).Article 

Google Scholar 
Faith, J. J. et al. The long-term stability of the human gut microbiota. Science 341, 1237439 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gajer, P. et al. Temporal dynamics of the human vaginal microbiota. Sci. Transl. Med. 4, 132ra52–132ra52 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. J. Mach. Learn. Res. 9, 2579–2605 (2008).
Google Scholar 
Bunin, G. Ecological communities with Lotka-Volterra dynamics. Phys. Rev. E 95, 042414 (2017).Article 
PubMed 

Google Scholar  More