Philippa Kaur

Petropoulos, G. A. Fenugreek © 2002. (2002).Megias, C. et al. Free amino acids, Including Canavanine, in the Seeds from 24 Wild Mediterranean Legumes. J. Food Chem. Nanotechnol. 2, 178–183 (2016).Article 

Google Scholar 
Legume Phylogeny Working Group. Legume phylogeny and classification in the 21st century: Progress , prospects and lessons for other species-rich clades. Taxon. 62, 217–248 (2013).Grela, E. R., Kiczorowska, B., Samolińska, W. & Matras, J. Chemical composition of leguminous seeds : part I — content of basic nutrients, amino acids, phytochemical compounds, and antioxidant activity. Eur. Food Res. Technol. 243, 1385–1395 (2017).CAS 
Article 

Google Scholar 
Bradshaw, A. D. Producing patterns in plants. New Phytol. 170, 639–641 (2006).Article 

Google Scholar 
Brunetti, C., George, R. M., Tattini, M., Field, K. & Davey, M. P. Metabolomics in plant environmental physiology. J. Exp. Bot. 64, 4011–4020 (2013).CAS 
Article 

Google Scholar 
Allevato, D. M., Kiyota, E., Mazzafera, P. & Nixon, K. C. Ecometabolomic analysis of wild populations of Pilocarpus pennatifolius (Rutaceae ) Using Unimodal Analyses. Front. Plant Sci. 10 (2019).Chen, W., Hou, L., Zhang, Z., Pang, X. & Li, Y. Genetic diversity, population structure, and linkage disequilibrium of a core collection of Ziziphus jujuba assessed with genome-wide SNPs developed by genotyping-by-sequencing and SSR. Markers. 8, 1–14 (2017).
Google Scholar 
Aljuhaimi, F., Şimşek, Ş., Özcan, M. M., Ghafoor, K. & Babiker, E. E. Effect of location on chemical properties, amino acid and fatty acid compositions of fenugreek (Trigonella foenum-graecum L.) seed and oils. J. Food Process. Preserv. 42, e13569 (2018).Kapoor, N. & Pande, V. Antioxidative defense to salt stress in Trigonella foenum-graecum L. Curr. discov. 2, 123–127 (2015).
Google Scholar 
Kyani, A. & Niknam, V. Comparative responses of two Trigonella species to salinity and drought stresses in vitro. Prog. Biol. Sci. 5, 233–248 (2015).
Google Scholar 
Saberali, S. F. & Moradi, M. Effect of salinity on germination and seedling growth of Trigonella foenum-graecum, Dracocephalum moldavica, Satureja hortensis and Anethum graveolens. J. Saudi Soc. Agric. Sci. 18, 316–323 (2019).
Google Scholar 
Ahari, D. S., Kashi, A. K., Hassandokht, M. R., Amri, A. & Alizadeh, K. Assessment of drought tolerance in Iranian fenugreek landraces. J. Food Agric. Environ. 7, 414–419 (2009).Meena, S. et al. Water stress induced biochemical changes in fenugreek (Trigonella foenum graecum L .) genotypes. International J. Seed Spices. 6, 61–70 (2016).
Google Scholar 
Nour, A. A. M. & Magboul, B. I. Chemical and amino acid composition of fenugreek seeds grown in Sudan. Food Chem. 22, 1–5 (1986).CAS 
Article 

Google Scholar 
Hassanzadeh, E., Chaichi, M. R., Mazaheri, D., Rezazadeh, S. & Badi, H. A. N. Physical and chemical variabilities among domestic Iranian Fenugreek (Trigonella foenum-graceum) seeds. Asian J. Plant Sci. 10, 323–330 (2011).Article 

Google Scholar 
Nagulapalli Venkata, K. C., Swaroop, A., Bagchi, D. & Bishayee, A. A small plant with big benefits: Fenugreek (Trigonella foenum-graecum Linn.) for disease prevention and health promotion. Mol. Nutr. Food Res. 61 (2017).Robinson, A. R., Ukrainetz, N. K., Kang, K., Mansfield, S. D. & Mansfield, S. D. Metabolite profiling of Douglas-fir (Pseudotsuga menziesii ) field trials reveals strong environmental and weak genetic variation. New Phytol . 174, 762–773 (2003).Article 

Google Scholar 
Wang, C. et al. Extraction of sensitive bands for monitoring the winter wheat (Triticum aestivum) growth status and yields based on the spectral reflectance. PLoS ONE. 12, 1–16 (2017).
Google Scholar 
Mehrafarin, A. et al. Bioengineering of important secondary metabolites and metabolic pathways in fenugreek (Trigonella foenum-graecum L.). J. Med. Plants 9, 1–18 (2010).CAS 

Google Scholar 
Bhutia, P. H. & Sharangi, A. B. Influence of dates of sowing and irrigation scheduling on phenology, growth and yield dynamics of fenugreek (Trigonella foenum greacum L .). Legume Res. 41, 275–280 (2018).
Google Scholar 
Guillermo A. A. Dosio, Luis A. N. Aguirreza´bal,* Fernando H. Andrade & ABSTRACT, V. R. P. Solar Radiation Intercepted during Seed Filling and Oil Production in Two Sunflower Hybrids. Crop Sci. 40, 1637–1644 (2000).Ishimaru, T. et al. High temperature and low solar radiation during ripening differentially affect the composition of milky-white grains in rice (Oryza sativa L.). Plant Prod. Sci. 21, 370–379 (2018).Larsson, S., Wirén, A., Lundgren, L. & Ericsson, T. Effects of light and nutrient stress on leaf phenolic chemistry in salix dasyclados and susceptibility to galerucella lineola (Coleoptera). Oikos. 47, 205–210 (1986).CAS 
Article 

Google Scholar 
Chua, I. Y. P., King, P. J. H., Ong, K. H., Sarbini, S. R. & Yiu, P. H. Influence of light intensity and temperature on antioxidant activity in Premna serratifolia L. J. Soil Sci. Plant Nutr. 15, 605–614 (2015).
Google Scholar 
Sharma, A., Shahzad, B., Rehman, A., Bhardwaj, R., Landi, M. Response of phenylpropanoid pathway and the role of polyphenols in plants under abiotic stress. Molecules. 4, 2452. https://www.mdpi.com/1420-3049/24/13/2452Vaast, P. et al. Shade: A key factor for coffee sustainability and quality. 20th Int. Conf. Coffee Sci. 4, 887–896 (2005).Borges, C. V., Minatel, I. O., Gomez-Gomez, H. A. & Lima, G. P. P. Medicinal plants: Influence of environmental factors on the content of secondary metabolites. Med. Plants Environ. Challenges 259–277 (2017). https://doi.org/10.1007/978-3-319-68717-9_15Hanifah, A., Maharijaya, A., Putri, S. P., Laviña, W. A. & Sobir. Untargeted metabolomics analysis of eggplant (Solanum melongena L.) fruit and its correlation to fruit morphologies. Metabolites 8, (2018).Szakiel, A. & Henry, M. Influence of environmental biotic factors on the content of saponins in plants Influence of environmental abiotic factors on the content of saponins in plants. Phytochem Rev. 10, (2011). https://doi.org/10.1007/s11101-010-9177-xObata, T. & Fernie, A. R. The use of metabolomics to dissect plant responses to abiotic stresses. Cell. Mol. Life Sci. 69, 3225–3243 (2012).CAS 
Article 

Google Scholar 
Hamidou, M. et al. Genetic Variability and its implications on early generation sorghum lines selection for yield, Yield contributing traits, and resistance to sorghum midge. 2018, (2018).Tierno, R. & Galarreta, J. I. R. De. Heritability of target bioactive compounds and hydrophilic antioxidant capacity in purple- and red- fl eshed tetraploid potatoes. Crop Pasture Sci. 67, 1309–1317 (2016).Culley, D. Variation of anthocyanin and carotenoid contents and associated antioxidant values in potato breeding Lines. J. Am. Soc. Hortic. Sci. 130 (2005). https://doi.org/10.21273/JASHS.130.2.174Stephens, M. J., Hall, H. K. & Alspach, P. A. Variation and heritabilities of antioxidant activity and total phenolic content estimated from a red raspberry factorial experiment. J. Am. Soc. Hortic. Sci. 130, 403–411 (2015). https://doi.org/10.21273/JASHS.130.3.403Antoine, M., Nicolas, Y., Noubissié, T. J., Marcel, R. & Martin, J. Genetics of seed flavonoid content and antioxidant activity in cowpea (Vigna unguiculata L . Walp .). Crop J. 4, 391–397 (2016).Matros, A. et al. Genome-metabolite associations revealed low heritability, high genetic complexity, and causal relations for leaf metabolites in winter wheat (Triticum aestivum L.). J. Exp. Bot
.
68, 415–428 (2017).Labarrere, B., Prinzing, A., Dorey, T., Chesneau, E., Hennion, F. Variations of secondary metabolites among natural suggest functional redundancy and versatility. Plants. 19, 234 (2019).Ghaffari, M. R., Shahinnia, F. & Schreiber, F. The metabolic signature of biomass formation in barley. Plant Cell Physiol. 57, 1943–1960 (2016).Aryal S, Baniya MK, Danekhu K, Kunwar P, Gurung R, Koirala N. Total phenolic content, flavonoid content and antioxidant potential of wild vegetables from Western Nepal. Plants. 11, 96 (2019).Praksh, R., Singh, D., Meena, B. L., Kumari, R. & Meena, S. K. Assessment of genetic variability, heritability and genetic advance for quantitative traits in Fenugreek (Trigonella foenum-graecum L.). Int. J. Curr. Microbiol. App.Sci. 6, 2389–2399 (2017).Haefelé, C., Bonfils, C. & Sauvaire, Y. Characterization of a dioxygenase from Trigonella foenum-graecum involved in 4-hydroxyisoleucine biosynthesis. Phytochemistry 44, 563–566 (1997).Article 

Google Scholar 
Zafar, M. I. & Gao, F. 4-Hydroxyisoleucine: A Potential New Treatment for Type 2 Diabetes Mellitus. BioDrugs 30, 255–262 (2016).CAS 
Article 

Google Scholar 
Hosamath, J. V., Hegde, R. V & Venugopal, C. K. Studies on genetic variability, heritability and genetic advance in Fenugreek (Trigonella foenum-graecum L .). Int. J. Curr. Microbiol. App.Sci. 6, 4029–4036 (2017).Al-Naggar AM, El-Salam R, Badran AE, Boulos ST, El-Moghazi M. Heritability and genetic advance from selection for morphological, biochemical and anatomical traits of Chenopodium quinoa under water stress. Bionature. 38, 66–85 (2018).Yadav, T. C., Meena, R. S. & Dhakar, L. Genetic variability analysis in Fenugreek (Trigonella foenum-graecum L.) Genotypes. Int. J. Curr. Microbiol. App. Sci. 7, 2998–3003 (2018).Di Martino, C., Delfine, S., Pizzuto, R., Loreto, F. & Fuggi, A. Free amino acids and glycine betaine in leaf osmoregulation of spinach responding to increasing salt stress. New Phytol. 158, 455–463 (2003).Article 

Google Scholar 
Haji, M. H., Sadat, E. S., Amanzadeh, Y., Izaddoust, M., Givi, E. Identification and quantitative determination of 4-hydroxyisoleucine in Trigonella foenum-graecum L. from Iran. J. Med. Plants 9, 29–34 (2010).Zhuo, R., Wang, L., Wang, L., Xiao, H. & Cai, S. Determination of trigonelline in Trigonella foenum-graecum L. by hydrophilic interaction chromatography. Se pu. 28, 379—382 (2010).CAS 
PubMed 

Google Scholar 
Kim, D. O., Jeong, S. W. & Lee, C. Y. Antioxidant capacity of phenolic phytochemicals from various cultivars of plums. Food Chem. 81, 321–326 (2003).CAS 
Article 

Google Scholar 
Molyneux, P. The use of the stable free radical diphenylpicryl- hydrazyl (DPPH) for estimating antioxidant activity. Songklanakarin J. Sci. Technol. 26, 211–216 (2004)CAS 

Google Scholar 
Wei, T. Title visualization of a correlation matrix. R Packag. 56, 1–17 (2017).
Google Scholar 
Husson, F., Josse, J. and Pages, J., 2010. Principal component methods-hierarchical clustering-partitional clustering: why would we need to choose for visualizing data. Applied Mathematics Department, 17 (2010)Hanson, C. H., Robinson, H. F. & Comstock, R. E. Biometrical studies of yield in segregating populations of Korean Lespedeza1. Agron. J. 48, 268–272 (1956).Article 

Google Scholar 
Herbert, W., Robinson, H. F. & Comstock, R. E. Estimates of Genetic and Environmental Variability in Soybeans. Agron J. 46, 314–318 (1955).
Google Scholar 
Oksanen, J. et al. Package ‘vegan’ title community ecology package. Community Ecol. Packag. 2, 1–297 (2019).
Google Scholar 
R Foundation for Statistical Computing. R: A language and environment for statistical computing. Vienna, Austria 2, (2008). More

Dobson, A. P. et al. Road will ruin Serengeti. Nature 467, 272–273 (2010).CAS 
PubMed 
Article 

Google Scholar 
Larsen, F. et al. Wildebeest migration drives tourism demand in the Serengeti. Biol. Conserv. 248, 108688 (2020).Article 

Google Scholar 
Aikens, E. O. et al. The greenscape shapes surfing of resource waves in a large migratory herbivore. Ecol. Lett. 20, 741–750 (2017).PubMed 
Article 

Google Scholar 
Bischof, R. et al. A migratory northern ungulate in the pursuit of spring: jumping or surfing the green wave? Am. Nat. 180, 407–424 (2012).PubMed 
Article 

Google Scholar 
Merkle, J. A. et al. Large herbivores surf waves of green-up during spring. Proc. Biol. Sci. 283, 20160456 (2016).PubMed 
PubMed Central 

Google Scholar 
Fryxell, J. M., Greever, J. & Sinclair, A. R. E. Why are migratory ungulates so abundant? Am. Nat.131, 781–798 (1988).Article 

Google Scholar 
Staver, A. C. & Hempson, G. P. Seasonal dietary changes increase the abundances of savanna herbivore species. Sci. Adv. 6, eabd2848 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Kauffman, M. J. et al. Causes, consequences, and conservation of ungulate migration. Annu. Rev. Ecol. Evol. Syst. 52, 453–478 (2021).Article 

Google Scholar 
Lundberg, J. & Moberg, F. Mobile link organisms and ecosystem functioning: implications for ecosystem resilience and management. Ecosystems 6, 0087–0098 (2003).Article 

Google Scholar 
Bauer, S. & Hoye, B. J. Migratory animals couple biodiversity and ecosystem functioning worldwide. Science 344, 1242552 (2014).CAS 
PubMed 
Article 

Google Scholar 
Bolger, D. T., Newmark, W. D., Morrison, T. A., & Doak, D. F. The need for integrative approaches to understand and conserve migratory ungulates. Ecol. Lett. 11, 63–77 (2007).PubMed 

Google Scholar 
Fryxell, J. M. & Holt, R. D. Environmental change and the evolution of migration. Ecology 94, 1274–1279 (2013).CAS 
PubMed 
Article 

Google Scholar 
Shaw, A. K. Drivers of animal migration and implications in changing environments. Evol. Ecol. 30, 991–1007 (2016).Article 

Google Scholar 
Hebblewhite, M. & Merrill, E. H. Trade-offs between predation risk and forage differ between migrant strategies in a migratory ungulate. Ecology 90, 3445–3454 (2009).PubMed 
Article 

Google Scholar 
Nelson, M. E. Development of migratory behavior in northern white-tailed deer. Can. J. Zool. 76, 426–432 (1998).Article 

Google Scholar 
Berg, J. E., Hebblewhite, M., St. Clair, C. C. & Merrill, E. H. Prevalence and mechanisms of partial migration in ungulates. Front. Ecol. Evol. 7, 325 (2019).Article 

Google Scholar 
Jesmer, B. R. et al. Is ungulate migration culturally transmitted? Evidence of social learning from translocated animals. Science 361, 1023–1025 (2018).CAS 
PubMed 
Article 

Google Scholar 
Sih, A., Bell, A. & Johnson, J. C. Behavioral syndromes: an ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378 (2004).PubMed 
Article 

Google Scholar 
Found, R. & St. Clair, C. C. Behavioural syndromes predict loss of migration in wild elk. Anim. Behav. 115, 35–46 (2016).Article 

Google Scholar 
Abraham, J. O., Hempson, G. P., Faith, J. T. & Staver, A. C.Seasonal strategies differ between tropical and extratropical herbivores. J. Anim. Ecol. 91, 681–692 (2022).PubMed 
Article 

Google Scholar 
Whitehead, H., Laland, K. N., Rendell, L., Thorogood, R. & Whiten, A. The reach of gene–culture coevolution in animals. Nat. Commun. 10, 2405 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Scanlon, T. M., Caylor, K. K., Manfreda, S., Levin, S. A. & Rodriguez-Iturbe, I. Dynamic response of grass cover to rainfall variability: implications for the function and persistence of savanna ecosystems. Adv. Water Res. 28, 291–302 (2005).Article 

Google Scholar 
Staver, A. C., Wigley-Coetsee, C. & Botha, J. Grazer movements exacerbate grass declines during drought in an African savanna. J. Ecol. 107, 1482–1491 (2019).Article 

Google Scholar 
Fryxell, J. M. & Sinclair, A. R. Causes and consequences of migration by large herbivores. Trends Ecol. Evol. 3, 237–241 (1988).CAS 
PubMed 
Article 

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

Google Scholar 
Langvatn, R., Albon, S. D., Burkey, T. & Clutton-Brock, T. H. Climate, plant phenology and variation in age of first reproduction in a temperate herbivore. J. Anim. Ecol. 65, 653–670 (1996).Article 

Google Scholar 
Webber, Q. M. R. & McGuire, L. P. Heterothermy, body size, and locomotion as ecological predictors of migration in mammals. Mamm. Rev. 52, 82–95 (2022).Article 

Google Scholar 
Mann, D. H., Groves, P., Gaglioti, B. V. & Shapiro, B. A. Climate-driven ecological stability as a globally shared cause of Late Quaternary megafaunal extinctions: the Plaids and Stripes Hypothesis. Biol. Rev. Camb. Philos. Soc. 94, 328–352 (2018).PubMed Central 
Article 

Google Scholar 
Jarman, P. J. The social organisation of antelope in relation to their ecology. Behaviour 48, 215–267 (1974).Article 

Google Scholar 
Hein, A. M., Hou, C. & Gillooly, J. F. Energetic and biomechanical constraints on animal migration distance. Ecol. Lett. 15, 104–110 (2012).PubMed 
Article 

Google Scholar 
Abraham, J. O., Hempson, G. P. & Staver, A. C. Drought-response strategies of savanna herbivores. Ecol. Evol. 9, 7047–7056 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Owen-Smith, R. N. Megaherbivores: the Influence of Very Large Body Size on Ecology (Cambridge Univ. Press, 1988).Book 

Google Scholar 
Gonzalez-Voyer, A. & von Hardenberg, A. in Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology: Concepts and Practice (ed. Garamszegi, L. Z.) 201–229 (Springer, 2014).Pérez-Barbería, F. J., Gordon, I. J. & Nores, C. Evolutionary transitions among feeding styles and habitats in ungulates. Evol. Ecol. Res. 3, 221–230 (2001).
Google Scholar 
Staver, A. C., Abraham, J. O., Hempson, G. P., Karp, A. T. & Faith, J. T. The past, present, and future of herbivore impacts on savanna vegetation. J. Ecol. 109, 2804–2822 (2021).Article 

Google Scholar 
Janis, C. M. in The Ecology of Browsing and Grazing (eds Gordon, I. J. & Prins, H. H. T.) 21–45 (Springer, 2008).Janis, C. M. Tertiary mammal evolution in the context of changing climates, vegetation, and tectonic events. Annu. Rev. Ecol. Syst. 24, 467–500 (1993).Article 

Google Scholar 
Zachos, J., Pagani, M., Sloan, L., Thomas, E. & Billups, K. Trends, rhythms, and aberrations in global climate 65 Ma to present. Science 292, 686–693 (2001).CAS 
PubMed 
Article 

Google Scholar 
Edwards, E. J. et al. The origins of C4 grasslands: integrating evolutionary and ecosystem. Science 328, 587–591 (2010).CAS 
PubMed 
Article 

Google Scholar 
Bhat, U., Kempes, C. P. & Yeakel, J. D. Scaling the risk landscape drives optimal life-history strategies and the evolution of grazing. Proc. Natl Acad. Sci. USA 117, 1580–1586 (2020).CAS 
PubMed 
Article 

Google Scholar 
Fagan, W. F. et al. Spatial memory and animal movement. Ecol. Lett. 16, 1316–1329 (2013).PubMed 
Article 

Google Scholar 
Merkle, J. A. et al. Spatial memory shapes migration and its benefits: evidence from a large herbivore. Ecol. Lett. 22, 1797–1805 (2019).PubMed 
Article 

Google Scholar 
Mueller, T., O’Hara, R. B., Converse, S. J., Urbanek, R. P. & Fagan, W. F. Social learning of migratory performance. Science 341, 999–1002 (2013).CAS 
PubMed 
Article 

Google Scholar 
Wcislo, W. T. Behavioral environments and evolutionary change. Annu. Rev. Ecol. Syst. 20, 137–169 (1989).Article 

Google Scholar 
Wyles, J. S., Kunkel, J. G. & Wilson, A. C. Birds, behavior, and anatomical evolution. Proc. Natl Acad. Sci. USA 80, 4394–4397 (1983).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yeakel, J. D., Kempes, C. P. & Redner, S. Dynamics of starvation and recovery predict extinction risk and both Damuth’s law and Cope’s rule. Nat. Commun. 9, 657 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Purdon, A., Mole, M. A., Chase, M. J. & van Aarde, R. J. Partial migration in savanna elephant populations distributed across southern Africa. Sci. Rep. 8, 11331 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Upham, N. S., Esselstyn, J. A. & Jetz, W. Inferring the mammal tree: species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLoS Biol. 17, e3000494 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Abrahms, B. et al. Memory and resource tracking drive blue whale migrations. Proc. Natl Acad. Sci. USA 116, 5582–5587 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Barnosky, A. D., Koch, P. L., Feranec, R. S., Wing, S. L. & Shabel, A. B. Assessing the causes of late Pleistocene extinctions on the continents. Science 306, 70–75 (2004).CAS 
PubMed 
Article 

Google Scholar 
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).CAS 
PubMed 
Article 

Google Scholar 
Faith, J. T., Rowan, J. & Du, A. Early hominins evolved within non-analog ecosystems. Proc. Natl Acad. Sci. USA 116, 21478–21483 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Holdo, R. M. et al. A disease-mediated trophic cascade in the Serengeti and its implications for ecosystem C. PLoS Biol. 7, e1000210 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Janzen, D. H. & Martin, P. S. Neotropical anachronisms: the fruits the gomphotheres ate. Science 215, 19–27 (1982).CAS 
PubMed 
Article 

Google Scholar 
Dantas, V. L. & Pausas, J. G. The legacy of the extinct Neotropical megafauna on plants and biomes. Nat. Commun. 13, 129 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Harris, G., Thirgood, S., Hopcraft, J. G. C., Cromsigt, J. P. G. M. & Berger, J. Global decline in aggregated migrations of large terrestrial mammals. Endanger. Species Res. 7, 55–76 (2009).Article 

Google Scholar 
Seersholm, F. V. et al. Rapid range shifts and megafaunal extinctions associated with late Pleistocene climate change. Nat. Commun. 11, 2770 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Alroy, J. A multispecies overkill simulation of the end-Pleistocene megafaunal mass extinction. Science 292, 1893–1896 (2001).CAS 
PubMed 
Article 

Google Scholar 
Berger, J. The last mile: how to sustain long-distance migration in mammals. Conserv. Biol. 18, 320–331 (2004).Article 

Google Scholar 
Faurby, S. & Svenning, J.-C. Resurrection of the island rule: human-driven extinctions have obscured a basic evolutionary pattern. Am. Nat. 187, 812–820 (2016).PubMed 
Article 

Google Scholar 
Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).Article 

Google Scholar 
Smith, F. A. et al. Body mass of late Quaternary mammals. Ecology 84, 3403 (2003).Article 

Google Scholar 
IUCN. IUCN Red List of Threatened Species 2019 (IUCN, 2019).Toljagić, O., Voje, K. L., Matschiner, M., Liow, L. H. & Hansen, T. F. Millions of years behind: slow adaptation of ruminants to grasslands. Syst. Biol. 67, 145–157 (2018).PubMed 
Article 

Google Scholar 
Pinzon, J. E. & Tucker, C. J. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929–6960 (2014).Article 

Google Scholar 
R Core Team. R: a Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

Google Scholar 
Blomberg, S. P., Garland, T. Jr. & Ives, A. R. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution 57, 717–745 (2003).PubMed 
Article 

Google Scholar 
Orme, D. The caper package: Comparative analysis of phylogenetics and evolution in R. R package version 1.0.1 https://cran.r-project.org/web/packages/caper/vignettes/caper.pdf (2018).Beaulieu, J. M. & O’Meara, B. OUwie: Analysis of evolutionary rates in an OU framework. R package version 2.6 https://rdrr.io/cran/OUwie/ (2014).Cressler, C. E., Butler, M. A. & King, A. A. Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein–Uhlenbeck model. Syst. Biol. 64, 953–968 (2015).PubMed 
Article 

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

Google Scholar 
van der Bijl, W. phylopath: easy phylogenetic path analysis in R. PeerJ 6, e4718 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, L. et al. Large-scale ruminant genome sequencing provides insights into their evolution and distinct traits. Science 364, eaav6202 (2019).CAS 
PubMed 
Article 

Google Scholar  More

Update of the Zero Draft of the Post-2020 Global Biodiversity Framework (Convention on Biological Diversity, 2020); https://www.cbd.int/doc/c/3064/749a/0f65ac7f9def86707f4eaefa/post2020-prep-02-01-en.pdfCorlett, R. T. et al. Impacts of the coronavirus pandemic on biodiversity conservation. Biol. Conserv. 246, 108571 (2020).Article 

Google Scholar 
Singh, R. et al. Impact of the COVID-19 pandemic on rangers and the role of rangers as a planetary health service. Parks 27, 119–134 (2021).Article 

Google Scholar 
Hockings, M. et al. COVID‐19 and protected and conserved areas. Parks 26, 7–24 (2020).Article 

Google Scholar 
Waithaka, J. The Impact of COVID-19 Pandemic on Africa’s Protected Areas Operations and Programmes (IUCN, 2020); https://www.iucn.org/sites/dev/files/content/documents/2020/report_on_the_impact_of_covid_19_doc_july_10.pdfLindsey, P. et al. Conserving Africa’s wildlife and wildlands through the COVID-19 crisis and beyond. Nat. Ecol. Evol. 4, 1300–1310 (2020).Article 

Google Scholar 
Amador-Jiménez, M., Millner, N., Palmer, C., Pennington, R. T. & Sileci, L. The unintended impact of Colombia’s COVID-19 lockdown on forest fires. Environ. Resour. Econ. 76, 1081–1105 (2020).Article 

Google Scholar 
Poulter, B., Freeborn, P. H., Matt Jolly, W. & Morgan Varner, J. COVID-19 lockdowns drive decline in active fires in southeastern United States. Proc. Natl Acad. Sci. USA 118, e2015666118 (2021).Article 
CAS 

Google Scholar 
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).CAS 
Article 

Google Scholar 
Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).Article 
CAS 

Google Scholar 
Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl Acad. Sci. USA 116, 23209–23215 (2019).CAS 
Article 

Google Scholar 
Tabor, K. et al. Evaluating the effectiveness of conservation and development investments in reducing deforestation and fires in Ankeniheny–Zahemena Corridor, Madagascar. PLoS ONE 12, e0190119 (2017).Article 
CAS 

Google Scholar 
Cochrane, M. A. Fire science for rainforests. Nature 421, 913–919 (2003).CAS 
Article 

Google Scholar 
Driscoll, D. A. et al. How fire interacts with habitat loss and fragmentation. Biol. Rev. 96, 976–998 (2021).Article 

Google Scholar 
Nelson, A. & Chomitz, K. M. Effectiveness of strict vs. multiple use protected areas in reducing tropical forest fires: a global analysis using matching methods. PLoS ONE 6, e22722 (2011).CAS 
Article 

Google Scholar 
Carlson, K. M. et al. Effect of oil palm sustainability certification on deforestation and fire in Indonesia. Proc. Natl Acad. Sci. USA 115, 121–126 (2018).CAS 
Article 

Google Scholar 
Turco, M. et al. Skilful forecasting of global fire activity using seasonal climate predictions. Nat. Commun. 9, 2718 (2018).Article 
CAS 

Google Scholar 
Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl Acad. Sci. USA 113, 11770–11775 (2016).CAS 
Article 

Google Scholar 
Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362 (2017).CAS 
Article 

Google Scholar 
Brooks, T. M. et al. Global biodiversity conservation priorities. Science 313, 58–61 (2006).CAS 
Article 

Google Scholar 
Jones, J. P. G. et al. Last chance for Madagascar’s biodiversity. Nat. Sustain. 2, 350–352 (2019).Article 

Google Scholar 
Gardner, C. J. et al. The rapid expansion of Madagascar’s protected area system. Biol. Conserv. 220, 29–36 (2018).Article 

Google Scholar 
Hockley, N., Mandimbiniaina, R. & Rakotonarivo, O. S. Fair and equitable conservation: do we really want it, and if so, do we know how to achieve it? Madag. Conserv. Dev. 13, 3–5 (2018).Article 

Google Scholar 
Corson, C. in Conservation and Environmental Management in Madagascar (ed. Scales, I. R.) 193–215 (Routledge, 2014).Davies, B. et al. Community factors and excess mortality in first wave of the COVID-19 pandemic in England. Nat. Commun. 12, 3755 (2021).CAS 
Article 

Google Scholar 
Kull, C. A. & Lehmann, C. E. R. in The New Natural History of Madagascar (ed. Goodman, S. M.) 197–203 (Princeton Univ. Press, in the press).Razafindrakoto, M., Roubaud, F. & Wachsberger, J.-M. Puzzle and Paradox: A Political Economy of Madagascar (Cambridge Univ. Press, 2020).Ruggiero, P. G. C., Pfaff, A., Nichols, E., Rosa, M. & Metzger, J. P. Election cycles affect deforestation within Brazil’s Atlantic Forest. Conserv. Lett. 14, e12818 (2021).Article 

Google Scholar 
Morpurgo, J., Kissling, W. D., Tyrrell, P., Negret, P. J. & Allan, J. R. The role of elections as drivers of tropical deforestation. Preprint at bioRxiv https://doi.org/10.1101/2021.05.04.442551 (2021).Tourism in Madagascar (WorldData, 2021); https://www.worlddata.info/africa/madagascar/tourism.phpRapport annuel d’activites 2018 (Madagascar National Parks, 2018).Vyawahare, M. As minister and activists trade barbs, Madagascar’s forests burn. Mongabay (17 December 2020).Cochrane, M. A. in Tropical Fire Ecology: Climate Change, Land Use, and Ecosystem Dynamics (ed. Cochrane, M. A.) 389–426 (Springer-Verlag, 2009); https://doi.org/10.1007/978-3-540-77381-8_14Cochrane, M. A. in Tropical Rainforest Responses to Climatic Change (eds Bush, M. et al.) 213–240 (Springer, 2011); https://doi.org/10.1007/978-3-642-05383-2_7Mondal, N. & Sukumar, R. Fires in seasonally dry tropical forest: testing the varying constraints hypothesis across a regional rainfall gradient. PLoS ONE 11, e0159691 (2016).Article 
CAS 

Google Scholar 
Madagascar Economic Update: COVID-19 Increases Poverty, a New Reform Momentum is Needed to Build Back Stronger (World Bank, 2020); https://www.worldbank.org/en/country/madagascar/publication/madagascar-economic-update-covid-19-increases-poverty-a-new-reform-momentum-is-needed-to-build-back-strongerBaker, A. Climate, not conflict. Madagascar’s famine is the first in modern history to be solely caused by global warming. Time (20 July 2021).Graham, V. et al. Management resourcing and government transparency are key drivers of biodiversity outcomes in Southeast Asian protected areas. Biol. Conserv. 253, 108875 (2021).Article 

Google Scholar 
Geldmann, J. et al. A global analysis of management capacity and ecological outcomes in terrestrial protected areas. Conserv. Lett. 11, e12434 (2018).Article 

Google Scholar 
Gill, D. A. et al. Capacity shortfalls hinder the performance of marine protected areas globally. Nature 543, 665–669 (2017).CAS 
Article 

Google Scholar 
Eklund, J., Coad, L., Geldmann, J. & Cabeza, M. What constitutes a useful measure of protected area effectiveness? A case study of management inputs and protected area impacts in Madagascar. Conserv. Sci. Pract. 1, e107 (2019).
Google Scholar 
Nolte, C. & Agrawal, A. Linking management effectiveness indicators to observed effects of protected areas on fire occurrence in the Amazon rainforest. Conserv. Biol. 27, 155–165 (2013).Article 

Google Scholar 
Schleicher, J., Peres, C. A. & Leader-Williams, N. Conservation performance of tropical protected areas: how important is management? Conserv. Lett. 12, e12650 (2019).Article 

Google Scholar 
Schroeder, W., Oliva, P., Giglio, L. & Csiszar, I. A. The new VIIRS 375m active fire detection data product: algorithm description and initial assessment. Remote Sens. Environ. 143, 85–96 (2014).Article 

Google Scholar 
Forest Monitoring Designed for Action (Global Forest Watch, 2021); https://www.globalforestwatch.org/Musinsky, J. et al. Conservation impacts of a near real-time forest monitoring and alert system for the tropics. Remote Sens. Ecol. Conserv 4, 189–196 (2018).Article 

Google Scholar 
Ramo, R. et al. African burned area and fire carbon emissions are strongly impacted by small fires undetected by coarse resolution satellite data. Proc. Natl Acad. Sci. USA 118, e2011160118 (2021).CAS 
Article 

Google Scholar 
Global Economic Prospects, June 2021 (World Bank, 2021).Razanatsoa, E. et al. Fostering local involvement for biodiversity conservation in tropical regions: lessons from Madagascar during the COVID‐19 pandemic. Biotropica 53, 994–1003 (2021).Article 

Google Scholar 
Nolte, C., Agrawal, A., Silvius, K. M. & Soares-Filho, B. S. Governance regime and location influence avoided deforestation success of protected areas in the Brazilian Amazon. Proc. Natl Acad. Sci. USA 110, 4956–4961 (2013).CAS 
Article 

Google Scholar 
ArcGIS 10.8 for Desktop (ESRI, 2021).Python Language Reference v.3.8.5 (Python Software Foundation, 2021); http://www.python.orgR Core Team R: A Language and Environment for Statistical Computing. R version 4.0.2 (R Foundation for Statistical Computing, 2020); https://www.R-project.org/Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).The World Database on Protected Areas (WDPA) (UNEP-WCMC and IUCN, 2020); www.protectedplanet.netGoodman, S. M., Raherilalao, J. M. & Wohlhauser, S. The Terrestrial Protected Areas of Madagascar: Their History, Description, and Biota (Association Vahatra, 2018).Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).Article 

Google Scholar 
NRT VIIRS 375 m Active Fire Product VNP14IMGT (NASA, 2020); https://doi.org/10.5067/FIRMS/VIIRS/VNP14IMGT_NRT.002Chen, D., Shevade, V., Baer, A. E. & Loboda, T. V. Missing burns in the high northern latitudes: the case for regionally focused burned area products. Remote Sens. 13, 4145 (2021).Article 

Google Scholar 
Schroeder, W. & Giglio, L. NASA VIIRS Land Science Investigator Processing System (SIPS) Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m & 750 m Active Fire Products: Product User’s Guide Version 1.4 (NASA, 2018).Global Precipitation Measurement: Precipitation Data Directory (NASA, 2020); https://gpm.nasa.gov/data/directoryGlobal Precipitation Measurement: The Tropical Rainfall Measuring Mission (TRMM) (NASA, 2020) https://gpm.nasa.gov/missions/trmmHantson, S. et al. Rare, intense, big fires dominate the global tropics under drier conditions. Sci. Rep. 7, 14374 (2017).Article 
CAS 

Google Scholar 
Zeileis, A., Kleiber, C. & Jackman, S. Regression models for count data in R. J. Stat. Softw. https://doi.org/10.18637/jss.v027.i08 (2008).Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R 261–293 (Springer, 2009).Joseph, M. B. et al. Spatiotemporal prediction of wildfire extremes with Bayesian finite sample maxima. Ecol. Appl. 29, e01898 (2019).Article 

Google Scholar 
Guo, F. et al. Comparison of six generalized linear models for occurrence of lightning-induced fires in northern Daxing’an Mountains, China. J. For. Res. 27, 379–388 (2016).Article 

Google Scholar 
Garay, A. M., Hashimoto, E. M., Ortega, E. M. M. & Lachos, V. H. On estimation and influence diagnostics for zero-inflated negative binomial regression models. Comput. Stat. Data Anal. 55, 1304–1318 (2011).Article 

Google Scholar 
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).Shcherbakov, M. V. et al. A survey of forecast error measures. World Appl. Sci. J. 24, 171–176 (2013).
Google Scholar 
Efron, B. Bootstrap methods: another look at the jackknife. Ann. Stat. 7, 1–26 (1979).Article 

Google Scholar 
Canty, A. & Ripley, B. boot: Bootstrap R (S-Plus) Functions. R package version 1.3-28 (2021). More

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Study areaThe study was conducted in the headwaters of the Chalpi Grande River watershed, 95 km2, located inside the Cayambe-Coca National Park in the northern Andes of Ecuador at an elevation range of 3789 to 3835 m (S 0°16′ 45″, W 78° 4′49″). This watershed harbors the primary water supply system for Quito. The system includes two reservoirs and 10 water intakes placed on first and second-order streams that, altogether, provide 39% of Quito’s water supply28. We monitored the Chalpi Norte stream for ~1.5 years prior to conducting our experiment for ~0.5 years (176 days), and ~0.4 years after the manipulation. Further, in the nearby area, we monitored 21 stream sites distributed upstream and downstream water intakes from the supply system (Fig. S4).Experiment for flow manipulation and monitoring flow reduction and recoveryWe conducted our experimental flow manipulation between October 2018 and April 2019 in a mainly rain-fed stream45. The experiment manipulated natural flows encompassing stable low flows and sporadic spates characterizing the high temporal variability of headwaters45,28 (Figs. 2a, b and S1). We set up a full Before-After/Control- Impact (BACI) experiment29 to evaluate ecosystem variables under natural and manipulated flow conditions. We identified a free-flowing stream reach on the Chalpi Norte that was above any water intakes that allowed us to divert flow with an ecohydraulic structure31. The structure was located above a meander, which we used to divert flow and return it to the stream below the meander (Fig. S4). The experimental site was comprised of an upstream/free-flowing reach (L = 25 m) (reference conditions), located ~32 m above the ecohydraulic structure and a downstream/regulated reach (L = 97 m) located immediately below the flow manipulation structure (Fig. 1b–d)31. The control site was located in a free-flowing stream, a tributary of the Chalpi Norte stream, with an upstream reach separated from a downstream reach by a distance of 16 m. We manipulated the instantaneous flow of the Chalpi Norte stream through a series of fixed percentages using different v-notch weir pairs31. We started diversions to maintain in the meander 100, 80, 60, 50, 40, 30, and 20% of the incoming flow for 7-day periods (based on local observations of benthic algal colonization); then we maintained 10% of the upstream flow for 36 days. We started to return flow gradually to recover 20, 30, 40, 50, 60, 80, and 100% of the upstream flow. In response to a natural spate while we maintained the 10% of upstream flow, the manipulated flow briefly (during ~9 h) increased above the targeted reduction (i.e., 54% instead of 10%) (Fig. 2a). We registered the spate of flow on the upstream reach of the experimental site (Figs. 2b and S1).Stream monitoring in adjacent streamsWe monitored 21 stream sites between July 2017 and July 2019. We selected seven streams with water intakes placed on the main channel (Chalpi Norte, Gonzalito, Quillugsha 1, 2, 3, Venado, and Guaytaloma). We sampled one site upstream of the water intake and two sites (i.e., 10 m and 500 m) downstream to obtain a wide range of flow reduction levels (Fig. S4) (see, 30 for further details on stream sites).Global literature surveyWe performed a systematic literature review to explore benthic algae responses to flow alterations (increase or decrease), focusing on cyanobacteria in streams. We used ISI Web of Science, Google Scholar, and Google Search for the entries: “benthic cyanobacteria” + “stream”, and “river”, “benthic algal bloom” + “flow” and all available combinations (Table S1). We selected papers containing information on benthic cyanobacteria and algae biomass and flow or water level measurements; specifically, we explored detailed information regarding experiments, spatial studies with upstream and downstream sites, and temporal replicates, as well seasonal associated benthic cyanobacteria blooms. We used published and/or publicly available data to calculate the percent of flow alteration in streams and calculated a factor on cyanobacteria biomass increase or decrease (quantitative studies) according to reported baseline conditions (either temporal or spatial). Only three out of 53 study sites reported a qualitative decrease in benthic cyanobacteria biomass attributable to flow reduction (Fig. 1d). Most studies (94%, n = 50) reported biomass increases with flow reductions. Among these studies sites, 44% reported qualitative observations where low flows were proposed as one of the environmental drivers responsible for benthic cyanobacteria blooms. While 66% of study sites (n = 33) related cyanobacterium biomass increase in time or space due to flow reductions caused by droughts, extreme low flow events, water abstractions, and experimental flumes manipulations.Abiotic and biotic variables sampling and analysesWater level sensors recording every 30 min (HOBO U40L, Onset USA) were installed at both upstream and downstream sites of water intakes, and on the experimental and control stream reaches (BACI desing), where we conducted multiple wading-rod flow measurements to convert water level into discharge via stage-discharge relationships (ADC current meter, OTT Hydromet, Germany). Streamwater’s physical and chemical in situ parameters (i.e., pH, temperature, conductivity, dissolved oxygen) were measured three times during biotic sampling on both stream sites and adjacent streams using a portable sonde (YSI, Xylem, USA). We collected water samples (500 ml) during in situ samplings to analyze nutrients (i.e., nitrate and phosphate) at the water supply company’s (EPMAPS) laboratory. We also measured precipitation from a rain gauge (HOBO Onset USA) installed in the Chalpi Norte stream.Our biotic variables included three benthic algae: cyanobacteria, diatoms, and green algae), and aquatic invertebrates biomass (Table 1). To measure Chl-a from cyanobacteria and benthic algae on artificial substrates, we used a BenthoTorch® (bbe Moldaenke GmbH, Germany) on unglazed ceramic plates (200 mm × 400 mm) with a grid of 25 squares of 2500 mm2 to allow algal accrual on a standardized surface. We allowed 21 days for colonization (based on previous observations) and then we placed all substrates5 at the beginning of the experiment. We performed five readings on five squares randomly selected within each plate. To consider the effect of benthic invertebrates to flow variations, we sampled stream sites using a Surber net (mesh size = 250 µm, area = 0.0625 m2). On the experimental and control sites we measured biotic, physical, and chemical in situ parameters every two days (n = 1760), and nutrients and invertebrates every seven days (n = 500) for the duration of the flow manipulation (~0.5 years). On the monitored sites, we measured biotic, physical, and chemical in situ parameters every seven days (n = 1456) and nutrients and invertebrates every 30 days (n = 336). To evaluate differences we calculated mean abiotic and biotic variables during the different phases (BL: baseline, FR: flow reduction, FI: gradual reset to initial flow) in the four-stream reaches to apply the BACI design29: upstream and downstream reaches on the experimental and control sites. We applied a paired one-tail t-test at α = 0.05 to compare FR and FI phases to baseline conditions, based on the expected direction of the response 1,14.Statistics and reproducibilityTo quantify the relationships between environmental variables and cyanobacteria biomass under manipulated and natural flow conditions, including interaction among algae and with invertebrates, we used multivariate autoregressive state-space modeling (MARSS)14,30. We fitted models with Gaussian errors for flow, conductivity, pH, water temperature, nitrate, phosphate, cyanobacteria, benthic algae, and invertebrate biomass time series via maximum likelihood (MARSS R-package)48. The state processes Xt includes state measurements for all four benthic components (cyanobacteria, diatoms, green algae, and invertebrates’ biomasses) considering the interactions between benthic components and environmental covariates (flow, conductivity, pH, water temperature, nitrate, phosphate) evolving through time, as follows:$${X}_{t}={{BX}}_{t-1}+U+{C}_{{Ct}}+{W}_{t}; {W}_{t} sim {MVN}(0,Q)$$
(1)
$${Y}_{t}={{ZX}}_{t}+{V}_{t} ; {V}_{t} sim {MVN}(0,R)$$
(2)
with Xt a matrix of states at time t, Yt a matrix of observations at time t, Wt a matrix of process errors (multivariate normally distributed with mean 0 and variance Q), Vt is a matrix of observation errors (normally distributed with mean 0 and variance R). Z is a matrix linking the observations Yt and the correspondent state Xt. B is an interaction matrix with inter-specific interaction (diatom and green algae) and with invertebrate strengths, Ct is a matrix of environmental variables (flow, conductivity, pH, water temperature, nitrate, phosphate) at time t. C is a matrix of coefficients indicating the effect of Ct to states Xt. U describes the mean trend. We computed a total of 12 models from the most complete to the simplest, the best-fitting model was identified as having the lowest Akaike Information Criterion adjusted for small sample sizes (AICc)14,30. To detect structural breaks in cyanobacteria biomass time series we calculated the differences between the smoothed state estimates at time t and t-1 based on the multivariate models. Sudden changes in the level were detected when the standardized smoothed state residuals exceed the 95% confidence interval for a t-distribution. We estimated the strength of environmental variables on cyanobacteria biomass and fitted models independently for each stream reach.To analyze cyanobacteria biomass across a gradient of flow alterations we compared weekly paired data (n = 1456) from upstream and downstream sites (i.e., at 10 m and 500 m). We thus calculated how much downstream site(s) biomass changed in comparison to upstream site biomass and assigned a factor for the increase or decrease. We determined the relative fraction of the instantaneous upstream flow in the downstream site measured within a 30-min time-step. We applied the same analysis to data from experiments obtained on the web search. We applied the Ramer–Douglas–Peucker (RDP) algorithm to find a breakpoint (ε lower distance to breakpoint) and the best line of fit for the local and global survey data distribution, we used the kmlShape-R package 48.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Water samples were filtered twice (see Methods), first through a large filter (0.45 µm, LF or “Large Filter”) and then the filtrate was passed through a small filter (0.05 µm, UF or “Ultrafine Fraction”). Using whole genome shotgun metagenomics from four water samples (LF92 and UF92 from the 92 m depth, LF99 and UF99 from the 99 m depth) as well as one sediment sample, we provide the first comprehensive whole genome shotgun metagenomics investigation of this section of the lake and highlight both the taxonomic composition and potential metabolic strategies for survival, as well as identify areas for deeper investigation.Cell counts and dissolved nutrientsIn order to determine the habitability of the anoxic basin, the cell counts were measured in the oxycline (75 m depth) and the anoxic region (92 and 99 m depth), where oxygen content is  More

Tonitto, C., Woodbury, P. B. & McLellan, E. L. Environ. Sci. Policy 87, 64–73 (2018).Article 

Google Scholar 
Carlson, K. M. et al. Nat. Clim. Change 7, 63–68 (2017).ADS 
CAS 
Article 

Google Scholar 
Carson, R., Darling, L. & Darling, L. Silent Spring (Houghton Mifflin, 1962).Audsley, E., Stacey, K. F., Parsons, D. J. & Williams, A. G. Estimation of the Greenhouse Gas Emissions from Agricultural Pesticide Manufacture and Use (Cranfield Univ., 2009).Heimpel, G. E., Yang, Y., Hill, J. D. & Ragsdale, D. W. PLoS ONE 8, e72293 (2013).ADS 
CAS 
Article 

Google Scholar 
Lal, R. Environ. Int. 30, 981–990 (2004).CAS 
Article 

Google Scholar 
Crippa, M. et al. Nat. Food 2, 198–209 (2021).CAS 
Article 

Google Scholar 
Labrie, G. et al. PLoS ONE 15, e0229136 (2020).CAS 
Article 

Google Scholar 
Tang, F. H., Lenzen, M., McBratney, A. & Maggi, F. Nat. Geosci. 14, 206–210 (2021).ADS 
CAS 
Article 

Google Scholar 
Mason, P. G. Biological Control: Global Impacts, Challenges and Future Directions of Pest Management (CSIRO, 2021).Deguine, J. P. et al. Agron. Sustain. Dev. 41, 1–35 (2021).Article 

Google Scholar 
Wyckhuys, K. A. G. et al. J. Environ. Manage. 307, 114529 (2022).Article 

Google Scholar 
Van den Berg, H. & Jiggins, J. World Dev. 35, 663–686 (2007).Article 

Google Scholar 
Godfray, H. C. J. et al. Science 327, 812–818 (2010).ADS 
CAS 
Article 

Google Scholar 
Huang, J. et al. Environ. Res. Lett. 13, 064027 (2018).ADS 
Article 

Google Scholar 
Pecenka, J. R. et al. Proc. Natl Acad. Sci. USA 118, e2108429118 (2021).CAS 
Article 

Google Scholar 
Naranjo, S. E., Ellsworth, P. C. & Frisvold, G. B. Annu. Rev. Entomol. 60, 621–645 (2015).CAS 
Article 

Google Scholar 
Tamburini, G. et al. Sci. Adv. 6, eaba1715 (2020).ADS 
Article 

Google Scholar 
Wolf, S. A. & Ghosh, R. Land Use Policy 96, 103552 (2020).Article 

Google Scholar 
Wyckhuys, K. A. G. et al. Environ. Res. Lett. 13, 094005 (2018).ADS 
Article 

Google Scholar 
Bridge, G. et al. Prog. Hum. Geogr. 44, 724–742 (2020).Article 

Google Scholar 
Gautam, M. et al. Repurposing Agricultural Policies and Support: Options to Transform Agriculture and Food Systems to Better Serve the Health of People, Economies, and the Planet (The World Bank and IFPRI, 2022).Tooker, J. F., O’Neal, M. E. & Rodriguez-Saona, C. Annu. Rev. Entomol. 65, 81–100 (2020).CAS 
Article 

Google Scholar 
van Lenteren, J. C. et al. BioControl 63, 39–59 (2018).Article 

Google Scholar 
Parnell, J. J. et al. Front. Plant Sci. 7, 1110 (2016).Article 

Google Scholar 
Herrero, M. et al. Nat. Food 1, 266–272 (2020).Article 

Google Scholar 
Rosenzweig, C. et al. Nat. Food 1, 94–97 (2020).Article 

Google Scholar 
Rana, J. & Paul, J. J. Retail. Consum. Serv. 38, 157–165 (2017).Article 

Google Scholar  More

Richardson, D.M., & Rundel, P.W. Ecology and biogeography of Pinus: An introduction. in Ecology and Biogeography of Pinus (Richardson, D.M. Ed.). 3–40. (Cambridge Press, 1998).Keeley, J. E. Ecology and evolution of pine life histories. Ann. For. Sci. 69, 445–453 (2012).Article 

Google Scholar 
Agee, J.K. Fire and pine ecosystems. in Ecology and Biogeography of Pinus (Richardson, D.M. Ed.). 193–217. (Cambridge Press, 1998).Keeley, J.E., & Zedler, P.H. Evolution of life histories in Pinus. in Ecology and Biogeography of Pinus (Richardson, D.M. Ed.). 219–251. (Cambridge Press, 1998).Pausas, J. G., Bradstock, R., Keith, D. A. & Keeley, J. E. Plant functional traits in relation to fire in crown-fire ecosystems. Ecology 85, 1085–1100 (2004).Article 

Google Scholar 
Hare, R. C. Contribution of bark to fire resistance of southern trees. J. For. 63, 248–251 (1965).
Google Scholar 
Jackson, J. F., Adams, D. C. & Jackson, U. B. Allometry of constitutive defense: A model and a comparative test with tree bark and fire regime. Am. Nat. 153, 614–632 (1999).PubMed 
Article 

Google Scholar 
Stephens, S. L. & Libby, W. J. Anthropogenic fire and bark thickness in coastal and island pine populations from Alta and Baja California. J. Biogeogr. 33, 648–652 (2006).Article 

Google Scholar 
Chapman, H. H. Is the longleaf type a climax?. Ecology 13, 328–334 (1932).Article 

Google Scholar 
Pile, L. S., Wang, G. G., Knapp, B. O., Liu, G. & Yu, D. Comparing morphology and physiology of southeastern US Pinus seedlings: Implications for adaptation to surface fire regimes. Ann. For. Sci. 74, 68 (2017).Article 

Google Scholar 
Rodríguez-Trejo, D. A. & Fulé, P. Z. Fire ecology of Mexican pines and a fire management proposal. Int. J. Wildl. Fire 12, 23–37 (2003).Article 

Google Scholar 
Pausas, J. G. Bark thickness and fire regime. Funct. Ecol. 29, 315–327 (2015).Article 

Google Scholar 
Little, S. & Mergen, F. External and internal changes associated with basal-crook formation in pitch and shortleaf pines. For. Sci. 12, 268–275 (1966).
Google Scholar 
Kolström, T. & Kellomäki, S. Tree survival in wildfires. Silva Fenn. 27, 277–281 (1993).Article 

Google Scholar 
Schwilk, D. W. & Ackerly, D. D. Flammability and serotiny as strategies: Correlated evolution in pines. Oikos 94, 326–236 (2001).Article 

Google Scholar 
Reyes, O. & Casal, M. Effect of high temperatures on cone opening and on the release and viability of Pinus pinaster and P. radiata seeds in NW Spain. Ann. For. Sci. 59, 327–334 (2002).Article 

Google Scholar 
Pausas, J. G. & Keeley, J. E. Epicormic resprouting in fire-prone ecosystems. Trends Plant Sci. 22, 1008–1015 (2017).CAS 
PubMed 
Article 

Google Scholar 
Fonda, R. W., Bellanger, L. A. & Burley, L. L. Burning characteristics of western conifer needles. Northwest Sci. 72, 1–9 (1998).
Google Scholar 
Fonda, R. W. Burning characteristics of needles from eight pine species. For. Sci. 47, 390–396 (2001).
Google Scholar 
Anderson, H. E. Forest fuel ignitability. Fire Tech. 6, 312–319 (1970).CAS 
Article 

Google Scholar 
Martin, R.E., et al. Assessing the flammability of domestic and wildland vegetation. in Proceedings of the 12th Conference Fire and Forest Meteorology. Jekyll Island. 130–137. (1993)Varner, J. M., Kane, J. M., Kreye, J. K. & Engber, E. The flammability of forest and wildland litter: A synthesis. Curr. For. Rep. 1, 91–99 (2015).
Google Scholar 
Fernandes, P. M. & Cruz, M. G. Plant flammability experiments offer limited insight into vegetation–fire dynamics interactions. New Phytol. 194, 606–609 (2012).PubMed 
Article 

Google Scholar 
Wenk, E. S., Wang, G. G. & Walker, J. L. Within-stand variation in understorey vegetation affects fire behaviour in longleaf pine xeric sandhills. Int. J. Wildl. Fire 20, 866–875 (2012).Article 

Google Scholar 
Whelan, A. W., Bigelow, S. W. & O’Brien, J. J. Overstory longleaf pines and hardwoods create diverse patterns of energy release and fire effects during prescribed fire. Front. For. Glob. Change. 4, 25 (2021).Article 

Google Scholar 
Mutch, R. W. Wildland fires and ecosystems—A hypothesis. Ecology 51, 1046–1051 (1970).Article 

Google Scholar 
Troumbis, A. S. & Trabaud, L. Some questions about flammability in fire ecology. Acta Oecol. 10, 167–175 (1989).
Google Scholar 
Midgley, J. J. Flammability is not selected for, it emerges. Aust. J. Bot. 61, 102–106 (2013).Article 

Google Scholar 
Snyder, J. R. The role of fire: Mutch ado about nothing?. Oikos 43, 404–405 (1984).Article 

Google Scholar 
Bond, W. J. & Midgley, J. J. Kill thy neighbour: An individualistic argument for theevolution of flammability. Oikos 73, 79–85 (1995).Article 

Google Scholar 
Gagnon, P. R. et al. Does pyrogenicity protect burning plants?. Ecology 91, 3481–3486 (2010).PubMed 
Article 

Google Scholar 
Vines, R. G. Heat transfer through bark, and the resistance of trees to fire. Aust. J. Bot. 16, 499–514 (1968).Article 

Google Scholar 
Harmon, M. E. Survival of trees after low-intensity surface fires in Great Smoky Mountains National Park. Ecology 65, 796–802 (1984).Article 

Google Scholar 
Schwilk, D. W., Gaetani, M. S. & Poulos, H. M. Oak bark allometry and fire survival strategies in the Chihuahuan Desert Sky Islands, Texas, USA. PLoS ONE 8, e79285 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Stevens, J., Kling, M., Schwilk, D., Varner, J. M. & Kane, J. M. Biogeography of fire regimes in western US conifer forests: a trait-based approach. Glob. Ecol. Biogeogr. 29, 944–955 (2020).Article 

Google Scholar 
Rosell, J. A. Bark thickness across the angiosperms: More than just fire. New Phytol. 211, 90–102 (2016).CAS 
PubMed 
Article 

Google Scholar 
Kane, J. M., Varner, J. M. & Hiers, J. K. The burning characteristics of southeastern oaks: discriminating fire facilitators from fire impeders. For. Ecol. Manag. 256, 2039–2045 (2008).Article 

Google Scholar 
Engber, E. A. & Varner, J. M. Patterns of flammability of the California oaks: The role of leaf traits. Can. J. For. Res. 42, 1965–1975 (2012).Article 

Google Scholar 
Guyette, R. P., Stambaugh, M. C., Dey, D. C. & Muzika, R. Predicting fire frequency with chemistry and climate. Ecosystems 15, 322–335 (2012).Article 

Google Scholar 
Stambaugh, M.C., Varner, J.M., & Jackson, S.T. Biogeography: An interweave of climate, fire, and humans. in Ecological Restoration and Management of Longleaf Pine Forests (Kirkman, K., Jack, S. B. Eds.). 17–38. (CRC Press, 2017).Münkemüller, T. et al. How to measure and test phylogenetic signal. Methods Ecol. Evol. 3, 743–756 (2012).Article 

Google Scholar 
Schwilk, D. W. & Caprio, A. C. Scaling from leaf traits to fire behavior: community composition predicts fire severity in a temperate forest. J. Ecol. 99, 970–980 (2011).Article 

Google Scholar 
Ormeño, E. et al. The relationship between terpenes and flammability of leaf litter. For. Ecol. Manag. 257, 471–482 (2009).Article 

Google Scholar 
Mirov, N. T. The terpenes (in relation to the biology of genus Pinus). Ann. Rev. Biochem. 17, 521–540 (1948).CAS 
PubMed 
Article 

Google Scholar 
Mitić, Z. S. et al. Needle terpenes as chemotaxonomic markers in Pinus: Subsections Pinus and Pinaster. Chem. Biodivers. 14, e1600453 (2017).Article 

Google Scholar 
Baradat, P. & Yazdani, R. Genetic expression for monoterpenes in clones of Pinus sylvestris grown on different sites. Scand. J. For. Res. 3, 25–36 (1987).Article 

Google Scholar 
Hanover, J. W. Applications of terpene analysis in forest genetics. New For. 6, 159–178 (1992).Article 

Google Scholar 
He, T., Pausas, J. G., Belcher, C. M., Schwilk, D. W. & Lamont, B. B. Fire-adapted traits of Pinus arose in the fiery Cretaceous. New Phytol. 194, 751–759 (2012).PubMed 
Article 

Google Scholar 
Saladin, B. et al. Fossils matter: Improved estimates of divergence times in Pinus reveal older diversification. Evol. Biol. 17, 95 (2017).
Google Scholar 
Kreye, J. K. et al. Effects of solar heating on the moisture dynamics of forest floor litter in humid environments: Composition, structure, and position matter. Can. J. For. Res. 48, 1331–1342 (2018).Article 

Google Scholar 
Ganteaume, A., Jappiot, M., Curt, T., Lampin, C. & Borgniet, L. Flammability of litter sampled according to two different methods: Comparison of results in laboratory experiments. Int. J. Wildl. Fire 23, 1061–1075 (2014).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2019). https://www.R-project.org/.Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125, 1–15 (1985).Article 

Google Scholar 
Orme, D., et al. Caper: Comparative Analyses of Phylogenetics and Evolution in R. Version 1.0.1. https://CRAN.R-project.org/package=caper. (2018).Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).CAS 
PubMed 
Article 

Google Scholar 
Freckleton, R. P., Harvey, P. H. & Pagel, M. Phylogenetic analysis and comparative data: A test and review of evidence. Am. Nat. 160, 712–726 (2002).CAS 
PubMed 
Article 

Google Scholar 
Barton, K. MuMIn: Multi-Model Inference. R Package Version 1.43.6. https://CRAN.R-project.org/package=MuMIn. (2019).Little, E.L. Atlas of United States Trees. Vol. 1. Conifers and Important Hardwoods. 1–320. (Miscellaneous Publication 1146, USDA, Forest Service, 1971).Prasad, A.M. & Iverson, L.R. Little’s Range and FIA Importance Value Database for 135 Eastern US Tree Species. http://www.fs.fed.us/ne/delaware/4153/global/littlefia/index.html. (Northeastern Research Station, USDA Forest Service). More