Ecology

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

Joly, K. et al. Sci. Rep. 9, 15333 (2019).Article 

Google Scholar 
Shaw, A. K. Evol. Ecol 30, 991–1007 (2016).Article 

Google Scholar 
Bauer, S. & Hoye, B. J. Science 344, 1242552 (2014).CAS 
Article 

Google Scholar 
Abraham, J. O., Upham, N. S., Damian-Serrano, A. & Jesmer, B. R. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01749-4 (2022).Article 

Google Scholar 
Middleton, A. D. et al. Oikos 127, 1060–1068 (2018).Article 

Google Scholar 
Hein, A. M., Hou, C. & Gillooly, J. F. Ecol. Lett. 15, 104–110 (2012).Article 

Google Scholar 
Zachos, J., Pagani, M., Sloan, L., Thomas, E. & Billups, K. Science 292, 686–693 (2001).CAS 
Article 

Google Scholar 
Edwards, E. J. et al. Science 328, 587–591 (2010).CAS 
Article 

Google Scholar 
Aikens, E. O. et al. Curr. Biol. 30, 3444–3449 (2020).CAS 
Article 

Google Scholar 
Merkle, J. A. et al. Ecol. Lett. 22, 1797–1805 (2019).Article 

Google Scholar 
Jesmer, B. R. et al. Science 361, 1023–1025 (2018).CAS 
Article 

Google Scholar 
Harris, G., Thirgood, S., Hopcraft, J. G. C., Cromsigt, J. & Berger, J. Endanger. Species Res. 7, 55–76 (2009).Article 

Google Scholar 
Aikens, E. O. et al. Glob. Change Biol. 26, 4215–4225 (2020).Article 

Google Scholar 
Doughty, C. E. et al. Ecography 43, 1107–1117 (2020).Article 

Google Scholar 
Kauffman, M. J. et al. Science 372, 566–569 (2021).CAS 
Article 

Google Scholar  More

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

IPBES The IPBES Assessment Report on Land Degradation and Restoration (Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, 2018).Barlow, J. et al. Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature 535, 144–147 (2016).CAS 
Article 

Google Scholar 
The State of the World’s Forests 2020. Forests, Biodiversity and People (FAO and UNEP, 2020).Pendrill, F. et al. Agricultural and forestry trade drives large share of tropical deforestation emissions. Glob. Environ. Change 56, 1–10 (2019).Article 

Google Scholar 
Geist, H. J. & Lambin, E. F. What Drives Tropical Deforestation? LUCC Report Series 4 (LUCC International Project Office, 2001).Austin, K. G., González-Roglich, M., Schaffer-Smith, D., Schwantes, A. M. & Swenson, J. J. Trends in size of tropical deforestation events signal increasing dominance of industrial-scale drivers. Environ. Res. Lett. 12, 054009 (2017).Article 

Google Scholar 
Graesser, J., Ramankutty, N. & Coomes, O. T. Increasing expansion of large-scale crop production onto deforested land in sub-Andean South America. Environ. Res. Lett. 13, 084021 (2018).Article 

Google Scholar 
Meyfroidt, P. et al. Middle-range theories of land system change. Glob. Environ. Change 53, 52–67 (2018).Article 

Google Scholar 
Verburg, P. H. et al. Land system science and sustainable development of the Earth system: a global land project perspective. Anthropocene 12, 29–41 (2015).Article 

Google Scholar 
Václavík, T. et al. Investigating potential transferability of place-based research in land system science. Environ. Res. Lett. 11, 095002 (2016).Article 

Google Scholar 
Stocks, G., Seales, L., Paniagua, F., Maehr, E. & Bruna, E. M. The geographical and institutional distribution of ecological research in the tropics. Biotropica 40, 397–404 (2008).Article 

Google Scholar 
Schröder, J. M., Ávila Rodríguez, L. P. & Günter, S. Research trends: tropical dry forests: the neglected research agenda? For. Policy Econ. 122, 102333 (2021).Article 

Google Scholar 
Rodrigues, A. S. L. et al. Boom-and-bust development patterns across the Amazon deforestation frontier. Science 324, 1435–1437 (2009).CAS 
Article 

Google Scholar 
de Jong, E. B. P., Knippenberg, L. & Bakker, L. New frontiers: an enriched perspective on extraction frontiers in Indonesia. Crit. Asian Stud. 49, 330–348 (2017).Article 

Google Scholar 
Tyukavina, A. et al. Congo Basin forest loss dominated by increasing smallholder clearing. Sci. Adv. 4, eaat2993 (2018).Article 

Google Scholar 
Pacheco, P. et al. Deforestation Fronts: Drivers and Responses in a Changing World (WWF, 2021).Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
Article 

Google Scholar 
Oberlack, C. et al. Archetype analysis in sustainability research: meanings, motivations, and evidence-based policy making. Ecol. Soc. https://doi.org/10.5751/ES-10747-240226 (2019).Sietz, D. et al. Archetype analysis in sustainability research: methodological portfolio and analytical frontiers. Ecol. Soc. https://doi.org/10.5751/ES-11103-240334 (2019).Václavík, T., Lautenbach, S., Kuemmerle, T. & Seppelt, R. Mapping global land system archetypes. Glob. Environ. Change 23, 1637–1647 (2013).Article 

Google Scholar 
Vallejos, M. et al. Social-ecological functional types: connecting people and ecosystems in the Argentine Chaco. Ecosystems 23, 471–484 (2020).Article 

Google Scholar 
Oberlack, C., Tejada, L., Messerli, P., Rist, S. & Giger, M. Sustainable livelihoods in the global land rush? Archetypes of livelihood vulnerability and sustainability potentials. Glob. Environ. Change 41, 153–171 (2016).Article 

Google Scholar 
Miles, L. et al. A global overview of the conservation status of tropical dry forests. J. Biogeogr. 33, 491–505 (2006).Article 

Google Scholar 
Pennington, R. T., Lehmann, C. E. R. & Rowland, L. M. Tropical savannas and dry forests. Curr. Biol. 28, R541–R545 (2018).CAS 
Article 

Google Scholar 
Ribeiro, N. S., Katerere, Y., Chirwa, P. W. & Grundy, I. M. in Miombo Woodlands in a Changing Environment: Securing the Resilience and Sustainability of People and Woodlands (eds Ribeiro, N. S. et al.) 1–8 (Springer, 2020).Murphy, B. P., Andersen, A. N. & Parr, C. L. The underestimated biodiversity of tropical grassy biomes. Philos. Trans. R. Soc. B 371, 20150319 (2016).Article 

Google Scholar 
Chidumayo, E. & Marunda, C. in The Dry Forests and Woodlands of Africa (eds Chidumayo, E. N. & Gumbo, D.) 1–9 (Earthscan, 2010).Gasparri, N. I. & Grau, H. R. Deforestation and fragmentation of Chaco dry forest in NW Argentina (1972–2007). For. Ecol. Manag. 258, 913–921 (2009).Article 

Google Scholar 
Miranda, J., Börner, J., Kalkuhl, M. & Soares-Filho, B. Land speculation and conservation policy leakage in Brazil. Environ. Res. Lett. 14, 045006 (2019).Article 

Google Scholar 
Ingalls, M. L., Meyfroidt, P., To, P. X., Kenney-Lazar, M. & Epprecht, M. The transboundary displacement of deforestation under REDD+: problematic intersections between the trade of forest-risk commodities and land grabbing in the Mekong region. Glob. Environ. Change 50, 255–267 (2018).Article 

Google Scholar 
Davis, K. F. et al. Tropical forest loss enhanced by large-scale land acquisitions. Nat. Geosci. 13, 482–488 (2020).CAS 
Article 

Google Scholar 
Ordway, E. M., Asner, G. P. & Lambin, E. F. Deforestation risk due to commodity crop expansion in sub-Saharan Africa. Environ. Res. Lett. 12, 044015 (2017).Article 

Google Scholar 
Vancutsem, C. et al. Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7, eabe1603 (2021).Article 

Google Scholar 
Sunderland, T. et al. Global dry forests: a prologue. Int. For. Rev. 17, 1–9 (2015).
Google Scholar 
Grau, H. R. & Aide, M. Globalization and land-use transitions in Latin America. Ecol. Soc. https://doi.org/10.5751/es-02559-130216 (2008).le Polain de Waroux, Y. et al. Rents, actors, and the expansion of commodity frontiers in the Gran Chaco. Ann. Am. Assoc. Geogr. 108, 204–225 (2018).
Google Scholar 
Romero-Muñoz, A. et al. Fires scorching Bolivia’s Chiquitano forest. Science 366, 1082 (2019).Article 
CAS 

Google Scholar 
Hoang, N. T. & Kanemoto, K. Mapping the deforestation footprint of nations reveals growing threat to tropical forests. Nat. Ecol. Evol. 5, 845–853 (2021).Article 

Google Scholar 
Eigenbrod, F. et al. Identifying agricultural frontiers for modeling global cropland expansion. One Earth 3, 504–514 (2020).Article 

Google Scholar 
Nolte, C., le Polain de Waroux, Y., Munger, J., Reis, T. N. P. & Lambin, E. F. Conditions influencing the adoption of effective anti-deforestation policies in South America’s commodity frontiers. Glob. Environ. Change 43, 1–14 (2017).Article 

Google Scholar 
Volante, J. N. & Seghezzo, L. Can’t see the forest for the trees: can declining deforestation trends in the Argentinian Chaco region be ascribed to efficient law enforcement? Ecol. Econ. 146, 408–413 (2018).Article 

Google Scholar 
Chirwa, P. W. & Adeyemi, O. in Zero Hunger: Encyclopedia of the UN Sustainable Development Goals (eds Leal Filho, W. et al.) 1–15 (Springer, 2019).Pacheco, P. Actor and frontier types in the Brazilian Amazon: assessing interactions and outcomes associated with frontier expansion. Geoforum 43, 864–874 (2012).Article 

Google Scholar 
García, A. K., Meyfroidt, P., Abeygunawardane, D. & Sitoe, A. Waves and legacies: the making of an investment frontier in Niassa, Mozambique. Ecol. Soc. 27, 40 (2022).Leal, I. R., Da Silva, J. M. C., Tabarelli, M. & Lacher, T. E.Jr Changing the course of biodiversity conservation in the Caatinga of northeastern Brazil. Conserv. Biol. 19, 701–706 (2005).Article 

Google Scholar 
Osabuohien, E. S. & Karakara, A. A. in The Palgrave Handbook of Agricultural and Rural Development in Africa (ed. Osabuohien, E. S.) 627–640 (Springer, 2020).Gautier, D., Garcia, C., Negi, S. & Wardell, D. A. The limits and failures of existing forest governance standards in semi-arid contexts. Int. For. Rev. 17, 114–126 (2015).
Google Scholar 
Brandt, M. et al. An unexpectedly large count of trees in the West African Sahara and Sahel. Nature 587, 78–82 (2020).Article 
CAS 

Google Scholar 
Bastin, J. F. et al. The extent of forest in dryland biomes. Science 356, 635–638 (2017).CAS 
Article 

Google Scholar 
Fagan, M. E. A lesson unlearned? Underestimating tree cover in drylands biases global restoration maps. Glob. Change Biol. 26, 4679–4690 (2020).CAS 
Article 

Google Scholar 
Bey, A. & Meyfroidt, P. Improved land monitoring to assess large-scale tree plantation expansion and trajectories in Northern Mozambique. Environ. Res. Commun. https://doi.org/10.1088/2515-7620/ac26ab (2021).Harris, N., Goldman, E. D. & Gibbes, S. Spatial Database of Planted Trees (SDPT Version 1.0) (World Resources Institute, accessed 21 November 2021).Timberlake, W. J., Chidumayo, E. & Sawadogo, L. in The Dry Forests and Woodlands of Africa (eds Chidumayo, E. N. & Gumbo, D.) 11–41 (Earthscan, 2010).Portillo-Quintero, C. A. & Sánchez-Azofeifa, G. A. Extent and conservation of tropical dry forests in the Americas. Biol. Conserv. 143, 144–155 (2010).Article 

Google Scholar 
Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534–545 (2017).Article 

Google Scholar 
Murphy, P. G. & Lugo, A. E. Ecology of tropical dry forest. Annu. Rev. Ecol. Syst. 17, 67–88 (1986).Article 

Google Scholar 
Lock, J. M. in Neotropical Savannas and Seasonally Dry Forests (eds Pennington, R. T. & Ratter, J. A.) 449–467 (CRC Press, 2006).Malhi, Y. et al. Megafauna and ecosystem function from the Pleistocene to the Anthropocene. Proc. Natl Acad. Sci. USA 113, 838–846 (2016).CAS 
Article 

Google Scholar 
Baldi, G., Veron, S. R. & Jobbagy, E. G. The imprint of humans on landscape patterns and vegetation functioning in the dry subtropics. Glob. Change Biol. 19, 441–458 (2013).Article 

Google Scholar 
Lahsen, M., Bustamante, M. M. C. & Dalla-Nora, E. L. Undervaluing and overexploiting the Brazilian Cerrado at our peril. Environ. Sci. Policy Sustain. Dev. 58, 4–15 (2016).Article 

Google Scholar 
Sitoe, A., Chidumayo, E. & Alberto, M. in The Dry Forests and Woodlands of Africa (eds Chidumayo, E. N. & Gumbo, D.) 131–153 (Earthscan, 2010).Ozdogan, M. & Woodcock, C. E. Resolution dependent errors in remote sensing of cultivated areas. Remote Sens. Environ. 103, 203–217 (2006).Article 

Google Scholar 
Estes, L. et al. A large-area, spatially continuous assessment of land cover map error and its impact on downstream analyses. Glob. Change Biol. 24, 322–337 (2018).Article 

Google Scholar 
Dlamini, W. M. Mapping forest and woodland loss in Swaziland: 1990–2015. Remote Sens. Appl. Soc. Environ. 5, 45–53 (2017).
Google Scholar 
Geist, H. J. & Lambin, E. F. Proximate causes and underlying driving forces of tropical deforestation: tropical forests are disappearing as the result of many pressures, both local and regional, acting in various combinations in different geographical locations. BioScience 52, 143–150 (2002).Article 

Google Scholar 
Walker, R. Mapping process to pattern in the landscape change of the Amazonian frontier. Ann. Assoc. Am. Geogr. 93, 376–398 (2003).Article 

Google Scholar 
Baumann, M. et al. Frontier metrics for a process-based understanding of deforestation dynamics. Preprint at EarthArXiv https://doi.org/10.31223/X55S7J (2022).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 
Lesiv, M. et al. Estimating the global distribution of field size using crowdsourcing. Glob. Change Biol. 25, 174–186 (2019).Article 

Google Scholar 
Weiss, D. J. et al. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553, 333–336 (2018).CAS 
Article 

Google Scholar 
Global Agro-Ecological Zones (GAEZ v3. 0) (IIASA and FAO, accessed 24 July 2020).Heinimann, A. et al. A global view of shifting cultivation: recent, current, and future extent. PLoS ONE 12, e0184479 (2017).Article 
CAS 

Google Scholar 
Shamseer, L. et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ 349, g7647 (2015).Article 

Google Scholar  More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

Farmers at a Great Green Wall site in Niger. Researchers have found that the project is not always benefiting the most vulnerable people.Credit: Boureima Hama/AFP/Getty

It’s now 15 years since the African Union gave its blessing to Africa’s Great Green Wall, one of the world’s most ambitious ecological-restoration schemes. The project is intended to combat desertification across the width of Africa, and spans some 8,000 kilometres, from Senegal to Djibouti. Its ambition is staggering: it aims to restore 100 million hectares of degraded land by 2030, capturing 250 million tonnes of carbon dioxide and creating 10 million jobs in the process. But it continues to struggle.An assessment two years ago by independent experts commissioned by the United Nations stated that somewhere between 4% and 20% of the restoration target had been achieved (go.nature.com/39zqgkr). That figure has not changed, according to the latest edition of Global Land Outlook (go.nature.com/3kdjtw5) from the UN Convention to Combat Desertification (UNCCD), out last week. Equally concerning is the fact that funding for the project continues to lag. Africa’s governments and international donors need to find around US$30 billion to reach the 100-million-hectare target. So far, $19 billion has been raised.A pandemic — and now a cost-of-living crisis — has placed demands on all governments, and that means countries might be expected to reduce their green-wall commitments. But the project continues to be weighed down by other difficulties, including the complex system through which it is funded and governed, as well as how its success is measured. These problems can and must be fixed, otherwise it will struggle to achieve its goals.One potential solution — improved metrics — comes from an analysis published last year by Matthew Turner at the University of Wisconsin–Madison and his colleagues (M. D. Turner et al. Land Use Policy 111, 105750; 2021). The researchers explored limitations in the Great Green Wall project metrics by assessing the impact of World Bank funding from 2006 to 2020. As their work indicates, definitions of success depend on which measure is used.In Niger, for example, green-wall projects could be said to be succeeding if measured by the area of eroded soil that has been recovered or by the number of trees that have been planted. But the authors report that these gains were not necessarily benefiting the most vulnerable people. In places, women were being excluded from employment in green-wall projects, and in some cases, local administrations looked to privatize restored land that might instead have been owned by everyone in a community.Broader problems with metrics are highlighted in the UN’s latest land-degradation report. This estimates that nearly half of the land that has been pledged for restoration worldwide will be planted predominantly with fast-growing trees and plants. This will provide only a fraction of the ecosystem services produced by forests that are allowed to naturally regenerate, including significantly less carbon storage, groundwater recharge and wildlife habitat.The Great Green Wall project also needs more predictable funding and more transparent governance. The project was conceived by Africa’s leaders for the benefit of the continent’s people, on the basis of warnings from scientists about the risks of desertification and land degradation. The original idea was not brought to Africa by international donors, as is often the case in international science-based development projects. But it still relies on donor financing, and lots of it — and that brings other problems, among them coordination challenges.The project is the responsibility of an organization set up by the African Union called the Pan African Agency of the Great Green Wall, based in Nouakchott, Mauritania. But some donors, such as the European Union and the World Bank, are not providing most of their Great Green Wall funding through this agency. Instead, they often deal directly with individual governments, because this gives them more control over how their money is spent. It is unfair to expect the Pan African Agency to coordinate a raft of donors doing one-on-one deals with individual countries. Bypassing the Pan African Agency also creates a problem for transparency, because it makes it harder for the African Union to determine precisely who is funding what.In January 2021, at an international biodiversity summit hosted by France, Emmanuel Macron, the French president, announced that the Great Green Wall would receive an extra $14 billion in funding for 5 years. He also said that a new body, called the Great Green Wall Accelerator, based in Bonn, Germany, would be responsible for pulling together funding pledges and tracking progress against targets. This is well-intentioned, but the accelerator needs to coordinate its work with the Pan African Agency. It is not yet clear how this will happen.A potentially more transformative solution was proposed two years ago by a group of UN-appointed experts. They recommended that a single trust fund be set up that all donors could contribute to and through which they could decide funding priorities together. Regrettably, this has not happened, and observers say it is not likely to happen in the current climate.This month, the international community will come together in Abidjan, Côte d’Ivoire, for the 15th conference of the parties to the UNCCD. The green wall’s funders and participating countries will all be there. If a single trust fund is off the table, they must work together to find a better way to coordinate their green-wall project activities. It is also essential that they study the findings of Turner and colleagues’ review. Along with a focus on existing metrics, the Great Green Wall needs evaluation criteria that take better account of the needs of all people in participating countries, particularly the most vulnerable. More

Kollas, C., Körner, C. & Randin, C. F. Spring frost and growing season length co-control the cold range limits of broad-leaved trees. J. Biogeogr. 41, 773–783 (2014).Article 

Google Scholar 
Lenz, A., Hoch, G., Körner, C. & Vitasse, Y. Convergence of leaf-out towards minimum risk of freezing damage in temperate trees. Funct. Ecol. 30, 1480–1490 (2016).Article 

Google Scholar 
Körner, C. et al. Where, why and how? Explaining the low-temperature range limits of temperate tree species. J. Ecol. 104, 1076–1088 (2016).Article 
CAS 

Google Scholar 
Körner, C. Plant adaptation to cold climates. F1000Research 5, 2769 (2016).Article 

Google Scholar 
Vitra, A., Lenz, A. & Vitasse, Y. Frost hardening and dehardening potential intemperate trees from winter to budburst. New Phytol. 216, 113–123 (2017).PubMed 
Article 

Google Scholar 
Dy, G. & Payette, S. Frost hollows of the boreal forest as extreme environments for black spruce tree growth. Can. J. For. Res. 37, 492–504 (2007).Article 

Google Scholar 
Marquis, B., Bergeron, Y., Simard, M. & Tremblay, F. Growing-season frost is a better predictor of tree growth than mean annual temperature in boreal mixedwood forest plantations. Glob. Change Biol. 26, 6537–6554 (2020).ADS 
Article 

Google Scholar 
Hufkens, K. et al. Ecological impacts of a widespread frost event following early spring leaf-out. Glob. Change Biol. 18, 2365–2377 (2012).ADS 
Article 

Google Scholar 
Guo, X., Khare, S., Silvestro, R. & Rossi, S. Minimum spring temperatures at the provenance origin drive leaf phenology in sugar maple populations. Tree Physiol. 40, 1639–1647 (2020).CAS 
PubMed 
Article 

Google Scholar 
Marquis, B., Bergeron, Y., Simard, M. & Tremblay, F. Probability of spring frosts, not growing degree-days, drives onset of spruce bud burst in plantations at the boreal-temperate forest ecotone. Front. Plant Sci. 11, 1031 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Mahony, C. R., Cannon, A. J., Wang, T. & Aitken, S. N. A closer look at novel climates: New methods and insights at continental to landscape scales. Glob. Change Biol. 23, 3934–3955 (2017).ADS 
Article 

Google Scholar 
Roman-Palacios, C. & Wiens, J. Recent responses to climate change reveal the drivers of species extinction and survival. Proc. Natl. Acad. Sci. U.S.A. 117, 4211–4217 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chuine, I. A unified model for budburst of trees. J. Theor. Biol. 207, 337–347 (2000).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Horvath, P. et al. Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models. Biogeosciences 18, 95–112 (2021).ADS 
CAS 
Article 

Google Scholar 
Wullschleger, S. D. et al. Plant functional types in Earth system models: Past experiences and future directions for application of dynamic vegetation models in high-latitude ecosystems. Ann. Bot. Lond. 114, 1–16 (2014).CAS 
Article 

Google Scholar 
Deser, C. et al. Insights from earth system model initial-condition large ensembles and future prospects. Nat. Clim. Change 10, 277–286 (2020).ADS 
Article 

Google Scholar 
Machete, R. L. & Smith, L. A. Demonstrating the value of larger ensembles in forecasting physical systems. Tellus A Dyn. Meteorol. Oceanogr. 68, 28393 (2016).Article 

Google Scholar 
Deser, C., Phillips, A., Bourdette, V. & Teng, H. Uncertainty in climate change projections: The role of internal variability. Clim. Dyn. 38, 527–546 (2012).Article 

Google Scholar 
Leduc, M. et al. The ClimEx project: A 50-member ensemble of climate change projections at 12-km resolution over Europe and Northeastern North America with the Canadian regional climate model (CRM5). J. Appl. Meteor. Climatol. 58, 663–693 (2019).ADS 
Article 

Google Scholar 
Innocenti, S., Mailhot, A., Leduc, M., Cannon, A. J. & Frigon, A. Projected changes in the probability distributions, seasonality, and spatiotemporal scaling of daily and subdaily extreme precipitation simulated by a 50-member ensemble over Northeastern North America. J. Geophys. Res. Atmos. 124, 19 (2019).Article 

Google Scholar 
Kay, J. E. et al. The community earth system model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteor. Soc. 96, 1333–1349 (2015).ADS 
Article 

Google Scholar 
Thompson, D. W. J., Barnes, E. A., Deser, C., Foust, W. E. & Phillips, A. S. Quantifying the role of internal climate variability in future climate trends. J. Clim. 28, 6443–6456 (2015).ADS 
Article 

Google Scholar 
Kumar, D. & Ganguly, A. R. Intercomparison of model response and internal variability across climate model ensembles. Clim. Dyn. 51, 207–219 (2018).Article 

Google Scholar 
Gu, L. et al. The contribution of internal climate variability to climate change impacts on droughts. Sci. Total Environ. 684, 229–246 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Mittermeier, M., Braun, M., Hofstätter, M., Wang, Y. & Ludwig, R. Detecting climate change effects on Vb cyclones in a 50-member single-model ensemble using machine learning. Geophys. Res. Lett. 46, 14653–14661 (2019).ADS 
Article 

Google Scholar 
Fyfe, J. C. et al. Large near-term projected snowpack loss over the western United States. Nat. Commun. 8, 14996 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
Book 

Google Scholar 
Liu, Q. et al. Extension of the growing season increases vegetation exposure to frost. Nat. Commun. 9, 426 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zohner, C. M. et al. Late-spring frost risk between 1959 and 2017 decreased in North America but increased in Europe and Asia. Proc. Natl. Acad. Sci. U.S.A. 117, 12192–12200 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ma, Q., Huang, J.-G., Hänninen, H. & Berninger, F. Divergent trends in the risk of spring frost damage to trees in Europe with recent warming. Glob. Change Biol. 25, 351–360 (2018).ADS 
Article 

Google Scholar 
Cannell, M. G. R. & Smith, R. I. Climatic warming, spring budburst and forest damage on trees. J. Appl. Ecol. 23, 177–191 (1986).Article 

Google Scholar 
Morin, X. & Chuine, I. Will tree species experience increased frost damage due to climate change because of changes in leaf phenology? Can. J. For. Res. 44, 1555–1565 (2014).Article 

Google Scholar 
Fu, Y. et al. Progress in plant phenology modeling under global climate change. Sci. China Earth Sci. 63, 1237 (2020).ADS 
Article 

Google Scholar 
Gill, A. L. et al. Changes in autumn senescence in northern hemisphere deciduous trees: A meta-analysis of autumn phenology studies. Ann Bot. 116, 875–888 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Perrin, M., Rossi, S. & Isabel, S. N. Synchronisms between bud and cambium phenology in black spruce: Early-flushing provenances exhibit early xylem formation. Tree Physiol. 37, 593–603 (2017).PubMed 
Article 

Google Scholar 
Guo, X. et al. Common-garden experiment reveals clinical trends of bud phenology in black spruce populations from a latitudinal gradient in the boreal forest. J. Ecol. https://doi.org/10.1111/1365-2745.13582 (2021).Article 

Google Scholar 
Usmani, A. et al. Ecotypic differentiation of black spruce populations: Temperature triggers bud burst but not bud set. Trees 34, 1313–1321 (2020).Article 

Google Scholar 
Buttò, V., Rozenberg, P., Deslauriers, A., Rossi, S. & Morin, H. Environmental and developmental factors driving xylem anatomy and micro-density in black spruce. New Phytol. 230, 957. https://doi.org/10.1111/nph.17223 (2021).CAS 
Article 
PubMed 

Google Scholar 
Keenan, T. F. & Richardson, A. D. The timing of autumn senescence is affected by the timing of spring phenology: Implications for predictive models. Glob. Change Biol. 21, 2634–2641 (2015).ADS 
Article 

Google Scholar 
Zani, D., Crowther, T. W., Mo, L., Renner, S. S. & Zohner, C. M. Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees. Science 370, 1066–1071 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Silvestro, R. et al. From phenology to forest management: Ecotypes selection can avoid early or late frosts, but not both. For. Ecol. Manage. 436, 21–26 (2019).Article 

Google Scholar 
D’Orangeville, L. et al. Beneficial effects of climate warming on boreal tree growth may be transitory. Nat. Commun. 9, 3213 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Menezes-Silva, P. E. et al. Different ways to die in a changing world: Consequences of climate change for tree species performance and survival through an ecophysiological perspective. Ecol. Evol. 9, 11979–11999 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Schwarz, J. A., Saha, S. & Bauhus, J. Potential of forest thinning to mitigate drought stress: A meta-analysis. For. Ecol. Manage. 380, 261–273 (2016).Article 

Google Scholar 
Marquis, B., Bergeron, Y., Simard, M. & Tremblay, F. Disentangling the effect of topography and microtopography on near-ground growing-season frosts at the boreal-temperate forest ecotone (Québec, Canada). New For. https://doi.org/10.1007/s11056-021-09840-7 (2021).Article 

Google Scholar 
Marquis, B. growth stagnation of planted spruce in boreal mixedwoods: Importance of landscape, microsite, and growing-season frosts. For. Ecol. Manage. 479, 118533 (2021).Article 

Google Scholar 
Pedlar, J. et al. The implementation of assisted migration in Canadian forests. For. Chron. 87, 766–777 (2011).Article 

Google Scholar 
Marris, E. Planting the forest for the future. Nature 459, 906–908 (2009).CAS 
PubMed 
Article 

Google Scholar 
Klisz, M. et al. Limitations at the limit? Diminishing of genetic effects in Norway spruce provenance trials. Front. Plant Sci. 10, 306 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Pedlar, J. H., McKenney, D. W. & Lu, P. Critical seed transfer distances for selected tree species in eastern North America. J. Ecol. https://doi.org/10.1111/1365-2745.13605 (2021).Article 

Google Scholar 
Prudhomme, G. O. et al. Ecophysiology and growth of white spruce seedlings from various seed sources along a climatic gradient support the need for assisted migration. Front. Plant Sci. 8, 2214 (2017).Article 

Google Scholar 
Girardin, M. P. et al. Annual aboveground carbon uptake enhancements from assisted gene flow in boreal black spruce forests are not long-lasting. Nat. Commun. 12, 1169 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zohner, C. M., Mo, L., Sebald, V. & Renner, S. S. Leaf-out in northern ecotypes of wide-ranging trees requires less spring warming, enhancing the risk of spring frost damage at cold range limits. Glob. Ecol. Biogeogr. 29, 1065–1072 (2020).Article 

Google Scholar 
Saucier, J.-P., Baldwin, K., Krestov, P. & Jorgenson, T. Boreal forests. In Routledge Handbook of Forest Ecology (eds Peh, K.S.-H. et al.) 7–29 (Routledge, 2015).
Google Scholar 
Hutchinson, M. F. et al. Development and testing of Canada-wide interpolated spatial models of daily minimum-maximum temperature and precipitation for 1961–2003. J. Appl. Meteorol. Climatol. 48, 725–741 (2009).ADS 
Article 

Google Scholar 
Gennaretti, F., Sangelantoni, L. & Grenier, P. Toward daily climate scenarios for Canadian Arctic coastal zones with more realistic temperature-precipitation interdependence. J. Geophys. Res. Atmos. 120, 862–877 (2015).Article 

Google Scholar 
Mpelasoka, F. S. & Chiew, F. H. S. Influence of rainfall scenario construction methods on runoff projections. J. Hydrometeorol. 10, 1168–1183 (2009).ADS 
Article 

Google Scholar 
Giorgi, F. Thirty years of regional climate modeling: Where are we and where are we going next? J. Geophys. Res. 124, 5696–5723 (2019).Article 

Google Scholar 
Laughlin, G. P. & Kalma, J. D. Frost hazard assessment from local weather and terrain data. Agric. For. Meteorol. 40, 1–16 (1987).ADS 
Article 

Google Scholar 
Sørland, S. L., Schar, C., Luthi, D. & Kjellstrom, E. Bias patterns and climate change signals in GCM-RCM model chains. Environ. Res. Lett. 13, 074017 (2018).ADS 
Article 

Google Scholar 
von Trentini, F., Aalbers, E. E., Fischer, E. M. & Ludwig, R. Comparing interannual variability in three regional single-model initial-condition large ensembles (smiles) over Europe. Earth Syst. Dyn. 11, 1013–1031 (2020).ADS 
Article 

Google Scholar 
Hänninen, H. & Pelkonen, H. Effects of temperature on dormancy release in Norway spruce and Scots pine seedlings. Silva Fenn. 22, 5357 (1988).
Google Scholar 
Lechowicz, M. J. Why do temperate deciduous trees leaf out at different times? Adaptation and ecology of forest communities. Am. Nat. 124, 821–842 (1984).Article 

Google Scholar 
Olson, M. S. et al. The adaptive potential of Populus balsamifera L. to phenology requirements in a warmer global climate. Mol. Ecol. 22, 1214–1230 (2013).CAS 
PubMed 
Article 

Google Scholar 
Hawkins, C. D. B. & Dhar, A. Spring bud phenology of 18 Betula papyrifera populations in British Columbia. Scand. J. For. Res. 27, 507–519 (2012).Article 

Google Scholar 
Bronson, D. R., Gower, S. T., Tanner, M. & Van Herk, I. Effect of ecosystem warming on boreal black spruce bud burst and shoot growth. Glob. Change Biol. 15, 1534–1543 (2009).ADS 
Article 

Google Scholar 
Basler, D. Evaluating phenological models for the prediction of leaf-out dates in six temperate tree species across central Europe. Agric. For. Meteorol. 217, 10–21 (2016).ADS 
Article 

Google Scholar 
Man, R., Lu, P. & Dang, Q.-L. Insufficient chilling effects vary among boreal tree species and chilling duration. Front. Plant Sci. https://doi.org/10.3389/fpls.2017.01354 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
R Development Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020). http://www.R-project.org. More

Dordevic, B. & Dasic, T. Water storage reservoirs and their role in the development, utilization and protection of catchment. SPATIUM Int. Rev. 24, 9–15 (2011).
Google Scholar 
European Community. Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Official J. Eur. Union. 5, L327 (2000).

Google Scholar 
Riley, W. D. et al. Small water bodies in Great Britain and Ireland: Ecosystem function, human-generated degradation, and options for restorative action. Sci. Total Environ. 645, 1598–1616. https://doi.org/10.1016/j.scitotenv.2018.07.243 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kujawa, K., Arczyńska-Chudy, E., Janku, K. & Mana, M. Effect of buffer zone structure on diversity of aquatic vegetation in farmland water bodies. Pol. J. Ecol. 68(4), 263–282. https://doi.org/10.3161/15052249PJE2020.68.4.001 (2021).Article 

Google Scholar 
Akasaka, M., Takamura, N., Mitsuhashi, H. & Kadono, Y. Effects of land use on aquatic macrophyte diversity and water quality of ponds. Fresh Biol. 55, 909–922 (2010).CAS 
Article 

Google Scholar 
Pukacz, A., Pełechaty, M., Pełechata, A. & Siepak, M. The differential cover of submerged vegetation vs habitat conditions in the lakes of the Lubuskie Region. Limnol. Rev. 7(2), 95–100 (2007).
Google Scholar 
Scheffer, M., Hosper, S. H., Meijer, M. L., Moss, B. & Jeppesen, E. Alternative equilibria in shallow lakes. Trends Ecol. Evol. 8, 275–279 (1993).CAS 
Article 

Google Scholar 
Scheffer, M. & Jeppesen, E. Alternative stable states. Ecol. Stud. 131, 397–405 (1998).Article 

Google Scholar 
Beck, M. W., Tomcko, C., Valley, R. D. & Staples, D. F. Analysis of macrophyte indicator variation as a function of sampling, temporal and stressor effects. Ecol. Indic. 46, 322–335 (2014).Article 

Google Scholar 
Celewicz-Gołdyn, S. & Kuczyńska-Kippen, N. Ecological value of macrophyte cover in creating habitat for microalgae (diatoms) and zooplankton (rotifers and crustaceans) in small field and forest water bodies. PLoS ONE 12(5), e0177317. https://doi.org/10.1371/journal.pone.0177317 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wilk-Woźniak, E. et al. Effects of the environs of waterbodies on aquatic plants in oxbow lakes (habitat 3150). Ecol. Ind. 98, 736–742. https://doi.org/10.1016/j.ecolind.2018.11.025 (2019).CAS 
Article 

Google Scholar 
Bedla, D. & Misztal, A. Changeability of chemistry of small water reservoirs with diversified use structure of the adjoining areas. Annu. Set Environ. Protect. 16, 421–439 (2014) (ISSSN 1506-218X (in Polish)).
Google Scholar 
Mozgawa, J. Photointerpretation analysis of landscape structure in lake watersheds of Suwałki Landscape. Park. Ekol. Pol. 41, 53–74 (1993).
Google Scholar 
Mioduszewski, W. Small water reservoirs on agricultural areas. Wieś Jutra Zakład Zasobów Wodnych Instytut Melioracji i Użytków Zielonych Falenty. 10(123), 32–34 (2008) (in Polish).
Google Scholar 
Gołdyn, B., Kowalczewska-Madura, K. & Celewicz-Gołdyn, S. Drought and deluge: Influence of environmental factors on water quality of kettle holes in two subsequent years with different precipitation. Limnologica 54, 14–22. https://doi.org/10.1016/j.limno.2015.07.002 (2015).CAS 
Article 

Google Scholar 
Bylak, A. et al. Small stream catchment in a developing city context: The importance of land cover changes on the ecological status of streams and the possibilities for providing ecosystem services. Sci. Total Environ. 815, 151974. https://doi.org/10.1016/j.scitotenv.2021.151974 (2022).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Goyal, V. C. et al. Ecological health and water quality of village ponds in the subtropics limiting their use for water supply and groundwater recharge. J. Environ. Manage. 277, 111450. https://doi.org/10.1016/j.jenvman.2020.111450 (2021).CAS 
Article 
PubMed 

Google Scholar 
Kuczyńska-Kippen, N., Spoljar, M., Mleczek, M. & Zhang, Ch. Elodeides, but not helophytes, increase community diversity and reduce trophic state: Case study with rotifer indices in field ponds. Ecol. Ind. 128, 107829. https://doi.org/10.1016/j.ecolind.2021.107829 (2021).Article 

Google Scholar 
Novikmec, M. et al. Ponds and their catchments: size relationships and influence of land use across multiple spatial scales. Hydrobiologia 774, 155–166. https://doi.org/10.1007/s10750-015-2514-8 (2016).Article 

Google Scholar 
Dudzińska, A., Szpakowska, B. & Pajchrowska, M. Influence of land development on the ecological status of small water bodies. Oceanol. Hydrobiol. Stud. 49(4), 345–353. https://doi.org/10.1515/ohs-2020-0030 (2020).Article 

Google Scholar 
Szpakowska, B. Occurrence and Role of Organic Compounds Dissolved in Surface and Ground Waters of Agricultural Landscape (Wydawnictwo Uniwersytetu Mikołaja Kopernika, 1999) (in Polish).
Google Scholar 
Wysocka-Czubaszek, A. & Banaszuk, P. Migration of nitrogen compounds and the riparian zones in the Upper Narew Valley. Acta Agroph. 2(1), 349–354 (2003) (in Polish).
Google Scholar 
Szpakowska, B., Świerk, D., Pajchrowska, M. & Gołdyn, R. Verifying the usefulness of macrophytes as an indicator of the status of small waterbodies. Sci. Total Environ. 798, 149279. https://doi.org/10.1016/j.scitotenv.2021.149279 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Hermanowicz, W., Dojlido, J., Dożańska, W., Koziorowski, B. & Zerbe, J. Physico-Chemical Examinations of Water and Sludge (Arkady, 1999) (in Polish).
Google Scholar 
Aynur, M., Liming, N. & Fang, Y. Spatial evaluation of environmental suitability for human settlement of Kashgar, Northwest China. Fresenius Environ. Bull. 27(9), 5899–5907 (2018).
Google Scholar 
Pham, L., Brabyn, L. & Ashrof, S. Combining Quick Bird, LiDAR and GIS topography indices to identify a single native tree species in a complex landscape using an object-based classification approach. Int. J. Appl. Earth Obs. Geoinf. 50, 187–197. https://doi.org/10.1016/j.jag.2016.03.015 (2016).ADS 
Article 

Google Scholar 
Lepš, J. & Šmilauer, P. Multivariate Analysis of Ecological Data Using CANOCO (Cambridge Univeristy Press, 2003) (ISBN 9780521891080).Book 

Google Scholar 
Mohamed, Z. A. Macrophytes-cyanobacteria allelopathic interactions and their implications for water resources management—A review. Limnologica 63, 122–132. https://doi.org/10.1016/j.limno.2017.02.006 (2017).CAS 
Article 

Google Scholar 
Koc, J. Problems of small reservoirs protection on rural areas. In Problemy ochrony i użytkowania obszarów wiejskich o dużych walorach przyrodniczych (eds Radwan, S. & Lorkiewicz, Z.) 151–156 (UMCS, 2000).
Google Scholar 
Bell, V. A. et al. Long term simulations of macronutrients (C, N and P) in UK freshwaters. Sci. Total Environ. 776, 145813. https://doi.org/10.1016/j.scitotenv.2021.145813 (2021).ADS 
CAS 
Article 

Google Scholar 
Winton, R. S. et al. Anthropogenic influences on Zambian water quality: Hydropower and land-use change. Environ. Sci. Process. Impacts. 23, 981–994. https://doi.org/10.1039/D1EM00006C (2021).CAS 
Article 
PubMed 

Google Scholar 
Wang, Y. et al. Effect and risk assessment of animal manure pollution on Huaihe River Basin, China. Chin. Geogr. Sci. 31, 751–764. https://doi.org/10.1007/s11769-021-1222-8 (2021).Article 

Google Scholar 
Lawniczak-Malińska, A., Ptak, M., Celewicz, S. & Choiński, A. Impact of lake morphology and shallowing on the rate of overgrowth in hard-water eutrophic lakes. Water 10(12), 1827. https://doi.org/10.3390/w10121827 (2018).CAS 
Article 

Google Scholar 
Bosiacka, B., Pacewicz, K. & Pieńkowski, P. Spatial analysis of plant species distribution among small water bodies in an agricultural landscape. Acta Agrobot. 61(2), 93–101. https://doi.org/10.5586/aa.2008.037 (2008).Article 

Google Scholar 
Joniak, T., Kuczyńska-Kippen, N. & Gąbka, M. Effect of agricultural landscape characteristics on the hydrobiota structure in small water bodies. Hydrobiologia 793, 121–133. https://doi.org/10.1007/s10750-016-2913-5 (2017).CAS 
Article 

Google Scholar 
Barling, R. D. & Moore, I. D. Role of buffer strips in management of waterway pollution: A review. Environ. Manage. 18, 543–558. https://doi.org/10.1007/BF02400858 (1994).ADS 
Article 

Google Scholar 
Blanco-Cangui, H., & Lal, R. Buffer strips. In Principles of Soil Conservation and Management (eds Blanco, H. & Lal, R.) 223–257 (Springer, Netherlands, 2008).
Google Scholar 
Borin, M., Passoni, M., Thiene, M. & Tempesta, T. Multiple functions of buffer strips in farming areas. Eur. J. Agron. 32, 103–111. https://doi.org/10.1016/j.eja.2009.05.003 (2010).Article 

Google Scholar 
Ghobrial, M. G., Nassr, H. S. & Kamil, A. W. Bioactivity effect of two macrophyte extracts on growth performance of two bloom-forming cyanophytes. Egypt. J. Aquat. Res. 41(1), 69–81. https://doi.org/10.1016/j.ejar.2015.01.001 (2015).Article 

Google Scholar 
Lacas, J. G., Voltz, M., Gouy, V., Carluer, N. & Gril, J. J. Using grassed strips to limit pesticide transfer to surface water: A review. Agron. Sustain. Dev. 25, 253–266 (2005).CAS 
Article 

Google Scholar 
Świerk, D. & Szpakowska, B. An ecosystem valuation method for small water bodies. Ecol. Chem. Eng. S. 20(2), 397–418. https://doi.org/10.2478/eces-2013-0029 (2013).Article 

Google Scholar 
Marszałek, M., Kowalski, Z. & Makara, A. The possibility of contamination of water-soil environment as a results of the use of pig slurry. Ecol. Chem. Eng. S. 26(2), 313–330. https://doi.org/10.1515/eces-219-0022 (2019).Article 

Google Scholar 
Micek, G., Górecki, J. & Neo, H. Relations: Company and its local milieu in the context of foreign direct investment in Polish pig industry. In Człowiek i Środowisko (eds Górka, Z. & Zborowski, A.) 297–308 (UJ Kraków, 2009).
Google Scholar 
OECD. Agriculture Trade and The Environment: The Pig Sector (OECD, 2003).
Google Scholar 
Barałkiewicz, D. et al. Storm water contamination and its effect on the quality of urban surface waters. Environ. Monit. Assess. 186(10), 6789–6803. https://doi.org/10.1007/s10661-014-3889-0 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
So Fijanic, A., Hulley, M. & Loock, D. Stormwater quality assessment and management for the town of jasper in Alberta, Canada. Water Qual. Res. J. Can. 56(1), 45–56. https://doi.org/10.2166/wqrj.2021.012 (2021).CAS 
Article 

Google Scholar 
Zubala, T. Assessment of the variability of rainwater quality and the functioning of retention reservoirs in the urban area. Rocznik Ochrona Środowiska 22(2), 840–858 (2020).
Google Scholar 
Czerniawski, R., Sługocki, L., Krepski, T., Wilczak, A. & Pietrzak, K. Spatial changes in invertebrate structures as a factor of strong human activity in the bed and catchment area of a small urban stream. Water 12(3), 913. https://doi.org/10.3390/w12030913 (2020).CAS 
Article 

Google Scholar 
Gołdyn, R. et al. Influence of stormwater runoff on macroinvertebrates in a small urban river and a reservoir. Sci. Total Environ. 625, 743–751. https://doi.org/10.1016/j.scitotenv.2017.12.324 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Cao, Y., Zhi, Y., Jeppesen, E. & Li, W. Species-specific responses of submerged macrophytes to simulated extreme precipitation: A mesocosm study. Water 11(6), 1160. https://doi.org/10.3390/w11061160 (2019).CAS 
Article 

Google Scholar 
Hilt, S. et al. Restoration of submerged vegetation in shallow eutrophic lakes—A guideline and state of the art in Germany. Limnologica 36, 155–171 (2006).CAS 
Article 

Google Scholar 
Ryszkowski, L. & Kędziora, A. Modification of water flows and nitrogen fluxes by shelterbelts. Ecol. Eng. 29(4), 388–400. https://doi.org/10.1016/j.ecoleng.2006.09.023 (2007).Article 

Google Scholar  More