Philippa Kaur

Kalbitz, K. & Kaiser, K. Contribution of dissolved organic matter to carbon storage in forest mineral soils. J. Plant Nutr. Soil Sci. 171, 52–60 (2008).CAS 

Google Scholar 
Michalzik, B. et al. Modelling the production and transport of dissolved organic carbon in forest soils. Biogeochemistry 66, 241–264 (2003).CAS 

Google Scholar 
Sokol, N. W., Sanderman, J. & Bradford, M. A. Pathways of mineral-associated soil organic matter formation: Integrating the role of plant carbon source, chemistry, and point of entry. Glob. Change Biol. 25, 12–24 (2019).ADS 

Google Scholar 
Roth, V.-N. et al. Persistence of dissolved organic matter explained by molecular changes during its passage through soil. Nat. Geosci. https://doi.org/10.1038/s41561-019-0417-4 (2019).Article 

Google Scholar 
Jones, O. A. H. et al. Metabolomics and its use in ecology: Metabolomics in ecology. Austral Ecol. 38, 713–720 (2013).
Google Scholar 
Gołębiewski, M. et al. Rapid microbial community changes during initial stages of pine litter decomposition. Microb. Ecol. 77, 56–75 (2019).PubMed 

Google Scholar 
Chomel, M. et al. Plant secondary metabolites: A key driver of litter decomposition and soil nutrient cycling. J. Ecol. 104, 1527–1541 (2016).
Google Scholar 
Purahong, W., Wubet, T., Krüger, D. & Buscot, F. Molecular evidence strongly supports deadwood-inhabiting fungi exhibiting unexpected tree species preferences in temperate forests. ISME J. 12, 289–295 (2018).
Google Scholar 
Djukic, I. et al. Early stage litter decomposition across biomes. Sci. Total Environ. 628–629, 1369–1394 (2018).ADS 
PubMed 

Google Scholar 
Rousk, J. et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 4, 1340–1351 (2010).PubMed 

Google Scholar 
Wu, Y., Zeng, J., Zhu, Q., Zhang, Z. & Lin, X. pH is the primary determinant of the bacterial community structure in agricultural soils impacted by polycyclic aromatic hydrocarbon pollution. Sci. Rep. 7, 40093 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Griffiths, R. I. et al. The bacterial biogeography of British soils: Mapping soil bacteria. Environ. Microbiol. 13, 1642–1654 (2011).PubMed 

Google Scholar 
Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Büttner, H. et al. Bacterial endosymbionts protect beneficial soil fungus from nematode attack. Proc. Natl. Acad. Sci. USA 118, e2110669118 (2021).PubMed 
PubMed Central 

Google Scholar 
Lucas, J. M., Gora, E., Salzberg, A. & Kaspari, M. Antibiotics as chemical warfare across multiple taxonomic domains and trophic levels in brown food webs. Proc. R. Soc. B 286, 20191536 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Goldbeck, O. et al. Establishing recombinant production of pediocin PA-1 in Corynebacterium glutamicum. Metab. Eng. 68, 34–45 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu, X. et al. Microbial interactions with dissolved organic matter drive carbon dynamics and community succession. Front. Microbiol. 9, 1234 (2018).PubMed 
PubMed Central 

Google Scholar 
D’Andrilli, J., Junker, J. R., Smith, H. J., Scholl, E. A. & Foreman, C. M. DOM composition alters ecosystem function during microbial processing of isolated sources. Biogeochemistry 142, 281–298 (2019).
Google Scholar 
Benk, S. A. et al. Fueling diversity in the subsurface: Composition and age of dissolved organic matter in the critical zone. Front. Earth Sci. 7, 296 (2019).ADS 

Google Scholar 
Marschner, P., Umar, S. & Baumann, K. The microbial community composition changes rapidly in the early stages of decomposition of wheat residue. Soil Biol. Biochem. 43, 445–451 (2011).CAS 

Google Scholar 
Badri, D. V., Zolla, G., Bakker, M. G., Manter, D. K. & Vivanco, J. M. Potential impact of soil microbiomes on the leaf metabolome and on herbivore feeding behavior. New Phytol. 198, 264–273 (2013).CAS 
PubMed 

Google Scholar 
Kohlhepp, B. et al. Pedological and hydrogeological setting and subsurface flow structure of the carbonate-rock CZE Hainich in western Thuringia, Germany. Hydrol. Earth Syst. Sci. https://doi.org/10.5194/hess-2016-374 (2016).Dittmar, T., Koch, B. P., Hertkorn, N. & Kattner, G. A simple and efficient method for the solid-phase extraction of dissolved organic matter (SPE-DOM) from seawater. Limnol. Oceanogr. 6, 230–235 (2008).CAS 

Google Scholar 
Simon, C., Roth, V.-N., Dittmar, T. & Gleixner, G. Molecular signals of heterogeneous terrestrial environments identified in dissolved organic matter: A comparative analysis of orbitrap and ion cyclotron resonance mass spectrometers. Front. Earth Sci. 6, 138 (2018).ADS 

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

Google Scholar 
Herlemann, D. P. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kumar, S. et al. Thiosulfate- and hydrogen-driven autotrophic denitrification by a microbial consortium enriched from groundwater of an oligotrophic limestone aquifer. FEMS Microbiol. Ecol. 94, 10 (2018).
Google Scholar 
Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the miseq illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Taubert, M. et al. Tracking active groundwater microbes with D 2 O labelling to understand their ecosystem function: Tracking active groundwater microbes. Environ. Microbiol. 20, 369–384 (2018).CAS 
PubMed 

Google Scholar 
Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590-596 (2013).CAS 
PubMed 

Google Scholar 
Lohmann, P. et al. Function is what counts: How microbial community complexity affects species, proteome and pathway coverage in metaproteomics. Expert Rev. Proteomics 17, 163–173 (2020).CAS 
PubMed 

Google Scholar 
Starke, R. et al. Candidate brocadiales dominates C, N and S cycling in anoxic groundwater of a pristine limestone-fracture aquifer. J. Proteomics 152, 153–160 (2017).CAS 
PubMed 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2019).Oksanen, J. et al. vegan: Community Ecology Package (Springer, 2018).
Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 

Google Scholar 
Zoppi, J., Guillaume, J.-F., Neunlist, M. & Chaffron, S. MiBiOmics: an interactive web application for multi-omics data exploration and integration. BMC Bioinform. 22, 6 (2021).
Google Scholar 
Adler, D. & Murdoch, D. rgl: 3D Visualization Using OpenGL (Springer, 2019).
Google Scholar 
Aßhauer, K. P., Wemheuer, B., Daniel, R. & Meinicke, P. Tax4Fun: Predicting functional profiles from metagenomic 16S rRNA data: Fig. 1. Bioinformatics 31, 2882–2884 (2015).PubMed 
PubMed Central 

Google Scholar 
Fath, M. J. & Kolter, R. ABC transporters: Bacterial exporters. Microbiol. Rev. 57, 995 (1993).CAS 
PubMed 
PubMed Central 

Google Scholar 
Waters, C. M. & Bassler, B. L. Quorum sensing: Cell-to-cell communication in bacteria. Annu. Rev. Cell Dev. Biol. 21, 319–346 (2005).CAS 
PubMed 

Google Scholar 
Deutscher, J., Francke, C. & Postma, P. W. How phosphotransferase system-related protein phosphorylation regulates carbohydrate metabolism in bacteria. Microbiol. Mol. Biol. Rev. 70, 939–1031 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vogt, T. Phenylpropanoid biosynthesis. Mol. Plant 3, 2–20 (2010).CAS 
PubMed 

Google Scholar 
Polturak, G. et al. Engineered gray mold resistance, antioxidant capacity, and pigmentation in betalain-producing crops and ornamentals. PNAS 114, 9062–9067 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mille-Lindblom, C. & Tranvik, L. J. Antagonism between bacteria and fungi on decomposing aquatic plant litter. Microb. Ecol. 45, 173–182 (2003).CAS 
PubMed 

Google Scholar 
Chopra, I. & Roberts, M. Tetracycline antibiotics: Mode of action, applications, molecular biology, and epidemiology of bacterial resistance. Microbiol. Mol. Biol. Rev. 65, 232–260 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
Schiessl, K. T. et al. Phenazine production promotes antibiotic tolerance and metabolic heterogeneity in Pseudomonas aeruginosa biofilms. Nat. Commun. 10, 762 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Reading, C. & Cole, M. Clavulanic acid: A beta-lactamase-inhiting beta-lactam from Streptomyces clavuligerus. Antimicrob. Agents Chemother. 11, 852–857 (1977).CAS 
PubMed 
PubMed Central 

Google Scholar 
Šnajdr, J. et al. Transformation of Quercus petraea litter: Successive changes in litter chemistry are reflected in differential enzyme activity and changes in the microbial community composition: Transformation of Quercus petraea litter. FEMS Microbiol. Ecol. 75, 291–303 (2011).PubMed 

Google Scholar 
Voříšková, J. & Baldrian, P. Fungal community on decomposing leaf litter undergoes rapid successional changes. ISME J. 7, 477–486 (2013).PubMed 

Google Scholar 
Buresova, A. et al. Succession of microbial decomposers is determined by litter type, but site conditions drive decomposition rates. Appl. Environ. Microbiol. 85, e01760-19 (2019).PubMed 
PubMed Central 

Google Scholar 
Hopwood, D. A. Streptomyces in Nature and Medicine: The Antibiotic Makers (Oxford University Press, 2007).
Google Scholar 
Anaya-López, J. L., López-Meza, J. E. & Ochoa-Zarzosa, A. Bacterial resistance to cationic antimicrobial peptides. Crit. Rev. Microbiol. 39, 180–195 (2013).PubMed 

Google Scholar 
Lindner, K. R., Bonner, D. P. & Koster, W. H. Monobactams. Kirk-Othmer Encyclopedia of Chemical Technology (Wiley, 2000). https://doi.org/10.1002/0471238961.1315141512091404.a01.Book 

Google Scholar 
Eustáquio, A. S. et al. Novobiocin biosynthesis: Inactivation of the putative regulatory gene novE and heterologous expression of genes involved in aminocoumarin ring formation. Arch. Microbiol. 180, 25–32 (2003).PubMed 

Google Scholar 
Banerjee, S. et al. Network analysis reveals functional redundancy and keystone taxa amongst bacterial and fungal communities during organic matter decomposition in an arable soil. Soil Biol. Biochem. 97, 188–198 (2016).CAS 

Google Scholar 
Taubert, M., Stähly, J., Kolb, S. & Küsel, K. Divergent microbial communities in groundwater and overlying soils exhibit functional redundancy for plant-polysaccharide degradation. PLoS ONE 14, e0212937 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wallenstein, M. D., Hess, A. M., Lewis, M. R., Steltzer, H. & Ayres, E. Decomposition of aspen leaf litter results in unique metabolomes when decomposed under different tree species. Soil Biol. Biochem. 42, 484–490 (2010).CAS 

Google Scholar 
Backlund, I. et al. Extractive profiles of different lodgepole pine (Pinus contorta) fractions grown under a direct seeding-based silvicultural regime. Ind. Crops Prod. 58, 220–229 (2014).CAS 

Google Scholar  More

Spatiotemporal variations in N and P emissions from livestock manureManure N and P production and comparison with fertilizer usage in the Yangtze River DeltaAlthough the amounts of chemical fertilizers used in the Yangtze River Delta region fluctuated greatly before 2000, the overall trend was still increasing, and there was a continuous downward trend since 2000 (Fig. 1). Specifically, the amount of chemical fertilizer used in the Yangtze River Delta region reached a peak of nearly 40 years in 1985, with 1.87 × 109 kg of nitrogen fertilizer and 3.38 × 108 kg of phosphate fertilizer. After entering the low period in 1990, it began to recover slowly, but since the beginning of the twenty-first century, the amount of fertilizer application has shown a continuous downward trend. By 2018, the amount of fertilizer nitrogen and phosphorus applied in the Yangtze River Delta region was 7.79 × 108 kg and 1.31 × 108 kg, respectively, which was a decrease of 58.28% and 61.28% compared to 1985.Figure 1Application of N and P fertilizers and the amount of N and P emissions from organic fertilizers (manure) in the Yangtze River Delta from 1980 to 2018.Full size imageThe change trend of fertilizer application in the Yangtze River Delta may be a combined effect of the national policy and the change of cultivated land area. Since the 1970s, my country has vigorously promoted the fertilizer industry and agricultural fertilization47,48, which reached a peak in 1985. However, due to the adjustment of agricultural policies around 1990, the fertilizer market at this time is relatively chaotic49, resulting in fluctuations in the amount of fertilizer used. The amount of fertilizer application is closely related to the area of arable land. Around 2000, the area of arable land in China decreases, and the amount of fertilization is also affected. However, the use of chemical fertilizers per unit area still causes serious environmental pollution. In 2015, the Ministry of Agriculture formulated and issued the “Action Plan for Zero Growth of Chemical Fertilizer Use by 2020”, which provided new guidance for chemical fertilizer application, which led to a fundamental reduction.Over the past 40 years, emissions of N and P from manure in the Yangtze River Delta generally increased at first and then decreased. From 1980 to 2005, N and P emissions from manure in the Yangtze River Delta showed an increasing trend. Manure N increased from 2.13 × 108 kg in 1980 to 2.66 × 108 kg in 2005, increased by 24.77%. Manure P increased from 7.34 × 107 kg in 1980 to 1.03 × 108 kg in 2005, increased by 40.11%. After reaching a peak in 2005, emissions showed a continuous declining trend. In 2018, N and P emissions from manure used as organic fertilizer were 1.46 × 108 kg and 5.68 × 107 kg, respectively, back to 1980 levels.The change trend of manure nitrogen and phosphorus emissions in the Yangtze River Delta is mainly affected by economic development and national policies. From 1985 to 2005, the vigorous economic development also promoted the increasing demand for meat, eggs, and milk in people’s lives. The development of animal husbandry50, and there were almost no special regulations on the establishment of sound manure management in the livestock and poultry farming industry before 2000. Factors have contributed to the increase in the discharge of livestock and poultry manure. After 2000, the State Environmental Protection Administration successively promulgated many normative documents, which effectively curb the increase of nitrogen and phosphorus emissions from livestock manure.Spatiotemporal variation characteristics of N and P emissions from livestock manureThere have obvious temporal and spatial variabilities in manure N and P emissions in the soils among cities in the Yangtze River Delta over the past 40 years (Figs. 2, 3). N and P emissions from livestock manure in the soils showed a trend of increasing and then decreasing, with manure nutrient emissions from livestock in some areas showed a fluctuating trend.Figure 2Manure N emissions in cities in the Yangtze River Delta.Full size imageFigure 3Manure P emissions in cities in the Yangtze River Delta.Full size imageNantong and Shanghai have always been the geographic focus of livestock manure nutrient emissions in terms of spatial change. Average N and P emissions from livestock manure in Nantong over the past 40 years was 3.47 × 107 and 1.39 × 107 kg, respectively; average N and P emissions from livestock manure in Shanghai over the past 40 years was 2.67 × 107 and 9.70 × 106 kg, respectively. During the past 40 years, the total N and P emissions from livestock manure were lowest in Zhoushan. From 1980 to 2018, the average yields of N and P in livestock manure were only 1.18 × 106 and 4.34 × 105 kg, respectively, in Zhoushan. The N and P emissions from livestock manure in other cities fluctuated 3.54 × 105 to 3.01 × 107 kg and 1.35 × 105 to 1.14 × 107 kg, respectively.Nantong and Shanghai have become the geographic focus of livestock and poultry manure emissions due to the following reasons: on the one hand, rapid economic development, abundant population resources, urban density and high degree of agricultural intensification are all factors that have caused the rapid development of livestock and poultry breeding. On the other hand, arable land resources are extremely scarce, resulting in a unit arable land carrying capacity much higher than other cities, and the dense water network has also accelerated the loss of nitrogen and phosphorus in livestock manure. As Zhoushan is located at the intersection of the golden coastline of eastern China and the golden waterway of the Yangtze River, it is China’s largest seafood production base, and its development focus is not on the livestock and poultry breeding industry.Changes in manure loads of livestock in the Yangtze River Delta from 1980 to 2018Spatiotemporal pattern of manure N and P loads in livestockFrom 1980 to 2018, the manure N load in the soils of the Yangtze River Delta showed an overall trend of increasing first and then decreasing (Fig. 4). From 1980 to 2010, most regions of the study area showed an increasing trend in manure N load. In 2010, the on average manure N load was highest. The high load area was rapidly expanding from the north and the middle east to the south of the study area, and the load center shifted from the east to the middle and southwest. From 2010 to 2018, the manure N load decreased year by year, especially in the middle of the study area, and the high-load area transferred to the edge area. In 2018, manure N load recovered roughly to the level seen in the 1990s.Figure 4Spatiotemporal distribution of manure N load in the Yangtze River Delta from 1980 to 2018.Full size imageFrom 1980 to 2010, average manure N load increased from 27.46 to 50.61 kg hm−2, representing a growth of 84.30% to the maximum manure N load of the past 40 years. This trend is basically consistent with the gradual increase in the average nitrogen pollution load per unit of arable land in China as shown by the results of earlier studies48,51,52,53. Manure N load in Zhoushan (located in the eastern coastal area), Shanghai, Huzhou, and Jiaxing (located in the central area), and Hangzhou (located in the southwest region) increased significantly: Zhoushan saw the largest increase (295.25%) from 22.04 kg hm−2 in 1980 to 87.10 kg hm−2 in 2010. On the one hand, the livestock breeding industry is affected by the price regulation and management of the agricultural material market, and on the other hand, it is affected by production price factors. Therefore, the rapid increase in demand for livestock caused by the rapid economic development, the increase in prices, and the increase in the number of breeding industries are the main reasons for the rapid increase in the manure N load during this period. From 2010 to 2018, the average manure loads dropped dramatically, decreasing from 50.61 to 30.29 kg hm−2, representing a decline of 40.15%. The average manure N load in Wuxi, Suzhou, Jiaxing (located in the central part), and Zhoushan (located in the eastern coastal area) showed significant declines, with that in Jiaxing decreasing from 100.88 to 24.60 kg hm−2, representing a decrease of 75.62%. Feng et al.54 found that policy is an important factor that makes livestock breeding industry more standardized and the environment improved. Therefore, the reduction in the manure N load during this period was largely affected by policy regulation. Over the past 40 years, the manure N load in Shanghai has been maintained at a high level, with a load of over 50 kg hm−2 throughout the period. By contrast, the manure N load in Yangzhou has been maintained at a low level for a long time, which was below 30 kg hm−2, highlighting significant regional differences. The reason is the difference caused by the difference in the degree of urban construction and economic development in different regions. In areas with relatively developed economies, the demand for livestock products is large, and the amount of livestock is large, but the area of arable land is small, and the capacity of absorbing livestock and poultry manure is limited, resulting in a relatively large manure N and P loads on cultivated land55.From 1980 to 2018, the manure N load center moved from the central and northern regions of the study area to the northwestern and eastern, and then to the southwestern and eastern regions after a peak of manure loads was reached in each city. In the 1980s, the maximum livestock manure loads were located in the middle (Jiaxing) and the northern part (Wuxi) of the study area. Subsequently, the center of gravity of manure N emissions from livestock gradually shifted to the east. In the 1990s, the livestock manure N load gravity center was located in the northwestern (Zhenjiang) and eastern (Shanghai) areas. At the beginning of the twenty-first century, the livestock manure N load was maintained at a relatively high level in most cities, and the emission center gradually shifted to the east. In 2018, the livestock manure N load was centred in the southwestern region (Hangzhou) and the eastern region (Nantong, Shanghai).Considering that there is no systematic standard limit of organic fertilizer N in China, we used the European Union’s farmland manure N limit standard of as the basis for determining the manure N load51. From 1980 to 2018, the manure N load did not exceed the European Union’s standard (170 kg hm−2), but still showed an increasing trend. It can be seen that the discharge of livestock manure in each city has had adverse impact on the environment of the Yangtze River Delta.From 1980 to 2018, the spatiotemporal evolution pattern of livestock manure P load in the soils was very similar to that of the manure N load, with an overall trend of first increasing and then decreasing (Fig. 5). From 1980 to 2010, the livestock manure P load showed an increasing trend. By 2010, the livestock manure P load had reached its maximum over the past 40 years. Areas with high livestock manure P load spread from the central area to the surrounding cities, and finally radiated to the surrounding regions with the central area as the load center; from 2010 to 2018, the livestock manure P load decreased significantly, and areas with high manure P load migrated from the central region to the southwestern marginal region and the northeastern coastal cities.Figure 5Spatiotemporal distribution of manure P load in the Yangtze River Delta from 1980 to 2018.Full size imageFrom 1980 to 2010, the average livestock manure P load in the Yangtze River Delta increased from 9.36 to 19.47 kg hm−2, representing a growth rate of 108.02%. Average manure P loads in Zhoushan, Ningbo (located in the eastern coastal area), and Jiaxing, Hangzhou, and Huzhou (located in the central and southern regions) showed significant increases of 347.66%, 323.28%, 198.13%, 181.88%, and 155.39%, respectively. From 2010 to 2018, the average livestock manure P load was reduced from 19.47 to 11.74 kg hm−2, representing a reduction of 39.71%. Zhoushan (located in the eastern coastal area) and Jiaxing and Suzhou (located in the central area) saw the most obvious declines of 73.26%, 72.49%, and 68.05%, respectively. In addition, the average manure P load content was greater than 20 kg hm−2 in the central region (Huzhou, Jiaxing, Shanghai) and the southwestern region (Hangzhou), but lower than 15 kg hm−2 in the southeastern region (Tai) and northwestern region (Yangzhou, Taizhou).From 1980 to 2018, the center of the livestock manure P load shifted from the central and northern regions of the study area to the eastern and northwestern regions, and then to the northeastern and southwestern regions after reaching peak P loading in each city. In the 1980s, the livestock manure P load was mainly concentrated in the middle (Jiaxing) and the northern region (Wuxi); in the 1990s, the center of the livestock manure P load moved to the east (Shanghai) and the northwest (Zhenjiang); at the beginning of the twenty-first century, the livestock manure P load remained at high levels in most cities. By 2018, the center of the livestock manure P load had accumulated toward the edge of the Yangtze River Delta, mainly in the northeastern (Nantong) and southwestern regions (Hangzhou).It is generally believed that annual P application from manure should not exceed 35 kg hm−256, otherwise excessive P will result in leaching of soil P and eutrophication of the water body. Livestock manure P load in Jinxing exceeded this standard in 2010, having a certain negative impact on the local environment. The rational treatment and utilization of livestock manure is imminent57.In summary, the Yangtze River Delta’s livestock manure has increased first and then decreased in the past 40 years. The reason for its significant increase before 2010 is mainly due to the urban construction and rapid economic development in various regions, and it spreads to the surrounding areas. With urban development, the area of agricultural land has been greatly reduced, and the area carrying livestock manure nutrient has been relatively reduced, resulting in an increase in the manure N and P load in the Yangtze River Delta. After 2010, the reason for the decrease in the manure N and P load in the Yangtze River Delta may be related to the successive introduction and implementation of environmental protection policies for livestock breeding in various provinces and cities. After the promulgation of the “Pollution Prevention and Control Technology Policy for Livestock and Poultry Breeding Industry” in 2010, the state has strengthened the macro-control of the livestock breeding industry, the Yangtze River Delta has been listed as a restricted development zone, the industrial structure has been further optimized and adjusted, and the amount of livestock breeding has been significantly reduced. This leads to a decrease in the manure N and P load in the Yangtze River Delta. The manure N and P load has been weakened year by year. This change pattern is also consistent with the trend of policy measures. To a certain extent, it shows that the current livestock and poultry pollution prevention and control measures have achieved remarkable results58.Changes in livestock manure N and P loads from 1980 to 2018Between 1980 and 2018, manure N and P loads showed significant spatial variability (Fig. 6). Due to the influence of government guidance, large demand for production land, and environmental protection pressure, changes to animal husbandry space have been promoted59,60,61. Livestock manure N and P loads in the northwestern and central regions have decreased significantly, while manure N and P loads in the surrounding areas have increased to varying degrees, showing a general shift from the central region to the surrounding cities. Specifically, in 2018, the manure N and P loads in Nanjing, Wuxi, Suzhou, and Jiaxing showed a decreasing trend. Compared with 1980, the manure N load decreased by 41.55%, 48.26%, 44.49%, and 33.46%, respectively. Compared with 1980, the manure P load decreased by 21.63%, 43.08%, 43.47%, and 17.98%, respectively. The reduction in manure N and P loads in the central region is related to policies of livestock pollution prevention, which were successively promulgated in the Tai Lake area32,33,62. The relevant departments have optimized the regional layout of animal husbandry and comprehensively made use of the livestock manure to reduce pollution, thereby reducing the manure N and P loads. All the 11 cities of Yangzhou, Zhenjiang, Changzhou, Nantong, Shanghai, Huzhou, Hangzhou, Shaoxing, Ningbo, Zhoushan, and Tai saw different degrees of increase in their manure N and P loads, and Hangzhou increased most. Compared with 1980, the manure N and P load of Hangzhou in 2018 increased by 76.72% and 112.58%, respectively. The manure N and P loads in the remaining 10 cities increase less than 100%. Increases in manure N and P loads may be related to the small scale63,64 and scattered distribution of local farms, and lack of environmental protection awareness among residents65,66.Figure 6(a) Percentage change of manure N loads from 1980 to 2018; (b) Percentage change of manure P loads from 1980 to 2018.Full size imageIdentification of high-risk areas of soil pollution caused by livestock manureThe high-risk areas for manure N and P emissions in 2018 were mainly located in the northwestern and southern regions of the Yangtze River Delta (Fig. 7), while the manure N and P emissions in some northern cities could not meet the nutrient requirements of the local land. Manure N and P emissions in Changzhou were 215.60% and 334.54% of the land’s absorption capacity, while those in Nanjing were 102.18% and 71.02%, respectively. It shows that the imbalance between the supply and demand of planting and breeding may cause a greater risk of environmental pollution of livestock and poultry, and it is necessary to reduce the scale of breeding or expand the scale of planting67,68. Manure P emissions in Wuxi, Huzhou, Jiaxing, Hangzhou, and Zhoushan were close to the maximum land absorption capacity for livestock manure nutrients, indicating that the supply and demand for planting and breeding were balanced. Therefore, the use of local organic fertilizers can be appropriately increased to reduce the amount of chemical fertilizers used and reduce the potential pollution threat caused by the enrichment of manure nutrients69,70. In the northern region, the discharge of livestock manure in Yangzhou, Taizhou, Nantong, and Zhenjiang was only 0–20% of the land absorption capacity, indicating that livestock manure nutrient in these areas cannot meet the nutrient needs of local crops. Therefore, additional nutrient supply is needed to meet the normal growth of local crops71.Figure 7(a) Manure N emission relative to land absorption capacity in 2018; (b) manure P emission relative to land absorption capacity in 2018.Full size imageSelection of typical models and main control factors based on long-term manure N and P emissionsSystematic clustering analysis of manure N and P emissionsAccording to the similarity of manure N and P emissions in the cities, we carried out variable analysis based on the Ward minimum variance method72. Cities were divided into four categories based on the change trend of manure N emissions in the Yangtze River Delta73 (Fig. 8a). Class I: Yangzhou, Tai, Wuxi, Suzhou, Shaoxing, Ningbo, Zhoushan, Hangzhou; class II: Nantong, Taizhou, Changzhou; class III: Huzhou, Jiaxing; class IV: Nanjing, Shanghai, Zhenjiang.Figure 8(a) Systematic clustering of manure N emissions from 1980 to 2018; (b) systematic clustering of manure P emissions from 1980 to 2018.Full size imageSimilar to the manure N classification method, cities were divided into four categories based on the change trend of manure P emissions in the Yangtze River Delta (Fig. 8b). Class I: Nantong, Taizhou, Changzhou, Yangzhou, Tai, Shaoxing, Ningbo, Zhoushan, Hangzhou; class II: Huzhou, Jiaxing; class III: Wuxi, Suzhou; class IV: Nanjing, Shanghai, Zhenjiang.Principal component analysis of manure N and P emissionsBased on the results of systematic clustering, typical cities were extracted to establish a typical model of manure N and P emissions and the main control factors were selected74. For manure N emissions, Yangzhou, Nantong, Huzhou, and Shanghai were selected from Class I, Class II, Class III, and Class IV, respectively. Combining these with the rising and falling trend characteristics of manure N emissions over the long study period, we established four typical models of manure N emissions as “up-down-down” model, “down-up-up” model, “down-up-down” model, and “up-up-down” model. According to the clustering results of manure P emissions, Hangzhou, Jiaxing, Suzhou, and Shanghai were selected from Class I, Class II, Class III, and Class IV, respectively. Four typical models of manure P emissions were established based on rising and falling trend characteristics of manure N emissions over the long study period as “up-up-down” model, “down-up-down” model, “down-level-down” model, and “up-down-down” model.Analysis on the main control factors for manure N emissionsFor the “up-down-down” model in Yangzhou, the total variance of the two principal components accounted for 85% (Fig. 9a); Nantong was characterized as a typical “down-up-up” model city, where the variance of the two principal components ac-counted for 82% (Fig. 9b); Huzhou represented a typical “down-up-down” model city, where the sum of the variances of the two principal components was 82% (Fig. 9c); in the “up-up-down” model for Shanghai, the sum of variance of the two principal com-ponents was 96% (Fig. 9d).Figure 9Manure N emissions from 1980 to 2018 and main control factors based on principal component analysis.Full size imageThere was a significant positive correlation between changes in manure N emissions and the proportion of the primary industry in Yangzhou, indicating that the “up-down-down” model of Class I is mainly affected by the primary industry. The “Pollution Prevention and Control Plan for Livestock Breeding Industry in Yangzhou” proposes to delimit forbidden and restricted areas, regulate livestock breeding, and reduce pollutant discharge from livestock breeding. Therefore, the scale of livestock breeding in Yangzhou has decreased, and the total manure N has shown a downward trend, which is consistent with the interannual changes in the proportion of the primary industry. The primary industry in this class is mainly agriculture and animal husbandry; thus, the main control factor for the total manure N is the proportion of primary industry.The total manure N in Nantong first decreased and then increased, and finally tended to be flat over the study period. There was a clear correlation between changes in manure N emissions in Nantong, meat production, and the total output value of animal husbandry, indicating that Class II is dominated by these two factors. The livestock breeding industry in Class II is relatively developed75,76, and meat production showed a consistent trend with total manure N. Hence, meat production and the total output value of animal husbandry are the factors having the greatest impact on Class II.Total manure N in Huzhou showed a trend of decreasing, then increasing, and finally decreasing. Manure N emissions changes and meat production showed a relatively obvious positive correlation, indicating that Class III is mainly affected by meat production and has little correlation with factors such as GDP, which is consistent with the changing trend of meat production. Huzhou’s agriculture is dominated by planting and fishery77, and the impact of animal husbandry is not significant, so meat production is the factor having the greatest impact on such cities.Total manure N in Shanghai began to increase over the study period, consistent with changes in meat production. There was a strong positive correlation between changes in manure N emissions and meat production, indicating that Class IV is greatly affected by arable land area. After that, Shanghai issued relevant measures to regulate livestock breeding, such as the “Shanghai Livestock a Breeding Management Measures”. Due to a decline in farmland area, meat production decreased, and manure N showed a downward trend78. Total manure N and meat production in Shanghai both increased at first and then decreased. Therefore, meat production is the most influential factor in such cities.Selection of main control factors for manure P emissionsIn the “up-up-down” model for Hangzhou, the total variance of the two principal components accounted for 91% (Fig. 10a); as a typical “down-up-down” model, the total variance of the two principal components in Jiaxing accounted for 88% (Fig. 10b); Su-zhou represented a typical “down-flat-down” model, and the sum of the variance of the two principal components accounted for 95% (Fig. 10c); as an example of the “up-down-down” model, the variance of the two principal components in Shanghai accounted for 96% (Fig. 10d).Figure 10Manure P emissions from 1980 to 2018 and main control factors based on principal component analysis.Full size imageManure P in Hangzhou first increased and then decreased, essentially the same as the trend for Hangzhou’s meat production. There was a strong positive correlation between changes in manure P emissions and the total meat production in Yangzhou, indicating that the “up-up-down” model of Class I is mainly affected by meat production. Due to the serious pollution from livestock and poultry in Hangzhou79, relevant policies have been introduced to reduce the amount of livestock breeding, thereby reducing meat production.Manure P in Jiaxing showed a trend of first decline, then rise, and finally decline. There was a large positive correlation between changes in manure P emissions and meat production in Jiaxing, indicating that the “down-up-down” model of Class II is mainly affected by meat production. Class II is dominated by agriculture and animal husbandry34,35, and the breeding industry is relatively developed. Meat production also changes with these industries, and its inter-annual variation is consistent with that of manure P.Manure P in Suzhou showed a downward trend, consistent with inter-annual changes in the area of arable land. Changes in manure P emissions and the area of arable land showed a significant positive correlation, indicating that the Class III cities with a “decrease-level-decrease” model were mainly affected by the area of arable land and the proportion of the primary industry. Suzhou is an industrial development base that cannot be ignored. Local economic development is relatively rapid80, so its arable land area is continuously decreasing81.The livestock manure P in Shanghai increased at first and then decreased, which is consistent with inter-annual changes in meat production. In 2002, the Shanghai Municipal People’s Government highlighted a special plan for Shanghai’s animal husbandry, stipulating prohibition of breeding areas, control of breeding areas and moderate breeding areas. The city’s total livestock and poultry production decreased, and meat production decreased82. There was a strong positive correlation between the changes in livestock manure P emissions and meat production, indicating that the “up-down-down” model of Class IV is mainly affected by meat production. 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

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

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

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

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

Odonnell, C. Population dynamics and survivorship in bats. In Ecology and Behavioral Methods for the Study of Bats (eds Kunz, T. H. & Parsons, S.) 158–176 (The Johns University Press, 2009).
Google Scholar 
Lebreton, J.-D., Burnham, K. P., Clobert, J. & Anderson, D. R. Modeling survival and testing biological hypotheses using marked animals: A unified approach with case studies. Ecol. Monogr. 62, 67–118 (1992).Article 

Google Scholar 
Gibbons, J. W. & Andrews, K. M. PIT tagging: Simple technology at its best. Bioscience 54, 447–454 (2004).Article 

Google Scholar 
Ellison, L. E. et al. A comparison of conventional capture versus PIT reader techniques for estimating survival and capture probabilities of big brown bats (Eptesicus fuscus). Acta Chiropterologica 9, 149–160 (2007).Article 

Google Scholar 
van Harten, E. et al. High detectability with low impact: Optimizing large PIT tracking systems for cave-dwelling bats. Ecol. Evol. 9, 10916–10928 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Schorr, R. A., Ellison, L. E. & Lukacs, P. M. Estimating sample size for landscape-scale mark-recapture studies of North American migratory tree bats. Acta Chiropterologica 16, 231–239 (2014).Article 

Google Scholar 
Baker, G. B. et al. The effect of forearm bands on insectivorous bats (Microchiroptera) in Australia. Wildl. Res. 28, 229–237 (2001).Article 

Google Scholar 
O’Shea, T. J., Ellison, L. E. & Stanley, T. R. Survival estimation in bats: Historical overview, critical appraisal, and suggestions for new approaches. In Sampling Rare or Elusive Species: Concepts, Designs, and Techniques for Estimating Population Parameters (ed. Thompson, W. L.) 297–336 (Island Press, 2004).
Google Scholar 
O’Shea, T. J. et al. Recruitment in a Colorado population of big brown bats: Breeding probabilities, litter size, and first-year survival. J. Mammal. 91, 418–428 (2010).Article 

Google Scholar 
O’Shea, T. J., Ellison, L. E. & Stanley, T. R. Adult survival and population growth rate in Colorado big brown bats (Eptesicus fuscus). J. Mammal. 92, 433–443 (2011).Article 

Google Scholar 
Schorr, R. A. & Siemers, J. L. Population dynamics of little brown bats (Myotis lucifugus) at summer roosts: Apparent survival, fidelity, abundance, and the influence of winter conditions. Ecol. Evol. 11, 7427–7438 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
O’Donnell, C. F. J., Edmonds, H. & Hoare, J. M. Survival of PIT-tagged lesser short-tailed bats (Mystacina tuberculata) through a pest control operation using the toxin pindone in bait stations. N. Z. J. Ecol. 35, 291–295 (2011).
Google Scholar 
Edmonds, H., Pryde, M. & O’Donnell, C. Survival of PIT-tagged lesser short-tailed bats (Mystacina tuberculata) through an aerial 1080 pest control operation. N. Z. J. Ecol. 41, 186–192 (2017).
Google Scholar 
Reusch, C. et al. Differences in seasonal survival suggest species-specific reactions to climate change in two sympatric bat species. Ecol. Evol. 9, 7957–7965 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
IUCN. The IUCN red list of threatened species. Version 2020-2. http://www.iucnredlist.org (2020).Lentini, P. E., Bird, T. J., Griffiths, S. R., Godinho, L. N. & Wintle, B. A. A global synthesis of survival estimates for microbats. Biol. Lett. 11, 20150371 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Culina, A., Linton, D. M. & Macdonald, D. W. Age, sex, and climate factors show different effects on survival of three different bat species in a woodland bat community. Glob. Ecol. Conserv. 12, 263–271 (2017).Article 

Google Scholar 
Frick, W. F., Reynolds, D. S. & Kunz, T. H. Influence of climate and reproductive timing on demography of little brown myotis Myotis lucifugus. J. Anim. Ecol. 79, 128–136 (2010).PubMed 
Article 

Google Scholar 
Schorcht, W., Bontadina, F. & Schaub, M. Variation of adult survival drives population dynamics in a migrating forest bat. J. Anim. Ecol. 78, 1182–1190 (2009).PubMed 
Article 

Google Scholar 
Sendor, T. & Simon, M. Population dynamics of the pipistrelle bat: Effects of sex, age and winter weather on seasonal survival. J. Anim. Ecol. 72, 308–320 (2003).Article 

Google Scholar 
Sripathi, K., Raghuram, H., Rajasekar, R., Karuppudurai, T. & Abraham, S. G. Population size and survival in the indian false vampire bat Megaderma lyra. Acta Chiropterologica 6, 145–154 (2004).Article 

Google Scholar 
Papadatou, E., Butlin, R. K., Pradel, R. & Altringham, J. D. Sex-specific roost movements and population dynamics of the vulnerable long-fingered bat, Myotis capaccinii. Biol. Conserv. 142, 280–289 (2009).Article 

Google Scholar 
López-Roig, M. & Serra-Cobo, J. Impact of human disturbance, density, and environmental conditions on the survival probabilities of pipistrelle bat (Pipistrellus pipistrellus). Popul. Ecol. 56, 471–480 (2014).Article 

Google Scholar 
Wilkinson, G. S. & Adams, D. M. Recurrent evolution of extreme longevity in bats. Biol. Lett. 15, 20180860 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
DELWP. National Recovery Plan for the Southern Bent-wing Bat Miniopterus orianae bassanii (2020).Lumsden, L. & Gray, P. Longevity record for a southern bent-wing bat Miniopterus schreibersii bassanii. Australas. Bat Soc. Newsl. 16, 43–44 (2001).
Google Scholar 
Holz, P. H. et al. Virus survey in populations of two subspecies of bent-winged bats (Miniopterus orianae bassanii and oceanensis) in south-eastern Australia reveals a high prevalence of diverse herpesviruses. PLoS ONE 13, e0197625 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Holz, P. H., Lumsden, L. F., Marenda, M. S., Browning, G. F. & Hufschmid, J. Two subspecies of bent-winged bats (Miniopterus orianae bassanii and oceanensis) in southern Australia have diverse fungal skin flora but not Pseudogymnoascus destructans. PLoS ONE 13, e0204282 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Holz, P. H., Lumsden, L. F. & Hufschmid, J. Ectoparasites are unlikely to be a primary cause of population declines of bent-winged bats in south-eastern Australia. Int. J. Parasitol. Parasites Wildl. 7, 423–428 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Holz, P. H., Lumsden, L. F., Legione, A. R. & Hufschmid, J. Polychromophilus melanipherus and haemoplasma infections not associated with clinical signs in southern bent-winged bats (Miniopterus orianae bassanii) and eastern bent-winged bats (Miniopterus orianae oceanensis). Int. J. Parasitol. Parasites Wildl. 8, 10–18 (2019).PubMed 
Article 

Google Scholar 
Holz, P. H., Clark, P., McLelland, D. J., Lumsden, L. F. & Hufschmid, J. Haematology of southern bent-winged bats (Miniopterus orianae bassanii) from the Naracoorte Caves National Park, South Australia. Comp. Clin. Pathol. 29, 231–237 (2020).CAS 
Article 

Google Scholar 
Dwyer, P. D. The population pattern of Miniopterus schreibersii (Chiroptera) in north-eastern New South Wales. Aust. J. Zool. 14, 1073–1137 (1966).Article 

Google Scholar 
Dwyer, P. D. Mortality factors of the bent-winged bat. Vic. Nat. 83, 31–36 (1966).
Google Scholar 
Dwyer, P. D. Seasonal changes in activity and weight of Miniopterus schreibersii blepotis (Chiroptera) in north-eastern NSW. Aust. J. Zool. 12, 52–69 (1964).Article 

Google Scholar 
Bureau of Meteorology. Drought archive. http://www.bom.gov.au/climate/drought/archive.shtml (2019).Dwyer, P. D. Population ranges of Miniopterus schreibersii (Chiroptera) in south-eastern Australia. Aust. J. Zool. 17, 665–686 (1969).Article 

Google Scholar 
Fleischer, T., Gampe, J., Scheuerlein, A. & Kerth, G. Rare catastrophic events drive population dynamics in a bat species with negligible senescence. Sci. Rep. 7, 7370 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Thomas, D. W. Hibernating bats are sensitive to nontactile human disturbance. J. Mammal. 76, 940–946 (1995).Article 

Google Scholar 
Reeder, D. M. et al. Frequent arousal from hibernation linked to severity of infection and mortality in bats with white-nose syndrome. PLoS ONE 7, e38920 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Turbill, C., Bieber, C. & Ruf, T. Hibernation is associated with increased survival and the evolution of slow life histories among mammals. Proc. R. Soc. B Biol. Sci. 278, 3355–3363 (2011).Article 

Google Scholar 
van Harten, E. Population Dynamics of the Critically Endangered, Southern Bent-Winged Bat Miniopterus orianae bassanii (La Trobe University, 2020).
Google Scholar 
PIRSA. History of the south east drainage system – summary. https://www.pir.sa.gov.au/aghistory/natural_resources/water_resources_ag_dev/history_of_the_south_east_drainage_system_-_summary/history_of_the_south_east_drainage_system_-_summary#_ftnref2 (2017).Harding, C., Herpich, D. & Cranswick, R. H. Examining temporal and spatial changes in surface water hydrology of groundwater dependent ecosystems using WOfS (Water Observations from Space): Southern Border Groundwaters Agreement area, South East South Australia. (2018).Holz, P. H., Lumsden, L. F., Reardon, T., Gray, P. & Hufschmid, J. Does size matter? Morphometrics of southern bent-winged bats (Miniopterus orianae bassanii) and eastern bent-winged bats (Miniopterus orianae oceanensis). Aust. Zool. AZ https://doi.org/10.7882/AZ.2019.019 (2020).Article 

Google Scholar 
Rashid, M. M. & Beecham, S. Characterization of meteorological droughts across South Australia. Meteorol. Appl. 26, 556–568 (2019).Article 

Google Scholar 
Culina, A., Linton, D. M., Pradel, R., Bouwhuis, S. & Macdonald, D. W. Live fast, don’t die young: Survival–reproduction trade-offs in long-lived income breeders. J. Anim. Ecol. 88, 746–756 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Kunz, T. H., Whitaker, J. O. & Wadanoli, M. D. Dietary energetics of the insectivorous Mexican free-tailed bat (Tadarida brasiliensis) during pregnancy and lactation. Oecologia 101, 407–415 (1995).CAS 
PubMed 
Article 

Google Scholar 
Adams, R. A. & Hayes, M. A. Water availability and successful lactation by bats as related to climate change in arid regions of western North America. J. Anim. Ecol. 77, 1115–1121 (2008).PubMed 
Article 

Google Scholar 
Henry, M., Thomas, D. W., Vaudry, R. & Carrier, M. Foraging distances and home range of pregnant and lactating little brown bats (Myotis lucifugus). J. Mammal. 83, 767–774 (2002).Article 

Google Scholar 
Lučan, R. & Radil, J. Variability of foraging and roosting activities in adult females of Daubenton’s bat (Myotis daubentonii) in different seasons. Biologia (Bratisl.) 65 (2010).Amorim, F., Jorge, I., Beja, P. & Rebelo, H. Following the water? Landscape-scale temporal changes in bat spatial distribution in relation to Mediterranean summer drought. Ecol. Evol. 8, 5801–5814 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
O’Donnell, C. F. J. Timing of breeding, productivity and survival of long-tailed bats Chalinolobus tuberculatus (Chiroptera: Vespertilionidae) in cold-temperate rainforest in New Zealand. J. Zool. 257, 311–323 (2002).Article 

Google Scholar 
Holz, P. H., Stent, A., Lumsden, L. F. & Hufschmid, J. Trauma found to be a significant cause of death in a pathological investigation of bent-winged bats (Miniopterus orianae). J. Zoo Wildl. Med. 50, 966–971 (2020).PubMed 
Article 

Google Scholar 
Hughes, P. M., Rayner, J. M. V. & Jonesg, G. Ontogeny of ‘true’ flight and other aspects of growth in the bat Pipistrellus pipistrellus. J. Zool. 236, 291–318 (1995).Article 

Google Scholar 
Wund, M. A. Learning and the development of habitat-specific bat echolocation. Anim. Behav. 70, 441–450 (2005).Article 

Google Scholar 
McGuire, L. P. et al. Common condition indices are no more effective than body mass for estimating fat stores in insectivorous bats. J. Mammal. 99, 1065–1071 (2018).Article 

Google Scholar 
Mispagel, C. et al. DDT and metabolites residues in the southern bent-wing bat (Miniopterus schreibersii bassanii) of south-eastern Australia. Chemosphere 55, 997–1003 (2004).CAS 
PubMed 
Article 

Google Scholar 
Allinson, G. et al. Organochlorine and trace metal residues in adult southern bent-wing bat (Miniopterus schreibersii bassanii) in southeastern Australia. Chemosphere 64, 1464–1471 (2006).CAS 
PubMed 
Article 

Google Scholar 
Kolkert, H., Andrew, R., Smith, R., Rader, R. & Reid, N. Insectivorous bats selectively source moths and eat mostly pest insects on dryland and irrigated cotton farms. Ecol. Evol. https://doi.org/10.1002/ece3.5901 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Sherwin, H. A., Montgomery, W. I. & Lundy, M. G. The impact and implications of climate change for bats. Mammal Rev. 43, 171–182 (2013).Article 

Google Scholar 
O’Shea, T. J., Cryan, P. M., Hayman, D. T. S., Plowright, R. K. & Streicker, D. G. Multiple mortality events in bats: A global review. Mammal Rev. 46, 175–190 (2016).Article 

Google Scholar 
Mundinger, C., Scheuerlein, A. & Kerth, G. Long-term study shows that increasing body size in response to warmer summers is associated with a higher mortality risk in a long-lived bat species. Proc. R. Soc. B Biol. Sci. 288, 20210508 (2021).Article 

Google Scholar 
Adams, R. A. & Hayes, M. A. Assemblage-level analysis of sex-ratios in Coloradan bats in relation to climate variables: A model for future expectations. Glob. Ecol. Conserv. 14, e00379 (2018).Article 

Google Scholar 
Crichton, E. G., Seamark, R. F. & Krutzsch, P. H. The status of the corpus luteum during pregnancy in Miniopterus schreibersii (Chiroptera: Vespertilionidae) with emphasis on its role in developmental delay. Cell Tissue Res. 258, 183–201 (1989).CAS 
PubMed 
Article 

Google Scholar 
Olsen, I. C. The analysis of continuous mark-recapture data (Norwegian University of Science and Technology, 2006).
Google Scholar 
Barbour, A. B., Ponciano, J. M. & Lorenzen, K. Apparent survival estimation from continuous mark-recapture/resighting data. Methods Ecol. Evol. 4, 846–853 (2013).Article 

Google Scholar 
van Harten, E. et al. Recovery of southern bent-winged bats (Miniopterus orianae bassanii) after PIT-tagging and the use of surgical adhesive. Aust. Mammal. 42, 216–219 (2020).Article 

Google Scholar 
McDonald, T. L., Amstrup, S. C. & Manly, B. F. Tag loss can bias Jolly-Seber capture-recapture estimates. Wildl. Soc. Bull. 31, 814–822 (2003).
Google Scholar 
van Harten, E. et al. Low rates of PIT-tag loss in an insectivorous bat species. J. Wildl. Manag. 85, 1739–1743 (2021).Article 

Google Scholar 
Lebl, K. & Ruf, T. An easy way to reduce PIT-tag loss in rodents. Ecol. Res. 25, 251–253 (2010).Article 

Google Scholar 
Rigby, E. L., Aegerter, J., Brash, M. & Altringham, J. D. Impact of PIT tagging on recapture rates, body condition and reproductive success of wild Daubenton’s bats (Myotis daubentonii). Vet. Rec. 170, 101 (2012).CAS 
PubMed 
Article 

Google Scholar 
Locatelli, A. G., Ciuti, S., Presetnik, P., Toffoli, R. & Teeling, E. Long-term monitoring of the effects of weather and marking techniques on body condition in the Kuhl’s pipistrelle bat, Pipistrellus kuhlii. Acta Chiropterologica 21, 87–102 (2019).Article 

Google Scholar 
Paniw, M. et al. The myriad of complex demographic responses of terrestrial mammals to climate change and gaps of knowledge: A global analysis. J. Anim. Ecol. 90, 1398–1407 (2021).PubMed 
Article 

Google Scholar 
Frick, W. F., Kingston, T. & Flanders, J. A review of the major threats and challenges to global bat conservation. Ann. N. Y. Acad. Sci. 1469, 5–25 (2020).PubMed 
Article 

Google Scholar 
Brunet-Rossinni, A. K. & Wilkinson, G. S. Methods for age estimation and the study of senescence in bats. In Ecological and Behavioral Methods for the Study of Bats (eds Kunz, T. H. & Parsons, S.) 315–325 (Johns Hopkins University Press, 2009).
Google Scholar 
Churchill, S. Australian Bats (Allen and Unwin, 2008).
Google Scholar 
Laake, J. L. RMark: An R interface for analysis of capture-recapture data with MARK. 25 (2013).Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference (Springer, 2002). https://doi.org/10.1007/b97636.Book 
MATH 

Google Scholar 
Caswell, H. Matrix population models. In Encyclopedia of Environmetrics (eds El-Shaarawi, A. H. & Piegorsch, W. W.) (Wiley, Berlin, 2006). https://doi.org/10.1002/9780470057339.vam006m.Chapter 

Google Scholar 
Dwyer, P. D. The breeding biology of Miniopterus schreibersii blepotis (Termminck) (Chiroptera) in north-eastern NSW. Aust. J. Zool. 11, 219–240 (1963).Article 

Google Scholar 
Richardson, E. G. The biology and evolution of the reproductive cycle of Miniopterus schreibersii and M. australis (Chiroptera: Vespertilionidae). J. Zool. 183, 353–375 (1977).Article 

Google Scholar  More