Ecology

Zhu, X. China’s karst tiankeng and its value for science and tourism. Sci. Technol. Rev. 19, 60–63 (2001).
Google Scholar 
Zhu, X. et al. A brift study on karst tiankeng. Carsol. Sin. 22, 51–65 (2003).
Google Scholar 
Zhu, X. & Waltham, T. Tiankeng: Definition and description. Cave Karst Sci. 32, 75–79 (2005).
Google Scholar 
Zhu, X. & Chen, W. Tiankengs in the karst of China. Cave Karst Sci. 32, 55–56 (2005).
Google Scholar 
Shui, W., Chen, Y., Wang, Y., Su, Z. & Zhang, S. Origination, study progress and prospect of karst tiankeng research in China. Acta Geogr. Sin. 70, 431–446 (2015).
Google Scholar 
Alexander, K. Cave un-roofing as a large-scale geomorphic process. Carsol. Sin. 4, 1–11 (2006).
Google Scholar 
Palmer, A. & Palmer, M. Hydraulic processes in the origin of tiankengs. Speleogenesis Evol. Karst Aquifers 4, 8 (2006).
Google Scholar 
Waltham, T. Collapse processes at the tiankengs of Xingwen. Cave Karst Sci. 32, 107–110 (2005).
Google Scholar 
White, W. & White, E. Size scales for closed depression landforms: The place of tiankengs. Cave Karst Sci. 32, 111–118 (2005).
Google Scholar 
Chen, W., Zhu, X., Zhu, D. & Ma, Z. Karst geological relics and development of Xiaozhai Tiankeng and Tianjinxia Fissure Gorge, Fengjie, Chongqing. J. Mountain Sci. 22, 22–29 (2004).CAS 

Google Scholar 
Yue, Y., Wang, K., Zhang, W., Chen, H. & Wang, M. Relationships between soil and environment in Peak-Cluster Depression areas of karst region based on canonical correspondence analysis. Environ. Sci. 29, 1400–1405 (2008).
Google Scholar 
Huang, B., Cai, W., Xue, Y. & Zhu, X. Research on tourism resource characteristics of tiankeng group in Dashiwei, Guangxi. Geogr. Geo-Inf. Sci. 20, 109–112 (2004).CAS 

Google Scholar 
Zhu, X. Discovery of erosional tiankeng in Houping, Wulong and its value of science and tourism. Carsol. Sin. 2, 93–98 (2006).
Google Scholar 
Yuan, D. The development of modern karstology in China. Geol. Rev. 52, 733–736 (2006).CAS 

Google Scholar 
Gunn, J. Turloughs and tiankengs: Distinctive doline forms. Cave Karst Sci. 32, 83–84 (2005).
Google Scholar 
Klimchou, A. Cave un-roofing as a large-scale geomorphic process. Cave Karst Sci. 32, 93–98 (2005).
Google Scholar 
Zhu, X., Chen, W. & Erin, L. Wulong karst systems and as an indicator of local tectonic uplift. Carsol. Sin. 26, 119–125 (2007).
Google Scholar 
Shui, W. & Wang, X. Geological expedition and analysis on formation and evolvement of erosive Karst Tiankeng: A case study of Xingwen World Geopark. Adv. Mater. Res. 250–253, 2002–2006 (2011).Article 

Google Scholar 
Su, S., Huang, K. & Ma, B. Diversity study on pteridophyte flora in the area of Dashiwei Tiankeng group of Leye County. Hubei Agric. Sci. 51, 948–950 (2012).
Google Scholar 
Huang, K. & Su, S. Resource investigation and application research of pteridophyte flora resource in the area of Dashiwei Tiankeng Group. Anhui Agric. Sci. Bull. 21, 74–80 (2015).CAS 

Google Scholar 
Su, Y., Xue, Y., Fan, B., Mo, F. & Feng, H. Plant community structure and species diversity in Liuxing tiankeng of Guangxi. Acta Botan. Boreali-Occiden. Sin. 36, 2300–2306 (2016).
Google Scholar 
Li, W., Xiang, Y., Du, Y. & Wu, X. Underground forest communities in Zhanyi, Yunnan Province. For. Sci. Technol. 20–25 (2001).Jian, X. et al. Interspecific relationships of grassland plant community’s dominant species in moderate-degraded tiankeng of Yunnan, China. Chin. J. Appl. Ecol. 29, 1–14. https://doi.org/10.13287/j.1001 (2018).Article 

Google Scholar 
Chen, W., Zhu, D. & Zhu, X. Characteristics and evaluation of karst landscape in tiankeng-difeng scenery site, Fengjie, Chongqing. Geogr. Geo-Inf. Sci. 20, 80–83 (2004).CAS 

Google Scholar 
Wang, J. & Guo, C. Comparison between the positive and negative topographic ecosystem in karst mountainous areas and its bearing capability. Guizhou Agric. Sci. 35, 85–87 (2007).MathSciNet 

Google Scholar 
Tony, W. Tiankengs of the world, outside China. Speleogenesis Evol. Karst Aquifers 4, 1–12 (2006).
Google Scholar 
Su, Y., Tang, Q., Mo, F. & Xue, Y. Karst tiankengs as refugia for indigenous tree flora amidst a degraded landscape in southwestern China. Sci. Rep. 7, 1–10 (2017).ADS 
Article 
CAS 

Google Scholar 
Bátori, Z. et al. A comparison of the vegetation of forested and non-forested solution dolines in Hungary: A preliminary study. Biologia 69, 1339–1348 (2014).Article 
CAS 

Google Scholar 
Bátori, Z. et al. The conservation value of karst dolines for vascular plants in woodland habitats of Hungary: Refugia and climate change. Int. J. Speleol. 43, 15–26 (2014).Article 

Google Scholar 
Bátori, Z. et al. Importance of karst sinkholes in preserving relict, mountain, and wet-woodland plant species under sub-Mediterranean climate: A case study from southern Hungary. J. Cave Karst Stud. Natl. Speleol. Soc. Bull. 74, 127–134 (2012).Article 

Google Scholar 
Bátori, Z. et al. Large-and small-scale environmental factors drive distributions of cool-adapted plants in karstic microrefugia. Ann. Bot. 119, 301–309 (2017).PubMed 
Article 

Google Scholar 
Vilisics, F. et al. Small scale gradient effects on isopods (Crustacea: Oniscidea) in karstic sinkholes. Biologia 66, 499–505 (2011).Article 

Google Scholar 
Dolinar, B. & Vreš, B. Pregled flore Mišje doline in zgornjega porečja Rašice (Dolenjska, Slovenija). Hladnikia 30, 3–37 (2012).
Google Scholar 
Raschmanová, N., Miklisová, D., Ľubomír, K. & Šustr, V. Community composition and cold tolerance of soil Collembola in a collapse karst doline with strong microclimate inversion. Biologia 70, 802–811 (2016).Article 

Google Scholar 
Macarthur, R. & Wilson, E. The Theory of Island Biogeography (Princeton University Press, 1967).
Google Scholar 
Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Natl. Acad. Sci. U. S. A. 104, 5925 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhou, F. & Yu, S. Study on the plant diversity of island-like habitats in Karst Mountain Areas. Guizhou For. Sci. Technol. 40, 18–22 (2012).CAS 

Google Scholar 
Hu, F., Lou, Q. & Sun, Y. Community composition and species diversity of different island habitat on Karst Mountainous in Central Guizhou. Guizhou Sci. 29, 23–28 (2011).
Google Scholar 
Culver, D. Karst environment. Z. Geomorphol. Suppl. 60, 103–117 (2016).Article 

Google Scholar 
Daily, G. Restoring value to the world’s degraded lands. Science 269, 350–354 (1995).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Bartgis, R. The endangered sedge Scirpus ancistrochaetus and the flora of sinkhole ponds in Maryland and West Virginia. Castanea 57, 46–51 (1992).
Google Scholar 
Yu, X., Li, Y. & Ma, Z. A preliminary study on flora diversity of karst microhabitat in Shilin Park, Yunnan, China. J. Mountain Sci. 25, 438–447 (2007).
Google Scholar 
Dang, G., Feng, H., Tang, Q., Mo, F. & Xue, Y. New recorded species in Guangxi, China. J. Guangxi Normal Univ. (Nat. Sci. Edit.) 34, 147–150 (2016).
Google Scholar 
Eigenbrod, F., Gonzalez, P., Dash, J. & Steyl, I. Vulnerability of ecosystems to climate change moderated by habitat intactness. Glob. Change Biol. 21, 275–286 (2015).ADS 
Article 

Google Scholar 
Cornell, H. & Lawton, J. Species interactions, local and regional processes, and limits to the richness of ecological communities: A theoretical perspective. J. Anim. Ecol. 61, 1–12 (1992).Article 

Google Scholar 
Helmus, M., Mahler, D. & Losos, J. Island biogeography of the Anthropocene. Nature 513, 543–546 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Yuan, T., Zhang, H., Ou, Z. & Tan, Y. Effects of topography on the diversity and distribution pattern of ground plants in karst montane forests in Southwest Guangxi, China. Chin. J. Appl. Ecol. 25, 2803–2810 (2014).
Google Scholar 
Wen, L. et al. The succession characteristics and its driving mechanisms of plant community in karst region, Southwest China. Acta Ecol. Sin. 35, 5822–5833 (2015).Article 

Google Scholar 
Zhang, Z., Hu, G. & Ni, J. Erratum to: Effects of topographical and edaphic factors on the distribution of plant communities in two subtropical Karst forests, Southwestern China. J. Mt. Sci. 10, 337–338 (2013).Article 

Google Scholar 
Du, H. et al. Plant community characteristics and its coupling relationships with soil in depressions between karst hills, North Guangxi, China. Chin. J. Plant Ecol. 37, 197–208 (2013).Article 

Google Scholar 
Liu, S., Zhang, B., Yang, Q., Hu, C. & Su, C. Species composition and diversity of plant communities in Xiaoyanwan Garden of Xingwen Karst National Geopark, Sichuan Province. Subtrop. Plant Sci. 38, 37–40 (2009).
Google Scholar 
Tan, C. The preliminary discussion about Haifeng’s wetland ecosystem. For. Sci. Technol. 1–8 (2002).Ma, K. & Liu, Y. Measurement of biodiversity: The measurement of α diversity. Chin. Biodivers. 2, 231–239 (1995).
Google Scholar 
Pielou, E. The measurement of diversity in different types of biological collections. J. Theor. Biol. 13, 131–144 (1966).ADS 
Article 

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

Google Scholar  More

Study areaThe study was conducted on two rivers in north-eastern Amazonia sensu lato, including the Guiana Shield and the Amazon River drainage (Fig. 2). The climate of the entire study area is homogeneous and the region is covered by dense, uniform lowland primary rainforest51. The altitude is in the range of 0–860 m a.s.l. The regional climate is equatorial, and the annual rainfall ranges from 3600 mm in the northeast to 2000 mm in the southwest. The Maroni River is 612 km long from its source to its estuary, and its watershed covers a surface of >68,000 km2 in Suriname and French Guiana. The Oyapock River (length, 404 km; area, 26,800 km2) is located in the state of Amapa in Brazil and in French Guiana.The foregoing river basins host nearly 400 freshwater fish species and more than 180 mammal species52,53. Most of the mammal species have a large distribution range, covering the entire study area53. The fish species have a less homogeneous distribution and a distinct upstream-downstream composition gradient54,55. Here, only large rivers were considered and most fish species were widespread over the whole study area. As habitat availability increases with river size, species richness is expected to increase upstream to dowsntream31,32. The Oyapock and Maroni river basins are among the last remaining wilderness areas on Earth17. Nevertheless, ecological disturbances are increasing there because of a growing human population and the development of small-scale gold mining activity. These disturbances have caused limited but diffuse deforestation23,56. The deforested areas currently comprise 0.67% of all Maroni and Oyapock catchments.SamplingEnvironmental DNA (eDNA) was collected from water samples at 74 locations (hereafter, sites) along the main channel and the large tributaries of the Maroni and Oyapock rivers (Fig. 2). Thirty-seven sites were sampled at each river basin. The minimum and maximum distances between adjacent sites were 1.07 and 50.20 km, respectively. The mean and median distances between adjacent sites were 10.18 and 9.14 km, respectively, and the standard deviation (SD) was 7.79 km. The sites were located from sea level to 157 m a.s.l. At all sites, the river was wider than 20 m and deeper than 1 m (Strahler orders 4–8; Supplementary Fig. 5). The physicochemical properties of the water slightly varied among sites. The temperature, pH, and conductivity were in the ranges of 28.4–33.2 °C, 6.5–7.6, and 16.9–54.6 µS/cm, respectively, at all sites except two estuarine locations where the conductivity was relatively high because of seawater incursion (Supplementary Data 2).The eDNA samples were collected during the dry seasons (October–November) of 2017 and 2018 for Maroni and Oyapock, respectively. At both rivers, the sites were sequentially sampled from downstream to upstream at a rate of 1–4 sites per day depending on the distance and travel time between sites. Following the protocol of ref. 45, we collected the eDNA by filtering two replicates of 34 L of water per site. A peristaltic pump (Vampire Sampler; Buerkle GmbH, Bad Bellingen, Germany) and single-use tubing were used to pump the water into a single-use filtration capsule (VigiDNA, pore size 0.45 μm; filtration surface 500 cm2, SPYGEN, Bourget-du-Lac, France). The tubing input was placed a few centimetres below the water surface in zones with high water flow as recommended by Cilleros et al.43. Sampling was performed in turbulent areas with rapid hydromorphologic units to ensure optimal eDNA homogeneity throughout the water column. To avoid eDNA cross-contamination among sites, the operator remained on emerging rocks downstream from the filtration area. At the end of filtration, the capsule was voided, filled with 80 mL CL1 preservation buffer (SPYGEN), and stored in the dark up to one month before the DNA extraction. No permits were required for the eDNA sampling and the access to all sites was legally permitted. The study complies with access and benefit permits ABSCH-IRCC-FR-246820-1 and ABSCH-IRCC-FR-245902-1, authorizing collection, transport and analysis of all environmental DNA samples used in this study.Laboratory procedures and bioinformatic analysesFor the DNA extraction, each filtration capsule was agitated on an S50 shaker (Ingenieurbüro CAT M. Zipperer GmbH, Ballrechten-Dottingen, Germany) at 800 rpm for 15 min, decanted into a 50 mL tube, and centrifuged at 15,000 × g and 6 °C for 15 min. The supernatant was removed with a sterile pipette, leaving 15 mL of liquid at the bottom of the tube. Subsequently, 33 mL of ethanol and 1.5 mL of 3 M sodium acetate were added to each 50 mL tube, and the mixtures were stored at −20 °C for at least one night. The tubes were then centrifuged at 15,000 × g and 6 °C for 15 min, and the supernatants were discarded. Then, 720 µL of ATL buffer from a DNeasy Blood & Tissue Extraction Kit (Qiagen, Hilden, Germany) was added. The tubes were vortexed, and the supernatants were transferred to 2 mL tubes containing 20 µL proteinase K. The tubes were then incubated at 56 °C for 2 h. DNA extraction was performed using a NucleoSpin Soil kit (Macherey-Nagel GmbH, Düren, Germany) starting from step six of the manufacturer’s instructions. Elution was performed by adding 100 µL of SE buffer twice. After the DNA extraction, the samples were tested for inhibition by qPCR following the protocol in ref. 57. Briefly, quantitative PCR was performed in duplicate for each sample. If at least one of the replicates showed a different Ct (Cycle threshold) than expected (at least 2 Cts), the sample was considered inhibited and diluted 5-fold before the amplification.For the fish, the “teleo” primers58 (forward: 3ʹ-ACACCGCCCGTCACTCT-5ʹ; reverse: 3ʹ-CTTCCGGTACACTTACCATG-5ʹ) were used as they efficiently discriminated local fish species43,45. For the mammals, the 12S-V5 vertebrate marker59 (forward: 3ʹ-TAGAACAGGCTCCTCTAG-5ʹ; reverse: 3ʹ-TTAGATACCCCACTATGC-5ʹ) was used as it also effectively distinguishes local mammal species44,60. The DNA amplifications were performed in a final volume of 25 μL containing 1 U AmpliTaq Gold DNA Polymerase (Applied Biosystems, Foster City, CA, USA), 0.2 μM of each primer, 10 mM Tris-HCl, 50 mM KCl, 2.5 mM MgCl2, 0.2 mM of each dNTP, and 3 μL DNA template. Human blocking primer was added to the mixture for the “teleo”58 (5′-ACCCTCCTCAAGTATACTTCAAAGGAC-C3-3′) and the “12S-V5” primers61 (5′-CTATGCTTAGCCCTAAACCTCAACAGTTAAATCAACAAAACTGCT-C3-3′) at final concentrations of 4 μM and 0.2 μg/μL bovine serum albumin (BSA; Roche Diagnostics, Basel, Switzerland). Twelve PCR replicates were performed per field sample. The forward and reverse primer tags were identical within each PCR replicate. The PCR mixture was denatured at 95 °C for 10 min, followed by 50 cycles of 30 s at 95 °C, 30 s at 55 °C for the “teleo” primers and 50 °C for the 12S-V5 primers, 1 min at 72 °C, and a final elongation step at 72 °C for 7 min. This step was conducted in a dedicated room for DNA amplification that is kept under negative air pressure and is physically separated from the DNA extraction rooms maintained under positive air pressure. The purified PCR products were pooled in equal volumes to achieve an expected sequencing depth of 500,000 reads per sample before DNA library preparation.For the fish analyses, 10 libraries were prepared using a PCR-free library protocol (https://www.fasteris.com/metafast) at Fasteris, Geneva, Switzerland. Four libraries were sequenced on an Illumina HiSeq 2500 (2 × 125 bp) (Illumina, San Diego, CA, USA) with a HiSeq SBS Kit v4 (Illumina), three were sequenced on a MiSeq (2 × 125 bp) (Illumina) with a MiSeq Flow Cell Kit Version3 (Illumina), and three libraries were sequenced on a NextSeq (2 × 150 bp + 8) (Illumina) with a NextSeq Mid kit (Illumina). The libraries run on the NextSeq were equally distributed in four lanes. Sequencing was performed according to the manufacturer’s instructions at Fasteris. For the mammal analyses, eight libraries were prepared using a PCR-free library protocol (https://www.fasteris.com/metafast) at Fasteris. Two libraries were sequenced on an Illumina HiSeq 2500 (2 × 125 bp) (Illumina) using a HiSeq Rapid Flow Cell v2 and a HiSeq Rapid SBS Kit v2 (Illumina), three libraries were prepared on a MiSeq (2 × 125 bp) (Illumina) with a MiSeq Flow Cell Kit Version3 (Illumina), and three libraries were prepared using a NextSeq (2 × 150 bp + 8) (Illumina) and a NextSeq Mid kit (Illumina). The libraries run on the NextSeq were equally distributed in four lanes. As different sequencing platforms were used (MiSeq and NextSeq for the Maroni and HiSeq 2500 and MiSeq for the Oyapock; Supplementary Fig. 6 and Supplementary Data 3), the possible influences of the platforms on the sequencing results were verified. To this end, we compared the differences in species numbers between the sample replicates assigned to the same platform (accounting for replicate effect only) against those of the sample replicates assigned to different platforms (accounting for replicate and platform effects). As there were more sites with their two replicates sequenced with the same platform than sites with their replicates sequenced with different platforms (see Supplementary Fig. 6), sites with replicates on the same platform were randomly selected for the comparisons. We repeated this procedure 50 times. The number of species between replicates sequenced on the same platform and those sequenced on different platforms did not differ for >98.5% of all fish and mammal samples (Supplementary Fig. 7 and Supplementary Note 2). Similar to these results, a previous study on 16 S rRNA amplicon has shown that the samples were not influenced by the Illumina sequencing platform used62.To monitor for contaminants, 13 negative extraction controls were performed for each of the primers (“teleo” and “12S-V5”); one control was amplified twice. All of them were amplified and sequenced by the same methods as the samples and in parallel to them. Therefore, for the negative extraction controls, 168 amplifications were prepared with the “teleo” primers (13 negative controls; one amplified and sequenced twice) and 156 amplifications with the “12S-V5” primers (13 negative controls). Fourteen negative PCR controls (ultrapure water; 12 replicates) were amplified and sequenced in parallel to the samples. Eight were amplified with the “teleo” primers and six were amplified with the “12S-V05” primers. Thus, for the PCR negative controls, there were 96 amplifications with the “teleo” primers and 72 amplifications with the Vert01 primers. Sequencing information for the controls is shown in Supplementary Data 3c.An updated version of the reference database from ref. 43 was used. There were 265 Guianese species for the fish analyses (ref. 47). The GenBank nucleotide database was consulted, but it contained little information on the Guianese fish species. Most of the sequences were derived from ref. 43. For the mammal analyses, the vertebrate database was built using ecoPCR software63 from the releases 134 and 138 of the European Nucleotide Archive (ENA), for the Maroni and Oyapock river samples, respectively. The two releases were compared, and it was established that the new mammal species added to each version did not originate from French Guiana. Hence, the results were not influenced by the EMBL release number. The relevant metabarcoding fragment was extracted from this database with ecoPCR63 and OBITools64. Therefore, the reference database comprised the local database of French Guianese mammals60, which references 576 specimens from 164 species as well as all available vertebrate species in EMBL.The sequence reads were analyzed with the OBITools package according to the protocol described by Valentini et al.58. Briefly, the forward and reverse reads were assembled with the illuminapairedend programme. The ngsfilter programme was then used to assign the sequences to each sample. A separate dataset was created for each sample by splitting the original dataset into several files with obisplit. Sequences shorter than 20 bp or occurring less than 10 times per sample were discarded. The obiclean program was used to identify amplicon sequence variants (ASVs) that have likely arisen due to PCR or sequencing errors. It uses the information of sequence counts and sequence similarities to classify whether a sequence is a variant (“internal”) of a more abundant (“head”) ASV64. After this step, we matched the ASV with the reference database to obtain the taxonomic assignation for each ASV. Sequences labelled by the obiclean programme as ‘internal’’ and probably corresponding to PCR errors were discarded. The ecotag programme was then used for taxonomic assignment of molecular operational taxonomic units (MOTUs). The taxonomic assignments from ecotag were corrected to avoid overconfidence in assignments. Species-level assignments were validated only for ≥98% sequence identity with the reference database. Sequences below this threshold were discarded.Measuring disturbance intensity using GIS dataIn riverine systems, the disturbances may accumulate because of hydrologic connectivity, which is the downstream transfer of matter and pollutants4. Hence, the upstream sub-basin drainage network was considered to determine the size of the upstream sub-basin affecting local biodiversity (Fig. 1). The sub-basins were delineated by applying a flow accumulation algorithm to the SRTM global 30 m digital elevation model65. Deforestation was measured over 14 upstream spatial extents with radii of 0.5, 1.5, 3, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, and 90 km for each sampling site. Then, these 14 upstream spatial extents were intersected with the sub-basin drainage network. In addition, mammals and fish can also be affected by disturbances other than those mediated by hydrologic connectivity. Thus, deforestation was also measured upstream and downstream from the eDNA sampling sites using the same foregoing 14 radii.At each sampling site, deforestation intensity was quantified for each of the 14 spatial extents. We summed upstream (only accounting for disturbances mediated by river hydrologic connectivity) or upstream and downstream (not only considering disturbances mediated by hydrologic connectivity) deforested surfaces from Landsat satellite image datasets. Forest loss surfaces were obtained from the Global Forest Change dataset66. The Global Forest Change dataset identifies areas deforested between 2001 and 2017 on a 30 m spatial scale. To incorporate deforested areas prior to 2000, tree canopy cover data for that year were also used. Except for river courses, all pixels with More

We express our gratitude to Lukáš Falteisek, Richard Dorrell, Jan Petrášek, Stanislav Volsobě, Kateřina Schwarzerová and Jana Krtková for constructive discussions. English has been kindly corrected by William Bourland. Furthermore, we thank to Dovilė Barcytė, William Bourland, Antonio Calado, Dora Čertnerová, Yana Eglit, Ivan Fiala, Martina Hálová, Miroslav Hyliš, Dagmar Jirsová, Petr Kaštánek, Viktorie Kolátková, Alena Kubátová, Alexander Kudryavtsev, Frederik Leliaert, Julius Lukeš, Jan Mach, Joost Mansour, Jan Mourek, Yvonne Němcová, Fabrice Not, Vladimír Scholtz, Alastair Simpson, Pavel Škaloud, Jan Šťastný, Róbert Šuťák, Daria Tashyreva, Dana Savická, Jan Šobotník, Zdeněk Verner, Jan Votýpka for kindly providing cultures and taxonomic identifications. More

Controlled fires can be used to reduce the risk of wildfires.Credit: David Hoffmann/Alamy

Indigenous oral accounts have helped scientists to reconstruct a 3,000-year history of a large fire-prone forest in California. The results suggest that parts of the forest are denser than ever before, and are at risk of severe wildfires1. The research is part of a growing effort to combine Indigenous knowledge with other scientific data to improve understanding of ecosystem histories.Wildfires are a substantial threat to Californian forests. Clarke Knight, a palaeo-ecosystem scientist at the US Geological Survey in Menlo Park, California, and her colleagues wanted to understand how Indigenous communities helped shape the forest by managing this risk in the state’s lush western Klamath Mountains. Specifically, they studied Indigenous peoples’ use of cultural burning — small, controlled fires that keep biomass low and reduce the risk of more widespread burning. The results are published in the Proceedings of the National Academy of Science.“When I was a little kid, my grandmother used to burn around the house,” says Rod Mendes, fire chief for the Yurok Tribe fire department, whose family is part of the Karuk Tribe of northern California. The Karuk and Yurok tribes have called the Klamath Mountains home for thousands of years. “She was just keeping the place clean. Native people probably did some of the first prescribed fire operations in history,” says Mendes.Understanding how Indigenous tribes used fire is essential for managing forests to reduce wildfire risk, says Knight. “We need to listen to Native people and learn and understand why they managed the landscape the way they did,” adds Mendes.Collaboration for corroborationTo map the region’s forest history, the team drew on historical accounts and oral histories from Karuk, Yurok and Hoopa Valley Tribe members collected by study co-author Frank Lake, a US Forest Service research ecologist in Arcata, California, and a Karuk descendant, as part of his PhD thesis in 2007. These accounts described the tribes’ fire and land use. For instance, members lit small fires to keep trails clear; this also reduced the amount of vegetation, preventing expansion of wildfires from lightning strikes. Larger fires, called broadcast burning, were used to improve visibility, hunting and nut-harvesting conditions in the forest. The effects of fire on the vegetation lasted for decades.Knight says that it was important to collaborate with the tribes given their knowledge of the region. The Karuk Resources Advisory Board approved a proposal for the study before it began. “In a way, it’s decolonizing the existing academic model that hasn’t been very inclusive of Indigenous histories,” says Lake.The researchers also analysed sediment cores collected near two low-elevation lakes in the Klamath Mountains that are culturally important to the tribes. Layers of pollen in the cores were used to infer the approximate tree density in the area at various times, and modelling helped date the cores so they could estimate how that density changed.The team also measured charcoal in the cores’ layers, which helped to map fluctuations in the amount of fire in the region. Burn scars on tree stumps pointed to specific instances of fire in between 1700-1900. Because the stumps’ rings serve as an ecological calendar, the researchers were able to compare periods of fire with corresponding tree-density data. They then pieced together how this density fluctuated with fire incidence. Although these empirical methods could not specifically confirm that the fires were lit by the tribes, patterns suggested when this was more probable, says Knight. For instance, increased burning in cool, wet periods, when fires caused by lightning were probably less common, suggested a human influence.Combining multiple lines of evidence, Knight and her team show that the tree density in this region of Klamath Mountains started to increase as the area was colonized, partly because the European settlers prevented Indigenous peoples from practising cultural burning. In the twentieth century, total fire suppression became a standard management practice, and fires of any kind were extinguished or prevented — although controlled burns are currently used in forest management. The team reports that in some areas, the tree density is higher than it has been for thousands of years, owing in part to fire suppression.Healthy forestA dense forest isn’t necessarily a healthy one, says Knight. The Douglas-fir, which dominate the low-land Klamath forests, are less fire resilient and more prone to calamitous wildfires. “This idea that we simply should let nature take its course is just not supported by this work,” she says. She adds that one of the study’s strengths is the multiple lines of evidence showing that past Indigenous burning helped to manage tree density.Fire ecologist Jeffrey Kane at the California State Polytechnic University Humboldt in Arcata says that the study’s findings of increased tree density are not surprising. He has made similar observations in the Klamath region. “There’s a lot more trees than were there just 120 years ago,” he says.Dominick DellaSala, chief scientist at forest-protection organization Wild Heritage in Talent, Oregon, points out that the results suggesting record tree densities cannot be applied to the entire Klamath region, owing to the limited range of the study’s lakeside data.Knight, however, says that the results can be extrapolated to other similar low-elevation lake sites that have similar vegetation types.More Indigenous voicesPalaeoecology studies are increasingly incorporating Indigenous knowledge — but there’s still a long way to go, says physical geographer Michela Mariani at the University of Nottingham, UK. In Australia, Mariani has also found that tree density began to increase after British colonization hampered cultural burning. “It’s very important that we now include Indigenous people in the discussion in fire management moving on,” Mariani says. “They have a deeper knowledge of the landscape we simply don’t have.”Including Indigenous voices in research is also crucial for decolonizing conventional scientific methods, Lake emphasizes. It “becomes a form of justice for those Indigenous people who have long been excluded, marginalized and not acknowledged”, he says. More

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

Google Scholar 
Gibbs, H. K. et al. Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s. Proc. Natl Acad. Sci. USA 107, 16732–16737 (2010).CAS 
Article 

Google Scholar 
Payn, T. et al. Changes in planted forests and future global implications. Ecol. Manag. 352, 57–67 (2015).Article 

Google Scholar 
Pendrill, F., Persson, U. M., Godar, J. & Kastner, T. Deforestation displaced: trade in forest-risk commodities and the prospects for a global forest transition. Environ. Res. Lett. 14, 055003 (2019).Article 

Google Scholar 
Hurni, K. & Fox, J. The expansion of tree-based boom crops in mainland Southeast Asia: 2001 to 2014. J. Land Use Sci. 13, 198–219 (2018).Article 

Google Scholar 
Vijay, V. et al. The impacts of oil palm on recent deforestation and biodiversity loss. PLoS ONE 11, e0159668 (2016).Heilmayr, R., Echeverría, C. & Lambin, E. F. Impacts of Chilean forest subsidies on forest cover, carbon and biodiversity. Nat. Sustain. 3, 701–709 (2020).Article 

Google Scholar 
le Maire, G., Dupuy, S., Nouvellon, Y., Loos, R. A. & Hakamada, R. Mapping short-rotation plantations at regional scale using MODIS time series: case of eucalypt plantations in Brazil. Remote Sens. Environ. 152, 136–149 (2014).Article 

Google Scholar 
Wang, M. M. H., Carrasco, L. R. & Edwards, D. P. Reconciling rubber expansion with biodiversity conservation. Curr. Biol. 30, 3825–3832 (2020).CAS 
Article 

Google Scholar 
Lewis, S. L., Wheeler, C. E., Mitchard, E. T. A. & Koch, A. Restoring natural forests is the best way to remove atmospheric carbon. Nature 568, 25–28 (2019).CAS 
Article 

Google Scholar 
Dave, R. et al. Second Bonn Challenge Progress Report: Application of the Barometer in 2018 (IUCN, 2019).Sloan, S., Meyfroidt, P., Rudel, T. K., Bongers, F. & Chazdon, R. The forest transformation: planted tree cover and regional dynamics of tree gains and losses. Glob. Environ. Change 59, 101988 (2019).Article 

Google Scholar 
Petersen, R. et al. Mapping Tree Plantations with Multispectral Imagery: Preliminary Results for Seven Tropical Countries (WRI, 2016).Erb, K.-H. et al. Land management: data availability and process understanding for global change studies. Glob. Change Biol. 23, 512–533 (2017).Article 

Google Scholar 
Souza, C. M. et al. Reconstructing three decades of land use and land cover changes in Brazilian biomes with Landsat Archive and Earth Engine. Remote Sens. 12, 2735 (2020).Article 

Google Scholar 
Miettinen, J. et al. Extent of industrial plantations on Southeast Asian peatlands in 2010 with analysis of historical expansion and future projections. GCB Bioenergy 4, 908–918 (2012).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).Puyravaud, J.-P., Davidar, P. & Laurance, W. F. Cryptic destruction of India’s native forests. Conserv. Lett. 3, 390–394 (2010).Article 

Google Scholar 
Fagan, M. E. et al. Mapping pine plantations in the southeastern U.S. using structural, spectral, and temporal remote sensing data. Remote Sens. Environ. 216, 415–426 (2018).Article 

Google Scholar 
Tropek, R. et al. Comment on “High-resolution global maps of 21st-century forest cover change”. Science 344, 981 (2014).CAS 
Article 

Google Scholar 
Global Forest Resources Assessment 2020 (FAO, 2020).FAOSTAT Agricultural Statistics Database (FAO, 2019); http://faostat.fao.org/site/291/default.aspxCook-Patton, S. C. et al. Mapping carbon accumulation potential from global natural forest regrowth. Nature 585, 545–550 (2020).CAS 
Article 

Google Scholar 
Hurni, K., Schneider, A., Heinimann, A., Nong, D. H. & Fox, J. Mapping the expansion of boom crops in mainland Southeast Asia using dense time stacks of Landsat data. Remote Sens. 9, 320 (2017).Article 

Google Scholar 
Miettinen, J., Shi, C. & Liew, S. C. 2015 Land cover map of Southeast Asia at 250 m spatial resolution. Remote Sens. Lett. 7, 701–710 (2016).Article 

Google Scholar 
Torbick, N., Ledoux, L., Salas, W. & M. Zhao, M. Regional mapping of plantation extent using multisensor imagery. Remote Sens. 8, 236 (2016).Azizan, F. A., Kiloes, A. M., Astuti, I. S. & Abdul Aziz, A. Application of optical remote sensing in rubber plantations: a systematic review. Remote Sens. 13, 429 (2021).Article 

Google Scholar 
Bégué, A. et al. Remote sensing and cropping practices: a review. Remote Sens. 10, 99 (2018).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. 3, 115009 (2021).Jucker, T. et al. Topography shapes the structure, composition and function of tropical forest landscapes. Ecol. Lett. 21, 989–1000 (2018).Article 

Google Scholar 
Féret, J.-B. & Asner, G. P. Spectroscopic classification of tropical forest species using radiative transfer modeling. Remote Sens. Environ. 115, 2415–2422 (2011).Article 

Google Scholar 
Poortinga, A. et al. Mapping plantations in Myanmar by fusing Landsat-8, Sentinel-2 and Sentinel-1 data along with systematic error quantification. Remote Sens. 11, 831 (2019).Article 

Google Scholar 
Gutiérrez-Vélez, V. H. et al. High-yield oil palm expansion spares land at the expense of forests in the Peruvian Amazon. Environ. Res. Lett. 6, 044029 (2011).Article 

Google Scholar 
Descals, A. et al. High-resolution global map of smallholder and industrial closed-canopy oil palm plantations. Earth Syst. Sci. Data. 13, 1211–1231 (2021).Article 

Google Scholar 
Ordway, E. M., Naylor, R. L., Nkongho, R. N. & Lambin, E. F. Oil palm expansion and deforestation in Southwest Cameroon associated with proliferation of informal mills. Nat. Commun. 10, 114 (2019).CAS 
Article 

Google Scholar 
Heilmayr, R., Echeverría, C., Fuentes, R. & Lambin, E. F. A plantation-dominated forest transition in Chile. Appl. Geogr. 75, 71–82 (2016).Article 

Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
Article 

Google Scholar 
Bond, W. J., Stevens, N., Midgley, G. F. & Lehmann, C. E. R. The trouble with trees: afforestation plans for Africa. Trends Ecol. Evol. 34, 963–965 (2019).Article 

Google Scholar 
Veldman, J. W. et al. Where tree planting and forest expansion are bad for biodiversity and ecosystem services. Bioscience 65, 1011–1018 (2015).Article 

Google Scholar 
Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).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).Bastin, J. F. et al. The extent of forest in dryland biomes. Science 356, 635–638 (2017).CAS 
Article 

Google Scholar 
Fagan, M. E., Reid, J. L., Holland, M. B., Drew, J. G. & Zahawi, R. A. How feasible are global forest restoration commitments? Conserv. Lett. 13, e12700 (2020).Article 

Google Scholar 
Malkamäki, A. et al. A systematic review of the socio-economic impacts of large-scale tree plantations, worldwide. Glob. Environ. Change 53, 90–103 (2018).Article 

Google Scholar 
Schwartz, N. B., Aide, T. M., Graesser, J., Grau, H. R. & Uriarte, M. Reversals of reforestation across Latin America limit climate mitigation potential of tropical forests. Front. For. Glob. Change 3, 85 (2020).Article 

Google Scholar 
Noojipady, P. et al. Managing fire risk during drought: the influence of certification and El Niño on fire-driven forest conversion for oil palm in Southeast Asia. Earth Syst. Dynam. 8, 749–771 (2017).Bullock, E. L., Woodcock, C. E., Souza, C. Jr. & Olofsson, P. Satellite-based estimates reveal widespread forest degradation in the Amazon. Glob. Change Biol. 26, 2956–2969 (2020).Article 

Google Scholar 
Sloan, S. & Sayer, J. A. Forest Ecology and Management Forest Resources Assessment of 2015 shows positive global trends but forest loss and degradation persist in poor tropical countries. Ecol. Manag. 352, 134–145 (2015).Article 

Google Scholar 
Heinrich, V. H. A. et al. Large carbon sink potential of secondary forests in the Brazilian Amazon to mitigate climate change. Nat. Commun. 12, 1785 (2021).CAS 
Article 

Google Scholar 
Potapov, P. et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165 (2021).Article 

Google Scholar 
Bernal, B., Murray, L. T. & Pearson, T. R. H. Global carbon dioxide removal rates from forest landscape restoration activities. Carbon Balance Manag. 13, 22 (2018).CAS 
Article 

Google Scholar 
Li, W., Goodchild, M. F. & Church, R. An efficient measure of compactness for two-dimensional shapes and its application in regionalization problems. Int. J. Geogr. Inf. Sci. 27, 1227–1250 (2013).Article 

Google Scholar 
Asner, G. P. Cloud cover in Landsat observations of the Brazilian Amazon. Int. J. Remote Sens. 22, 3855–3862 (2001).Article 

Google Scholar 
Wilson, A. M. & Jetz, W. Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biol. 14, e1002415 (2016).Article 
CAS 

Google Scholar 
Gutiérrez-Vélez, V. H. & DeFries, R. Annual multi-resolution detection of land cover conversion to oil palm in the Peruvian Amazon. Remote Sens. Environ. 129, 154–167 (2013).Article 

Google Scholar 
Reiche, J. et al. Combining satellite data for better tropical forest monitoring. Nat. Clim. Change 6, 120–122 (2016).Article 

Google Scholar 
Erinjery, J. J., Singh, M. & Kent, R. Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery. Remote Sens. Environ. 216, 345–354 (2018).Article 

Google Scholar 
Shimada, M. et al. New global forest/non-forest maps from ALOS PALSAR data (2007–2010). Remote Sens. Environ. 155, 13–31 (2014).Article 

Google Scholar 
Torres, R. et al. GMES Sentinel-1 mission. Remote Sens. Environ. 120, 9–24 (2012).Article 

Google Scholar 
Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017).Article 

Google Scholar 
World Database on Protected Areas User Manual 1.4 (UNEP-WCMC, 2016).AutoML: Automatic Machine Learning (H2O.ai, 2020); https://h2o-release.s3.amazonaws.com/h2o/rel-yau/5/docs-website/h2o-docs/automl.htmlHealey, S. P. et al. Mapping forest change using stacked generalization: an ensemble approach. Remote Sens. Environ. 204, 717–728 (2018).Article 

Google Scholar 
Lagomasino, D. et al. Measuring mangrove carbon loss and gain in deltas. Environ. Res. Lett. 14, 25002 (2019).Article 

Google Scholar 
Bunting, P. et al. The global mangrove watch—a new 2010 global baseline of mangrove extent. Remote Sens. 10, 1669 (2018).Article 

Google Scholar 
Pickens, A. H. et al. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sens. Environ. 243, 111792 (2020).Article 

Google Scholar 
Olofsson, P. et al. Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 148, 42–57 (2014).Article 

Google Scholar 
Stehman, S. V. Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes. Int. J. Remote Sens. 35, 4923–4939 (2014).Article 

Google Scholar 
Olofsson, P. et al. Mitigating the effects of omission errors on area and area change estimates. Remote Sens. Environ. 236, 111492 (2020).Article 

Google Scholar 
Database of Global Administrative Areas (GADM) v.3.6 (GADM, 2018); https://gadm.org/download_country_v3.htmlHijmans, R. J., Williams, E., Vennes, C. M. & Hijmans, M. R. J. Package ‘geosphere’ version 1.5-10. Spherical trigonometry (2017).Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience 51, 933–938 (2001).Article 

Google Scholar 
Mittermeier, R. A., Turner, W. R., Larsen, F. W., Brooks, T. M. & Gascon, C. in Biodiversity Hotspots: Distribution and Protection of Conservation Priority Areas (eds Zachos, F. E. & Habel, J. C.) 3–22 (Springer, 2011).Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017). More

Yuuka Fukui
Yuuka Fukui received Ph.D. degree from Keio University in 2012 under the supervision of Prof. Keiji Fujimoto. She was a JSPS research fellow (DC2) from 2010 to 2012. She joined the laboratory of Prof. Keiji Fujimoto at Keio university as a research associate in 2012 and was promoted to an assistant professor in 2017. Her research interests focus on the design and synthesis of polymeric materials (particles, membranes, porous structures) and organic-inorganic hybrid materials inspired from biological systems.About the award article: The authors reported a new technique to prepare nanoparticles from biomass-derived polymers, which will be utilized as an eco-friendly alternative to synthetic particulate plastics. Nanosized agarose gel particles were produced via sol-to-gel transition of agarose inside water nanodroplets prepared by W/O miniemulsion method. Subsequently, the water evaporation was carried out to generate xerogel nanoparticles (AgarX). The morphologies and crystal structure of AgarX were controlled by changing the pressure and temperature during the water evaporation. The resultant AgarX possessed high crystallinity and exhibited a water dispersibility and a water resistance.

Mikihiro Hayashi
Mikihiro Hayashi received his Ph.D. degree from Nagoya University (Prof. Yushu Matsushita group) in 2015. During his doctor course, he had been selected as a JSPS research fellow (DC2) and experienced researches in ESPCI Paris-Tech (Prof. Ludwik Leibler) and in Shanghai Jiao Tong University (Prof. Xinyuan Zhu). He then re-joined Ludwik Leibler’s group as a postdoc, and experienced another postdoc in Prof. Masatoshi Tokita in Tokyo institute of technology. In 2017, he became an assistant professor in Prof. Akinori Takasu group (Nagoya institute of technology), and currently manages his own laboratory as a PI. His research interest is the design of functional cross-linked materials.About the award article: the authors reported a preparation vitrimer-like elastomers with dynamic bond-exchangeable cross-links. A poly(ethyl acrylate)-based copolymer bearing random pyridine groups was synthesized, which was cross-linked by quaternization reaction with dibromo cross-linkers. In this system, the bond exchange was operated via trans-N-alkylation of the quaternized pyridine groups, showing useful sustainable functions, such as reprocessability, recyclability, and dissolution ability in some selective solvents.

Ryohei Ishige
Ryohei Ishige received his Ph.D. from Tokyo Institute of Technology in 2011 under the supervision of Prof. Junji Watanabe. He joined Prof. Atsushi Takahara’s laboratory at Kyushu University (2011–2013) and Prof. Yoshinobu Tsujii’s laboratory at Kyoto University (2013–2014). From 2014, he joined Prof. Shinji Ando’s laboratory at Tokyo Institute of Technology as an assistant professor and was promoted to an associate professor in 2021. His research interests are liquid-crystalline aromatic polymers and those structure-property relationships.About the award article: the authors developed a novel analytical technique integrating spectroscopies (infrared pMAIRS, and spectroscopic ellipsometry) and scattering methods (GI-WAXS), applied to the process where thin film polyimide, PI, is generated from linear poly(amic ester), PAE, precursors whose backbone consists of para-linkage. They revealed that PAE-based thin PI films form heterogeneous structure composed of non-oriented amorphous region and oriented ordered region which includes anisotropic nanopores causing structural birefringence. This method enables comprehensive evaluation of the evolution in complex hierarchical structures following chemical reactions for every noncrystalline thin film polymers.

Ryohei Kakuchi
Ryohei Kakuchi received his Ph.D. degree from the Hokkaido University in 2009 with a JSPS (Japan Society for Promotion of Science) research fellowship. After the Ph.D., he has made postdoctoral works in Germany from 2009 to 2014 and joined Kanazawa University as a research assistant professor in 2014. Based on the Leading Initiative for Excellent Young Researchers program, he was then appointed as an assistant professor (PI) at Gunma University in 2017. His research interests are the novel polymer synthesis based on unique organic transformation reactions including multicomponent reactions.About the award article: The authors proposed a new synthetic strategy to utilize wood-biomass sourced compounds in a green fashion. To achieve sustainable material chemistry, the intrinsic reactivity of lignin-derived poly(methacrylated vanillin) (PMV) was spotlighted because many multicomponent reactions employ aldehydes as a reactant. First, the Passerini three-component reaction (Passerini-3CR) of the PMV was revealed to proceed with >90% aldehyde conversions. Taking advantage of this high reactivity of the PMV, its immobilized cellulose fabric, a wood-biomass sourced organic hybrid, was revealed to accept the surface Passerini-3CR with amino acid derivatives, thereby demonstrating a fully bio-based material fabrication. More

Site descriptionThe respective sites were classified into five climatic regions in California, containing San Cruz and San Rose in region 1, Saint Helena and San Jose in region 2, Livermore and Cloverdale in region 3, Davis, Lodi and Fontana in region 4, Fresno and Bakerfield in region 5 (Fig. 1). There were differences in annual mean temperature among five climatic regions, ranging from 14.3°C to 18.6°C. In each region, the GHDs, quality of musts and wines, and wine tasting notes were recorded for 148 cultivars from 1935 to 1941. Meanwhile, in region 2, namely in Napa, the GHDs and must sugar content (in °Brix) were recorded for four representative cultivars (Cabernet Sauvignon, Chardonnay, Merlot and Sauvignon Blanc) during 1991–2018.Fig. 1The locations of five climatic regions for wine grape classed by Winkler index in California. The insert plot represents the distinct Winkler index (WI) during 1935–1941 in five climatic regions.Full size imageClimate dataThe climate data was collected from five stations for over one hundred year-period (1911–2018), including daily average, maximum and minimum temperature (Table 1). Climate data was retrieved from the National Oceanic and Atmospheric Administration (NOAA)’s National Centers for Environmental Information (NCEI). The database from which the data was retrieved was the “Global Historical Climatology Network – Daily (GHCN-Daily), Version 3” (https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/by_station/)25,26. Table 1 showed the search codes and names of five stations in the website. The climate data of region 1 and region 5 were for the periods of 1911–2011 and 1938–2018, respectively.Table 1 Description of weather stations and time-span in five climatic regions.Full size tableBioclimatic indicesHere, we presented seven temperature-related indices to explore the changing climate in five climatic regions during the last 100 years. We compared the changes of these indices between the past (1935–1941) and current climate conditions (1991–2018). Thereafter, four indices were chosen to describe annual changes, including average, maximum, minimum temperature and diurnal temperature range (DTR). Furthermore, other indices were used to analyse growing season temperature (GST), Winkler index (WI) and Huglin index (HI) for the grape-growing season5,27,28. The equations used to calculate the bioclimatic indices of grape-growing season are:$$GST=frac{{sum }_{Apr1}^{Oct31}frac{{T}_{max}+{T}_{min}}{2}}{n}$$
(1)
$$WI={sum }_{Apr1}^{Oct31}left(frac{{T}_{max}+{T}_{min}}{2}-10right)$$
(2)
$$HI={sum }_{Apr1}^{Sep30}left(frac{{T}_{max}+{T}_{ave}}{2}-10right)times K$$
(3)
where Tmax, Tmin and Tave represent daily maximum, minimum and average temperatures, respectively. K is a length of day coefficient ranging from 1.02 to 1.06 between 40 and 50 of latitude in the northern hemisphere.Sample collection, harvest dates, quality of musts and wines measurementSample collection, harvest dates, quality of musts and wines measurement were detailed in the report of Amerine and Winkler24. Briefly, grape berries (22–220 kg) were picked in the morning from representative vines of variety collections or commercial vineyards by Amerine and Winkler24, as well as numerous vineyard owners. The harvest dates were recorded after picking. All grapes picked were crushed within 24 hours except for a few samples in 1935. The clear juice was taken after the coarse sediment had settled, in order to measure total soluble solids (°Brix), total acid (grams per 100 cc), and pH of must. The must was placed in an open oak fermenting tank. After fermentation, it was completed in a closed oak container. Then, the alcohol (percent by volume), extract (grams per 100 cc), tannin (grams per 100 cc), and fixed acid (grams per 100 cc) of wine were measured. The must °Brix was measured with a Brix hydrometer floating in a cylinder, must total acid was determined by titration with sodium hydroxide to a phenolphthalein end point, and must pH was measured with a quinhydrone electrode or a Beckman pH meter. In addition, wine alcohol was measured by the hydrometer and reported as percentage by volume, the extract and tannin of wine were measured by means of a special 0° to 8° Balling hydrometer and the Association of Official Agricultural Chemists method24. Note that the fixed acid of wine are equal to total acid minus volatile acid, where the total acid was measured by titration with phenolphthalein as an indicator while the volatile acid was determined also by titration with pretreated wines by method II of the Association of Official Agricultural Chemists24.Wine tasting notesThe purpose of wine tasting was to evaluate the cultivars based on the merits and defects of wine. The descriptive terms used for recording the results of the organoleptic examination contained appearance, color, odors, volatile acidity, total acidity, dryness, body, taste, smoothness and astringency, and general quality. More

West, S. A., Diggle, S. P., Buckling, A., Gardner, A. & Griffin, A. S. The social lives of microbes. Annu. Rev. Ecol. Evol. Syst. 38, 53–77 (2007).Article 

Google Scholar 
Diggle, S. P., Griffin, A. S., Campell, G. S. & West, S. A. Cooperation and conflict in quorum-sensing bacterial populations. Nature 450, 411–414 (2007).CAS 
PubMed 
Article 

Google Scholar 
Ebrahimi, A., Schwartzman, J. & Cordero, O. X. Cooperation and spatial self-organization determine rate and efficiency of particulate organic matter degradation in marine bacteria. Proc. Natl Acad. Sci. USA 116, 23309–23316 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yan, J., Monaco, H. & Xavier, J. B. The ultimate guide to bacterial swarming: An experimental model to study the evolution of cooperative behavior. Annu. Rev. Microbiol. 73, 293–312 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kramer, J., Özkaya, Ö. & Kümmerli, R. Bacterial siderophores in community and host interactions. Nat. Rev. Microbiol. 18, 152–163 (2020).CAS 
PubMed 
Article 

Google Scholar 
Griffin, A., West, S. A. & Buckling, A. Cooperation and competition in pathogenic bacteria. Nature 430, 1024–1027 (2004).CAS 
PubMed 
Article 

Google Scholar 
Sandoz, K. M., Mitzimberg, S. M. & Schuster, M. Social cheating in Pseudomonas aeruginosa quorum sensing. Proc. Natl Acad. Sci. USA 104, 15876–15881 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Xavier, J. B., Kim, W. & Foster, K. R. A molecular mechanism that stabilizes cooperative secretions in Pseudomonas aeruginosa. Mol. Microbiol. 79, 166–179 (2011).CAS 
PubMed 
Article 

Google Scholar 
Drescher, K., Nadell, C. D., Stone, H. A., Wingreen, N. S. & Bassler, B. L. Solutions to the public goods dilemma in bacterial biofilms. Curr. Biol. 24, 50–55 (2014).CAS 
PubMed 
Article 

Google Scholar 
Nadal Jimenez, P. et al. The multiple signaling systems regulating virulence in Pseudomonas aeruginosa. Microbiol. Mol. Biol. Rev. 76, 46–65 (2012).PubMed Central 
Article 
CAS 

Google Scholar 
Schuster, M., Sexton, D. J., Diggle, S. P. & Greenberg, E. P. Acyl-homoserine lactone quorum sensing: from evolution to application. Annu. Rev. Microbiol. 67, 43–63 (2013).CAS 
PubMed 
Article 

Google Scholar 
Papenfort, K. & Bassler, B. L. Quorum sensing signal-response systems in Gram-negative bacteria. Nat. Rev. Microbiol. 14, 576–588 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Darch, S. E., West, S. A., Winzer, K. & Diggle, S. P. Density-dependent fitness benefits in quorum-sensing bacterial populations. Proc. Natl Acad. Sci. USA 109, 8259–8263 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ross-Gillespie, A. & Kümmerli, R. Collective decision-making in microbes. Front. Microbiol. 5, 54 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Whiteley, M., Diggle, S. P. & Greenberg, E. P. Progress in and promise of bacterial quorum sensing research. Nature 551, 313–320 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Avery, A. Microbial cell individuality and the underlying sources of heterogeneity. Nat. Rev. Microbiol. 4, 577–587 (2006).CAS 
PubMed 
Article 

Google Scholar 
Eldar, A. & Elowitz, M. B. Functional roles for noise in genetic circuits. Nature 467, 167–173 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ackermann, M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat. Rev. Microbiol. 13, 497–508 (2015).CAS 
PubMed 
Article 

Google Scholar 
Visca, P., Imperi, F. & Lamont, I. L. Pyoverdine siderophores: From biogenesis to biosignificance. Trends Microbiol. 15, 22–30 (2007).CAS 
PubMed 
Article 

Google Scholar 
Youard, Z. A., Wenner, N. & Reimmann, C. Iron acquisition with the natural siderophore enantiomers pyochelin and enantio-pyochelin in Pseudomonas species. Biometals 24, 513–522 (2011).CAS 
PubMed 
Article 

Google Scholar 
Schalk, I. J. & Cunrath, O. An overview of the biological metal uptake pathways in Pseudomonas aeruginosa. Environ. Microbiol. 18, 3227–3246 (2016).CAS 
PubMed 
Article 

Google Scholar 
Schalk, I. J., Rigouin, C. & Godet, J. An overview of siderophore biosynthesis among fluorescent Pseudomonads and new insights into their complex cellular organization. Environ. Microbiol. 22, 1447–1466 (2020).PubMed 
Article 

Google Scholar 
Ochsner, U. A. & Vasil, M. L. Gene repression by the ferric uptake regulator in Pseudomonas aeruginosa: Cycle selection of iron-regulated genes. Proc. Natl Acad. Sci. USA 93, 4409–4414 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Leoni, L., Ciervo, A., Orsi, N. & Visca, P. Iron-regulated transcription of the pvdA gene in Pseudomonas aeruginosa: effect of Fur and PvdS on promoter activity. J. Bacteriol. 178, 2299–2313 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Escolar, L., Pérez-Martín, J. & de Lorenzo, V. Opening the iron box: Transcriptional metalloregulation by the fur protein. J. Bacteriol. 181, 6223–6229 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dumas, Z., Ross-Gillespie, A. & Kümmerli, R. Switching between apparently redundant iron-uptake mechanisms benefits bacteria in changeable environments. Proc. R. Soc. B 280, 20131055 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lamont, I. L., Beare, P., Ochsner, U., Vasil, A. I. & Vasil, M. L. Siderophore-mediated signaling regulates virulence factor production in Pseudomonas aeruginosa. Proc. Natl Acad. Sci. USA 99, 7072–7077 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Edgar, R. J. et al. Interactions between an anti-sigma protein and two sigma factors that regulate the pyoverdine signaling pathway in Pseudomonas aeruginosa. BMC Microbiol. 14, 287 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Heinrichs, D. E. & Poole, K. Cloning and sequence analysis of a gene (pchR) encoding an AraC family activator of pyochelin and ferripyochelin receptor synthesis in Pseudomonas aeruginosa. J. Bacteriol. 175, 5882–5889 (1993).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Michel, L., Gonzalez, N., Jagdeep, S., Nguyen-Ngoc, T. & Reimmann, C. PchR-box recognition by the AraC-type regulator PchR of Pseudomonas aeruginosa requires the siderophore pyochelin as an effector. Microbiology 58, 495–509 (2005).CAS 

Google Scholar 
Michel, L., Bachelard, A. & Reimmann, C. Ferripyochelin uptake genes are involved in pyochelin-mediated signalling in Pseudomonas aeruginosa. Microbiology 153, 1508–1518 (2007).CAS 
PubMed 
Article 

Google Scholar 
Cornelis, P. & Dingemans, J. Pseudomonas aeruginosa adapts its iron uptake strategies in function of the type of infections. Front. Cell. Infect. Microbiol. 3, 75 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brandel, J. et al. Pyochelin, a siderophore of Pseudomonas aeruginosa: Physicochemical characterization of the iron(III), copper(II) and zinc(II) complexes. Dalton Trans. 41, 2820–2834 (2012).CAS 
PubMed 
Article 

Google Scholar 
Perraud, Q. et al. Phenotypic adaptation of Pseudomonas aeruginosa in the presence of siderophore-antibiotic conjugates during epithelial cell infection. Microorganisms 8, 1820 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Mossialos, D. et al. Quinolobactin, a new siderophore of Pseudomonas fluorescens ATCC 17400, the production of which Is repressed by the cognate pyoverdine. Appl. Environ. Microbiol. 66, 487–492 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tyrrell, J. et al. Investigation of the multifaceted iron acquisition strategies of Burkholderia cenocepacia. BioMetals 28, 367–380 (2015).CAS 
PubMed 
Article 

Google Scholar 
Wei, Q. et al. Global regulation of gene expression by OxyR in an important human opportunistic pathogen. Nucleic Acids Res. 40, 4320–4333 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Frangipani, E. et al. The Gac/Rsm and cyclic-di-GMP signalling networks coordinately regulate iron uptake in Pseudomonas aeruginosa. Environ. Microbiol. 16, 676–688 (2014).CAS 
PubMed 
Article 

Google Scholar 
Schulz, S. et al. Elucidation of sigma factor-associated networks in Pseudomonas aeruginosa reveals a modular architecture with limited and function-specific crosstalk. PLoS Pathog. 11, e1004744 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Cunrath, O. et al. The pathogen Pseudomonas aeruginosa optimizes the production of the siderophore pyochelin upon environmental challenges. Metallomics 12, 2108–2120 (2020).CAS 
PubMed 
Article 

Google Scholar 
Wang, T. et al. An atlas of the binding specificities of transcription factors in Pseudomonas aeruginosa directs prediction of novel regulators in virulence. eLife 10, e61885 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tiburzi, F., Imperi, F. & Visca, P. Intracellular levels and activity of PvdS, the major iron starvation sigma factor of Pseudomonas aeruginosa. Mol. Microbiol. 67, 213–227 (2008).CAS 
PubMed 
Article 

Google Scholar 
Kümmerli, R., Jiricny, N., Clarke, L. S., West, S. A. & Griffin, A. S. Phenotypic plasticity of a cooperative behaviour in bacteria. J. Evol. Biol. 22, 589–598 (2009).PubMed 
Article 

Google Scholar 
Harrison, F. Dynamic social behaviour in a bacterium: Pseudomonas aeruginosa partially compensates for siderophore loss to cheats. J. Evol. Biol. 26, 1370–1378 (2013).CAS 
PubMed 
Article 

Google Scholar 
Schiessl, K. T. et al. Individual- versus group-optimality in the production of secreted bacterial compounds. Evolution 73, 675–688 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Cunrath, O. et al. A cell biological view of the siderophore pyochelin iron uptake pathway in Pseudomonas aeruginosa. Environ. Microbiol. 17, 171–185 (2015).CAS 
PubMed 
Article 

Google Scholar 
Leinweber, A., Weigert, M. & Kümmerli, R. The bacterium Pseudomonas aeruginosa senses and gradually responds to interspecific competition for iron. Evolution 72, 1515–1528 (2018).CAS 
PubMed Central 
Article 

Google Scholar 
Julou, T. et al. Cell-cell contacts confine public goods diffusion inside Pseudomonas aeruginosa clonal microcolonies. Proc. Natl Acad. Sci. USA 110, 12577–12582 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Weigert, M. & Kümmerli, R. The physical boundaries of public goods cooperation between surface-attached bacterial cells. Proc. R. Soc. B 284, 20170631 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Jayaraman, P., Sakharkar, M. K., Lim, C. S., Hock Tang, T. & Sakharkar, K. R. Activity and interactions of antibiotic and phytochemical combinations against Pseudomonas aeruginosa in vitro. Int. J. Biol. Sci. 6, 556–568 (2010).Kapoor, G., Saigal, S. & Elongavan, A. Action and resistance mechanisms of antibiotics: A guide for clinicians. J. Anaesthesiol. Clin. Pharmacol. 33, 300–305 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wrobel, A., Arciszewska, K., Maliszewski, D. & Drozdowska, D. Trimethoprim and other nonclassical antifolates an excellent template for searching modifications of dihydrofolate reductase enzyme inhibitors. J. Antibiot. 73, 5–27 (2020).CAS 
Article 

Google Scholar 
van der Veen, D. R. et al. Flexible clock systems: Adjusting the temporal programme. Phil. Trans. R. Soc. B 372, 20160254 (2017).Helm, B. et al. Two sides of a coin: ecological and chronobiological perspectives of timing in the wild. Phil. Trans. R. Soc. B 372, 0246 (2017).Rivera, M. Bacterioferritin: structure, dynamics, and protein–protein interactions at play in iron storage and mobilization. Acc. Chem. Res. 50, 331–340 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Soldano, A., Yao, H., Chandler, J. R. & Rivera, M. Inhibiting iron mobilization from bacterioferritin in Pseudomonas aeruginosa impairs biofilm formation irrespective of environmental iron availability. ACS Infectious Dis. 6, 447–458 (2020).Andrews, S. C., Robinson, A. K. & Rodriguez-Quinones, F. Bacterial iron homeostasis. FEMS Microbiol. Rev. 27, 215–237 (2003).CAS 
PubMed 
Article 

Google Scholar 
Alqarni, B., Colley, B., Klebensberger, J., McDougald, D. & Rice, S. A. Expression stability of 13 housekeeping genes during carbon starvation of Pseudomonas aeruginosa. J. Microbiol. Methods 127, 182–187 (2016).CAS 
PubMed 
Article 

Google Scholar 
Veening, J.-W., Smits, W. K. & Kuipers, O. P. Bistability, epigenetics, and bet-hedging in bacteria. Annu. Rev. Microbiol. 62, 193–210 (2008).CAS 
PubMed 
Article 

Google Scholar 
Ratcliff, W. C. & Denison, R. F. Individual-level bet hedging in the bacterium Sinorhizobium meliloti. Curr. Biol. 20, 1740–1744 (2010).CAS 
PubMed 
Article 

Google Scholar 
Schreiber, F. et al. Phenotypic heterogeneity driven by nutrient limitation promotes growth in fluctuating environments. Nat. Microbiol. 1, 16055 (2016).CAS 
PubMed 
Article 

Google Scholar 
Conradt, L. & Roper, T. J. Consensus decision making in animals. Trends Ecol. Evol. 20, 449–456 (2005).PubMed 
Article 

Google Scholar 
Sumpter, D. J. T. The principles of collective animal behaviour. Philos. Trans. R. Soc. B 361, 5–22 (2006).CAS 
Article 

Google Scholar 
Couzin, I. D. Collective cognition in animal groups. Trends Cogn. Sci. 13, 36–43 (2009).PubMed 
Article 

Google Scholar 
Bose, T., Reina, A. & Marshall, J. A. R. Collective decision-making. Curr. Opin. Behav. Sci. 16, 30–34 (2017).Article 

Google Scholar 
Dussutour, A., Ma, Q. & Sumpter, D. Phenotypic variability predicts decision accuracy in unicellular organisms. Proc. R. Soc. B 286, 20182825 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Ross-Gillespie, A., Dumas, Z. & Kümmerli, R. Evolutionary dynamics of interlinked public goods traits: an experimental study of siderophore production in Pseudomonas aeruginosa. J. Evol. Biol. 28, 29–39 (2015).CAS 
PubMed 
Article 

Google Scholar 
Choi, K.-H. & Schweizer, H. P. mini-Tn7 insertion in bacteria with single attTn7 sites: Example Pseudomonas aeruginosa. Nat. Protoc. 1, 153–161 (2006).CAS 
PubMed 
Article 

Google Scholar 
Rezzoagli, C., Granato, E. T. & Kümmerli, R. In-vivo microscopy reveals the impact of Pseudomonas aeruginosa social interactions on host colonization. ISME J. 13, 2403–2414 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Minoia, M. et al. Stochasticity and bistability in horizontal transfer control of a genomic island in Pseudomonas. Proc. Natl Acad. Sci. USA 105, 20792–20797 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mellini, M. et al. Generation of genetic tools for gauging multiple-gene expression at the single-cell level. Appl. Environ. Microbiol. 87, e02956–02920 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, S., Crooks, P. A., Wei, X. & de Leon, J. Toxicity of dipyridyl compounds and related compounds. Crit. Rev. Toxicol. 34, 447–460 (2004).CAS 
PubMed 
Article 

Google Scholar 
Liu, Y., Yang, L. & Molin, S. Synergistic activities of an efflux pump inhibitor and iron chelators against Pseudomonas aeruginosa growth and biofilm formation. Antimicrob. Agents Chemother. 54, 3960–3963 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Henriquez, T., Stein, N. V. & Jung, H. Resistance to bipyridyls mediated by the TtgABC efflux system in Pseudomonas putida KT2440. Front. Microbiol. 11, 1974 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Meyer, J.-M., Neely, A., Stintzi, A., Georges, C. & Holder, I. A. Pyoverdin is essential for viruence of Pseudomonas aeruginosa. Infect. Immun. 64, 518–523 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
de Jong, I. G., Beilharz, K., Kuipers, O. P. & Veening, J. W. Live cell imaging of Bacillus subtilis and Streptococcus pneumoniae using automated time-lapse microscopy. J. Vis. Exp. 53, e3145 (2011).
Google Scholar 
Berg, S. et al. ilastik: Interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).CAS 
PubMed 
Article 

Google Scholar 
Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).Mridha, S. and Kuemmerli, R. Mridha_Kummerli_2022_CommsBiol_raw_data_figshare.xlsx. figshare. Dataset. https://doi.org/10.6084/m9.figshare.19681962.v1 (2022) More