Philippa Kaur

Our research involved work with animal subjects (unionid mussels and Round Goby fishes) and was conducted following relevant regulations and standard procedures. The field collections were carried out under Pennsylvania Fish and Boat Commission permits (# 2018-01-0136 and 2019-01-0026). The experimental protocols were approved by Penn State University’s Institutional Animal Care and Use Committee (IACUC# 201646941 and 201646962). All new DNA sequencing data are made publicly available in GenBank (with accession numbers provided in Table 1) and a BioProject (# PRJNA813547) of the National Center for Biotechnology Information40.Propensity of Round Goby to consume unionid mussels in a controlled lab settingStream table setupWe conducted lab experiments to observe the potential predation of juvenile freshwater mussels by the Round Goby, following standard research protocols for work with animal subjects (IACUC# 201646962, Penn State University). We constructed four artificial stream tables in an aquatic laboratory, each measuring 3 × 2 m and featuring two run and two pool sections (each 0.63 × 0.56 × 0.46 m). Water flow was produced using eight Homsay 920 GPH submersible water pumps, which pumped water from a central reservoir tub into each table at the start of each run section. The water flow direction was clockwise for stream tables 1 and 3, and counterclockwise for stream tables 2 and 4. Water pumped into the stream tables exited via two drains located medially of each run section, where it flowed back to the central reservoir tub. Each stream table was filled with a 6 mm layer of substrate consisting of a mixture of sand, gravel (4–6 mm), and crushed stone (size 2B, with an average size of ~ 19 mm). The day before each experiment, field technicians traveled to local streams and collected macroinvertebrates using one minute D-frame kick net samples for each of the four stream tables. The macroinvertebrates and associated substrate were transported back to the facility and were introduced into each stream table system.Preferential feeding experimentsBefore each experiment commenced, juvenile Plain Pocketbook mussels (Lampsillis cardium) were introduced into each stream table (with 165 mussel specimens for experiment 1 and 100 mussels for experiments 2 and 3). This  widespread and abundant species is not imperiled in Pennsylvania, and mussels were provided for this study by the White Sulphur Springs National Fish Hatchery located in southeast West Virgina. The mussels were allowed to acclimate in the stream tables for 2 h before commencing each experiment. Ten Round Gobies were introduced into each stream system (stream tables 1 and 2 for experiment 1, and all tables for experiments 2 and 3). The total length (from nose tip to caudal tip) of each fish was measured prior to introduction and after the termination of experiments 2 and 3. Experiment 1 was conducted for 3 weeks, while experiments 2 and 3 were conducted for 8 days. During these experiments, Round Gobies were allowed to exist in the systems and feed preferentially, on the mussels and macroinvertebrates, for the allotted time before each investigation concluded. We acknowledge that in these experiments, the mussel abundances are higher and macroinvertebrate densities lower and less rich than commonly occur in the natural stream environment. Further, the Round Goby fish densities used are much higher than currently in the French Creek watershed, though are comparable to what is currently seen in parts of the Great Lakes basin. Nonetheless, the experiment scenarios allowed us to observe if Round Gobies would consume the mussels when given the choice to feed on a variety of food items.Evaluation of unionids consumed by fishRound Gobies were removed from the stream tables upon completion of each experiment. They were euthanized using  > 250 mg/L buffered (pH ~ 7) tricaine-S (MS222) solution. The fish were submerged for 10 min beyond the cessation of opercular movement to ensure proper euthanasia, and tissues were collected after we confirmed complete euthanasia—compliant with AVMA guidelines and approved by the IACUC protocol. The Round Gobies were placed in a 10% solution of formalin for preservation, and after 2 weeks, they were rinsed with clean water and were placed in 70% ethanol for long-term storage. After fish were removed from the system, the water was drained, and the substrate was sifted to recover the remaining mussels. Mussels were counted, and live individuals were returned to holding tanks for use in subsequent experiments. To further assess whether Round Gobies had consumed mussels during the investigation, Round Gobies were x-rayed using a Bruker Skyscan 1176 micro-CT scanner. After that, the stomachs of each fish were excised, and the contents examined using a Leica CME dissection scope to confirm the identity of Plain Pocketbook mussels. Contents posterior to the stomach were not analyzed because they could not be reliably counted and identified.DNA metabarcoding to identify mussel species consumed by Round Goby in a stream settingFish and mussel sample acquisitionWe collected 39 Round Gobies directly from streams within the French Creek watershed—their newly invaded natural stream habitats—in June 2018. We aimed to quantify which species, if any, of unionid mussels they consumed. Fish collection locations included LeBoeuf Creek at Moore Road and 100 m below the confluence of French Creek and LeBoeuf Creek. The unionid mussel populations and the environmental field settings at these locations are detailed by Clark et al.19. A team of field technicians collected fish by kick seining (3 m × 1 m × 9.5 mm nylon mesh) while moving downstream. Seining was the sampling method of choice compared to electrofishing to avoid possible regurgitation of food items prior to excision of fishes’ stomachs. The stream reaches sampled at each location were between 100 and 200 m in length and included riffle, run, and pool habitats. In addition to fish samples, unionid mussel samples from French Creek were also collected for analysis (under Pennsylvania Fish & Boat Commission collectors permits # 2018-01-0136 and 2019-01-0026). Following standard research protocols (under IACUC# 201646941, Penn State University), the Round Gobies collected were euthanized using buffered Tricaine-S (MS222) solution; and stomachs were excised using sterilized utensils before being placed in sterilized tubes filled with 97% ethanol. After excision of stomachs, fishes were placed in a 10% formalin solution for preservation. After 2 weeks, fishes were rinsed with clean water and transferred to 70% ethanol for storage. The stomach samples were immediately placed in ethanol and on ice in the field. Samples were stored in a freezer before being shipped to the US Geological Survey’s Eastern Ecological Science Center for various molecular ecology analyses. Once the fish and mussel samples arrived at this lab, they were recorded and stored at four °C until analysis.Primer developmentSpecific primers targeting a moderately conserved region of the mitochondrial COI gene for 25 species of unionids inhabiting French Creek were designed. Previously a PCR-based amplification method utilizing restriction enzyme digests was used to identify genetic fingerprints of 25 unionid species inhabiting French Creek41. Here, we designed a new degenerate PCR primer set modified with sequencing overhangs to facilitate compatibility with a MiSeq amplicon sequencing method previously designed for 16S Amplicon sequencing. We targeted the locus of the mitochondrial COI gene of unionids known to inhabit the Atlantic Slope Drainage. Consensus sequences were derived using Multalin analysis and a tiling method to identify conserved primer binding regions flanking an ~ 300 bp region of the COI gene. This gene was targeted in part due to the availability of partial or complete sequences representing these target species in the NCBI reference database40. Cytochrome oxidase sequences were downloaded for the 25 unionid mussel species of interest. However, a COI sequence for the Rabbitsfoot (Theliderma cylindrica) mussel was absent from the NCBI database, which required us to sequence this region for an in-house reference (which is described later in the paper). We designed a degenerate primer cocktail specific to all mussel species of interest that amplified a ~ 289 bp product, with forward and reverse primers used for the amplification of unionid specific COI presented as supplemental information (see Table S-230. We evaluated the suitability of the primers using samples from field identified mussels. For primer optimization, PCR was run across a gradient of annealing temperatures to determine suitability. In addition, we used Round Goby DNA as a template to evaluate specificity. In addition to Round Goby stomach samples, mussel samples of several species collected from French Creek were included as positive controls.DNA extraction from tissue samplesFollowing the manufacturer’s protocols, tissue samples (including fish stomach and mussel tissue) were extracted with the Zymo Research ZymoBIOMICS 96 MagBead DNA Kit (San Diego, CA). Random samples of DNA extracts were analyzed on an Agilent 2100 Bioanalyzer using a high-sensitivity assay kit. Fragments in the target amplicon range were apparent (albeit not known to be of mussel origin). All samples were stored at − 20 °C until PCR was performed. DNA from both the T. cylindrica and L. complanata samples were analyzed for DNA quality.Rolling circle amplification of mitochondrial genomesTo acquire COI sequences for T. cylindrica and L. complanata, we subjected archived DNA samples to rolling circle amplification (RCA) followed by amplicon sequencing on the MiSeq. In short, 2 µl of DNA template was added to 2 µl Equiphi29 DNA polymerase reaction buffer containing 1 µl of Exonuclease-resistant random primers (ThermoFisher). Samples were denatured by heating to 95 °C for 3 min followed immediately by cooling on ice for more than 5 min. A volume of 5 µl was added to an RCA master mix containing 1.5 µl of 10 × Equiphi29 DNA polymerase reaction buffer, 0.2 µl of 100 mM dithiothreitol, 8 µl of 2.5 mM dNTPs, 1 µl of Eqiphi29 DNA polymerase (10U) and 4.3 µl of nuclease-free water. The samples were heated to 45 °C for 3 h and then 65 °C for 10 min. Samples were then placed in ice and then frozen at − 20 °C. All RCA products were normalized to 0.2 ng/µl in 10 mM Tris–HCl, pH 8.5. Normalized RCA product was utilized as a template for an Illumina Nextera XT library preparation. Sequencing libraries were prepared following the Nextera XT Library Preparation Reference Guide (CT# 15031942 v01) using the Nextera XT Library Preparation Kit (Illumina, San Diego, CA). Final libraries were analyzed for size and quality using the Agilent BioAnalyzer with the accompanying DNA 1000 Kit (Agilent, Santa Clara, CA). Libraries were quantified using the Qubit H.S. Assay Kit (Invitrogen, Carlsbad, CA) and normalized to 4 nM using 10 mM Tris, pH 8.5. Libraries were pooled and run on the Illumina MiSeq at a concentration of 10 pM with a 5% PhiX spike with run parameters of 1 × 150. Bioinformatic processing of this data is outlined below.Amplification of the cytochrome oxidase 1 geneExtracted genomic DNA was used as template for end-point PCR. Samples evaluated were from mussels and round gobies (see supporting Table S-330). The ~ 289 bp COI region was amplified with the mussel primers as follows. The amplification reaction contained 0.15 µM of each primer, 1 µL of the initial amplification product, and Promega Go Taq Green Master Mix following manufacturer recommendations for a 25 µL reaction. The thermocycler program consisted of an initial denaturing step of 95 °C for 3 min, followed by 30 cycles of 30 s at 95 °C, 30 s at 52 °C, and 1 min at 72 °C. Products were subjected to a final extension of 72 °C for 5 min then held until collection at 12 °C. An appropriately sized amplification product was confirmed for each reaction by electrophoresis of 5 µL of the reaction product through a 1.5% I.D. N.A. agarose gel (FMC Bioproducts) at 100 V for 45 min. PCR products were cleaned with the Qiagen Qiaquick PCR purification kit (Valencia, CA) and quantified using the Qubit dsDNA H.S. Assay Kit (Thermofisher Scientific, Grand Island, NY). Samples were diluted in 10 mM Tris buffer (pH 8.5) to a final concentration of 5 ng/µL.Generation of mock mussel samplesTo better understand and minimize sources of error or bias in the taxonomic assignment, we created a mock extraction by mixing sequences from known mussel taxa at defined concentrations. For each mussel, approximately 25-mg of tissue was extracted with the ZymoBIOMICS 96 MagBead DNA Kit (San Diego, CA) following the manufacturer’s protocol. The COI sequence was amplified from each species using the same primer-protocol combination described above. A total of 5 PCR products were mixed at equal concentration (mass/volume) to generate the mock sample (“Mock” hereafter). To confirm the identity of these inputs, each COI region was amplified and sequenced on the Illumina MiSeq during the same run as the Mock and samples.Sequencing library preparation and quality assessmentNext-generation sequencing was performed on the Illumina MiSeq platform to observe species-specific sequences and determine the diet of the Round Goby. Inclusion of the overhangs on the amplification primers allowed us to utilize the Illumina 16S Metagenomic Sequencing Library Preparation protocol42. Amplicon libraries were prepared following the same manufacturer’s protocol. All samples were indexed using the Illumina Nextera XT multiplex library indices. DNA read size spectra were determined with the Agilent 2100 Bioanalyzer using the Agilent DNA 1000 Kit (Santa Clara, Calif.). Libraries were quantified with the Qubit dsDNA H.S. Assay Kit (ThermoFisher Scientific, Grand Island, N.Y.) and normalized to 4 nM (nM) using 10 mM (mM) Tris (hydroxymethyl) aminomethane buffer pH 8.5. A final concentration of 10 picomolar library with a 6.5% PhiX control spike was created with the combined pool of all indexed libraries. All bioinformatic operations were completed on CLC Genomic Workbench v20 (Qiagen, Valencia, Calif.).Read filtering, trimming, and RNAseq metabarcoding assemblyFASTQ files from the sequencing runs were imported as paired-end reads into CLC Genomics Workbench v20.0.4 (Qiagen Bioinformatics, Redwood City, Calif.) for initial filtering of exogenous sequence adaptors and poor-quality base calls. The trimmed overlapping paired-end reads were mapped to the 25 target unionid sequences specific for the species of interest. Several mapping iterations were run using different levels of stringency. We utilized + 2/− 3 match-mismatch scoring and set the length fraction to 0.90. Analyses were iterated using different similarity fractions ranging from 0.90 to 0.99. Reads were annotated, and relative abundance was determined using a curated reference library (see supporting Datasets S-1 and S-230). More

Hu ZM, Li SG, Guo Q, Niu SL, He NP, Li LH. et al. A synthesis of the effect of grazing exclusion on carbon dynamics in grasslands in China. Global Change Biol. 2016;22:1385–93.Article 

Google Scholar 
Lyu X, Li XB, Gong JR, Wang H, Dang DL, Dou HS, et al. Comprehensive grassland degradation monitoring by remote sensing in Xilinhot, Inner Mongolia, China. Sustainability. 2020;12:3682.Article 

Google Scholar 
O’Mara FP. The role of grasslands in food security and climate change. Ann Bot-London. 2012;110:1263–70.Article 

Google Scholar 
Bryan BA, Gao L, Ye YQ, Sun XF, Connor JD, Crossman ND, et al. China’s response to a national land-system sustainability emergency. Nature. 2018;559:193–204.CAS 
PubMed 
Article 

Google Scholar 
Bardgett RD, Bullock JM, Lavorel S, Manning P, Schaffner U, Ostle N. et al. Combatting global grassland degradation. Nat Rev Earth Environ. 2021;2:720–35.Article 

Google Scholar 
Chang JF, Ciais P, Gasser T, Smith P, Herrero M, Havlik P, et al. Climate warming from managed grasslands cancels the cooling effect of carbon sinks in sparsely grazed and natural grasslands. Nat Commun. 2021;12:118.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wardle DA, Bardgett RD, Klironomos JN, Setälä H, van der Putten WH, Wall DH. Ecological linkages between aboveground and belowground biota. Science. 2004;304:1629–33.CAS 
PubMed 
Article 

Google Scholar 
Feeney DS, Crawford JW, Daniell T, Hallett PD, Nunan N, Ritz K, et al. Three-dimensional microorganization of the soil-root-microbe system. Microb Ecol. 2006;52:151–8.PubMed 
Article 

Google Scholar 
Harris J. Soil microbial communities and restoration ecology: Facilitators or followers? Science. 2009;325:573–4.CAS 
PubMed 
Article 

Google Scholar 
Vecrin MP, Muller S. Top-soil translocation as a technique in the re-creation of species-rich meadows. Appl Veg Sci. 2003;6:271–8.Article 

Google Scholar 
Middleton EL, Bever JD. Inoculation with a native soil community advances succession in a grassland restoration. Restor Ecol. 2012;20:218–26.Article 

Google Scholar 
Wubs ERJ, van der Putten WH, Bosch M, Bezemer TM. Soil inoculation steers restoration of terrestrial ecosystems. Nat Plants. 2016;2:16107.PubMed 
Article 

Google Scholar 
Wubs ERJ, van Heusden T, Melchers PD, Bezemer TM. Soil inoculation steers plant-soil feedback, suppressing ruderal plant species. Front Ecol Evol. 2019;7:451.Article 

Google Scholar 
Bever JD. Feedback between plants and their soil communities in an old field community. Ecology. 1994;75:1965–77.Article 

Google Scholar 
Bennett JA, Maherali H, Reinhart KO, Lekberg Y, Hart MM, Klironomos J. Plant-soil feedbacks and mycorrhizal type influence temperate forest population dynamics. Science. 2017;355:181–4.CAS 
PubMed 
Article 

Google Scholar 
Contos P, Wood JL, Murphy NP, Gibb H. Rewilding with invertebrates and microbes to restore ecosystems: Present trends and future directions. Ecol Evol. 2021;11:7187–200.PubMed 
PubMed Central 
Article 

Google Scholar 
Emam T. Local soil, but not commercial AMF inoculum, increases native and non-native grass growth at a mine restoration site. Restor Ecol. 2016;24:35–44.Article 

Google Scholar 
Moradi J, Vicentini F, Simackova H, Pizl V, Tajovsky K, Stary J. An investigation into the long-term effect of soil transplant in bare spoil heaps on survival and migration of soil meso and macrofauna. Ecol Eng. 2018;110:158–64.Article 

Google Scholar 
Carbajo V, den Braber B, van der Putten WH, De Deyn GB. Enhancement of late successional plants on ex-arable land by soil inoculations. Plos One. 2011;6:e21943.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ma W, Liang XS, Wang ZW, Luo WT, Yu Q, Han XG. Resistance of steppe communities to extreme drought in northeast China. Plant Soil. 2022;473:181–194.IUSS Working Group WRB. World Reference Base for Soil Resources 2014, update 2015 International soil classification system for naming soils and creating legends for soil maps. World Soil Resources Reports No. 106. FAO, Rome, 2015.Jaunatre R, Buisson E, Dutoit T. Topsoil removal improves various restoration treatments of a Mediterranean steppe (La Crau, southeast France). Appl Veg Sci. 2014;17:236–45.Article 

Google Scholar 
Kuo S. Methods of soil analysis. Part 3: chemical methods. Soil Science Society of America: Madison, 1996.Biddle JF, Fitz-Gibbon S, Schuster SC, Brenchley JE, House CH. Metagenomic signatures of the Peru Margin subseafloor biosphere show a genetically distinct environment. P Natl Acad Sci USA. 2008;105:10583–8.CAS 
Article 

Google Scholar 
De Beeck MO, Lievens B, Busschaert P, Declerck S, Vangronsveld J, Colpaert JV. Comparison and validation of some ITS primer pairs useful for fungal metabarcoding studies. Plos One. 2014;9:e97629.Article 

Google Scholar 
Magoč T, Salzberg SL. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011;27:2957–63.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.CAS 
PubMed 
Article 

Google Scholar 
Chen SF, Zhou YQ, Chen YR, Gu J. Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:i884–90.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Edgar RC. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.CAS 
PubMed 
Article 

Google Scholar 
Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microb. 2007;73:5261–7.CAS 
Article 

Google Scholar 
Quast C, Pruesse E, Gerken J, Peplies J, Yarza P, Yilmaz P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:590–6.Article 
CAS 

Google Scholar 
Kõljalg U, Larsson K-H, Abarenkov K, Nilsson RH, Alexander IJ, Eberhardt U, et al. UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. New Phytol. 2005;166:1063–8.PubMed 
Article 
CAS 

Google Scholar 
Oostenbrink M. Estimating nematode populations by some selected methods. Nematology, Chapel Hill, 1960.Townshend JL. A modification and evaluation of the apparatus for the Oostenbrink direct cotton wool filter extraction method. Nematologica. 1963;9:106–10.Article 

Google Scholar 
Bongers T. De Nematoden van Nederland. In: Vormgeving en technische realisatie. Uitgeverij Pirola, Schoorl, 1994.Ahmad W, Jairjpuri MS. Mononchida: the predaceous nematodes. Nematology Monographs and Perspectives. Brill, Boston, 2010.Li Q, Liang WJ, Zhang XK, Mahamood M. Soil nematodes of grasslands in Northern China. Academic Press: San Diego, 2017.Wu ZY, Raven PH, Hong DY. Flora of China. Science Press: Beijing, 2013.Munson SM, Long AL, Wallace CSA, Webb RH. Cumulative drought and land-use impacts on perennial vegetation across a North American dryland region. Appl Veg Sci. 2016;19:430–41.Article 

Google Scholar 
Li YH, Wang W, Liu ZL, Jiang S. Grazing gradient versus restoration succession of leymus chinensis (Trin.) Tzvel. grassland in inner mongolia. Restor Ecol. 2008;16:572–83.Article 

Google Scholar 
Liang C, Michalk DL, Millar GD. The ecology and growth patterns of Cleistogenes species in degraded grasslands of eastern Inner Mongolia, China. J Appl Ecol. 2002;39:584–94.Article 

Google Scholar 
Liu ZG, Li ZQ. Effects of different grazing regimes on the morphological traits of Carex duriuscula on the Inner Mongolia steppe. China. New Zeal J Agr Res. 2010;53:5–12.Article 

Google Scholar 
Liu M, Gong JR, Pan Y, Luo QP, Zhai ZW, Yang LL, et al. Response of dominant grassland species in the temperate steppe of Inner Mongolia to different land uses at leaf and ecosystem levels. Photosynthetica. 2018;56:921–31.Article 

Google Scholar 
Bates D, Machler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67:1–48.Article 
CAS 

Google Scholar 
Dixon P. Vegan, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.Article 

Google Scholar 
McMurdie PJ, Holmes S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. Plos One. 2013;8:e61217.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.CAS 
PubMed 
Article 

Google Scholar 
De Cáceres M, Legendre P, Moretti M. Improving indicator species analysis by combining groups of sites. Oikos. 2010;119:1674–84.Article 

Google Scholar 
Hartman K, van der Heijden MGA, Wittwer RA, Banerjee S, Walser JC, Schlaeppi K. Cropping practices manipulate abundance patterns of root and soil microbiome members paving the way to smart farming. Microbiome. 2018;6:14.PubMed 
PubMed Central 
Article 

Google Scholar 
Aitchison J. A new approach to null correlations of proportions. Mathematical Geology. 1981;13:175–89.Article 

Google Scholar 
Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. Plos Comput Biol. 2015;11:e1004226.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Cao YP, Lin W, Li HZ. Two-sample tests of high-dimensional means for compositional data. Biometrika. 2018;105:115–32.Article 

Google Scholar 
Csardi G, Nepusz T. The igraph software package for complex network research. InterJ Complex Syst. 2006;1695:1–9.Banerjee S, Schlaeppi K, van der Heijden MGA. Keystone taxa as drivers of microbiome structure and functioning. Nat Rev Microbiol. 2018;16:567–76.CAS 
PubMed 
Article 

Google Scholar 
Banerjee S, Schlaeppi K, van der Heijden MGA. Reply to ‘Can we predict microbial keystones?’. Nat Rev Microbiol. 2019;17:194–194.CAS 
PubMed 
Article 

Google Scholar 
Zheng HP, Yang TJ, Bao YZ, He PP, Yang KM, Mei XL, et al. Network analysis and subsequent culturing reveal keystone taxa involved in microbial litter decomposition dynamics. Soil Biol Biochem. 2021;157:108230.CAS 
Article 

Google Scholar 
Kardol P, Wardle DA. How understanding aboveground-belowground linkages can assist restoration ecology. Trends Ecol Evol. 2010;25:670–9.PubMed 
Article 

Google Scholar 
Wubs ERJ, van der Putten WH, Mortimer SR, Korthals GW, Duyts H, Wagenaar R, et al. Single introductions of soil biota and plants generate long-term legacies in soil and plant community assembly. Ecol Lett. 2019;22:1145–51.PubMed 
PubMed Central 
Article 

Google Scholar 
St-Denis A, Kneeshaw D, Belanger N, Simard S, Laforest-Lapointe I, Messier C. Species-specific responses to forest soil inoculum in planted trees in an abandoned agricultural field. Appl Soil Ecol. 2017;112:1–10.Article 

Google Scholar 
Kitto JAJ, Gray DP, Greig HS, Niyogi DK, Harding JS. Meta-community theory and stream restoration: evidence that spatial position constrains stream invertebrate communities in a mine impacted landscape. Restor Ecol. 2015;23:284–91.Article 

Google Scholar 
Ofek M, Hadar Y, Minz D. Ecology of root colonizing Massilia (Oxalobacteraceae). Plos One. 2012;7:e40117.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lyu D, Backer R, Smith DL. Three plant growth-promoting rhizobacteria alter morphological development, physiology, and flower yield of Cannabis sativa L. Ind Crop Prod. 2022;178:114583.CAS 
Article 

Google Scholar 
Kulmatiski A, Beard KH. Long-term plant growth legacies overwhelm short-term plant growth effects on soil microbial community structure. Soil Biol Biochem. 2011;43:823–30.CAS 
Article 

Google Scholar 
Brewer TE, Handley KM, Carini P, Gilbert JA, Fierer N. Genome reduction in an abundant and ubiquitous soil bacterium ‘Candidatus Udaeobacter copiosus’. Nat Microbiol. 2017;2:16198.Article 
CAS 

Google Scholar 
Reme J. Development and present state of close-to-nature silviculture. J Landscape Ecol. 2018;11:17–32.Article 

Google Scholar  More

Lieth, H. Phenology and Seasonality Modeling Vol. 8 (Springer, 2013).Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).Article 

Google Scholar 
Shen, M. et al. Can changes in autumn phenology facilitate earlier green-up date of northern vegetation? Agric. For. Meteorol. 291, 108077 (2020).Article 

Google Scholar 
Menzel, A. et al. Climate change fingerprints in recent European plant phenology. Glob. Change Biol. 26, 2599–2612 (2020).Article 

Google Scholar 
Shen, X. et al. Asymmetric effects of daytime and nighttime warming on spring phenology in the temperate grasslands of China. Agric. For. Meteorol. 259, 240–249 (2018).Article 

Google Scholar 
Rudolf, V. H. W. The role of seasonal timing and phenological shifts for species coexistence. Ecol. Lett. 22, 1324–1338 (2019).
Google Scholar 
Zhu, J., Zhang, Y. & Wang, W. Interactions between warming and soil moisture increase overlap in reproductive phenology among species in an alpine meadow. Biol. Lett. 12, 20150749 (2016).Article 

Google Scholar 
Chen, J. et al. Plants with lengthened phenophases increase their dominance under warming in an alpine plant community. Sci. Total Environ. 728, 138891 (2020).Article 

Google Scholar 
Lian, X. et al. Summer soil drying exacerbated by earlier spring greening of northern vegetation. Sci. Adv. 6, eaax0255 (2020).Article 

Google Scholar 
Wolkovich, E. M. & Donahue, M. J. How phenological tracking shapes species and communities in non-stationary environments. Biol. Rev. Camb. Philos. Soc. 96, 2810–2827 (2021).Article 

Google Scholar 
Xu, X., Riley, W. J., Koven, C. D., Jia, G. & Zhang, X. Earlier leaf-out warms air in the north. Nat. Clim. Chang. 10, 370–375 (2020).Article 

Google Scholar 
D’Amato, G. et al. The effects of climate change on respiratory allergy and asthma induced by pollen and mold allergens. Allergy 75, 2219–2228 (2020).Article 

Google Scholar 
Garcia-Mozo, H. Poaceae pollen as the leading aeroallergen worldwide: a review. Allergy 72, 1849–1858 (2017).Article 

Google Scholar 
Ge, Q., Dai, J., Liu, J., Zhong, S. & Liu, H. The effect of climate change on the fall foliage vacation in China. Tour. Manag. 38, 80–84 (2013).Article 

Google Scholar 
Liu, J., Cheng, H., Jiang, D. & Huang, L. Impact of climate-related changes to the timing of autumn foliage colouration on tourism in Japan. Tour. Manag. 70, 262–272 (2019).Article 

Google Scholar 
Fan, B. et al. Earlier vegetation green-up has reduced spring dust storms. Sci. Rep. 4, 6749 (2014).Article 

Google Scholar 
Minoli, S. et al. Global response patterns of major rainfed crops to adaptation by maintaining current growing periods and irrigation. Earths Future 7, 1464–1480 (2019).Article 

Google Scholar 
Shen, M. et al. Plant phenological responses to climate change on the Tibetan Plateau: research status and challenges. Natl Sci. Rev. 22, 454–467 (2015).Article 

Google Scholar 
You, Q., Wang, D., Jiang, Z. & Kang, S. Diurnal temperature range in CMIP5 models and observations on the Tibetan Plateau. Q. J. R. Meteorol. Soc. 143, 1978–1989 (2017).Article 

Google Scholar 
You, Q. et al. Temperature dataset of CMIP6 models over China: evaluation, trend and uncertainty. Clim. Dyn. 57, 17–35 (2021).Article 

Google Scholar 
Zhu, Y.-Y. & Yang, S. Evaluation of CMIP6 for historical temperature and precipitation over the Tibetan Plateau and its comparison with CMIP5. Adv. Clim. Change Res. 11, 239–251 (2020).Article 

Google Scholar 
Lun, Y. et al. Assessment of GCMs simulation performance for precipitation and temperature from CMIP5 to CMIP6 over the Tibetan Plateau. Int. J. Climatol. 41, 3994–4018 (2021).Article 

Google Scholar 
Song, L., Zhuang, Q., Yin, Y., Wu, S. & Zhu, X. Intercomparison of model-estimated potential evapotranspiration on the Tibetan Plateau during 1981–2010. Earth Interact. 21, 1–22 (2017).Article 

Google Scholar 
You, Q., Min, J. & Kang, S. Rapid warming in the Tibetan Plateau from observations and CMIP5 models in recent decades. Int. J. Climatol. 36, 2660–2670 (2016).Article 

Google Scholar 
He, J.-S. et al. Above-belowground interactions in alpine ecosystems on the roof of the world. Plant Soil 458, 1–6 (2020).Article 

Google Scholar 
Kuang, X. & Jiao, J. J. Review on climate change on the Tibetan Plateau during the last half century. J. Geophys. Res. Atmos. 121, 3979–4007 (2016).Article 

Google Scholar 
Shen, M., Piao, S., Cong, N., Zhang, G. & Jassens, I. A. Precipitation impacts on vegetation spring phenology on the Tibetan Plateau. Glob. Change Biol. 21, 3647–3656 (2015).Article 

Google Scholar 
Shen, M., Tang, Y., Chen, J., Zhu, X. & Zheng, Y. Influences of temperature and precipitation before the growing season on spring phenology in grasslands of the central and eastern Qinghai-Tibetan Plateau. Agric. For. Meteorol. 151, 1711–1722 (2011).Article 

Google Scholar 
Ganjurjav, H. et al. Warming and precipitation addition interact to affect plant spring phenology in alpine meadows on the central Qinghai-Tibetan Plateau. Agric. For. Meteorol. 287, 107943 (2020).Article 

Google Scholar 
Peng, J., Wu, C., Wang, X. & Lu, L. Spring phenology outweighed climate change in determining autumn phenology on the Tibetan Plateau. Int. J. Climatol. 41, 3725–3742 (2021).Article 

Google Scholar 
Chen, X., An, S., Inouye, D. W. & Schwartz, M. D. Temperature and snowfall trigger alpine vegetation green-up on the world’s roof. Glob. Change Biol. 21, 3635–3646 (2015).Article 

Google Scholar 
Zheng, Z. et al. Continuous but diverse advancement of spring-summer phenology in response to climate warming across the Qinghai-Tibetan Plateau. Agric. For. Meteorol. 223, 194–202 (2016).Article 

Google Scholar 
Zhu, W. et al. Divergent shifts and responses of plant autumn phenology to climate change on the Qinghai-Tibetan Plateau. Agric. For. Meteorol. 239, 166–175 (2017).Article 

Google Scholar 
Sun, Q., Li, B., Jiang, Y., Chen, X. & Zhou, G. Declined trend in herbaceous plant green-up dates on the Qinghai–Tibetan Plateau caused by spring warming slowdown. Sci. Total Environ. 772, 145039 (2021).Article 

Google Scholar 
Sun, Q., Li, B., Zhou, G., Jiang, Y. & Yuan, Y. Delayed autumn leaf senescence date prolongs the growing season length of herbaceous plants on the Qinghai–Tibetan Plateau. Agric. For. Meteorol. 284, 107896 (2020).Article 

Google Scholar 
Jiang, Y. et al. Divergent shifts in flowering phenology of herbaceous plants on the warming Qinghai–Tibetan plateau. Agric. For. Meteorol. 307, 108502 (2021).Article 

Google Scholar 
Cong, N., Shen, M. & Piao, S. Spatial variations in responses of vegetation autumn phenology to climate change on the Tibetan Plateau. J. Plant Ecol. 10, 744–752 (2016).
Google Scholar 
Shi, C. et al. Effects of warming on chlorophyll degradation and carbohydrate accumulation of Alpine herbaceous species during plant senescence on the Tibetan Plateau. PLoS ONE 9, e107874 (2014).Article 

Google Scholar 
Morisette, J. T. et al. Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century. Front. Ecol. Environ. 7, 253–260 (2009).Article 

Google Scholar 
Kharouba, H. M. et al. Global shifts in the phenological synchrony of species interactions over recent decades. Proc. Natl Acad. Sci. USA 115, 5211–5216 (2018).Article 

Google Scholar 
Vitasse, Y. et al. Phenological and elevational shifts of plants, animals and fungi under climate change in the European Alps. Biol. Rev. Camb. Philos. Soc. 96, 1816–1835 (2021).Article 

Google Scholar 
Richardson, A. D. et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. For. Meteorol. 169, 156–173 (2013).Article 

Google Scholar 
Keenan, T. F. et al. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Chang. 4, 598–604 (2014).Article 

Google Scholar 
Estiarte, M. & Penuelas, J. Alteration of the phenology of leaf senescence and fall in winter deciduous species by climate change: effects on nutrient proficiency. Glob. Change Biol. 21, 1005–1017 (2015).Article 

Google Scholar 
Penuelas, J., Rutishauser, T. & Filella, I. Ecology. Phenology feedbacks on climate change. Science 324, 887–888 (2009).Article 

Google Scholar 
Piao, S. et al. Weakening temperature control on the interannual variations of spring carbon uptake across northern lands. Nat. Clim. Chang. 7, 359–363 (2017).Article 

Google Scholar 
Ran, Y., Li, X. & Cheng, G. Climate warming over the past half century has led to thermal degradation of permafrost on the Qinghai–Tibet Plateau. Cryosphere 12, 595–608 (2018).Article 

Google Scholar 
Gao, T. et al. Accelerating permafrost collapse on the eastern Tibetan Plateau. Environ. Res. Lett. 16, 054023 (2021).Article 

Google Scholar 
Sun, R. et al. Interannual variability of the North Pacific mixed layer associated with the spring Tibetan Plateau thermal forcing. J. Clim. 32, 3109–3130 (2019).Article 

Google Scholar 
Zhang, J., Wu, L., Huang, G., Zhu, W. & Zhang, Y. The role of May vegetation greenness on the southeastern Tibetan Plateau for East Asian summer monsoon prediction. J. Geophys. Res. Atmos. 116, D05106 (2011).Article 

Google Scholar 
Wu, G. et al. Tibetan Plateau climate dynamics: recent research progress and outlook. Natl Sci. Rev. 2, 100–116 (2015).Article 

Google Scholar 
Wang, Y., Zhao, P., Yu, R. & Rasul, G. Inter-decadal variability of Tibetan spring vegetation and its associations with eastern China spring rainfall. Int. J. Climatol. 30, 856–865 (2010).Article 

Google Scholar 
Yu, H., Luedeling, E. & Xu, J. Winter and spring warming result in delayed spring phenology on the Tibetan Plateau. Proc. Natl Acad. Sci. USA 107, 22151–22156 (2010).Article 

Google Scholar 
Shen, M. et al. Increasing altitudinal gradient of spring vegetation phenology during the last decade on the Qinghai–Tibetan Plateau. Agric. For. Meteorol. 189-190, 71–80 (2014).Article 

Google Scholar 
Wang, X. et al. No consistent evidence for advancing or delaying trends in spring phenology on the Tibetan Plateau. J. Geophys. Res. Biogeosci. 122, 3288–3305 (2017).Article 

Google Scholar 
Wang, C. et al. Assessing phenological change and climatic control of alpine grasslands in the Tibetan Plateau with MODIS time series. Int. J. Biometeorol. 59, 11–23 (2015).Article 

Google Scholar 
Wang, K. et al. Snow effects on alpine vegetation in the Qinghai-Tibetan Plateau. Int. J. Digit. Earth 8, 58–75 (2013).Article 

Google Scholar 
Meng, F., Huang, L., Chen, A., Zhang, Y. & Piao, S. Spring and autumn phenology across the Tibetan Plateau inferred from normalized difference vegetation index and solar-induced chlorophyll fluorescence. Big Earth Data 5, 182–200 (2021).Article 

Google Scholar 
Wang, X., Wu, C., Peng, D., Gonsamo, A. & Liu, Z. Snow cover phenology affects alpine vegetation growth dynamics on the Tibetan Plateau: satellite observed evidence, impacts of different biomes, and climate drivers. Agric. For. Meteorol. 256–257, 61–74 (2018).Article 

Google Scholar 
Li, P. et al. Change in autumn vegetation phenology and the climate controls from 1982 to 2012 on the Qinghai–Tibet Plateau. Front. Plant Sci. 10, 1677 (2019).Article 

Google Scholar 
Zhu, W., Zheng, Z., Jiang, N. & Zhang, D. A comparative analysis of the spatio-temporal variation in the phenologies of two herbaceous species and associated climatic driving factors on the Tibetan Plateau. Agric. For. Meteorol. 248, 177–184 (2018).Article 

Google Scholar 
Xia, J. et al. Interannual variation in the start of vegetation growing season and its response to climate change in the Qinghai–Tibet Plateau derived from MODIS data during 2001 to 2016. J. Appl. Remote Sens. 13, 048506 (2019).Article 

Google Scholar 
Huang, K. et al. Impacts of snow cover duration on vegetation spring phenology over the Tibetan Plateau. J. Plant Ecol. 12, 583–592 (2019).Article 

Google Scholar 
Li, P. et al. Dynamics of vegetation autumn phenology and its response to multiple environmental factors from 1982 to 2012 on Qinghai-Tibetan Plateau in China. Sci. Total Environ. 637-638, 855–864 (2018).Article 

Google Scholar 
Liu, X. et al. Driving forces of the changes in vegetation phenology in the Qinghai–Tibet Plateau. Remote Sens. 13, 4952 (2021).Article 

Google Scholar 
Piao, S. et al. Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai–Xizang Plateau. Agric. For. Meteorol. 151, 1599–1608 (2011).Article 

Google Scholar 
Wang, Z. et al. Causes for the unimodal pattern of biomass and productivity in alpine grasslands along a large altitudinal gradient in semi-arid regions. J. Veg. Sci. 24, 189–201 (2013).Article 

Google Scholar 
Du, M. et al. in Proc. MODSIM 2007 Int. Congr. Model. Simul. (eds Oxley, L. & Kulasiri, D.) 2146–2152 (Modelling and Simulation Society of Australia and New Zealand, 2007).Wang, S. P. et al. Asymmetric sensitivity of first flowering date to warming and cooling in alpine plants. Ecology 95, 3387–3398 (2014).Article 

Google Scholar 
Che, M. et al. Spatial and temporal variations in the end date of the vegetation growing season throughout the Qinghai–Tibetan Plateau from 1982 to 2011. Agric. For. Meteorol. 189–190, 81–90 (2014).Article 

Google Scholar 
Zhang, G., Zhang, Y., Dong, J. & Xiao, X. Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011. Proc. Natl Acad. Sci. USA 110, 4309–4314 (2013).Article 

Google Scholar 
Maisongrande, P., Duchemin, B. & Dedieu, G. VEGETATION/SPOT: an operational mission for the Earth monitoring; presentation of new standard products. Int. J. Remote Sens. 25, 9–14 (2010).Article 

Google Scholar 
Didan, K., Munoz, A. B., Solano, R. & Huete, A. MODIS vegetation index user’s guide (MOD13 series) version 3.00, June 2015 (collection 6) (Univ. Arizona, 2015).Beck, H. E. et al. Global evaluation of four AVHRR–NDVI data sets: intercomparison and assessment against Landsat imagery. Remote Sens. Environ. 115, 2547–2563 (2011).Article 

Google Scholar 
Zhang, Y., Song, C., Band, L. E., Sun, G. & Li, J. Reanalysis of global terrestrial vegetation trends from MODIS products: browning or greening? Remote Sens. Environ. 191, 145–155 (2017).Article 

Google Scholar 
Zhang, Y., Joiner, J., Alemohammad, S. H., Zhou, S. & Gentine, P. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences 15, 5779–5800 (2018).Article 

Google Scholar 
Ding, M. et al. Temperature dependence of variations in the end of the growing season from 1982 to 2012 on the Qinghai–Tibetan Plateau. GISci. Remote Sens. 53, 147–163 (2015).Article 

Google Scholar 
Cheng, M., Jin, J. & Jiang, H. Strong impacts of autumn phenology on grassland ecosystem water use efficiency on the Tibetan Plateau. Ecol. Indic. 126, 107682 (2021).Article 

Google Scholar 
Pedelty, J. et al. in Proc. 2007 IEEE Int. Geosci. Remote Sensing Symp. 1021–1025 (IEEE, 2007).Pinzon, J. & Tucker, C. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929–6960 (2014).Article 

Google Scholar 
Liu, Y., Liu, R. & Chen, J. M. Retrospective retrieval of long-term consistent global leaf area index (1981–2011) from combined AVHRR and MODIS data. J. Geophys. Res. Biogeosci. 117, G04003 (2012).Article 

Google Scholar 
Yang, B. et al. New perspective on spring vegetation phenology and global climate change based on Tibetan Plateau tree-ring data. Proc. Natl Acad. Sci. USA 114, 6966–6971 (2017).Article 

Google Scholar 
Shishov, V. V. et al. VS-oscilloscope: a new tool to parameterize tree radial growth based on climate conditions. Dendrochronologia 39, 42–50 (2016).Article 

Google Scholar 
Zhao, Y., Zhou, T., Zhang, W. & Li, J. Change in precipitation over the Tibetan Plateau projected by weighted CMIP6 models. Adv. Atmos. Sci. 39, 1133–1150 (2022).Article 

Google Scholar 
Lalande, M., Ménégoz, M., Krinner, G., Naegeli, K. & Wunderle, S. Climate change in the High Mountain Asia in CMIP6. Earth Syst. Dyn. 12, 1061–1098 (2021).Article 

Google Scholar 
Jin, Z. et al. Temporal variability in the thermal requirements for vegetation phenology on the Tibetan plateau and its implications for carbon dynamics. Clim. Change 138, 617–632 (2016).Article 

Google Scholar 
Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).Article 

Google Scholar 
Cao, R., Shen, M., Zhou, J. & Chen, J. Modeling vegetation green-up dates across the Tibetan Plateau by including both seasonal and daily temperature and precipitation. Agric. For. Meteorol. 249, 176–186 (2018).Article 

Google Scholar 
Li, P. et al. Combined control of multiple extreme climate stressors on autumn vegetation phenology on the Tibetan Plateau under past and future climate change. Agric. For. Meteorol. 308–309, 108571 (2021).Article 

Google Scholar 
Lang, W., Chen, X., Qian, S., Liu, G. & Piao, S. A new process-based model for predicting autumn phenology: how is leaf senescence controlled by photoperiod and temperature coupling? Agric. For. Meteorol. 268, 124–135 (2019).Article 

Google Scholar 
Yang, Z. et al. Phylogenetic conservatism in heat requirement of leaf-out phenology, rather than temperature sensitivity, in Tibetan Plateau. Agric. For. Meteorol. 304-305, 108413 (2021).Article 

Google Scholar 
Gao, B., Li, J. & Wang, X. Impact of frozen soil changes on vegetation phenology in the source region of the Yellow River from 2003 to 2015. Theor. Appl. Climatol. 141, 1219–1234 (2020).Article 

Google Scholar 
Jiang, H. et al. The impacts of soil freeze/thaw dynamics on soil water transfer and spring phenology in the Tibetan Plateau. Arct. Antarct. Alp. Res. 50, e1439155 (2018).Article 

Google Scholar 
Li, G., Jiang, C., Cheng, T. & Bai, J. Grazing alters the phenology of alpine steppe by changing the surface physical environment on the northeast Qinghai-Tibet Plateau, China. J. Environ. Manage. 248, 109257 (2019).Article 

Google Scholar 
Du, J. et al. Interacting effects of temperature and precipitation on climatic sensitivity of spring vegetation green-up in arid mountains of China. Agric. For. Meteorol. 269–270, 71–77 (2019).Article 

Google Scholar 
Liu, L. et al. Effects of elevation on spring phenological sensitivity to temperature in Tibetan Plateau grasslands. Chin. Sci. Bull. 59, 4856–4863 (2014).Article 

Google Scholar 
Cong, N. et al. Little change in heat requirement for vegetation green-up on the Tibetan Plateau over the warming period of 1998–2012. Agric. For. Meteorol. 232, 650–658 (2017).Article 

Google Scholar 
Shen, M. et al. Strong impacts of daily minimum temperature on the green-up date and summer greenness of the Tibetan Plateau. Glob. Change Biol. 22, 3057–3066 (2016).Article 

Google Scholar 
Du, J. et al. Daily minimum temperature and precipitation control on spring phenology in arid-mountain ecosystems in China. Int. J. Climatol. 40, 2568–2579 (2020).Article 

Google Scholar 
Shen, M. Spring phenology was not consistently related to winter warming on the Tibetan Plateau. Proc. Natl Acad. Sci. USA 108, E91–E92 (2011).Article 

Google Scholar 
An, S. et al. Precipitation and minimum temperature are primary climatic controls of alpine grassland autumn phenology on the Qinghai-Tibet Plateau. Remote Sens. 12, 431 (2020).Article 

Google Scholar 
Zu, J. et al. Biological and climate factors co-regulated spatial-temporal dynamics of vegetation autumn phenology on the Tibetan Plateau. Int. J. Appl. Earth Obs. Geoinf. 69, 198–205 (2018).
Google Scholar 
Qiao, C. et al. Vegetation phenology in the Qilian mountains and its response to temperature from 1982 to 2014. Remote Sens. 13, 286 (2021).Article 

Google Scholar 
Yang, Z. et al. Asymmetric responses of the end of growing season to daily maximum and minimum temperatures on the Tibetan Plateau. J. Geophys. Res. Atmos. 122, 13,78–13,287 (2017).
Google Scholar 
Dorji, T. et al. Plant functional traits mediate reproductive phenology and success in response to experimental warming and snow addition in Tibet. Glob. Change Biol. 19, 459–472 (2013).Article 

Google Scholar 
Li, X., Zhang, L. & Luo, T. Rainy season onset mainly drives the spatiotemporal variability of spring vegetation green-up across alpine dry ecosystems on the Tibetan Plateau. Sci. Rep. 10, 18797 (2020).Article 

Google Scholar 
Zhang, X. et al. Effects of climate change on the growing season of alpine grassland in Northern Tibet, China. Glob. Ecol. Conserv. 23, e01126 (2020).Article 

Google Scholar 
Sun, Q. et al. A prognostic phenology model for alpine meadows on the Qinghai–Tibetan Plateau. Ecol. Indic. 93, 1089–1100 (2018).Article 

Google Scholar 
Zhu, J., Zhang, Y. & Jiang, L. Experimental warming drives a seasonal shift of ecosystem carbon exchange in Tibetan alpine meadow. Agric. For. Meteorol. 233, 242–249 (2017).Article 

Google Scholar 
Shen, M. et al. No evidence of continuously advanced green-up dates in the Tibetan Plateau over the last decade. Proc. Natl Acad. Sci. USA 110, E2329 (2013).
Google Scholar 
Fu, Y. S. et al. Variation in leaf flushing date influences autumnal senescence and next year’s flushing date in two temperate tree species. Proc. Natl Acad. Sci. USA 111, 7355–7360 (2014).Article 

Google Scholar 
Delpierre, N. et al. Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France. Agric. For. Meteorol. 149, 938–948 (2009).Article 

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

Google Scholar 
Meng, F. D. et al. Changes in flowering functional group affect responses of community phenological sequences to temperature change. Ecology 98, 734–740 (2017).Article 

Google Scholar 
Wang, S. et al. Timing and duration of phenological sequences of alpine plants along an elevation gradient on the Tibetan plateau. Agric. For. Meteorol. 189–190, 220–228 (2014).Article 

Google Scholar 
Jiang, L. L. et al. Relatively stable response of fruiting stage to warming and cooling relative to other phenological events. Ecology 97, 1961–1969 (2016).Article 

Google Scholar 
Li, X. et al. Responses of sequential and hierarchical phenological events to warming and cooling in alpine meadows. Nat. Commun. 7, 12489 (2016).Article 

Google Scholar 
Meng, F. et al. Nonlinear responses of temperature sensitivities of community phenophases to warming and cooling events are mirroring plant functional diversity. Agric. For. Meteorol. 253–254, 31–37 (2018).Article 

Google Scholar 
Meng, F. et al. Divergent responses of community reproductive and vegetative phenology to warming and cooling: asymmetry versus symmetry. Front. Plant Sci. 10, 1310 (2019).Article 

Google Scholar 
Zhang, Z., Niu, K., Liu, X., Jia, P. & Du, G. Linking flowering and reproductive allocation in response to nitrogen addition in an alpine meadow. J. Plant Ecol. 7, 231–239 (2013).Article 

Google Scholar 
Xi, Y. et al. Nitrogen addition alters the phenology of a dominant alpine plant in Northern Tibet. Arct. Antarct. Alp. Res. 47, 511–518 (2018).Article 

Google Scholar 
Yin, T.-F., Zheng, L.-L., Cao, G.-M., Song, M.-H. & Yu, F.-H. Species-specific phenological responses to long-term nitrogen fertilization in an alpine meadow. J. Plant Ecol. 10, 301–309 (2016).
Google Scholar 
Liu, L. et al. Altered precipitation patterns and simulated nitrogen deposition effects on phenology of common plant species in a Tibetan Plateau alpine meadow. Agric. For. Meteorol. 236, 36–47 (2017).Article 

Google Scholar 
Liu, Y. et al. Effects of nitrogen addition and mowing on reproductive phenology of three early-flowering forb species in a Tibetan alpine meadow. Ecol. Eng. 99, 119–125 (2017).Article 

Google Scholar 
Zhu, J., Zhang, Y. & Liu, Y. Effects of short-term grazing exclusion on plant phenology and reproductive succession in a Tibetan alpine meadow. Sci. Rep. 6, 27781 (2016).Article 

Google Scholar 
Li, Y. et al. The effects of grazing regimes on phenological stages, intervals and divergences of alpine plants on the Qinghai–Tibetan Plateau. J. Veg. Sci. 30, 134–145 (2019).Article 

Google Scholar 
Dorji, T. et al. Impacts of climate change on flowering phenology and production in alpine plants: the importance of end of flowering. Agric. Ecosyst. Environ. 291, 106795 (2020).Article 

Google Scholar 
Meng, F. et al. Opposite effects of winter day and night temperature changes on early phenophases. Ecology 100, e02775 (2019).Article 

Google Scholar 
Meng, F. et al. Temperature sensitivity thresholds to warming and cooling in phenophases of alpine plants. Clim. Change 139, 579–590 (2016).Article 

Google Scholar 
Suonan, J., Classen, A. T., Sanders, N. J. & He, J. S. Plant phenological sensitivity to climate change on the Tibetan Plateau and relative to other areas of the world. Ecosphere 10, e02543 (2019).Article 

Google Scholar 
Ganjurjav, H. et al. Phenological changes offset the warming effects on biomass production in an alpine meadow on the Qinghai–Tibetan Plateau. J. Ecol. 109, 1014–1025 (2020).Article 

Google Scholar 
Jiang, Z. et al. Extreme climate events in China: IPCC-AR4 model evaluation and projection. Clim. Change 110, 385–401 (2011).Article 

Google Scholar 
Huang, X. et al. Spatiotemporal dynamics of snow cover based on multi-source remote sensing data in China. Cryosphere 10, 2453–2463 (2016).Article 

Google Scholar 
Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2019).Article 

Google Scholar 
Wang, C. & Tang, Y. Responses of plant phenology to nitrogen addition: a meta-analysis. Oikos 128, 1243–1253 (2019).Article 

Google Scholar 
Chen, H., Zhu, Q., Wu, N., Wang, Y. & Peng, C. H. Delayed spring phenology on the Tibetan Plateau may also be attributable to other factors than winter and spring warming. Proc. Natl Acad. Sci. USA 108, E93 (2011).
Google Scholar 
Zhang, L. et al. Effect of warming and degradation on phenophases of Kobresia pygmaea and Potentilla multifida on the Tibetan Plateau. Agric. Ecosyst. Environ. 300, 106998 (2020).Article 

Google Scholar 
Lin, X. et al. Fluxes of CO2, CH4, and N2O in an alpine meadow affected by yak excreta on the Qinghai-Tibetan plateau during summer grazing periods. Soil Biol. Biochem. 41, 718–725 (2009).Article 

Google Scholar 
Sa, C. et al. Spatiotemporal variation in snow cover and its effects on grassland phenology on the Mongolian Plateau. J. Arid Land 13, 332–349 (2021).Article 

Google Scholar 
Zheng, J., Xu, X., Jia, G. & Wu, W. Understanding the spring phenology of Arctic tundra using multiple satellite data products and ground observations. Sci. China Earth Sci. 63, 1599–1612 (2020).Article 

Google Scholar 
Wu, W., Sun, Y., Xiao, K. & Xin, Q. Development of a global annual land surface phenology dataset for 1982–2018 from the AVHRR data by implementing multiple phenology retrieving methods. Int. J. Appl. Earth Obs. Geoinf. 103, 102487 (2021).
Google Scholar 
Karkauskaite, P., Tagesson, T. & Fensholt, R. Evaluation of the plant phenology index (PPI), NDVI and EVI for start-of-season trend analysis of the Northern Hemisphere boreal zone. Remote Sens. 9, 485 (2017).Article 

Google Scholar 
Yang, Y., Guan, H., Shen, M., Liang, W. & Jiang, L. Changes in autumn vegetation dormancy onset date and the climate controls across temperate ecosystems in China from 1982 to 2010. Glob. Change Biol. 21, 652–665 (2015).Article 

Google Scholar 
Zhang, J. et al. Comparison of land surface phenology in the Northern Hemisphere based on AVHRR GIMMS3g and MODIS datasets. ISPRS J. Photogramm. Remote Sens. 169, 1–16 (2020).Article 

Google Scholar 
Shen, M. et al. Earlier-season vegetation has greater temperature sensitivity of spring phenology in northern hemisphere. PLoS ONE 9, e88178 (2014).Article 

Google Scholar 
Zhang, H., Yuan, W., Liu, S., Dong, W. & Fu, Y. Sensitivity of flowering phenology to changing temperature in China. J. Geophys. Res. Biogeosci. 120, 1658–1665 (2015).Article 

Google Scholar 
Cook, B. I. et al. Sensitivity of spring phenology to warming across temporal and spatial climate gradients in two independent databases. Ecosystems 15, 1283–1294 (2012).Article 

Google Scholar 
Wang, C., Cao, R., Chen, J., Rao, Y. & Tang, Y. Temperature sensitivity of spring vegetation phenology correlates to within-spring warming speed over the Northern Hemisphere. Ecol. Indic. 50, 62–68 (2015).Article 

Google Scholar 
Gao, M. et al. Three-dimensional change in temperature sensitivity of northern vegetation phenology. Glob. Change Biol. 26, 5189–5201 (2020).Article 

Google Scholar 
Zohner, C. M., Benito, B. M., Fridley, J. D., Svenning, J. C. & Renner, S. S. Spring predictability explains different leaf-out strategies in the woody floras of North America, Europe and East Asia. Ecol. Lett. 20, 452–460 (2017).Article 

Google Scholar 
Fu, Y. H. et al. Daylength helps temperate deciduous trees to leaf-out at the optimal time. Glob. Change Biol. 25, 2410–2418 (2019).Article 

Google Scholar 
Huang, J. G. et al. Photoperiod and temperature as dominant environmental drivers triggering secondary growth resumption in Northern Hemisphere conifers. Proc. Natl Acad. Sci. USA 117, 20645–20652 (2020).Article 

Google Scholar 
Iler, A. M., CaraDonna, P. J., Forrest, J. R. K. & Post, E. Demographic consequences of phenological shifts in response to climate change. Annu. Rev. Ecol. Evol. Syst. 52, 221–245 (2021).Article 

Google Scholar 
Chen, S., Huang, Y., Gao, S. & Wang, G. Impact of physiological and phenological change on carbon uptake on the Tibetan Plateau revealed through GPP estimation based on spaceborne solar-induced fluorescence. Sci. Total Environ. 663, 45–59 (2019).Article 

Google Scholar 
Jin, J. et al. Grassland production in response to changes in biological metrics over the Tibetan Plateau. Sci. Total Environ. 666, 641–651 (2019).Article 

Google Scholar 
Kang, X. et al. Variability and changes in climate, phenology, and gross primary production of an alpine wetland ecosystem. Remote Sens. 8, 391 (2016).Article 

Google Scholar 
Zheng, Z., Zhu, W. & Zhang, Y. Direct and lagged effects of spring phenology on net primary productivity in the alpine grasslands on the Tibetan Plateau. Remote Sens. 12, 1223 (2020).Article 

Google Scholar 
Wang, S. et al. Responses of net primary productivity to phenological dynamics in the Tibetan Plateau, China. Agric. For. Meteorol. 232, 235–246 (2017).Article 

Google Scholar 
Li, S., Zhang, H., Zhou, X., Yu, H. & Li, W. Enhancing protected areas for biodiversity and ecosystem services in the Qinghai–Tibet Plateau. Ecosyst. Serv. 43, 101090 (2020).Article 

Google Scholar 
Meng, F. et al. Enhanced spring temperature sensitivity of carbon emission links to earlier phenology. Sci. Total Environ. 745, 140999 (2020).Article 

Google Scholar 
Hu, G. et al. The divergent impact of phenology change on the productivity of alpine grassland due to different timing of drought on the Tibetan Plateau. Land Degrad. Dev. 32, 4033–4041 (2021).Article 

Google Scholar 
Li, P., Zhu, W. & Xie, Z. Diverse and divergent influences of phenology on herbaceous aboveground biomass across the Tibetan Plateau alpine grasslands. Ecol. Indic. 121, 107036 (2021).Article 

Google Scholar 
He, M. et al. Relationships between wood formation and cambium phenology on the Tibetan Plateau during 1960–2014. Forests 9, 86 (2018).Article 

Google Scholar 
Wang, J., Li, M., Yu, C. & Fu, G. The change in environmental variables linked to climate change has a stronger effect on aboveground net primary productivity than does phenological change in alpine grasslands. Front. Plant Sci. 12, 798633 (2022).Article 

Google Scholar 
Shen, W., Zhang, L. & Luo, T. Causes for the increase of early-season freezing events under a warmer climate at alpine treelines in southeast Tibet. Agric. For. Meteorol. 316, 108863 (2022).Article 

Google Scholar 
Ye, D.-Z. & Wu, G.-X. The role of the heat source of the Tibetan Plateau in the general circulation. Meteorol. Atmos. Phys. 67, 181–198 (1998).Article 

Google Scholar 
Cao, R., Feng, Y., Liu, X., Shen, M. & Zhou, J. Uncertainty of vegetation green-up date estimated from vegetation indices due to snowmelt at northern middle and high latitudes. Remote Sens. 12, 190 (2020).Article 

Google Scholar 
Zeng, L., Wardlow, B. D., Xiang, D., Hu, S. & Li, D. A review of vegetation phenological metrics extraction using time-series, multispectral satellite data. Remote Sens. Environ. 237, 111511 (2020).Article 

Google Scholar 
Cao, R. et al. A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky-Golay filter. Remote Sens. Environ. 217, 244–257 (2018).Article 

Google Scholar 
Chen, J. et al. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens. Environ. 91, 332–344 (2004).Article 

Google Scholar 
Wang, C. et al. A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems. Remote Sens. Environ. 196, 1–12 (2017).Article 

Google Scholar 
Yang, W. et al. A semi-analytical snow-free vegetation index for improving estimation of plant phenology in tundra and grassland ecosystems. Remote Sens. Environ. 228, 31–44 (2019).Article 

Google Scholar 
Wang, C., Chen, J., Tang, Y., Black, T. A. & Zhu, K. A novel method for removing snow melting-induced fluctuation in GIMMS NDVI3g data for vegetation phenology monitoring: a case study in deciduous forests of North America. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 11, 800–807 (2018).Article 

Google Scholar 
Helman, D. Land surface phenology: What do we really ‘see’ from space? Sci. Total Environ. 618, 665–673 (2018).Article 

Google Scholar 
Steltzer, H. & Post, E. Ecology. Seasons and life cycles. Science 324, 886–887 (2009).Article 

Google Scholar 
Liang, L., Schwartz, M. D. & Fei, S. Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest. Remote Sens. Environ. 115, 143–157 (2011).Article 

Google Scholar 
Li, R. et al. Leaf unfolding of Tibetan alpine meadows captures the arrival of monsoon rainfall. Sci. Rep. 6, 20985 (2016).Article 

Google Scholar 
Tang, J. et al. Emerging opportunities and challenges in phenology: a review. Ecosphere 7, e01436 (2016).Article 

Google Scholar 
Van Nuland, M. E. et al. Natural soil microbiome variation affects spring foliar phenology with consequences for plant productivity and climate-driven range shifts. New Phytol. 232, 762–775 (2021).Article 

Google Scholar 
Mutz, J., McClory, R., van Dijk, L. J. A., Ehrlen, J. & Tack, A. J. M. Pathogen infection influences the relationship between spring and autumn phenology at the seedling and leaf level. Oecologia 197, 447–457 (2021).Article 

Google Scholar 
Radville, L., McCormack, M. L., Post, E. & Eissenstat, D. M. Root phenology in a changing climate. J. Exp. Bot. 67, 3617–3628 (2016).Article 

Google Scholar 
Gao, M. et al. Divergent changes in the elevational gradient of vegetation activities over the last 30 years. Nat. Commun. 10, 2970 (2019).Article 

Google Scholar  More

Ethical statementEthics approval was obtained for this study from the ethics committee of the University of Goettingen (Chair: Prof. Dr. Peter-Tobias Stoll) under the reference number 17./04.22Wurz.Study areaAll plots were situated in northeastern Madagascar in the SAVA region (Supplementary Fig. 1). The natural vegetation is tropical lowland rainforest, but deforestation rates are high30,67.The region is globally and nationally one of the most biodiverse places with high levels of endemism17,68. Forest loss is mainly driven by slash-and-burn shifting hill rice cultivation58. The region is characterized by a warm and humid climate with an annual rainfall of 2255 mm and a mean annual temperature of 23,9 °C (mean value of 60 plots extracted from CHELSA climatology69). Vanilla is the main cash crop in the SAVA region, making Madagascar the main vanilla producer globally21,22. Vanilla prices have shown strong fluctuations over the past years, with a price boom between 2014 and 2019 triggering an expansion of vanilla agroforestry in the region22,23.Study designWe selected 10 villages based on the 60 villages selected within the Diversity Turn in Land Use Science project22 (Supplementary Fig. 1). We selected the villages based on the list of villages for our study region from official election lists which listed all villages within a fokontany individually22. Village boundaries, demographics, infrastructure were defined based on a rapid survey with the village chief. Among the 60 villages, we considered all villages without coconut plantations, with less than 40% water (river, sea, and lakes) to avoid a strong influence of water elements and with forest fragments and shifting cultivation present within a 2 km radius around the village. Two of these 17 villages overlapped within a 2 km radius of the villages, thus we randomly selected one of them, resulting in 14 villages. We visited these 14 villages in a randomized order and stopped after we found 10 villages which fulfilled the necessary criteria (all land-use types present, willing to participate). In each of the 10 villages, we selected three vanilla agroforests, one forest fragment, and two fallows. Overall, we studied 60 plots across 10 villages and 10 plots in one protected old-growth forest (Marojejy National Park). All plots had a minimum distance of 260 m and a mean minimum distance of 794 m (SD = 468 m) to each other. Plot elevation ranged between 10 and 819 m.a.s.l. (mean  = 205 m, SD = 213 m; Supplementary Table 20).Plot selectionIn each of the 10 villages, we selected three vanilla agroforests with low, medium, and high canopy closure, respectively, covering a within village canopy cover gradient. To refine our vanilla agroforest classification, we used interviews with the plot owners to categorize all vanilla agroforests based on land-use history into fallow- and forest-derived agroforests15. Forest-derived vanilla agroforests are established within forest fragments, which have been manually thinned of dense understory vegetation. Fallow-derived vanilla agroforests are established on formerly slashed and burned plots, where vegetation has been cleared for hill rice production (shifting cultivation system locally called tavy). Out of our 30 vanilla agroforests, 20 vanilla agroforests were fallow-derived and 10 vanilla agroforests were forest-derived, roughly matching the proportion of fallow- and forest-derived vanilla agroforests across the study region (70% are fallow-derived vanilla agroforests, 27% are forest-derived vanilla agroforests and 3% of unknown origin22.In addition to vanilla agroforests, we selected one forest fragment in each village. Forest fragments were located inside the agricultural landscape and were remnants of the once continuous forest; these fragments are frequently used for natural product extraction. Forest fragments have not been burned or clear cut in living memory, yet the ongoing resource extraction results in a much simplified stand structure and fewer large trees compared to old-growth forest12. Furthermore, we chose one herbaceous and one woody fallow in each of the 10 study villages. Both fallow types form part of the shifting hill rice production cycle and represent the fallow period at different stages after the crop production. Herbaceous fallows have been slashed and burned multiple times with the last cultivation cycle at the end of 2016, one year prior to the first species data collection in 2017, and thereafter left fallow11. The continuous succession of herbaceous fallows turns them into woody fallows with the domination of woody plants including shrubs, trees, and sometimes bamboo. Our 10 woody fallows have last burned 4–16 years before data collection. In this study, we combine both herbaceous and woody fallows into the category “fallow”. Generally, fallows occur in different forms in the study region. The characteristics of fallows depend on the frequency of past fires and the length of fallow periods in between crop cultivation11. Frequent burning results in a loss of native and woody species and a dominance of exotic species and grasses11. In later fallow cycles, fern species increasingly appear11.Due to the commonly repeated slashing and burning, secondary forests are very rare in the study region. Shifting cultivation prevails in Madagascar70, because it is an important option for people to grow food because means for agricultural intensification are scarce. According to our baseline survey (performed in 60 villages in our study region), 90% of the interviewed farmers grow rice for subsistence in addition to growing vanilla22. Out of this sample, 64% of farmers grow rice in irrigated paddies and 26% of farmers use shifting cultivation.We also studied 10 plots at two sites in Marojejy National Park, the only remaining, continuous old-growth forest at a low altitude in our study area71. We chose accessible old-growth forest plots with a minimum distance of 250 m from the forest edge. Five of the 10 old-growth forest plots were located in Manantenina Valley, the other five old-growth forest plots were situated in the eastern part of Marojejy National Park, called Bangoabe area. Illegal selective logging has occurred in some parts of the park. During our plot selection, we avoided sites with traces of selective logging.Land-use history classificationTo collect information on the land-use history or farm history, interviews with farmers are common72,73. We did interviews with the plot owner. Questions on land-use history were binary (forest-derived or fallow-derived) and did not include information on the detailed land-use history (e.g. frequency of burning, past crop systems). Thus, we consider this selfreported data very reliable. The land-use categorization derived by farmers was confirmed by our visual plot inspections (forest-derived vanilla agroforests do have a quite distinctive vegetation structure compared to fallow-derived vanilla agroforests). Additionally, data on tree species composition and soil characteristics show evident differences between the categories and back up the binary land-use history categorization. Analysis of tree species composition showed that fallow- and forest-derived vanilla agroforests differ significantly in tree species composition12. Soil analysis (see Fig. S9) showed that our fallow-derived vanilla agroforests are associated with fertility-related variables such as an increase in calcium, pH, nitrogen, and phosphorus, which is common after slas-and-burn agriculture74,75.Plot designWe collected species data on plots with a radius of 25 m (1964 m2, 0.1964 ha). We established our circular plots in a homogeneous area of the land-use type or forest. Adjacent land uses were usually different because farmers generally own small-scale land with a mean size of 0.66 ha (mean size of agroforests). We assessed vanilla plant data (yield, vine length, vine age, planting density) on 36 vanilla pieds on each of 30 circular vanilla plots (Supplementary Fig. 8). We defined one vanilla pied (foot in French) as the combination of a vanilla vine and a minimum of one support tree. The 36 vanilla pieds were evenly selected in each of the circular plots based on a sampling protocol to ensure comprehensive and unbiased sampling. We chose vanilla pieds independent of age, length or health condition. We marked the 36 selected vanilla pieds per plot with a unique barcode to assess vanilla yield (April 2018) and other plant health variables on the same plant (not used in this study). However, for 37 vanilla pieds (out of a total of 1080 marked vanilla pieds), the barcodes were lost or unreadable and we selected a new plant closest to the original position (independent of age, length, or condition) and marked it with a new unique barcode. We measured the size of the vanilla agroforest by walking with the agroforest owner and a hand-held GPS device at the perimeter of the plot.Vanilla planting densityWe counted each vanilla pied on each 25 m circular plot by dividing the plot in four-quarter segments. We calculated the area of each 25 m radius plot including slope correction and calculated vanilla planting density (vanilla pieds per hectare) by dividing the number of vanilla pieds by the slope-corrected plot area.Vanilla yieldWe measured yield on 30 vanilla plantations (10 forest-derived vanilla plantations and 20 fallow-derived vanilla plantations); three in each of our 10 study villages. We measured vanilla yield on a total of 36 vanilla pieds between March and April 2018. We assessed the vanilla yield before harvest to ensure an accurate yield assessment due to two reasons. Firstly, vanilla pods are commonly harvested successively due to their differing pollination date and maturity requiring multiple visits over several weeks. Secondly, theft of vanilla pods is commonplace around harvest time. We, therefore, estimated the weight of the on-plant-hanging vanilla pods by measuring pod volume and relating this to a prior established volume–weight correlation. This is possible because vanilla pods only grow in length and width in the first 8 weeks of their development76. Our yield assessment consisted of one interview part with the plot owner and one measurement part. The interview part included questions about the occurrence of theft and early harvest on the plantation. During the measurement part, we assessed the number, diameter, and length of all vanilla pods. We measured vanilla pod length with a ruler starting at the junction of stem and pod until the tip of the pod without considering the bending of the pod. We measured the diameter at the widest part of the pod using a caliper. We firstly calculated pod volume based on the standard volume cylinder formula using the measured diameter (cm) and length (cm): V = πr2h.Secondly, we calculated the weight (g) of each pod by using the linear regression equation (y = bx + a) of a weight–volume correlation of 114 vanilla pods from 114 different agroforests (weight, length, and diameter of these 114 green vanilla was assessed post-harvest in 2017). We calculated the weight of all measured pods of the harvest in 2018 based on the formula:$${{{{{rm{volume}}}}}}={{{{{rm{pi }}}}}}({{{{{rm{diameter}}}}}}({{{{{rm{mm}}}}}})/20)^wedge 2ast {{{{{rm{length}}}}}}({{{{{rm{cm}}}}}})$$Here, we divided the pod diameter (mm) by 20 to obtain the radius and to transform millimeters to centimeters. Weight was defined as volume*0.5662 + 0.9699. No vanilla pods were stolen or already harvested on our 36 vanilla pieds and hence we did not need to account for it in our vanilla yield calculation.Vanilla vine lengthWe assessed vanilla vine length for all 36 vanilla pieds (same vanilla pieds as used for the yield assessment) on each plot by measuring the total length of the vine from the lowest to the highest part with a measuring stick. If the vanilla vine was looped on the support tree (= vanilla vine is hanging in multiple loops on the support tree), we measured from the top height of the looping of the vanilla vine until the lowest height of the vine. At the medium height of the vanilla vine, we counted the number of times the vanilla vine passed through. We calculated the total length of the liana by multiplying the maximum height of the vanilla vine by the number of times the vine passed through the middle. In some cases, the vanilla vine looped at two different heights, we thus considered the middle between the two looping heights as the top height. If vanilla vines grew on two different support trees, we considered them as one vanilla pieds if support trees were More

Nathan, R. et al. A movement ecology paradigm for unifying organismal movement research. Proc. Natl. Acad. Sci. 105, 19052–19059 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lima, S. L. & Dill, L. M. Behavioral decisions made under the risk of predation: A review and prospectus. Can. J. Zool. 68, 619–640 (1990).Article 

Google Scholar 
Kays, R., Crofoot, M. C., Jetz, W. & Wikelski, M. Terrestrial animal tracking as an eye on life and planet. Science 348, 1122–1133. https://doi.org/10.1126/science.aaa2478 (2015).CAS 
Article 

Google Scholar 
Allen, A. M. & Singh, N. J. Linking movement ecology with wildlife management and conservation. Front. Ecol. Evol. 3, 1–13. https://doi.org/10.3389/fevo.2015.00155 (2016).ADS 
Article 

Google Scholar 
Fraser, K. C. et al. Tracking the conservation promise of movement ecology. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2018.00150 (2018).Article 

Google Scholar 
Boutin, S. Food supplementation experiments with terrestrial vertebrates: Patterns, problems, and the future. Can. J. Zool. 68, 203–220 (1990).Article 

Google Scholar 
Adams, E. S. Approaches to the study of territory size and shape. Annu. Rev. Ecol. Syst. 32, 277–303. https://doi.org/10.1146/annurev.ecolsys.32.081501.114034 (2001).Article 

Google Scholar 
Ruffino, L., Salo, P., Koivisto, E., Banks, P. B. & Korpimaki, E. Reproductive responses of birds to experimental food supplementation: A meta-analysis. Front. Ecol. Evol. 11, 1–13. https://doi.org/10.1186/s12983-014-0080-y (2014).CAS 
Article 

Google Scholar 
Taylor, E. N., Malawy, M. A., Browning, D. M., Lemar, S. V. & DeNardo, D. F. Effects of food supplementation on the physiological ecology of female western diamond-backed rattlesnakes (Crotalus atrox). Oecologia 144, 206–213. https://doi.org/10.1007/s00442-005-0056-x (2005).ADS 
Article 
PubMed 

Google Scholar 
Wasko, D. K. & Sasa, M. Food resources influence spatial ecology, habitat selection, and foraging behavior in an ambush-hunting snake (Viperidae: Bothrops asper): An experimental study. Zoology 115, 179–187. https://doi.org/10.1016/j.zool.2011.10.001 (2012).Article 
PubMed 

Google Scholar 
Glaudas, X. & Alexander, G. J. Food supplementation affects the foraging ecology of a low-energy, ambush-foraging snake. Behav. Ecol. Sociobiol. 71, 1–11. https://doi.org/10.1007/s00265-016-2239-3 (2017).Article 

Google Scholar 
Secor, S. M. & Nagy, K. A. Bioenergetic correlates of foraging mode for the snakes Crotalus cerastes and Masticophis flagellum. Ecology 75, 1600–1614 (1994).Article 

Google Scholar 
Christy, M. T., Savidge, J. A., Yackel Adams, A. A., Gragg, J. E. & Rodda, G. H. Experimental landscape reduction of wild rodents increases movements in the invasive brown treesnake (Boiga irregularis). Manag. Biol. Invasions 8, 455–467. https://doi.org/10.3391/mbi.2017.8.4.01 (2017).Article 

Google Scholar 
Neilson, E. W., Avgar, T., Burton, A. C., Broadley, K. & Boutin, S. Animal movement affects interpretation of occupancy models from camera-trap surveys of unmarked animals. Ecosphere 9, 1–15. https://doi.org/10.1002/ecs2.2092 (2018).Article 

Google Scholar 
Efford, M. G. & Dawson, D. K. Occupancy in continuous habitat. Ecosphere 3, 1–15. https://doi.org/10.1890/ES11-00308.1 (2012).Article 

Google Scholar 
Tang, Z., Huang, Q., Wu, H., Kuang, L. & Fu, S. The behavioral response of prey fish to predators: The role of predator size. PeerJ 5, 1–13. https://doi.org/10.7717/peerj.3222 (2017).Article 

Google Scholar 
Thorsen, M., Shorten, R., Lucking, R. & Lucking, V. Norway rats (Rattus norvegicus) on Fregate Island, Seychelles: The invasion; subsequent eradication attempts and implications for the island’s fauna. Biol. Cons. 96, 133–138 (2000).Article 

Google Scholar 
Rodda, G. H. Foraging behavior of the brown tree snake, Boiga irregularis. Herpetol. J. 2, 110–114 (1992).
Google Scholar 
Savidge, J. A. Extinction of an island forest avifauna by an introduced snake. Ecology 68, 660–668 (1987).Article 

Google Scholar 
Rodda, G. H., McCoid, M. J., Fritts, T. H. & Campbell, E. W. III. Population trends and limiting factors in Boiga irregularis. In Problem Snake Management: The Habu and the Brown Treesnake (eds Rodda, G. H. et al.) 236–256 (Cornell University Press, 1999).Chapter 

Google Scholar 
Yackel Adams, A. A., Lardner, B., Knox, A. J. & Reed, R. N. Inferring the absence of an incipient population during a rapid response for an invasive species. PLoS ONE 13, 1–13 (2018).Article 
CAS 

Google Scholar 
Clark, L., Clark, C. & Siers, S. Brown tree snake methods and approaches for control. In Ecology and Management of Terrestrial Vertebrate Invasive Species in the United States (eds Pitt, W. C. et al.) 107–134 (CRC Press, 2018).
Google Scholar 
Christy, M. T., Yackel Adams, A. A., Rodda, G. H., Savidge, J. A. & Tyrrell, C. L. Modelling detection probabilities to evaluate management and control tools for an invasive species. J. Appl. Ecol. 47, 106–113 (2010).Article 

Google Scholar 
Tyrrell, C. L. et al. Evaluation of trap capture in a geographically closed population of brown treesnakes on Guam. J. Appl. Ecol. 46, 128–135 (2009).Article 

Google Scholar 
Siers, S. R., Yackel Adams, A. A. & Reed, R. N. Behavioral differences following ingestion of large meals and consequences for management of a harmful invasive snake: A field experiment. Ecol. Evol. 8, 10075–10093 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Santana-Bendix, M. A. Movements, Activity Patterns and Habitat Use of Boiga irregularis (Colubridae), an Introduced Predator in the Island of Guam (University of Arizona, 1994).
Google Scholar 
Tobin, M. E., Sugihara, R. T., Pochop, P. A. & Linnell, M. A. Nightly and seasonal movements of Boiga irregularis on Guam. J. Herpetol. 33, 281–291 (1999).Article 

Google Scholar 
Lardner, B., Savidge, J. A., Reed, R. N. & Rodda, G. H. Movements and activity of juvenile brown treesnakes (Boiga irregularis). Copeia 2014, 428–436 (2014).Article 

Google Scholar 
Siers, S. R., Savidge, J. A. & Reed, R. N. Invasive brown treesnake movements at road edges indicate road-crossing avoidance. J. Herpetol. 48, 500–505 (2014).Article 

Google Scholar 
Wiewel, A. S., Yackel Adams, A. A. & Rodda, G. H. Distribution, density, and biomass of introduced small mammals in the southern Marian Islands. Pac. Sci. 63, 205–222 (2009).Article 

Google Scholar 
Camp, R. J., Amidon, F. A., Marshall, A. P. & Pratt, T. K. Bird populations on the island of Tinian; Persistence despite wholesale loss of native forests. Pac. Sci. 66, 283–298. https://doi.org/10.2984/66.3.3 (2012).Article 

Google Scholar 
Lardner, B., Yackel Adams, A. A., Knox, A. J., Savidge, J. A. & Reed, R. N. Do observer fatigue and taxon bias compromise visual encounter surveys for small vertebrates?. Wildl. Res. 46, 127–135 (2019).Article 

Google Scholar 
Mathies, T., Levine, B., Engeman, R. & Savidge, J. A. Pheromonal control of the invasive brown treesnake: Potency of female sexual attractiveness pheromone varies with ovarian state. Int. J. Pest Manag. https://doi.org/10.1080/09670874.2013.784374 (2013).Article 

Google Scholar 
Boback, S. M., Nafus, M. G., Yackel Adams, A. A. & Reed, R. N. Use of visual surveys and radiotelemetry reveals sources of detection bias for a cryptic snake at low densities. Ecosphere https://doi.org/10.1002/ecs2.3000 (2020).Article 

Google Scholar 
Harper, G. A. & Rutherford, M. Home range and population density of black rats (Rattus rattus) on a seabird island: A case for a marine subsidised effect?. N. Z. J. Ecol. 40, 219–228 (2016).
Google Scholar 
Hochachka, W. M., Martin, K., Doyle, F. & Krebs, C. J. Monitoring vertebrate populations using observational data. Can. J. Zool. 78, 521–529 (2000).Article 

Google Scholar 
Wiewel, A. S., Yackel Adams, A. A. & Rodda, G. H. Evaluating abundance estimate precision and the assumptions of a count-based index for small mammals. J. Wildl. Manag. 73, 761–771. https://doi.org/10.2193/2008-180 (2009).Article 

Google Scholar 
Fauteux, D. et al. Evaluation of invasive and non-invasive methods to monitor rodent abundance in the Arctic. Ecosphere 9, 1–18. https://doi.org/10.1002/ecs2.2124 (2018).Article 

Google Scholar 
Siers, S. R. et al. Assessment of brown treesnake activity and bait take following large-scale snake suppression in Guam. (ed APHIS USDA, WS, NWRC) (Final Report QA-2438, Hilo, HI, 2018).McQueen, D. J., Post, J. R. & Mills, E. L. Trophic relationships in fresh-water pelagic ecosystems. Can. J. Fish. Aquat. Sci. 43, 1571–1581 (1986).Article 

Google Scholar 
Sih, A., Crowley, P., McPeek, M., Petranka, J. & Strohmeier, K. Predation, competition, and prey communities: A review of field experiments. Annu. Rev. Ecol. Syst. 16, 269–311 (1985).Article 

Google Scholar 
Dorcas, M. E. et al. Severe mammal declines coincide with proliferation of invasive Burmese pythons in Everglades National Park. Proc. Natl. Acad. Sci. 109, 2418–2422. https://doi.org/10.1073/pnas.1115226109 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
de Miranda, E. B. P. The plight of reptiles as ecological actors in the tropics. Front. Ecol. Evol. 5, 1–15. https://doi.org/10.3389/fevo.2017.00159 (2017).Article 

Google Scholar 
Campbell, E. W. III., Yackel Adams, A. A., Converse, S. J., Fritts, T. H. & Rodda, G. H. Do predators control prey species abundance? An experimental test with brown treesnakes on Guam. Ecology 93, 1194–1203 (2012).PubMed 
Article 

Google Scholar 
Lindell, L. E. & Forsman, A. Density effects and snake predation: Prey limitation and reduced growth rate of adders at high density of conspecifics. Can. J. Zool. 74, 1000–1007 (1996).Article 

Google Scholar 
Schoener, T. W., Spiller, D. A. & Losos, J. B. Predation on a common Anolis lizard: Can the food-web effects of a devastating predator be reversed?. Ecol. Monogr. 72, 383–407 (2002).Article 

Google Scholar 
McCleery, R. A. et al. Marsh rabbit mortalities tie pythons to the precipitous decline of mammals in the Everglades. Proc. R. Soc. Lond. 282, 20150120. https://doi.org/10.1098/rspb.2015.0120 (2015).Article 

Google Scholar 
Plummer, M. V. & Congdon, J. D. Radiotelemetric study of activity and movements of racers (Coluber constrictor) associated with a Carolina bay in South Carolina. Copeia 1994, 20–26 (1994).Article 

Google Scholar 
Madsen, T. & Shine, R. Seasonal migration of predators and prey—A study of pythons, and rats in tropical Australia. Ecology 77, 149–156 (1996).Article 

Google Scholar 
Chandler, C. J., Van Helden, B., Close, P. G. & Speldewinde, P. C. 2D or not 2D? Three-dimensional home range analysis better represents space use by an arboreal mammal. Acta Oecol. 105, 103576. https://doi.org/10.1016/j.actao.2020.103576 (2020).Article 

Google Scholar 
Udyawer, V., Simpfendorfer, C. A. & Heupel, M. R. Diel patterns in three-dimensional use of space by sea snakes. Anim. Biotelem. 3, 1–9. https://doi.org/10.1186/s40317-015-0063-6 (2015).Article 

Google Scholar 
Shine, R. Reproduction in Australian elapid snakes II. Female reproductive cycles. Aust. J. Zool. 25, 655–666 (1977).Article 

Google Scholar 
Murcia, C. Edge effects in fragmented forests: Implications for conservation. Trends Ecol. Evol. 10, 58–62 (1995).CAS 
PubMed 
Article 

Google Scholar 
Matlack, G. R. Microenvironment variation within and among forest edge sites in the eastern United States. Biol. Cons. 66, 185–194 (1993).Article 

Google Scholar 
Kapos, V. Effects of isolation on the water status of forest patches in the Brazilian Amazon. Trop. Ecol. 5, 173–185 (1989).Article 

Google Scholar 
Williams-Linera, G. Vegetation structure and environmental conditions of forest edges in Panama. J. Ecol. 78, 356–373 (1990).Article 

Google Scholar 
Matlack, G. R. Vegetation dynamics of the forest edge: Trends in space and successional time. J. Ecol. 82, 113–123 (1994).Article 

Google Scholar 
Chen, J., Franklin, J. F. & Spies, T. A. Vegetation responses to edge environments in old-growth douglas-fir forests. Ecol. Appl. 2, 387–396 (1992).PubMed 
Article 

Google Scholar 
Gates, J. E. Powerline corridors, edge effects, and wildlife in forested landscapes of the central Appalachians. In Wildlife and Habitats in Managed Landscapes (eds Rodiek, J. E. & Bolen, E. G.) 13–32 (Island Press, 1991).
Google Scholar 
Kroodsma, R. L. Edge effect on breeding forest birds along a power-line corridor. J. Appl. Ecol. 19, 361–370 (1982).Article 

Google Scholar 
Morgan, K. A. & Gates, J. E. Bird population patterns in forest edge and strip vegetation at Remington Farms, Maryland. J. Wildl. Manag. 46, 933–944 (1982).Article 

Google Scholar 
Weatherhead, P. J. & Charland, M. B. Habitat selection in an Ontario population of the snake, Elaphe obsoleta. J. Herpetol. 19, 12–19 (1985).Article 

Google Scholar 
Durner, G. M. & Gates, J. E. Spatial ecology of black rat snakes on Remington Farms, Maryland. J. Wildl. Manag. 57, 812–826 (1993).Article 

Google Scholar 
Mushinsky, H. R. Foraging ecology. In Snakes: Ecology and Evolutionary Biology (eds Seigel, R. A. et al.) 302–334 (Macmillan Publishing Company, 1987).
Google Scholar 
Fritts, T. H., Scott, N. J. Jr. & Smith, B. J. Trapping Boiga irregularis on Guam using bird odors. J. Herpetol. 23, 189–192 (1989).Article 

Google Scholar 
Shivik, J. A. Brown tree snake response to visual and olfactory cues. J. Wildl. Manag. 62, 105–111 (1998).Article 

Google Scholar 
Simkova, O., Frydlova, P., Zampachova, B., Frynta, D. & Landova, E. Development of behavioral profile in the Northern common boa (Boa imperator): Repeatable independent traits or personality?. PLoS ONE 12, 1–35. https://doi.org/10.1371/journal.pone.0177911 (2017).CAS 
Article 

Google Scholar 
Fritts, T. H., McCoid, M. J. & Gomez, D. M. Dispersal of snakes to extralimital islands: Incidents of the brown treesnake, Boiga irregularis, dispersing to islands in ships and aircraft. In Problem Snake Management: The Habu and the Brown Treesnake (eds Rodda, G. H. et al.) 209–223 (Cornell University Press, 1999).
Google Scholar 
Yackel Adams, A. A. et al. Can we prove that an undetected species is absent? Evaluating whether brown treesnakes are established on the island of Saipan using surveillance and expert opinion. Manag. Biol. Invas. 12, 901–926 (2021).Article 

Google Scholar 
Siers, S. R. & Savidge, J. A. Restoration Plan for the Habitat Management Unit, Naval Support Activity Andersen, Guam 1–238 (Colorado State University, 2017).
Google Scholar 
Dorr, B. S., Clark, C. S. & Savarie, P. (USDA APHIS WS National Wildlife Research Center, Fort Collins, CO, 2016).Reinert, H. K. & Cundall, D. An improved surgical implantation method for radio-tracking snakes. Copeia 1982, 702–705 (1982).Article 

Google Scholar 
Shine, R. Strangers in a strange land: Ecology of the Australian colubrid snakes. Copeia 1991, 120–131 (1991).Article 

Google Scholar 
Savidge, J. A., Qualls, F. J. & Rodda, G. H. Reproductive biology of the brown tree snake, Boiga irregularis (Reptilia: Colubridae), during colonization of Guam and comparison with that in their native range. Pac. Sci. 61, 191–199 (2007).Article 

Google Scholar 
Yackel Adams, A. A. & Nafus, M. G. Brown Treesnake visual survey and radiotelemetry data, Guam 2015: U.S. Geological Survey data release. https://doi.org/10.5066/P939BM0W (2020).Savidge, J. A. Food habits of Boiga irregularis, an introduced predator on Guam. J. Herpetol. 22, 275–282 (1988).Article 

Google Scholar 
Reed, R. N. & Boback, S. M. Does body size predict dates of species description among North American and Australian reptiles and amphibians?. Glob. Ecol. Biogeogr. 11, 41–47 (2002).Article 

Google Scholar 
Duong, T. ks: Kernel density estimation and kernel discriminant analysis for multivariate data in R. J. Stat. Softw. 21, 1–16 (2007).Article 

Google Scholar 
R Foundation for Statistical Computing. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).
Google Scholar 
Simpfendorfer, C. A., Olsen, E. M., Heupel, M. R. & Moland, E. Three-dimensional kernel utilization distributions improve estimates of space use in aquatic animals. Can. J. Fish. Aquat. Sci. 69, 565–572 (2012).Article 

Google Scholar 
Gitzen, R. A., Millspaugh, J. J. & Kernohan, B. J. Bandwidth selection for fixed-kernel analysis of animal utilization distributions. J. Wildl. Manag. 70, 1334–1344 (2006).Article 

Google Scholar 
Cooper, N. W., Sherry, T. W. & Marra, P. P. Modeling three-dimensional space use and overlap in birds. Auk 131, 681–693 (2014).Article 

Google Scholar 
ArcGIS Desktop (Environmental Systems Research, 2017).Nafus, M. G., Boback, S. M., Klug, P. E., Yackel Adams, A. A. & Reed, R. N. Brown treesnake movement following snake suppression in the Habitat Management Unit on Northern Guam from 2015. U.S Geological Survey data release. https://doi.org/10.5066/P95QJ2PE (2022). More

Authors and AffiliationsSmithsonian Tropical Research Institute – STRI, Balboa, Republic of PanamaGustavo A. Castellanos-Galindo, D. Ross Robertson, Diana M. T. Sharpe & Mark E. TorchinLeibniz Centre for Tropical Marine Research (ZMT), Bremen, GermanyGustavo A. Castellanos-GalindoAuthorsGustavo A. Castellanos-GalindoD. Ross RobertsonDiana M. T. SharpeMark E. TorchinCorresponding authorCorrespondence to
Gustavo A. Castellanos-Galindo. More

Sophie Gilbert left a tenured position to join a start-up that allows small private landowners to sell carbon credits for preserving forests on their land.Credit: Sophie Gilbert

It was during a car journey to California in temperatures sometimes exceeding 40 °C that Sophie Gilbert decided she needed to make a major career change.Driving to visit family from her home in Moscow, Idaho, she passed columns of wildfire smoke, the oppressive heat limiting the time she could spend out of her air-conditioned car. The two-day drive midway through last year helped to crystallize a feeling that she urgently needed to do something more concrete to help deal with the threat of climate change.“It hit at a gut level,” says Gilbert. “Climate change isn’t something that’s going to happen to someone else later on. It felt deeply, viscerally real for me and my family and what I care about.”Given her role as a wildlife ecologist at the University of Idaho in Moscow, it might seem that Gilbert was already well placed to have a positive impact on climate change. But the slow, incremental pace of academia, and the difficulty of getting policymakers to act on her findings, left her feeling that she was not making as much of a difference as she’d hoped.“I’ve been studying how wildlife responds to environmental change to inform conservation planning for 15 years now, researching and publishing and waiting for something to happen and then having it not happen, even when I’ve worked closely with wildlife and land-management agencies,” she says. “The system just isn’t designed to respond to the urgent challenges we’re facing,” she says.Gilbert took stock of her skills and knowledge, and how they could be put to use, settling on nature-based solutions such as forest-carbon storage and biodiversity. She made a shortlist of companies and non-governmental organizations (NGOs) doing that kind of work and started contacting them to discuss her options.In April this year, a month after securing tenure, Gilbert joined Natural Capital Exchange, a start-up firm based in San Francisco, California. The company allows small private landowners to sell carbon credits for preserving forests on their land. Gilbert’s role as senior lead for natural capital involves adding biodiversity credits to the company’s offerings, to provide incentives for conserving functioning, well-managed forests.Giving up the security and freedom that tenure offers was a big step, but Gilbert says that the hardest part of the decision was actually breaking the news to her graduate students, whose reactions ranged from anger, to understanding, to some combination of the two. “There’s a lot of mentoring and mutual responsibility there, so telling them and helping them through the process of finding a new adviser has been by far the most emotionally gruelling part,” she says.But she is excited to be taking up the challenge of working in the fast-paced world of a start-up company. “The company is full of rigorous, smart people who want to do good work,” she says. “It’s going to be a wild and exciting ride.”Spreading the wordIt’s a ride that Alice Bell knows well. By 2015, she had spent 11 years working as a lecturer in science communication at Imperial College London, and as a research fellow in the Science Policy Research Unit at the University of Sussex in Brighton, UK. She decided to leave academia for good and took up a position as head of communications at the climate-change campaign group Possible, based in London.The move came about partly by necessity — Bell’s contract was due to end, and she felt that UK government cuts were making academia an ever-more precarious occupation — but it stemmed mainly from a desire to be more directly involved in tackling the climate crisis.While at Imperial, she had built and launched a college-wide interdisciplinary course on climate change that had forced her to look more deeply into the issue. “I felt a greater urgency to put my skills somewhere they would be best utilized,” she says.Bell says leaving academia was the right choice. She thinks she is having a bigger impact on the climate crisis, and that her work–life balance has improved; she also feels more engaged in her work. “I feel more intellectually stimulated in workshops with NGOs than I did in most academic meetings,” she says, adding that she finds it liberating to be freed from academia’s pressure to publish, and from the weight of that pressure on career progression.But there are some drawbacks. “When you’re working for a small charity, no one knows who you are,” says Bell. “I was taken more seriously when I could say I was from Imperial.”Some might fear that leaving academia could arouse suspicions that they weren’t good enough to stay. “Ignore that voice,” she advises. “For many individuals, it could well be the best decision to give up.”Change from withinNot everyone, however, is ready or willing to give up on an academic career that they have spend years building up. And some find opportunities to get more involved in concrete climate solutions from within academia.

Meade Krosby provides natural-resource managers and policymakers with scientific evidence on climate-change impacts and adaptation actions.Credit: Eric Bruns

Since 2017, Meade Krosby has combined an academic post as a senior scientist at the University of Washington’s Climate Impacts Group in Seattle, where she works on climate vulnerability assessment and adaptation planning, with a director’s role at the university’s Northwest Climate Adaptation Science Center. The centre provides natural-resource managers and policymakers in the region with scientific evidence on climate-change impacts and adaptation actions. Krosby calls it a “boundary organization”, an interface between science and society, “acting as a conduit between the two”.“We bring applied science to decision-making around climate change, and bring decision-makers’ and communities’ concerns and knowledge back into academia to inform the kind of research that is done,” she says.Between 2016 and 2018, Krosby collaborated with Indigenous scholars, tribal organizations and other university scientists to develop the Tribal Climate Tool, a free online resource that aims to get the best available climate projections into the hands of Indigenous communities, to inform their planning for climate change. The tool, which launched in 2018, is now being used in many hazard-mitigation plans, such as the Samish Indian Nation’s 2019 climate-change vulnerability assessment. Krosby is also writing a paper on its development and use, producing a more conventional academic output to complement a tool that makes a difference in the real world.“You can do really useful work that doesn’t look like basic science, but it’s not always a trade-off between doing cool science and useful science,” she says.Funding challengeKrosby knew early on in her academic career that she wanted to make practical contributions that would help society to prepare for climate change. She started looking for this kind of applied work in 2009, during her postdoctoral research at the University of Washington, but found it hard at first to find funding — either from federal funding agencies or from private foundations. Then, in 2010, she received funding from the US Department of the Interior to look at species mobility and connectivity, and was able to use that to create a position for herself in the Climate Impacts Group.But she quickly found that her experience in more conventional academic settings had not prepared her for the kinds of project that the group undertook, with the aim of making science useful for policymakers and the public. “It was shocking how ill-prepared I was for transdisciplinary work,” she says. “We’re not trained to do, or to value, those kinds of collaborations.” The centre now supports fellowships and training in societally engaged research, and Krosby teaches a graduate course on how to connect science to society. “It’s an opportunity to train early-career scientists to do the work we never got trained to do,” she says. In 2020, she co-authored a paper1 calling for changes in how scientists are trained, by emphasizing skills such as collaboration and communication1.Academic career structures are not set up to promote and reward work that requires lots of collaboration with people outside the university, and which doesn’t necessarily result in a typical scientific publication, says Krosby. “The work I want to do wouldn’t be rewarded in a tenure-track position,” she adds. “To do this effectively, universities need to think about their incentive structure. Is a peer-reviewed paper really the most important outcome?”Reef encounterJulia Baum, a marine ecologist at the University of Victoria in Canada, has found a way to do practical, climate-focused work in a standard academic job. For her, the turning point came in 2015, when a massive marine heatwave nearly wiped out the tropical reef she was studying. “I watched a beautiful pristine reef melt down in 10 months,” she says. “I used to think overfishing was the biggest threat — then climate change came and hit me over the head.”

Julia Baum records data on the Pacific atoll of Kiritimati, after a marine heatwave in 2015 nearly destroyed the coral reef.Credit: Kristina Tietjen

That experience prompted her to completely overhaul her research programme to focus exclusively on climate impacts and how to mitigate them. “I want to do more than just document a sinking ship — I want to help right it,” she says.Baum’s tenured position offers her the flexibility of making that change, and she says she felt a moral obligation to apply her knowledge in a way that would help address the biggest threat facing the planet. As well as redirecting her research, Baum is designing a cross-university graduate-training programme focused on coastal climate solutions. This will offer training in professional skills that are crucial for climate work but are rarely taught in universities — such as how to collaborate and negotiate with non-academic partners, and how to deal with the media.But, like Krosby, Baum says she and many of her colleagues feel frustrated that a lot of universities don’t seem to value or support any kind of work outside conventional academic publications. Those who want to apply their findings to real-world problems often have to do it on their own, with no real benefit to their academic career. “Universities need to rise to the challenge and find innovative ways to support their faculty, by valuing and rewarding solutions work in their hiring and promotion criteria,” she says.If they don’t, universities risk losing more dedicated researchers such as Gilbert and Bell to the private sector. “If there comes a point when the climate-solutions impact I can have within academia seems too small, then yes, I would make the leap,” says Baum.Maximum impactFor academics looking for a way to take on a bigger role in the fight against climate change, there are a lot of options — from finding or making your own position in a university, to leaving for a company or charity that is doing more immediate, hands-on work. But the first step is working out where you can have the most impact, and what you can bring to the table. “For many people, the biggest impact you can have is through your students,” says Gilbert. “If you can focus on that and feel satisfied, that’s great.”For those who choose to leave, however, it pays to spend some time doing your research, finding companies and organizations that are doing the kind of work you are interested in, and talking to them about what you could offer. You might be surprised to find just how useful your skills can be outside academia — not just the disciplinary knowledge you have gained, but transferable skills such as technical writing and the ability to review and synthesize complex research. “The list of things we’re good at is pretty awesome,” says Gilbert. More

Ramoliya, P. & Pandey, A. Effect of salinization of soil on emergence, growth and survival of seedlings of Cordia rothii. For. Ecol. Manage. 176, 185–194 (2003).Article 

Google Scholar 
Müller, H. M. et al. The desert plant Phoenix dactylifera closes stomata via nitrate-regulated SLAC1 anion channel. New Phytol. 216, 150–162 (2017).PubMed 
Article 
CAS 

Google Scholar 
Hazzouri, K. M. et al. Prospects for the study and improvement of abiotic stress tolerance in date palms in the post-genomics era. Front. Plant Sci. 11, 293 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Abdelfattah, M. A. Integrated suitability assessment: A way forward for land use planning and sustainable development in Abu Dhabi, United Arab Emirates. Arid Land Res. Manage. 27, 41–64 (2013).Article 

Google Scholar 
Al-Muaini, A. et al. Water requirements for irrigation with saline groundwater of three date-palm cultivars with different salt-tolerances in the hyper-arid United Arab Emirates. Agric. Water Manage. 222, 213–220 (2019).Article 

Google Scholar 
Guo, H., Shi, X., Ma, L., Yang, T. & Min, W. Long-term irrigation with saline water decreases soil nutrients, diversity of bacterial communities, and cotton yields in a gray desert soil in China. Pol. J. Environ. Stud. 29, 4077–4088 (2020).CAS 
Article 

Google Scholar 
Blaskó, L. Salinity, physical effects on soils. In Encyclopedia of Agrophysics (eds Gliński, J. et al.) 723–725 (Springer, 2011).Chapter 

Google Scholar 
Rengasamy, P. Irrigation water quality and soil structural stability: A perspective with some new insights. Agronomy 8, 72 (2018).Article 
CAS 

Google Scholar 
Trivedi, P., Leach, J. E., Tringe, S. G., Sa, T. & Singh, B. K. Plant–microbiome interactions: From community assembly to plant health. Nat. Rev. Microbiol. 18, 607–621 (2020).CAS 
PubMed 
Article 

Google Scholar 
Masmoudi, K. et al. Metagenomics of beneficial microbes in abiotic stress tolerance of date palm. In The Date Palm Genome, Vol. 2: Omics and Molecular Breeding (eds Al-Khayri, J. M. et al.) 203–214 (Springer, 2021).Chapter 

Google Scholar 
Boncompagni, E., Østerås, M., Poggi, M.-C. & Le Rudulier, D. Occurrence of choline and glycine betaine uptake and metabolism in the family rhizobiaceae and their roles in osmoprotection. Appl. Environ. Microbiol. 65, 2072–2077 (1999).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, C. & Beattie, G. A. Characterization of the osmoprotectant transporter opuc from Pseudomonas syringae and demonstration that cystathionine-β-synthase domains are required for its osmoregulatory function. J. Bacteriol. 189, 6901–6912 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rath, H. et al. Management of osmoprotectant uptake hierarchy in Bacillus subtilis via a SigB-dependent antisense RNA. Front. Microbiol. 11, 622 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Singh, R. P. & Jha, P. N. The PGPR Stenotrophomonas maltophilia SBP-9 augments resistance against biotic and abiotic stress in wheat plants. Front. Microbiol. 8, 1945 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Ferjani, R. et al. The date palm tree rhizosphere is a niche for plant growth promoting bacteria in the oasis ecosystem. Biomed Res. Int. 2015, 1–10 (2015).Article 

Google Scholar 
Sanka Loganathachetti, D., Alhashmi, F., Chandran, S. & Mundra, S. Irrigation water salinity structures the bacterial communities of date palm (Phoenix dactylifera)-associated bulk soil. Front. Plant Sci. https://doi.org/10.3389/fpls.2022.944637 (2022).Article 

Google Scholar 
Chen, L. J. et al. An integrative influence of saline water irrigation and fertilization on the structure of soil bacterial communities. J. Agric. Sci. 157, 693–700 (2019).CAS 
Article 

Google Scholar 
Li, Y. Q. et al. Bacterial community in saline farmland soil on the Tibetan plateau: Responding to salinization while resisting extreme environments. BMC Microbiol. 21, 119 (2021).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mosqueira, M. J. et al. Consistent bacterial selection by date palm root system across heterogeneous desert oasis agroecosystems. Sci. Rep. 9, 4033 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Cherif, H. et al. Oasis desert farming selects environment-specific date palm root endophytic communities and cultivable bacteria that promote resistance to drought. Environ. Microbiol. Rep. 7, 668–678 (2015).CAS 
PubMed 
Article 

Google Scholar 
FAO. Standard Operating Procedure for Soil Electrical Conductivity, Soil/Water, 1:5. (2021).Nelson, D. W. & Sommers, L. E. Total carbon, organic carbon, and organic matter. In Chemical Methods-SSSA Book Series No. 5 (eds Bigham, J. M. et al.) (Soil Science Society of America and American Society of Agronomy, 1996).
Google Scholar 
Mizrahi-Man, O., Davenport, E. R. & Gilad, Y. Taxonomic classification of bacterial 16S rRNA genes using short sequencing reads: Evaluation of effective study designs. PLoS ONE 8, e53608 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Martin-Sanchez, P. M. et al. Analysing indoor mycobiomes through a large-scale citizen science study in Norway. Mol. Ecol. 30, 2689–2705 (2021).CAS 
PubMed 
Article 

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–D596 (2012).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Dai, T. et al. Identifying the key taxonomic categories that characterize microbial community diversity using full-scale classification: A case study of microbial communities in the sediments of Hangzhou Bay. FEMS Microbiol. Ecol. 92, 150 (2016).Article 
CAS 

Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package (2020).Blanchet, F. G., Legendre, P. & Borcard, D. Forward selection of explanatory variables. Ecology 89, 2623–2632 (2008).PubMed 
Article 

Google Scholar 
Emirates Soil Museum. Emirates Soil Museum. https://www.emiratessoilmuseum.org/index.php/ (Accessed 08 July 2022).Jackson, O., Quilliam, R. S., Stott, A., Grant, H. & Subke, J.-A. Rhizosphere carbon supply accelerates soil organic matter decomposition in the presence of fresh organic substrates. Plant Soil 440, 473–490 (2019).CAS 
Article 

Google Scholar 
Xie, E. et al. Short-term effects of salt stress on the amino acids of Phragmites australis root exudates in constructed wetlands. Water 12, 569 (2020).CAS 
Article 

Google Scholar 
Korber, D. R., Choi, A., Wolfaardt, G. M. & Caldwell, D. E. Bacterial plasmolysis as a physical indicator of viability. Appl. Environ. Microbiol. 62, 3939–3947 (1996).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang, K. et al. Salinity is a key determinant for soil microbial communities in a desert ecosystem. mSystems 4, e00225 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hessini, K. et al. Interactive effects of salinity and nitrogen forms on plant growth, photosynthesis and osmotic adjustment in maize. Plant Physiol. Biochem. 139, 171–178 (2019).CAS 
PubMed 
Article 

Google Scholar 
Lammel, D. R. et al. Direct and indirect effects of a pH gradient bring insights into the mechanisms driving prokaryotic community structures. Microbiome 6, 106 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Lopes, L. D., Hao, J. & Schachtman, D. P. Alkaline soil pH affects bulk soil, rhizosphere and root endosphere microbiomes of plants growing in a Sandhills ecosystem. FEMS Microbiol. Ecol. 97, 028 (2021).Article 
CAS 

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 
Article 

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

Google Scholar 
Kumar, A., Mann, A., Kumar, A., Kumar, N. & Meena, B. L. Physiological response of diverse halophytes to high salinity through ionic accumulation and ROS scavenging. Int. J. Phytoremediat. 23, 1041–1051 (2021).CAS 
Article 

Google Scholar 
Kalam, S. et al. Recent understanding of soil acidobacteria and their ecological significance: A critical review. Front. Microbiol. https://doi.org/10.3389/fmicb.2020.580024 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Boukhatem, Z. F., Merabet, C. & Tsaki, H. Plant growth promoting actinobacteria, the most promising candidates as bioinoculants? Front. Agron. https://doi.org/10.3389/fagro.2022.849911 (2022).Article 

Google Scholar 
Köberl, M. et al. Comparisons of diazotrophic communities in native and agricultural desert ecosystems reveal plants as important drivers in diversity. FEMS Microbiol. Ecol. 92, 166 (2016).Article 
CAS 

Google Scholar 
Speirs, L. B. M., Rice, D. T. F., Petrovski, S. & Seviour, R. J. The phylogeny, biodiversity, and ecology of the Chloroflexi in activated sludge. Front. Microbiol. 10, 2015 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Hou, Y. et al. Responses of the soil microbial community to salinity stress in maize fields. Biology (Basel) 10, 1114 (2021).CAS 

Google Scholar 
Patil, A., Kale, A., Ajane, G., Sheikh, R. & Patil, S. Plant growth-promoting rhizobium: Mechanisms and biotechnological prospective. Rhizobium Biol. Biotechnol. https://doi.org/10.1007/978-3-319-64982-5_7 (2017).Article 

Google Scholar 
Lima Guimarães, S. et al. Effects of inoculation of Rhizobium on nodulation and nitrogen accumulation in cowpea subjected to water availabilities. Am. J. Plant Sci. 06, 1378–1384 (2015).Article 

Google Scholar 
Ghadbane, M., Medjekal, S., Benderradji, L., Belhadj, H. & Daoud, H. Assessment of arbuscular mycorrhizal fungi status and Rhizobium on date palm (Phoenix dactylifera L.) cultivated in a Pb contaminated soil. In Recent Advances in Environmental Science from the Euro-Mediterranean and Surrounding Regions 2nd edn (eds Ksibi, M. et al.) 703–707 (Springer, 2021).
Google Scholar 
Saeed, E. E. et al. Streptomyces globosus UAE1, a potential effective biocontrol agent for black scorch disease in date palm plantations. Front. Microbiol. 8, 1455 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Falagán, C. & Johnson, D. B. Acidibacter ferrireducens gen. nov., sp. nov.: An acidophilic ferric iron-reducing gammaproteobacterium. Extremophiles 18, 1067–1073 (2014).PubMed 
Article 
CAS 

Google Scholar 
Schulze-Makuch, D. et al. Transitory microbial habitat in the hyperarid Atacama desert. Proc. Natl. Acad. Sci. 115, 2670–2675 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhao, K. et al. Actinobacteria associated with Glycyrrhiza inflata Bat. are diverse and have plant growth promoting and antimicrobial activity. Sci. Rep. 8, 13661 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
An, S.-U. et al. Invasive Spartina anglica greatly alters the rates and pathways of organic carbon oxidation and associated microbial communities in an intertidal wetland of the Han river estuary, Yellow Sea. Front. Mar. Sci. 7, 59 (2020).ADS 
Article 

Google Scholar 
Khan, M. A. et al. Rhizospheric Bacillus spp. rescues plant growth under salinity stress via regulating gene expression, endogenous hormones, and antioxidant system of Oryza sativa L.. Front. Plant Sci. 12, 1145 (2021).
Google Scholar 
Schimel, J., Balser, T. C. & Wallenstein, M. Microbial stress-response physiology and its implications for ecosystem function. Ecology 88, 1386–1394 (2007).PubMed 
Article 

Google Scholar 
Mukhtar, S., Mehnaz, S., Mirza, M. S., Mirza, B. S. & Malik, K. A. Diversity of bacillus-like bacterial community in the rhizospheric and non-rhizospheric soil of halophytes (Salsola stocksii and Atriplex amnicola), and characterization of osmoregulatory genes in halophilic Bacilli. Can. J. Microbiol. 64, 567–579 (2018).CAS 
PubMed 
Article 

Google Scholar 
Yeager, C. M. et al. Polysaccharide degradation capability of actinomycetales soil isolates from a semiarid grassland of the colorado plateau. Appl. Environ. Microbiol. 83, e03020-e3116 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ortúzar, M., Trujillo, M. E., Román-Ponce, B. & Carro, L. Micromonospora metallophores: A plant growth promotion trait useful for bacterial-assisted phytoremediation? Sci. Total Environ. 739, 139850 (2020).ADS 
PubMed 
Article 
CAS 

Google Scholar 
El-Tarabily, K. A. et al. Growth promotion of Salicornia bigelovii by Micromonospora chalcea UAE1, an endophytic 1-aminocyclopropane-1-carboxylic acid deaminase-producing actinobacterial isolate. Front. Microbiol. 10, 1694 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Carro, L. et al. Genome-based classification of micromonosporae with a focus on their biotechnological and ecological potential. Sci. Rep. 8, 525 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Li, M. et al. Composition and function of rhizosphere microbiome of Panax notoginseng with discrepant yields. Chin. Med. 15, 85 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rufián, J. S., Rueda-Blanco, J., Beuzón, C. R. & Ruiz-Albert, J. Protocol: An improved method to quantify activation of systemic acquired resistance (SAR). Plant Methods 15, 16 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Bhise, K. K., Bhagwat, P. K. & Dandge, P. B. Synergistic effect of Chryseobacterium gleum sp. SUK with ACC deaminase activity in alleviation of salt stress and plant growth promotion in Triticum aestivum L.. 3 Biotech 7, 105 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Cao, C., Tao, S., Cui, Z. & Zhang, Y. Response of soil properties and microbial communities to increasing salinization in the meadow grassland of Northeast China. Microb. Ecol. 82, 722–735 (2021).CAS 
PubMed 
Article 

Google Scholar  More