Philippa Kaur

Daszak, P. Emerging infectious diseases of wildlife-threats to biodiversity and human health. Science 287, 443–449 (2000).Article 
ADS 
CAS 

Google Scholar 
Jones, K. E. et al. Global trends in emerging infectious diseases. Nature 451, 990–993 (2008).Article 
ADS 
CAS 

Google Scholar 
Altizer, S., Ostfeld, R. S., Johnson, P. T. J., Kutz, S. & Harvell, C. D. Climate change and infectious diseases: From evidence to a predictive framework. Science 1979(341), 514–519 (2013).Article 
ADS 

Google Scholar 
Kilpatrick, A. M., Briggs, C. J. & Daszak, P. The ecology and impact of chytridiomycosis: An emerging disease of amphibians. Trends Ecol. Evol. 25, 109–118 (2010).Article 

Google Scholar 
Blehert, D. S. et al. Bat white-nose syndrome: An emerging fungal pathogen?. Science 1979(323), 227–227 (2009).Article 

Google Scholar 
Wilfert, L. et al. Deformed wing virus is a recent global epidemic in honeybees driven by Varroa mites. Science 1979(351), 594–597 (2016).Article 
ADS 

Google Scholar 
Garamszegi, L. Z. Climate change increases the risk of malaria in birds. Glob. Change Biol. 17, 1751–1759 (2011).Article 
ADS 

Google Scholar 
Zamora-Vilchis, I., Williams, S. E. & Johnson, C. N. Environmental temperature affects prevalence of blood parasites of birds on an elevation gradient: Implications for disease in a warming climate. PLoS ONE 7, e39208 (2012).Article 
ADS 
CAS 

Google Scholar 
Harvell, D., Altizer, S., Cattadori, I. M., Harrington, L. & Weil, E. Climate change and wildlife diseases: When does the host matter the most?. Ecology 90, 912–920 (2009).Article 

Google Scholar 
Burge, C. A. et al. Climate change influences on marine infectious diseases: Implications for management and society. Ann. Rev. Mar. Sci. 6, 249–277 (2014).Article 

Google Scholar 
Tracy, A. M., Pielmeier, M. L., Yoshioka, R. M., Heron, S. F. & Harvell, C. D. Increases and decreases in marine disease reports in an era of global change. Proc. R. Soc. B Biol. Sci. 286, 20191718 (2019).Article 

Google Scholar 
Lejeusne, C., Chevaldonné, P., Pergent-Martini, C., Boudouresque, C. F. & Pérez, T. Climate change effects on a miniature ocean: The highly diverse, highly impacted Mediterranean Sea. Trends Ecol. Evol. 25, 250–260 (2010).Article 

Google Scholar 
Basso, L. et al. The Pen Shell, Pinna nobilis: A review of population status and recommended research priorities in the Mediterranean Sea. Adv. Mar. Biol. 71, 109–160 (2015).Article 

Google Scholar 
Catanese, G. et al. Haplosporidium pinnae sp. nov., a haplosporidan parasite associated with mass mortalities of the fan mussel, Pinna nobilis, in the Western Mediterranean Sea. J. Invertebr. Pathol. 157, 9–24 (2018).Article 
CAS 

Google Scholar 
Vázquez-Luis, M. et al. S.O.S. Pinna nobilis: A mass mortality event in western Mediterranean sea. Front. Mar. Sci. 4, 220 (2017).Article 

Google Scholar 
García-March, J. R. et al. Can we save a marine species affected by a highly infective, highly lethal, waterborne disease from extinction?. Biol. Conserv. 243, 108498 (2020).Article 

Google Scholar 
Prado, P. et al. Pinna nobilis in suboptimal environments are more tolerant to disease but more vulnerable to severe weather phenomena. Mar. Environ. Res. 163, 105220 (2021).Article 
CAS 

Google Scholar 
Cabanellas-Reboredo, M. et al. Tracking a mass mortality outbreak of pen shell Pinna nobilis populations: A collaborative effort of scientists and citizens. Sci. Rep. 9, 13355 (2019).Article 
ADS 

Google Scholar 
Kersting, D. K. et al. Recruitment disruption and the role of unaffected populations for potential recovery after the Pinna nobilis mass mortality event. Front. Mar. Sci. 7, 1–11 (2020).Article 
ADS 

Google Scholar 
Box, A. et al. Reduced antioxidant response of the fan mussel Pinna nobilis related to the presence of haplosporidium pinnae. Pathogens 9, 1–14 (2020).Article 

Google Scholar 
Peyran, C., Morage, T., Nebot-Colomer, E., Iwankow, G. & Planes, S. Unexpected residual habitats raise hope for the survival of the fan mussel Pinna nobilis along the Occitan coast (Northwest Mediterranean Sea). Endanger Species Res. 48, 123–137 (2022).Article 

Google Scholar 
Rosa, R. D. et al. A hemocyte gene expression signature correlated with predictive capacity of oysters to survive Vibrio infections. BMC Genomics 13, 1–12 (2012).Article 

Google Scholar 
van de Vijver, M. J. et al. A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med. 347, 1999–2009 (2002).Article 

Google Scholar 
Seppey, M., Manni, M. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness. In Methods in Molecular Biology 227–245 https://doi.org/10.1007/978-1-4939-9173-0_14 (2019).Smith-Unna, R., Boursnell, C., Patro, R., Hibberd, J. M. & Kelly, S. TransRate: Reference-free quality assessment of de novo transcriptome assemblies. Genome Res. 26, 1134–1144 (2016).Article 
CAS 

Google Scholar 
Guo, X. & Ford, S. E. Infectious diseases of marine mollusks and host responses as revealed by genomic tools. Philos. Trans. R. Soc. B Biol. Sci. https://doi.org/10.1098/rstb.2015.0206 (2016).Article 

Google Scholar 
Pauletto, M. et al. Deep transcriptome sequencing of Pecten maximus hemocytes: A genomic resource for bivalve immunology. Fish Shellfish Immunol. 37, 154–165 (2014).Article 
CAS 

Google Scholar 
Caurcel, C. et al. MolluscDB: A genome and transcriptome database for molluscs. Philos. Trans. R. Soc. Lond. B Biol. Sci. 376, 20200157 (2021).Article 
CAS 

Google Scholar 
de Oliveira, A. L. et al. Comparative transcriptomics enlarges the toolkit of known developmental genes in mollusks. BMC Genomics 17, 1–23 (2016).Article 

Google Scholar 
Richardson, M. F. & De Sherman, C. D. H. De novo assembly and characterization of the invasive Northern Pacific Seastar transcriptome. PLoS ONE 10, e0142003 (2015).Article 

Google Scholar 
Zhang, D., Wang, F., Dong, S. & Lu, Y. D. De novo assembly and transcriptome analysis of osmoregulation in Litopenaeus vannamei under three cultivated conditions with different salinities. Gene 578, 185–193 (2016).Article 
CAS 

Google Scholar 
Werner, G. D. A., Gemmell, P., Grosser, S., Hamer, R. & Shimeld, S. M. Analysis of a deep transcriptome from the mantle tissue of Patella vulgata Linnaeus (Mollusca: Gastropoda: Patellidae) reveals candidate biomineralising genes. Mar. Biotechnol. 15, 230–243 (2013).Article 
CAS 

Google Scholar 
Ding, J. et al. Transcriptome sequencing and characterization of Japanese scallop Patinopecten yessoensis from different shell color lines. PLoS ONE 10, e0116406 (2015).Article 

Google Scholar 
Harney, E. et al. De novo assembly and annotation of the European abalone Haliotis tuberculata transcriptome. Mar Genomics 28, 11–16 (2016).Article 

Google Scholar 
Khalturin, K., Hemmrich, G., Fraune, S., Augustin, R. & Bosch, T. C. G. More than just orphans: Are taxonomically-restricted genes important in evolution?. Trends Genet. 25, 404–413. https://doi.org/10.1016/j.tig.2009.07.006 (2009).Article 
CAS 

Google Scholar 
Gibson, A. K., Smith, Z., Fuqua, C., Clay, K. & Colbourne, J. K. Why so many unknown genes? Partitioning orphans from a representative transcriptome of the lone star tick Amblyomma americanum. BMC Genomics 14, 135 (2013).Article 
CAS 

Google Scholar 
Albertin, C. B. et al. The octopus genome and the evolution of cephalopod neural and morphological novelties. Nature 524, 220–224 (2015).Article 
ADS 
CAS 

Google Scholar 
Vogeler, S., Galloway, T. S., Lyons, B. P. & Bean, T. P. The nuclear receptor gene family in the Pacific oyster, Crassostrea gigas, contains a novel subfamily group. BMC Genomics 15, 369 (2014).Article 

Google Scholar 
Allam, B. & Raftos, D. Immune responses to infectious diseases in bivalves. J. Invertebr. Pathol. 131, 121–136. https://doi.org/10.1016/j.jip.2015.05.005 (2015).Article 
CAS 

Google Scholar 
Allam, B. & Pales Espinosa, E. Bivalve immunity and response to infections: Are we looking at the right place?. Fish Shellfish Immunol. 53, 4–12. https://doi.org/10.1016/j.fsi.2016.03.037 (2016).Article 
CAS 

Google Scholar 
Qiu, L., Song, L., Xu, W., Ni, D. & Yu, Y. Molecular cloning and expression of a Toll receptor gene homologue from Zhikong Scallop, Chlamys farreri. Fish Shellfish Immunol. 22, 451–466 (2007).Article 
CAS 

Google Scholar 
Zhang, L., Li, L., Zhu, Y., Zhang, G. & Guo, X. Transcriptome analysis reveals a rich gene set related to innate immunity in the eastern oyster (Crassostrea virginica). Mar. Biotechnol. 16, 17–33 (2014).Article 

Google Scholar 
Moreira, R. et al. Transcriptomics of in vitro immune-stimulated hemocytes from the Manila clam Ruditapes philippinarum using high-throughput sequencing. PLoS ONE 7, e35009 (2012).Article 
ADS 
CAS 

Google Scholar 
Toubiana, M. et al. Toll-like receptors and MyD88 adaptors in Mytilus: Complete cds and gene expression levels. Dev. Comp. Immunol. 40, 158–166 (2013).Article 
CAS 

Google Scholar 
He, Y. et al. Transcriptome analysis reveals strong and complex antiviral response in a mollusc. Fish Shellfish Immunol. 46, 131–144 (2015).Article 
CAS 

Google Scholar 
Zhang, L. et al. Massive expansion and functional divergence of innate immune genes in a protostome. Sci. Rep. 5, 8693 (2015).Article 
CAS 

Google Scholar 
Casadevall, A. & Pirofski, L. A. Host–pathogen interactions: The attributes of virulence. J. Infect. Dis. 184, 337–344. https://doi.org/10.1086/322044 (2001).Article 
CAS 

Google Scholar 
Jones, B., Pascopella, L. & Falkow, S. Entry of microbes into the host: Using M cells to break the mucosal barrier. Curr. Opin. Immunol. 7, 474–478 (1995).Article 
CAS 

Google Scholar 
Liévin-Le Moal, V. & Servin, A. L. The front line of enteric host defense against unwelcome intrusion of harmful microorganisms: Mucins, antimicrobial peptides, and microbiota. Clin. Microbiol. Rev. 19, 315–337. https://doi.org/10.1128/CMR.19.2.315-337.2006 (2006).Article 
CAS 

Google Scholar 
Trigos, S., Vicente, N., Prado, P. & Espinós, F. J. Adult spawning and early larval development of the endangered bivalve Pinna nobilis. Aquaculture 483, 102–110 (2018).Article 

Google Scholar 
Vázquez-Luis, M., Nebot-Colomer, E., Deudero, S., Planes, S. & Boissin, E. Natural hybridization between pen shell species: Pinna rudis and the critically endangered Pinna nobilis may explain parasite resistance in P. nobilis. Mol. Biol. Rep. https://doi.org/10.1007/s11033-020-06063-5 (2021).Article 

Google Scholar 
Katsares, V., Tsiora, A., Galinou-Mitsoudi, S. & Imsiridou, A. Genetic structure of the endangered species Pinna nobilis (Mollusca: Bivalvia) inferred from mtDNA sequences. Biologia 63, 412–417 (2008).Article 
CAS 

Google Scholar 
Gonzalez-Wanguemert, M. et al. Highly polymorphic microsatellite markers for the Mediterranean endemic fan mussel Pinna nobilis. Mediterr. Mar. Sci. 16, 31 (2014).Article 

Google Scholar 
Peyran, C., Planes, S., Tolou, N., Iwankow, G. & Boissin, E. Development of 26 highly polymorphic microsatellite markers for the highly endangered fan mussel Pinna nobilis and cross-species amplification. Mol. Biol. Rep. 47, 2551–2559 (2020).Article 
CAS 

Google Scholar 
Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28, 2537–2539 (2012).Article 
CAS 

Google Scholar  More

Piao, S., Friedlingstein, P., Ciais, P., Viovy, N. & Demarty, J. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Glob. Biogeochem. Cycles 21, GB3018 (2007).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 
Xia, J., Niu, S., Ciais, P. & Janssens, I. A. Joint control of terrestrial gross primary productivity by plant phenology and physiology. Proc. Natl Acad. Sci. USA 112, 2788–2793 (2015).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Yang, J. et al. Divergent shifts in peak photosynthesis timing of temperate and alpine grasslands in China. Remote Sens. Environ. 233, 111395 (2019).Article 

Google Scholar 
Huang, K., Xia, J., Wang, Y. & Ahlstrom, A. Enhanced peak growth of global vegetation and its key mechanisms. Nat. Ecol. Evol. 2, 1897–1905 (2018).Article 
PubMed 

Google Scholar 
Park, T., Chen, C. & Macias-Fauria, M. Changes in timing of seasonal peak photosynthetic activity in northern ecosystems. Glob. Change Biol. 25, 2382–2395 (2019).Article 

Google Scholar 
Medlyn, B. E. Physiological basis of the light use efficiency model. Tree Physiol. 18, 167 (1998).Article 
PubMed 

Google Scholar 
Turner, D. P., Urbanski, S., Bremer, D., Wofsy, S. C. & Gregory, M. A cross-biome comparison of daily light use efficiency for gross primary production. Glob. Change Biol. 9, 383–395 (2003).Article 

Google Scholar 
Monteith, J. L. Solar radiation and productivity in tropical ecosystems. Appl. Ecol. 9, 747–766 (1972).Article 

Google Scholar 
Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat. Plants 3, 734–741 (2017).Article 
PubMed 
CAS 

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 
CAS 

Google Scholar 
Frankenberg, C. et al. New global observations of the terrestrial carbon cycle from GOSAT: patterns of plant fluorescence with gross primary productivity. Geophys. Res. Lett. 38, L17706 (2011).Article 

Google Scholar 
Yuan, H., Dai, Y., Xiao, Z., Ji, D. & Shangguan, W. Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling. Remote Sens. Environ. 115, 1171–1187 (2011).Article 

Google Scholar 
Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).Article 
PubMed 
CAS 

Google Scholar 
Wang, X. et al. Globally consistent patterns of asynchrony in vegetation phenology derived from optical, microwave, and fluorescence satellite data. J. Geophys. Res. Biogeosci. 125, e2020JG005732 (2020).Article 

Google Scholar 
Poorter, H. et al. Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. New Phytol. 193, 30–50 (2012).Article 
PubMed 
CAS 

Google Scholar 
Zhang, Y., Commane, R., Zhou, S., Williams, A. P. & Gentine, P. Light limitation regulates the response of autumn terrestrial carbon uptake to warming. Nat. Clim. Change 10, 739–743 (2020).Article 
CAS 

Google Scholar 
Yuan, W. et al. Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database. Agric. For. Meteorol. 192-193, 108–120 (2014).Article 

Google Scholar 
Reich, P. B. et al. Temperature drives global patterns in forest biomass distribution in leaves, stems, and roots. Proc. Natl Acad. Sci. USA 111, 13721–13726 (2014).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Wright, I. J., Reich, P. B. & Westoby, M. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).Article 
PubMed 
CAS 

Google Scholar 
Reich, P. B., Oleksyn, J. & Wright, I. J. Leaf phosphorus influences the photosynthesis–nitrogen relation: a cross-biome analysis of 314 species. Oecologia 160, 207–212 (2009).Article 
PubMed 

Google Scholar 
Chen, Y., Han, W., Tang, L., Tang, Z. & Fang, J. Leaf nitrogen and phosphorus concentrations of woody plants differ in responses to climate, soil and plant growth form. Ecography 36, 178–184 (2013).Article 

Google Scholar 
Jiang, M., Caldararu, S., Zaehle, S., Ellsworth, D. S. & Medlyn, B. E. Towards a more physiological representation of vegetation phosphorus processes in land surface models. New Phytol. 222, 1223–1229 (2019).Article 
PubMed 

Google Scholar 
Kergoat, L., Lafont, S., Arneth, A., Le Dantec, V. & Saugier, B. Nitrogen controls plant canopy light-use efficiency in temperate and boreal ecosystems. J. Geophys. Res. Biogeosci. 113, G04017 (2008).Article 

Google Scholar 
Du, E. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat. Geosci. 13, 221–226 (2020).Article 
CAS 

Google Scholar 
Cleveland, C. C. et al. Patterns of new versus recycled primary production in the terrestrial biosphere. Proc. Natl Acad. Sci. USA 110, 12733–12737 (2013).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Veneklaas, E. J. et al. Opportunities for improving phosphorus-use efficiency in crop plants. New Phytol. 195, 306–320 (2012).Article 
PubMed 
CAS 

Google Scholar 
Janssens, I. A. & Luyssaert, S. Nitrogen’s carbon bonus. Nat. Geosci. 2, 318–319 (2009).Article 
CAS 

Google Scholar 
Luo, X. et al. Global variation in the fraction of leaf nitrogen allocated to photosynthesis. Nat. Commun. 12, 4866 (2021).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Lambers, H., Iii, F. & Pons, T. L. Plant Physiological Ecology (Springer, 2008).Vose, J. M. et al. Factors influencing the amount and distribution of leaf area of pine stands. Ecol. Bull. 43, 102−114 (1994).Carter, S. K., Saenz, D. & Rudolf, V. H. W. Shifts in phenological distributions reshape interaction potential in natural communities. Ecol. Lett. 21, 1143–1151 (2018).Article 
PubMed 

Google Scholar 
Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).Article 

Google Scholar 
Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).Article 
PubMed 
CAS 

Google Scholar 
Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere–biosphere system. Glob. Biogeochem. Cycles 19, GB1015 (2005).Article 

Google Scholar 
Murray-Tortarolo, G. et al. Evaluation of land surface models in reproducing satellite-derived LAI over the high-latitude Northern Hemisphere. Part I: Uncoupled DGVMs. Remote Sens. 5, 4819–4838 (2013).Article 

Google Scholar 
Lawrence, D. M. et al. The community land model version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst. 11, 4245–4287 (2019).Article 

Google Scholar 
Goll, D. S., Winkler, A. J. & Raddatz, T. Carbon–nitrogen interactions in idealized simulations with JSBACH (version 3.10). Geosci. Model Dev. 10, 2009–2030 (2017).Article 
CAS 

Google Scholar 
Goll, D. S., Vuichard, N. & Maignan, F. A representation of the phosphorus cycle for ORCHIDEE (revision 4520). Geosci. Model Dev. 10, 3745–3770 (2017).Article 
CAS 

Google Scholar 
Sun, Y., Goll, D. S. & Chang, J. Global evaluation of the nutrient-enabled version of the land surface model ORCHIDEE-CNP v1.2 (r5986). Geosci. Model Dev. 14, 1987–2010 (2021).Article 
CAS 

Google Scholar 
Clark, D. B., Mercado, L. M. & Sitch, S. The Joint UK Land Environment Simulator (JULES), model description—Part 2: Carbon fluxes and vegetation dynamics. Geosci. Model Dev. 4, 701–722 (2011).Article 

Google Scholar 
Terrer, C. et al. Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass. Nat. Clim. Change 9, 684–689 (2019).Article 
CAS 

Google Scholar 
Reyes-Fox, M. et al. Elevated CO2 further lengthens growing season under warming conditions. Nature 510, 259–262 (2014).Article 
PubMed 
CAS 

Google Scholar 
Guanter, L. et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl Acad. Sci. USA 111, E1327–E1333 (2014).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Sun, Y. et al. OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence. Science 358, eaam5747 (2017).Article 
PubMed 

Google Scholar 
Joiner, J. et al. The seasonal cycle of satellite chlorophyll fluorescence observations and its relationship to vegetation phenology and ecosystem atmosphere carbon exchange. Remote Sens. Environ. 152, 375–391 (2014).Article 

Google Scholar 
Chu, D. et al. Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion. Remote Sens. Environ. 264, 112632 (2021).Joiner, J. et al. Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2. Atmos. Meas. Tech. 6, 2803–2823 (2013).Article 

Google Scholar 
Zhang, Y., Joiner, J., Gentine, P. & Zhou, S. Reduced solar-induced chlorophyll fluorescence from GOME-2 during Amazon drought caused by dataset artifacts. Glob. Change Biol. 24, 2229–2230 (2018).Article 

Google Scholar 
Rodell, M., Houser, P. R. & Jambor, U. The Global Land Data Assimilation System. Bull. Am. Meteorol. Soc. 85, 381–394 (2004).Article 

Google Scholar 
Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Reichstein, M. et al. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob. Change Biol. 11, 1424–1439 (2005).Article 

Google Scholar 
LASSLOP, G. et al. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. Glob. Change Biol. 16, 187–208 (2010).Article 

Google Scholar 
Vautard, R., Yiou, P. & Ghil, M. Singular-spectrum analysis: a toolkit for short, noisy chaotic signals. Phys. D. 58, 95–126 (1992).Article 

Google Scholar 
Zhou, S. et al. Dominant role of plant physiology in trend and variability of gross primary productivity in North America. Sci. Rep. 7, 41366 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Butler, E. E., Datta, A. & Flores-Moreno Mapping local and global variability in plant trait distributions. Proc. Natl Acad. Sci. USA 114, E10937–E10946 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosci. 116, G04021 (2011).Article 

Google Scholar 
Ellis, E. C., Antill, E. C. & Kreft, H. All is not loss: plant biodiversity in the anthropocene. PLoS ONE 7, e30535 (2012).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Kier, G., Mutke, J., Dinerstein, E., Ricketts, T. H. & Barthlott, W. Global patterns of plant diversity and floristic knowledge. J. Biogeogr. 32, 1107–1116 (2005).Article 

Google Scholar 
Boles, S. H. et al. Land cover characterization of temperate East Asia using multi-temporal VEGETATION sensor data. Remote Sens. Environ. 90, 477–489 (2004).Article 

Google Scholar  More

Field parameters and major ionsThe results of the hydrochemistry and environmental isotopes for the 40 lakes are presented spatially in Figs. S1–S11 and are located in Tables S1 and S2.The lake waters are oxic (8.6–12.6 mg l−1) and range from slightly acidic (pH 6.0) to slightly alkaline (pH 9.2). Lake water temperatures are generally highest for lakes along the west coast (greater than 10 °C, Table S2). Phosphate concentrations are below detection level (0.1 mg l−1) for all lakes and nitrate was low ranging from below detection limit ( More

Study areaPenang is situated in the northern part of Peninsular Malaysia and lies within the latitudes 5°12’N to 5°30′ N and longitudes 100°09’E to 100°26’E (Fig. 7). Penang with a land area of 295 Km2, has an estimated population of 720,000 and is regarded as the most populated island in Malaysia. Penang shares the same border on the north and east with Kedah State and the south with Perak State. There are two main parts of Penang State: Penang Island and the mainland which is also regarded as Seberang Perai. These two parts of the State are connected by the two bridges. The eastern part of Penang Island is the most urbanized area comprising industries, commercial centres and residential buildings. However, the western part is less developed comprising mainly hilly terrain and forests22. This study is focused on the Island part of Penang. This island is endowed with a yearly equatorial climate (hot and humid). It has a mean annual temperature ranging between 27 and 30 °C while the mean annual relative humidity ranges between 70 and 90%. Also, the mean annual rainfall is about 267–624 cm.Figure 7The map of Penang State showing the Penang Island (created by the authors using ArcMap 10.8 software).Full size imageData acquisitionThe flow chart of the methodology is presented in Fig. 8. Landsat satellite images were used for the assessment of changes in land use covering a period of 2010–2021 (11 years).Figure 8The flow chart of the methodology.Full size imageThese images were gotten from the website of the United State Geological Survey (https://earthexplorer.usgs.gov). The Landsat images include the Landsat 5 TM (thematic mapper) and Landsat 8 OLI / TIRS (operational land imager / thermal infrared sensor). These were downloaded from the Landsat level 1 dataset (Table 6) with additional criteria which reduced the.Table 6 The characteristics of the satellite data used.Full size tableDetermination of LST and NDVI for Landsat 5 and 8Band 6 of Landsat 5 and band 10 of Landsat 8 were used for the determination of the land surface temperature (LST). The LST and normalized difference vegetation index were determined using the following steps:Conversion of top of atmosphere (TOA) radianceUsing the radiance rescaling factor, thermal infra-red digital numbers were converted to TOA spectral radiance using the equation below29: (frac{Red – NIR}{{Red + NIR}}) (frac{Red – NIR}{{Red + NIR}}) For Landsat 8,$$ {text{L}}lambda = left( {{text{ML}} times {text{ Qcal}}} right) + left( {{text{AL}} – {text{Oi}}} right) $$
(1)
For Landsat 5,$$ {text{L}}lambda = left( {{ }frac{{{text{LMax}}lambda – {text{LMin}}lambda }}{{{text{QcalMax}} – {text{QcalMin}}}}} right) times left( {left( {{text{Qcal }} – {text{QcalMin}}} right) + {text{LMin}}lambda } right) $$
(2)
where Lλ is TOA spectral radiance, ML is radiance multiplicative band Number, AL is radiance add band number, Qcal is quantized and calibrated standard product pixel values (DN for band 6 or band 10), Oi is the correction value for the respective bands, LMaxλ is spectral radiance scaled to QcalMax, LMinλ is spectral radiance scaled to QcalMin, QcalMax is maximum quantized calibrated pixel value, and QcalMin is minimum quantized calibrated pixel value.Conversion to TOA brightness temperature (BT)Spectral radiance data were converted to TOA brightness temperature using the thermal constant values in the Metadata file29.Kelvin (K) to Celcius (°C) degrees$$ BT = {raise0.5exhbox{$scriptstyle {K2}$} kern-0.1em/kern-0.15em lower0.25exhbox{$scriptstyle {ln left( {frac{K1}{{{text{L}}lambda { } + { }1}}} right)}$}} – 273.15 $$
(3)
where BT is the Top of atmosphere brightness temperature (°C), Lλ is TOA spectral radiance (W.m−2 .sr−1 .µm−1)), K1 is the K1 constant band number, and K2 is the K2 constant band number. For Landsat 5, K1 is 607.76, and K2 is 1260.56.Normalized difference vegetation index (NDVI)The Normalized Difference Vegetation Index (NDVI) is a standardized vegetation index which reveals the intensity of greenness and surface radiant temperature of the area30,31. The index value of NDVI usually ranges from − 1 to 1. The higher NDVI value indicates that the vegetation of the area is denser and healthier. This shows that the NDVI values of normal healthy vegetation range from 0.1– 0.75, while it is almost zero for rock and soil, and negative value for water bodies24. The NDVI is calculated using the followings:$$ {text{NDVI }} = frac{{left( {{text{NIR }}{-}{text{ RED}}} right){ }}}{{left( {{text{NIR }} + {text{ RED}}} right)}} $$
(4)
In Landsat 4–7$$ {text{NDVI }} = , left( {{text{Band 4 }}{-}{text{ Band 3}}} right) , / , left( {{text{Band 4 }} + {text{ Band 3}}} right) $$In Landsat 8$$ {text{NDVI }} = , left( {{text{Band 5 }}{-}{text{ Band 4}}} right) , / , left( {{text{Band 5 }} + {text{ Band 4}}} right) $$where: RED = DN values from the RED band, and NIR = DN values from the Near Infra-red band.Land Surface Emissivity (LSE)Land Surface Emissivity is the average emissivity of an element on the surface of the earth calculated from NDVI values.$$ {text{PV }} = left{ {frac{{left( {{text{NDVI }} – {text{ NDVImin}}} right)}}{{left( {{text{NDVImax }} – {text{ NDVImin}}} right)}}} right}^{2} $$
(5)
where PV is the Proportion of vegetation, NDVI is the DN value from the NDVI image, NDVImin is the minimum DN value from the NDVI image, and NDVImax is the maximum DN value from the NDVI image.$$ {text{E }} = left( {0.004{ } times {text{PV}}} right) + 0.986 $$
(6)
where E is land surface emissivity, PV is the Proportion of vegetation, 0.986 corresponds to a correction value of the equation.Land Surface Temperature (LST)Land Surface Temperature (LST) is the radiative temperature which is calculated using top of atmosphere brightness temperature, the wavelength of emitted radiance and land surface emissivity.$$ {text{LST}} = {raise0.5exhbox{$scriptstyle {BT}$} kern-0.1em/kern-0.15em lower0.25exhbox{$scriptstyle {left( {1 + left( {{lambda } times { }frac{{{text{BT}}}}{{{text{c}}2}}} right) times {text{ln}}left( {text{E}} right)} right)}$}} $$
(7)
Here c2 is 14388. The value of λ for Landsat 5 (Band 6) is 11.5 µm and Landsat 8 (Band 10) is 10.8 µm.Where BT is the top of atmosphere brightness temperature, λ is the wavelength of emitted radiance, and E is land surface emissivity.c2 = h*c/s (1.4388*10–2 mK = 14388 mK), h is Planck’s constant (6.626*1034 Js), s is Boltzmann constant (1.38*1023 JK), c is velocity of light (2.998*108 m/s).Determination of land use and land cover (LULC) of the study areaThe Landsat images were pre-screened and subjected to clipping and classification32. The boundary shape file of Penang was used to clip out the area of study.Image classificationThe unsupervised method involving a random assignment of sample training points and supervised methods of satellite image classification was employed in this study for determining the LULC types. This mixture of image classification methods has been reported as vital in achieving a high accuracy level33. Bands 5, 4 and 3 were used to classify Landsat 8 while bands 4, 3 and 2 were used for classifying Landsat 5. We used the extraction by mask in the spatial analyst tool of ArcMap 10.2.1 software to extract the study area from the selected bands of the Landsat satellite images. A widely used supervised image classification method was adopted for classifying the Landsat bands in this study32,34. The principle of operation of this method involves the identification of known sample training points which are then used to classify other unknown points with related spectral signatures35. The three monochromatic satellite bands were combined to produce the false colour composite (FCC) using the data management tool36. This involves drawing polygons on the LULC type to select the training points. The LULC types adopted for this study include urbanized areas, agricultural land, rocks, forests, bare surfaces, and water bodies. These were modified LULC types from IPOC Good Practice Guidance37. To achieve this, a minimum of 40 sample points were selected randomly for each category of LULC type36. Having prior knowledge of the study area assisted in the selection of the training points38.The multivariate maximum likelihood classification (MLC) technique was used for transforming the images. Other image transformation techniques have been used by researchers. These include the fuzzy set classifier, neural networks (NN) classifier, extraction and classification of homogenous objects (ECHO) classifier, per-field classifier, sub-pixel classifier, decision trees (DTs), support vector machines (SVMs), minimum distance classifier (MDC) and so-on39. The adoption of any of these techniques is dependent on the knowledge of the area of study, band selection, accessibility of data, the complexity of the landscape, the classification algorithm, and the proficiency of the analyst39. We preferred MLC to other techniques in this study due to its reported high level of accuracy in tropical regions32,34. Another reason for choosing MLC is that it is readily incorporated in many widely used GIS software packages. This MLC algorithm operates based on assigning pixels to the highest probability class and establishing the class ownership of such pixels. It is also regarded as a parametric classifier whose data follows almost a normal distribution39. We ensured the accuracy of this classifier by assigning a large number of training sample points using our prior knowledge of the study area.Description of the LULC categoriesThe urbanized area is the developed part of the study area. This includes houses, roads, railways, and industries. This is also known to be a settlement in other literature40. Agricultural land is the part of the study area dominated by agricultural activities and herbaceous plants and grasses. Agricultural land is generally a product of deforestation36. Rocks are part of the study area comprising solid mineral materials (rocks). Bare land is the bare soil which is either made open by natural or human activities.Forests are parts of the study area dominated by trees. They can be primary or secondary forests depending on the rate of disturbances. According to41, forest land is an area having more than 0.5 ha of flora comprising trees (height is above 5 m) with a canopy greater than 10%. The forests in Penang are generally both primary and secondary42. Water bodies are parts of the study area covered by water seasonally or permanently. These include seas, rivers, lakes, ponds, streams, or reservoirs40.Determination of change in the LULCThe rate and extent of change in the LULC of Penang within the periods under consideration were determined following the formula below43:$$ {text{Changed area }}left( {{text{C}}_{{text{a}}} } right) , = {text{ T}}_{{text{a}}} left( {text{year 2}} right) , {-}{text{ T}}_{{text{a}}} left( {text{year 1}} right) $$
(8)
$$ {text{Changed extent }}left( {{text{C}}_{{text{e}}} } right) , = {text{ C}}_{{text{a}}} /{text{ T}}_{{text{a}}} left( {text{year 1}} right) $$
(9)
$$ {text{Percentage of change }} = {text{ C}}_{{text{e}}} {text{x 1}}00 $$
(10)
where Ta means the total area.Determination of relationship between LST and NDVIThe values of LST and NDVI at 20 random points of each LULC class were used. The relationship between the LST and NDVI across all the LULC classes in each year was determined using the bivariate linear regression analysis. This was done in Paleontological Statistical (PAST) package 3.0.Classification accuracy assessmentThe classification accuracy was assessed by taking ground truth coordinate data of the LULC of the study area using a geographical positioning system (GPS) device (Garmin Etrex 10). These data were compared with the LULC classified in this study32. Consequently, an error matrix was generated. This normally uses ground truth data to explain the accuracy of the classified LULC. The error matrix comprises the user’s accuracy, the producer’s accuracy, overall accuracy and the Kappa index32.The producer’s accuracy (omission error) represents the probability of the correctly classified reference pixel and it is determined using this formula below:$${text{Producer’s accuracy }}left( % right) , = { 1}00% , – {text{ error of omission}} $$
(11)
Also, the user’s accuracy (commission error) represents the probability that the classified pixel matches the one on the ground36 and it is determined using the formula below:$$ {text{User’s accuracy }}left( % right) , = { 1}00% , – {text{ error of commission}} $$
(12)
The statistical accuracy of the matrix was determined using the Kappa coefficient44. This Kappa coefficient ranges from − 1 to + 145. Therefore, the overall accuracy of the classification was determined by dividing the total number of correctly classified pixels by the total number of sampled ground truth data40. More

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In this episode:00:45 World’s oldest DNA shows that mastodons roamed ancient GreenlandDNA recovered from ancient permafrost has been used to reconstruct what an ecosystem might have looked like two million years ago. Their work suggests that Northern Greenland was much warmer than the frozen desert it is today, with a rich ecosystem of plants and animals.Research Article: Kjær et al.Nature Video: The world’s oldest DNA: Extinct beasts of ancient Greenland08:21 Research HighlightsWhy low levels of ‘good’ cholesterol don’t predict heart disease risk in Black people, and how firework displays affect the flights of geese.Research Highlight: ‘Good’ cholesterol readings can lead to bad results for Black peopleResearch Highlight: New Year’s fireworks chase wild geese high into the sky10:31 Modelling the potential emissions of plasticsWhile the global demand for plastics is growing, the manufacturing and disposal of these ubiquitous materials is responsible for significant CO2 emissions each year. This week, a team have modelled how CO2 emissions could vary in the context of different strategies for mitigating climate change. They reveal how under specific conditions the industry could potentially become a carbon sink.Research Article: Stegmann et al.News and Views: Plastics can be a carbon sink but only under stringent conditionsSubscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.Never miss an episode. Subscribe to the Nature Podcast on Apple Podcasts, Google Podcasts, Spotify or your favourite podcast app. An RSS feed for Nature Podcast is available too. More

RESEARCH BRIEFINGS
07 December 2022

Ancient environmental DNA from northern Greenland opens a new chapter in genetic research, demonstrating that it is possible to track the ecology and evolution of biological communities two million years ago. The record shows an open boreal-forest ecosystem inhabited by large animals such as mastodons and reindeer. More

Fernández-Martínez, M. et al. Nutrient availability as the key regulator of global forest carbon balance. Nat. Clim. Chang. 4, 471–476 (2014).Article 
ADS 

Google Scholar 
Wright, S. J. Plant responses to nutrient addition experiments conducted in tropical forests. Ecol. Monogr. 89, e01382 (2019).Article 

Google Scholar 
Levy-Varon, J. H. et al. Tropical carbon sink accelerated by symbiotic dinitrogen fixation. Nat. Commun. 10, 5637 (2019).Article 
ADS 
CAS 

Google Scholar 
Batterman, S. A. et al. Key role of symbiotic dinitrogen fixation in tropical forest secondary succession. Nature 502, 224–227 (2013).Article 
ADS 
CAS 

Google Scholar 
Ter Steege, H. et al. Continental-scale patterns of canopy tree composition and function across Amazonia. Nature 443, 444–447 (2006).Article 
ADS 

Google Scholar 
Hedin, L. O., Brookshire, E. N. J., Menge, D. N. L. & Barron, A. R. The nitrogen paradox in tropical forest ecosystems. Annu. Rev. Ecol. Evol. Syst. 40, 613–635 (2009).Article 

Google Scholar 
Menge, D. N. L. et al. Patterns of nitrogen-fixing tree abundance in forests across Asia and America. J. Ecol. 107, 2598–2610 (2019).Article 
CAS 

Google Scholar 
Matson, W. J.Jr Herbivory in relation to plant nitrogen content. Annu. Rev. Ecol. Syst. 11, 119–161 (1980).Article 

Google Scholar 
Coley, P. D., Bateman, M. L. & Kusar, T. A. The effects of plant quality on caterpillar growth and defense against natural enemies. Oikos 115, 219–228 (2006).Article 

Google Scholar 
Wieder, W. R., Cleveland, C. C., Smith, W. K. & Todd-Brown, K. Future productivity and carbon storage limited by terrestrial nutrient availability. Nat. Geosci. 8, 441–444 (2015).Article 
ADS 
CAS 

Google Scholar 
Barron, A. R., Purves, D. W. & Hedin, L. O. Facultative nitrogen fixation by canopy legumes in a lowland tropical forest. Oecologia 165, 511–520 (2011).Article 
ADS 

Google Scholar 
McCulloch, L. A. & Porder, S. Light fuels while nitrogen suppresses symbiotic nitrogen fixation hotspots in neotropical canopy gap seedlings. New Phytol. 231, 1734–1745 (2021).Article 
CAS 

Google Scholar 
Brookshire, E. N. J. et al. Symbiotic N fixation is sufficient to support net aboveground biomass accumulation in a humid tropical forest. Sci Rep. 9, 7571 (2019).Article 
ADS 
CAS 

Google Scholar 
Gei, M. et al. Legume abundance along successional and rainfall gradients in Neotropical forests. Nat. Ecol. Evol. 2, 1104–1111 (2018).Article 

Google Scholar 
Vance, C. P. in Nitrogen-fixing Leguminous Symbioses. Nitrogen Fixation: Origins, Applications, and Research Progress, Vol. 7 (eds Dilworth, M. J. et al.) (Springer, 2008).Vitousek, P. M. & Howarth, R. W. Nitrogen limitation on land and in the sea: how can it occur? Biogeochemistry 13, 87–115 (1991).Article 

Google Scholar 
Menge, D. N. L., Levin, S. A. & Hedin, L. O. Evolutionary tradeoffs can select against nitrogen fixation and thereby maintain nitrogen limitation. Proc. Natl Acad. Sci. USA 105, 1573–1578 (2008).Article 
ADS 
CAS 

Google Scholar 
Sheffer, E., Batterman, S. A., Levin, S. A. & Hedin, L. O. Biome-scale nitrogen fixation strategies selected by climatic constraints on nitrogen cycle. Nat. Plants 1, 15182 (2015).Article 
CAS 

Google Scholar 
Vitousek, P. M. & Field, C. B. Ecosystem constraints to symbiotic nitrogen fixers: a simple model and its implications. Biogeochemistry 46, 179–202 (1999).Article 
CAS 

Google Scholar 
Coley, P. D. & Barone, J. A. Herbivory and plant defenses in tropical forests. Annu. Rev. Ecol. Syst. 27, 305–335 (1996).Article 

Google Scholar 
Fyllas, N. M. et al. Basin-wide variations in foliar properties of Amazonian forest: phylogeny, soils and climate. Biogeosciences 6, 2677–2708 (2009).Article 
ADS 

Google Scholar 
Batterman, S. A. et al. Phosphatase activity and nitrogen fixation reflect species differences, not nutrient trading or nutrient balance, across tropical rainforest trees. Ecol. Lett. 21, 1486–1495 (2018).Article 

Google Scholar 
Menge, D. N. L., Wolf, A. A. & Funk, J. L. Diversity of nitrogen fixation strategies in Mediterranean legumes. Nat. Plants 1, 15064 (2015).Article 
CAS 

Google Scholar 
Ritchie, M. E. & Tilman, D. Responses of legumes to herbivores and nutrients during succession on a nitrogen-poor soil. Ecol. Soc. Am. 76, 2648–2655 (1995).
Google Scholar 
Taylor, B. N. & Ostrowsky, L. R. Nitrogen-fixing and non-fixing trees differ in leaf chemistry and defence but not herbivory in a lowland Costa Rican rain forest. J. Trop. Ecol. 35, 270–279 (2019).Article 

Google Scholar 
Endara, M.-J. et al. Coevolutionary arms race versus host defense chase in a tropical herbivore–plant system. Proc. Natl Acad. Sci. USA 114, E7499–E7505 (2017).Article 
CAS 

Google Scholar 
Kursar, T. A. & Coley, P. D. Convergence in defense syndromes of young leaves in tropical rainforests. Biochem. Syst. Ecol. 31, 929–949 (2003).Article 
CAS 

Google Scholar 
Kursar, T. A. et al. The evolution of antiherbivore defenses and their contribution to species coexistence in the tropical tree genus Inga. Proc. Natl Acad. Sci. USA 106, 18073–18078 (2009).Article 
ADS 
CAS 

Google Scholar 
Taylor, B. N. & Menge, D. N. L. Light regulates tropical symbiotic nitrogen fixation more strongly than soil nitrogen. Nat. Plants 4, 655–661 (2018).Article 
CAS 

Google Scholar 
Adams, M., Turnbull, T., Sprent, J. & Buchmann, N. Legumes are different: leaf nitrogen, photosynthesis, and water use efficiency. Proc. Natl Acad. Sci. USA 113, 4098–4103 (2016).Article 
ADS 
CAS 

Google Scholar 
Coley, P. D. Effects of plant growth rate and leaf lifetime on the amount and type of anti-herbivore defense. Oecologia 74, 531–536 (1988).Article 
ADS 
CAS 

Google Scholar 
Batterman, S. A., Wurzburger, N. & Hedin, L. O. Nitrogen and phosphorus interact to control tropical symbiotic N2 fixation: a test in Inga punctata. J. Ecol. 101, 1400–1408 (2013).Article 
CAS 

Google Scholar 
Eichhorn, M. P., Nilus, R., Compton, S. G., Hartley, S. E. & Burslem, D. F. R. P. Herbivory of tropical rain forest tree seedlings correlates with future mortality. Ecology 91, 1092–1101 (2010).Article 

Google Scholar 
Wink, M. Evolution of secondary metabolites in legumes (Fabaceae). South African J. Bot. 89, 164–175 (2013).Article 
CAS 

Google Scholar 
Currano, E. D. & Jacobs, B. F. Bug-bitten leaves from the early Miocene of Ethiopia elucidate the impacts of plant nutrient concentrations and climate on insect herbivore communities. Glob. Planet. Change 207, 103655 (2021).Article 

Google Scholar 
Wieder, W. R., Cleveland, C. C., Lawrence, D. M. & Bonan, G. B. Effects of model structural uncertainty on carbon cycle projections: biological nitrogen fixation as a case study. Environ. Res. Lett. 10, 044016 (2015).Article 
ADS 

Google Scholar 
Sprent, J. I. Legume Nodulation: A Global Perspective (John Wiley, 2009).Leigh, E. G. Jr Tropical Forest Ecology: A View from Barro Colorado Island (Oxford Univ. Press, 1999).Comita, L. S., Muller-Landau, H. C., Aguilar, S. & Hubbell, S. P. Asymmetric density dependence shapes species abundances in a tropical tree community. Science 329, 330–332 (2010).Article 
ADS 
CAS 

Google Scholar 
Queenborough, S. A., Metz, M. R., Valencia, R. & Wright, S. J. Demographic consequences of chromatic leaf defence in tropical tree communities: do red young leaves increase growth and survival? Ann. Bot. 112, 677–684 (2013).Article 

Google Scholar 
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671 (2012).Article 
CAS 

Google Scholar 
Pasquini, S. C. & Santiago, L. S. Nutrients limit photosynthesis in seedlings of a lowland tropical forest tree species. Oecologia 168, 311–319 (2012).Article 
ADS 
CAS 

Google Scholar 
Collalti, A. & Prentice, I. C. Is NPP proportional to GPP? Waring’s hypothesis 20 years on. Tree Physiol. 39, 1473–1483 (2019).Article 
CAS 

Google Scholar 
Westbrook, J. W. et al. What makes a leaf tough? Patterns of correlated evolution between leaf toughness traits and demographic rates among 197 shade-tolerant woody species in a Neotropical forest. Am. Nat. 177, 800–811 (2011).Article 

Google Scholar 
Wright, S. J. et al. Functional traits and the growth–mortality trade‐off in tropical trees. Ecology 91, 3664–3674 (2010).Article 

Google Scholar 
Kitajima, K. et al. How cellulose-based leaf toughness and lamina density contribute to long leaf lifespans of shade-tolerant species. New Phytol. 195, 640–652 (2012).Article 

Google Scholar 
Kitajima, K., Wright, S. J. & Westbrook, J. W. Leaf cellulose density as the key determinant of inter- and intra-specific variation in leaf fracture toughness in a species-rich tropical forest. Interface Focus https://doi.org/10.1098/rsfs.2015.0100 (2016).Sedio, B. E., Echeverri, J. C. R., Boya, C. A. & Wright, S. J. Sources of variation in foliar secondary chemistry in a tropical forest tree community. Ecology 98, 616–623 (2017).Article 

Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
Smithson, M. & Verkuilen, J. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol. Methods 11, 54–71 (2006).Article 

Google Scholar 
Murphy, S. J., Xu, K. & Comita, L. S. Tree seedling richness, but not neighborhood composition, influences insect herbivory in a temperate deciduous forest community. Ecol. Evol. 6, 6310–6319 (2016).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. Preprint at https://arxiv.org/abs/1406.5823 (2014).Moles, A. T. & Westoby, M. Do small leaves expand faster than large leaves, and do shorter expansion times reduce herbivore damage? Oikos 90, 517–524 (2000).Article 

Google Scholar 
Bürkner, P. C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. https://doi.org/10.18637/jss.v080.i01 (2017). More

Keeling, P. J. & Burki, F. Progress towards the tree of eukaryotes. Curr. Biol. 29, R808–R817 (2019).Article 
CAS 

Google Scholar 
Gawryluk, R. M. R. et al. Non-photosynthetic predators are sister to red algae. Nature 572, 240–243 (2019).Article 
CAS 

Google Scholar 
Janouškovec, J. et al. A new lineage of eukaryotes illuminates early mitochondrial genome reduction. Curr. Biol. 27, 3717–3724 (2017).Article 

Google Scholar 
Lax, G. et al. Hemimastigophora is a novel supra-kingdom-level lineage of eukaryotes. Nature 564, 410–414 (2018).Article 
ADS 
CAS 

Google Scholar 
Oren, A. Prokaryote diversity and taxonomy: current status and future challenges. Philos. Trans. R. Soc. Lond. B 359, 623–638 (2004).Article 
CAS 

Google Scholar 
Shu, W. S. & Huang, L. N. Microbial diversity in extreme environments. Nat. Rev. Microbiol. 20, 219–235 (2022).Article 
CAS 

Google Scholar 
Massana, R., del Campo, J., Sieracki, M. E., Audic, S. & Logares, R. Exploring the uncultured microeukaryote majority in the oceans: reevaluation of ribogroups within stramenopiles. ISME J. 8, 854–866 (2014).Article 

Google Scholar 
de Vargas, C. et al. Eukaryotic plankton diversity in the sunlit ocean. Science 348, 1261605 (2015).Article 

Google Scholar 
Flegontova, O. et al. Extreme diversity of diplonemid eukaryotes in the ocean. Curr. Biol. 26, 3060–3065 (2016).Article 
CAS 

Google Scholar 
Ahlering, M. A. & Carrel, J. E. Predators are rare even when they are small. Oikos 95, 471–475 (2001).Article 

Google Scholar 
Hehenberger, E. et al. Novel predators reshape holozoan phylogeny and reveal the presence of a two-component signaling system in the ancestor of animals. Curr. Biol. 27, 2043–2050 (2017).Article 
CAS 

Google Scholar 
Tikhonenkov, D. V. et al. Description of Colponema vietnamica sp. n. and Acavomonas peruviana n. gen. n. sp., two new alveolate phyla (Colponemidia nom. nov. and Acavomonidia nom. nov.) and their contributions to reconstructing the ancestral state of alveolates and eukaryotes. PLoS ONE 9, e95467 (2014).Article 
ADS 

Google Scholar 
Tikhonenkov, D. V. et al. New lineage of microbial predators adds complexity to reconstructing the evolutionary origin of animals. Curr. Biol. 30, 4500–4509 (2020).Article 
CAS 

Google Scholar 
Mylnikov, A. P. & Tikhonenkov, D. V. The new alveolate carnivorous flagellate Colponema marisrubri sp. n. (Colponemida, Alveolata) from the Red Sea. Zool. Zh. 88, 1163–1169 (2009).
Google Scholar 
Strassert, J. F. H., Irisarri, I., Williams, T. A. & Burki, F. A molecular timescale for eukaryote evolution with implications for the origin of red algal-derived plastids. Nat. Commun. 12, 1879 (2021).Article 
ADS 
CAS 

Google Scholar 
Rodriguez-Ezpeleta, N. et al. Detecting and overcoming systematic errors in genome-scale phylogenies. Syst. Biol. 56, 389–399 (2007).Article 
CAS 

Google Scholar 
Strassert, J. F. H., Jamy, M., Mylnikov, A. P., Tikhonenkov, D. V. & Burki, F. New phylogenomic analysis of the enigmatic phylum Telonemia further resolves the eukaryote tree of life. Mol. Biol. Evol. 36, 757–765 (2019).Article 
CAS 

Google Scholar 
Lanfear, R., Kokko, H. & Eyre-Walker, A. Population size and the rate of evolution. Trends Ecol. Evol. 29, 33–41 (2014).Article 

Google Scholar 
Bahler, M. & Rhoads, A. Calmodulin signaling via the IQ motif. FEBS Lett. 513, 107–113 (2002).Article 
CAS 

Google Scholar 
Schaffer, D. E., Iyer, L. M., Burroughs, A. M. & Aravind, L. Functional innovation in the evolution of the calcium-dependent system of the eukaryotic endoplasmic reticulum. Front. Genet. 11, 34 (2020).Article 

Google Scholar 
Morita-Yamamuro, C. et al. The Arabidopsis gene CAD1 controls programmed cell death in the plant immune system and encodes a protein containing a MACPF domain. Plant Cell Physiol. 46, 902–912 (2005).Article 
CAS 

Google Scholar 
Rosado, C. J. et al. The MACPF/CDC family of pore-forming toxins. Cell. Microbiol. 10, 1765–1774 (2008).Article 
CAS 

Google Scholar 
Ishino, T., Chinzei, Y. & Yuda, M. A Plasmodium sporozoite protein with a membrane attack complex domain is required for breaching the liver sinusoidal cell layer prior to hepatocyte infection. Cell. Microbiol. 7, 199–208 (2005).Article 
CAS 

Google Scholar 
Satoh, H., Oshiro, N., Iwanaga, S., Namikoshi, M. & Nagai, H. Characterization of PsTX-60B, a new membrane-attack complex/perforin (MACPF) family toxin, from the venomous sea anemone Phyllodiscus semoni. Toxicon 49, 1208–1210 (2007).Article 
CAS 

Google Scholar 
Tikhonenkov, D. V., Mazei, Y. A. & Embulaeva, E. A. Degradation succession of heterotrophic flagellate communities in microcosms. Zh. Obs. Biol. 69, 57–64 (2008).CAS 

Google Scholar 
Tikhonenkov, D. V. et al. On the origin of TSAR: morphology, diversity and phylogeny of Telonemia. Open Biol. 12, 210325 (2022).Article 
CAS 

Google Scholar 
Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).Article 
CAS 

Google Scholar 
Keeling, P. J., Poulson, N. & McFadden, G. I. Phylogenetic diversity of parabasalian symbionts from termites, including the phylogenetic position of Pseudotrypanosoma and Trichonympha. J. Eukaryot. Microbiol. 45, 643–650 (1998).Article 
CAS 

Google Scholar 
Medlin, L., Elwood, H. J., Stickel, S. & Sogin, M. L. The characterization of enzymatically amplified eukaryotic 16S-like rRNA-coding regions. Gene 71, 491–499 (1988).Article 
CAS 

Google Scholar 
Tikhonenkov, D. V., Janouškovec, J., Keeling, P. J. & Mylnikov, A. P. The morphology, ultrastructure and SSU rRNA gene sequence of a new freshwater flagellate, Neobodo borokensis n. sp. (Kinetoplastea, Excavata). J. Eukaryot. Microbiol. 63, 220–232 (2016).Article 
CAS 

Google Scholar 
Andrews, S. FastQC: a quality control tool for high throughput sequence data (Babraham Bioinformatics, 2010); https://www.bioinformatics.babraham.ac.uk/projects/fastqc/.Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620 (2013).Article 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
CAS 

Google Scholar 
Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).Article 
CAS 

Google Scholar 
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).Article 
CAS 

Google Scholar 
Laetsch, D. R. & Blaxter, M. L. BlobTools: interrogation of genome assemblies. F1000Research 6, 1287 (2017).Article 

Google Scholar 
Haas, B. J. et al. Denovo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512 (2013).Article 
CAS 

Google Scholar 
Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).Article 
CAS 

Google Scholar 
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).Article 
CAS 

Google Scholar 
Shen, W. & Ren, H. TaxonKit: a practical and efficient NCBI taxonomy toolkit. J. Genet. Genomics 48, 844–850 (2021).Richter, D. J. et al. EukProt: a database of genome-scale predicted proteins across the diversity of eukaryotes. Peer Community Journal 2, e56 (2022).Simao, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).Article 
CAS 

Google Scholar 
Kanehisa, M., Furumichi, M., Sato, Y., Ishiguro-Watanabe, M. & Tanabe, M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Res. 49, D545–D551 (2021).Article 
CAS 

Google Scholar 
Moriya, Y., Itoh, M., Okuda, S., Yoshizawa, A. C. & Kanehisa, M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 35, W182–W185 (2007).Article 

Google Scholar 
Burki, F. The eukaryotic tree of life from a global phylogenomic perspective. Cold Spring Harb. Perspect. Biol. 6, a016147 (2014).Article 

Google Scholar 
Waskom, M. et al. mwaskom/Seaborn: v0.8.1 (September 2017). Zenodo https://doi.org/10.5281/zenodo.883859 (2017).Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).Article 
ADS 
MathSciNet 
CAS 

Google Scholar 
Finn, R. D. et al. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 44, D279–D285 (2016).Article 
CAS 

Google Scholar 
Letunic, I. & Bork, P. 20 years of the SMART protein domain annotation resource. Nucleic Acids Res. 46, D493–D496 (2018).Article 
CAS 

Google Scholar 
Almagro Armenteros, J. J. et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat. Biotechnol. 37, 420–423 (2019).Article 
CAS 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).Article 
CAS 

Google Scholar 
Burns, J. A., Pittis, A. A. & Kim, E. Gene-based predictive models of trophic modes suggest Asgard archaea are not phagocytotic. Nat. Ecol. Evol. 2, 697–704 (2018).Article 

Google Scholar 
Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).Article 

Google Scholar 
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).Article 
CAS 

Google Scholar 
Hall, T. A. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp. Ser. 41, 95–98 (1999).CAS 

Google Scholar 
Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).Article 
CAS 

Google Scholar 
Whelan, S., Irisarri, I. & Burki, F. PREQUAL: detecting non-homologous characters in sets of unaligned homologous sequences. Bioinformatics 34, 3929–3930 (2018).CAS 

Google Scholar 
Capella-Gutierrez, S., Silla-Martinez, J. M. & Gabaldon, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).Article 
CAS 

Google Scholar 
Roure, B., Rodriguez-Ezpeleta, N. & Philippe, H. SCaFoS: a tool for selection, concatenation and fusion of sequences for phylogenomics. BMC Evol. Biol. 7, S2 (2007).Article 

Google Scholar 
Lartillot, N., Rodrigue, N., Stubbs, D. & Richer, J. PhyloBayes MPI: phylogenetic reconstruction with infinite mixtures of profiles in a parallel environment. Syst. Biol. 62, 611–615 (2013).Article 
CAS 

Google Scholar 
Dayhoff, M., Schwartz, R. & Orcutt, B. in Atlas of Protein Sequence and Structure (ed. Dayhoff, M.) 345–352 (National Biomedical Research Foundation, 1978).Susko, E. & Roger, A. J. On reduced amino acid alphabets for phylogenetic inference. Mol. Biol. Evol. 24, 2139–2150 (2007).Article 
CAS 

Google Scholar 
Lartillot, N. & Philippe, H. A Bayesian mixture model for across-site heterogeneities in the amino-acid replacement process. Mol. Biol. Evol. 21, 1095–1109 (2004).Article 
CAS 

Google Scholar 
Quang le, S., Gascuel, O. & Lartillot, N. Empirical profile mixture models for phylogenetic reconstruction. Bioinformatics 24, 2317–2323 (2008).Article 

Google Scholar 
Wang, H. C., Minh, B. Q., Susko, E. & Roger, A. J. Modeling site heterogeneity with posterior mean site frequency profiles accelerates accurate phylogenomic estimation. Syst. Biol. 67, 216–235 (2018).Article 
CAS 

Google Scholar 
Kück, P. & Struck, T. H. BaCoCa—a heuristic software tool for the parallel assessment of sequence biases in hundreds of gene and taxon partitions. Mol. Phylogenet. Evol. 70, 94–98 (2014).Article 

Google Scholar 
Shimodaira, H. An approximately unbiased test of phylogenetic tree selection. Syst. Biol. 51, 492–508 (2002).Article 

Google Scholar 
Kumar, S., Stecher, G. & Tamura, K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).Article 
CAS 

Google Scholar 
Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).Article 
MathSciNet 
CAS 

Google Scholar 
Dierckxsens, N., Mardulyn, P. & Smits, G. NOVOPlasty: de novo assembly of organelle genomes from whole genome data. Nucleic Acids Res. 45, e18 (2017).
Google Scholar 
Kuznetsov, A. & Bollin, C. J. in Multiple Sequence Alignment (ed. Katoh, K.) 261–295 (Springer, 2021).Lohse, M., Drechsel, O., Kahlau, S. & Bock, R. OrganellarGenomeDRAW—a suite of tools for generating physical maps of plastid and mitochondrial genomes and visualizing expression data sets. Nucleic Acids Res. 41, W575–W581 (2013).Article 

Google Scholar 
Johnson, P. Z., Kasprzak, W. K., Shapiro, B. A. & Simon, A. E. RNA2Drawer: geometrically strict drawing of nucleic acid structures with graphical structure editing and highlighting of complementary subsequences. RNA Biol. 16, 1667–1671 (2019).Article 

Google Scholar 
Burger, G., Gray, M. W., Forget, L. & Lang, B. F. Strikingly bacteria-like and gene-rich mitochondrial genomes throughout jakobid protists. Genome Biol. Evol. 5, 418–438 (2013).Article 

Google Scholar 
Criscuolo, A. & Gribaldo, S. BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments. BMC Evol. Biol. 10, 210 (2010).Article 

Google Scholar 
Zhang, D. et al. PhyloSuite: an integrated and scalable desktop platform for streamlined molecular sequence data management and evolutionary phylogenetics studies. Mol. Ecol. Resour. 20, 348–355 (2020).Article 

Google Scholar 
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).Article 
CAS 

Google Scholar 
Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084–1097 (2019).Article 
CAS 

Google Scholar 
Massana, R. et al. Marine protist diversity in European coastal waters and sediments as revealed by high-throughput sequencing. Environ. Microbiol. 17, 4035–4049 (2015).Article 
CAS 

Google Scholar 
Gendron, E. M. S., Darcy, J. L., Hell, K. & Schmidt, S. K. Structure of bacterial and eukaryote communities reflect in situ controls on community assembly in a high-alpine lake. J. Microbiol. 57, 852–864 (2019).Article 
CAS 

Google Scholar 
Minerovic, A. D. et al. 18S-V9 DNA metabarcoding detects the effect of water-quality impairment. Ecol. Indic. 113, 106225 (2020).Article 
CAS 

Google Scholar 
Pearman, J. K. et al. Cross-shelf investigation of coral reef cryptic benthic organisms reveals diversity patterns of the hidden majority. Sci. Rep. 8, 8090 (2018).Article 
ADS 
CAS 

Google Scholar 
Rodas, A. M. et al. Eukaryotic plankton communities across reef environments in Bocas del Toro Archipelago, Panamá. Coral Reefs 39, 1453–1467 (2020).Article 

Google Scholar 
Schoenle, A. et al. High and specific diversity of protists in the deep-sea basins dominated by diplonemids, kinetoplastids, ciliates and foraminiferans. Commun. Biol. 4, 501 (2021).Article 
CAS 

Google Scholar 
Schulhof, M. A. et al. Sierra Nevada mountain lake microbial communities are structured by temperature, resources and geographic location. Mol. Ecol. 29, 2080–2093 (2020).Article 
CAS 

Google Scholar 
Yi, Z. et al. High-throughput sequencing of microbial eukaryotes in Lake Baikal reveals ecologically differentiated communities and novel evolutionary radiations. FEMS Microbiol. Ecol. 93, fix073 (2017). More