Philippa Kaur

Azam, F. & Malfatti, F. Microbial structuring of marine ecosystems. Nat. Rev. Microbiol. 5, 782–791 (2007).CAS 
PubMed 
Article 

Google Scholar 
Martiny, J. B. H. et al. Microbial biogeography: Putting microorganisms on the map. Nat. Rev. Microbiol. 4, 102–112 (2006).CAS 
PubMed 
Article 

Google Scholar 
Hanson, C. A., Fuhrman, J. A., Horner-Devine, M. C. & Martiny, J. B. H. Beyond biogeographic patterns: Processes shaping the microbial landscape. Nat. Rev. Microbiol. 10, 497–506 (2012).CAS 
PubMed 
Article 

Google Scholar 
Grossart, H. P. Ecological consequences of bacterioplankton lifestyles: Changes in concepts are needed. Environ. Microbiol. Rep. 2, 706–714 (2010).PubMed 
Article 

Google Scholar 
Simon, M., Grossart, H. P., Schweitzer, B. & Ploug, H. Microbial ecology of organic aggregates in aquatic ecosystems. Aquat. Microb. Ecol. 28, 175–211 (2002).Article 

Google Scholar 
Smith, D. C., Simon, M., Alldredge, A. L. & Azam, F. Intense hydrolytic enzyme activity on marine aggregates and implication for rapid particle dissolution. Nature 359, 139–141 (1992).ADS 
CAS 
Article 

Google Scholar 
Grossart, H. P., Tang, K. W., Kiørboe, T. & Ploug, H. Comparison of cell-specific activity between free-living and attached bacteria using isolates and natural assemblages. FEMS Microbiol. Lett. 206, 194–200 (2007).Article 
CAS 

Google Scholar 
Rieck, A., Herlemann, D. P. R., Jürgens, K. & Grossart, H. Particle-associated differ from free-living bacteria in surface waters of the Baltic Sea. Front. Microbiol. 6, 1297 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Karner, M. & Herndl, G. J. Extracellular enzymatic activity and secondary production in free-living and marine-snow-associated bacteria. Mar. Biol. 113, 341–347 (1992).CAS 
Article 

Google Scholar 
Lyons, M. M. & Dobbs, F. C. Differential utilization of carbon substrates by aggregate-associated and water-associated heterotrophic bacterial communities. Hydrobiologia 686, 181–193 (2012).CAS 
Article 

Google Scholar 
Simon, H. M., Smith, M. W. & Herfort, L. Metagenomic insights into particles and their associated microbiota in a coastal margin ecosystem. Front. Microbiol. 5, 466 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Smith, M. W., Allen, L. Z., Allen, A. E., Herfort, L. & Simon, H. M. Contrasting genomic properties of free-living and particle-attached microbial assemblages within a coastal ecosystem. Front. Microbiol. 4, 120 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mestre, M. et al. Spatial variability of marine bacterial and archaeal communities along the particulate matter continuum. Mol. Ecol. 26, 6827–6840 (2017).CAS 
PubMed 
Article 

Google Scholar 
Bižic-Ionescu, M. et al. Comparison of bacterial communities on limnic versus coastal marine particles reveals profound differences in colonization. Environ. Microbiol. 17, 3500–3514 (2015).PubMed 
Article 
CAS 

Google Scholar 
Hollibaugh, J. T., Wong, P. S. & Murrell, M. C. Similarity of particle-associated and free-living bacterial communities in northern San Francisco Bay, California. Aquat. Microb. Ecol. 21, 103–114 (2000).Article 

Google Scholar 
Ortega-Retuerta, E., Joux, F., Jeffrey, W. H. & Ghiglione, J. F. Spatial variability of particle-attached and free-living bacterial diversity in surface waters from the Mackenzie River to the Beaufort Sea (Canadian Arctic). Biogeosciences 10, 2747–2759 (2013).ADS 
Article 

Google Scholar 
Noble, P. A., Bidle, K. D. & Fletcher, M. Natural microbial community compositions compared by a back-propagating neural network and cluster analysis of 5S rRNA. Appl. Environ. Microbiol. 63, 1762–1770 (1997).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhou, J. & Ning, D. Stochastic community assembly: Does it matter in microbial ecology?. Microbiol. Mol. Biol. Rev. 81, e00002-17 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Jain, A., Balmonte, J. P., Singh, R., Bhaskar, P. V. & Krishnan, K. P. Spatially resolved assembly, connectivity and structure of particle-associated and free-living bacterial communities in a high Arctic fjord. FEMS Microbiol. Ecol. 97, 1–12 (2021).Article 
CAS 

Google Scholar 
Yao, Z. et al. Bacterial community assembly in a typical estuarine marsh. Appl. Environ. Microbiol. 85, e02602-18 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, J. et al. Assembly processes and source tracking of planktonic and benthic bacterial communities in the Yellow River estuary. Environ. Microbiol. 23, 2578–2591 (2021).PubMed 
Article 

Google Scholar 
Balmonte, J. P. et al. Sharp contrasts between freshwater and marine microbial enzymatic capabilities, community composition, and DOM pools in a NE Greenland fjord. Limnol. Oceanogr. 65, 77–95 (2019).ADS 
Article 
CAS 

Google Scholar 
Fortunato, C. S., Herfort, L., Zuber, P., Baptista, A. M. & Crump, B. C. Spatial variability overwhelms seasonal patterns in bacterioplankton communities across a river to ocean gradient. ISME J. 6, 554–563 (2012).CAS 
PubMed 
Article 

Google Scholar 
Yawata, Y., Carrara, F., Menolascina, F. & Stocker, R. Constrained optimal foraging by marine bacterioplankton on particulate organic matter. Proc. Natl. Acad. Sci. USA 117, 25571–25579 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hu, Y. et al. The relationships between the free-living and particle-attached bacterial communities in response to elevated eutrophication. Front. Microbiol. 11, 423 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Lima-Mendez, G. et al. Determinants of community structure in the grobal plankton interactome. Science (80-) 348, 1262073-1–10 (2015).Article 
CAS 

Google Scholar 
Milici, M. et al. Co-occurrence analysis of microbial taxa in the Atlantic ocean reveals high connectivity in the free-living bacterioplankton. Front. Microbiol. 7, 649 (2016).PubMed 
PubMed Central 

Google Scholar 
Herren, C. M. & McMahon, K. D. Cohesion: A method for quantifying the connectivity of microbial communities. ISME J. 11, 2426–2438 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Deng, Y. et al. Molecular ecological network analyses. BMC Bioinform. 13, 113 (2012).Article 

Google Scholar 
Labry, C. et al. High alkaline phosphatase activity in phosphate replete waters: The case of two macrotidal estuaries. Limnol. Oceanogr. 61, 1513–1529 (2016).ADS 
Article 

Google Scholar 
Crump, B. C. et al. Quantity and quality of particulate organic matter controls bacterial production in the Columbia River estuary. Limnol. Oceanogr. 62, 2713–2731 (2017).ADS 
CAS 
Article 

Google Scholar 
Canuel, E. A. & Hardison, A. K. Sources, ages, and alteration of organic matter in Estuaries. Ann. Rev. Mar. Sci. 8, 409–434 (2016).PubMed 
Article 

Google Scholar 
He, W., Chen, M., Schlautman, M. A. & Hur, J. Dynamic exchanges between DOM and POM pools in coastal and inland aquatic ecosystems: A review. Sci. Total Environ. 551–552, 415–428 (2016).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Bianchi, T. S. The role of terrestrially derived organic carbon in the coastal ocean: A changing paradigm and the priming effect. Proc. Natl. Acad. Sci. 108, 19473–19481 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Auffret, G. A. Dynamique sédimentaire de la marge continentale celtique-Evolution Cénozoïque-Spécificité du Pleistocène supérieur et de l’Holocène (Université de Bordeaux I, 1983).
Google Scholar 
Delmas, R. & Tréguer, P. Évolution saisonnière des nutriments dans un écosystème eutrophe d’Europe occidentale (la rade de Brest). Interactions marines et terrestres. Oceanol. Acta 6, 345–356 (1983).CAS 

Google Scholar 
Bassoullet, P. Etude de la dynamique des sédiments en suspension dans l’estuaire de l’Aulne (rade de Brest) (Université de Bretagne Occidentale, 1979).
Google Scholar 
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).CAS 
PubMed 
Article 

Google Scholar 
Bolyen, E. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).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 
Olesen, S. W., Duvallet, C. & Alm, E. J. dbOTU3: A new implementation of distribution-based OTU calling. PLoS ONE 12, 1–13 (2017).Article 
CAS 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).Article 
CAS 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (2013).Whickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).Book 

Google Scholar 
Lê, S., Josse, J. & Husson, F. FactoMineR: An R package for multivariate analysis. J. Stat. Softw. 25, 1–18 (2008).Article 

Google Scholar 
Wei, T. & Simko, V. R package ‘corrplot’: Visualization of a Correlation Matrix (2011).McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oksanen, J. et al. Vegan: Community Ecology Package (2022).Liu, C., Cui, Y., Li, X. & Yao, M. Microeco: An R package for data mining in microbial community ecology. FEMS Microbiol. Ecol. 97, fiaa255 (2021).CAS 
PubMed 
Article 

Google Scholar 
Kandlikar, G. ranacapa: Utility Functions and ‘shiny’ App for Simple Environmental DNA Visualizations and Analyses (2021).Cao, Y. microbiomeMarker: microbiome biomarker analysis toolkit (2021).Tsirogiannis, C. & Brody, S. PhyloMeasures: Fast and Exact Algorithms for Computing Phylogenetic Biodiversity Measures (2017).McKnight, D. T. et al. Methods for normalizing microbiome data: An ecological perspective. Methods Ecol. Evol. 10, 389–400 (2019).Article 

Google Scholar 
Paradis, E. & Schliep, K. Ape 50: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics https://doi.org/10.1093/bioinformatics/bty633 (2019).Article 
PubMed 

Google Scholar 
Legendre, P. & Legendre, L. Numerical Ecology (Third English Edition) (Elsevier, 2012).MATH 

Google Scholar 
Stegen, J. C., Lin, X., Fredrickson, J. K. & Konopka, A. E. Estimating and mapping ecological processes influencing microbial community assembly. Front. Microbiol. 6, 370 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Stegen, J. C., Lin, X., Konopka, A. E. & Fredrickson, J. K. Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J. 6, 1653–1664 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Naimi, B. usdm: Uncertainty Analysis for Species Distribution Models (2017).Wu, W., Xu, Z., Dai, M., Gan, J. & Liu, H. Homogeneous selection shapes free-living and particle-associated bacterial communities in subtropical coastal waters. Divers. Distrib. 00, 1–14 (2020).
Google Scholar 
Wang, Y. et al. Patterns and processes of free-living and particle-associated bacterioplankton and archaeaplankton communities in a subtropical river-bay system in South China. Limnol. Oceanogr. 65, 161–179 (2020).
Google Scholar 
Zhou, L. et al. Environmental filtering dominates bacterioplankton community assembly in a highly urbanized estuarine ecosystem. Environ. Res. 196, 110934 (2021).CAS 
PubMed 
Article 

Google Scholar 
Graham, E. B. & Stegen, J. C. Dispersal-based microbial community assembly decreases biogeochemical function. Processes 5, 65 (2017).Article 

Google Scholar 
Campbell, B. J. & Kirchman, D. L. Bacterial diversity, community structure and potential growth rates along an estuarine salinity gradient. ISME J. 7, 210–220 (2013).CAS 
PubMed 
Article 

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

Google Scholar 
Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146 (2015).CAS 
PubMed 
Article 

Google Scholar 
Sunagawa, S. et al. Structure and function of the global ocean microbiome. Science (80-) 348, 1261359 (2015).Article 
CAS 

Google Scholar 
Buchan, A., LeCleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686–698 (2014).CAS 
PubMed 
Article 

Google Scholar 
Martinez-Garcia, M. et al. Capturing single cell genomes of active polysaccharide degraders: An unexpected contribution of verrucomicrobia. PLoS ONE 7, e35314 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Reintjes, G., Arnosti, C., Fuchs, B. M. & Amann, R. An alternative polysaccharide uptake mechanism of marine bacteria. ISME J. 11, 1640–1650 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gralka, M., Szabo, R., Stocker, R. & Cordero, O. X. Trophic interactions and the drivers of microbial community assembly. Curr. Biol. 30, R1176–R1188 (2020).CAS 
PubMed 
Article 

Google Scholar 
Liu, J., Meng, Z., Liu, X. & Zhang, X. H. Microbial assembly, interaction, functioning, activity and diversification: a review derived from community compositional data. Mar. Life Sci. Technol. 1, 112–128 (2019).ADS 
Article 

Google Scholar 
Hernandez, D. J., David, A. S., Menges, E. S., Searcy, C. A. & Afkhami, M. E. Environmental stress destabilizes microbial networks. ISME J. 15, 1722–1734 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Herren, C. M. & McMahon, K. D. Keystone taxa predict compositional change in microbial communities. Environ. Microbiol. 20, 2207–2217 (2018).PubMed 
Article 

Google Scholar 
Liénart, C. et al. Dynamics of particulate organic matter composition in coastal systems: A spatio-temporal study at multi-systems scale. Prog. Oceanogr. 156, 221–239 (2017).Article 

Google Scholar 
Fraisse, S., Bormans, M. & Lagadeuc, Y. Morphofunctional traits reflect differences in phytoplankton community between rivers of contrasting flow regime. Aquat. Ecol. 47, 315–327 (2013).Article 

Google Scholar 
Treguer, P. & Queguiner, B. Seasonal variations in conservative and nonconservative mixing of nitrogen compounds in a West European macrotidal estuary. Oceanol. Acta 12, 371–380 (1989).CAS 

Google Scholar 
Grossart, H. P. & Tang, K. W. Communicative & integrative biology. Commun. Integr. Biol. 3, 491–494 (2010).PubMed 
PubMed Central 
Article 

Google Scholar  More

Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol. 14, e2001104 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science 354, aaf7671 (2016).PubMed 
Article 
CAS 

Google Scholar 
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Wingfield, J. C. et al. Organism-environment interactions in a changing world: a mechanistic approach. J. Ornithol. 152, 279–288 (2011).Article 

Google Scholar 
Mendoza-Gonzalez, G., Martinez, M. L., Rojas-Soto, O. R., Vazquez, G. & Gallego-Fernandez, J. B. Ecological niche modeling of coastal dune plants and future potential distribution in response to climate change and sea level rise. Glob. Change Biol. 19, 2524–2535 (2013).ADS 
Article 

Google Scholar 
Saunders, S. P. et al. Community science validates climate suitability projections from ecological niche modeling. Ecol. Appl. 30, 17 (2020).Article 

Google Scholar 
Peterson, A. T., Cobos, M. E. & Jimenez-Garcia, D. Major challenges for correlational ecological niche model projections to future climate conditions. Ann. N. Y. Acad. Sci. 1429, 66–77 (2018).ADS 
PubMed 
Article 

Google Scholar 
Mays, H. L. et al. Genomic analysis of demographic history and Ecological niche modeling in the endangered Sumatran Rhinoceros Dicerorhinus sumatrensis. Curr. Biol. 28, 70–76 (2018).CAS 
PubMed 
Article 

Google Scholar 
Malcolm, R. J., Liu, C., Neilson, P. R., Hansen, L. & Hannah, L. A. Global warming and extinctions of endemic species from biodiversity hotspots. Conserv. Biol. 20, 538–548 (2005).Article 

Google Scholar 
Fitzpatrick, M. C. & Keller, S. R. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett. 18, 1–16 (2015).PubMed 
Article 

Google Scholar 
Gotelli, J. N. & Stanton-Geddes, J. Climate change, genetic markers and species distribution modelling. J. Biogeogr. 42, 1577–1585 (2015).Article 

Google Scholar 
Ruegg, K. et al. Ecological genomics predicts climate vulnerability in an endangered southwestern songbird. Ecol. Lett. 21, 1085–1096 (2018).PubMed 
Article 

Google Scholar 
Razgour, O. et al. Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proc. Natl Acad. Sci. USA 116, 10418–10423 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Valladares, F. et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014).PubMed 
Article 

Google Scholar 
Bay, R. A. et al. Genomic signals of selection predict climate-driven population declines in a migratory bird. Science 359, 83–86 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Rhone, B. et al. Pearl millet genomic vulnerability to climate change in West Africa highlights the need for regional collaboration. Nat. Commun. 11, 5274 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rahbek, C. et al. Building mountain biodiversity: geological and evolutionary processes. Science 365, 1114–1119 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Fjeldså, J., Bowie, R. C. K. & Rahbek, C. The role of mountain ranges in the diversification of birds. Annu. Rev. Ecol. Evol. Syst. 43, 249–265 (2012).Article 

Google Scholar 
Freeman, B. G., Scholer, M. N., Ruiz-Gutierrez, V. & Fitzpatrick, J. W. Climate change causes upslope shifts and mountaintop extirpations in a tropical bird community. Proc. Natl Acad. Sci. USA 115, 11982–11987 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
He, J. K., Lin, S. L., Li, J. T., Yu, J. H. & Jiang, H. S. Evolutionary history of zoogeographical regions surrounding the Tibetan Plateau. Commun. Biol. 3, 9 (2020).Article 
CAS 

Google Scholar 
Wu, Y. J. et al. Explaining the species richness of birds along a subtropical elevational gradient in the Hengduan Mountains. J. Biogeogr. 40, 2310–2323 (2013).Article 

Google Scholar 
del Hoyo, J., Elliott, A., Sargatal, J. & Christie, D. A. Handbook of the Birds of the World (Lynx Edicions, 2013).Qu, Y. et al. Lineage diversification and historical demography of a montane bird Garrulax elliotii – implications for the Pleistocene evolutionary history of the eastern Himalayas. BMC Evolut. Biol. 11, 174 (2011).Article 

Google Scholar 
Qu, Y. et al. Long-term isolation and stability explain high genetic diversity in the Eastern Himalaya. Mol. Ecol. 23, 705–720 (2014).PubMed 
Article 

Google Scholar 
Wang, W. J. et al. Glacial expansion and diversification of an East Asian montane bird, the green-backed tit (Parus monticolus). J. Biogeogr. 40, 1156–1169 (2013).Article 

Google Scholar 
Simão, 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).PubMed 
Article 
CAS 

Google Scholar 
Laine, V. N. et al. Evolutionary signals of selection on cognition from the great tit genome and methylome. Nat. Commun. 7, 9 (2016).Article 
CAS 

Google Scholar 
Ellis, N., Smith, S. J. & Pitcher, C. R. Gradient forests: calculating importance gradients on physical predictors. Ecology 93, 156–168 (2012).PubMed 
Article 

Google Scholar 
Giorgetta, M. A. Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J. Adv. Model. Earth Syst. 5, 572–597 (2013).ADS 
Article 

Google Scholar 
Gent, P. R. et al. The community climate system model version 4. J. Clim. 24, 4973–4991 (2011).ADS 
Article 

Google Scholar 
Watanabe, M. et al. Improved climate simulation by MIROC5: mean states, variability, and climate sensitivity. J. Clim. 23, 6312–6335 (2010).ADS 
Article 

Google Scholar 
Voldoire, A. et al. The CNRM-CM5.1 global climate model: description and basic evaluation. Clim. Dyn. 40, 2091–2121 (2013).Article 

Google Scholar 
Frichot, E., Schoville, S. D., Bouchard, G. & Francois, O. Testing for associations between loci and environmental gradients using latent factor mixed models. Mol. Biol. Evol. 30, 1687–1699 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Forester, B. R., Jones, M. R., Joost, S., Landguth, E. L. & Lasky, J. R. Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes. Mol. Ecololgy 25, 104–120 (2016).CAS 
Article 

Google Scholar 
Forester, B. R., Lasky, J. R., Wagner, H. H. & Urban, D. L. Comparing methods for detecting multilocus adaptation with multivariate genotype-environment associations. Mol. Ecol. 27, 2215–2233 (2018).CAS 
PubMed 
Article 

Google Scholar 
Li, C. et al. Two Antarctic penguin genomes reveal insights into their evolutionary history and molecular changes related to the Antarctic environment. Gigascience 3, 27 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Pirri, F. et al. Selection-driven adaptation to the extreme Antarctic environment in Emperor penguin. Preprint at bioRxiv https://doi.org/10.1101/2021.12.14.471946 (2021).Wang, L. C. et al. Involvement of the Arabidopsis HIT1/AtVPS53 tethering protein homologuein the acclimation of the plasma membrane to heat stess.J. Exp. Bot. 62, 3609–3620 (2011).CAS 
PubMed 
Article 

Google Scholar 
Piñol, R. A. et al. Preoptic BRS3 neurons increase body temperature and heart rate via multiple pathways. Cell Metab. 33, 1389–1403 (2021).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Guilherme, A. et al. Neuronal modulation of brown adipose activity through perturbation of white adipocyte lipogenesis. Mol. Metab. 16, 116–125 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang, Y., Guo, W., zhang, Y., Zhang, H. & Wu, C. Insights into hypoxic adaptation in Tibetan chicken embryos from comparative proteomics. Comp. Biochem. Physiol. Part D. 31, 100602 (2019).CAS 

Google Scholar 
Pizzagalli, M. D., Bensimon, A. & Superti-Furga, G. A guide to plasma membrane solute carrier proteins. FEBS J. 288, 2784–2835 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Qu, Y. et al. Rapid phenotypic evolution with shallow genomic differentiation during early stages of high elevation adaptation in Eurasian Tree Sparrows. Natl Sci. Rev. 7, 113–127 (2020).PubMed 
Article 

Google Scholar 
Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ferrier, S., Manion, G., Elith, J. & Richardson, K. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Diversity Distrib. 13, 252–264 (2007).Article 

Google Scholar 
Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD – a platform for ensemble forecasting of species distributions. Ecography 32, 369–373 (2009).Article 

Google Scholar 
Chen, Y. et al. Large-scale genome-wide reveals climate adaptive variability in a cosmopolitan pest. Nat. Commun. 12, 7206 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Clarke, R. T., Rothery, P. & Raybould, A. F. Confidence limits for regression relationships between distance matrices: Estimating gene flow with distance. J. Agric. Biol. Environ. Stat. 7, 361–372 (2002).Article 

Google Scholar 
Excoffier, L., Dupanloup, I., Huerta-Sanchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Foden, W. B. et al. Climate change vulnerability assessment of species. WIREs Clim. Change 10, e551 (2019).Article 

Google Scholar 
Smith, T. B. et al. Genomic vulnerablity and soci-economic threats under climate change in an African rainforest bird. Evolut. Appl. 14, 1239–1247 (2021).Article 

Google Scholar 
Liu, B., Liang, E. Y., Liu, K. & Camarero, J. J. Species- and elevation-dependent growth responses to climate warming of mountain forests in the Qinling Mountains, central China. Forests 9, 11 (2018).
Google Scholar 
Dang, H. S., Zhang, Y. J., Zhang, K. R., Jiang, M. X. & Zhang, Q. F. Climate-growth relationships of subalpine fir (Abies fargesii) across the altitudinal range in the Shennongjia Mountains, central China. Clim. Change 117, 903–917 (2013).ADS 
Article 

Google Scholar 
Lingua, E., Cherubini, P., Motta, R. & Nola, P. Spatial structure along an altitudinal gradient in the Italian central Alps suggests competition and facilitation among coniferous species. J. Veg. Sci. 19, 425–436 (2008).Article 

Google Scholar 
Zhang, D. C., Zhang, Y. H., Boufford, D. E. & Sun, H. Elevational patterns of species richness and endemism for some important taxa in the Hengduan Mountains, southwestern China. Biodivers. Conserv. 18, 699–716 (2009).Article 

Google Scholar 
Zhang, R. Z., Zheng, D., Yang, Q. Y. & Liu, Y. H. Physical Geography of Hengduan Mountains (Science Press, 1997).Liu, Y. et al. Sino-Himalayan mountains act as cradles of diversity and immigration centres in the diversification of parrotbills (Paradoxornithidae). J. Biogeogr. 43, 1488–1501 (2016).Bush, A. et al. Incorporating evolutionary adaptation in species distribution modeling reduces projected vulnerability to climate change. Ecol. Lett. 17, 1468–148 (2016).Article 

Google Scholar 
Sparks, M. M., Westley, A. A. H., Falke, J. A. & Quinn, T. P. Thermal adaptation and phenotypic plasticity in a warming world: insights from common garden experiments on Alaskan sockeye salmon. Glob. Change Biol. 23, 5203–5217 (2017).ADS 
Article 

Google Scholar 
Merow, C., Wilson, A. M. & Jetz, W. Integrating occurrence data and expert maps for improved species range predictions. Glob. Ecol. Biogeogr. 26, 243–258 (2017).Article 

Google Scholar 
Weisenfeld, N. I., Kumar, V., Shah, P., Church, D. M. & Jaffe, D. B. Direct determination of diploid genome sequences. Genome Res. 27, 757–767 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Luo, R. et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience 1, 18 (2012).PubMed 
PubMed Central 
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).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
She, R., Chu, J. S. C., Wang, K., Pei, J. & Chen, N. GenBlastA: enabling BLAST to identify homologous gene sequences. Genome Res. 19, 143–149 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Birney, E., Clamp, M. & Durbin, R. GeneWise and genomewise. Genome Res. 14, 988–995 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McKenna, A. et al. The genome analysis toolkit: a mapreduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
Robinson, J. D., Bunnefeld, L., Hearn, J., Stone, G. N. & Hickerson, M. J. ABC inference of multi-population divergence with admixture from unphased population genomic data. Mol. Ecol. 23, 4458–4471 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nazareno, A. G., Bemmels, J. B., Dick, C. W. & Lohmann, L. G. Minimum sample sizes for population genomics: an empirical study from an Amazonian plant species. Mol. Ecol. Resour. 17, 1136–1147 (2017).CAS 
PubMed 
Article 

Google Scholar 
Willing, E. M., Dreyer, C. & van Oosterhout, C. Estimates of genetic differentiation measured by FST do not necessary require large sample size when using many SNP markers. PLoS One 7, e2649 (2012).Article 
CAS 

Google Scholar 
Keenan, K., Mcginnity, P., Cross, T. F., Crozier, W. W. & Prodöhl, P. A. diveRsity: an Rpackage for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol. 4, 782–788 (2013).Article 

Google Scholar 
Rellstab, C., Gugerli, F., Eckert, I. A., Hancock, M. A. & Holderegger, R. A practical guide to environmental assocaition analysis in landscape genomics. Mol. Ecol. 24, 4348–4370 (2015).PubMed 
Article 

Google Scholar 
Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Xie, C. et al. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 39, W316–W322 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).PubMed 
Article 

Google Scholar 
Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Boria, R. A., Olson, L. E., Goodman, S. M. & Anderson, R. P. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol. Model. 275, 73–77 (2014).Article 

Google Scholar 
Anderson, R. P. & Raza, A. The effect of the extent of the study region on GISmodels of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela. J. Biogeogr. 37, 1378–1393 (2010).Article 

Google Scholar 
Pearson, R. G., Raxworthy, C., Nakamura, M. & Peterson, A. T. Predicting species distributions from small numbers of occurrence records: a test case using crypticgeckos in Madagascar. J. Biogeogr. 34, 102–117 (2007).Article 

Google Scholar 
Heming, N. M., Dambros, C. & Gutiérrez, E. E. ENMwizard: advanced techniques for Ecological Niche Modeling made easy. https://github.com/HemingNM/ENMwizard (2018).Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling. Ecography 37, 191–203 (2014).Article 

Google Scholar 
Muscarella, R. et al. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for MAXENT ecological niche models. Methods Ecol. Evol. 5, 1198–1205 (2014).Article 

Google Scholar 
Owens, H. L. et al. Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas. Ecol. Model. 263, 10–18 (2013).Article 

Google Scholar 
Akaike, H. New look at statistical-model identification. IEEE Trans. Autom. Control AC19, 716–723 (1974).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
Phillips, S. J. et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).PubMed 
Article 

Google Scholar 
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: how, where and how many? Methods Ecol. Evol. 3, 327–338 (2012).Article 

Google Scholar 
Bellard, C. et al. Will climate change promote future invasions? Glob. Change Biol. 19, 3740–3748 (2013).ADS 
Article 

Google Scholar 
Elith, J., Kearney, M. & Phillips, S. The art of modelling range-shifting species. Methods Ecol. Evol. 1, 330–342 (2010).Article 

Google Scholar 
Anantharaman, R., Hall, K., Shah, V. B. & Edelman, A. Circuitscape in Julia: high performance connectivity modelling to support conservation decisions. Proc. JuliaCon Conf. 1, 58 (2020).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Anderson, D. R. & Burnham, K. P. Avoiding pitfalls when using information-theoretic methods. J. Wildl. Manag. 66, 912–918 (2002).Article 

Google Scholar 
Van Strien, M. J., Keller, D. & Holderegger, R. A new analytical approach to landscape genetic modelling: least-cost transect analysis and linear mixed models. Mol. Ecol. 21, 4010–4023 (2012).Article 

Google Scholar 
Bartoń, K. MuMIn: multi-model inference, R package version 1.9.13 (2013).Zhang, G. et al. Comparative genomics reveal insights into avian genome evolution and adaptation. Science 346, 1311–1320 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Roesti, M., Kueng, B., Moser, D. & Berner, D. The genomics of ecological vicariance in threespine stickleback fish. Nat. Commun. 6, 8767 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar  More

Climate models need to capture a full spectrum of data from forests such as the Brazilian Amazon.Credit: Florence Goisnard/AFP/Getty

Humanity’s understanding of how forests are responding to climate change is disconcertingly fragile. Take carbon fertilization, for example — the phenomenon by which plants absorb more carbon dioxide as its concentration in the atmosphere increases. This is one of the principal mechanisms by which nature has so far saved us from the worst of climate change, but there’s little understanding of its future trajectory. In fact, researchers don’t fully understand how climate change interacts with a multitude of forest processes. Complex, unsolved questions include how climate warming affects forest health; how it affects the performance of forests as a carbon sink; and whether it alters the ecosystem services that forests provide. Forests are our life-support system, and we should be more serious about taking their pulse.Six papers in this week’s Nature provide important insights into those questions. They also underline some of the challenges that must be overcome if we are to fully understand forests’ potential in the fight against climate change. These challenges are not only in the science itself, but also relate to how forest scientists collaborate, how they are funded (especially where data collection is concerned) and how they are trained.Forest science is an amalgam of disciplines. Ecologists and plant scientists measure tree growth, soil nutrients and other parameters in thousands of forest plots around the world. Physical scientists monitor factors such as forest height and above-ground forest biomass using remote-sensing data from drones or satellites. Experimental scientists investigate how forests might behave in a warming world by artificially altering factors such as temperature or carbon dioxide levels in experimental plots. Some of the data they generate are absorbed by yet another community: the modellers, who have created dynamic global vegetation models (DGVMs). These simulate how carbon and water cycles change with climate and, in turn, inform broader earth-system and climate models of the type that feed into policymaking.Different DGVMs make different predictions about how long forests will continue to absorb anthropogenic CO2. One reason for these differences is that models are sensitive to assumptions made about the processes in forests. There are many influences — including temperature, moisture, fire and nutrients — that are generally studied in isolation. Yet they interact with each other.Not all DGVMs account for the dampening effect that a lack of soil phosphorus can have on carbon fertilization, for example. Much of central and eastern Amazonia is poor in phosphorus, and research has shown that introducing phosphorus limitation into DGVMs can cut the carbon-fertilization effect1. This week, Hellen Fernanda Viana Cunha at the National Institute for Amazonian Research in Manaus, Brazil, and her colleagues report2 a powerful experimental demonstration of how the soil’s poor phosphorus content limits carbon absorption in an old-growth Amazonian forest.Models simulating the northward spread of boreal forest as temperatures rise are also missing key drivers3, according to Roman Dial at Alaska Pacific University in Anchorage and his colleagues. They report today that a white-spruce population has migrated surprisingly far north into the Arctic tundra. To explain this, it is necessary to take into account winter winds (which facilitate long-distance dispersal) along with the availability of deep snow and soil nutrients (which promote plant growth).Models are often based on a small number of ‘functional tree types’ — for example, ‘evergreen broadleaf’ or ‘evergreen needle leaf’. These are chosen as a proxy for the behaviour of the planet’s more than 60,000 known tree species. Yet ecologists are discovering that the biology of individual species matters when it comes to a tree’s response to climate change.David Bauman at the Environmental Change Institute at the University of Oxford, UK, and his co-workers reported in May that tree mortality on 24 moist tropical plots in northern Australia has doubled in the past 35 years (and life expectancy has halved), apparently owing to the increasing dryness of the air4. But that was an average of the 81 dominant tree species: mortality rates varied substantially between species, a variation that seemed to be related to the density of their wood.Peter Reich at the Institute for Global Change Biology at the University of Michigan in Ann Arbor and his colleagues now report that modest alterations in temperature and rainfall led to varying rates of growth and survival5 for different species in southern boreal-forest trees. The species that prospered were rare.Failure to examine multiple factors simultaneously means that scientists are making findings that challenge the assumptions in models. Spring is coming earlier for temperate forests and most models assume that, by prolonging the growing season, this increases woody-stem biomass. However, observational work carried out in temperate deciduous forests by Kristina Anderson-Teixeira at the Smithsonian Conservation Biology Institute in Front Royal, Virginia, and her colleagues found no sign of this happening6.Modellers are all too aware of the need to incorporate more complexity into their models, and of the potential that increasing amounts of computing power have to assist them in this endeavour. But they need more data.Continuity problemTo obtain comprehensive, valuable data for the models, continuous, long-term observations need to be made, and that depends on the availability of long-term funding. Achieving such continuity is a problem for both remote-sensing and ground-based operations. The former can cost hundreds of millions of dollars, but the value of its long-term data sets is immense, as demonstrated by a team led by Giovanni Forzieri at the University of Florence in Italy. The authors used 20 years of satellite data to show that nearly one-quarter of the world’s intact forests have already reached their critical threshold for abrupt decline7. But even field-based data collection, which costs a pittance by comparison, struggles to achieve financial security.Important ground-based operations include the Forest Global Earth Observatory (ForestGEO), part of the Smithsonian Tropical Research Institute, which is headquartered in Washington DC. This monitors 7.5 million individual trees in plots around the world. The amount of work that goes into this monitoring is formidable. For example, at present, ForestGEO is conducting the eighth five-yearly census of a plot in Peninsular Malaysia. This involves determining the species for each of the 350,000 trees (there are some 800 species growing there) and measuring the circumference of each trunk. It will take 16 skilled people a year to measure all the trees. Delays in the provision of funding to ForestGEO have held up similar censuses at plots in countries including Papua New Guinea, Vietnam, Brunei and Ecuador.

A ForestGEO researcher making tree measurements at a forest plot in Barro Colorado Island, Panama.Credit: Jorge Aleman, STRI

The future of the plots in North Queensland, which supplied Bauman with a rare 49 years’ worth of continuous data, is uncertain. They have been monitored since the mid-1970s by the Australian public research-funding agency CSIRO — initially every two years, then, more recently, every five years. In 2019, monitoring of the plots was switched to every 50 years because of funding shortages at CSIRO, leaving scientists searching for new sources of funding.Without continuity of funding, organizations such as ForestGEO can’t equip researchers with the requisite skills or collect data over periods longer than an individual’s time in a specific post or a funder’s cycle. “We have trained people and then lost them due to job insecurity,” says Stuart Davies, who leads ForestGEO.Different groups of forest researchers are trying to address these problems. ForestGEO is coordinating the Alliance for Tropical Forest Science in an effort to make it easier to share data, and to bolster the morale and careers of the skilled technicians and scientists — many of whom live in low- and middle-income countries — who do the bulk of the data collection.But we also need more-imaginative funding mechanisms that lift long-term observational plots out of three- to five-year funding cycles. Space agencies that fund remote-sensing satellites could collaborate with other funding agencies, for example, so that earth-observation missions include a fully funded component for ground-based data collection — which is, after all, crucial for calibrating their results. Journals, too, could do more to value and incentivise the production of long-term data sets.And there is a need for more interdisciplinarity. The US Department of Energy is funding a project called NGEE–Tropics (Next-Generation Ecosystem Experiments–Tropics) in which modellers will work with empirical researchers, both observational and experimental, who study tropical forests to create a full, process-rich model of such forests. This is encouraging, and the idea could be pushed further. What is needed is an initiative that pulls the disciplines together towards a goal of building a better understanding of forest processes. Among other things, such an initiative would encourage researchers in different disciplines to take each other’s data needs into account when planning their projects.For this to work, we need to remember that the edifice of forest science relies on the long-term data that scientists wring from forests over decades. Our chances of overcoming climate change are small, but they will diminish further if we forget the basics of monitoring our home planet. More

Experimental set-upFour vegetation species were selected: Spartina maritima, Salicornia europaea, Halimione portulacoides and Juncus maritimus. These species were chosen for a broad representation of the biomechanical properties and morphological characteristics of saltmarsh species42,43. Plants were collected in Cantabrian estuaries in late summer and early autumn (from early September to late October) during low tide (please refer to the “Methods” section). A total of 105 boxes were collected, of which 94 boxes were used to build a 9.05 m long and 0.58 m wide meadow in a flume (Fig. 1). Five boxes were used to directly estimate the meadow standing biomass in the field (Sample 1 in Table 1), leaving 6 extra boxes for possible contingencies.Figure 1(A) Shows a sketch of the experimental flume, where the vegetation box distribution in the 100% and 50% density cases is displayed in the two upper panels and a lateral view in the bottom panel. The green boxes indicate the vegetated area in each case. Free surface sensors are displayed by blue lines and numbers. (B) Shows the four species within the flume. From left to right: view of the Spartina sp. frontal edge, aerial view of Salicornia sp., frontal view of Juncus sp. and top view of the Halimione sp. rear edge.Full size imageTable 1 Standing biomass (g/m2) and plant height (m) for the four species.Full size tableExperiments were conducted in a flume 20.71 m long and 0.58 m wide at the University of Cantabria. The flume is equipped with a piston wave maker at its left end and a dissipation beach at the rear end. The 94 vegetation boxes used to create a meadow were introduced into the flume following the pattern shown in panel A of Fig. 1 to minimize any edge effects along the edges of the boxes. To ensure a smooth transition from the bottom of the channel to the vegetated area, two false bottoms were constructed with wood, and a thin sediment layer was glued to the wood to mimic the field roughness.Three meadow densities per species were considered. The meadow density directly determined in the field was chosen under the 100% density scenario. To consider a second meadow density, and therefore a second standing biomass value, plants were removed from half of the boxes following the pattern shown in Panel A of Fig. 1 to prevent creating preferential flow channels along the meadow. This case was considered the 50% density scenario. The study of these two biomass scenarios for each vegetation species is carried out with the aim of covering a wide range of standing biomass values, including low values that may be more representative of meadow winter conditions, thus facilitating the applicability of obtained results. Finally, a second cut was made, in which all plants were removed, resulting in the final scenario with a zero density. Plants were cut from above to avoid any damage along the meadow surface (as shown in Supplementary Fig. S2). In each cut, plants in 5 boxes along the leading edge and in 5 boxes at the center of the meadow were collected to quantify the standing biomass (Samples 2 and 3 for the first cut and Sample 4 and 5 for the second cut in Table 1). Therefore, the standing biomass could be monitored throughout the entire duration of the experiments, from the field until the second cut, when all plants were removed.Once located in the flume, the meadow was evaluated under regular and random wave conditions considering three water depths, i.e., h = 0.20, 0.30 and 0.40 m. Regular waves were generated using Stokes II-, III- and V-order and Cnoidal theories when applicable. Wave heights ranging from 0.05 to 0.15 m and wave periods varying between 1.5 and 4 s were considered. Random waves were generated using a Jonswap spectrum with a peak enhancement factor of 3.3, a significant wave height varying between 0.05 and 0.15 m and a peak wave period ranging from 1.8 to 4.8 s (please refer to Supplementary Table S1). Additionally, all wave conditions were considered under the zero-density scenario with bare soil for each species. The wave height evolution along the flume was recorded using 15 capacitive free surface gauges, as shown in Fig. 1 (please refer to Supplementary Table S2 for detailed coordinates).Meadow characteristics analysisThe characteristics of the vegetation meadows were analyzed by measuring the standing biomass throughout the full duration of the experiments and by measuring the individual plant height (please refer to the “Methods” section). The mean standing biomass value obtained for each species was considered the value associated with the 100% density scenario. Then, half of the standing biomass value was considered under the 50% density scenarios since half of the boxes was randomly cut, and the standing biomass values obtained after the second cut agreed with those obtained after the first cut and in the field, as indicated in Table 1. The plant height for each species was also measured (please refer to the “Methods” section), and the resultant mean value detailed in Table 1 was considered.Wave height attenuation analysisWave height attenuation analysis was performed following previous studies reported in the literature assessing the capacity by fitting a damping coefficient6,7,35,44. The18 formulation was used for regular waves, and that of19 was used for random waves (please refer to the “Methods” section). Cases with a zero density were also considered in this analysis to quantify the influence of bare soil friction by determining the corresponding damping coefficient, ({beta }_{B}). Consequently, β was obtained in the 100% and 50% density cases and the cases without vegetation (please refer to Supplementary Tables S3, S4 and S5 to find the obtained coefficients for all cases). This allowed the determination of a new damping coefficient isolating the effect of the standing biomass, ({beta }_{SB}), following24 (please refer to the “Methods” section). Figure 2 shows an example of wave height attenuation analysis for the four species and the different densities under wave condition JS07 (Supplementary Table S1).Figure 2Analysis of wave attenuation under wave condition JS07 for Spartina sp. 100% (S100), 50% (S050) and zero density (S000); Salicornia sp. 100% (L100), 50% (L050) and zero density (L000); Juncus sp. 100% (J100), 50% (J050) and zero density (J000); and Halimione sp. 100% (H100), 50% (H050) and zero density (H000). The damping coefficients for the bare soil cases, ({beta }_{B}), are displayed in blue. The damping coefficients for the 100% and 50% density cases, (beta ), are displayed in dark and light green, respectively. The damping coefficients obtained after subtracting the dissipation obtained in the bare soil cases, ({beta }_{SB}), are displayed in black and dark gray. 95% confidence interval is shown in brackets and correlation coefficient (({rho }^{2})) for each fit is also displayed.Full size imageThe damping coefficients for the bare soil cases shown in Fig. 2, ({beta }_{B}), are consistent with the soil properties observed in the field. Spartina sp. was collected in a muddy area, whereas the other three species were collected in areas with coarser sediments and exhibited a mixture of sand and mud. For all species, wave dissipation was significantly higher under the 100% density scenario than that under the 50% density cases, as expected, highlighting the importance of the standing biomass in wave energy dissipation. It was also observed that bottom friction-induced dissipation plays a more important role for the pioneer species, i.e., Spartina sp. and Salicornia sp., than for the upper marsh species, i.e., Juncus sp. and Halimione sp., which can dissipate wave energy to a greater extent.The importance of wave parameters in the resultant wave attenuation has been highlighted by several works in the literature. Therefore, not only vegetation characteristics but also incident wave conditions determine the coastal protection capacity. Figure 3 shows a comparison of the obtained wave height attenuation due to Halimione sp. under the different wave conditions.Figure 3Analysis of wave attenuation under the different irregular wave conditions for the Halimione sp. 100% (H100) and zero-density (H000) cases. The top panel shows two cases with different h but equal Hs and Tp values (JS01 and JS08), the middle panel shows two cases with different Tp but equal h and Hs values (JS10 and JS11), and the bottom panel shows two cases with different Hs but equal h and Tp values (JS09 and JS12). 95% confidence interval is shown in brackets and correlation coefficient (({rho }^{2})) for each fit is also displayed.Full size imageThe top panel in Fig. 3 shows two cases where Hs and Tp are equal, i.e., JS01 and JS08 in Supplementary Table S1, and two water depths are considered, namely, h = 0.2 and 0.3 m. As can be observed, wave damping is higher for the smallest water depth, where most of the water column is covered by vegetation since the mean vegetation height for Halimione sp. reaches 0.187 m (Table 1). The importance of the water depth with respect to the plant height in terms of wave height attenuation has been reported by several authors44,45,46 who have highlighted this aspect based on the submergence ratio, i.e., the plant height divided by the water depth, revealing higher attenuation at lower submergence ratios on a consistent basis. Bottom friction attenuation is also higher for the smallest water depth, as expected.The middle panel of Fig. 3 shows two cases with equal h and Hs but different Tp values, namely, JS10 and JS11 in Supplementary Table S1. Wave height attenuation is higher for the shortest wave period, as well as the damping produced by bottom friction. This is in line with previous studies, such as35 and44, who conducted experiments involving simulated and real saltmarshes, respectively. Finally, the bottom panel of Fig. 3 shows two cases with different Hs but equal h and Tp values, i.e., JS09 and JS12 in Supplementary Table S1. As widely reported in the literature, e.g.,7,47,48, wave height attenuation increases with the wave height, as shown in the bottom panel of Fig. 3. Bottom friction also increases with the wave height, as expected.A set of damping coefficients was obtained via the 288 tests conducted in the laboratory, 144 tests involving regular waves and 144 tests involving random waves. Additionally, in all cases, the damping coefficient considering the isolated effect of the standing biomass, ({beta }_{SB}), was determined. The relationship of these damping coefficients to the measured standing biomass is explored in the next section with the aim of establishing a new relationship to estimate the wave damping effect of the different saltmarsh species based on the standing biomass, without the need for data fitting.Wave damping coefficient as a function of the standing biomassThe mean standing biomass obtained for the different species, Table 1, is considered here to analyze the relationship with the wave damping coefficients obtained by fitting18 formulation to wave heights measured along the meadow for regular waves and19 formulation for random waves. The plant height was highly variable among the different species (Table 1), ranging from 0.170 m for Spartina sp. to 0.714 m for Juncus sp. Then, some species were submerged at all tested water depths, while other species remained above water in all tests. In the latter cases, there remained a portion of each plant above the water level, thus not contributing to wave attenuation. To consider the actual interaction between the standing biomass and flow conditions and assuming a uniform vertical distribution, the effective standing biomass, (ESB), can be defined as follows:$$ESB=DryWeight*frac{minleft{{h}_{v},hright}}{{h}_{v}}$$
(1)
where (DryWeight) denotes the measured dry weight for each species (g/m2), ({h}_{v}) is the mean plant height and (h) is the water depth. Additionally, in the submerged cases, the same (ESB) value will impact flow differently depending on the submergence ratio, (SR), as defined in Eq. (2). To consider this effect, the standing biomass ratio, (SBR) in Eq. (3), can be defined as follows:$$SR=frac{{h}_{v}}{h}, ;;where ;; SR=1 ;;for ;;{h}_{v} >h$$
(2)
$$SBR=ESB*SR$$
(3)
Figure 4 shows the relationship between (SBR) and the measured wave damping coefficient, (beta ). The results for regular and random waves are displayed for each water depth, and a linear fit was found under each condition.Figure 4Wave damping coefficient, (beta ), as a function of the standing biomass ratio, (SBR), under all regular (left panels) and random (right panels) wave conditions. Each panel shows the wave trains assessed at each water depth, h = 0.20, 0.30 and 0.40 m. The results for the 100% density case are marked with circles and those for the 50% density case are marked with squares. The linear fitting results obtained under each wave condition are also displayed.Full size imageUnder each wave condition, a linear fitting relationship between (beta ) and (SBR) was obtained for the eight (SBR) values, as shown in Fig. 4. For similar (SBR) values, the highest (beta ) values were consistently obtained at the smallest water depth, highlighting the notable influence of this parameter on the obtained wave attenuation. Following previous works, such as those of24 and25, who considered the vegetation submerged solid volume fraction to estimate the resulting wave attenuation and established a common relationship for different water depths, the volumetric standing biomass, (VSB), can be defined as follows:$$VSB= SBR*frac{1}{h}$$
(4)
(VSB) is expressed in units of g/m3, which is the weight per unit volume. Exploring the relationship of (beta ) with this new parameter, it was found that the results for the three water depths could be fitted with a single linear relationship, as shown in Fig. 5. However, despite the linear trend observed in Fig. 5, notable data scatter was observed for each (VSB) value. Each of these groups corresponds to a certain water depth and (SBR) value, which were determined under different wave heights and wave periods.Figure 5Wave damping coefficient, (beta ), as a function of the volumetric standing biomass, (VSB), under all regular (top panel) and random (bottom panel) wave conditions. The obtained linear fitting results are displayed in both panels. 95% confidence interval is shown in brackets and correlation coefficient (({rho }^{2})) for each fit is also displayed.Full size imageFinally, to account for the characteristics of the incident wave conditions, including the wave height and period, two nondimensional parameters were considered. The first parameter, considering the wave height, is the relative wave height, defined as the ratio of the incident wave height to the water depth, (H/h). Previous studies have highlighted the importance of this parameter in the resultant wave attenuation (e.g.24,44). Under random wave conditions, the considered wave height is ({H}_{rms}), according to wave attenuation analysis. The second parameter, considering the effect of the different wave periods and the importance of the number of wave lengths inside the vegetation length49, is the relative meadow length, defined as the ratio of the meadow length to the wave length, ({L}_{v}/L). To ensure consistency with the above wave attenuation analysis, in which the wave damping amount per unit length was obtained, the unit meadow length was considered here. Thus, the hydraulic standing biomass, (HSB), can be defined as:$$HSB=VSB*frac{H}{h}*frac{{L}_{v}}{L}$$
(5)
Figure 6 shows the relationship obtained between (beta ) and this new variable under all regular and random conditions following the linear fitting relationship of (beta =A*HSB+B), where (A) and (B) are fitting constants with units of (g/m2)−1 and m−1, respectively.Figure 6Wave damping coefficient, (beta ), as a function of the hydraulic standing biomass, (HSB), under all regular (top panel) and random (bottom panel) wave conditions. Both panels show linear fitting results obtained without considering the saturation point, indicated by the black solid line, and those obtained considering the saturation point, indicated by the gray solid line. The black dashed line indicates the saturation point. 95% confidence interval is shown in brackets and correlation coefficient (({rho }^{2})) for each fit is also displayed.Full size imageThe linear fitting results obtained between (beta ) and (HSB) under regular and random wave conditions are shown in Fig. 6 as solid black lines and expressed as Eqs. (6) and (7), respectively, where values between brackets are the 95% confidence interval for each coefficient.$$beta =9.206cdot {10}^{-4} left(9.006cdot {10}^{-5}right)*HSB+0.103 (0.021)$$
(6)
$$beta =1.192 cdot {10}^{-3} left(9.124 cdot {10}^{-5}right)*HSB+0.071 (0.016)$$
(7)
The inclusion of incident wave condition characteristics reduces the resulting data scatter, highlighting the role of the wave height and period in the obtained wave attenuation, as described in the previous section. An interesting aspect observed in Fig. 6 is that the four cases with the highest wave damping coefficients yielded similar values for the different (HSB) values. Under regular wave conditions, the mean (beta ) value for these four cases is 0.76, and under random wave conditions, the value reaches 0.68. This may indicate that the damping coefficient has reached its maximum value and no longer increases with increasing (HSB) value. To analyze this aspect in more detail, the wave height evolution measured for the four tests in which (beta ) reaches its maximum value are plotted (as shown in Supplementary Fig. S3). These tests correspond to Halimione sp. with a density of 100% and the shallowest water depth, h = 0.20 m. This species achieved the highest standing biomass value among the species considered in these experiments, and for h = 0.20 m, almost the entire water column was covered by vegetation. For these tests, a notable wave height attenuation was observed, where the wave height strongly decayed along the first 5 m of vegetation, and the wave height entirely dissipated along the last 4 m (as shown in Supplementary Fig. S3). The wave damping equation cannot suitably reproduce the strong wave decay within this few meters. Then, an almost constant wave damping coefficient value is reached under the different considered wave conditions, and a saturation regime is observed, in which the wave height beyond the meadow can be assumed to be negligible. To consider this phenomenon, a two-section fitting relationship is proposed, as shown in Fig. 6. The value of the saturation damping coefficient, chosen as the mean value of the four cases analyzed, is plotted as a dashed gray line, and a linear fit is obtained for the remaining data. The two-section fitting relationship is expressed in Eqs. (8) and (9) for both regular and random waves, respectively, where values between brackets are the 95% confidence interval for each coefficient.$$beta =left{begin{array}{ll}1.020 cdot {10}^{-3}left(1.112 cdot {10}^{-4}right)*HSB+0.088 ; (0.020) \ 0.758; (0.027)end{array}right. begin{array}{l} ;;0 < HSB < 659\ ;; HSB > 659end{array}$$
(8)
$$beta =left{begin{array}{l}1.310cdot {10}^{-3}left(1.232cdot {10}^{-4}right)*HSB+0.059; (0.017) \ 0.684 ;(0.066)end{array}right. begin{array}{l};;0474end{array}$$
(9)
All damping coefficients considered in the previous analysis were obtained without subtracting any additional source of dissipation such as bottom and wall friction. Previous works, such as24, highlighted the high importance of considering any other sources of wave dissipation besides the effect of vegetation elements when quantifying the wave height attenuation capacity. In this case, the flume walls were made of glass, and the friction induced by these walls could be considered negligible. However, bottom friction could be significant, as observed in tests run after removing all vegetation stems. Then, the wave damping coefficient obtained after subtracting the bottom friction contribution, ({beta }_{SB}), is studied here. Figure 7 shows the relationship obtained between this damping coefficient, ({beta }_{SB}), and hydraulic standing biomass, (HSB).Figure 7Wave damping coefficient, ({beta }_{SB}), as a function of the hydraulic standing biomass, (HSB), under all regular (top panel) and random (bottom panel) wave conditions. Both panels show linear fitting results obtained without considering the saturation point, indicated by the black solid line, and those obtained considering the saturation point, indicated by the gray solid line. The black dashed line indicates the saturation point. 95% confidence interval is shown in brackets and correlation coefficient (({rho }^{2})) for each fit is also displayed.Full size imageA linear relationship was also obtained for ({beta }_{SB}), revealing correlation coefficients similar to those obtained when analyzing (beta ). The obtained linear relationships under regular and random wave conditions are expressed as Eqs. (10) and (11), respectively, where values between brackets are the 95% confidence interval for each coefficient. A two-section fitting relationship, Eqs. (12) and (13), was also included considering the saturation regime obtained in the Halimione sp. 100% density and h = 0.20 m cases with a ({beta }_{SB}=) 0.69 and 0.63 under regular and random wave conditions, respectively.$${beta }_{SB}=1.051*{10}^{-3} left(7.063cdot {10}^{-5}right)*HSB$$
(10)
$${beta }_{SB}=1.296*{10}^{-3} left(6.894cdot {10}^{-5}right)*HSB$$
(11)
$${beta }_{SB}=left{begin{array}{l}1.151cdot {10}^{-3} left(7.445cdot {10}^{-5}right)*HSB \ 0.685 ;(0.047)end{array}right. begin{array}{l} ;; 0599end{array}$$
(12)
$${beta }_{SB}=left{begin{array}{l}1.396cdot {10}^{-3}left(7.919cdot {10}^{-5}right)*HSB \ 0.631 ;left(0.055right)end{array}right. begin{array}{l};; 0451end{array}$$
(13)
As can be noted, the ({beta }_{SB}) values are significantly lower than those obtained for (beta ), especially in the shallowest water depth cases where bottom friction is the highest, as discussed above. The estimation of (beta ) and ({beta }_{SB}) allows two possible approaches to determine the wave damping effect of a saltmarsh. The first approach, based on (beta ), includes wave damping induced by the combined effect of vegetation and bottom friction. Therefore, the consideration of (beta ) in analytical or numerical analysis could provide the total dissipation induced by the species under study, and sediment characteristics are not necessary for analysis. Considering that saltmarsh species grow in muddy to sandy environments and that the major contribution to the obtained wave attenuation is associated with vegetation, this approach may be the best option if soil properties are not thoroughly characterized.The second approach relies on the definition of ({beta }_{SB}). In this case, the wave damping contributions of vegetation drag and bottom friction are separated. Then, ({beta }_{SB}) can be used in cases where the effect of both momentum sinks can be separately evaluated. To quantify the wave damping contribution of vegetation drag only, ({beta }_{SB}) can be used, and then, the additional friction due to the bottom effect can be added considering the soil properties in each case. This second approach assumes a linear sum of both momentum sinks and could be applicable when soil properties are thoroughly characterized. More

Zelezniak A, Andrejev S, Ponomarova O, Mende DR, Bork P, Patil KR. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc Natl Acad Sci. 2015;112:6449–54.CAS 
Article 

Google Scholar 
D’Souza G, Shitut S, Preussger D, Yousif G, Waschina S, Kost C. Ecology and evolution of metabolic cross-feeding interactions in bacteria. Natural Product Reports. 2018;35:455–88.Article 

Google Scholar 
Garcia SL, Buck M, McMahon KD, Grossart H-P, Eiler A, Warnecke F. Auxotrophy and intrapopulation complementary in the ‘interactome’ of a cultivated freshwater model community. Mol Ecology. 2015;24:4449–59.CAS 
Article 

Google Scholar 
Johnson WM, Alexander H, Bier RL, Miller DR, Muscarella ME, Pitz KJ, et al. Auxotrophic interactions: A stabilizing attribute of aquatic microbial communities? FEMS Microbiol Ecology. 2020;96:1–14.D’Souza G, Waschina S, Pande S, Bohl K, Kaleta C, Kost C. Less is more: Selective advantages can explain the prevalent loss of biosynthetic genes in bacteria. Evolution. 2014;68:2559–70.Article 

Google Scholar 
Oliveira NM, Niehus R, Foster KR. Evolutionary limits to cooperation in microbial communities. Proc Natl Acad Sci. 2014;111:17941–6.CAS 
Article 

Google Scholar 
Pande S, Kost C. Bacterial unculturability and the formation of intercellular metabolic networks. Trends Microbiol. 2017;25:349–61.CAS 
Article 

Google Scholar 
Douglas AE. The microbial exometabolome: ecological resource and architect of microbial communities. Philos Trans R Soc B: Biological Sci. 2020;375:20190250.CAS 
Article 

Google Scholar 
Paczia N, Nilgen A, Lehmann T, Gätgens J, Wiechert W, Noack S. Extensive exometabolome analysis reveals extended overflow metabolism in various microorganisms. Microbial Cell Factories. 2012;11:122.CAS 
Article 

Google Scholar 
Sokolovskaya OM, Shelton AN, Taga ME. Sharing vitamins: Cobamides unveil microbial interactions. Science. 2020;369:eaba0165.CAS 
Article 

Google Scholar 
Zomorrodi AR, Segrè D. Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities. Nat Commun. 2017;8:1563.Article 

Google Scholar 
D’Souza G, Kost C. Experimental evolution of metabolic dependency in bacteria. PLoS Genetics. 2016;12:e1006364.Article 

Google Scholar 
Giri S, Oña L, Waschina S, Shitut S, Yousif G, Kaleta C, et al. Metabolic dissimilarity determines the establishment of cross-feeding interactions in bacteria. Curr Biology. 2021;31:5547–57.CAS 
Article 

Google Scholar 
Jiang X, Zerfaß C, Feng S, Eichmann R, Asally M, Schäfer P, et al. Impact of spatial organization on a novel auxotrophic interaction among soil microbes. ISME J. 2018;12:1443–56.CAS 
Article 

Google Scholar 
Konstantinidis D, Pereira F, Geissen E-M, Grkovska K, Kafkia E, Jouhten P, et al. Adaptive laboratory evolution of microbial co-cultures for improved metabolite secretion. Mol Syst Biology. 2021;17:e10189.CAS 
Article 

Google Scholar 
Harcombe WR, Chacón JM, Adamowicz EM, Chubiz LM, Marx CJ. Evolution of bidirectional costly mutualism from byproduct consumption. Proc Natl Acad Sci. 2018;115:12000–4.CAS 
Article 

Google Scholar 
Giri S, Waschina S, Kaleta C, Kost C. Defining division of labor in microbial communities. J Mol Biol. 2019;431:4712–31.CAS 
Article 

Google Scholar 
Sanchez A, Gore J. Feedback between population and evolutionary dynamics determines the fate of social microbial populations. PLOS Biology. 2013;11:e1001547.CAS 
Article 

Google Scholar 
Preussger D, Giri S, Muhsal LK, Oña L, Kost C. Reciprocal fitness feedbacks promote the evolution of mutualistic cooperation. Curr Biology. 2020;30:3580–3590.e7.CAS 
Article 

Google Scholar 
McNally CP, Borenstein E. Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss. BMC Syst Biology. 2018;12:69.Article 

Google Scholar 
Estrela S, Morris JJ, Kerr B. Private benefits and metabolic conflicts shape the emergence of microbial interdependencies. Environ Microbiol. 2016;18:1415–27.Article 

Google Scholar 
Libby E, Hébert-Dufresne L, Hosseini S-R, Wagner A. Syntrophy emerges spontaneously in complex metabolic systems. PLOS Comput Biology. 2019;15:e1007169.Article 

Google Scholar 
Rabbers I, Gottstein W, Feist A, Teusink B, Bruggeman FJ, Bachmann H Selection for cell yield does not reduce overflow metabolism in E. coli. bioRxiv. 2021:2021.05.24.445453.Pacheco AR, Moel M, Segrè D. Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems. Nat Commun. 2019;10:103.Article 

Google Scholar 
Gude S, Pherribo GJ, Taga ME. Emergence of metabolite provisioning as a by-product of evolved biological functions. mSystems. 2020;5:e00259–20.CAS 
Article 

Google Scholar 
Campbell K, Herrera-Dominguez L, Correia-Melo C, Zelezniak A, Ralser M. Biochemical principles enabling metabolic cooperativity and phenotypic heterogeneity at the single cell level. Current Opinion in. Syst Biology. 2018;8:97–108.
Google Scholar 
Morris JJ. Black Queen evolution: the role of leakiness in structuring microbial communities. Trends Genetics. 2015;31:475–82.CAS 
Article 

Google Scholar 
van Tatenhove-Pel RJ, Rijavec T, Lapanje A, van Swam I, Zwering E, Hernandez-Valdes JA, et al. Microbial competition reduces metabolic interaction distances to the low µm-range. ISME J. 2021;15:688–701.Article 

Google Scholar 
Shitut S, Ahsendorf T, Pande S, Egbert M, Kost C. Nanotube-mediated cross-feeding couples the metabolism of interacting bacterial cells. Environ Microbiol. 2019;21:1306–20.CAS 
Article 

Google Scholar 
Klee SM, Sinn JP, Finley M, Allman EL, Smith PB, Aimufua O, et al. Erwinia amylovora auxotrophic mutant exometabolomics and virulence on apples. Appl Environ Microbiol. 2019;85:e00935–19.CAS 
Article 

Google Scholar 
Hibbing ME, Fuqua C, Parsek MR, Peterson SB. Bacterial competition: surviving and thriving in the microbial jungle. Nat Rev Microbiol. 2010;8:15–25.CAS 
Article 

Google Scholar 
Wienhausen G, Noriega-Ortega BE, Niggemann J, Dittmar T, Simon M The exometabolome of two model strains of the Roseobacter group: A marketplace of microbial metabolites. Front Microbiol. 2017;8.Pinu FR, Granucci N, Daniell J, Han T-L, Carneiro S, Rocha I, et al. Metabolite secretion in microorganisms: the theory of metabolic overflow put to the test. Metabolomics. 2018;14:43.Article 

Google Scholar 
Shiio I, Ôtsuka S-I, Takahashi M. Effect of biotin on the bacterial formation of glutamic acid: I. Glutamate formation and cellular permeability of amino acids. J Biochem. 1962;51:56–62.CAS 
Article 

Google Scholar 
Konings WN, Poolman B, Driessen AJM. Can the excretion of metabolites by bacteria be manipulated? FEMS Microbiol Rev. 1992;8:93–108.CAS 
Article 

Google Scholar 
Zampieri M, Hörl M, Hotz F, Müller NF, Sauer U. Regulatory mechanisms underlying coordination of amino acid and glucose catabolism in Escherichia coli. Nat Commun. 2019;10:3354.Article 

Google Scholar 
Kochanowski K, Okano H, Patsalo V, Williamson J, Sauer U, Hwa T. Global coordination of metabolic pathways in Escherichia coli by active and passive regulation. Mol Syst Biology. 2021;17:e10064.CAS 
Article 

Google Scholar 
Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 1999;27:29–34.CAS 
Article 

Google Scholar 
Baba T, Ara T, Hasegawa M, Takai Y, Okumura Y, Baba M, et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol Syst Biology. 2006;2:2006.0008.Article 

Google Scholar 
Thomason LC, Costantino N, Court DL E. coli genome manipulation by P1 transduction. Curr Protocols Mol Biology. 2007;79:1.17.1-1.8.Pande S, Shitut S, Freund L, Westermann M, Bertels F, Colesie C, et al. Metabolic cross-feeding via intercellular nanotubes among bacteria. Nat Commun. 2015;6:6238.CAS 
Article 

Google Scholar 
Oña L, Giri S, Avermann N, Kreienbaum M, Thormann KM, Kost C. Obligate cross-feeding expands the metabolic niche of bacteria. Nat Ecology Evolut. 2021;5:1224–32.Article 

Google Scholar 
Choi K-H, Gaynor JB, White KG, Lopez C, Bosio CM, Karkhoff-Schweizer RR, et al. A Tn7-based broad-range bacterial cloning and expression system. Nat Methods. 2005;2:443–8.CAS 
Article 

Google Scholar 
Vanstockem M, Michiels K, Vanderleyden J, Van Gool AP. Transposon Mutagenesis of Azospirillum brasilense and Azospirillum lipoferum: Physical analysis of Tn5 and Tn5-Mob insertion mutants. Appl Environ Microbiology. 1987;53:410–5.CAS 
Article 

Google Scholar  More

Given Caprinae life history and plausible combinations of mean recruitment and adult female survivorship, we evaluated population persistence and estimated population MVP. The values describing adult female survivorship and recruitment, plus the variability we employed match values found in other populations of Caprinae. We do not pool data across different Caprinae populations or species. Our approach and results directly inform the conservation and management of many Caprinae, especially those for which the acquisition of demographic data remains beyond reach.Our work embodies the characteristics of a high-quality PVA: clear objectives, appropriate demographic data, model structure matching species life histories, stochasticity, examination of extinction probability, appropriate time interval, use of mean values and associated variability6. As with most ecological models, the quest for more data remains problematic, not debilitating, and is addressed by creatively and aptly using existing information to generate meaningful results3.Wildlife agencies generate lamb:adult female ratios from Caprinae surveys, recognizing that yearlings can be mistaken for adult females, causing miscounts. Excluding yearlings from the ratio’s denominator assumes that no miscounts are occurring, yet an unknown and inconsistent number of yearlings remain in the adult female category across survey events. For these reasons, surveyors of other species, like Dall’s sheep and caribou, pool counts of yearlings and adult females, generating lamb:“adult female-like” ratios instead15,23,24,25.Managers of Caprinae populations can follow these precedents and produce lamb:(adult female + yearling) ratios. Consistency would help standardize methods for building comparisons and meta-analyses across populations of Caprinae, while reducing variability across surveys due to differing techniques.Typically, metrics like elasticity (proportional) and sensitivity (additive) describe the influences of demographic parameters on population growth13,14,22,26. For Caprinae, when adult female survivorship is 0.90 and recruitment 0.30, the elasticity in survivorship and recruitment are 0.61 (90% CIs 0.40–0.75) and 0.24 (90% CIs 0.13–0.40) respectively (elasticity in young adult survivorship is 0.16 (90% CIs 0.12–0.21). For ungulates in general, the elasticity values for survival tend to be higher than those for recruitment27. Our results match this pattern, as the elasticity results indicate that a change in adult survival has a 2.5 times greater effect on λ than an equivalent change in recruitment. Relatedly, other theoretical work reports that demographic parameters with more temporal variability have lower elasticities, indicating less impact on population fitness (e.g.28,29).Our work centers on applications. Since most management actions affect these demographic parameters simultaneously, at issue is the practicality (e.g. feasibility and affordability) of management to increase these parameters, and understanding how such changes could impact λ. For example, imagine a population with mean recruitment of 0.30 and adult survival 0.85, with a biologist interested in increasing recruitment or adult female survival to acquire λ ≥ 1. The answer is to increase either value by 0.02 (Fig. 1, Supplementary Data S1). Similarly, one can set a λ target and determine the amount of recruitment and adult female survival necessary for acquiring it (Fig. 1, Supplementary Data S1).Minimum abundance targetA minimum population of 50 adult females meets the persistence criteria, given intermediate levels of recruitment and survival producing λ ~ 1 (Table 2). The risk of population collapse wanes as populations increase above the minimum threshold (Table 2; Fig. 1). For example, a population of ~ 100 adult females always meets persistence criteria (Table 2). Populations of adult females should be somewhat larger than 50 when modest declines (λ ~ 0.97) are suspected, providing a cushion to address the causes of decline, and mitigate further reductions.Translocation of 5 adult females during each of 5 years, or 10 in each of 3 years, requires a starting abundance of 70 adult females for the population to maintain the persistence criteria, never reach a lower confidence interval of 0, and for the population to return to the starting population size within 30 years. If managers mistakenly target a population having  More

Gregg T. M., Mead L., Burns J. H., Takabayashi M. Puka mai he ko ‘a: the significance of corals in Hawaiian culture. In: Ethnobiology of Corals and Coral Reefs). (Springer, 2015).Chevin, L.-M., Lande, R. & Mace, G. M. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLoS Biol. 8, e1000357 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hochachka P. W., Somero G. N. Biochemical adaptation: mechanism and process in physiological evolution. (Oxford university press, 2002).Munday, P. L., Warner, R. R., Monro, K., Pandolfi, J. M. & Marshall, D. J. Predicting evolutionary responses to climate change in the sea. Ecol. Lett. 16, 1488–1500 (2013).PubMed 
Article 

Google Scholar 
Seebacher, F., White, C. R. & Franklin, C. E. Physiological plasticity increases resilience of ectothermic animals to climate change. Nat. Clim. Change 5, 61–66 (2015).ADS 
Article 

Google Scholar 
Coles, S. L., Jokiel, P. L. & Lewis, C. R. Thermal tolerance in tropical versus subtropical Pacific reef corals. Pac. Sci. 30, 159–166 (1976).
Google Scholar 
Pandolfi, J. M. et al. Global trajectories of the long-term decline of coral reef ecosystems. Science 301, 955–958 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hoegh-Guldberg, O. et al. Coral reefs under rapid climate change and ocean acidification. Science 318, 1737–1742 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
LaJeunesse, T. C. et al. Systematic Revision of Symbiodiniaceae Highlights the Antiquity and Diversity of Coral Endosymbionts. Curr. Biol. 28, 2570–2580 (2018).CAS 
PubMed 
Article 

Google Scholar 
Glynn, P. W. Coral reef bleaching: ecological perspectives. Coral Reefs 12, 1–17 (1993).ADS 
Article 

Google Scholar 
Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Van Hooidonk, R. et al. Local-scale projections of coral reef futures and implications of the Paris Agreement. Sci. Rep. 6, 1–8 (2016).Article 
CAS 

Google Scholar 
Palumbi, S. R., Barshis, D. J., Traylor-Knowles, N. & Bay, R. A. Mechanisms of reef coral resistance to future climate change. Science 344, 895–898 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Silverstein, R. N., Cunning, R. & Baker, A. C. Change in algal symbiont communities after bleaching, not prior heat exposure, increases heat tolerance of reef corals. Glob. Change Biol. 21, 236–249 (2015).ADS 
Article 

Google Scholar 
Ziegler, M. et al. Coral bacterial community structure responds to environmental change in a host-specific manner. Nat. Commun. 10, 1–11 (2019).ADS 
CAS 
Article 

Google Scholar 
Rivera, H. E. et al. A framework for understanding gene expression plasticity and its influence on stress tolerance. Mol. Ecol. 30, 1381–1397 (2021).CAS 
PubMed 
Article 

Google Scholar 
Drury C. & Lirman D. Genotype by environment interactions in coral bleaching. Proceedings of the Royal Society B: Biological Sciences 288, 20210177 (2021).Drury, C., Manzello, D. & Lirman, D. Genotype and local environment dynamically influence growth, disturbance response and survivorship in the threatened coral, Acropora cervicornis. PLoS ONE 12, e0174000 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Todd, P. A. Morphological plasticity in scleractinian corals. Biol. Rev. 83, 315–337 (2008).PubMed 
Article 

Google Scholar 
Eirin-Lopez J. M. & Putnam H. M. Marine environmental epigenetics. Annual review of marine science 11, 335–368 (2019).Putnam, H. M., Davidson, J. M. & Gates, R. D. Ocean acidification influences host DNA methylation and phenotypic plasticity in environmentally susceptible corals. Evolut. Appl. 9, 1165–1178 (2016).CAS 
Article 

Google Scholar 
Dixon, G., Liao, Y., Bay, L. K. & Matz, M. V. Role of gene body methylation in acclimatization and adaptation in a basal metazoan. Proc. Natl Acad. Sci. 115, 13342–13346 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rodriguez‐Casariego, J. A., Cunning, R., Baker, A. C. & Eirin‐Lopez, J. M. Symbiont shuffling induces differential DNA methylation responses to thermal stress in the coral Montastraea cavernosa. Mol. Ecol. 31, 588–602 (2022).PubMed 
Article 
CAS 

Google Scholar 
Meyer, E., Aglyamova, G. & Matz, M. Profiling gene expression responses of coral larvae (Acropora millepora) to elevated temperature and settlement inducers using a novel RNA‐Seq procedure. Mol. Ecol. 20, 3599–3616 (2011).CAS 
PubMed 

Google Scholar 
Barshis, D. J. et al. Genomic basis for coral resilience to climate change. Proc. Natl Acad. Sci. 110, 1387–1392 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dixon, G. B. et al. Genomic determinants of coral heat tolerance across latitudes. Science 348, 1460–1462 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Dixon, G., Abbott, E. & Matz, M. Meta‐analysis of the coral environmental stress response: Acropora corals show opposing responses depending on stress intensity. Mol. Ecol. 29, 2855–2870 (2020).CAS 
PubMed 
Article 

Google Scholar 
Traylor-Knowles, N., Rose, N. H., Sheets, E. A. & Palumbi, S. R. Early transcriptional responses during heat stress in the coral Acropora hyacinthus. Biol. Bull. 232, 91–100 (2017).CAS 
PubMed 
Article 

Google Scholar 
Majerová, E., Carey, F. C., Drury, C. & Gates, R. D. Preconditioning improves bleaching tolerance in the reef‐building coral Pocillopora acuta through modulations in the programmed cell death pathways. Mol. Ecol. 30, 3560–3574 (2021).PubMed 
Article 
CAS 

Google Scholar 
Vidal-Dupiol, J. et al. Thermal stress triggers broad Pocillopora damicornis transcriptomic remodeling, while Vibrio coralliilyticus infection induces a more targeted immuno-suppression response. PLoS ONE 9, e107672 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Middlebrook, R., Hoegh-Guldberg, O. & Leggat, W. The effect of thermal history on the susceptibility of reef-building corals to thermal stress. J. Exp. Biol. 211, 1050–1056 (2008).PubMed 
Article 

Google Scholar 
Bellantuono, A. J., Granados-Cifuentes, C., Miller, D. J., Hoegh-Guldberg, O. & Rodriguez-Lanetty, M. Coral thermal tolerance: tuning gene expression to resist thermal stress. PLoS ONE 7, e50685 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bay, R. A. & Palumbi, S. R. Rapid acclimation ability mediated by transcriptome changes in reef-building corals. Genome Biol. Evol. 7, 1602–1612 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ainsworth, T. D. et al. Climate change disables coral bleaching protection on the Great Barrier Reef. Science 352, 338–342 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
van Oppen, M. J., Oliver, J. K., Putnam, H. M. & Gates, R. D. Building coral reef resilience through assisted evolution. Proc. Natl Acad. Sci. 112, 2307–2313 (2015).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
National Academies of Sciences E, and Medicine. A Research Review of Interventions to Increase the Persistence and Resilience of Coral Reefs. (The National Academies Press, 2019).Kellett M., Hoffmann A. A., Mckechnie S. W. Hardening capacity in the Drosophila melanogaster species group is constrained by basal thermotolerance. Funct. Ecol. 19, 853–858 (2005).Gerken, A. R., Eller, O. C., Hahn, D. A. & Morgan, T. J. Constraints, independence, and evolution of thermal plasticity: probing genetic architecture of long-and short-term thermal acclimation. Proc. Natl Acad. Sci. 112, 4399–4404 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Calosi, P., Bilton, D. T. & Spicer, J. I. Thermal tolerance, acclimatory capacity and vulnerability to global climate change. Biol. Lett. 4, 99–102 (2008).PubMed 
Article 

Google Scholar 
Nyamukondiwa, C., Terblanche, J. S., Marshall, K. & Sinclair, B. Basal cold but not heat tolerance constrains plasticity among Drosophila species (Diptera: Drosophilidae). J. Evolut. Biol. 24, 1927–1938 (2011).CAS 
Article 

Google Scholar 
Bellantuono, A. J., Hoegh-Guldberg, O. & Rodriguez-Lanetty, M. Resistance to thermal stress in corals without changes in symbiont composition. Proc. R. Soc. Lond. B: Biol. Sci. 279, 1100–1107 (2011).
Google Scholar 
DeMerlis, A. et al. Pre-exposure to a variable temperature treatment improves the response of Acropora cervicornis to acute thermal stress. Coral Reefs, 41, 1–11 (2022).Oliver, T. & Palumbi, S. Do fluctuating temperature environments elevate coral thermal tolerance? Coral Reefs 30, 429–440 (2011).ADS 
Article 

Google Scholar 
Klepac, C. & Barshis, D. Reduced thermal tolerance of massive coral species in a highly variable environment. Proc. R. Soc. B 287, 20201379 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bahr, K. D., Jokiel, P. L. & Rodgers, K. S. The 2014 coral bleaching and freshwater flood events in Kāneʻohe Bay, Hawaiʻi. PeerJ 3, e1136 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Cunning, R., Ritson-Williams, R. & Gates, R. D. Patterns of bleaching and recovery of Montipora capitata in Kāne’ohe Bay, Hawai’i, USA. Mar. Ecol. Prog. Ser. 551, 131–139 (2016).ADS 
CAS 
Article 

Google Scholar 
Innis, T., Cunning, R., Ritson-Williams, R., Wall, C. & Gates, R. Coral color and depth drive symbiosis ecology of Montipora capitata in Kāne’ohe Bay, O’ahu, Hawai’i. Coral Reefs 37, 423–430 (2018).ADS 
Article 

Google Scholar 
Wall C. B., Ritson-Williams R., Popp B. N., Gates R. D. Spatial variation in the biochemical and isotopic composition of corals during bleaching and recovery. Limnol. Oceanogr. 64, 2011–2028 (2019).Ritson-Williams, R. & Gates, R. D. Coral community resilience to successive years of bleaching in Kane ‘ohe Bay, Hawai ‘i. Coral Reefs 10, 757–769 (2020).Article 

Google Scholar 
Drury, C. et al. Intrapopulation adaptive variance supports thermal tolerance in a reef-building coral. Commun. Biol. 5, 1–10 (2022).Article 
CAS 

Google Scholar 
Dilworth J., Caruso C., Kahkejian V. A., Baker A. C., Drury C. Host genotype and stable differences in algal symbiont communities explain patterns of thermal stress response of Montipora capitata following thermal pre-exposure and across multiple bleaching events. Coral Reefs 40, 151–163 (2020).Pinzón, J. H. et al. Whole transcriptome analysis reveals changes in expression of immune-related genes during and after bleaching in a reef-building coral. R. Soc. Open Sci. 2, 140214 (2015).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Thomas, L. & Palumbi, S. R. The genomics of recovery from coral bleaching. Proc. R. Soc. Lond. B: Biol. Sci. 284, 20171790 (2017).
Google Scholar 
Bertucci, A., Foret, S., Ball, E. & Miller, D. J. Transcriptomic differences between day and night in Acropora millepora provide new insights into metabolite exchange and light‐enhanced calcification in corals. Mol. Ecol. 24, 4489–4504 (2015).CAS 
PubMed 
Article 

Google Scholar 
Drury, C. Resilience in reef-building corals: the ecological and evolutionary importance of the host response to thermal stress. Mol. Ecol. 00, 1–18 (2019).CAS 

Google Scholar 
Whitehead, A. & Crawford, D. L. Neutral and adaptive variation in gene expression. Proc. Natl Acad. Sci. 103, 5425–5430 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kenkel, C. D. & Matz, M. V. Gene expression plasticity as a mechanism of coral adaptation to a variable environment. Nat. Ecol. Evol. 1, 1–6 (2016).
Google Scholar 
Cunning, R. & Baker, A. C. Thermotolerant coral symbionts modulate heat stress‐responsive genes in their hosts. Mol. Ecol. 29, 2940–2950 (2020).CAS 
PubMed 
Article 

Google Scholar 
van Heerwaarden, B. & Kellermann, V. Does plasticity trade off with basal heat tolerance? Trends Ecol. Evol. 35, 874–885 (2020).PubMed 
Article 

Google Scholar 
Sasaki, M. C. & Dam, H. G. Negative relationship between thermal tolerance and plasticity in tolerance emerges during experimental evolution in a widespread marine invertebrate. Evolut. Appl. 14, 2114–2123 (2021).Article 

Google Scholar 
Roach T.N., Dilworth J., Jones A.D., Quinn R.A., Drury C. Metabolomic signatures of coral bleaching history. Nat. Ecol. Evol. 5, 1–9 (2021).Snider, J., Thibault, G. & Houry, W. A. The AAA+ superfamily of functionally diverse proteins. Genome Biol. 9, 1–8 (2008).Article 
CAS 

Google Scholar 
Moon, S. Y. & Zheng, Y. Rho GTPase-activating proteins in cell regulation. Trends Cell Biol. 13, 13–22 (2003).CAS 
PubMed 
Article 

Google Scholar 
Hobbs, G. A., Zhou, B., Cox, A. D. & Campbell, S. L. Rho GTPases, oxidation, and cell redox control. Small GTPases 5, e28579 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Majerová E., Drury C. A BI-1 mediated cascade improves redox homeostasis during thermal stress and prevents oxidative damage in a preconditioned reef-building coral. bioRxiv, (2021).Coleman, M. & Olson, M. Rho GTPase signalling pathways in the morphological changes associated with apoptosis. Cell Death Differ. 9, 493–504 (2002).CAS 
PubMed 
Article 

Google Scholar 
Opalińska, M. & Jańska, H. AAA proteases: guardians of mitochondrial function and homeostasis. Cells 7, 163 (2018).PubMed Central 
Article 
CAS 

Google Scholar 
Matsuda, S. et al. Coral bleaching susceptibility is predictive of subsequent mortality within but not between coral species. Front. Ecol. Evol. 8, 1–14 (2020).Barott, K. L. et al. Coral bleaching response is unaltered following acclimatization to reefs with distinct environmental conditions. Proc. Natl Acad. Sci. 118, 1–8 (2021).Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).Article 

Google Scholar 
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Williams, A. et al. Multi-omic characterization of the thermal stress phenome in the stony coral Montipora capitata. PeerJ 9, e12335 (2021).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shumaker, A. et al. Genome analysis of the rice coral Montipora capitata. Sci. Rep. 9, 2571 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kriventseva, E. V. et al. OrthoDB v10: sampling the diversity of animal, plant, fungal, protist, bacterial and viral genomes for evolutionary and functional annotations of orthologs. Nucleic Acids Res. 47, D807–D811 (2019).CAS 
PubMed 
Article 

Google Scholar 
Hunter, S. et al. InterPro: the integrative protein signature database. Nucleic Acids Res. 37, D211–D215 (2009).CAS 
PubMed 
Article 

Google Scholar 
Bateman, A. et al. The Pfam protein families database. Nucleic Acids Res. 32, D138–D141 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Leggat, W., Heron, S. F., Fordyce, A., Suggett, D. J. & Ainsworth, T. D. Experiment Degree Heating Week (eDHW) as a novel metric to reconcile and validate past and future global coral bleaching studies. J. Environ. Manag. 301, 113919 (2022).Article 

Google Scholar 
Ritz, C., Baty, F., Streibig, J. C. & Gerhard, D. Dose-response analysis using R. PloS ONE 10, e0146021 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).Article 
CAS 

Google Scholar 
Philip, D. VEGAN, a package of R functions for community ecology. Journal of Vegetation Science 14, 927–930 (2003).Wright, R. M. et al. Positive genetic associations among fitness traits support evolvability of a reef-building coral under multiple stressors. Glob. Change Biol. 25, 3294–3304 (2019).ADS 
Article 

Google Scholar 
Drury C., Dilworth J., Majerová E., Caruso C., Greer J. B. Expression plasticity regulates intraspecific variation in the acclimatization potential of a reef-building coral [dataset]. Zenodo https://doi.org/10.5281/zenodo.6877825 (2022). More

Tittensor, D. P. et al. A mid-term analysis of progress toward international biodiversity targets. Science 346, 241–243 (2014).CAS 
Article 

Google Scholar 
IPBES Secretariat Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science—Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019).Bruner, A. G., Gullison, R. E., Rice, R. E. & Fonseca, G. A. Bda Effectiveness of parks in protecting tropical biodiversity. Science 291, 125–128 (2001).CAS 
Article 

Google Scholar 
Geldmann, J., Joppa, L. N. & Burgess, N. D. Mapping change in human pressure globally on land and within protected areas. Conserv. Biol. 28, 1604–1616 (2014).Article 

Google Scholar 
Laurance, W. F. et al. Averting biodiversity collapse in tropical forest protected areas. Nature 489, 290–293 (2012).CAS 
Article 

Google Scholar 
Conference of the Parties, The Strategic Plan for Biodiversity 2011–2020 and the Aichi Biodiversity Targets, COP-10 Decision X/2 (CBD, 2010).Protected Planet Report 2018 (UNEP-WCMC IUCN & NGS, 2018).Craigie, I. D. et al. Large mammal population declines in Africa’s protected areas. Biol. Conserv. 143, 2221–2228 (2010).Article 

Google Scholar 
Joppa, L. N., Bailie, J. E. M. & Robinson, J. G. Protected Areas: Are They Safeguarding Biodiversity?. (Wiley Blackwell, 2016).Book 

Google Scholar 
Rada, S. et al. Protected areas do not mitigate biodiversity declines: a case study on butterflies. Divers. Distrib. 25, 217–224 (2019).Article 

Google Scholar 
Jetz, W., McPherson, J. M. & Guralnick, R. P. Integrating biodiversity distribution knowledge: toward a global map of life. Trends Ecol. Evol. 27, 151–159 (2012).Article 

Google Scholar 
Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238 (2013).Article 

Google Scholar 
Kindsvater, H. K. et al. Overcoming the data crisis in biodiversity conservation. Trends Ecol. Evol. 33, 676–688 (2018).Article 

Google Scholar 
Sutherland, W. J., Pullin, A. S., Dolman, P. M. & Knight, T. M. The need for evidence-based conservation. Trends Ecol. Evol. 19, 305–308 (2004).Article 

Google Scholar 
Ferraro, P. J. & Pattanayak, S. K. Money for nothing? A call for empirical evaluation of biodiversity conservation investments. PLoS Biol. 4, 482–488 (2006).CAS 
Article 

Google Scholar 
Polaina, E., González-Suárez, M. & Revilla, E. Socioeconomic correlates of global mammalian conservation status. Ecosphere 6, 1–34. (2015).Article 

Google Scholar 
Ferraro, P. J. & Pressey, R. L. Measuring the difference made by conservation initiatives: protected areas and their environmental and social impacts. Philos. Trans. R. Soc. Lond. Biol. Sci. 370, 20140270 (2015).Article 

Google Scholar 
Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl Acad. Sci. U.S.A. 116, 23209–23215 (2019).CAS 
Article 

Google Scholar 
McGinnis, M. D. & Ostrom, E. Social-ecological system framework: initial changes and continuing challenges. Ecol. Soc. 19, 30 (2014).Article 

Google Scholar 
Barnes, M. D. et al. Wildlife population trends in protected areas predicted by national socio-economic metrics and body size. Nat. Commun. 7, 12747 (2016).CAS 
Article 

Google Scholar 
Palomo, I. et al. Incorporating the social-ecological approach in protected areas in the anthropocene. BioScience 64, 181–191 (2014).Article 

Google Scholar 
Poteete, A. R., Janssen, M. A., & Ostrom, E. Working Together: Collective Action, the Commons, and Multiple Methods in Practice (Princeton Univ. Press, 2010).Wilson, D. S., Ostrom, E. & Cox, M. E. Generalizing the core design principles for the efficacy of groups. J. Econ. Behav. Organ. 90, S21–S32 (2013).Article 

Google Scholar 
Tebet, G., Trimble, M. & Pereira Medeiros, R. Using Ostrom’s principles to assess institutional dynamics of conservation: lessons from a marine protected area in Brazil. Mar. Policy 88, 174–181 (2018).Article 

Google Scholar 
Ban, N. C. et al. Social and ecological effectiveness of large marine protected areas. Glob. Environ. Change 43, 82–91 (2017).Article 

Google Scholar 
Fleischman, F. D. et al. Governing large-scale social-ecological systems: lessons from five cases. Int. J. Commons 8, 428–456 (2014).Article 

Google Scholar 
Faff, R., Ho, Y. K., Lin, W. & Yap, C. M. Diminishing marginal returns from R&D investment: evidence from manufacturing firms. Appl. Econ. 45, 611–622 (2013).Article 

Google Scholar 
Gill, D. A. et al. Capacity shortfalls hinder the performance of marine protected areas globally. Nature 543, 665–669 (2017).CAS 
Article 

Google Scholar 
Bowles, S. & Polanía-Reyes, S. Economic incentives and social preferences: substitutes or complements? J. Econ. Lit. 50, 368–425 (2012).Article 

Google Scholar 
Irwin, K., Mulder, L. & Simpson, B. The detrimental effects of sanctions on intragroup trust: comparing punishments and rewards. Soc. Psychol. Q. 77, 253–272 (2014).Article 

Google Scholar 
Pacifici, M. et al. Assessing species vulnerability to climate change. Nat. Clim. Change 5, 215–225. (2015).Article 

Google Scholar 
Urban, M. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).CAS 
Article 

Google Scholar 
Lovett, G. M. et al. Effects of air pollution on ecosystems and biological diversity in the eastern United States. Ann. N. Y. Acad. Sci. 1162, 99–135 (2009).CAS 
Article 

Google Scholar 
Backhaus, T., Snape, J. & Lazorchak, J. The impact of chemical pollution on biodiversity and ecosystem services: the need for an improved understanding. Integr. Environ. Assess. Manag. 8, 575–576 (2012).CAS 
Article 

Google Scholar 
Benítez-López, A. et al. The impact of hunting on tropical mammal and bird populations. Science 356, 180–183 (2017).Article 
CAS 

Google Scholar 
Calabrese, A. et al. Conservation status of Asian elephants: the influence of habitat and governance. Biodivers. Conserv. 26, 2067–2081 (2017).Article 

Google Scholar 
Shaffer, L. J., Khadka, K. K., Van Den Hoek, J. & Naithani, K. J. Human–elephant conflict: a review of current management strategies and future directions. Front. Ecol. Evol. 6, 235 (2019).Article 

Google Scholar 
Klaassen, R. H. G. et al. When and where does mortality occur in migratory birds? Direct evidence from long-term satellite tracking of raptors. J. Anim. Ecol. 83, 176–184 (2014).Article 

Google Scholar 
Güneralp, P. & Seto, K. C. Futures of global urban expansion: uncertainties and implications for biodiversity conservation. Environ. Res. Lett. 8, 014025 (2013).Article 

Google Scholar 
Sherry, T.W., Johnson, M.D. & Strong, A. in Birds of Two Worlds. The Ecology and Evolution of Migration (eds Greenberg, R. & Marra, P. P.) 414–425 (The John Hopkins Univ. Press, 2005).Sanderson, F. J., Donald, P. F., Pain, D. J., Burfield, I. J. & Van Bommel, F. P. Long-term population declines in Afro-Palearctic migrant birds. Biol. Conserv. 131, 93–105 (2006).Article 

Google Scholar 
Runge, C. A. et al. Protected areas and global conservation of migratory birds. Science 350, 1255–1258 (2015).CAS 
Article 

Google Scholar 
Balme, G. A., Slotow, R. & Hunter, L. T. B. Edge effects and the impact of non-protected areas in carnivore conservation: leopards in the Phinda-Mkhuze Complex, South Africa. Anim. Conserv. 13, 315–323 (2010).Article 

Google Scholar 
Chase, J. M., Blowes, S. A., Knight, T. M., Gerstner, K. & May, F. Ecosystem decay exacerbates biodiversity loss with habitat loss. Nature 584, 238–243 (2020).CAS 
Article 

Google Scholar 
Ostrom, E. Governing the Commons: The Evolution of Institutions for Collective Action (Cambridge Univ. Press, 1990).Lacroix, K. & Richards, G. An alternative policy evaluation of the British Columbia carbon tax: broadening the application of Elinor Ostrom’s design principles for managing common-pool resources. Ecol. Soc. 20, 38 (2015).Article 

Google Scholar 
Bennett, N. J. et al. Mainstreaming the social sciences in conservation. Conserv. Biol. 31, 56–66 (2017).Article 

Google Scholar 
Dasgupta, P. The Economics of Biodiversity: The Dasgupta Review (HM Treasury, 2021).Resasco, J. Meta-analysis on a decade of testing corridor efficacy: what new have we learned? Curr. Landsc. Ecol. Rep. 4, 61–69 (2019).Article 

Google Scholar 
Andrade, G. S. M. & Rhodes, J. R. Protected areas and local communities: an inevitable partnership toward successful conservation strategies? Ecol. Soc. https://doi.org/10.5751/ES-05216-170414 (2012).Morell, V. Massive wolf kill disrupts long-running Yellowstone Park study. Science 375, 482–482 (2022).CAS 
Article 

Google Scholar 
Post, G. & Geldmann, J. Exceptional responders in conservation. Conserv. Biol. 32, 576–583 (2018).Article 

Google Scholar 
Wauchope, H. S. et al. Protected areas have a mixed impact on waterbirds, but management helps. Nature 605, 103–107 (2022).CAS 
Article 

Google Scholar 
Ostrom, E. A general framework for analyzing sustainability of social–ecological systems. Science 325, 419–422 (2009).CAS 
Article 

Google Scholar 
Kline, M. A., Waring, T. M. & Salerno, J. D. Designing cultural multilevel selection research for sustainability science. Sustainability Sci. 13, 9–19 (2017).Article 

Google Scholar 
Lindsey, P. A. et al. The performance of African protected areas for lions and their prey. Biol. Conserv. 209, 137–149 (2017).Article 

Google Scholar 
The World Database on Protected Areas (WDPA) (IUCN & UNEP‐WCMC, 2018); https://www.protectedplanet.net/en/search-areas?geo_type=country&filters%5Bdb_type%5D%5B%5D=wdpaCoad, L. et al. Measuring impact of protected area management interventions: current and future use of the global database of protected area management effectiveness. Phil. Trans. R. Soc. B 370, 20140281 (2015).Article 

Google Scholar 
Geldmann, J. et al. A global analysis of management capacity and ecological outcomes in terrestrial protected areas. Conserv. Lett. 11, e12434 (2018).Article 

Google Scholar 
Living Planet Database (LPD) (Zoological Society of London, 2018); http://www.livingplanetindex.orgKühl, H., Williamson, L., Sanz, C. M., Morgan, D. & Boesch, C. Launch of A.P.E.S. database. Gorilla Journal 34, 20–21 (2007).
Google Scholar 
Koerner, S. E., Poulsen, J. R., Blanchard, E. J., Okouyi, J. & Clark, C. J. Vertebrate community composition and diversity declines along a defaunation gradient radiating from rural villages in Gabon. J. Appl. Ecol. 54, 805–814 (2017).Article 

Google Scholar 
Bauer, H. et al. Lion (Panthera leo) populations are declining rapidly across Africa, except in intensively managed areas. Proc. Natl Acad. Sci. U.S.A. 112, 14894–14899 (2015).CAS 
Article 

Google Scholar 
Barr, D., Levy, R., Scheepers, C. & Tily, H. J. Random effects structure for confirmatory hypothesis testing: keep it maximal. J. Mem. Lang. 68, 1–43 (2014).
Google Scholar 
Schielzeth, H. & Forstmeier, W. Conclusions beyond support: overconfident estimates in mixed models. Behav. Ecol. 20, 416–420 (2009).Article 

Google Scholar 
McElreath, R. in Statistical Rethinking: A Bayesian Course with Examples in R and Stan (CRC Press, 2016).Bürkner, P. C. (2017). brms: an R package for Bayesian multilevel models using Stan. J. Stat. Software https://doi.org/10.18637/jss.v080.i01 (2017).Schielzeth, H. Simple means to improve the interpretability of regression coefficients. Methods Ecol. Evol. 1, 103–113 (2010).Article 

Google Scholar 
R Core Team R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2019).Gelman, A., Carlin, J. B. B., Stern, H. S. S. & Rubin, D. B. B. Bayesian Data Analysis (CRC Press, 2014).Protected Planet: The World Database on Protected Areas (WDPA) (UNEP-WCMC & IUCN, 2019); www.protectedplanet.netChamberlain, S. rphylopic: Get ‘Silhouettes’ of ‘Organisms’ from ‘Phylopic’. R version 0.3.3.91 https://github.com/sckott/rphylopic (2022). More