Ecology

External datasetsWoody biomass carbon dataThe dataset by ref. 16 maps annual global woody biomass carbon densities for 2000–2019 at a spatial resolution of ~10 km. The annual estimates represent averages for the tropical regions and growing-season (April–October) averages for the extra-tropical regions. Ref. 16 analyse global trends of gains and losses in woody biomass carbon for 2000–2019. Overall, they find that grid cells with (significant) net gains of vegetation carbon are by a factor of 1.4 more abundant than grid cells with net losses of vegetation carbon, indicating that there is a global greening trend when only considering the areal extent of biomass gains and not the magnitude of carbon gains. Their regionally distinct analysis of trends shows that almost all regions, except for the tropical moist forests in South America and parts of Southeast Asia, experienced net gains in biomass carbon. On the country scale, the largest net increase in biomass carbon is shown in China, which is mainly attributed to the large-scale afforestation programs in the southern part of the country and increased carbon uptake of established forests. On the other hand, the largest vegetation carbon losses are shown for Brazil and Indonesia, which is partly attributed to deforestation, degradation, and drought events. All of the mentioned trends have been found to be significant16. The decreasing carbon sink in Brazil is in line with ref. 44, who, considering both natural and anthropogenic fluxes, show that the southeastern Amazon has even turned from a carbon sink to a carbon source, mainly owing to fire emissions from forest clearing. Isolating carbon fluxes in intact, old-growth Amazonian rainforests (i.e., SLAND,B), ref. 45 also find evidence for a significantly decreasing carbon sink due to the negative effects of increasing temperatures and droughts on carbon uptake since the 1990s.The dataset was remapped to the BLUE resolution of 0.25∘ through conservative remapping (i.e., area-weighted averaging).ERA-5 dataThe ERA-5 variables were downloaded from the Copernicus Climate Data Store (https://cds.climate.copernicus.eu/cdsapp#!/home). Monthly air temperature (Ta) at 2 m height was averaged over each year, and annual precipitation was calculated by taking the sum of the monthly total precipitation (P). Both variables were regridded from the original resolution of ~0.1° to 0.25° resp. to the TRENDY resolution of 0.5° through conservative remapping.TRENDY dataWe used the TRENDY model ensemble version 8 (conducted for the 2019 GCB; ref. 8). We used net biome production (NBP) and annual vegetation carbon stocks (cVeg) for 2000–2018 from four different model setups (S2, S3, S5, and S6) and eight resp. 13 DGVMs (depending on the data available). The selection of DGVMs is done as in ref. 19 (Supplementary Tab. 3), but we included one additional model (ISAM) for the S2 simulations. The terrestrial biomass carbon sink (SLAND,B) was calculated for 13 DGVMs following the GCB 2020 approach, i.e., from the S2 simulation, which is the simulation without LULCC (i.e., fixed pre-industrial land cover) under transient environmental conditions (climate, nitrogen deposition, CO2 evolution). SLAND,B is the annual difference of cVeg and makes no statements about the further fate of biomass if cVeg decreases. SLAND,B, therefore, should not be interpreted as equivalent to the flux to/from the atmosphere, since parts of cVeg may be transferred to litter, dead wood, or soil. The same applies to our BLUE estimates of SLAND,B, ensuring comparability between our BLUE estimates and the TRENDY estimates. Increases (decreases) of cVeg between two years are a net uptake (release) of carbon from the terrestrial biosphere. The global sums of biomass carbon stocks under transient climate and CO2 were calculated from the S3 setup (LULCC under historical environmental conditions), whereas the S5 setup provides biomass carbon under constant present-day environmental forcing (closest to the classical bookkeeping approach). In line with the GCB, ELUC was calculated under historical environmental conditions as the difference in NBP between the S2 and S3 simulations (ELUC = NBP_S2 – NBP_S3). ELUC under constant present-day environmental forcing was calculated as the difference in NBP between the S6 (fixed pre-industrial land cover under present-day environmental forcing) and S5 simulations (ELUC = NBP_S6 – NBP_S5)19. All datasets were remapped to a common resolution of 0.5∘ through conservative remapping (area-weighted average) for the data analysis.Assimilation of observed woody biomass carbon in BLUEThe observed woody biomass carbon densities by ref. 16 are assimilated in BLUE in several steps.Carbon transfer in the default setup of BLUEThe BLUE simulation is started in AD 850. Biomass and soil vegetation carbon densities are based on ref. 17, which are converted to exponential time constants. A detailed explanation of the exponential model can be found in ref. 5.While in the default setup, changes are only due to LULCC, our assimilation approach now introduces environmental effects on woody vegetation carbon by assimilating the observed woody biomass carbon densities in BLUE from 2000 onward according to the methodological considerations explained below.Calculation of woody biomass carbon densities for different land cover types and PFTsWithin each 0.25° cell of the global grid, the (remapped) woody biomass carbon density from ref. 16 must be the sum of woody biomass carbon stored in all woody PFTs of all woody land cover types. The distribution of the woody biomass carbon across PFTs and land cover types is achieved by distributing the observed (i.e., actual) woody biomass carbon densities (ρBa) from ref. 16 across the two land cover types (j) and the eight PFTs (l) that can be woody vegetation (primary land, called virgin, “v” in BLUE and secondary, “s”, land) according to the fraction of total woody biomass carbon (fB) contained in each land cover type and each PFT (fB,j,l) as estimated by BLUE. fB,j,l varies for different PFTs and land cover types, depending on their history of LULCC and their potential for carbon uptake (i.e., the potential carbon densities).fB,j,l is extracted from the default simulations for the first year of the time series (i.e., 2000) and calculated for subsequent years from the BLUE simulations using the assimilated woody vegetation carbon densities for that year:$${f}_{B,j,l}(t)=frac{{C}_{B,j,l}(t)}{{C}_{B}(t)}$$
(1)
where CB is the woody biomass carbon stock.Consequently, the assimilated woody biomass carbon stock per cover type and PFT (CB_as,j,l) at each time step can be calculated as:$${C}_{B_as,j,l}(t)={rho }_{Ba}(t);*;A;*;{f}_{B,j,l}(t)$$
(2)
with j{v, s}; l{1. . 8}; t{2000. . 2019}. A is the area per grid cell.Thresholds for excluding inconsistent woody biomass carbon densitiesWe eliminate unrealistically large values for woody biomass carbon densities that our assimilation framework produces. Woody biomass carbon densities in BLUE that exceed the highest value (~374 t ha−1) of the original dataset indicate inconsistencies between the observed woody biomass carbon estimates and the fractional grid cell areas per PFT and land cover types that BLUE simulates. To account for uncertainties related to the criteria for exclusion of grid cells, multiple threshold approaches are applied and the results are compared. To maintain a temporally and spatially consistent time series of woody biomass carbon, grid cells that are excluded according to the chosen threshold approach are interpolated through linear barycentric interpolation. A first approach relies on a uniform upper threshold of More

We focused on the Northern High Latitudes (NHL, latitude > 50°N, excluding Greenland) due to their importance for carbon (CO2-C, the same hereafter)-climate feedbacks in the Earth system. To minimize the potential human influence on the CO2 cycle, we excluded areas under agricultural management (croplands, cropland/natural vegetation mosaic, and urban types), and considered only pixels of natural vegetation defined from the MODIS MCD12Q1 (v006) based IGBP land cover classification. Our main focus was the NHL permafrost region because permafrost plays a critical role in the ecology, environment, and society in the NHL. Permafrost, or permanently frozen ground, is defined as ground (soil, sediment, or rock) that remains at or below 0 °C for at least two consecutive years. The occurrence of permafrost is primarily controlled by temperature and has a strong effect on hydrology, soils, and vegetation composition and structure. Based on the categorical permafrost map from the International Permafrost Association58, the permafrost region (excluding permanent snow/ice and barren land), including sporadic (10–50%), discontinuous (50–90%), and continuous ( >90%) permafrost, encompasses about 15.7 × 106 km2, accounts for 57% of the NHL study dominion, and is dominated by tundra (shrubland and grass) and deciduous needleleaf (i.e., larch) forest that is regionally abundant in Siberia. The NHL non-permafrost region covers about 11.9 × 106 km2 and is dominated by mixed and evergreen needleleaf boreal forests (Fig. S1).Atmospheric CO2 inversions (ACIs)ACIs provide regionally-integrated estimates of surface-to-atmosphere net ecosystem CO2 exchange (NEEACI) fluxes by utilizing atmospheric CO2 concentration measurements and atmospheric transport models59. ACIs differ from each other mainly in their underlying atmospheric observations, transport models, spatial and temporal flux resolutions, land surface models used to predict prior fluxes, observation uncertainty and prior error assignment, and inversion methods. We used an ensemble mean of six different ACI products, each providing monthly gridded NEEACI at 1-degree spatial resolution, including Carbon‐Tracker 2019B (2000-2019, CT2019)60, Carbon‐Tracker Europe 2020 (2000–2019, CTE2020)61, Copernicus Atmosphere Monitoring Service (1979–2019, CAMS)62, Jena CarboScope (versions s76_v4.2 1976–2017, and s85_v4.2 1985-2017)63,64, and JAMSTEC (1996–2017)65. The monthly gridded ensemble mean NEEACI at 1-degree spatial resolution was calculated using the available ACIs from 1980-2017. Monthly ACI ensemble mean NEEACI data were summed to seasonal and annual values, and used to calculate the spatial and temporal trends of net CO2 uptake, and to investigate its relationship to climate and environmental controls.Productivity datasetDirect observations of vegetation productivity do not exist at a circumpolar scale. We therefore used two long-term gridded satellite-based estimates of vegetation productivity, including gross primary production (GPP) derived using a light use efficiency (LUE) approach (LUE GPP, 1982–1985)21,66 and satellite observations of Normalized Difference Vegetation Index (NDVI) from the Global Inventory Modeling and Mapping Studies (GIMMS NDVI, 1982–1985)67. LUE GPP (monthly, 0.5° spatial resolution, 1982–2015) is calculated from satellite observations of NDVI from the Advanced Very High-Resolution Radiometer (AVHRR; 1982 to 2015) combined with meteorological data, using the MOD17 LUE approach. LUE GPP has been extensively validated with a global array of eddy-flux tower sites68,69,70 and tends to provide better estimates in ecosystems with greater seasonal variability at high latitudes. Following66,71, we used the ensemble mean of GPP estimates from three of the most commonly used meteorological data sets: National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis; NASA Global Modeling and Assimilation Office (GMAO) Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2); and European Center for Medium-Range Weather Forecasting (ECMWF). GIMMS NDVI (bimonthly, 1/12 spatial resolution, 1982–2015) provides the longest satellite observations of vegetation “greenness”, and is widely used in studies of phenology, productivity, biomass, and disturbance monitoring as it has proven to be an effective surrogate of vegetation photosynthetic activity72.The gridded GPP data were resampled to 1-degree resolution at monthly time scales, to be consistent with NEEACI, and used to test (H1) whether greater temperature sensitivity of vegetation productivity explains the different trends in net CO2 uptake across the NHL. LUE GPP was also used to calculate monthly total ecosystem respiration (TER) as the difference between GPP and NEEACI (i.e., TERresidual =  GPP– NEEACI) from 1982-2015, as global observations of respiration do not exist. The NEEACI, GPP and TERresidual were used as observation-constrained top-down CO2 fluxes to investigate mechanisms underlying the seasonal CO2 dynamics in the structural equation modeling and additional decision tree-based analysis.Eddy Covariance (EC) measurements of bottom-up CO2 fluxesA total of 48 sites with at least three years of data representing the major NHL ecosystems were obtained from the FLUXNET2015 database (Table S1 and Fig. S1). EC measurements provide direct observations of net ecosystem CO2 exchange (NEE) and estimate the GPP and TER flux components of NEE using other climate variables. Daily GPP and TER were estimated as the mean value from both the nighttime partitioning method73 and the light response curve method74. More details on the flux partitioning and gap-filling methods used are provided by75. Daily fluxes were summed into seasonal and annual values and used to compare with trends from ACIs (Fig. S7), to estimate the climate and environmental controls on the CO2 cycle in the pathway analysis (Fig. 5), and to calculate the net CO2 uptake sensitivity to spring temperature (Fig. S14).Ensemble of dynamic global vegetation models (TRENDY simulations)The TRENDY intercomparison project compiles simulations from state-of-the-art dynamic global vegetation models (DGVMs) to evaluate terrestrial energy, water, and net CO2 exchanges76. The DGVMs provide a bottom-up approach to evaluate terrestrial CO2 fluxes (e.g., net biome production [NBP]) and allow deeper insight into the mechanisms driving changes in carbon stocks and fluxes. We used monthly NBP, GPP, and TER (autotrophic + heterotrophic respiration; Ra + Rh) from ten TRENDY v7 DGVMs76, including CABLE-POP, CLM5.0, OCN, ORCHIDEE, ORCHIDEE-CNP, VISIT, DLEM, LPJ, LPJ-GUESS, and LPX. We analyzed the “S3” simulations that include time-varying atmospheric CO2 concentrations, climate, and land use. All simulations were based on climate forcing from the CRU-NCEPv4 climate variables at 6-hour resolution. CO2 flux outputs were summarized monthly at 1-degree spatial resolution from 1980 to 2017. Monthly ensemble mean NBP, GPP, and TER were summed to seasonal and annual values, and then used to compare with observation-constrained ACI top-down CO2 fluxes (Figs. 4 and 5).Satellite data-driven carbon flux estimates (SMAP L4C)We also used a much finer spatio-temporal simulation of carbon fluxes from the NASA Soil Moisture Active Passive (SMAP) mission Level 4 Carbon product (L4C) to quantify the temperature and moisture sensitivity of NHL CO2 exchange77. The SMAP L4C provides global operational daily estimates of NEE and component CO2 fluxes for GPP and TER at 9 km resolution since 2015; whereas, an offline version of the L4C model provides a similar Nature Run (NR) carbon flux record over a longer period (2000-present), but without the influence of SMAP observational inputs. The L4C model has been calibrated against FLUXNET tower CO2 flux measurements and shows favorable performance and accuracy in high latitude regions4,77. In this analysis, daily gridded CO2 fluxes at 9-km resolution from the L4C NR record were summed to seasonal and annual values, and used to calculate the sensitivity of net C uptake in response to spring temperature (Fig. S14).CO2 fluxes in this analysis are defined with respect to the biosphere so that a positive value indicates the biosphere is a net sink of CO2 absorbed from the atmosphere. The different data products described above use different terminology (e.g., NEE, NBP) with slightly different meanings; however, they all provide estimates of net land-atmosphere CO2 exchange78.Climate, tree cover, permafrost, and soil moisture dataMonthly gridded air temperatures at 0.5-degree spatial resolution from 1980 to 2017 were obtained from the Climate Research Unit (CRU TS v4.02) at the University of East Anglia79. Air temperature was summarized at seasonal and annual scales to calculate temperature sensitivities of net CO2 uptake and to investigate the mechanism underlying the seasonal CO2 dynamics.Percent tree cover (%TC) at 0.05-degree spatial resolution was averaged over a 35-year (1982-2016) period using annual %TC layers derived from the Advanced Very High-Resolution Radiometer (AVHRR) (Fig. 1a)42. %TC was binned using 5% TC intervals to assess its relation to net CO2 uptake, or aggregated at a regional scale (e.g., TC  > 50% or TC  90%), discontinuous permafrost (DisconP, 10% < P  90%), discontinuous (DisconP, 10% < P  0.05 indicate a good fitting model), Bentler’s comparative fit index (CFI, where CFI ≈ 1 indicates a good fitting model), and the root mean square error of approximation (RMSEA; where RMSEA ≤ 0.05 and p  > 0.1 indicate a good fitting model). The standardized regression coefficient can be interpreted as the relative influences of exogenous (independent) variables. The R2 indicates the total variation in an endogenous (dependent) variable explained by all exogenous (independent) variables.Direct and legacy effects of temperature on seasonal net CO2 uptakeBecause landscape thawing and snow conditions regulate the onset of vegetation growth and influence the seasonal and annual CO2 cycles in the NHL24,84, we also analyzed the legacy effects of spring (May–Jun) temperature on seasonal net CO2 uptake. We regressed seasonal and annual net CO2 uptake from the site-level EC observations, regional-level ACI ensemble, and the TRENDY NBP ensemble against spring (May-June) air temperature. For EC observations, net CO2 uptake (i.e., NEE) and air temperature were summarized from site-level measurements. For the ACIs and TRENDY ensemble, net CO2 uptake (i.e., NEEACI and NBP) was summarized as regional means from the ACIs and TRENDY ensemble outputs, and air temperature was summarized as regional means from CRU temperature. The slope of the regression line was interpreted as the spring temperature sensitivity of the CO2 cycle. Simple linear regression was used here mainly due to the strong influence of spring temperature on the seasonal and annual CO2 cycle in NHL ecosystems30. Temperature sensitivity (γ: g C m−2 day−1 K−1) is the change in net CO2 flux (g C m−2 day−1) in response to a 1-degree temperature change. The sensitivity of net CO2 uptake to warm spring anomalies was calculated for different seasons (EGS, LGS, and annual) and regions (i.e., permafrost and non-permafrost), and the T-test was used to test for the difference in γ among different regions, seasons, and datasets. Similarly, direct effects of temperature on net CO2 uptake were calculated using the same season data (Fig. S14).Observationally-constrained estimates (EC and ACIs) showed that the sensitivity of net CO2 uptake in the EGS to spring temperature is positive (γ  > 0) and not statistically different (p  > 0.05) between permafrost and non-permafrost regions (({gamma }_{{ACI}}^{{np}})=0.125 ± 0.020 gC m−2 d−1 K−1; ({gamma }_{{EC}}^{{np}}) = 0.052 ± 0.013 gC m−2 d−1 K−1). In contrast, the sensitivity of net CO2 uptake in LGS to spring temperature is negative (γ  More

Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).CAS 
PubMed 
Article 

Google Scholar 
van der Sande, M. T. et al. Biodiversity in species, traits, and structure determines carbon stocks and uptake in tropical forests. Biotropica 49, 593–603 (2017).Article 

Google Scholar 
Grace, O. M. et al. Plant power: opportunities and challenges for meeting sustainable energy needs from the plant and fungal kingdoms. Plants People Planet 2, 446–462 (2020).Article 

Google Scholar 
Howes, M. J. R. et al. Molecules from nature: reconciling biodiversity conservation and global healthcare imperatives for sustainable use of medicinal plants and fungi. Plants People Planet 2, 463–481 (2020).Article 

Google Scholar 
Ulian, T. et al. Unlocking plant resources to support food security and promote sustainable agriculture. Plants People Planet 2, 421–445 (2020).Article 

Google Scholar 
Brondizio, E., Diaz, S., Settele, J. & Ngo, H. T. (eds) Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on biodiversity and ecosystem services. Zenodo https://doi.org/10.5281/zenodo.3831673 (2019).Bennun, L. et al. The value of the IUCN Red List for business decision-making. Conserv. Lett. 11, e12353 (2018).Betts, J. et al. A framework for evaluating the impact of the IUCN Red List of threatened species. Conserv. Biol. 34, 632–643 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Maira, L. et al. Achieving international species conservation targets: closing the gap between top-down and bottom-up approaches. Conserv. Soc. 19, 25–33 (2021).Article 

Google Scholar 
IUCN Red List version 2022-2: Table 1a (IUCN, 2022); https://www.iucnredlist.org/resources/summary-statistics#Figure2Rivers, M. The global tree assessment—red listing the world’s trees. BGjournal 14, 16–19 (2017).
Google Scholar 
Nic Lughadha, E. et al. Extinction risk and threats to plants and fungi. Plants People Planet 2, 389–408 (2020).Article 

Google Scholar 
Silva, S. V. et al. Global estimation and mapping of the conservation status of tree species using artificial intelligence. Front. Plant Sci. 13, 839792 (2022).ThreatSearch Online Database (Botanic Gardens Conservation International, accessed 12 October 2021); https://tools.bgci.org/threat_search.phpBachman, S. P., Nic Lughadha, E. M. & Rivers, M. C. Quantifying progress toward a conservation assessment for all plants. Conserv. Biol. 32, 516–524 (2018).PubMed 
Article 

Google Scholar 
Rondinini, C., Di Marco, M., Visconti, P., Butchart, S. H. M. & Boitani, L. Update or outdate: long-term viability of the IUCN Red List. Conserv. Lett. 7, 126–130 (2014).Article 

Google Scholar 
Cazalis, V. et al. Bridging the research–implementation gap in IUCN Red List assessments. Trends Ecol. Evol. 37, 359–370 (2022).PubMed 
Article 

Google Scholar 
Dauby, G. et al. ConR: an R package to assist large-scale multispecies preliminary conservation assessments using distribution data. Ecol. Evol. 7, 11292–11303 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Stévart, T. et al. A third of the tropical African flora is potentially threatened with extinction. Sci. Adv. 5, eaax9444 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Bland, L. M., Collen, B., Orme, C. D. L. & Bielby, J. Predicting the conservation status of data-deficient species. Conserv. Biol. 29, 250–259 (2015).PubMed 
Article 

Google Scholar 
Darrah, S. E., Bland, L. M., Bachman, S. P., Clubbe, C. P. & Trias-Blasi, A. Using coarse-scale species distribution data to predict extinction risk in plants. Divers. Distrib. 23, 435–447 (2017).Article 

Google Scholar 
Pelletier, T. A., Carstens, B. C., Tank, D. C., Sullivan, J. & Espíndola, A. Predicting plant conservation priorities on a global scale. Proc. Natl Acad. Sci. USA 115, 13027–13032 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zizka, A., Silvestro, D., Vitt, P. & Knight, T. M. Automated conservation assessment of the orchid family with deep learning. Conserv. Biol. 35, 897–908 (2021).PubMed 
Article 

Google Scholar 
Walker, B. E., Leão, T. C. C., Bachman, S. P., Bolam, F. C. & Nic Lughadha, E. Caution needed when predicting species threat status for conservation prioritization on a global scale. Front. Plant Sci. 11, 520 (2020).Lughadha, E. N. et al. The use and misuse of herbarium specimens in evaluating plant extinction risks. Philos. Trans. R. Soc. B 374, 20170402 (2019).Article 

Google Scholar 
Walker, B. E., Leão, T. C. C., Bachman, S. P., Lucas, E. & Nic Lughadha, E. M. Evidence-based guidelines for developing automated assessment methods. Preprint at https://ecoevorxiv.org/zxq6s/ (2021).Isaac, N. J. B., Turvey, S. T., Collen, B., Waterman, C. & Baillie, J. E. M. Mammals on the EDGE: conservation priorities based on threat and phylogeny. PLoS ONE 2, e296 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
Grenié, M., Denelle, P., Tucker, C. M., Munoz, F. & Violle, C. funrar: an R package to characterize functional rarity. Divers. Distrib. 23, 1365–1371 (2017).Article 

Google Scholar 
Lindegren, M., Holt, B. G., MacKenzie, B. R. & Rahbek, C. A global mismatch in the protection of multiple marine biodiversity components and ecosystem services. Sci. Rep. 8, 4099 (2018).Pollock, L. J. et al. Protecting biodiversity (in all its complexity): new models and methods. Trends Ecol. Evol. 35, 1119–1128 (2020).PubMed 
Article 

Google Scholar 
Arnan, X., Cerdá, X. & Retana, J. Relationships among taxonomic, functional, and phylogenetic ant diversity across the biogeographic regions of Europe. Ecography 40, 448–457 (2017).Article 

Google Scholar 
Wong, J. S. Y. et al. Comparing patterns of taxonomic, functional and phylogenetic diversity in reef coral communities. Coral Reefs 37, 737–750 (2018).Article 

Google Scholar 
Devictor, V. et al. Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: the need for integrative conservation strategies in a changing world. Ecol. Lett. 13, 1030–1040 (2010).PubMed 

Google Scholar 
Brum, F. T. et al. Global priorities for conservation across multiple dimensions of mammalian diversity. Proc. Natl Acad. Sci. USA 114, 7641–7646 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pollock, L. J., Thuiller, W. & Jetz, W. Large conservation gains possible for global biodiversity facets. Nature 546, 141–144 (2017).CAS 
PubMed 
Article 

Google Scholar 
Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).CAS 
PubMed 
Article 

Google Scholar 
Cámara-Leret, R. et al. Fundamental species traits explain provisioning services of tropical American palms. Nat. Plants 3, 16220 (2017).Saslis-Lagoudakis, C. H. et al. Phylogenies reveal predictive power of traditional,medicinein bioprospecting. Proc. Natl Acad. Sci. USA 109, 15835–15840 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
van Kleunen, M. et al. Economic use of plants is key to their naturalization success. Nat. Commun. 11, 3201 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Molina-Venegas, R., Rodríguez, M., Pardo-de-Santayana, M., Ronquillo, C. & Mabberley, D. J. Maximum levels of global phylogenetic diversity efficiently capture plant services for humankind. Nat. Ecol. Evol. 5, 583–588 (2021).PubMed 
Article 

Google Scholar 
Molina-Venegas, R. Conserving evolutionarily distinct species is critical to safeguard human well-being. Sci. Rep. 11, 24187 (2021).Zaman, W. et al. Predicting potential medicinal plants with phylogenetic topology: inspiration from the research of traditional Chinese medicine. J. Ethnopharmacol. 281, 114515 (2021).CAS 
PubMed 
Article 

Google Scholar 
Cámara-Leret, R. et al. Climate change threatens New Guinea’s biocultural heritage. Sci. Adv. 5, eaaz1455 (2019).Lima, V. P. et al. Climate change threatens native potential agroforestry plant species in Brazil. Sci. Rep. 12, 2267 (2022).Johnson, D. V. Tropical Palms 2010 Revision Non-Wood Forest Products 10 (FAO, 2010).Johnson, D. V. & Sunderland, T. C. H. Rattan Glossary and Compendium Glossary with Emphasis on Africa Non-Wood Forest Products 16 (FAO, 2004).Ter Steege, H. et al. Hyperdominance in the Amazonian tree flora. Science 342, 1243092 (2013).PubMed 
Article 
CAS 

Google Scholar 
Zona, S. & Henderson, A. A review of animal-mediated seed dispersal of palms. Selbyana 11, 6–21 (1989).
Google Scholar 
Kissling, W. D. et al. PalmTraits 1.0, a species-level functional trait database of palms worldwide. Sci. Data 6, 178 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Tomlinson, P. B. The uniqueness of palms. Bot. J. Linn. Soc. 151, 5–14 (2006).Article 

Google Scholar 
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).PubMed 
Article 
CAS 

Google Scholar 
Muscarella, R. et al. The global abundance of tree palms. Glob. Ecol. Biogeogr. 29, 1495–1514 (2020).Article 

Google Scholar 
Dransfield, J. et al. Genera Palmarum: The Evolution and Classification of Palms (Kew Publishing, 2008).Diazgranados, M. et al. World Checklist of Useful Plant Species (Royal Botanic Gardens, Kew, 2020).Couvreur, T. L. P. & Baker, W. J. Tropical rain forest evolution: palms as a model group. BMC Biol. 11, 2–5 (2013).Article 

Google Scholar 
Faurby, S., Eiserhardt, W. L., Baker, W. J. & Svenning, J. Molecular phylogenetics and evolution: an all-evidence species-level supertree for the palms (Arecaceae). Mol. Phylogenet. Evol. 100, 57–69 (2016).PubMed 
Article 

Google Scholar 
The IUCN Red List of Threatened Species Version 2021-2 (IUCN, accessed 12 October 2021); https://www.iucnredlist.orgBaker, W. J. & Dransfield, J. Beyond genera Palmarum: progress and prospects in palm systematics. Bot. J. Linn. Soc. 182, 207–233 (2016).Article 

Google Scholar 
Henderson, A. A revision of Calamus (Arecaceae, Calamoideae, Calameae, Calaminae). Phytotaxa https://doi.org/10.11646/phytotaxa.445.1.1 (2020).Rakotoarinivo, M., Dransfield, J., Bachman, S. P., Moat, J. & Baker, W. J. Comprehensive red list assessment reveals exceptionally high extinction risk to Madagascar palms. PLoS ONE 9, e103684 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Cosiaux, A. et al. Low extinction risk for an important plant resource: conservation assessments of continental African palms (Arecaceae/Palmae). Biol. Conserv. 221, 323–333 (2018).Article 

Google Scholar 
Johnson, D. & UICN/SSC Palm Specialist Group (eds) Palms, Their Conservation and Sustained Utilization—Status Survey and Conservation Action Plan (Union Internationale pour la Conservation de la Nature et de ses Ressources, 1996).Bachman, S., Walker, B. E., Barrios, S., Copeland, A. & Moat, J. Rapid least concern: towards automating red list assessments. Biodivers. Data J. 8, e47018 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Enquist, B. J. et al. The commonness of rarity: global and future distribution of rarity across land plants. Sci. Adv. https://doi.org/10.1126/sciadv.aaz0414 (2019).Vieilledent, G. et al. Combining global tree cover loss data with historical national forest cover maps to look at six decades of deforestation and forest fragmentation in Madagascar. Biol. Conserv. 222, 189–197 (2018).Article 

Google Scholar 
Gaveau, D. L. A. et al. Rise and fall of forest loss and industrial plantations in Borneo (2000–2017). Conserv. Lett. 12, e12622 (2019).Gamoga, G., Turia, R., Abe, H., Haraguchi, M. & Iuda, O. The forest extent in 2015 and the drivers of forest change between 2000 and 2015 in Papua New Guinea: deforestation and forest degradation in Papua New Guinea. Case Stud. Environ. 5, 1442018 (2021).Cámara-Leret, R. & Bascompte, J. Language extinction triggers the loss of unique medicinal knowledge. Proc. Natl Acad. Sci. USA 118, e2103683118 (2021).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Henderson, A., Fischer, B., Scariot, A., Whitaker Pacheco, M. A. & Pardini, R. Flowering phenology of a palm community in a central Amazon forest. Brittonia 52, 149–159 (2000).Article 

Google Scholar 
Olivares, I. & Galeano, G. Leaf and inflorescence production of the wine palm (Attalea butyracea) in the dry Magdalena river valley, Colombia. Caldasia 35, 37–48 (2013).
Google Scholar 
Voeks, R. A. Disturbance pharmacopoeias: medicine and myth from the humid tropics. Ann. Assoc. Am. Geogr. 94, 868–888 (2004).
Google Scholar 
Pironon, S. et al. Potential adaptive strategies for 29 sub-Saharan crops under future climate change. Nat. Clim. Change 9, 758–763 (2019).Article 

Google Scholar 
Govaerts, R., Dransfield, J., Zona, S. & Henderson, A. World Checklist of Arecaceae (Royal Botanic Gardens, Kew, accessed 1 March 2018); http://wcsp.science.kew.org/Chamberlain, S. et al. rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.6.0 (2021).Zizka, A. et al. CoordinateCleaner: standardized cleaning of occurrence records from biological collection databases. Methods Ecol. Evol. 10, 744–751 (2019).Article 

Google Scholar 
Plants of the World Online (Royal Botanic Gardens, Kew, accessed 1 March 2018); http://www.plantsoftheworldonline.org/South, A. rworldmap v.1.3-6: Mapping global data (2016).Bivand, R. et al. maptools v.0.9-2: Tools for handling spatial objects (2017).Arel-Bundock, V., Enevoldsen, N. & Yetman, C. countrycode: an R package to convert country names and country codes. J. Open Source Softw. 3, 848 (2018).Article 

Google Scholar 
Becker, R. A., Wilks, A. R., Brownrigg, R., Minka, T. P. & Deckmyn, A. maps v.3.3.0: Draw geographical maps (2018).Pebesma, E. et al. sp v.1.2-7: Classes and methods for spatial data (2018).Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).Article 

Google Scholar 
Wickham, H., Hester, J. & Chang, W. devtools v.1.13.5: Tools to make developing R packages easier (2018).World Geographic Scheme for Recording Plant Distributions Standard (TDWG, 2001); http://www.tdwg.org/standards/109Brummitt, R. K. World Geographical Scheme for Recording Plant Distributions (Hunt Institute for Botanical Documentation, 2001).Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51, 933–938 (2001).Article 

Google Scholar 
Moat, J. & Bachman, S. P. rCAT v.0.1.6: Conservation assessment tools (2017).Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Plants of the World Online (Royal Botanic Gardens, Kew, accessed 10 June 2020); http://www.plantsoftheworldonline.org/Csárdi, G. & FitzJohn, R. progress v.1.2.2: Terminal progress bars (2019).Microsoft Corporation & Weston, S. doParallel: Foreach parallel adaptor for the ‘parallel’ package. R package version 1.0.16 (2020).Microsoft Corporation & Weston, S. foreach: Provides foreach looping construct. R package version 1.5.0 (2020).Ooms, J., Lang, D. T. & Hilaiel, L. jsonlite v.1.7.2: A simple and robust JSON parser and generator for R (2020).Wickham, H. httr v.1.4.2: Tools for working with URLs and HTTP (2020).Global Human Footprint (Geographic), v2 (1995 – 2004) (SEDAC, accessed 14 May 2018); https://doi.org/10.7927/H4M61H5FFick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

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

Google Scholar 
Wickham, H. plyr v.1.8.6: Tools for splitting, applying and combining data (2021).Wickham, H. & RStudio. tidyr v.1.1.4: Tidy messy data (2021).Wickham, H., François, R., Henry, L. & Müller, K. dplyr v.1.0.7: A grammar of data manipulation (2021).Bivand, R. et al. rgdal v.1.5-8: Bindings for the ‘geospatial’ data abstraction library (2020).Greenberg, J. A. & Mattiuzzi, M. gdalUtils v.2.0.3.2: Wrappers for the Geospatial data Abstraction Library (GDAL) utilities (2020).Hijmans, R. J. et al. raster v.3.1-5: Geographic data analysis and modeling (2020).The IUCN Red List of Threatened Species (IUCN, accessed 22 March 2018); https://www.iucnredlist.org/ThreatSearch Online Database (Botanic Gardens Conservation International, accessed 1 March 2018); https://tools.bgci.org/threat_search.phpChamberlain, S., ROpenSci & Salmon, M. rredlist: ‘IUCN’ Red List client (2020).Wickham, H. stringr v.1.4.0: Simple, consistent wrappers for common string operations (2019).Gagolewski, M. & Tartanus, B. stringi v.1.7.5: Character string processing facilities (2021).Kuhn, M. caret: Classification and regression training. R package version 6.0-86 (2020).Torgo, L. Data Mining with R, Learning with Case Studies (Chapman and Hall/CRC, 2010).Chawla, N. V., Bowyer, K. W., Hall, L. O. & Kegelmeyer, P. SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2020).Article 

Google Scholar 
Stokely, M. HistogramTools: Utility functions for R histograms. R package version 0.3.2 (2015).Sarkar, D. et al. lattice v.0.20-40: Trellis graphics for R (2020).Wickham, H. ggplot2 Elegant Graphics for Data Analysis (Springer, 2016).Auguie, B. & Antonov, A. gridExtra v.2.3: Miscellaneous functions for ‘grid’ graphics (2017).Pruim, R., Kaplan, D. T. & Horton, N. J. mosaic v.1.6.0: Project MOSAIC statistics and mathematics teaching utilities (2020).Meyer, D. & Buchta, C. proxy v.0.4-23: Distance and similarity measures (2019).Wickham, H. & Seidel, D. scales v.1.1: Scale functions for visualization (2019).Branco, P., Ribeiro, R. & Torgo, L. UBL v.0.0.6: An implementation of re-sampling approaches to utility-based learning for both classification and regression tasks (2017).Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
Google Scholar 
Cohen, J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20, 37–46 (1960).Article 

Google Scholar 
Ripley, B. & Venables, W. nnet v.7.3-13: Feed-forward neural networks and multinomial log-linear models (2020).Warnes, G. R. et al. gdata v.2.18.0: Various R programming tools for data manipulation (2017).Wright, M. N., Wager, S. & Probst, P. ranger v.0.12.1: A fast implementation of random forests (2020).Arya, S., Mount, D., Kemp, S. E. & Jefferis, G. RANN v.2.6.1: Fast nearest neighbour search (wraps ANN Library) using L2 metric (2019).Meyer, D. et al. e1071 v.1.7-3: Misc Functions of the Department of Statistics, Probability Theory Group (formerly: E1071), TU Wien (2019).Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, 4765–4774 (2017).
Google Scholar 
Greenwell, B. fastshap v.0.0.7: Fast approximate Shapley values (2021).Greenwell, B. vip v.0.3.2: Variable importance plots (2020).Donoghoe, M. W. glm2 v.1.2.1: Fitting generalized linear models (2018).Wickham, H. reshape2 v.1.4.4: Flexibly reshape data: a reboot of the reshape package (2020).Robin, X. et al. pROC v.1.18.0: Display and analyze ROC curves (2020).Warnes, G. R. et al. gplots v.3.0.3: Various R programming tools for plotting data (2019).Müller, K. & Bryan, J. here v.1.0.1: A simpler way to find your files (2017).Wickham, H., Hester, J., Francois, R., Jylänki, J. & Jørgensen, M. readr v.1.3.1: Read rectangular text data (2018).Wickham, H. et al. readxl v.1.3.1: Read Excel files (2019).Henry, L. & Wickham, H. purrr v.0.3.4: Functional programming tools (2020).Lin Pedersen, T. ggforce v.0.3.1: Accelerating ‘ggplot2’ (2019).Lin Pedersen, T. patchwork v.1.0.0: The composer of plots (2019).Hester, J. glue v.1.3.1: Interpreted string literals (2019).Ooms, J. & McNamara, J. writexl v.1.2: Export data frames to Excel ‘xlsx’ format (2019).Horikoshi, M. et al. ggfortify v.0.4.8: Data visualization tools for statistical analysis results (2019).Liaw, A. randomForest v.4.6-14: Breiman and Cutler’s random forests for classification and regression (2018).Kassambara, A. ggpubr v.0.2.5: ‘ggplot2’ based publication ready plots (2020).Gruca, M., Blach-Overgaard, A. & Balslev, H. African palm ethno-medicine. J. Ethnopharmacol. 165, 227–237 (2015).PubMed 
Article 

Google Scholar 
Cámara–Leret, R. & Dennehy, Z. Indigenous knowledge of New Guinea’s useful plants: a review. Econ. Bot. 73, 405–415 (2019).Article 

Google Scholar 
Macía, M. J. et al. Palm uses in Northwestern South America: a quantitative review. Bot. Rev. 77, 462–570 (2011).Article 

Google Scholar 
Orme, D. et al. caper: Comparative analyses of phylogenetics and evolution in R. R package version 1.0.1 https://cran.r-project.org/package=caper (2018).Kowarik, A. & Templ, M. Imputation with the R package VIM. J. Stat. Softw. 74, 1–16 (2016).Alfons, A. & Templ, M. Estimation of social exclusion indicators from complex surveys: the R package laeken. J. Stat. Softw. 54, 1–25 (2013).Article 

Google Scholar 
Milliken, W., Walker, B. E., Howes, M. J. R., Forest, F. & Nic Lughadha, E. Plants used traditionally as antimalarials in Latin America: mining the tree of life for potential new medicines. J. Ethnopharmacol. 279, 114221 (2021).PubMed 
Article 

Google Scholar 
Fritz, S. A. & Purvis, A. Selectivity in mammalian extinction risk and threat types: a new measure of phylogenetic signal strength in binary traits. Conserv. Biol. 24, 1042–1051 (2010).PubMed 
Article 

Google Scholar 
Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018).Paradis, E. & Schliep, K. Ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).CAS 
PubMed 
Article 

Google Scholar 
Govaerts, R., Nic Lughadha, E., Black, N., Turner, R. & Paton, A. The World Checklist of Vascular Plants, a continuously updated resource for exploring global plant diversity. Sci. Data 8, 215 (2021).Yu, G. ggplotify v.0.0.4: Convert plot to ‘grob’ or ‘ggplot’ object (2019).Yu, G. aplot v.0.0.3: Decorate a ‘ggplot’ with associated information (2020).Slowikowski, K. et al. ggrepel v.0.8.1: Automatically position non-overlapping text labels with ‘ggplot2’ (2019).Schloerke, B. et al. GGally v.1.4.0: Extension to ‘ggplot2’ (2018).Rubis, B. et al. hrbrthemes v.0.6.0: Additional themes, theme components and utilities for ‘ggplot2’ (2019).Henry, L., Wickham, H. & Chang, W. ggstance v.0.3.3: Horizontal ‘ggplot2’ components (2019).Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T. Y. Ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8, 28–36 (2017).Article 

Google Scholar 
Brown, C. hash v.2.2.6.1: Full feature implementation of hash/associated arrays/dictionaries (2019).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).RStudio Team. RStudio: Integrated Development for R (RStudio, 2021).Bellot, S. et al. Workflow and code used to perform palm extinction risk and regional palm use resilience analyses. Zenodo https://doi.org/10.5281/zenodo.6678122 (2022). More

Bellot, S. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01858-0 (2022).Article 

Google Scholar 
Eiserhardt, W. L. et al. Ann. Bot. 108, 1391–1416 (2011).Article 

Google Scholar 
Muscarella, R. et al. Glob. Ecol. Biogeogr. 29, 1495–1514 (2020).Article 

Google Scholar 
Cámara-Leret, R. et al. Nat. Plants 3, 16220 (2017).Article 

Google Scholar 
The IUCN Red List of Threatened Species (IUCN, 2018).BGCI ThreatSearch Online Database (BGCI, 2018).Carlos-Júnior, L. A. et al. Divers. Distrib. 25, 743–757 (2019).Article 

Google Scholar 
Pollock, L. J., Thuiller, W. & Jetz, W. Nature 546, 141–144 (2017).CAS 
Article 

Google Scholar 
Rice, J. et al. The IPBES Regional Assessment Report on Biodiversity and Ecosystem Services for the Americas (IPBES, 2018).Coelho de Souza, F. et al. Nat. Ecol. Evol. 3, 1754–1761 (2019).Article 

Google Scholar  More

Literature values of R
To and Q10 of leaf respirationData of RTo were read from texts, tables, and figures in all available literature (18 species; Supplementary Tables 1, 2) when measured more than once within a period of darkness in lab- and field studies where measurement temperature, To, was kept constant. The RTo-initial was defined as the initial measurement of RTo for each study/species, and further values of RTo at later points within the same night of the same study were read as well.Apparent- and inherent temperature sensitivities (Q10, Equation 1; Fig. 2b) were obtained from all available literature (ten species; Supplementary Table 2) where in the same study/species, both nocturnal values of Q10,app and of Q10,inh were obtained in response to long-term natural T-changes in the environment during the night (hours) and nocturnal values were obtained in response to short-term artificial T-changes (max 30 min), respectively.Measurements of R
To and Q10 of leaf respirationIn the field (United Kingdom, Denmark, Panama, Colombia and Brazil), RTo (µmol CO2 m−2 s−1) in 16 species (Supplementary Tables 1, 3) was measured through nocturnal periods at constant To (controlled either by block-T or leaf-T) with infra-red gas analysers (Li-Cor-6400(XT) or Li-Cor-6800, Lincoln, Nebraska, USA). Mature, attached leaves positioned in the sunlight throughout the day were chosen. Target [CO2] in the leaf cuvette was set to ambient, ranging from 390 to 410 ppm, depending on when measurements were made, and target RH = 65 ± 10%, with a flow rate of 300 µmol s–1. The RTo-initial was defined as RTo at first measurement after darkness 30 min after sunset (to conservatively avoid light-enhanced dark respiration, LEDR50,51. Leak tests were conducted prior to measurements52. The temporal resolution of measurements varied between every three minutes to once per hour for the different species. Data were subsequently binned in hourly bins.Measurements to derive Q10,inh and Q10,app were conducted in two species in a T-controlled growth cabinet and in six species in the field (Supplementary Table 2), where Q10,inh was measured in response to 10–30 min of artificial changes in T and Q10,app was calculated from measurements of RT in response to T of the environment (growth cabinet or field) at the beginning of the night and again at the end of the night (hours apart).Tree level measurements in whole-tree chambersThe night-time respiratory efflux of the entire above-ground portion (crown and bole) in large growing trees of Eucalyptus tereticornis was measured in whole-tree chambers (WTCs) in Richmond, New South Wales (Australia, (33°36ʹ40ʺS, 150°44ʹ26.5ʺE). The WTCs are large cylindrical structures topped with a cone that enclose a single tree rooted in soil (3.25 m in diameter, 9 m in height, volume of ~53 m3) and under natural sunlight, air temperature and humidity conditions. An automated system measured the net exchange of CO2 between the canopy and the atmosphere within each chamber at 15-min resolution. During the night, we used the direct measurements of CO2 evolution (measured with an infra-red gas analyser; Licor 7000, Li-Cor, Inc., Lincoln, NE)53,54 as a measure of respiration.Due to the high noise-to-signal ratio in the CO2-exchange measurements from this system when analysing the high-resolution temporal variation through each night, we chose to only analyse temporal variation in tree-RT for the nights when tree-RT-initial were amongst the top 10% of CO2-exchange signals for the entire data set. The resulting data spanned 62 nights and included hourly average measurements from three replicate chambers.Data analysis of R
To
Measurements of nocturnal leaf respiration under constant temperature conditions (RTo) were divided by the initial rate of respiration (RTo-initial) at the onset of each night. Hourly means of RTo/RTo-initial were calculated for each leaf replicate to remove measurement noise and reduce bias due to the measurement of some species at more frequent intervals throughout the night. For species with multiple leaf replicates, these hourly means of RTo/RTo-initial were then combined to create hourly averages of RTo/RTo-initial at the species level. For each species, these values were plotted as a function of time to demonstrate how RTo/RTo-initial decreases with time since the onset of darkness, from sunset until sunrise (Supplementary Fig. 1). For each species, hourly means of RTo/RTo-initial plotted as a function of time were linearised by log-transforming data and the slope of the relationship determined. To test whether the slopes of the lines differed significantly within plant functional groups (woody, non-woody), species originating from the same biome (temperate, tropical) or species measured under the same conditions (lab, field), the slopes of the lines for all species from a given functional group, biome or measurement condition were tested pairwise against each other using the slope, standard error and sample size (number of points on the x-axis) for each line and applying a 0.05 cut-off for p values after Bonferroni correction for multiple testing. 11 out of 701 comparisons came out as being significantly different, which is why within-group slope differences were considered to be overall non-significant for this analysis. t-tests were used to test whether the slopes differed between plant functional groups (tree, non-woody), species originating from different biomes (temperate, tropical) and species measured under different environmental conditions (lab, field). In these tests, the degrees of freedom varied according to the different sample sizes. Since RTo/RTo-initial plotted as a function of time always starts at 1, the intercepts do not differ between species. t-tests were performed on linearised power functions by log-transforming data in order to test potential differences between lab and field, origin of species, between woody and non-woody species and between temperate and tropical biomes. Since these functions were statistically indistinguishable in each pairing, all measurements of nocturnal leaf respiration under constant temperature conditions (n = 967 nights, 31 species) were collated into a single plot. The data were binned hourly since some studies had very few measurements on half-hourly steps. A power function was fitted with a weighting of each hourly binned value using 1/(standard error of the mean). The power function was chosen as it, better than the exponential- or linear function, can capture both sudden steep- as well as slower decrease in RTo/RTo-initial in different species. The 95% confidence interval of the power function, following the new model equation, overlaps with all the 95% confidence intervals of the hourly binned values (Fig. 1a). All data analysis, including statistical analysis and figures were performed using Python version 3.9.4.Evaluation of new equationWe performed four sets of simulations (S1-S4) using different representations of leaf and plant respiration as outlined in Supplementary Table 4. Evaluation of Equation 4 (S2; Equation 3 from Fig. 1a merged with Equation 1) in comparison with Equation 1 (S1) and Equation 5 (S4) in comparison with Equation 2 (S3), respectively, for predictions of nocturnal variation in response to natural variation in temperature, was conducted by use of independent sets of leaf level data and tree scale data. The effect of including variable nocturnal RTo is estimated as the difference between S1 and S2 and between S3 and S4, respectively.The first data set used for the evaluation consists of nine broad-leaf species for which spot measurements of leaf respiration under ambient conditions were taken at sunset and before sunrise in the field (Fig. 1b and Supplementary Fig. 2a). Of these nine species, three species (Fig. 1c) were further measured throughout the night at ambient conditions. Further, whole-tree measurements measured throughout the night at ambient conditions (Supplementary Fig. 3a–d) were also used for evaluation. Finally, comparisons of Q10,inh with Q10,app in another ten species were used to test if RTo appeared constant as assumed in Equation 1 (Supplementary Tables 2, 3 and Fig. 2b).To validate the suitability of Equation 4 and Equation 5 over equations with full temporal control, modelled respiration values were compared against observed measurements for three species at the leaf level (Supplementary Fig. 2b–d) and for Eucalyptus tereticornis at the whole-tree level using three chamber replicates and during 62 nights using hourly measurements (Supplementary Fig. 3a–d). Linear fits were applied, using ordinary least squares regressions, to plots of normalised respiration (({R}_{T}/{R}_{{T}_{0}})) predicted by the four models against the observed values. The first measurements of the night were excluded from the fits, as these were necessarily equal to unity. The standardised residuals (S) in Supplementary Figs. 2c, 3b are calculated using the equation ({S}_{i}=({R}_{{{{{{{rm{modelled}}}}}}}_{i}}/{R}_{{{{{{{rm{Modelled}}}}}}}_{0}}-{R}_{{T}_{i}}/{R}_{{T}_{0}})/sqrt{(mathop{sum }nolimits_{i}^{N}{({R}_{{{{{{{rm{modelled}}}}}}}_{i}}/{R}_{{{{{{{rm{Modelled}}}}}}}_{0}}-{R}_{{T}_{i}}/{R}_{{T}_{0}})}^{2})/{df}}), for the residual of the ith measurement, where the sum is over all measurements, df is the number of degrees of freedom, and Rmodelled are the respiration values modelled by the four equations in Supplementary Table 4.Evaluation is done by comparing observed and simulated RT/RT, initial. We evaluate the nocturnal evolution of RT/RT, initial and use (i) one-to-one line figures that include fitted regression line, R2, p value and RMSE, (ii) Taylor diagrams and (iii) use plots of standardised residuals against temperature and hours since darkness for a qualitative assessment of the simulations, to identify whether there are any model biases at specific times or temperatures. Model evaluation, statistical analysis and figures were done using python version 3.9.4.Global scale modelling of plant R and NPP
We applied the novel formulation derived in this study (Equation 4 and Equation 5) to quantify the impact of incorporating variable RTo on simulated plant R and NPP globally using the JULES land surface model32,33 following simulations outlined in Supplementary Table 4.Plant respiration in JULES and simulations for this study: The original leaf respiration representation in JULES follows either eqn 1 ({{R}_{T}={R}_{{T}_{0}}{Q}}_{10}^{(T-{T}_{0})/10}) with Q10 = 2 and To = 25 oC or Equation 1 with an additional denominator ({{R}_{T}={R}_{{T}_{0}}{Q}}_{10}^{(T-{T}_{0})/10}/leftlfloor left(1+{e}^{0.3(T-{T}_{{upp}})}right)times left(1+{e}^{0.3({T}_{{low}}-T)}right)rightrfloor) (Equation 6). For the purpose of this application, we have used Equation 1 to represent leaf respiration in standard JULES simulations. The remaining components of maintenance respiration in JULES, i.e. fine root and wood are represented as a function of leaf to root and leaf to wood nitrogen ratios and leaf respiration rates following RT (β + (Nr + Ns)/Nl) (Equation 6) with RT as leaf respiration, Nr, Ns and Nl as root, stem and leaf Nitrogen content respectively and β as a soil water factor (Equation 42 in ref. 32). This implies that any variation in leaf respiration is passed to root and wood respiration as well30,31,35. Growth respiration is estimated as a fraction (25%) of the difference between GPP and maintenance respiration (Rm) expressed as Rg = 0.25 (GPP-Rm).JULES version 5.2 was modified to simulate leaf and plant respiration using the various descriptions (Equations 1–5) outlined in the modelling protocol in Supplementary Table 4. JULES uses standard astronomical equations to calculate the times of sunrise and sunset on a given day at each grid point. We used the model leaf temperature and RT at the timestep at or immediately preceding sunset to represent Tsunset, and RT,sunset and at every timestep through the night, the time since sunset (h) was updated. We performed global simulations for the period 2000–2018 with JULES, using the global physical configuration GL8, which is an update from GL755. We used WFDEI meteorological forcing data56 available at 0.5-degree spatial resolution and 3-h temporal resolution, and disaggregated and run in JULES with a 15 min timestep. Simulations were performed using nine plant functional types (PFTs)33. To isolate the effects of the new formulation on simulated Rp and NPP from possible impacts on leaf area index (LAI) or vegetation dynamics, we prescribed vegetation phenology via seasonal LAI fields and vegetation fractional cover based on the European Space Agency’s Land Cover Climate Change Initiative (ESA LC_CCI) global vegetation distribution57, processed to the JULES nine PFTs and re-gridded to the WFDEI grid. Annual variable fields of CO2 concentrations are based on annual mean observations from Mauna Loa58. JULES was spun up using the three cycles of the 2000–2018 meteorological forcing data to equilibrate the soil moisture stores. The mean annual output of Rp and NPP over the study period (2000–2018) is computed for all simulations and the effect of the new formulation is presented as the difference between the temporal mean of simulations with and without nocturnal variation in whole plant RTo for NPP and vice versa for Rp and as percentage respect to simulations without nocturnal variation in RTo. Results are presented for grid cells where grid level NPP is >50 g m−2 yr −1 in the standard simulations to avoid excessively large % effects at very low NPP. Output from JULES was analysed and plotted using python version 2.7.16.PermitsNo permit was required in Denmark as measurements were taken in private land (of author) and public land and measurements were non-destructive. Data were collected under the Panama Department of the Environment (current name MiAmbiente) research permit under the name of Dr Kaoru Kitajima. Permit number: SE/P-16-12. Data in Brazil were collected under the minister of Environment (Ministério do Meio Ambiente—MMA), Instituto Chico Mendes de Conservação da Biodiversidade—ICMBio, Sistema de Autorização e Informação em Biodiversidade—SISBIO permit number 47080-3. No permit was required in Colombia as measurements were taken on private land, no plant samples were collected, and trees were part of an existing experiment for which one of the co-authors is the lead. No access permits were required in the UK as they were conducted on the campus of own university plus in their own private garden.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Experimental designA 2-year field experiment was conducted at the Modern Agricultural Research and Development Base of Henan Province (113° 35′–114° 15′ E, 34° 53′–35° 11′ N). In order to enhance the diversity of LAI data, a split-plot design with a variety of field management measures and three replications was selected for the experiment (Fig. 1). The size of each experiment plot was 40 m2, the soil texture was predominantly sandy loam and sandy clay loam, as determined by textural analysis of soil samples collected before planting. Maize cultivar Dedan-5 was used in the experiment, which was planted on June 12, 2019, and June 20, 2020, with a row spacing of 42 cm and a planting density of 7 seedlings·m−2. The soil and cultivar in field experiments were representatives of those in the region. The irrigation, pesticide, and herbicide control practices followed local management for maize production.Figure 1The experimental design.Full size imageLAI measurements and UAV-based image acquisitionThe measurements of LAI were conducted at four growth stages including the tasseling stage (TS), flowering stage (FS), grain-filling stage (GS), and milk-ripe stage (MS) of maize in 2019 and 2020, a total of 264 LAI data of maize were collected during the 2-year field trial (Table 1). In order to reduce the impact of plant variability, the random sampling method was used to collect LAI samples. For each plot, three plants were randomly selected to measure the total green leaf area with the non-destructive portable leaf area meter (Laser Area Meter CI-203; CID Inc.). And the average leaf area of selected plants represented the single plant leaf area in each experiment plot. The LAI of each plot wasTable 1 Description of samplings.Full size table$$mathrm{LAI}=mathrm{LA}*mathrm{D}$$
(1)
where (mathrm{LA}) is the leaf area of a single plant in each plot; (mathrm{D}) is the planting density in one square meter.PHANTOM 4 PRO (DJI-Innovations Inc., Shenzhen, China) is a multi-rotor UAV equipped with a 20-megapixel visible-light camera that was employed to capture digital images. Aerial observations were conducted on the same dates as the LAI measurements, which was between 10:30 a.m. and 2:00 p.m. local time when the solar zenith angle was minimal. The UAV was flown automatically based on preset flight parameters and waypoints, with a forward overlap of 80% and a side overlap of 60%. A three-axis gimbal integrated with the inertial navigation system stabilized the camera, the automatic camera mode with fixed ISO (100) and a fixed exposure was used during the flight. Altogether, 4192 images were taken in eight flights from a flight height of 29.36 m above ground, with a spatial resolution of 0.008 m.The measurements of maize LAI were carried out with permission from the Modern Agricultural Research and Development Base of Henan Province. All experiments were carried out in accordance with relevant institutional, national, and international guidelines and legislation.Image pre-processingDJI Terra (version 2.3.3) was used to generate ortho-rectified images based on the structure from motion algorithms and a mosaic blending model. The main procedures are as follows: (1) extract feature points and match features according to the longitude, latitude, elevation, roll angle, pitch angle, and heading angle of each image; (2) build dense 3D point clouds by using dense multi-view stereo matching algorithm; (3) build a 3D polygonal mesh based on the vector relationship between each point in the dense cloud; (4) establish a 3D model with both external image and internal structure by merging the mosaic image into the 3D model; (5) generate digital orthophoto map (DOM).Vegetation indices (VIs) derived from the UAV-based digital imageryDigital imagery records the intensity of visible red (R), green (G), and blue (B) bands in individual pixels24. In order to enhance the vegetation parameters contained in the digital image, fourteen commonly used RGB-based VIs were collected, and their correlation with the LAI of maize at different growth stages was evaluated. Table 2 shows the detailed information of the selected RGB-based VIs.Table 2 RGB-based VIs for LAI estimation.Full size tableCentered on the point where LAI was measured, regions of interests (ROIs) with a size of 100*100 were clipped from the digital image. Python 3.7.3 was used for extracting the R, G, B information of maize and computing the RGB-based VIs from ROIs. In order to reduce the effects of light and shadow, the R, G, B color space of the image was normalized according to the followings:$$mathrm{r}=frac{R}{R+G+B}$$
(2)
$$g=frac{G}{R+G+B}$$
(3)
$$b=frac{B}{R+G+B}$$
(4)
where r, g, and b are the normalized values. R, G, B are the pixel values from the digital images based on each band.Pearson correlation analysisBefore regression analysis, the Pearson correlation analysis was performed to determine the relationship between maize LAI and different RGB-based VIs extracted from the digital image. Pearson correlation coefficient ((mathrm{r})) reflects the degree of linear correlation between two variables, which is between − 1 and 1. The calculation formula of Pearson correlation coefficient was expressed as follows:$$mathrm{r}= frac{sum_{i=1}^{n}left({X}_{i}-overline{X }right)left({Y}_{i}-overline{Y }right)}{sqrt{sum_{i=1}^{n}{left({X}_{i}-overline{X }right)}^{2}}sqrt{sum_{i=1}^{n}{left({Y}_{i}-overline{Y }right)}^{2}}}$$
(5)
where (X), (mathrm{Y}) are variables, (n) is the number of variables.Regression methodsLinear regression (LR)Linear regression is an approach for modelling the relationship between dependent and independent variables. The case of one independent variable is called unary linear regression (ULR), the expressions can be expressed as follows:$$mathrm{y}={beta }_{0}+{beta }_{1}x+varepsilon $$
(6)
where (varepsilon ) is deviation, which satisfies the normal distribution. (x), (mathrm{y}) are variables. ({beta }_{0}), ({beta }_{1}) are the intercept and slope of the regression line, respectively.For more than one independent variable, the regression process is called multiple linear regression (MLR), the expressions can be expressed as:$$mathrm{y}={beta }_{0}+{beta }_{1}{x}_{1}+{beta }_{2}{x}_{2}+dots +{beta }_{n}{x}_{n}$$
(7)
where ({x}_{1}),( {x}_{2}), …, ({x}_{n}), (mathrm{y}) are variables, ({beta }_{0}), ({beta }_{1}), ({beta }_{2}), …, ({beta }_{n}) are coefficients that determined by least square method and gradient descent method38.The RGB-based VIs with the highest Pearson correlation coefficient was used to establish the ULR model, and VIs with a correlation coefficient higher than 0.7 were used to establish the MLR model. In each growth stage, 70% of observation data were randomly selected for establishing models, and the remaining 30% of data were used as the testing dataset to assess the model performance.Back propagation neural networks (BPNN)In this study, a three-layer BPNN model was established for LAI estimation (Fig. 2). RGB-based VIs with a correlation coefficient higher than 0.7 were selected as the input variables. Tan-Sigmoid activation function was used in the hidden layer, and the Levenberg–Marquardt algorithm was selected as the training function. The maximum epoch of BPNN training was set to 1000, the learning rate was set to 0.005, and the MSE was set to 0.001. The observation data set was split into the training set and the testing dataset with a ratio of 7:3. The training dataset was used to fit the weights and bias of the BPNN model, the testing dataset was used to evaluate the model performance. Before training, data normalization was conducted for the input and output variables, and the denormalization was required to convent the output variable back into the original units after training.Figure 2Three-layer BPNN model.Full size imageRandom forest (RF)RF is a non-parametric ensemble ML method that operates by constructing a multitude of decision trees at training time and outputting the average prediction of the individual trees (Fig. 3). The bootstrapping approach was used to collect different sub-training data from the input training dataset to construct individual decision trees.Figure 3Random forest model.Full size imageThe construction process of RF regression model is as follows:

(1)

The value of (mathrm{n}_mathrm{estimators}) was tested from 50 to 1000 in increments of 50, and the value of 500 was finally selected according to higher R2 and lower RMSE.

(2)

At each node per tree, (mathrm{m}_mathrm{try}) RGB-based VIs was randomly selected from all 14 vegetation indices, and the best split was chosen according the lowest Gini Index. (mathrm{m}_mathrm{try}) was tested from 3 to 10, and the final value was 6.

(3)

The other parameters in the RF model were kept as default values according to the (mathrm{RandomForestRegressor}) function in (mathrm{Scikit}-mathrm{learn library}).

(4)

For each tree, the data splitting process in each internal node was repeated from the root node until a pre-defined stop condition was reached.

(5)

Similar with LR and BPNN model, the RGB-based VIs with a correlation coefficient higher than 0.7 were selected as the input variables, and the output variable is LAI.

Data analysis and performance evaluationThe repeated random sampling validation method was used to evaluate the generalization performance of different models. The training and testing dataset were randomly split 500 times. For each split, the LR, BPNN, and RF models were fitted to the training dataset, and the estimation accuracy was evaluated using the testing dataset. The coefficient of determination (R2), root mean square error (RMSE), and Akaike information criterion (AIC) of the training dataset were used for the assessment of models39, and the estimation accuracy was evaluated by R2 and RMSE of the testing dataset. Mathematically, a higher R2 corresponds to a smaller RMSE, and thus represents better model performance. The procedures of LAI inversion using UAV-based digital imagery and ML methods were shown in Fig. 4.Figure 4Flowchart of LAI inversion using UAV-based remote sensing and ML methods.Full size imageThe construction and evaluation of models was performed using Python 3.7.3 in Windows 10 operating system with Intel Core i7-9700 processor, 3.00 GHz CPU, and 32 GB RAM. The processing software is Spyder. The statistical analysis and figure plotting were performed in R × 64 4.0.3. More

Woodward SE, Krekhno Z, Finlay BB. Here, There, and Everywhere: How Pathogenic Escherichia coli Sense and Respond to Gastrointestinal Biogeography. Cell Microbiol. 2019;21:e13107.CAS 
Article 

Google Scholar 
Collins JW, Keeney KM, Crepin VF, Rathinam VAK, Fitzgerald KA, Finlay BB, et al. Citrobacter rodentium: Infection, Inflammation and the Microbiota. Nat Rev Microbiol. 2014;12:612–23.CAS 
Article 

Google Scholar 
Mowat AM, Agace WW. Regional Specialization Within the Intestinal Immune System. Nat Rev Immunol. 2014;14:nri3738.Article 

Google Scholar 
Population Dynamics – Latest Research and News | Nature [Internet]. [cited 2017 Nov 5]. Available from: https://www.nature.com/subjects/population-dynamicsAbel S, Abel zur Wiesch P, Davis BM, Waldor MK. Analysis of Bottlenecks in Experimental Models of Infection. PLoS Pathog. 2015;11:e1004823.Global Burden of Disease Study. 2013 Collaborators. Global, Regional, and National Incidence, Prevalence, and Years Lived with Disability for 301 Acute and Chronic Diseases and Injuries in 188 Countries, 1990-2013: A Systematic Analysis for the Global Burden of Disease Study 2013. Lancet Lond Engl 2015;386:743–800.Article 

Google Scholar 
WHO | Diarrhoeal Disease [Internet]. WHO. [cited 2017 Oct 22]. Available from: http://www.who.int/mediacentre/factsheets/fs330/en/.Crepin VF, Collins JW, Habibzay M, Frankel G. Citrobacter rodentium Mouse Model of Bacterial Infection. Nat Protoc. 2016;11:1851–76.CAS 
Article 

Google Scholar 
Bhinder G, Sham HP, Chan JM, Morampudi V, Jacobson K, Vallance BA. The Citrobacter rodentium Mouse Model: Studying Pathogen and Host Contributions to Infectious Colitis. J Vis Exp. 2013;19:e50222.
Google Scholar 
Barthold SW, Coleman GL, Bhatt PN, Osbaldiston GW, Jonas AM. The Etiology of Transmissible Murine Colonic Hyperplasia. Lab Anim Sci. 1976;26:889–94.CAS 
PubMed 

Google Scholar 
Deng W, Vallance BA, Li Y, Puente JL, Finlay BB. Citrobacter rodentium Translocated Intimin Receptor (Tir) is an Essential Virulence Factor Needed for Actin Condensation, Intestinal Colonization and Colonic Hyperplasia in Mice. Mol Microbiol. 2003;48:95–115.CAS 
Article 

Google Scholar 
Schauer DB, Falkow S. Attaching and Effacing Locus of a Citrobacter freundii Biotype That Causes Transmissible Murine Colonic Hyperplasia. Infect Immun. 1993;61:2486–92.CAS 
Article 

Google Scholar 
Elliott SJ, Yu J, Kaper JB. The Cloned Locus of Enterocyte Effacement From Enterohemorrhagic Escherichia coli O157:H7 is Unable to Confer the Attaching and Effacing Phenotype Upon E. coli K-12. Infect Immun. 1999;67:4260–3.CAS 
Article 

Google Scholar 
Mellies JL, Elliott SJ, Sperandio V, Donnenberg MS, Kaper JB. The Per Regulon of Enteropathogenic Escherichia coli: Identification of a Regulatory Cascade and a Novel Transcriptional Activator, the Locus of Enterocyte Effacement (LEE)-Encoded Regulator (Ler). Mol Microbiol. 1999;33:296–306.CAS 
Article 

Google Scholar 
Jarvis KG, Girón JA, Jerse AE, McDaniel TK, Donnenberg MS, Kaper JB. Enteropathogenic Escherichia coli Contains a Putative Type III Secretion System Necessary for the Export of Proteins Involved in Attaching and Effacing Lesion Formation. Proc Natl Acad Sci. 1995;92:7996–8000.CAS 
Article 

Google Scholar 
Moon HW, Whipp SC, Argenzio RA, Levine MM, Giannella RA. Attaching and Effacing Activities of Rabbit and Human Enteropathogenic Escherichia coli in Pig and Rabbit Intestines. Infect Immun. 1983;41:1340–51.CAS 
Article 

Google Scholar 
Abel S, Abel zur Wiesch P, Chang HH, Davis BM, Lipsitch M, Waldor MK. Sequence Tag–based Analysis of Microbial Population Dynamics. Nat Methods. 2015;12:223–6.CAS 
Article 

Google Scholar 
Cavalli-Sforza LL, Edwards AW. Phylogenetic analysis. Models and Estimation Procedures. Am J Hum Genet. 1967;19:233–57. 3.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang T, Abel S, Abel Zur Wiesch P, Sasabe J, Davis BM, Higgins DE, et al. Deciphering the Landscape of Host Barriers to Listeria monocytogenes Infection. Proc Natl Acad Sci . 2017;114:6334–9.CAS 
Article 

Google Scholar 
Hullahalli K, Waldor MK. Pathogen Clonal Expansion Underlies Multiorgan Dissemination and Organ-Specific Outcomes During Murine Systemic Infection. eLife 2021;10:e70910.CAS 
Article 

Google Scholar 
Petty NK, Feltwell T, Pickard D, Clare S, Toribio AL, Fookes M, et al. Citrobacter rodentium is an Unstable Pathogen Showing Evidence of Significant Genomic Flux. PLOS Pathog. 2011;7:e1002018.CAS 
Article 

Google Scholar 
Yasutomi E, Hoshi N, Adachi S, Otsuka T, Kong L, Ku Y, et al. Proton Pump Inhibitors Increase the Susceptibility of Mice to Oral Infection with Enteropathogenic Bacteria. Dig Dis Sci. 2018;63:881–9.CAS 
Article 

Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-Resolution Sample Inference From Illumina Amplicon Data. Nat Methods. 2016;13:581–3.CAS 
Article 

Google Scholar 
Krimbas CB, Tsakas S. The Genetics of Dacus Oleae. v. Changes of Esterase Polymorphism in a Natural Population Following Insecticide Control-Selection or Drift? Evol Int J Org Evol. 1971;25:454–60.Article 

Google Scholar 
Peter BM, Slatkin M. Detecting Range Expansions From Genetic Data. Evol Int J Org Evol. 2013;67:3274–89.Article 

Google Scholar 
Schwarz R, Kaspar A, Seelig J, Künnecke B. Gastrointestinal Transit Times in Mice and Humans Measured with 27Al and 19F Nuclear Magnetic Resonance. Magn Reson Med. 2002;48:255–61.Article 

Google Scholar 
McMurdie PJ, Holmes S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PloS One. 2013;8:e61217.CAS 
Article 

Google Scholar 
Petty NK, Bulgin R, Crepin VF, Cerdeño-Tárraga AM, Schroeder GN, Quail MA, et al. The Citrobacter rodentium Genome Sequence Reveals Convergent Evolution with Human Pathogenic Escherichia coli. J Bacteriol. 2010;192:525–38.CAS 
Article 

Google Scholar 
Mundy R, Pickard D, Wilson RK, Simmons CP, Dougan G, Frankel G. Identification of a Novel Type IV Pilus Gene Cluster Required for Gastrointestinal Colonization of Citrobacter rodentium. Mol Microbiol. 2003;48:795–809.CAS 
Article 

Google Scholar 
Darwin AJ, Miller VL. Identification of Yersinia enterocolitica Genes Affecting Survival in an Animal Host Using Signature-Tagged Transposon Mutagenesis. Mol Microbiol. 1999;32:51–62.CAS 
Article 

Google Scholar 
Maroncle N, Balestrino D, Rich C, Forestier C. Identification of Klebsiella pneumoniae Genes Involved in Intestinal Colonization and Adhesion Using Signature-Tagged Mutagenesis. Infect Immun. 2002;70:4729–34.CAS 
Article 

Google Scholar 
Wiles S, Dougan G, Frankel G. Emergence of a ‘Hyperinfectious’ Bacterial State After Passage of Citrobacter rodentium Through the Host Gastrointestinal Tract. Cell Microbiol. 2005;7:1163–72.CAS 
Article 

Google Scholar 
Wiles S, Clare S, Harker J, Huett A, Young D, Dougan G, et al. Organ Specificity, Colonization and Clearance Dynamics In Vivo Following Oral Challenges with the Murine Pathogen Citrobacter rodentium. Cell Microbiol. 2004;6:963–72.CAS 
Article 

Google Scholar 
Kitamoto S, Nagao-Kitamoto H, Kuffa P, Kamada N. Regulation of Virulence: The Rise and Fall of Gastrointestinal Pathogens. J Gastroenterol. 2016;51:195–205.Article 

Google Scholar 
Takumi K, de Jonge R, Havelaar A. Modelling Inactivation of Escherichia coli by Low pH: Application to Passage Through the Stomach of Young and Elderly People. J Appl Microbiol. 2000;89:935–43.CAS 
Article 

Google Scholar 
Pienaar JA, Singh A, Barnard TG. Acid-Happy: Survival and Recovery of Enteropathogenic Escherichia coli (EPEC) in Simulated Gastric Fluid. Micro Pathog. 2019;128:396–404.CAS 
Article 

Google Scholar 
McConnell EL, Basit AW, Murdan S. Measurements of Rat and Mouse Gastrointestinal pH, Fluid and Lymphoid Tissue, and Implications for In-Vivo Experiments. J Pharm Pharm. 2008;60:63–70.CAS 
Article 

Google Scholar 
Tan A, Petty NK, Hocking D, Bennett-Wood V, Wakefield M, Praszkier J, et al. Evolutionary Adaptation of an AraC-Like Regulatory Protein in Citrobacter rodentium and Escherichia Species. Infect Immun. 2015;83:1384–95.CAS 
Article 

Google Scholar 
Sanchez KK, Chen GY, Schieber AMP, Redford SE, Shokhirev MN, Leblanc M, et al. Cooperative Metabolic Adaptations in the Host Can Favor Asymptomatic Infection and Select for Attenuated Virulence in an Enteric Pathogen. Cell 2018;175:146–.e15.CAS 
Article 

Google Scholar 
Cunningham R, Jones L, Enki DG, Tischhauser R. Proton Pump Inhibitor Use as a Risk Factor for Enterobacteriaceal Infection: A Case-Control Study. J Hosp Infect. 2018;100:60–4.CAS 
Article 

Google Scholar 
Kelly OB, Dillane C, Patchett SE, Harewood GC, Murray FE. The Inappropriate Prescription of Oral Proton Pump Inhibitors in the Hospital Setting: A Prospective Cross-Sectional Study. Dig Dis Sci. 2015;60:2280–6.CAS 
Article 

Google Scholar 
Federici S, Suez J, Elinav E. Our Microbiome: On the Challenges, Promises, and Hype. Results Probl Cell Differ. 2020;69:539–57.CAS 
Article 

Google Scholar  More

Gardner, T. A., Côté, I. M., Gill, J. A., Grant, A. & Watkinson, A. R. Long-term region-wide declines in Caribbean corals. Science 301, 958–960 (2003).ADS 
PubMed 
Article 

Google Scholar 
Bruno, J. F. & Selig, E. R. Regional decline of coral cover in the Indo-Pacific: Timing, extent, and subregional comparisons. PLoS ONE 2, e711 (2007).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
De’Ath, G., Fabricius, K. E., Sweatman, H. & Puotinen, M. The 27-year decline of coral cover on the Great Barrier Reef and its causes. Proc. Natl. Acad. Sci. U. S. A. 109, 17995–17999 (2012).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hughes, T. P., Graham, N. A. J., Jackson, J. B. C., Mumby, P. J. & Steneck, R. S. Rising to the challenge of sustaining coral reef resilience. Trends Ecol. Evol. 25, 633–642 (2010).PubMed 
Article 

Google Scholar 
Pandolfi, J. M., Connolly, S. R., Marshall, D. J. & Cohen, A. L. Projecting coral reef futures under global warming and ocean acidification. Science 333, 418–422 (2011).ADS 
PubMed 
Article 

Google Scholar 
Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).ADS 
PubMed 
Article 

Google Scholar 
Bindoff, N. L. et al. Chapter 5: Changing Ocean, Marine Ecosystems, and Dependent Communities. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (2019).Richmond, R. H. Reproduction and recruitment in corals: Critical links in the persistence of reefs. In Life and Death of Coral Reefs (ed. Birkeland, C. E.) 175–197 (Springer, 1997).Chapter 

Google Scholar 
Trapon, M. L., Pratchett, M. S., Hoey, A. S. & Baird, A. H. Influence of fish grazing and sedimentation on the early post-settlement survival of the tabular coral Acropora cytherea. Coral Reefs 32, 1051–1059 (2013).ADS 
Article 

Google Scholar 
Gallagher, C. & Doropoulos, C. Spatial refugia mediate juvenile coral survival during coral–predator interactions. Coral Reefs 36, 51–61 (2017).ADS 
Article 

Google Scholar 
Vermeij, M. J. A. & Sandin, S. A. Density-dependent settlement and mortality structure the earliest life phases of a coral population. Ecology 89, 1994–2004 (2008).PubMed 
Article 

Google Scholar 
Vermeij, M. J. A., Smith, J. E., Smith, C. M., Vega Thurber, R. & Sandin, S. A. Survival and settlement success of coral planulae: Independent and synergistic effects of macroalgae and microbes. Oecologia 159, 325–336 (2009).ADS 
PubMed 
Article 

Google Scholar 
Ricardo, G. F., Jones, R. J., Nordborg, M. & Negri, A. P. Settlement patterns of the coral Acropora millepora on sediment-laden surfaces. Sci. Total Environ. 609, 277–288 (2017).ADS 
PubMed 
Article 

Google Scholar 
Brunner, C. A., Uthicke, S., Ricardo, G. F., Hoogenboom, M. O. & Negri, A. P. Climate change doubles sedimentation-induced coral recruit mortality. Sci. Total Environ. 768, 143897 (2021).ADS 
PubMed 
Article 

Google Scholar 
Birrell, C. L., McCook, L. J., Willis, B. L. & Diaz-Pulido, G. A. Effects of benthic algae on the replenishment of corals and the implications for the resilience of coral reefs. In Oceanography and Marine Biology: An Annual Review 25–63 (CRC Press, 2008).Chapter 

Google Scholar 
Karcher, D. B. et al. Nitrogen eutrophication particularly promotes turf algae in coral reefs of the central Red Sea. PeerJ 2020, 1–25 (2020).
Google Scholar 
Kirschner, C. M. & Brennan, A. B. Bio-inspired antifouling strategies. Annu. Rev. Mater. Res. 42, 211–229 (2012).ADS 
Article 

Google Scholar 
Webster, N. S. et al. Metamorphosis of a scleractinian coral in response to microbial biofilms. Appl. Environ. Microbiol. 70, 1213–1221 (2004).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Heyward, A. J. & Negri, A. P. Natural inducers for coral larval metamorphosis. Coral Reefs 18, 273–279 (1999).Article 

Google Scholar 
Negri, A. P., Webster, N. S., Hill, R. T. & Heyward, A. J. Metamorphosis of broadcast spawning corals in response to bacteria isolated from crustose algae. Mar. Ecol. Prog. Ser. 223, 121–131 (2001).ADS 
Article 

Google Scholar 
Tebben, J. et al. Induction of larval metamorphosis of the coral Acropora millepora by tetrabromopyrrole isolated from a Pseudoalteromonas bacterium. PLoS ONE 6, 1–8 (2011).Article 

Google Scholar 
Sneed, J. M., Sharp, K. H., Ritchie, K. B. & Paul, V. J. The chemical cue tetrabromopyrrole from a biofilm bacterium induces settlement of multiple Caribbean corals. Proc. R. Soc. B Biol. Sci. 281, 1–9 (2014).
Google Scholar 
Tebben, J. et al. Chemical mediation of coral larval settlement by crustose coralline algae. Sci. Rep. 5, 1–11 (2015).Article 

Google Scholar 
Carpenter, R. C. & Edmunds, P. J. Local and regional scale recovery of Diadema promotes recruitment of scleractinian corals. Ecol. Lett. 9, 268–277 (2006).Article 

Google Scholar 
Box, S. J. & Mumby, P. J. Effect of macroalgal competition on growth and survival of juvenile Caribbean corals. Mar. Ecol. Prog. Ser. 342, 139–149 (2007).ADS 
Article 

Google Scholar 
Linares, C., Cebrian, E. & Coma, R. Effects of turf algae on recruitment and juvenile survival of gorgonian corals. Mar. Ecol. Prog. Ser. 452, 81–88 (2012).ADS 
Article 

Google Scholar 
McCook, L. J., Jompa, J. & Diaz-Pulido, G. Competition between corals and algae on coral reefs: A review of evidence and mechanisms. Coral Reefs 19, 400–417 (2001).ADS 
Article 

Google Scholar 
Nugues, M. M., Smith, G. W., Van Hooidonk, R. J., Seabra, M. I. & Bak, R. P. M. Algal contact as a trigger for coral disease. Ecol. Lett. 7, 919–923 (2004).Article 

Google Scholar 
Fong, J. et al. Allelopathic effects of macroalgae on Pocillopora acuta coral larvae. Mar. Environ. Res. 151, 104745. https://doi.org/10.1016/j.marenvres.2019.06.007 (2019).Article 
PubMed 

Google Scholar 
Hauri, C., Fabricius, K. E., Schaffelke, B. & Humphrey, C. Chemical and physical environmental conditions underneath mat- and canopy-forming macroalgae, and their effects on understorey corals. PLoS ONE 5, 1–9 (2010).Article 

Google Scholar 
Bay, L. K. et al. Reef Restoration and Adaptation Program : Intervention Technical Summary. A report provided to the Australian Government by the Reef Restoration and Adaptation Program. (2019).Anthony, K. R. N. et al. Interventions to help coral reefs under global change—A complex decision challenge. PLoS ONE 15, 1–14 (2020).Article 

Google Scholar 
Vardi, T. et al. Six priorities to advance the science and practice of coral reef restoration worldwide. Restor. Ecol. 29, 1–7 (2021).Article 

Google Scholar 
Heyward, A. J., Rees, M. & Smith, L. D. Coral spawning slicks harnessed for large-scale coral culture. Progr. Abstr. Int. Conf. Sci. Asp. Coral Reef Assess. Monit. Restor. 104, 188–189 (1999).
Google Scholar 
Harrison, P., Villanueva, R. & De la Cruz, D. Coral Reef Restoration using Mass Coral Larval Reseeding (Southern Cross University, 2016).
Google Scholar 
de la Cruz, D. W. & Harrison, P. L. Enhancing coral recruitment through assisted mass settlement of cultured coral larvae. PLoS ONE 15, e0242847. https://doi.org/10.1371/journal.pone.0242847 (2020).Article 

Google Scholar 
Chamberland, V. F., Snowden, S., Marhaver, K. L., Petersen, D. & Vermeij, M. J. A. The reproductive biology and early life ecology of a common Caribbean brain coral, Diploria labyrinthiformis (Scleractinia: Faviinae). Coral Reefs 36, 83–94 (2017).ADS 
Article 

Google Scholar 
Randall, C. J. et al. Sexual production of corals for reef restoration in the Anthropocene. Mar. Ecol. Prog. Ser. 635, 203–232 (2020).ADS 
Article 

Google Scholar 
Miller, M. W. et al. Settlement yields in large-scale in situ culture of Caribbean coral larvae for restoration. Restor. Ecol. https://doi.org/10.1111/rec.13512 (2021).Article 

Google Scholar 
Baria-Rodriguez, M. V., de la Cruz, D. W., Dizon, R. M., Yap, H. T. & Villanueva, R. D. Performance and cost-effectiveness of sexually produced Acropora granulosa juveniles compared with asexually generated coral fragments in restoring degraded reef areas. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 891–900 (2019).Article 

Google Scholar 
Doropoulos, C., Elzinga, J., ter Hofstede, R., van Koningsveld, M. & Babcock, R. C. Optimizing industrial-scale coral reef restoration: Comparing harvesting wild coral spawn slicks and transplanting gravid adult colonies. Restor. Ecol. 27, 758–767 (2019).Article 

Google Scholar 
Kuffner, I. B., Andersson, A. J., Jokiel, P. L., Rodgers, K. S. & MacKenzie, F. T. Decreased abundance of crustose coralline algae due to ocean acidification. Nat. Geosci. 1, 114–117 (2008).ADS 
Article 

Google Scholar 
Webster, N. S., Uthicke, S., Botté, E. S., Flores, F. & Negri, A. P. Ocean acidification reduces induction of coral settlement by crustose coralline algae. Glob. Change Biol. 19, 303–315 (2013).ADS 
Article 

Google Scholar 
Randall, C. J., Giuliano, C., Heyward, A. J. & Negri, A. P. Enhancing coral survival on deployment devices with microrefugia. Front. Mar. Sci. 8, 662263. https://doi.org/10.3389/fmars.2021.662263 (2021).Article 

Google Scholar 
Kuffner, I. B. et al. Inhibition of coral recruitment by macroalgae and cyanobacteria. Mar. Ecol. Prog. Ser. 323, 107–117 (2006).ADS 
Article 

Google Scholar 
Arnold, S. N., Steneck, R. S. & Mumby, P. J. Running the gauntlet: Inhibitory effects of algal turfs on the processes of coral recruitment. Mar. Ecol. Prog. Ser. 414, 91–105 (2010).ADS 
Article 

Google Scholar 
Speare, K. E., Duran, A., Miller, M. W. & Burkepile, D. E. Sediment associated with algal turfs inhibits the settlement of two endangered coral species. Mar. Pollut. Bull. 144, 189–195 (2019).PubMed 
Article 

Google Scholar 
Tebben, J., Guest, J. R., Sin, T. M., Steinberg, P. D. & Harder, T. Corals like it waxed: Paraffin-based antifouling technology enhances coral spat survival. PLoS ONE 9, 1–8 (2014).Article 

Google Scholar 
Almeida, E., Diamantino, T. C. & de Sousa, O. Marine paints: The particular case of antifouling paints. Prog. Org. Coat. 59, 2–20 (2007).Article 

Google Scholar 
Negri, A. P., Smith, L. D., Webster, N. S. & Heyward, A. J. Understanding ship-grounding impacts on a coral reef: Potential effects of anti-foulant paint contamination on coral recruitment. Mar. Pollut. Bull. 44, 111–117 (2002).PubMed 
Article 

Google Scholar 
Smith, L. D., Negri, A. P., Philipp, E., Webster, N. S. & Heyward, A. J. The effects of antifoulant-paint-contaminated sediments on coral recruits and branchlets. Mar. Biol. 143, 651–657 (2003).Article 

Google Scholar 
Jacobson, A. H. & Willingham, G. L. Sea-nine antifoulant: An environmentally acceptable alternative to organotin antifoulants. Sci. Total Environ. 258, 103–110 (2000).ADS 
PubMed 
Article 

Google Scholar 
Silva, V. et al. Isothiazolinone biocides: Chemistry, biological, and toxicity profiles. Molecules 25, 991. https://doi.org/10.3390/molecules25040991 (2020).Article 
PubMed Central 

Google Scholar 
da Silva, A. R., da Guerreiro, A. S., Martins, S. E. & Sandrini, J. Z. DCOIT unbalances the antioxidant defense system in juvenile and adults of the marine bivalve Amarilladesma mactroides (Mollusca: Bivalvia). Comp. Biochem. Physiol. Part C 250, 109169 (2021).
Google Scholar 
Cima, F. et al. Preliminary evaluation of the toxic effects of the antifouling biocide Sea-Nine 211TM in the soft coral Sarcophyton cf. glaucum (Octocorallia, Alcyonacea) based on PAM fluorometry and biomarkers. Mar. Environ. Res. 83, 16–22 (2013).PubMed 
Article 

Google Scholar 
Wendt, I., Backhaus, T., Blanck, H. & Arrhenius, Å. The toxicity of the three antifouling biocides DCOIT, TPBP and medetomidine to the marine pelagic copepod Acartia tonsa. Ecotoxicology 25, 871–879 (2016).PubMed 
Article 

Google Scholar 
Chen, L. et al. Identification of molecular targets for 4,5-dichloro-2-n-octyl-4-isothiazolin-3-one (DCOIT) in teleosts: New insight into mechanism of toxicity. Environ. Sci. Technol. 51, 1840–1847 (2017).ADS 
PubMed 
Article 

Google Scholar 
Martins, S. E., Fillmann, G., Lillicrap, A. & Thomas, K. V. Review: Ecotoxicity of organic and organo-metallic antifouling co-biocides and implications for environmental hazard and risk assessments in aquatic ecosystems. Biofouling 34, 34–52 (2018).PubMed 
Article 

Google Scholar 
Moon, Y. S., Kim, M., Hong, C. P., Kang, J. H. & Jung, J. H. Overlapping and unique toxic effects of three alternative antifouling biocides (Diuron, Irgarol 1051 ®, Sea-Nine 211 ® ) on non-target marine fish. Ecotoxicol. Environ. Saf. 180, 23–32 (2019).PubMed 
Article 

Google Scholar 
Su, Y. et al. Toxicity of 4,5-dichloro-2-n-octyl-4-isothiazolin-3-one (DCOIT) in the marine decapod Litopenaeus vannamei. Environ. Pollut. 251, 708–716 (2019).PubMed 
Article 

Google Scholar 
Fonseca, V. B., da Guerreiro, A. S., Vargas, M. A. & Sandrini, J. Z. Effects of DCOIT (4,5-dichloro-2-octyl-4-isothiazolin-3-one) to the haemocytes of mussels Perna perna. Comp. Biochem. Physiol Part C 232, 108737. https://doi.org/10.1016/j.cbpc.2020.108737 (2020).Article 

Google Scholar 
Ferreira, V. et al. Effects of nanostructure antifouling biocides towards a coral species in the context of global changes. Sci. Total Environ. 799, 149324 (2021).ADS 
PubMed 
Article 

Google Scholar 
de Campos, B. G. et al. A preliminary study on multi-level biomarkers response of the tropical oyster Crassostrea brasiliana to exposure to the antifouling biocide DCOIT. Mar. Pollut. Bull. 174, 112141 (2022).Article 

Google Scholar 
Maia, F. et al. Incorporation of biocides in nanocapsules for protective coatings used in maritime applications. Chem. Eng. J. 270, 150–157 (2015).Article 

Google Scholar 
Santos, J. V. N. et al. Can encapsulation of the biocide DCOIT affect the anti-fouling efficacy and toxicity on tropical bivalves?. Appl. Sci. 10, 1–12 (2020).Article 

Google Scholar 
Detty, M. R., Ciriminna, R., Bright, F. V. & Pagliaro, M. Environmentally benign sol-gel antifouling and foul-releasing coatings. Acc. Chem. Res. 47, 678–687 (2014).PubMed 
Article 

Google Scholar 
Korschelt, K., Tahir, M. N. & Tremel, W. A Step into the future: Applications of nanoparticle enzyme mimics. Chemistry 24, 9703–9713 (2018).PubMed 
Article 

Google Scholar 
Herget, K. et al. Haloperoxidase mimicry by CeO2-x nanorods combats biofouling. Adv. Mater. 29, 1–8 (2017).Article 

Google Scholar 
Korschelt, K. et al. CeO2-: X nanorods with intrinsic urease-like activity. Nanoscale 10, 13074–13082 (2018).PubMed 
Article 

Google Scholar 
Herget, K., Frerichs, H., Pfitzner, F., Tahir, M. N. & Tremel, W. Functional enzyme mimics for oxidative halogenation reactions that combat biofilm formation. Adv. Mater. 30, 1–28 (2018).Article 

Google Scholar 
Doropoulos, C., Ward, S., Marshell, A., Diaz-Pulido, G. & Mumby, P. J. Interactions among chronic and acute impacts on coral recruits: The importance of size-escape thresholds. Ecology 93, 2131–2138 (2012).PubMed 
Article 

Google Scholar 
Ji, Z. et al. Designed synthesis of CeO2 nanorods and nanowires for studying toxicological effects of high aspect ratio nanomaterials. ACS Nano 6, 5366–5380 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Herget, K. et al. Supporting Information: Haloperoxidase mimicry by CeO2-x nanorods combats biofouling. Adv. Mater. 29, 1603823 (2017).Article 

Google Scholar 
Sokolova, A. et al. Spontaneous multiscale phase separation within fluorinated xerogel coatings for fouling-release surfaces. Biofouling 28, 143–157 (2012).PubMed 
Article 

Google Scholar 
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
ImageJ Release Notes. https://imagej.nih.gov/ij/notes.html.Arganda-Carreras, I. et al. Trainable Weka Segmentation: A machine learning tool for microscopy pixel classification. Bioinformatics 33, 2424–2426 (2017).PubMed 
Article 

Google Scholar 
Arganda-Carreras, I. et al. Supplementary Data: Trainable Weka Segmentation: A Machine Learning Tool for Microscopy Pixel Classification: Trainable Weka Segmentation User Manualhttps://doi.org/10.1093/bioinformatics/btx180 (2017).Vyas, N., Sammons, R. L., Addison, O., Dehghani, H. & Walmsley, A. D. A quantitative method to measure biofilm removal efficiency from complex biomaterial surfaces using SEM and image analysis. Sci. Rep. 6, 2–11 (2016).Article 

Google Scholar 
Carbone, D. A., Gargano, I., Pinto, G., De Natale, A. & Pollio, A. Evaluating microalgae attachment to surfaces: A first approach towards a laboratory integrated assessment. Chem. Eng. Trans. 57, 73–78 (2017).
Google Scholar 
Moreno Osorio, J. H. et al. Early colonization stages of fabric carriers by two Chlorella strains. J. Appl. Phycol. 32, 3631–3644 (2020).Article 

Google Scholar 
Ricardo, G. F. et al. Impacts of water quality on Acropora coral settlement: The relative importance of substrate quality and light. Sci. Total Environ. 777, 146079. https://doi.org/10.1016/j.scitotenv.2021.146079 (2021).ADS 
Article 
PubMed 

Google Scholar 
Macadam, A., Nowell, C. J. & Quigley, K. Machine learning for the fast and accurate assessment of fitness in coral early life history. Remote Sens. 13, 1–17 (2021).Article 

Google Scholar 
Negri, A. P. & Heyward, A. J. Inhibition of Fertilization and Larval Metamorphosis of the Coral Acropora millepora (Ehrenberg, 1834) by Petroleum Products. Mar. Pollut. Bull. 41, 420–427 (2000).Article 

Google Scholar 
Nordborg, F. M., Flores, F., Brinkman, D. L., Agustí, S. & Negri, A. P. Phototoxic effects of two common marine fuels on the settlement success of the coral Acropora tenuis. Sci. Rep. 8, 1–12 (2018).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (2021).Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686. https://doi.org/10.21105/joss.01686 (2019).ADS 
Article 

Google Scholar 
Pinheiro, J., Bates, D., Debroy, S., Sarkar, D. & R Core Team. Linear and nonlinear mixed effects models contact. Linear nonlinear Mix. Eff. Model. 3, 103–135 (2021).
Google Scholar 
Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage Publications, 2019).
Google Scholar 
Lenth, R. V. Emmeans: Estimated Marginal Means. https://cran.r-project.org/package=emmeans (2021).Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
Dafforn, K. A., Lewis, J. A. & Johnston, E. L. Antifouling strategies: History and regulation, ecological impacts and mitigation. Mar. Pollut. Bull. 62, 453–465 (2011).PubMed 
Article 

Google Scholar 
Wu, R. et al. Room temperature synthesis of defective cerium oxide for efficient marine anti-biofouling. Adv. Compos. Hybrid Mater. https://doi.org/10.1007/s42114-021-00256-7 (2021).Article 

Google Scholar 
Hu, M. et al. Nanozymes in nanofibrous mats with haloperoxidase-like activity to combat biofouling. ACS Appl. Mater. Interfaces 10, 44722–44730 (2018).PubMed 
Article 

Google Scholar 
He, X. et al. Haloperoxidase mimicry by CeO2-x nanorods of different aspect ratios for antibacterial performance. ACS Sustain. Chem. Eng. 8, 6744–6752 (2020).Article 

Google Scholar 
Saxena, P. & Harish,. Nanoecotoxicological reports of engineered metal oxide nanoparticles on algae. Curr. Pollut. Rep. 4, 128–142 (2018).Article 

Google Scholar 
Xu, Y. et al. Effects of cerium oxide nanoparticles on bacterial growth and behaviors: Induction of biofilm formation and stress response. Environ. Sci. Pollut. Res. 26, 9293–9304 (2019).Article 

Google Scholar 
Xu, Y. et al. Mechanistic understanding of cerium oxide nanoparticle-mediated biofilm formation in Pseudomonas aeruginosa. Environ. Sci. Pollut. Res. 25, 34765–34776 (2018).Article 

Google Scholar 
Tang, Y. et al. Hybrid xerogel films as novel coatings for antifouling and fouling release. Biofouling 21, 59–71 (2005).PubMed 
Article 

Google Scholar 
Gunari, N. et al. The control of marine biofouling on xerogel surfaces with nanometer-scale topography. Biofouling 27, 137–149 (2011).PubMed 
Article 

Google Scholar 
Maia, F. et al. Silica nanocontainers for active corrosion protection. Nanoscale 4, 1287–1298 (2012).ADS 
PubMed 
Article 

Google Scholar 
Martins, R. et al. Effects of a novel anticorrosion engineered nanomaterial on the bivalve: Ruditapes philippinarum. Environ. Sci. Nano 4, 1064–1076 (2017).Article 

Google Scholar 
Gutner-Hoch, E. et al. Antimacrofouling efficacy of innovative inorganic nanomaterials loaded with booster biocides. J. Mar. Sci. Eng. 6, 15. https://doi.org/10.3390/jmse6010006 (2018).Article 

Google Scholar 
Negri, A. P. & Heyward, A. J. Inhibition of coral fertilisation and larval metamorphosis by tributyltin and copper. Mar. Environ. Res. 51, 17–27 (2001).PubMed 
Article 

Google Scholar 
Morse, D. E., Hooker, N., Morse, A. N. C. & Jensen, R. A. Control of larval metamorphosis and recruitment in sympatric agariciid corals. J. Exp. Mar. Biol. Ecol. 116, 193–217 (1988).Article 

Google Scholar 
Harrington, L., Fabricius, K., De’ath, G. & Negri, A. Recognition and selection of settlement substrata determine post-settlement survival in corals. Ecology 85, 3428–3437 (2004).Article 

Google Scholar 
Jorissen, H., Baumgartner, C., Steneck, R. S. & Nugues, M. M. Contrasting effects of crustose coralline algae from exposed and subcryptic habitats on coral recruits. Coral Reefs 39, 1767–1778 (2020).Article 

Google Scholar 
Figueiredo, J. et al. Toxicity of innovative anti-fouling nano-based solutions to marine species. Environ. Sci. Nano 6, 1418–1429 (2019).Article 

Google Scholar 
Shafir, S., Abady, S. & Rinkevich, B. Improved sustainable maintenance for mid-water coral nursery by the application of an anti-fouling agent. J. Exp. Mar. Biol. Ecol. 368, 124–128 (2009).Article 

Google Scholar  More