Ecology

Arneth, A. et al. Terrestrial biogeochemical feedbacks in the climate system. Nat. Geosci. 3, 525–532 (2010).CAS 
Article 

Google Scholar 
Green, J. K. et al. Regionally strong feedbacks between the atmosphere and terrestrial biosphere. Nat. Geosci. 10, 410–414 (2017).CAS 
Article 

Google Scholar 
Heimann, M. & Reichstein, M. Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature 451, 289–292 (2008).CAS 
Article 

Google Scholar 
Beer, C. et al. Temporal and among-site variability of inherent water use efficiency at the ecosystem level. Glob. Biogeochem. Cycles 23, 1–13 (2009).Article 

Google Scholar 
Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324–327 (2013).CAS 
Article 

Google Scholar 
Frank, D. C. et al. Water-use efficiency & transpiration across European forests during the Anthropocene. Nat. Clim. Change 5, 579–583 (2015).CAS 
Article 

Google Scholar 
Mastrotheodoros, T. et al. Linking plant functional trait plasticity and the large increase in forest water use efficiency. J. Geophys. Res. Biogeosci. 122, 2393–2408 (2017).Article 

Google Scholar 
Lavergne, A. et al. Observed and modelled historical trends in the water-use efficiency of plants and ecosystems. Glob. Change Biol. 25, 2242–2257 (2019).Article 

Google Scholar 
Huxman, T. E. et al. Convergence across biomes to a common rain-use efficiency. Nature 429, 651–654 (2004).CAS 
Article 

Google Scholar 
Yang, Y. et al. Contrasting responses of water use efficiency to drought across global terrestrial ecosystems. Sci. Rep. 6, 23284 (2016).CAS 
Article 

Google Scholar 
Huang, L. et al. A global examination of the response of ecosystem water-use efficiency to drought based on MODIS data. Sci. Total Environ. 601–602, 1097–1107 (2017).Article 

Google Scholar 
Reichstein, M. et al. Severe drought effects on ecosystem CO2 and H2O fluxes at three Mediterranean evergreen sites: revision of current hypotheses? Glob. Change Biol. 8, 999–1017 (2002).Article 

Google Scholar 
Reichstein, M. et al. Inverse modeling of seasonal drought effects on canopy CO2/H2O exchange in three Mediterranean ecosystems. J. Geophys. Res. Atmos. 108, 4726 (2003).Article 

Google Scholar 
Cooley, S. S. et al. Assessing regional drought impacts on vegetation and evapotranspiration: a case study in Guanacaste, Costa Rica. Ecol. Appl. 29, e01834 (2019).Article 

Google Scholar 
Medrano, H., Flexas, J. & Galmés, J. Variability in water use efficiency at the leaf level among Mediterranean plants with different growth forms. Plant Soil 317, 17–29 (2008).Article 

Google Scholar 
Soh, W. K. et al. Rising CO2 drives divergence in water use efficiency of evergreen and deciduous plants. Sci. Adv. 5, eaax7906 (2019).CAS 
Article 

Google Scholar 
Wang, M., Chen, Y., Wu, X. & Bai, Y. Forest-type-dependent water use efficiency trends across the northern hemisphere. Geophys. Res. Lett. 45, 8283–8293 (2018).Article 

Google Scholar 
Enquist, B. et al. Scaling from traits to ecosystems: developing a general trait driver theory via integrating trait-based and metabolic scaling theories. Adv. Ecol. Res. 52, 249–318 (2015).Article 

Google Scholar 
Gross, N. et al. Functional trait diversity maximizes ecosystem multifunctionality. Nat. Ecol. Evol. 1, 0132 (2017).Article 

Google Scholar 
Bagousse‐Pinguet, Y. L. et al. Testing the environmental filtering concept in global drylands. J. Ecol. 105, 1058–1069 (2017).Article 

Google Scholar 
Ponce Campos, G. E. et al. Ecosystem resilience despite large-scale altered hydroclimatic conditions. Nature 494, 349–352 (2013).CAS 
Article 

Google Scholar 
Fisher, J. B. et al. The future of evapotranspiration: global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resour. Res. 53, 2618–2626 (2017).Article 

Google Scholar 
Xue, B.-L. et al. Global patterns, trends, and drivers of water use efficiency from 2000 to 2013. Ecosphere 6, art174 (2015).Article 

Google Scholar 
Fisher, J. B. et al. ECOSTRESS: NASA’s next generation mission to measure evapotranspiration from the International Space Station. Water Resour. Res. 56, e2019WR026058 (2020).Article 

Google Scholar 
Higgins, M. A. et al. Geological control of floristic composition in Amazonian forests. J. Biogeogr. 38, 2136–2149 (2011).Article 

Google Scholar 
De Kauwe, M. G., Keenan, T. F., Medlyn, B. E., Prentice, I. C. & Terrer, C. Satellite based estimates underestimate the effect of CO2 fertilization on net primary productivity. Nat. Clim. Change 6, 892–893 (2016).Article 

Google Scholar 
Huang, M. et al. Seasonal responses of terrestrial ecosystem water-use efficiency to climate change. Glob. Change Biol. 22, 2165–2177 (2016).Article 

Google Scholar 
Lin, Y.-S. et al. Optimal stomatal behaviour around the world. Nat. Clim. Change 5, 459–464 (2015).CAS 
Article 

Google Scholar 
Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Glob. Change Biol. 17, 2134–2144 (2011).Article 

Google Scholar 
Peters, W. et al. Increased water-use efficiency and reduced CO2 uptake by plants during droughts at a continental scale. Nat. Geosci. 11, 744–748 (2018).CAS 
Article 

Google Scholar 
Cheng, L. et al. Recent increases in terrestrial carbon uptake at little cost to the water cycle. Nat. Commun. 8, 110 (2017).Article 

Google Scholar 
Fisher, J. B. ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS): Level-3 Evapotranspiration L3(ET_PT-JPL) Algorithm Theoretical Basis Document. Jet Propulsion Laboratory, California Institute of Technology (2018).Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. BioScience 54, 547–560 (2004).Article 

Google Scholar 
Heinsch, F. et al. Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations. IEEE Trans. Geosci. Remote Sens. 44, 1908–1925 (2006).Article 

Google Scholar 
Zhao, M., Heinsch, F., Nemani, R. & Running, S. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens. Environ. 95, 164–176 (2005).Article 

Google Scholar 
Ryu, Y. et al. Integration of MODIS land and atmosphere products with a coupled-process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales. Glob. Biogeochem. Cycles 25, GB4017 (2011).Article 

Google Scholar  More

Xu, Y. et al. Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016. Earth Syst. Sci. Data 12, 847–867 (2020).Article 

Google Scholar 
Meijaard, E. et al. The environmental impacts of palm oil in context. Nat. Plants 6, 1418–1426 (2020).Article 

Google Scholar 
Guillaume, T. et al. Carbon costs and benefits of Indonesian rainforest conversion to plantations. Nat. Commun. 9, 2388 (2018).Article 

Google Scholar 
Ordway, E. M. & Asner, G. P. Carbon declines along tropical forest edges correspond to heterogeneous effects on canopy structure and function. Proc. Natl Acad. Sci. USA 117, 7863–7870 (2020).CAS 
Article 

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

Google Scholar 
Santoro, M. et al. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth Syst. Sci. Data 13, 3927–3950 (2021).Article 

Google Scholar 
The World Database on Protected Areas (WDPA) (UNEP-WCMC and IUCN, accessed 12 February 2020); www.protectedplanet.netMahmud, A., Rehrig, M. & Hills, G. Improving the Livelihoods of Palm Oil Smallholders: The Role of the Private Sector (FSG, 2010).Lasco, R. Forest carbon budgets in Southeast Asia following harvesting and land cover change. Sci. China 45, 55–64 (2002).Article 

Google Scholar 
Historical Greenhouse Gas Emissions (Climate Watch, accessed 6 October 2021); https://www.climatewatchdata.org/Euler, M., Schwarze, S., Siregar, H. & Qaim, M. Oil palm expansion among smallholder farmers in Sumatra, Indonesia. J. Agric. Econ. 67, 658–676 (2016).Article 

Google Scholar 
Donofrio, S., Rothrock, P. & Leonard, J. J. F. T. Supply Change: Tracking Corporate Commitments to Deforestation-free SupplyChains, 2017 (Forest Trends, 2017).Rist, L., Feintrenie, L. & Levang, P. The livelihood impacts of oil palm: smallholders in Indonesia. Biodivers. Conserv. 19, 1009–1024 (2010).Article 

Google Scholar 
Saadun, N. et al. Socio-ecological perspectives of engaging smallholders in environmental-friendly palm oil certification schemes. Land Use Policy 72, 333–340 (2018).Article 

Google Scholar 
Hansen, M. C., Stehman, S. V. & Potapov, P. V. Quantification of global gross forest cover loss. Proc. Natl Acad. Sci. USA 107, 8650 (2010).CAS 
Article 

Google Scholar 
Santoro, M. & Cartus, O. ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the year 2017 v.1 (Centre for Environmental Data Analysis, 2019); https://doi.org/10.5285/bedc59f37c9545c981a839eb552e4084Busch, J. et al. Reductions in emissions from deforestation from Indonesia’s moratorium on new oil palm, timber, and logging concessions. Proc. Natl Acad. Sci. USA 112, 1328–1333 (2015).CAS 
Article 

Google Scholar 
McGarigal, K., Cushman, S. A. & Ene, E. FRAGSTATS v.4: spatial pattern analysis program for categorical and continuous maps (Univ. Massachusetts, 2012). More

Identification of two Zn/Co responsive proteins in diatomsZn and Co growth rate experiments in which Zn or Co (omitting the other) were added to the growth media were conducted and harvested for proteomic analysis. Growth rates of the marine diatom species Thalassiosira pseudonana CCMP1335, Phaeodactylum tricornutum CCMP632, Pseudo-nitzschia delicatissima UNC1205 and Chaetoceros sp. RS19 (Chaetoceros RS19 herein) were conducted in a consistent media composition to allow for intercomparison among species (see “Methods”). The onset of growth limitation by Zn and Co was evident by decreased growth rates under low [Zn2+] and [Co2+], and the ability to use Co to restore Zn-limited growth was species-specific and consistent with prior results for the diatoms T. pseudonana, P. tricornutum and P. delicatissima (Fig. 1a, b)9 and for other eukaryotic algae2,8,10. Growth rates of Chaetoceros RS19 were not stimulated by increasing [Co2+] up to 23.5 pM in the absence of added Zn. This inability to substitute Co for Zn in Chaetoceros RS19 was clearly distinct from that of other diatoms, but was consistent with previous observations in Chaetoceros calcitrans10, implying a genus-wide attribute.Fig. 1: Growth responses of diatoms to varying [Zn2+] and [Co2+] and initial detection of ZCRPs in T. pseudonana.Growth rates of four diatoms over a range of a [Zn2+] and b [Co2+]. Data are presented as mean values of biological duplicate cultures. Data is available in Supplementary Table 1. Global proteomic analyses comparing the proteomes of pooled biological duplicate cultures (n = 2) of T. pseudonana in c high vs. low added Zn and d high vs. low added Co. Each point is an identified protein with the mean of technical triplicate abundance scores in one treatment plotted against the mean of abundance scores in another treatment. The solid line denotes 1:1 abundance. Error bars in c are the standard deviation of technical triplicate measurements.Full size imageThe proteome as a function of Zn2+ and Co2+ was explored in the marine diatom T. pseudonana harvested during log phase growth. Global proteomic analysis comparing low (1.1 pM) versus high (10.2 pM) added [Zn2+] and low (2.3 pM) versus high (23.4 pM) added [Co2+] revealed two uncharacterized diatom proteins that greatly increased in abundance at low [Zn2+] or [Co2+] (Fig. 1c, d). These proteins were annotated as a CobW/HypB/UreG, nucleotide binding domain and a bacterial extra-cellular solute binding domain, respectively, within the manually curated JGI Thaps3 T. pseudonana genome17 and were identified in T. pseudonana cultures with high confidence (≥9 exclusive unique peptides, 100% protein probability; Supplementary Fig. 1). BLAST sequence alignments showed these proteins to be homologous with CobW-like proteins (with 31.69% identity relative to Pseudomonas denitrificans CobW) and with the bacterial nickel transport protein NikA (with 30.5% identity relative to E. coli NikA), respectively. Based on their clear response to Zn and Co in the proteomes of multiple diatom species (Fig. 2a–d), the lack of definitive annotations in diatoms, and their genetic distance from bacterial homologs, these proteins are referred to as ZCRP-A and ZCRP-B (Zn/Co Responsive Protein A and B) in this study. Abundance patterns of these proteins were also investigated in P. tricornutum, P. delicatissima and Chaetoceros RS19. ZCRP-A spectral abundance counts were significantly (Kendall correlation, p  10 times. j Topology predictions from five sub-methods (OCTOPUS, Philius, PolyPhobius, SCAMPI, and SPOCTOPUS), consensus prediction (TOPCONS), and predicted ΔG values for P. tricornutum ZCRP-B generated using the TOPCONS webserver (https://topcons.cbr.su.se/)27,28. k Extent of Co uptake after 24 h for wild-type (WT), ZCRPA-knockout (KO), and ZCRPA-overexpression (OE) lines of P. tricornutum normalized to fluorescence units (fsu). Data are presented as mean values ± the standard deviation of biological triplicate cultures (n = 3). Individual data points are overlaid as white circles. The extent of Co uptake was found to be significantly larger in the ZCRPA-OE line compared to the wild-type via one-way ANOVA (f(3) = 23.16, p = 0.000268) and post hoc Dunnett test (p = 0.00048).Full size imageTo date, connections between COG0523 proteins and utilization of Zn and Co have been explored primarily in prokaryotic organisms. For example, the COG0523 protein CobW has a role in vitamin B12 biosynthesis and thus Co use19,21. In contrast, a subgroup of other COG0523 proteins (YjiA, YeiR, ZigA, and ZagA) have been implicated in Zn2+ metabolism8,13,14,15,16, and a client protein to the metallochaperone ZagA in Bacillus subtilis has been identified22.Compared to bacteria, less is known about the function of COG0523 proteins in marine phytoplankton, though COG0523 protein family members are known to occur in all kingdoms8,23. A recent study described the presence of COG0523 domain proteins upregulated under low Zn in the coccolithophore Emiliania huxleyi, but without further functional characterization24, implying a potential Zn-related function of a COG0523 protein in a marine alga distinct from the marine diatoms included in this study.Although various proteins belonging to the COG0523 subgroup share similar conserved domains, they possess different metal binding abilities and thus likely have different functions among the diverse organisms in which they are found. For example, recent work has established that CobW preferentially binds Co2+ as the cognate metal and acts as a Co2+ chaperone ultimately supplying vitamin B12 in bacteria, whereas the closely related putative metal chaperones YeiR and YjiA (homologs of CobW) bind Zn2+19. We can infer from homology and the response to low Zn and low Co in the present study that Zn2+ and Co2+ are likely both cognate metals for diatom ZCRP-A. Further metal binding and affinity assays can confirm and characterize metal binding in this protein.Frustule morphologyPhenotypic plasticity in P. tricornutum is well documented. Two basic cell morphotypes, fusiform and triradiate, are found in natural liquid environments. It is thought that by adopting the triradiate form, a cell increases its surface area and thus the area of membrane available for enzymatic activity or molecular diffusion of dissolved inorganic carbon (DIC) into the cell. The triradiate form is known to be more common under DIC limiting conditions, which supports this hypothesis25. Distinct morphological differences resulted from the knockout (KO) of the ZCRP-A gene. In P. tricornutum, ZCRP-A knockout cells consistently adopted a triradiate shape while wild-type cells were fusiform (Fig. 4i). Normally, triradiate cells of P. tricornutum spontaneously revert to fusiform across generations26, thus it is notable that ZCRP-A knockout cells have consistently maintained their triradiate shape for 4+ years in culture irrespective of media [Zn2+]. As Zn2+ is the predominant metal cofactor used in diatom CAs, the adoption of the triradiate form in knockout P. tricornutum cells may be a response to a disruption of the carbon concentrating mechanism caused by a reduction in Zn acquisition capability due to ZCRP-A knockout. This is consistent with the observed relative increase in Mn2+-utilizing CA (ι-CA) in the knockout line compared to the wild-type (Supplementary Fig. 5).ZCRP-B sequence analysis and cellular localizationUnlike COG0523 proteins, the relationship of ZCRP-B abundance to environmental Zn and Co concentrations does not appear to have been previously described. Topology predictions of P. tricornutum ZCRP-B using TOPCONS27,28 revealed a single predicted transmembrane domain near the N-terminus, with the majority of the protein predicted to be oriented outside the membrane (Fig. 4j). Overexpression and fluorescent tagging of ZCRP-B confirmed localization to the cell membrane (Fig. 4e–h; Supplementary Fig. 3b). A single predicted transmembrane domain contrasts with the Zrt/Irt-like divalent metal transporters (ZIPs) in eukaryotic algae, which have 7+ transmembrane domains and are key Zn transporters in many organisms29,30. It is therefore most likely that ZCRP-B is not a transporter itself, but one part of a multi-protein membrane complex and potentially interacts with the ZIP system. A sequence database similarity search (BLASTp, NCBI) found the ZCRP-B protein to be homologous with NikA, a protein subunit of the bacterial ATP-binding cassette (ABC) type Ni transport system protein Nik (30.5% identity with E. coli NikA, E = 7e−49, Supplementary Fig. 6). This transporter is well characterized in bacteria and is comprised of five subunits NikA-E. NikB and NikC are two pore-forming integral inner membrane proteins, NikD and NikE are two inner membrane-associated proteins with ATPase activity, and NikA is the periplasmic component that functions as the initial metal receptor31. No proteins with homology to NikB nor NikC were detected in the P. tricornutum proteomes generated in this study. Two uncharacterized P. tricornutum proteins were homologous with NikD (28.8% identity, E = 1e−14) and NikE (34.9% identity, E = 1.33e−8), though neither had abundance trends similar to ZCRP-B, implying that their function and regulation are independent of ZCRP-B.The sequence of a functionally similar bacterial ABC transport complex, CntABCDF (cobalt nickel transporter, also known as Opp1) from Staphylococcus aureus was also compared to NikA and ZCRP-B (Supplementary Fig. 6). CntA shares 25.6% identity with ZCRP-B (E = 3e−28), and similar to NikA, is an extra-cytoplasmic solute-binding protein that transports Ni, Zn and Co. CntA functions as a Ni/Co acquisition system in Zn-limited S. aureus32. Although the Nik and Cnt systems serve Ni and Co transport in bacteria, ZCRP-B responds to Zn and Co in marine diatoms, which have a significant Zn demand. This may imply a recruitment and repurposing of this bacterial Ni transporter component as part of the Zn acquisition systems during the evolution of marine diatoms.ZCRP-B as a putative high-affinity ligandSequence similarity to the extracellular transport components NikA and CntA (Supplementary Fig. 6), localization to the plasma membrane (Fig. 4b; Supplementary Fig. 3b), and increased abundance under low Zn and Co conditions (Fig. 2b) of P. tricornutum ZCRP-B suggests a metal-binding role as part of a high-affinity transport complex. The induction of ZCRP-B expression at low [Zn2+] (Fig. 2a–c) fits the description of a high-affinity Zn uptake system observed in marine algae that is known to be induced at low free [Zn2+]33,34, suggesting that this protein is involved in an adaptive response to extremely scarce Zn availability. Furthermore, ZCRP-B could contribute to the pool of high-affinity organic ligands that complex dissolved Zn, either by dissociation from living cells or upon cell death by viral lysis and grazing, in the upper water column12,35.The identification of a membrane-associated Zn-Co responsive protein-containing putative metal-binding sites allows us to reconsider the mechanisms of cellular metal uptake in diatoms. Prior physiological experiments observed Zn uptake in marine diatoms to approach the limits of diffusion33, and predicted kinetic control with fast cell surface metal binding and uptake relative to dissociation and release back to the seawater environment36. To enable this transport capability, it was postulated that transporters might be so abundant that the membrane becomes crowded37. Here, the observation of a putative Zn-binding, membrane-associated protein with only 1 predicted transmembrane domain instead implies a separation of the Zn concentrating function at the cell surface relative to its transport into the cell. In this scenario when Zn is scarce, biosynthesis of ZCRP-B increases and is tethered to the cell surface to compete Zn away from natural dissolved Zn ligands35 and/or chelate Zn atoms that make it through the diffusive boundary layer to the membrane. In this manner, ZCRP-B would increase the surface Zn concentration in the vicinity of Zn transporters, and multiple ZCRP-B proteins could supply nearby surface ZIP transporters or be endocytosed, avoiding the predicted membrane crowding of transporters problem. Aristilde and colleagues have previously demonstrated that weak natural Zn-binding ligands containing cysteine do indeed enhance cellular Zn uptake within the diatom Thalassiosira weissflogii, with heightened effects in Zn-limited compared to Zn-replete cells38. They proposed the formation of a transient tertiary complex between the Zn-bound ligand and Zn transporters (ZIPs and heavy metal P-type ATPases) at the cell surface, which could be mediated by a surface-tethered Zn binding ligand such as ZCRP-B. Future studies could examine the mechanism of Zn exchange between ZCRP-B and Zn/Co transporters such as the ZIPs in eukaryotic algae, which were also detected at lower Zn and Co abundances in P. tricornutum but with relatively lower spectral counts (Supplementary Fig. 7a, b), consistent with this model. Furthermore, the proposed mechanism of ZCRP-B binding is similar to that of the high-affinity Fe3+ binding protein ISIP2a, previously characterized in marine algae as an iron starvation-induced protein39. ISIP2a has been characterized as a phytotransferrin involved in endocytosis-mediated high-affinity Fe uptake in P. tricornutum that acts to concentrate Fe at the cell surface and is an extracellular protein anchored to the membrane with one transmembrane domain39. As the protein sequences of P. tricornutum ZCRP-B and ISIP2a share no significant similarity, it is possible that the uptake mechanism of ZCRP-B is similar to that of ISIP2a, but specific to high-affinity Zn and Co uptake rather than Fe. This suggests a common strategy of using extracellular membrane-anchored metal acquisition proteins in marine algae faced with metal limitation.Co uptake in wild-type and mutant diatom strainsAs ZCRP-A and ZCRP-B abundance is related to media [Co2+] (Fig. 2a–d), we investigated differences in the extent of Co uptake after 24 h among Zn/Co-limited wild-type, ZCRP-A knockout, ZCRP-A overexpression, and ZCRP-B overexpression lines of P. tricornutum via addition of the radiotracer 57Co (see methods). The extent of Co uptake among genetically modified P. tricornutum lines was observed to be significantly different via one-way ANOVA (f(3) = 23.16, p = 0.000268). A Dunnet post hoc test revealed that uptake was significantly greater (2.6× larger) in the ZCRP-A overexpression line compared to wild-type (p = 0.00048, Fig. 4k). We interpret this result as the overexpression of ZCRP-A creating a larger intracellular binding capacity for Co, thus protecting it from intracellular sensor or regulatory systems and/or efflux pumps. In contrast, no significant difference in Co uptake rates was observed when comparing ZCRP-A knockout, ZCRP-B overexpression, and wild-type lines, suggesting that P. tricornutum ZCRP-A knockout cells are capable of compensating for knockout to maintain Co metabolism, perhaps through the use of low-affinity transporters33. This is consistent with these uptake experiments being conducted using seawater media with a relatively abundant concentration of Zn (background of 7.7 pM Co and 4.0 nM Zn in the absence of EDTA), thus the use of low-affinity transporters was likely sufficient to acquire Zn and Co for growth, and neither ZCRP-A knockout nor ZCRP-B overexpression would be expected to add any metabolic benefit (Fig. 4k). Moreover, if ZCRP-B is only one part of a multi-protein acquisition and transport complex as hypothesized, overexpression of the single protein may not result in enhanced functionality.Abundance patterns of CAs in two diatomsCarbonic anhydrase enzymes constitute a major reservoir of Zn and Co within marine diatoms7. Within the stroma, intracellular chloroplastic CAs are essential in supplying CO2 to RUBISCO as they convert HCO3−, the predominant species of inorganic carbon in the pyrenoid, into CO240,41. Seven subclasses of CAs have been identified in marine diatoms to date and are designated as alpha, beta, gamma, delta, zeta, theta, and iota (α, β, γ, δ, ζ, θ, and ι). While Zn2+ is the cofactor most commonly used in algal CAs, utilization of both cadmium (Cd2+) and cobalt (Co2+) in place of Zn2+ at the active site of ζ-CA (CDCA) and a δ-CA, respectively, has been previously documented2,5,42. Overall, Zn-utilizing CAs increased in abundance with increasing Zn, consistent with the need for rapid HCO3− conversion at faster growth rates (Fig. 5; Supplementary Fig. 7). Specifically, spectral abundance counts of two β-type CAs, PtCA1 and PtCA2, became abundant in high [Co2+] (23.4 pM) and [Zn2+] ( > 1.1 pM) and were inversely related to ZCRP-A abundance (Supplementary Fig. 7). Both PtCA1 and PtCA2 are known to localize to the chloroplast pyrenoid41,43. Moreover, the increasing abundance trends of the Zn-utilizing α-CAs (CA-II and CA-VI) and the θ-CA Pt43233, which localize to the periplastidial compartment, chloroplast endoplasmic reticulum, and thylakoid lumen, respectively, at higher and Zn/Co provide further evidence for this strategy of increasing CA use under Zn-replete and higher growth rate conditions (Fig. 5; Supplementary Fig. 7)43,44.Fig. 5: Comparison of α-CA, ι-CA, and ZCRP abundances.Spectral counting abundance scores of a alpha CA, iota CA, and b ZCRP-A and ZCRP-B detected in Zn and Co treatments of P. tricornutum measured by global proteomic analysis. Data are plotted as means ± the standard deviation of technical triplicate measurements of pooled biological duplicate cultures (n = 2). Protein names are shown with their corresponding JGI protein ID.Full size imageIn contrast, abundance trends of the recently discovered ι-CA were inversely related to Zn2+ (Fig. 5). Originally identified in T. pseudonana, ι-CA was found to localize to the inner chloroplast membrane surrounding the stroma and is unusual in that it prefers Mn2+ to Zn2+ as a cofactor45. In the present study, spectral counts of P. tricornutum ι-CA decreased as metal concentrations increased, similar to that observed for ZCRP-A and ZCRP-B (Fig. 5). This ι-CA response was consistent with a Zn sparing strategy under low [Zn2+] and [Co2+] used to prioritize the use of Zn2+ for other metalloenzyme functions.Due to the inverse relationship between the abundances of ZCRP-A and chloroplastic Zn2+-requiring CAs in P. tricornutum (that is, all CAs detected with the exception of ι-CA) and the various types of CAs in T. pseudonana (Supplementary Fig. 7), it seems unlikely ZCRP-A directly interacts with CAs. These results are instead consistent with the hypothesis that ZCRP-A functions as a Zn2+ allocation and prioritization mechanism during Zn limitation. The role of Zn2+ in key transcriptional and translational proteins such as RNA polymerase and ribosomal proteins is well known, and major reservoirs of Zn are associated with these transcription and translation systems in the fast-growing copiotrophic bacterium Pseudoalteromonas6. The availability of Zn in ribosomes and the ER is therefore likely also a cellular priority in diatoms, and could benefit from utilizing the putative chaperone and trafficking capability of ZCRP-A when Zn is scarce. We, therefore, posit that ZCRP-A may serve as a Zn2+ trafficking or storage protein that contributes to the prioritization and movement of Zn2+ to the ER or CER, while the Mn-utilizing Mn ι-CA compensates for the lowered Zn availability in the chloroplast. The increased biosynthesis of ZCRP-A may be an important function to shift Zn homeostasis, competing for intracellular Zn and trafficking it towards the ER or CER.Distribution of putative ZCRP homologs among oceanic taxaPutative ZCRP homologs among eukaryotic oceanic taxa were identified by BLAST searching the P. tricornutum ZCRP-A and ZCRP-B protein sequences against all available transcriptomes in the Marine Microbial Eukaryotic Transcriptome Sequencing Project (MMETSP) database, which includes over 650 assembled and annotated transcriptomes of oceanic microbial eukaryotes46. Phylogenetic analysis revealed the presence of putative ZCRP-A and ZCRP-B homologs in a wide variety of organisms belonging to the Chromista kingdom that could be further categorized into Bacillariophyceae, Dinophyceae, and Prymnesiophyceae classes (Supplementary Figs. 8 and  9). Notably, the Chaetoceros RS-19 ZCRP-A homolog did not phylogenetically cluster with the other diatoms (Bacillariophyceae), but instead appears to be more closely related to E. coli YjiA (Supplementary Fig. 8). Furthermore, the lack of the conserved G2/Switch I region in the Chaetoceros RS-19 homolog (Fig. 3) is anomalous in comparison to other putative homologs identified within the MMETSP database. Overall, ZCRPs are not exclusive to oceanic diatoms, but rather are widely distributed amongst oceanic taxa.Metaproteomic detection of ZCRP-A and ZCRP-BTo investigate the use of ZCRP-A and ZCRP-B in the natural environment, we searched metaproteomic data collected during the KM1128 METZYME (Metals and Enzymes in the Pacific) research expedition on the R/V Kilo Moana October 1–25, 2011 from Oʻahu, Hawaiʻi, to Apia, Samoa (Fig. 6a). dZn followed a nutrient-like distribution as described previously, with an average surface (40 m) dZn concentration of 1.21 nM and average deep water (3000 m) concentration of 10.37 nM47 (Fig. 6b). dCo was highly depleted in the upper photic zone as the result of biological uptake48,49 (Fig. 6c). Eukaryotic homologs of ZCRP-A and ZCRP-B were detected at multiple stations at surface ( More

To estimate the potential increase in biodiversity decline and the national level of conservation investment needed to counteract it in post-conflict Colombia, we used a model developed by Waldron et al.19. This quantitative model predicts national biodiversity status change, the biodiversity decline score (BDS), based on investment in conservation actions in relation to human development pressures. The model uses seven predictors related to the economy of each country, its biodiversity status or dynamics, and its conservation spending19.ScenariosWe used the Waldron et al.19 model to predict (1) the expected increase in biodiversity decline immediately after the peace agreement (the post-conflict period), (2) the conservation funding needed to prevent this additional decline and (3) the investment necessary to avoid biodiversity decline. We used four scenarios to examine our questions.The baseline scenario was the War BDS scenario, which estimated the BDS of the last 12 years of the conflict, before the peace agreement in 2016. Predictor variables related to human pressures were from 4–5 years before to appropriately represent the lag in the modelled effect19. We used the most recent available value of ‘strict-sense’ conservation investment19. The following three scenarios examined post-conflict options and were compared with this War BDS scenario.The Peace BDS scenario predicted the BDS for a 12-year period post-conflict. The predictor variables related to human pressures were from the 11-year period immediately after the peace agreement. We assumed the same conservation spending as for the War BDS. The Lower BDS scenario estimated the necessary investment to achieve the War BDS. This represented a situation where the biodiversity loss during the conflict did not change post-conflict. For this scenario, we held the human pressure variables the same as in the Peace BDS scenario. The Prevented BDS scenario was exactly the same as the Lower BDS scenario, but we set a target of no biodiversity decline (BDS = 0).We used the War and Peace BDS estimates to calculate the expected additional biodiversity decline post-conflict. Then, we used the model with data from the Lower BDS scenario to calculate the investment needed to prevent any additional biodiversity decline post-conflict. Finally, we used data from the Prevented BDS scenario to estimate the conservation investment necessary to halt biodiversity decline in the post-conflict period.Data for predictor variablesWe modified the predictors related to agriculture and economic growth to examine anticipated changes in human pressures. This revision allowed us to consider the expected agricultural expansion, in the form of percentage of agricultural land and growth, and economic growth, as the gross domestic product (GDP) and GDP growth. We also modified the function so that we could use it to estimate funding needs given a target BDS.For the War BDS scenario, data on GDP, GDP growth, agricultural land area and agricultural land area growth were either available or easily computed. The data for GDP and the percentage of agricultural land from 2001–2012 were obtained from The World Bank28. The agricultural land growth was calculated as the difference between the percentage of agricultural land of consecutive years, and GDP growth was calculated from the GDP per capita data from The World Bank28.For the Peace, Lower and Prevented BDS scenarios, we made projections about the predictors. For the GDP we used projections for 2017–2019, and for the GDP growth projections for 2019–2022 (ref. 33), and then selected an annual increase in the GDP growth of 0.3 percentage points for the remaining 5 years, corresponding to the most conservative estimate found in ref. 34. We then used our estimates of GDP growth for the whole time period to calculate the GDP per capita for the last 10 years, and used population projection to compute the GDP for the next 10 years.To estimate the agricultural land and growth for the Peace, Lower and Prevented BDS scenarios, we used projections on deforestation. We developed our model to reflect the immediate consequences in agricultural expansion and deforestation post-conflict. Thus, we estimated the percentage agricultural land area using projected values of deforestation35. We support this approach based on two observations. First, at least 90% of deforested land was transformed to agriculture during past years36. Second, forest transformation to agriculture has been more aggressive since the peace agreement7,10,11. Thus, the processes that fuel agricultural conversion are stronger. For each year we added the deforested area to the previous agricultural land area. We then calculated the yearly percentage agricultural land area and computed the agricultural growth as the percentage difference between the agricultural land area of consecutive years. We took the minimum and maximum values of deforestation projections to create best- and worst-case scenarios.We acknowledge that our use of the Waldron et al.19 model has limitations because we did not update all the predictors. Specifically, two ‘inertia’ terms that account for the effect of biodiversity decline occurring immediately before the time period of interest19. The coefficients associated with these terms have a positive effect on the BDS, which means that a more intense decline in the past will increase the predicted biodiversity decline. Given the increase in human pressures, the actual inertia terms are probably larger than the ones we used. Thus, the Peace BDS and the actual increase in biodiversity decline post-FARC may be larger.The ModelTo create a broad proxy for the expected cost of potential conservation interventions across Colombia, we estimated the OCC for agriculture at the 1 km2 scale. We estimated the OCC by building a spatially explicit probability model of forest conversion to agriculture and then paired it with the net present value of the expected return of different agricultural activities.We calculated the OCC following the methodology proposed by Naidoo and Adamowicz24. Their approach models the expected net present value of potential net rents resulting from agricultural uses of a forested parcel, while accounting for the probability of conversion to agriculture. Provided that each agricultural use k has its own annual expected return per area of land Rk, and that each parcel i has a probability of conversion Pik from forest to agricultural use k, the expected value for a given discount rate δ is$${{{mathrm{OCC}}}} = mathop {sum}limits_{i = 1}^{{I}} {mathop {sum}limits_{k = 1}^{{K}} {{{P}}_{i,k}} } frac{{{{R}}_k}}{{delta }}$$
(1)
Thus, the OCC of an area composed of several parcels is equal to the sum of the expected returns of the probable agricultural uses, weighted according to their probability of conversion, in each of the parcels, summed across all of the parcels.We calculated the OCC for forested areas in three steps. First, we built a probability model to obtain the general risk of forest conversion (Pdef). Next, we built a second model that, given that a parcel had been transformed, predicted the probability of forest conversion to different types of agricultural activities (({{P}}_{{{{mathrm{ag}}}}_k})). We used both models to compute the total probability of conversion to each type of agricultural activity k in a parcel i (({{P}}_{ik} = {{P}}_{{{{mathrm{def}}}}_i} times {{P}}_{{{{mathrm{ag}}}}_{i,k}})). We then estimated the net present value of the expected return of each agricultural activity (Rk/δ) using literature and commercial prices and the costs of agricultural products.Types of agricultural land use modelledOur OCC model needed to represent relevant agricultural activities. Below, we justify our selection of three types of agricultural land uses: cattle ranching, coca crops and other crops.Cattle ranching is expected to be a major driver of post-conflict deforestation11. This activity has accounted for 50% of deforestation, in the form of forest conversion to pasture, in past years36, and has considerably expanded post-conflict7.Illegal coca crops are expected to be, and have been observed to be, an important driver of post-conflict deforestation12. This activity is at risk of increase where the withdrawal of FARC and the absence of state presence left a ‘power vacuum’ that facilitated other illegal groups gaining control of such crops in the territory7,11,12. Indeed, evidence shows that deforestation associated with coca cultivation increased as the conflict became less intense37.Other crops were grouped into a single category with cattle ranching due to their small percentage contribution to forest conversion in our time frame (3%) compared with cattle ranching and coca crops (47 and 50%, respectively). We proxy for the extent of all other crops by using data on the distribution of three relevant agricultural products in the post-conflict period: cacao, oil palm and coffee. The cacao crop has high potential in most of the key post-conflict areas in Colombia, so it could have a major role in the peace transition38. Oil palm is important owing to its steep increase in cultivation during the last few years12, to the point that Colombia is now the largest producer in South America39. The relevance of coffee resides in its impact on the rural population, given that coffee crops are the only source of income for approximately 563,000 families and generates over 726,000 rural jobs40.Landscape features dataWe selected ten factors relevant to deforestation in Colombia to model the probability of forest conversion: proximity to roads, presence of FARC (binary: presence or no presence), population density, slope23, elevation, proximity to deforested areas, to rivers, to mining areas and to oil wells, and belonging to national and regional PAs10. National PAs restrict economic activities and are managed by the System of National Natural Parks, while regional PAs allow multiple-use activities and are managed by regional environmental authorities8,41. We did not include indigenous reserves or Afro-Colombian lands.We used deforested areas from 1990 to 2000 from the Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM)42, the water bodies map from the Department of Environment and Sustainable Development43 and maps from the Instituto Geográfico Agustín Codazzi (IGAC)44 to calculate the distance to already deforested areas, rivers, roads, mining areas and oil wells. The elevation map was obtained from NASA’s (National Aeronautics and Space Administration’s) Land Topography digital images45, and we calculated the slope using the elevation map. We computed population density as the mean value of the 32 mainland administrative departments from 2000 to 2012 using data from the Departamento Administrativo Nacional de Estadística46 (DANE; see Supplementary Table 3 for dataset details). We obtained a map showing the presence of FARC from the Fundación Paz y Reconciliación (PARES)47. All spatial data calculations were performed using software QGIS (https://www.qgis.org/en/site/, version 3.12.2) and R (https://www.r-project.org/, version 3.6.2).Forest conversion and agricultural use modelWe used a two-stage modelling process. First, we modelled the probability of an area being deforested by any driver (not exclusively due to agricultural expansion), using the total deforested area in the country in a 12-year period to parametrize our model (forest conversion model). Second, we modelled the probability that the deforestation was due to a particular agricultural activity (agricultural use model). To parametrize this second model, we used patches of land that were indeed transformed to an agricultural use in this same 12-year period. We combined these two models to obtain the probability that a patch of land was deforested to a particular agricultural activity.We used a binomial logistic regression model to build our forest conversion model, which estimates the probability of forest conversion (Pdef). We used the land cover change from 2000 to 2012 across the country, available from IDEAM42, and reclassified each pixel cell as forested or transformed. We used the bayesglm function from the R arm package48.For our agricultural use model, we built a second binomial logistic regression model to estimate ({{P}}_{{{{mathrm{ag}}}}_k}), the probability of conversion to each type of agricultural activity (cattle and other crops or coca crops) for a parcel that had been transformed. We employed data on forested areas in 2000 that had been converted by 2012. The coca crops cover map was obtained from the Sistema Integrado de Control de Cultivos Ilícitos (BIESIMCI)49. For the cattle ranching map, we used forested areas converted to pasture. Our other crop data contained temporary and permanent crops obtained from a land cover map43.It should be noted that in logistic regression models, the probability of conversion does not change in a linear fashion, but the ratio of probabilities (odds) does. For the agricultural model, the odds describe the probability of conversion to coca crops over the joint probability of conversion to cattle and other crops. This implies that the variation between the probabilities, not the probability itself, changes constantly.To check for spatial autocorrelation, we plotted spatial correlograms of the models’ residuals with Moran’s I. Because spatial patterns were present, we subsampled for pixel cells at a minimum distance of 20 km between points, which reduced the spatial effects adequately for our purposes, although it was most effective for the forest conversion model (Extended Data Fig. 1). We checked for collinearity in the predictor variables using variance inflation factor scores and removed the variables with a value >3 (distance to mines and oil wells; Supplementary Tables 4 and 5). We performed tenfold cross-validation to test the prediction accuracy of the models. This process splits the data into ten subsets and repeatedly fits the model with the data of nine of the subsets to compare its predictions with the remaining subset. We calculated the percentage of correct predictions (overall accuracy) each time and computed the mean as the final forecasting accuracy indicator.Estimation of annual net rentWe estimated the net present values of the expected return of each agricultural activity to estimate the OCC of forested areas in Colombia. For cattle, we used annual net rent from a beef company50. The total annual net rent for other crops was calculated as the weighted average of the net rents for oil palm, cacao and coffee proportional to their land area in 2016 and 2017 (refs. 51,52,53). For coca crops, we used the average net profit for farmers who sell coca leaves54. We selected three discount rate values: 5, 10 and 20% (Supplementary Tables 6 and 7).Predicting forest conversion and OCCTo predict the probability of forest conversion, we updated our spatial information on roads, deforested areas from 2007 to 2017 (ref. 42), FARC presence as the presence of FARC dissidents and deserters in 2017 (ref. 47), and population density as the mean population density by department from 2017 to 2023 (ref. 55). Together with the annual net rent for each agricultural activity, we used the probabilities of conversion of the two models to compute the OCC, or expected land value, of each forested pixel cell for the three discount rates using Eq. (1).We recognize that the simplified national context of social violence when predicting the probability of forest conversion can limit the application of our results. Our models included FARC presence, and we used the presence of dissidents and deserters in this forecasting stage. However, this ignores other criminal groups that might influence the risk of forest conversion, particularly to coca crops, due to the ‘power vacuum’ left by the withdrawal of FARC and lack of state presence11. Because we overlooked the potential impact of other criminal groups, the probability of forest conversion, particularly to coca crops, could have been underestimated. This would imply an underestimation of the OCC in the areas with presence of these other criminal groups.We used the rural cadastral values56 to validate our OCC results by comparing our predicted mean land values by administrative department in the country. Although rural cadastral values might not reflect the value of illegal coca crops, they were, to the best of our knowledge, the best available data for our purposes.The STAR metricThe STAR metric is a measurement of the potential benefit to threatened and near-threatened species of actions aimed at reducing threats and restoring habitat20. The metric can be disaggregated spatially using the area of habitat for each species, showing the proportional potential contributions of conservation actions in particular regions. We focused on the STAR threat-abatement score (START) only. The START score can be further disaggregated by threat according to the contribution of each threat to the species’ risk of extinction, which allows analysis of potential abatement of species extinction risk by particular activities at particular locations. We took advantage of this trait and used the START metric in a specialized way, focusing on the threats posed by agriculture only on all the species with an area of habitat in Colombia. This resulted in 475 species considered (246 amphibians, 172 birds and 57 mammals), of which 169 are vulnerable, 124 near-threatened, 130 endangered and 52 critically endangered. Agriculture accounted for 52% of the total START. This focus on agriculture includes annual and perennial non-timber crops, wood and pulp plantations, and livestock farming and ranching, so we treated land converted to cattle and crops in the same way even though each land-use type has different impacts on species.The use of the STAR metric has some limitations associated with the spatial distribution of the threat due to agriculture. First, the STAR metric is based on documented ongoing and expected future threats to the species according to the International Union for Conservation of Nature Red List. The majority of documented threats are ongoing, thus the majority of species threatened by agriculture are already being negatively impacted. This causes uncertainty in the assumption that avoiding further agricultural conversion will reduce species extinction risk, as additional activities to mitigate the impact of current agricultural activities on the species may also be required. Nevertheless, species assessed as threatened by agriculture are known to be vulnerable to this pressure, meaning that they would almost certainly suffer negative impacts under future agricultural expansion.Second, there is uncertainty in the potential spatial distribution of agricultural expansion. Therefore, the STAR metric as we used it helped us identify sites with urgent potential benefits of avoiding agriculture. This could under-represent territories of great biodiversity value that are not currently impacted by agriculture, like the Amazon region.Prioritization mapsWe wanted to achieve a coarse methodology that could help decision-makers direct national conservation funding to the territories with the most potential benefits of halting forest conversion to agriculture. To pair the STAR scores with our modelled OCC, we divided the total range of STAR scores and OCC into high, medium and low values. Given the distribution of STAR scores, we divided the total range in the logarithmic scale. We classified each forested pixel cell into one of nine combinations of STAR scores and OCC. This analysis was later translated to the municipality resolution by calculating the mean STAR score and mean OCC of all forested pixel cells in each municipality, and applying the same classification system used at the pixel resolution. The distributions of aggregated STAR scores and OCC at the municipality resolution follow a similar pattern to the distribution by pixel cell, with small differences due to the grouping of the values in means (Extended Data Fig. 2b,c).Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Lotsy, J. P. Evolution by Means of Hybridization (Martinus Nijhoff, 1916).Abbott, R. J. et al. Hybridization and speciation. J. Evol. Biol. 26, 229–246 (2013).CAS 
PubMed 
Article 

Google Scholar 
Schumer, M., Rosenthal, G. G. & Andolfatto, P. How common is homoploid hybrid speciation? Evolution 68, 1553–1560 (2014).PubMed 
Article 

Google Scholar 
Payseur, B. A. & Rieseberg, L. H. A genomic perspective on hybridization and speciation. Mol. Ecol. 25, 2337–2360 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, Z. F. et al. Hybrid speciation via inheritance of alternate alleles of parental isolating genes. Mol. Plant 14, 208–222 (2021).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Müntzing, A. Outlines to a genetic monograph for the genus Galeopsis: with special reference to the nature and inheritance of partial sterility. Hereditas 13, 185–341 (1930).Article 

Google Scholar 
Schumer, M., Cui, R., Rosenthal, G. G. & Andolfatto, P. Reproductive isolation of hybrid populations driven by genetic incompatibilities. Plos. Genet. 11, e1005041 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Taylor, S. A. & Larson, E. L. Insights from genomes into the evolutionary importance and prevalence of hybridization in nature. Nat. Ecol. Evol. 3, 170–177 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
Kong, S. & Kubatko, L. S. Comparative performance of popular methods for hybrid detection using genomic data. Syst. Biol. 70, 891–907 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
Goulet, B. E., Roda, F. & Hopkins, R. Hybridization in plants: old ideas, new techniques. Plant Physiol. 173, 65–78 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Jiang, Y. F. et al. Differentiating homoploid hybridization from ancestral subdivision in evaluating the origin of the D lineage in wheat. N. Phytol. 228, 409–414 (2020).Article 

Google Scholar 
Rokas, A. & Holland, P. Rare genomic changes as a tool for phylogenetics. Trends Ecol. Evol. 15, 454–459 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Bapteste, E. & Philippe, H. The potential value of indels as phylogenetic markers: position of trichomonads as a case study. Mol. Biol. Evol. 19, 972–977 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Mavárez, J. et al. Speciation by hybridization in Heliconius butterflies. Nature 441, 868–871 (2006).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
Lamichhaney, S. et al. Rapid hybrid speciation in Darwin’s finches. Science 359, 224–228 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Zhang, B. W. et al. Phylogenomics reveals an ancient hybrid origin of the Persian walnut. Mol. Biol. Evol. 36, 2451–2461 (2019).CAS 
Article 

Google Scholar 
Guo, X., Thomas, D. C. & Saunders, R. M. K. Gene tree discordance and coalescent methods support ancient intergeneric hybridisation between Dasymaschalon and Friesodielsia (Annonaceae). Mol. Phylogenet. Evol. 127, 14–29 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
Winkler, H. Betulaceae. In: Pflanzenreich IV (Verlag von Wilhelm Engelmann, 1904).Li, P. Q. & Skvortsov, A. K. Betulaceae. In: Flora of China (Science Press & Missouri Botanical Garden Press, 1999).Crane, P. R. Betulaceous leaves and fruits from the British Upper Palaeocene. Bot. J. Linn. Soc. 83, 103–136 (1981).Article 

Google Scholar 
Li, P. Q. & Cheng, S. X. Betulaceae. In: Flora Reipublicae Popularis Sinicae (Science Press, 1979).Yoo, K. O. & Wen, J. Phylogeny and biogeography of Carpinus and subfamily Coryloideae (Betulaceae). Int. J. Plant Sci. 163, 641–650 (2002).Article 

Google Scholar 
Li, J. H. Sequences of low-copy nuclear gene support the monophyly of Ostrya and paraphyly of Carpinus (Betulaceae). J. Sys. Evol. 46, 333–340 (2008).
Google Scholar 
Yang, X. Y. et al. Plastomes of Betulaceae and phylogenetic implications. J. Sys. Evol. 57, 508–518 (2019).Article 

Google Scholar 
Yang, Y. Z. et al. Genomic effects of population collapse in a critically endangered ironwood tree Ostrya rehderiana. Nat. Commun. 9, 5449 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yang, X. Y. et al. A chromosome-level reference genome of the hornbeam, Carpinus fangiana. Sci. Data 7, 24 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, Y. et al. The Corylus mandshurica genome provides insights into the evolution of Betulaceae genomes and hazelnut breeding. Hortic. Res. 8, 54 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Salojärvi, J. et al. Genome sequencing and population genomic analyses provide insights into the adaptive landscape of silver birch. Nat. Genet. 49, 904–912 (2017).PubMed 
Article 
CAS 

Google Scholar 
Tajima, F. Evolutionary relationship of DNA-sequences in finite populations. Genetics 105, 437–460 (1983).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Durand, E. Y., Patterson, N., Reich, D. & Slatkin, M. Testing for ancient admixture between closely related populations. Mol. Biol. Evol. 28, 2239–2252 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Blischak, P. D., Chifman, J., Wolfe, A. D. & Kubatko, L. S. HyDe: a Python package for genome-scale hybridization detection. Syst. Biol. 67, 821–829 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Kubatko, L. S. & Chifman, J. An invariants-based method for efficient identification of hybrid species from large-scale genomic data. BMC Evol. Biol. 19, 112 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Baack, E., Melo, M. C., Rieseberg, L. H. & Ortiz-Barrientos, D. The origins of reproductive isolation in plants. N. Phytol. 207, 968–984 (2015).Article 

Google Scholar 
Sobel, J. M. & Chen, G. F. Unification of methods for estimating the strength of reproductive isolation. Evolution 68, 1511–1522 (2014).PubMed 
Article 

Google Scholar 
Imura, Y. et al. CRYPTIC PRECOCIOUS/MED12 is a novel flowering regulator with multiple target steps in Arabidopsis. Plant Cell Physiol. 53, 287–303 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kim, S.-J. & Bassham, D. C. TNO1 is involved in salt tolerance and vacuolar trafficking in Arabidopsis. Plant Physiol. 156, 514–526 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang, F. et al. Control of leaf blade outgrowth and floral organ development by LEUNIG, ANGUSTIFOLIA3 and WOX transcriptional regulators. N. Phytol. 223, 2024–2038 (2019).CAS 
Article 

Google Scholar 
Liu, Z. C., Franks, R. G. & Klink, V. P. Regulation of gynoecium marginal tissue formation by LEUNIG and AINTEGUMENTA. Plant Cell 12, 1879–1891 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sitaraman, J., Bui, M. & Liu, Z. LEUNIG_HOMOLOG and LEUNIG perform partially redundant functions during Arabidopsis embryo and floral development. Plant Physiol. 147, 672–681 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, C. L. et al. Phylotranscriptomics reveals extensive gene duplication in the subtribe Gentianinae (Gentianaceae). J. Sys. Evol. 59, 1198–1208 (2021).Article 

Google Scholar 
Morales-Briones, D. F. et al. Disentangling sources of gene tree discordance in phylogenomic data sets: testing ancient hybridizations in Amaranthaceae s.l. Syst. Biol. 70, 219–235 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
Yang, Y. Z. et al. Prickly waterlily and rigid hornwort genomes shed light on early angiosperm evolution. Nat. Plants 6, 215–222 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stull, G. W. et al. Gene duplications and phylogenomic conflict underlie major pulses of phenotypic evolution in gymnosperms. Nat. Plants 7, 1015–1025 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
Luo, X. et al. Chasing ghosts: allopolyploid origin of Oxyria sinensis (Polygonaceae) from its only diploid congener and an unknown ancestor. Mol. Ecol. 26, 3037–3049 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Grover, C. E. et al. Re-evaluating the phylogeny of allopolyploid Gossypium L. Mol. Phylogenet. Evol. 92, 45–52 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
Edger, P. P., McKain, M. R., Bird, K. A. & VanBuren, R. Subgenome assignment in allopolyploids: challenges and future directions. Curr. Opin. Plant Biol. 42, 76–80 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Doyle, J. J. & Doyle, J. L. A rapid DNA isolation procedure for small amounts of fresh leaf tissue. Phytochem. Bull. 19, 11–15 (1987).
Google Scholar 
Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. Plos ONE 9, e112963 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Burton, J. N. et al. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nat. Biotechnol. 31, 1119–1125 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr. Protoc. Bioinf. 5, 4.10.1–4.10.14 (2004).Article 

Google Scholar 
Haas, B. J. et al. Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies. Nucleic Acids Res. 31, 5654–5666 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stanke, M. et al. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 34, W435–W439 (2006).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biol. 9, R7 (2008).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bairoch, A. & Apweiler, R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28, 45–48 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Marchler-Bauer, A. et al. CDD: a conserved domain database for the functional annotation of proteins. Nucleic Acids Res. 39, D225–D229 (2011).CAS 
PubMed 
Article 
PubMed Central 

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

Google Scholar 
Conesa, A. & Götz, S. Blast2GO: a comprehensive suite for functional analysis in plant genomics. Int. J. Plant Genomics 2008, 619832 (2008).PubMed 
Article 
CAS 
PubMed Central 

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

Google Scholar 
Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ye, G. et al. De novo genome assembly of the stress tolerant forest species Casuarina equisetifolia provides insight into secondary growth. Plant J. 97, 779–794 (2019).CAS 
PubMed 
Article 

Google Scholar 
Marrano, A. et al. High-quality chromosome-scale assembly of the walnut (Juglans regia L.) reference genome. GigaScience 9, giaa050 (2020).PubMed 
PubMed Central 
Article 
CAS 

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

Google Scholar 
Löytynoja, A. Phylogeny-aware alignment with PRANK. In: Multiple Sequence Alignment Methods, Methods in Molecular Biology (Humana Press, 2014).Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, Y. P. et al. MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 40, e49 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kielbasa, S. M., Wan, R., Sato, K., Horton, P. & Frith, M. C. Adaptive seeds tame genomic sequence comparison. Genome Res. 21, 487–493 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang, C., Rabiee, M., Sayyari, E. & Mirarab, S. ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees. BMC Bioinform. 19, 153 (2018).Article 

Google Scholar 
Sukumaran, J. & Holder, M. T. DendroPy: a Python library for phylogenetic computing. Bioinformatics 26, 1569–1571 (2010).CAS 
PubMed 
Article 

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

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

Google Scholar 
Malinsky, M., Matschiner, M. & Svardal, H. Dsuite—Fast D-statistics and related admixture evidence from VCF files. Mol. Ecol. Resour. 21, 584–595 (2021).PubMed 
Article 

Google Scholar 
Hudson, R. R., Kreitman, M. & Aguadé, M. A test of neutral molecular evolution based on nucleotide data. Genetics 116, 153–159 (1987).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Mao, Y. et al. Bivalve production in China (eds. Smaal, A., Ferreira, J., Grant, J., Petersen, J, & Strand, Ø.) 51–72 (Springer, New York, 2019).CFSY. China fishery statistical yearbook. (China Agriculture Publishing House, Beijing, 2021).Muller-Feuga, A. Microalgae for aquaculture: the current global situation and future trends (ed. Richmond, A.) 352–364 (Blackwell Science, Hoboken, 2004).Lindahl, O. Mussel farming as a tool for re‐eutrophication of coastal waters: experiences from Sweden (ed. Shumway, S. E.) 217–237 (Wiley-Blackwell, Hoboken, 2011).Petersen, J. K., Hasler, B., Timmermann, K., Nielsen, P. & Holmer, M. Mussels as a tool for mitigation of nutrients in the marine environment. Mar. Pollut. Bull. 82, 137–143 (2014).CAS 

Google Scholar 
Petersen, J. K., Saurel, C., Nielsen, P. & Timmermann, K. The use of shellfish for eutrophication control. Aquacult. Int. 24, 857–878 (2016).
Google Scholar 
Hily, C., Grall, J., Chauvaud, L., Lejart, M. & Clavier, J. CO2 generation by calcified invertebrates along rocky shores of Brittany, France. Mar. Freshwater. Res. 64, 91–101 (2013).CAS 

Google Scholar 
Filgueira, R. Strohmeier, T. & Strand, Ø. Regulating services of bivalve molluscs in the context of the carbon cycle and implications for ecosystem valuation (eds. Smaal, A., Ferreira, J., Grant, J., Petersen, J. & Strand, Ø.) 231–251 (Springer, New York, 2019).Newell, R. I. Ecosystem influences of natural and cultivated populations of suspension-feeding bivalve molluscs: a review. J. Shellfish. Res. 23, 51–62 (2004).
Google Scholar 
Benemann, J. R. Microalgae aquaculture feeds. J. Appl. Phycol. 4, 233–245 (1992).
Google Scholar 
Brown, M. R. & Blackburn, I. Live microalgae as feeds in aquaculture hatcheries (eds. Allan, G. & Burnell, G.) 117–156 (Woodhead Publishing Series in Food Science, Technology and Nutrition, 2013).Thajuddin, N. & Subramanian, G. Cyanobacterial biodiversity and potential applications in biotechnology. Curr. Sci. 89, 47–57 (2005).CAS 

Google Scholar 
Caers, M., Coutteau, P. & Sorgeloos, P. Dietary impact of algal and artificial diets, fed at different feeding rations, on the growth and fatty acid composition of Tapes philippinarum (L.) spat. Aquaculture 170, 307–322 (1999).CAS 

Google Scholar 
Chen, S. M., Tseng, K. Y. & Huang, C. H. Fatty acid composition, sarcoplasmic reticular lipid oxidation, and immunity of hard clam (Meretrix lusoria) fed different dietary microalgae. Fish. Shellfish. Immunol. 45, 141–145 (2015).CAS 

Google Scholar 
Rosa, M., Ward, J. E. & Shumway, S. E. Selective capture and ingestion of particles by suspension-feeding bivalve molluscs: a review. J. Shellfish. Res. 37, 727–746 (2018).
Google Scholar 
Ward, J. E. & Shumway, S. E. Separating the grain from the chaff: particle selection in suspension-and deposit-feeding bivalves. J. Exp. Mar. Biol. Ecol. 300, 83–130 (2004).
Google Scholar 
Tang, B., Liu, B., Wang, G., Tao, Z. & Xiang, J. Effects of various algal diets and starvation on larval growth and survival of Meretrix meretrix. Aquaculture 254, 526–533 (2006).
Google Scholar 
Espinosa, E. P., Cerrato, R. M., Wikfors, G. H. & Allam, B. Modeling food choice in the two suspension-feeding bivalves, Crassostrea virginica and Mytilus edulis. Mar. Biol. 163, 1–13 (2016).
Google Scholar 
Jones, J., Allam, B. & Espinosa, E. P. Particle selection in suspension-feeding bivalves: does one model fit all?. Biol. Bull. 238, 41–53 (2020).CAS 

Google Scholar 
Pales Espinosa, E., Cerrato, R. M., Wikfors, G. H. & Allam, B. Modeling food choice in the two suspension-feeding bivalves, Crassostrea virginica and Mytilus edulis. Mar. Biol. 163, 1–13 (2016).CAS 

Google Scholar 
Barillé, L., Prou, J., Héral, M. & Bourgrier, S. No influence of food quality, but ration-dependent retention efficiencies in the Japanese oyster Crassostrea gigas. J. Exp. Mar. Biol. Ecol. 171, 91–106 (1993).
Google Scholar 
Petersen, J. K. et al. Intercalibration of mussel Mytilus edulis clearance rate measurements. Mar. Ecol. Prog. Ser. 267, 187–194 (2004).ADS 

Google Scholar 
Zhang, T. et al. Effects of environmental factors on the survival and growth of juvenile hard clam Mercenaria mercenaria (Linnaeus,1758). Oceanol. Limnol. Sin. 34, 142–149 (2003).
Google Scholar 
Matias, D. et al. The influence of different microalgal diets on European clam (Ruditapes decussatus, Linnaeus, 1758) larvae culture performances. Aquacult. Res. 46, 2527–2543 (2015).
Google Scholar 
Liao, K. et al. qPCR analysis of bivalve larvae feeding preferences when grazing on mixed microalgal diets. PLoS ONE 12, e0180730 (2017).
Google Scholar 
Sautour, B., Artigas, L. F., Delmas, D., Herbland, A. & Laborde, P. Grazing impact of micro- and mesozooplankton during a spring situation in coastal waters off the Gironde estuary. J. Plankton. Res. 22, 531–552 (2000).
Google Scholar 
Manoylov, K. M. Taxonomic identification of algae (morphological and molecular): species concepts, methodologies, and their implications for ecological bioassessment. J. Phycol. 50, 409–424 (2014).
Google Scholar 
Shokralla, S. et al. Next-generation DNA barcoding: using next-generation sequencing to enhance and accelerate DNA barcode capture from single specimens. Mol. Ecol. Resour. 14, 892–901 (2014).CAS 

Google Scholar 
Hirai, J., Hidaka, K., Nagai, S. & Ichikawa, T. Molecular-based diet analysis of the early post-larvae of Japanese sardine Sardinops melanostictus and Pacific round herring Etrumeus teres. Mar. Ecol. Prog. Ser. 564, 99–113 (2017).ADS 
CAS 

Google Scholar 
Su, M., Liu, H., Liang, X., Gui, L. & Zhang, J. Dietary analysis of marine fish species: enhancing the detection of prey-specific dna sequences via high-throughput sequencing using blocking primers. Estuar. Coast. 41, 560–571 (2018).
Google Scholar 
Talwar, C., Nagar, S., Lal, R. & Negi, R. K. Fish gut microbiome: current approaches and future perspectives. Indian J. Microbiol. 58, 397–414 (2018).CAS 

Google Scholar 
Yi, X. et al. In situ diet of the copepod Calanus sinicus in coastal waters of the South Yellow Sea and the Bohai Sea. Acta. Oceanol. Sin. 36, 68–79 (2017).CAS 

Google Scholar 
Reis, A. D., Jeffs, A. G. & Lavery, S. D. From feeding habits to food webs: exploring the diet of an opportunistic benthic generalist. Mar. Ecol. Prog. Ser. 655, 107–121 (2020).ADS 

Google Scholar 
Yeh, H. D., Questel, J. M., Maas, K. R. & Bucklin, A. Metabarcoding analysis of regional variation in gut contents of the copepod Calanus finmarchicus in the North Atlantic Ocean. Deep Sea Res. II 180, 104738 (2020).
Google Scholar 
Zeale, M. R., Howeverlin, R. K., Barker, G. L., Lees, D. C. & Jones, G. Taxon-specific PCR for DNA barcoding arthropod prey in bat faeces. Mol. Ecol. Resour. 11, 236–244 (2011).CAS 

Google Scholar 
Sherwood, A. R. & Presting, G. G. Universal primers amplify a 23s rDNA plastid marker in eukaryotic algae and cyanobacteria. J. Phycol. 43, 605–608 (2007).
Google Scholar 
Qiao, L., Chang, Z., Li, J. & Chen, Z. Phytoplankton community succession in relation to water quality changes in the indoor industrial aquaculture system for Litopenaeus vannamei. Aquaculture 527, 735441 (2020).CAS 

Google Scholar 
Vahl, O. Efficiency of particle retention in Mytilus edulis L. Ophelia 10, 17–25 (1972).
Google Scholar 
Riisgård, H. U. Efficiency of particle retention and filtration rate in 6 species of northeast American bivalves. Mar. Ecol. Prog. Ser. 45, 217–223 (1988).ADS 

Google Scholar 
Rosa, M. et al. Examining the physiological plasticity of particle capture by the blue mussel, Mytilus edulis (L.): confounding factors and potential artifacts with studies utilizing natural seston. J. Exp. Mar. Biol. Ecol. 473, 207–217 (2015).
Google Scholar 
Shumway, S. E. et al. Flow cytometry: a new method for characterization of differential ingestion, digestion and egestion by suspension feeders. Mar. Ecol. Prog. Ser. 24, 201–204 (1985).ADS 

Google Scholar 
Dupuy, C. et al. Feeding rate of the oyster Crassostrea gigas in a natural planktonic community of the mediterranean thau lagoon. Mar. Ecol. Prog. Ser. 205, 171–184 (2000).ADS 

Google Scholar 
Strøhmeier, T., Strand, Ø., Alunno-Bruscia, M., Duinker, A. & Cranford, P. J. Variability in particle retention efficiency by the mussel Mytilus edulis. J. Exp. Mar. Biol. Ecol. 412, 96–102 (2012).
Google Scholar 
Yahel, G., Marie, D., Beninger, P. G., Eckstein, S. & Genin, A. In situ evidence for pre-capture qualitative selection in the tropical bivalve Lithophaga simplex. Aquat. Biol. 6, 235–246 (2009).
Google Scholar 
Bass, A. E., Malouf, R. E. & Shumway, S. E. Growth of northern quahogs, Mercenaria mercenaria (Linnaeus, 1758) fed on picophytoplankton. J. Shellfish. Res. 9, 299–307 (1990).
Google Scholar 
Leblanc, A. et al. Determination of isotopic labeling of proteins by precursor ion scanning liquid chromatography/tandem mass spectrometry of derivatized amino acids applied to nuclear magnetic resonance studies. Rapid Commun. Mass. Spectrom. 26, 1165–1174 (2012).ADS 
CAS 

Google Scholar 
Sonier, R. et al. Picoplankton contribution to Mytilus edulis growth in an intense culture environment. Mar. Biol. 163, 73–85 (2016).
Google Scholar 
Herdman, M., Castenholz, R. W., Waterbury, J. B. & Rippka, R. Form-genus XIII. Synechococcus (eds. Boone, D. R. & Castenholz, R. W.) 508–512 (Springer, New York, 2001).Hibberd, D. J. Notes on the taxonomy and nomenclature of the algal classes Eustigmatophyceae and Tribophyceae (synonym Xanthophyceae). Bot. J. Linn. Soc. 82, 93–119 (1981).
Google Scholar 
Wei, Y. Chrysochromulina parva Lackey Prymnesiophyceae new record in China and its seasonal fluctuation in Lake Donghu, Wuhan. Acta Hydrobiol. Sin. 20, 317–321 (1996).
Google Scholar 
Stockner, J. G. & Antia, N. J. Algal picoplankton from marine and freshwater ecosystems: a multidisciplinary perspective. Can. J. Fish. Aquat. Sci. 43, 2472–2503 (1986).
Google Scholar 
Gallager, S., Waterbury, J. & Stoecker, D. Efficient grazing and utilization of the marine cyanobacterium Synechococcus sp. by larvae of the bivalve Mercenaria mercenaria. Mar. Biol. 119, 251–259 (1994).
Google Scholar 
Seychelles, L. H., Audet, C., Tremblay, R., Fournier, R. & Pernet, F. Essential fatty acid enrichment of cultured rotifers (Brachionus plicatilis, Müller) using frozen-concentrated microalgae. Aqua. Nut. 15, 431–439 (2009).CAS 

Google Scholar 
Hughes, T. G. The sorting of food particles by Abra sp. (bivalvia: tellinacea). J. Exp. Mar. Biol. Ecol. 20, 137–156 (1975).
Google Scholar 
Hernroth, B., Larsson, A. & Edebo, L. Influence on uptake, distribution and elimination of Salmonella typhimurium in the blue mussel, Mytilus edulis, by the cell surface properties of the bacteria. J. Shellfish. Res. 19, 167–174 (2000).
Google Scholar 
Rosa, M. et al. Effects of particle surface properties on feeding selectivity in the eastern oyster Crassostrea virginica and the blue mussel Mytilus edulis. J. Exp. Mar. Biol. Ecol. 446, 320–327 (2013).
Google Scholar 
Rosa, M., Ward, J. E., Holohan, B. A., Shumway, S. E. & Wikfors, G. H. Physicochemical surface properties of microalgae and their combined effects on particle selection by suspension-feeding bivalve molluscs. J. Exp. Mar. Biol. Ecol. 486, 59–68 (2017).CAS 

Google Scholar 
Grasland, B., Mitalane, J., Briandet, R., Quemener, E. & Haras, D. Bacterial biofilm in seawater: cell surface properties of early-attached marine bacteria. Biofouling 19, 307–313 (2003).CAS 

Google Scholar 
Ozkan, A. & Berberoglu, H. Physico-chemical surface properties of microalgae. Colloids. Surf. B. 112, 287–293 (2013).CAS 

Google Scholar 
Dadon-Pilosof, A. et al. Surface properties of SAR 11 bacteria facilitate grazing avoidance. Nat. Microbiol. 2, 1608–1615 (2017).
Google Scholar 
Xiao, G., Zhang, J., Cai, X., Lu, R. & Fang, J. Studies on the filtration feeding, respiration ration and excretion of Ruditapes philippinarum juvenile. J. Oceanogr. Taiwan Strait 25, 30–35 (2006).
Google Scholar 
Atkins, D. On the ciliary mechanisms and interrelationships of lamellibranchs. VII: latero-frontal cilia of the gill filaments and their phylogenetic value. Q. J. Microsc. Sci. 80, 345–433 (1938).
Google Scholar 
Owen, G. & Mccrae, J. M. Further studies on the latero-frontal tracts of bivalves. Proc. R. Soc. London. 194, 527–544 (1976).ADS 

Google Scholar 
Owen, G. Classification and the bivalve gill. Phil. Trans. R. Soc. Lond. 284, 377–385 (1978).
Google Scholar 
Ward, J. E., Sanford, L. P. & Newell, R. A new explanation of particle capture in suspension- feeding bivalve molluscs. Limnol. Oceanogr. 43, 741–752 (1998).ADS 

Google Scholar 
Winter, J. E. A review on the knowledge of suspension-feeding in lamellibranchiate bivalves, with special reference to artificial aquaculture systems. Aquaculture 13, 1–33 (1978).
Google Scholar 
Newell, C. R., Wildish, D. J. & Macdonald, B. A. The effects of velocity and seston concentration on the exhalant siphon area, valve gape and filtration rate of the mussel Mytilus edulis. J. Exp. Mar. Biol. Ecol. 262, 91–111 (2001).
Google Scholar 
Jacobs, P., Troost, K., Riegman, R., Van der, M. & J.,. Length- and weight-dependent clearance rates of juvenile mussels (Mytilus edulis) on various planktonic prey items. Helgol. Mar. Res. 69, 101–112 (2015).ADS 

Google Scholar 
Ivlev, V. S. Experimental ecology of the feeding of fish. (Yale University Press New Haven, Connecticut, 1961) p 302.Strauss, R. E. Reliability estimates for Ivlevs electivity index the forage ratio and a proposed linear index of food selection. Trans. Am. Fish. Soc. 108, 344–352 (1979).
Google Scholar 
Puig, S., Videla, F., Cona, M. I. & Monge, A. S. Use of food availability by guanacos (Lama guanicoe) and livestock in Northern Patagonia (Mendoza, Argentina). J. Arid. Environ. 47, 291–308 (2001).ADS 

Google Scholar  More

Beyaert, I. & Hilker, M. Plant odour plumes as mediators of plant–insect interactions. Biol. Rev. 89, 68–81 (2014).
Google Scholar 
Simpraga, M., Takabayashi, J. & Holopainen, J. K. Language of plants: Where is the word?. J. Integr. Plant Biol. 58, 343–349 (2016).CAS 

Google Scholar 
Bruce, T. J. A., Wadhams, L. J. & Woodcock, C. M. Insect host location: A volatile situation. Trends Plant Sci. 10, 269–274 (2005).CAS 

Google Scholar 
Bruce, T. J. A. & Pickett, J. A. Perception of plant volatile blends by herbivorous insects—Finding the right mix. Phytochemistry 72, 1605–1611 (2011).CAS 

Google Scholar 
Raguso, R. A. Wake up and smell the roses: The ecology and evolution of floral scent. Annu. Rev. Ecol. Evol. S. 39, 549–569 (2008).
Google Scholar 
Schiestl, F. P. The evolution of floral scent and insect chemical communication. Ecol. Lett. 13, 643–656 (2010).
Google Scholar 
Arimura, G., Kost, C. & Boland, W. Herbivore-induced, indirect plant defences. Biochim. Biophys. Acta. 1734, 91–111 (2005).CAS 

Google Scholar 
Hare, J. D. Ecological role of volatiles produced by plants in response to damage by herbivorous insects. Annu. Rev. Entomol. 56, 161–180 (2011).CAS 

Google Scholar 
Laothawornkitkul, J., Taylor, J. E., Paul, N. D. & Hewitt, C. N. Biogenic volatile organic compounds in the earth system. New Phytol. 183, 27–51 (2009).CAS 

Google Scholar 
Dicke, M., van Loon, J. J. A. & Soler, R. Chemical complexity of volatiles from plant induced by multiple attack. Nature Chem. Biol. 5, 317–324 (2009).CAS 

Google Scholar 
Loreto, F. & Schnitzler, J. P. Abiotic stresses and induced BVOCs. Trends Plant Sci. 15, 154–166 (2010).CAS 

Google Scholar 
Tasin, M. et al. Synergism and redundancy in a plant volatile blend attracting grapevine moth females. Phytochemistry 68, 203–209 (2007).CAS 

Google Scholar 
Riffell, J. A., Lei, H., Christensen, T. A. & Hildebrand, J. G. Characterization and coding of behaviorally significant odor mixtures. Curr. Biol. 19, 335–340 (2009).CAS 

Google Scholar 
Riffell, J. A., Lei, H. & Hildebrand, J. G. Neural correlates of behavior in the moth Manduca sexta in response to complex odors. Proc. Natl. Acad. Sci. USA 106, 19219–19226 (2009).ADS 
CAS 

Google Scholar 
Atema, J. Eddy chemotaxis and odor landscapes: Exploration of nature with animal sensors. Biol. Bull. 191, 129–138 (1996).CAS 

Google Scholar 
Conchou, L. et al. Insect odorscapes: From plant volatiles to natural olfactory scenes. Front. Physiol. 10, 972 (2019).
Google Scholar 
Riffell, J. A., Abrell, L. & Hildebrand, J. G. Physical processes and real-time chemical measurement of the insect olfactory environment. J. Chem. Ecol. 34, 837–853 (2008).CAS 

Google Scholar 
Mylne, K. R., Davidson, M. J. & Thomson, D. J. Concentration fluctuation measurements in tracer plumes using high and low frequency response detectors. Bound-Lay. Meteorol. 79, 225–242 (1996).ADS 

Google Scholar 
Finelli, C. M., Pentcheff, N. D., Zimmer-Faust, R. K. & Wethey, D. S. Odor transport in turbulent flows: Constraints on animal navigation. Limnol. Oceanogr. 44, 1056–1071 (1999).ADS 
CAS 

Google Scholar 
Murlis, J., Elkinton, J. S. & Cardé, R. T. Odor plumes and how insects use them. Annu. Rev. Entomol. 37, 505–532 (1992).
Google Scholar 
Murlis, J., Willis, M. A. & Cardé, R. T. Spatial and temporal structures of pheromone plumes in fields and forests. Physiol. Entomol. 25, 211–222 (2000).CAS 

Google Scholar 
Kennedy, J. S. The visual response of flying mosquitoes. Proc. Zool. Soc. London Ser. A 109, 221–242 (1940).
Google Scholar 
Bursell, E. Observations on the orientation of tsetse flies (Glossina pallidipes) to wind-borne odours. Physio. Entomol. 9, 133–137 (1984).
Google Scholar 
Murlis, J., Elkinton, J. S. & Cardé, R. T. Odor plumes and how insects use them. Annu. Rev. Entomol. 37, 505–532 (1992).
Google Scholar 
Kennedy, J. S., Ludlow, A. R. & Sanders, C. J. Guidance of flying male moths by wind-borne sex-pheromone. Physiol. Entomol. 6, 395–412 (1981).
Google Scholar 
Koehl, M. A. R. The fluid mechanics of arthropod sniffing in turbulent odor plumes. Chem. Senses 31, 93–105 (2006).CAS 

Google Scholar 
Baker, T. C., Willis, M. A., Haynes, K. F. & Phelan, P. L. A pulsed cloud of sex pheromone elicits upwind flight in male moths. Physiol. Entomol. 10, 257–265 (1985).
Google Scholar 
Willis, M. A. & Baker, T. C. Effects of intermittent and continuous pheromone stimulation on the flight behavior of the oriental fruit moth, Grapholita molesta. Physiol. Entomol. 9, 341–358 (1984).
Google Scholar 
Mafraneto, A. & Cardé, R. T. Fine-scale structure of pheromone plumes modulates upwind orientation of flying moths. Nature 369, 142–144 (1994).ADS 
CAS 

Google Scholar 
Mafraneto, A. & Cardé, R. T. Dissection of the pheromone-modulated flight of moths using single-pulse response as a template. Experientia 52, 373–379 (1996).CAS 

Google Scholar 
Vickers, N. J. & Baker, T. C. Reiterative responses to single strands of odor promote sustained upwind flight and odor source location by moths. Proc. Natl. Acad. Sci. USA 91, 5756–5760 (1994).ADS 
CAS 

Google Scholar 
Lei, H., Riffell, J. A., Gage, S. L. & Hildebrand, J. G. Contrast enhancement of stimulus intermittency in a primary olfactory network and its behavioral significance. J. Biol. 8, 21 (2009).
Google Scholar 
Kuenen, L. & Carde, R. T. Strategies for recontacting a lost pheromone plume: Casting and upwind flight in the male gypsy moth. Physiol. Entomol. 19, 15–29 (1994).
Google Scholar 
Vickers, N. J. & Baker, T. C. Latencies of behavioral response to interception of filaments of sex pheromone and clean air influence flight track shape in Heliothis virescens (F.) males. J. Comp. Physiol. A. 178, 831–847 (1996).
Google Scholar 
Vickers, N. J. Mechanisms of animal navigation in odor plumes. Biol. Bull. 198, 203–212 (2000).CAS 

Google Scholar 
Cardé, R. T. & Willis, M. A. Navigational strategies used by insects to find distant, wind-borne sources of odor. J. Chem. Ecol. 34, 854–866 (2008).
Google Scholar 
Willis, M. A. & Baker, T. C. Effects of varying sex pheromone component ratios on the zigzagging flight movements of the oriental fruit moth, Grapholita molesta. J. Insect. Behav. 1, 357–371 (1988).
Google Scholar 
Voskamp, K. E., Den Otter, C. J. & Noorman, N. Electroantennogram responses of tsetse flies (Glossina pallidipes) to host odours in an open field and riverine woodland. Physiol. Entomol. 23, 176–183 (1998).
Google Scholar 
Cai, X. M., Xu, X. X., Bian, L., Luo, Z. X. & Chen, Z. M. Measurement of volatile plant compounds in field ambient air by thermal desorption–gas chromatography–mass spectrometry. Anal. Bioanal. Chem. 407, 9105–9114 (2015).CAS 

Google Scholar 
Zollner, G. E., Torr, S. J., Ammann, C. & Meixner, F. X. Dispersion of carbon dioxide plumes in African woodland: implications for host-finding by tsetse flies. Physiol. Entomol. 29, 381–394 (2004).
Google Scholar 
McFrederick, Q. S., Kathilankal, J. C. & Fuentes, J. D. Air pollution modifies floral scent trails. Atmos. Environ. 42, 2336–2348 (2008).ADS 
CAS 

Google Scholar 
Yuan, J. S., Himanen, S. J., Holopainen, J. K., Chen, F. & NealStewart, C. Jr. Smelling global climate change: mitigation of function for plant volatile organic compounds. Trends Ecol. Evol. 24, 323–331 (2009).
Google Scholar 
Weissburg, M. J. The fluid dynamical context of chemosensory behavior. Biol. Bull. 198, 188–202 (2000).CAS 

Google Scholar 
Atkinson, R. & Arey, J. Gas-phase tropospheric chemistry of biogenic volatile organic compounds: A review. Atmos. Environ. 37, 197–219 (2003).ADS 

Google Scholar 
Helmig, D., Bocquet, F., Pollmann, J. & Revermann, T. Analytical techniques for sesquiterpene emission rate studies in vegetation enclosure experiments. Atmos. Environ. 38, 557–572 (2004).ADS 
CAS 

Google Scholar 
Riffell, J. A, Shlizerman, E., Sanders, E., Abrell, L., Medina, B., Hinterwirth, A. J. & NathanKutz, J. Flower discrimination by pollinators in a dynamic chemical environment. Science 344, 1515–1518 (2014).Shorey, H. H. Animal communication by pheromones (Academic Press, 1976).Cardé, R. T. & Charlton, R. E. Olfactory sexual communication in Lepidoptera: Strategy, sensitivity and selectivity In Insect communication (ed. Lewis, T.) 241–265 (Academic Press, 1984).Elkinton, J. S., Schal, C., Ono, T. & Carde, R. T. Pheromone puff trajectory and upwind flight of male gypsy moths in a forest. Physiol. Entomol. 12, 399–406 (1987).
Google Scholar 
Baker, T. C., Fadamiro, H. Y. & Cosse, A. A. Moth uses fine tuning for odour resolution. Nature 393, 530 (1998).ADS 
CAS 

Google Scholar 
Szyszka, P., Stierle, J. S., Biergans, S. & Galizia, C. G. The speed of smell: Odor-object segregation within milliseconds. PLoS One 7, e36096 (2012).ADS 
CAS 

Google Scholar 
Hildebrand, J. G. Analysis of chemical signals by nervous systems. Proc. Natl. Acad. Sci. USA 92, 67–74 (1995).ADS 
CAS 

Google Scholar 
Cai, X. M. et al. Field background odour should be taken into account when formulating a pest attractant based on plant volatiles. Sci. Rep. 7, 41818 (2017).ADS 
CAS 

Google Scholar 
Xu, X. X. et al. Does background odor in tea gardens mask attractants? Screening and application of attractants for Empoasca onukii Matsuda. J. Econ. Entomol. 110, 2357–2363 (2017).CAS 

Google Scholar 
Hare, J. D. & Sun, J. J. Production of induced volatiles by Datura wrightii in response to damage by insects: Effect of herbivore species and time. J. Chem. Ecol. 37, 751–764 (2011).CAS 

Google Scholar 
Mumm, R., Tiemann, T., Schulz, S. & Hilker, M. Analysis of volatiles from black pine (Pinus nigra): Significance of wounding and egg deposition by a herbivorous sawfly. Phytochemistry 65, 3221–3230 (2004).CAS 

Google Scholar  More

Franco FP, Túler AC, Gallan DZ, Gonçalves FG, Favaris AP, Peñaflor MFGV, et al. Fungal phytopathogen modulates plant and insect responses to promote its dissemination. ISME J. 2021;15:3522–33.CAS 

Google Scholar 
Huang H, Ren L, Li H, Schmidt A, Gershenzon J, Lu Y, et al. The nesting preference of an invasive ant is associated with the cues produced by actinobacteria in soil. PLoS Pathog. 2020;16:e1008800.CAS 

Google Scholar 
Angleró-Rodríguez YI, Blumberg BJ, Dong Y, Sandiford SL, Pike A, Clayton AM, et al. A natural Anopheles-associated Penicillium chrysogenum enhances mosquito susceptibility to Plasmodium infection. Sci Rep. 2016;6:34084.
Google Scholar 
Davis TS, Landolt PJ. A survey of insect assemblages responding to volatiles from a ubiquitous fungus in an agricultural landscape. J Chem Ecol. 2013;39:860–8.CAS 

Google Scholar 
Flury P, Vesga P, Dominguez-Ferreras A, Tinguely C, Ullrich CI, Kleespies RG, et al. Persistence of root-colonizing Pseudomonas protegens in herbivorous insects throughout different developmental stages and dispersal to new host plants. ISME J. 2018;13:860–72.
Google Scholar 
Kandasamy D, Gershenzon J, Andersson MN, Hammerbacher A. Volatile organic compounds influence the interaction of the Eurasian spruce bark beetle (Ips typographus) with its fungal symbionts. ISME J. 2019;13:1788–800.CAS 

Google Scholar 
Keesey IW, Koerte S, Khallaf MA, Retzke T, Guillou A, Grosse-Wilde E, et al. Pathogenic bacteria enhance dispersal through alteration of Drosophila social communication. Nat Commun. 2017;8:265.
Google Scholar 
Paul GB, Gerhard F, Elżbieta R, Alexandra S, Arne H, Sébastien L, et al. Yeast, not fruit volatiles mediate Drosophila melanogaster attraction, oviposition and development. Funct Ecol. 2012;26:1365–2435.
Google Scholar 
Ganter PF. Yeast and invertebrate associations. In: Gábor P, Carlos R, editors. Biodiversity and ecophysiology of yeasts. Berlin, Heidelberg: Springer; 2006. pp 303–70.Anagnostou C, Legrand EA, Rohlfs M. Friendly food for fitter flies?—Influence of dietary microbial species on food choice and parasitoid resistance in Drosophila. Oikos. 2010;119:533–41.
Google Scholar 
Günther CS, Knight SJ, Jones R, Goddard MR. Are Drosophila preferences for yeasts stable or contextual? Ecol Evol. 2019;9:8075–86.
Google Scholar 
Luo Y, Johnson JC, Chakraborty TS, Piontkowski A, Gendron CM, Pletcher SD. Yeast volatiles double starvation survival in Drosophila. Sci Adv. 2021;7:eabf8896.CAS 

Google Scholar 
Fogleman S. Coadaptation of Drosophila and yeasts in their natural habitat. J Chem Ecol. 1986;12:1037–55.
Google Scholar 
Droby S, Eick A, Macarisin D, Cohen L, Rafael G, Stange R, et al. Role of citrus volatiles in host recognition, germination and growth of Penicillium digitatum and Penicillium italicum. Postharvest Biol Tec. 2008;49:386–96.CAS 

Google Scholar 
Stensmyr MC, Dweck HK, Farhan A, Ibba I, Strutz A, Mukunda L, et al. A conserved dedicated olfactory circuit for detecting harmful microbes in Drosophila. Cell. 2012;151:1345–57.CAS 

Google Scholar 
Melo N, Wolff GH, Costa-da-Silva AL, Arribas R, Triana MF, Gugger M, et al. Geosmin attracts Aedes aegypti mosquitoes to oviposition sites. Curr Biol. 2020;30:127–34.CAS 

Google Scholar 
Wei DD, He W, Lang N, Miao ZQ, Xiao LF, Dou W, et al. Recent research status of Bactrocera dorsalis: Insights from resistance mechanisms and population structure. Arch Insect Biochem. 2019;102:e21601.CAS 

Google Scholar 
Han P, Wang X, Niu CY, Dong YC, Zhu JQ, Desneux N. Population dynamics, phenology, and overwintering of Bactrocera dorsalis (Diptera: Tephritidae) in Hubei Province, China. J Pest Sci. 2011;84:289–95.
Google Scholar 
Duyck PF, David P, Quilici S. A review of relationships between interspecific competition and invasions in fruit flies (Diptera: Tephritidae). Ecol Entomol. 2004;29:511–20.
Google Scholar 
Wen T, Zheng L, Dong S, Gong Z, Sang M, Long X, et al. Rapid detection and classification of citrus fruits infestation by Bactrocera dorsalis (Hendel) based on electronic nose. Postharvest Biol Tec. 2019;147:156–65.
Google Scholar 
Li X, Yang H, Wang T, Wang J, Wei H. Life history and adult dynamics of Bactrocera dorsalis in the citrus orchard of Nanchang, a subtropical area from China: implications for a control timeline. ScienceAsia. 2019;45:212–20.
Google Scholar 
Chalupowicz D, Veltman B, Droby S, Eltzov E. Evaluating the use of biosensors for monitoring of Penicillium digitatum infection in citrus fruit. Sens Actuat B-Chem. 2020;311:127896.CAS 

Google Scholar 
Turlings TC, Lengwiler UB, Bernasconi ML, Wechsler D. Timing of induced volatile emissions in maize seedlings. Planta. 1998;207:146–52.CAS 

Google Scholar 
Wang B, Dong W, Li H, D’Onofrio C, Bai P, Chen R, et al. Molecular basis of (E)-β-farnesene-mediated aphid location in the predator Eupeodes corollae. Curr Biol. 2022;32:951–62.CAS 

Google Scholar 
Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2− ΔΔCT method. Methods. 2001;25:402–8.CAS 

Google Scholar 
Cellar NA, De Nison JE, Seipelt CT, Twohig M, Burgess JA. Title of subordinate document. In: Dramatic improvements in assay reproducibility for water-soluble vitamins using ACQUITY UPLC and the Ultra-Sensitive Xevo TQ-S Mass Spectrometer. 2013. https://www.waters.com/webassets/cms/library/docs/720004690en.pdf.Ren FR, Sun X, Wang TY, Yan JY, Yao YL, Li CQ, et al. Pantothenate mediates the coordination of whitefly and symbiont fitness. ISME J. 2021;15:1655–67.CAS 

Google Scholar 
Batta YA. Quantitative postharvest contamination and transmission of Penicillium expansum (Link) conidia to nectarine and pear fruit by Drosophila melanogaster (Meig.) adults. Postharvest Biol Tec. 2006;40:190–6.
Google Scholar 
Rohlfs M. Clash of kingdoms or why Drosophila larvae positively respond to fungal competitors. Front Zool. 2005;2:2.
Google Scholar 
Becher PG, Bengtsson M, Hansson BS, Witzgall P. Flying the fly: long-range flight behavior of Drosophila melanogaster to attractive odors. J Chem Ecol. 2010;36:599–607.CAS 

Google Scholar 
Dionigi C, Ahten T, Wartelle L. Effects of several metals on spore, biomass, and geosmin production by Streptomyces tendae and Penicillium expansum. J Ind Microbiol Biot. 1996;17:84–88.CAS 

Google Scholar 
Jin S, Zhou X, Gu F, Zhong G, Yi X. Olfactory plasticity: variation in the expression of chemosensory receptors in Bactrocera dorsalis in different physiological states. Front Physiol. 2017;8:672.
Google Scholar 
Li H, Ren L, Xie M, Gao Y, He M, Hassan B, et al. Egg-surface bacteria are indirectly associated with oviposition aversion in Bactrocera dorsalis. Curr Biol. 2020;30:4432–40.CAS 

Google Scholar 
Liu Y, Cui Z, Si P, Liu Y, Zhou Q, Wang G. Characterization of a specific odorant receptor for linalool in the Chinese citrus fly Bactrocera minax (Diptera: Tephritidae). Insect Biochem Molec. 2020;122:103389.CAS 

Google Scholar 
Ju JF, Bing XL, Zhao DS, Guo Y, Hong XY. Wolbachia supplement biotin and riboflavin to enhance reproduction in planthoppers. ISME J. 2019;14:1–12.
Google Scholar 
Liu F, Wickham JD, Cao Q, Lu M, Sun J. An invasive beetle–fungus complex is maintained by fungal nutritional-compensation mediated by bacterial volatiles. ISME J. 2020;14:2829–42.CAS 

Google Scholar 
Douglas AE. The B vitamin nutrition of insects: the contributions of diet, microbiome and horizontally acquired genes. Curr Opin Insect Sci. 2017;23:65–69.
Google Scholar 
Honda K, Ômura H, Hayashi N, Abe F, Yamauchi T. Conduritols as oviposition stimulants for the danaid butterfly, Parantica sita, identified from a host plant, Marsdenia tomentosa. J Chem Ecol. 2004;30:2285–96.CAS 

Google Scholar 
Soldano A, Alpizar YA, Boonen B, Franco L, Lopez-Requena A, Liu G, et al. Gustatory-mediated avoidance of bacterial lipopolysaccharides via TRPA1 activation in Drosophila. Elife. 2016;5:e13133.
Google Scholar 
Hussain A, Üçpunar HK, Zhang M, Loschek LF, Grunwald Kadow IC. Neuropeptides modulate female chemosensory processing upon mating in Drosophila. PLoS Biol. 2016;14:e1002455.
Google Scholar 
Stötefeld L, Holighaus G, Schütz S, Rohlfs M. Volatile-mediated location of mutualist host and toxic non-host microfungi by Drosophila larvae. Chemoecology. 2015;5:271–83.
Google Scholar 
Gou B, Liu Y, Guntur A, Stern U, Yang HC. Mechanosensitive neurons on the internal reproductive tract contribute to egg-laying-induced acetic acid attraction in Drosophila. Cell Rep. 2014;9:522–30.CAS 

Google Scholar 
Mezzera C, Brotas M, Gaspar M, Pavlou HJ, Goodwin SF, Vasconcelos ML. Ovipositor extrusion promotes the transition from courtship to copulation and signals female acceptance in Drosophila melanogaster. Curr Biol. 2020;30:3736–48.CAS 

Google Scholar 
Teimoori-Boghsani Y, Ganjeali A, Cernava T, Müller H, Asili J, Berg G. Endophytic fungi of native Salvia abrotanoides plants reveal high taxonomic diversity and unique profiles of secondary metabolites. Front Microbiol. 2020;10:3013–20.
Google Scholar 
Holden JT, Furman C, Snell EE. D-alanine and the vitamin B6 content of microorganisms. J Biol Chem. 1949;178:789–97.CAS 

Google Scholar 
Michalkova V, Benoit JB, Weiss BL, Attardo GM, Aksoy S. Vitamin B6 generated by obligate symbionts is critical for maintaining proline homeostasis and fecundity in tsetse flies. Appl Environ Micro. 2014;80:5844–53.
Google Scholar 
Ren FR, Sun X, Wang TY, Yao YL, Huang YZ, Zhang X, et al. Biotin provisioning by horizontally transferred genes from bacteria confers animal fitness benefits. ISME J. 2020;14:2542–53.CAS 

Google Scholar 
Salem H, Bauer E, Strauss AS, Vogel H, Marz M, Kaltenpoth M. Vitamin supplementation by gut symbionts ensures metabolic homeostasis in an insect host. Proc Biol Sci. 2014;281:20141838.
Google Scholar  More