Ecology

SettingThis study was conducted in approximately 40-year-old naturally regenerated P. densiflora stands in Wola National Experimental Forest in Gyeongnam province in South Korea (35°12′ N, 128°10′ E; Table 1). The productivity of this forest is low, with a dominant tree height of 10 m at 20 years of age. Over the last 10 years, the mean annual precipitation was 1490 mm, of which one third fell during summer (July–August), and the mean temperature was 13.1 °C. The vegetation growing season generally lasts for approximately 200 days, extending from early April to October. The soil texture is a silt loam originating from sandstone and shale (clay 13.0 ± 0.8%, silt 44.1 ± 1.3%, sand 42.9 ± 1.6%; n = 18). The given texture results in volumetric water contents at 13.4 ± 0.7% (m3 m−3) at permanent wilting point (1500 kPa) and 40.7 ± 1.2% at field capacity (10 kPa)55. The understory is covered with Lespedeza spp., Quercus variabilis, Q. serrata, Smilax china, and Lindera glauca.In 2010, we selected two adjacent P. densiflora stands approximately 100 m apart from each other (180 m and 195 m above sea level, on slopes of 15° and 33°, both stands face south). Following a completely randomized design, we established nine plots (10 × 10 m2 with a 5 m untreated buffer) within each stand, of which three were randomly assigned to annual NPK fertilization, three to PK fertilization, and the rest to a control treatment without fertilization. The fertilizer, composed of urea, fused superphosphate and potassium chloride (N3P4K1) or P4K1 was added manually by deposition on the forest floor for 3 years in April 2011, April 2012, and March 2013. Over these 3 years, the NPK plots received 33.9 g N, 45 g P, and 11.1 g K m−2, while the PK plots received 45 g P and 11.1 g K m−2.Tree and stand measurementsThe standing biomass of trees was estimated using a combination of site-specific allometric equations based on destructive harvesting56 and repeated measurements of the dimensions of all trees in each plot (5–18 trees plot−1). The stem diameter at 1.2 m (D) was measured for all trees in each plot for which D was ≥ 6 cm. Selecting a representative tree in size for each plot within the 4 × 4 m2 center of the plot, we measured the tree height (H) and crown base for the representative trees. Measurements were performed in April and September 2011, September 2012–2014, and October 2021. We observed no effect of fertilization on the relationship between D and H or between D and crown base, so we assumed no effect on the allometric functions for foliage or branch biomass. A dendrometer band (Series 5 Manual Band, Forestry Suppliers Inc., Jackson, MS, USA) was installed on 18 representative trees (one per plot) to monitor radial growth monthly.Three 0.25 m2 circular litter traps were installed 60 cm above the forest floor in each plot in April 2011. Litter was collected at 3-month intervals between June 2011 and March 2015. The litter from each trap was transported to the laboratory and then oven-dried at 65 °C for 48 h. All dried samples were separated into needles, bark, cones, branches, and miscellaneous components, and weighed separately.In September 2014, we estimated the biomass of understory vegetation, separately for woody plants and herbaceous plants. All woody plants  More

Description of the phenological, vegetative and yield traits of the accessions per habitatThe process of data management included the computation of mean squares for the assessed phenological, vegetative and yield traits of the accessions with the sampling habitats considered as the treatment factor. The error mean squares served in the multiple comparison of means reported in Table 1.Table 1 Means of the measured phonological, vegetative and flowering and yield traits of Cucurbita moschata genotypes sampled from seven habitats.Full size tableRegarding the phenological traits, the accessions from the habitat of Zh have the longest period from seeding to first male (102.39 d) and first female (108.14 d) flower appearances, and the longest period from seeding to physiological maturity (153.95 d). For those traits, the accessions from Tiassale and Soubre are not significantly different from those of Zh. And, accessions from Tiassale and Zh have the longest periods from seeding to 50% flowering. On the other hand, accessions from Korho, Ferke, Bondu and Burki develop their first male and female flowers and attain 50% flowering in a very short period. They also reach physiological maturity faster. Accessions from Korho, however, have the longest period from seeding to 50% emergence (6.07 d) and accessions from Bondu have the longest period from first female flower appearance to physiological maturity (53.04 d).
For the vegetative traits, accessions from Tiassale and Soubre have the largest girth size (4.43 cm and 4.63 cm, respectively). Accessions from Tiassale have the longest (24.98 cm) and widest (19.94 cm) leaves, the longest male (16.2 cm) and female (4.03 cm) peduncles and the longest petioles (34.94 cm). The measures for those organs on accessions from Soubre rank second to those of Tiassale. On the other hand, accessions from Korho, Ferke, Bondu and Burki are characterized by smaller girth size, smaller leaves, smaller petioles and smaller peduncles of male and female flowers. But the accessions from Bondu are the tallest (586.91 cm) followed by the accessions from Ferke (489.20 cm). And the accessions from Zh are the shortest (417.38 cm).For the flowering and yield traits, accessions from Tiassale and Soubre show the largest numbers of male (27.33 units and 22.58 units, respectively) and female (5.22 units and 6.05 units, respectively) flowers per plant, largest numbers of fruits per plant (2.78 units and 2.53 units, respectively) and largest measures of all fruit-related traits. Their seeds are very large, but in small numbers. In contrast, accessions from Korho, Ferke, Bondu and Burki have the smallest numbers of male and female flowers per plant, the smallest numbers of fruits per plant and the smallest measures of fruit-related traits. They have large numbers of seeds, but their seeds are smaller, except the seeds of the accessions from Burki. Refer to Table 1 for more detailed information.
Variability of the phenological, vegetative and yield traitsTable 2 shows the spread of the phenological and morphological traits of the assessed accessions of C. moschata. All the evaluated traits showed very wide ranges of distribution of the observations. Some conspicuously wide ranges of traits include number of days to 50% flowering (DTF) that goes from 52 to 152 d, plant height with a minimum of 48 cm and a maximum of 1510 cm, diameter of the fruit that is between 5.8 cm and 35 cm, weight of the fruit that varies between 150 g and 10,930 g and number of seeds per fruit that spreads in the interval from 32 units per fruit to 729 units per fruit. Excluding the number of days to 50% emergence (DTE), all the other assessed traits have remarkably wide ranges of phenotypic expressions (Table 2). All the traits but DTE, DTF, days from first female flower appearance to fruit maturity, fruit length and length of the dry seed, had outliers. The number of outliers ranged from 1 to 67. Except the outliers observed with the width of the dry seed, all the outliers were above 1.5*IQR + Q3 where IQR is the inter-quartile range and Q3 is the third quartile. The presence of outliers is indicative of the richness and large variability of the population of accessions. The outliers are exceptional performances that fall outside the normal distribution of the observations. They are a stock of unusual traits that can be used in a crop improvement program when beneficial. For example, the observed outliers for diameter of the fruit, weight of the fruit or thickness of the pulp can be used in a breeding program for the improvement of fruit yield. Similarly, outliers for beneficial traits related to the seed can be used to improve C. moschata crop for seed yield. Besides, the computed mean squares (data not reported) showed highly significant variations between accessions for the assessed traits. They all yielded p-values less than 0.01, providing additional support to the evidence of large variability among the accessions of C. moschata of Cote d’Ivoire. The computed standard deviation, and median absolute deviation for each trait are additional evidence. We should note that in most cases, the mean squares associated to year (data not reported) were not significant, indicating the relative stability of the assessed traits.Table 2 Minimum (Min), first quartile (Q1), median, third quartile (Q3), maximum (Max), standard deviation (SD), median absolute deviation (MAD) and outliers obtained from the phenological, vegetative and flowering and yield traits of 663 accessions of C. moschata.Full size tableThe components of variance, the quantitative genetic differentiation, the overall mean, and the coefficients of variation are reported in Table 3. The lme4 package37 used in the determination of the components of variance, does not provide p-values in the analysis of mixed or random models. The reported quantities in Table 3 are not accompanied with tests of significance. It is worth mentioning that the respective units of measure of the assessed traits are squared for the variances and the evaluated estimates will be reported without the units of measure. The phenotypic variance ((sigma_{p}^{2})) is partitioned into variance between morphotypes or genotypic variance ((sigma_{g}^{2})), and within morphotypes or residual variance ((sigma_{e}^{2})). For the class of phenological traits, considerable genotypic variances were observed with days to 50% flowering (266.21) and days to first male flower appearance (254.40), compared with their respective residual variances (148.13 and 199.50). Regarding the class of vegetative traits, only the peduncle length of male flowers had a genotypic variance (9.22) greater than its residual variance (8.86). In the class of flowering and yield traits, 8 of the 15 traits assessed showed large genotypic variances in comparison with their respective residual variances. They are number of female flowers per plant ((sigma_{g}^{2}) = 3.02 versus (sigma_{e}^{2}) = 2.36), length of the fruit ((sigma_{g}^{2}) = 53.96 versus (sigma_{e}^{2}) = 48.97), diameter of the fruit ((sigma_{g}^{2}) = 37.17 versus (sigma_{e}^{2}) = 16.76), volume of the fruit ((sigma_{g}^{2}) = 10,713,468 versus (sigma_{e}^{2}) = 3,904,590), weight of the fruit ((sigma_{g}^{2}) = 5,413,819 versus (sigma_{e}^{2}) = 1,420,187), diameter of the cavity enclosing the seed ((sigma_{g}^{2}) = 19.12 versus (sigma_{e}^{2}) = 7.75), thickness of the fruit pulp ((sigma_{g}^{2}) = 1.11 versus (sigma_{e}^{2}) = 0.94) and weight of the fruit pulp ((sigma_{g}^{2}) = 5,979,212 versus (sigma_{e}^{2}) = 1,088,750). For a trait to have a lager genotypic variance than the residual variance is synonymous to a relative ease of improvement of the crop for that trait through a breeding program.Table 3 Components of variances ((sigma_{p}^{2}), (sigma_{g}^{2}), (sigma_{e}^{2}), (sigma_{a}^{2})), quantitative genetic differentiation ((Q_{ST})), overall mean ((mu)), and coefficients of variation (%) ((CV_{p}),(CV_{g}),(CV_{e})), of the measured phenological, vegetative and yield traits of the accessions of C. moschata of Cote d’Ivoire.Full size tableThe coefficient of variation (CV) is another statistic that measures variation. It is actually the dispersion of a trait per unit measure of its mean, which can be used to compare variations of traits with different measurement units or different scales. As a rule-of-thumb, a coefficient of variation greater than 20% is indicative of large variation for the trait. The phenotypic coefficient of variation is considerably high for 25 of the 28 assessed traits. Only the number of days from seeding to physiological maturity, the first and second longest axes of the dry seed show coefficients of variation less than 20%. Traits with very large phenotypic coefficients of variation include the peduncle length of female flowers ((CV_{p}) = 93.98%), weight of the pulp ((CV_{p}) = 92.96%), volume of the fruit ((CV_{p}) = 89.17%), weight of the fruit ((CV_{p}) = 78.30%) and number of female flowers per plant ((CV_{p}) = 65.81%). With respect to the residual coefficients of variation, only the number of days from seeding to 50% emergence and number of days from first female flower appearance to physiological maturity have residual coefficients of variation greater than 20%, among the phenological traits. All the vegetative traits have residual coefficients of variation greater than 20%, and show a near-perfect linear relation (r = 0.98; p  More

Jayathilake PG, Jana S, Rushton S, Swailes D, Bridgens B, Curtis T, et al. Extracellular polymeric substance production and aggregated bacteria colonization influence the competition of microbes in biofilms. Front Microbiol. 2017;8:1865.Article 
PubMed 
PubMed Central 

Google Scholar 
Flemming H-C, Neu TR, Wingender J (eds). The Perfect Slime: Microbial Extracellular Polymeric Substances (EPS). IWA Publishing, London, 2016.Morales-García AL, Bailey RG, Jana S, Burgess JG. The role of polymers in cross-kingdom bioadhesion. Philos Trans R Soc Lond B Biol Sci. 2019;374:20190192.Article 
PubMed 
PubMed Central 

Google Scholar 
Davey ME, O’Toole GA. Microbial biofilms: From ecology to molecular genetics. Microbiol Mol. 2000;64:847–67.Article 
CAS 

Google Scholar 
Peters BM, Jabra-Rizk MA, O’May GA, Costerton JW, Shirtliff ME. Polymicrobial interactions: Impact on pathogenesis and human disease. Clin Microbiol Rev. 2012;25:193–213.Article 
PubMed 
PubMed Central 

Google Scholar 
Karygianni L, Ren Z, Koo H, Thurnheer T. Biofilm matrixome: Extracellular components in structured microbial communities. Trends Microbiol. 2020;28:668–81.Article 
CAS 
PubMed 

Google Scholar 
Morris BEL, Henneberger R, Huber H, Moissl-Eichinger C. Microbial syntrophy: Interaction for the common good. FEMS Microbiol Rev. 2013;37:384–406.Article 
CAS 
PubMed 

Google Scholar 
Decho AW, Gutierrez T. Microbial extracellular polymeric substances (EPSs) in ocean systems. Front Microbiol. 2017;8:922.Article 
PubMed 
PubMed Central 

Google Scholar 
Boleij M, Seviour T, Wong LL, van Loosdrecht MCM, Lin Y. Solubilization and characterization of extracellular proteins from anammox granular sludge. Water Res. 2019;164:114952.Article 
CAS 
PubMed 

Google Scholar 
Kim D, Barraza JP, Arthur RA, Hara A, Lewis K, Liu Y, et al. Spatial mapping of polymicrobial communities reveals a precise biogeography associated with human dental caries. Proc Natl Acad Sci USA. 2020;117:12375–86.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sadiq FA, Burmølle M, Heyndrickx M, Flint S, Lu W, Chen W, et al. Community-wide changes reflecting bacterial interspecific interactions in multispecies biofilms. Crit Rev Microbiol. 2021;47:338–58.Article 
PubMed 

Google Scholar 
Liu W, Jacquiod S, Brejnrod A, Russel J, Burmølle M, Sørensen SJ. Deciphering links between bacterial interactions and spatial organization in multispecies biofilms. ISME J. 2019;13:3054–66.Article 
PubMed 
PubMed Central 

Google Scholar 
Bridier A, Le Coq D, Dubois-Brissonnet F, Thomas V, Aymerich S, Briandet R. The spatial architecture of Bacillus subtilis biofilms deciphered using a surface-associated model and in situ imaging. PLOS One. 2011;6:e16177.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lee KWK, Periasamy S, Mukherjee M, Xie C, Kjelleberg S, Rice SA. Biofilm development and enhanced stress resistance of a model, mixed-species community biofilm. ISME J. 2014;8:894–907.Article 
CAS 
PubMed 

Google Scholar 
Myszka K, Czaczyk K. Characterization of adhesive exopolysaccharide (EPS) produced by Pseudomonas aeruginosa under starvation conditions. Curr Microbiol. 2009;58:541–6.Article 
CAS 
PubMed 

Google Scholar 
Harimawan A, Ting YP. Investigation of extracellular polymeric substances (EPS) properties of P. aeruginosa and B. subtilis and their role in bacterial adhesion. Colloids Surf B. 2016;146:459–67.Article 
CAS 

Google Scholar 
Yang X-R, Li H, Nie S-A, Su J-Q, Weng B-S, Zhu G-B, et al. Potential contribution of anammox to nitrogen loss from paddy soils in Southern China. Appl Environ Microbiol. 2015;81:938–47.Article 
PubMed 
PubMed Central 

Google Scholar 
Kartal B, van Niftrik L, Keltjens JT, Op den Camp HJM, Jetten MSM Chapter 3 – Anammox—Growth physiology, cell biology, and metabolism. In: Poole RK, editor. Adv Microb Physiol. 60: Academic Press; 2012. p. 211–62.Lu Y, Natarajan G, Nguyen TQN, Thi SS, Arumugam K, Seviour TW, et al. Species level enrichment of AnAOB and associated growth morphology under the effect of key metabolites. bioRxiv. 2020. 2020.02.04.934877Gonzalez-Gil G, Sougrat R, Behzad AR, Lens PN, Saikaly PE. Microbial community composition and ultrastructure of granules from a full-scale anammox reactor. Micro Ecol. 2015;70:118–31.Article 
CAS 

Google Scholar 
Kindaichi T, Yuri S, Ozaki N, Ohashi A. Ecophysiological role and function of uncultured Chloroflexi in an anammox reactor. Water Sci Technol. 2012;66:2556–61.Article 
CAS 
PubMed 

Google Scholar 
Qin Y, Han B, Cao Y, Wang T. Impact of substrate concentration on anammox-UBF reactors start-up. Bioresour Technol. 2017;239:422–9.Article 
CAS 
PubMed 

Google Scholar 
Chen Z, Meng Y, Sheng B, Zhou Z, Jin C, Meng F. Linking exoproteome function and structure to anammox biofilm development. Environ Sci Technol. 2019;53:1490–500.Article 
CAS 
PubMed 

Google Scholar 
Ali M, Shaw DR, Albertsen M, Saikaly PE. Comparative genome-centric analysis of freshwater and marine ANAMMOX cultures suggests functional redundancy in nitrogen removal processes. Front Microbiol. 2020;11:1637.Article 
PubMed 
PubMed Central 

Google Scholar 
Jia F, Yang Q, Liu X, Li X, Li B, Zhang L, et al. Stratification of extracellular polymeric substances (EPS) for aggregated anammox microorganisms. Environ Sci Technol. 2017;51:3260–8.Article 
CAS 
PubMed 

Google Scholar 
Hou X, Liu S, Zhang Z. Role of extracellular polymeric substance in determining the high aggregation ability of anammox sludge. Water Res. 2015;75:51–62.Article 
CAS 
PubMed 

Google Scholar 
Feng C, Lotti T, Lin Y, Malpei F. Extracellular polymeric substances extraction and recovery from anammox granules: Evaluation of methods and protocol development. Chem Eng J. 2019;374:112–22.Article 
CAS 

Google Scholar 
Lotti T, Carretti E, Berti D, Montis C, Del Buffa S, Lubello C, et al. Hydrogels formed by anammox extracellular polymeric substances: Structural and mechanical insights. Sci Rep. 2019;9:11633.Article 
PubMed 
PubMed Central 

Google Scholar 
Jakubovics NS, Goodman SD, Mashburn-Warren L, Stafford GP, Cieplik F. The dental plaque biofilm matrix. Periodontology 2000. 2021;86:32–56.Article 
PubMed 
PubMed Central 

Google Scholar 
Ðapa T, Leuzzi R, Ng YK, Baban ST, Adamo R, Kuehne SA, et al. Multiple factors modulate biofilm formation by the anaerobic pathogen Clostridium difficile. J Bacteriol. 2013;195:545–55.Article 
PubMed 
PubMed Central 

Google Scholar 
Honma K, Inagaki S, Okuda K, Kuramitsu HK, Sharma A. Role of a Tannerella forsythia exopolysaccharide synthesis operon in biofilm development. Micro Pathog. 2007;42:156–66.Article 
CAS 

Google Scholar 
Li X-R, Du B, Fu H-X, Wang R-F, Shi J-H, Wang Y, et al. The bacterial diversity in an anaerobic ammonium-oxidizing (anammox) reactor community. Syst Appl Microbiol. 2009;32:278–89.Article 
CAS 
PubMed 

Google Scholar 
Cho S, Takahashi Y, Fujii N, Yamada Y, Satoh H, Okabe S. Nitrogen removal performance and microbial community analysis of an anaerobic up-flow granular bed anammox reactor. Chemosphere 2010;78:1129–35.Article 
CAS 
PubMed 

Google Scholar 
Morgenroth E, Sherden T, Van Loosdrecht MCM, Heijnen JJ, Wilderer PA. Aerobic granular sludge in a sequencing batch reactor. Water Res. 1997;31:3191–4.Article 
CAS 

Google Scholar 
Wong LL, Natarajan G, Boleij M, Thi SS, Winnerdy FR, Mugunthan S, et al. Extracellular protein isolation from the matrix of anammox biofilm using ionic liquid extraction. Appl Microbiol Biotechnol. 2020;104:3643–54.Article 
CAS 
PubMed 

Google Scholar 
Law Y, Kirkegaard RH, Cokro AA, Liu X, Arumugam K, Xie C, et al. Integrative microbial community analysis reveals full-scale enhanced biological phosphorus removal under tropical conditions. Sci Rep. 2016;6:25719.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ondov BD, Bergman NH, Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Bioinform. 2011;12:385.Article 

Google Scholar 
Liu X, Arumugam K, Natarajan G, Seviour TW, Drautz-Moses DI, Wuertz S, et al. Draft genome sequence of a Candidatus brocadia bacterium enriched from activated sludge collected in a tropical climate. Genome Announc. 2018;6:e00406–18.Article 
PubMed 
PubMed Central 

Google Scholar 
Price MN, Dehal PS, Arkin AP. FastTree 2-approximately maximum-likelihood trees for large alignments. PloS One. 2010;5:e9490–e.Article 
PubMed 
PubMed Central 

Google Scholar 
Arkin AP, Cottingham RW, Henry CS, Harris NL, Stevens RL, Maslov S, et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat Biotechnol. 2018;36:566–9.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Buchfink B, Reuter K, Drost H-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat Methods. 2021;18:366–8.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.Article 
CAS 
PubMed 

Google Scholar 
Daims H, Nielsen JL, Nielsen PH, Schleifer K-H, Wagner M. In situ characterization of Nitrospira-like nitrite-oxidizing bacteria active in wastewater treatment plants. Appl Environ Microbiol. 2001;67:5273–84.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Seviour T, Wong LL, Lu Y, Mugunthan S, Yang Q, Shankari UDOCS, et al. Phase transitions by an abundant protein in the anammox extracellular matrix mediate cell-to-cell aggregation and biofilm formation. mBio 2020;11:e02052–20.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Protter DSW, Rao BS, Van Treeck B, Lin Y, Mizoue L, Rosen MK, et al. Intrinsically disordered regions can contribute promiscuous interactions to RNP granule assembly. Cell Rep. 2018;22:1401–12.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Fulton KM, Smith JC, Twine SM. Clinical applications of bacterial glycoproteins. Expert Rev Proteom. 2016;13:345–53.Article 
CAS 

Google Scholar 
Upreti RK, Kumar M, Shankar V. Bacterial glycoproteins: Functions, biosynthesis and applications. Proteomics 2003;3:363–79.Article 
CAS 
PubMed 

Google Scholar 
van Teeseling MCF, Maresch D, Rath CB, Figl R, Altmann F, Jetten MSM, et al. The S-layer protein of the anammox bacterium Kuenenia stuttgartiensiss is heavily O-glycosylated. Front Microbiol. 2016;7:1721.PubMed 
PubMed Central 

Google Scholar 
McGonigle JM, Lang SQ, Brazelton WJ, Parales RE. Genomic evidence for formate metabolism by Chloroflexi as the key to unlocking deep carbon in lost city microbial ecosystems. Appl Environ Microbiol. 2020;86:e02583–19.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Vuillemin A, Kerrigan Z, D’Hondt S, Orsi WD. Exploring the abundance, metabolic potential and gene expression of subseafloor Chloroflexi in million-year-old oxic and anoxic abyssal clay. FEMS Microbiol Ecol. 2020;96:fiaa223.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kartal B, de Almeida NM, Maalcke WJ, Op den Camp HJ, Jetten MS, Keltjens JT. How to make a living from anaerobic ammonium oxidation. FEMS Microbiol Rev. 2013;37:428–61.Article 
CAS 
PubMed 

Google Scholar 
Loera-Muro A, Guerrero-Barrera A, Tremblay DNY, Hathroubi S, Angulo C. Bacterial biofilm-derived antigens: A new strategy for vaccine development against infectious diseases. Expert Rev Vaccines. 2021;20:385–96.Article 
CAS 
PubMed 

Google Scholar 
Hobley L, Harkins C, MacPhee CE, Stanley-Wall NR. Giving structure to the biofilm matrix: An overview of individual strategies and emerging common themes. FEMS Microbiol Rev. 2015;39:649–69.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Elias S, Banin E. Multi-species biofilms: Living with friendly neighbors. FEMS Microbiol Rev. 2012;36:990–1004.Article 
CAS 
PubMed 

Google Scholar 
Teeseling MCFV, Almeida NMD, Klingl A, Speth DR, Camp HJMOD, Rachel R, et al. A new addition to the cell plan of anammox bacteria: Candidatus Kuenenia stuttgartiensis has a protein surface layer as the outermost layer of the cell. J Bacteriol. 2014;196:80–9.Article 
PubMed 
PubMed Central 

Google Scholar 
Paula AJ, Hwang G, Koo H. Dynamics of bacterial population growth in biofilms resemble spatial and structural aspects of urbanization. Nat Commun. 2020;11:1354.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kragelund C, Caterina L, Borger A, Thelen K, Eikelboom D, Tandoi V, et al. Identity, abundance and ecophysiology of filamentous Chloroflexi species present in activated sludge treatment plants. FEMS Microbiol Ecol. 2007;59:671–82.Article 
CAS 
PubMed 

Google Scholar 
Nierychlo M, Miłobędzka A, Petriglieri F, McIlroy B, Nielsen PH, McIlroy SJ. The morphology and metabolic potential of the Chloroflexi in full-scale activated sludge wastewater treatment plants. FEMS Microbiol Ecol. 2018;95.Kragelund C, Thomsen TR, Mielczarek AT, Nielsen PH. Eikelboom’s morphotype 0803 in activated sludge belongs to the genus Caldilinea in the phylum Chloroflexi. FEMS Microbiol Ecol. 2011;76:451–62.Article 
CAS 
PubMed 

Google Scholar 
Zhang J, Miao Y, Zhang Q, Sun Y, Wu L, Peng Y. Mechanism of stable sewage nitrogen removal in a partial nitrification-anammox biofilm system at low temperatures: Microbial community and EPS analysis. Bioresour Technol. 2020;297:122459.Article 
CAS 
PubMed 

Google Scholar 
Björnsson L, Hugenholtz P, Tyson GW, Blackall LL. Filamentous Chloroflexi (green non-sulfur bacteria) are abundant in wastewater treatment processes with biological nutrient removal. Microbiology 2002;148:2309–18.Article 
PubMed 

Google Scholar 
Boleij M, Pabst M, Neu TR, van Loosdrecht MCM, Lin Y. Identification of glycoproteins isolated from extracellular polymeric substances of full-scale anammox granular sludge. Environ Sci Technol. 2018;52:13127–35.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pabst M, Grouzdev DS, Lawson CE, Kleikamp HBC, de Ram C, Louwen R, et al. A general approach to explore prokaryotic protein glycosylation reveals the unique surface layer modulation of an anammox bacterium. ISME J. 2022;16:346–57.Article 
CAS 
PubMed 

Google Scholar 
Berlanga M, Guerrero R. Living together in biofilms: The microbial cell factory and its biotechnological implications. Micro Cell Fact. 2016;15:165.Article 

Google Scholar 
Liu T, Tian R, Li Q, Wu N, Quan X. Strengthened attachment of anammox bacteria on iron-based modified carrier and its effects on anammox performance in integrated floating-film activated sludge (IFFAS) process. Sci Total Environ. 2021;787:147679.Article 
CAS 
PubMed 

Google Scholar  More

Zhe, W. et al. Probing the nature of soil organic matter. Crit. Rev. Environ. Sci. Technol. 52, 4072–4093 (2022).Article 

Google Scholar 
Blanco-Canqui, H. & Lal, R. Mechanisms of carbon sequestration in soil aggregates. Crit. Rev. Plant Sci. 23, 481–504 (2004).Article 
CAS 

Google Scholar 
Six, J., Paustian, K., Elliott, E. T. & Combrink, C. Soil structure and organic matter: I. Distribution of aggregate–size classes and aggregate–associated carbon. Soil Sci. Soc. Am. J. 64, 681–689 (2000).Article 
ADS 
CAS 

Google Scholar 
Lehmann, J. & Kleber, M. The contentious nature of soil organic matter. Nature 528, 60–68 (2015).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Six, J., Bossuyt, H., Degryze, S. & Denef, K. A history of research on the link between (micro)aggregates, soil biota, and soil organic matter dynamics. Soil Tillage Res. 79, 7–31 (2004).Article 

Google Scholar 
Tisdall, J. M. & Oades, J. M. Organic matter and water-stable aggregates in soils. Eur. J. Soil Sci. 33, 141–163 (1982).Article 
CAS 

Google Scholar 
Luo, Y. et al. Rice rhizodeposition promotes the build-up of organic carbon in soil via fungal necromass. Soil Biol. Biochem. 160, 108345 (2021).Article 
CAS 

Google Scholar 
Wang, X. et al. Organic amendments drive shifts in microbial community structure and keystone taxa which increase C mineralization across aggregate size classes. Soil Biol. Biochem. 153, 108062 (2021).Article 
CAS 

Google Scholar 
Duan, Y. et al. Long–term fertilisation reveals close associations between soil organic carbon composition and microbial traits at aggregate scales. Agric. Ecosyst. Environ. 306, 107169 (2021).Article 
CAS 

Google Scholar 
Christensen, B. T. Physical fractionation of soil and structural and functional complexity in organic matter turnover. Eur. J. Soil Sci. 52, 345–353 (2001).Article 
CAS 

Google Scholar 
Olk, D. C. & Gregorich, E. G. Overview of the symposium proceedings, “meaningful pools in determining soil carbon and nitrogen dynamics”. Soil Sci. Soc. Am. J. 70, 967–974 (2006).Article 
ADS 
CAS 

Google Scholar 
Courtier-Murias, D. et al. Unraveling the long–term stabilization mechanisms of organic materials in soils by physical fractionation and NMR spectroscopy. Agric. Ecosyst. Environ. 171, 9–18 (2013).Article 
CAS 

Google Scholar 
Rodrigues, L. A. T. et al. Short– and long–term effects of animal manures and mineral fertilizer on carbon stocks in subtropical soil under no–tillage. Geoderma 386, 114913 (2021).Article 
ADS 
CAS 

Google Scholar 
Mao, J., Dan, C. O., Fang, X., He, Z. & Schmidt-Rohr, K. Influence of animal manure application on the chemical structures of soil organic matter as investigated by advanced solid–state NMR and FT–IR spectroscopy. Geoderma 146, 353–362 (2008).Article 
ADS 
CAS 

Google Scholar 
Simonetti, G. et al. Characterization of humic carbon in soil aggregates in a long–term experiment with manure and mineral fertilization. Soil Sci. Soc. Am. J. 25, 880–890 (2012).Article 

Google Scholar 
Cambardella, C. A. & Elliott, E. T. Particulate soil organic-matter changes across a grassland cultivation sequence. Soil Sci. Soc. Am. J. 56, 777–783 (1992).Article 
ADS 

Google Scholar 
Conant, R. T., Six, J. & Paustian, K. Land use effects on soil carbon fractions in the southeastern United States. I. Management-intensive versus extensive grazing. Biol. Fertil. Soils 38, 386–392 (2003).Article 
CAS 

Google Scholar 
Blanco-Moure, N., Gracia, R., Bielsa, A. C. & López, M. V. Soil organic matter fractions as affected by tillage and soil texture under semiarid Mediterranean conditions. Soil Tillage Res. 155, 381–389 (2016).Article 

Google Scholar 
Yu, H. et al. Accumulation of organic C components in soil and aggregates. Sci. Rep. 5, 13804 (2015).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Schöning, I., Morgenroth, G. & Kögel-Knabner, I. O/N–alkyl and alkyl C are stabilised in fine particle size fractions of forest soils. Biogeochemistry 73, 475–497 (2005).Article 

Google Scholar 
Solomon, D., Lehmann, J., Kinyangi, J., Liang, B. & Schäfer, T. Carbon K-edge NEXAFS and FTIR–ATR spectroscopic investigation of organic carbon speciation in soils. Soil Sci. Soc. Am. J. 13, 107–119 (2005).Article 

Google Scholar 
Yan, H., Chen, C., Xu, Z., Williams, D. & Xu, J. Assessing management impacts on soil organic matter quality in subtropical Australian forests using physical and chemical fractionation as well as 13C NMR spectroscopy. Soil Biol. Biochem. 41, 640–650 (2009).Article 

Google Scholar 
Masoom, H. et al. Soil organic matter in its native state: Unravelling the most complex biomaterial on earth. Environ. Sci. Technol. 50, 1670–1680 (2016).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Vogel, C. et al. Clay mineral composition modifies decomposition and sequestration of organic carbon and nitrogen in fine soil fractions. Biol. Fertil. Soils 51, 427–442 (2015).Article 
CAS 

Google Scholar 
Sharma, S., Singh, P., Angmo, P. & Satpute, S. Total and labile pools of organic carbon in relation to soil biological properties under contrasting land-use systems in a dry mountainous region. Carbon Manage. 13, 352–371 (2022).Article 
CAS 

Google Scholar 
Six, J., Elliott, E., Paustian, K. & Doran, J. Aggregation and soil organic matter accumulation in cultivated and native grassland soils. Soil Sci. Soc. Am. J. 62, 1367–1377 (1998).Article 
ADS 
CAS 

Google Scholar 
Elliott, E. T. Aggregate structure and carbon, nitrogen, and phosphorus in native and cultivated soils. Soil Sci. Soc. Am. J. 50, 627–633 (1986).Article 
ADS 

Google Scholar 
Yu, H., Ding, W., Luo, J., Geng, R. & Cai, Z. Long-term application of organic manure and mineral fertilizers on aggregation and aggregate-associated carbon in a sandy loam soil. Soil Tillage Res. 124, 170–177 (2012).Article 

Google Scholar 
Carter, M. R. & Gregorich, E. G. (eds) Soil Sampling and Methods of Analysis 2nd edn, 230–233 (Taylor & Francis Group, CRC, 2007).
Google Scholar 
Lu, R. (ed.) Soil and Agro-chemistry Analytical Methods 146–149 (China Agricultural Science and Technology Press, 1999).
Google Scholar 
Wu, J., Joergensen, R. G., Pommerening, B., Chaussod, R. & Brookes, P. C. Measurement of soil microbial biomass C by fumigation extraction: An automated procedure. Soil Biol. Biochem. 22, 1167–1169 (1990).Article 
CAS 

Google Scholar 
Zhang, X., Zhu, A., Yang, W. & Zhang, J. Accumulation of organic components and its association with macroaggregation in a sandy loam soil following conservation tillage. Plant Soil. 416, 1–15 (2017).Article 
CAS 

Google Scholar 
Skjemstad, J. O., Clarke, P., Taylor, J. A., Oades, J. M. & Newman, R. H. The removal of magnetic materials from surface soils—a solid state 13C CP/MAS NMR study. Soil Res. 32, 1215–1229 (1994).Article 
CAS 

Google Scholar 
Ringle, C. M., Wende, S. & Becker, J. M. SmartPLS 3.” Boenningstedt: SmartPLS GmbH. Preprint at http://www.smartpls.com (2015).Jerbi, M., Labidi, S., Lounès-Hadj Sahraoui, A., Chaar, H. & Ben Jeddi, F. Higher temperatures and lower annual rainfall do not restrict, directly or indirectly, the mycorrhizal colonization of barley (Hordeum vulgare L.) under rainfed conditions. PLoS ONE 15, e0241794 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cohen, J. Statistical power analysis for the behavioral sciences 2nd edn, 407–530 (Erlbaum Associates, Berlin, 1988).MATH 

Google Scholar 
Singh, P. & Benbi, D. K. Physical and chemical stabilization of soil organic matter in cropland ecosystems under rice–wheat, maize–wheat and cotton–wheat cropping systems in northwestern India. Carbon Manag. 12, 603–621 (2021).Article 
CAS 

Google Scholar 
Kiem, R. & Kögel-Knabner, I. Contribution of lignin and polysaccharides to the refractory carbon pool in C–depleted arable soils. Soil Biol. Biochem. 35, 101–118 (2003).Article 
CAS 

Google Scholar 
Lutzow, M. V. et al. Stabilization of organic matter in temperate soils: mechanisms and their relevance under different soil conditions: a review. Eur. J. Soil Sci. 57, 426–445 (2006).Article 

Google Scholar 
Yudina, A. V., Klyueva, V. V., Romanenko, K. A. & Fomin, D. S. Micro- within macro: How micro-aggregation shapes the soil pore space and water-stability. Geoderma 415, 115771 (2022).Article 
ADS 
CAS 

Google Scholar 
Tisdall, J. M., Smith, S. E. & Rengasamy, P. Aggregation of soil by fungal hyphae. Soil Res. 35, 55–60 (1997).Article 

Google Scholar 
Li, T. et al. Contrasting impacts of manure and inorganic fertilizer applications for nine years on soil organic carbon and its labile fractions in bulk soil and soil aggregates. CATENA 194, 104739 (2020).Article 
CAS 

Google Scholar 
Liang, Y. et al. Effect of chemical fertilizer and straw-derived organic amendments on continuous maize yield, soil carbon sequestration and soil quality in a Chinese Mollisol. Agric. Ecosyst. Environ. 314, 107403 (2021).Article 
CAS 

Google Scholar 
Liang, C., Kästner, M. & Joergensen, R. G. Microbial necromass on the rise: The growing focus on its role in soil organic matter development. Soil Biol. Biochem. 150, 108000 (2020).Article 
CAS 

Google Scholar 
Sharma, S., Singh, P. & Kumar, S. Responses of soil carbon pools, enzymatic activity, and crop yields to nitrogen and straw incorporation in a rice-wheat cropping system in North-Western India. Front. Sustain. Food Syst. 4, 532704 (2020).Article 

Google Scholar 
Puget, P., Chenu, C. & Balesdent, J. Dynamics of soil organic matter associated with particle–size fractions of water–stable aggregates. Eur. J. Soil Sci. 51, 595–605 (2000).Article 

Google Scholar  More

Finkelman, S., Navarro, S., Rindner, M. & Dias, R. Effect of low pressure on the survival of Trogoderma granarium Everts, Lasioderma serricorne (F.) and Oryzaephilus surinamensis (L.) at 30°C. J. Stored. Prod. Res. 42, 23–30 (2006).Article 

Google Scholar 
Hosseininaveh, V. A., Bandani, A. P., Azmayeshfard, P. S., Hosseinkhani, S. & Kazzazi, M. Digestive proteolytic and amylolytic activities in Trogoderma granarium Everts (Dermestidae: Coleoptera). J. Stored. Prod. Res. 43, 515–522 (2007).Article 
CAS 

Google Scholar 
Burges, H. D. Development of the khapra beetle, Trogoderma granarium, in the lower part of its temperature range. J. Stored. Prod. Res. 44, 32–35 (2008).Article 

Google Scholar 
Hagstrum D. W & Subramanyam, B. Stored-Product Insect Resource (AACC International, 2009).Beal, R. S. Synopsis of the economic species of Trogoderma occurring in the United States with description of a new species (Coleoptera: Dermestidae). Ann. Entomol. Soc. Am. 49, 559–566 (1956).Article 

Google Scholar 
Kerr, J. A. Khapra beetle returns. Pest Control 49(12), 24–25 (1984).
Google Scholar 
Sinha, R. N. & Utida, S. Climatic areas potentially vulnerable to stored product insects in Japan. Appl. Entomol. Zool. 2, 124–132 (1967).Article 

Google Scholar 
Banks, H. J. Distribution and establishment of Trogoderma granarium Everts (Coleoptera: Dermestidae): Climatic and other influences. J. Stored. Prod. Res. 13, 183–202 (1977).Article 

Google Scholar 
Kavallieratos, N. G., Athanassiou, C. G., Guedes, R. N. C., Drempela, J. D. & Boukouvala, M. C. Invader competition with local competitors: Displacement or coexistence among the invasive khapra beetle, Trogoderma granarium Everts (Coleoptera: Dermestidae), and two other major stored-grain beetles?. Front. Plant. Sci. 8, 1837 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Lampiri, E., Baliota, G. V., Morrison, W. M., Domingue, M. J. & Athanassiou, C. Comparative population growth of the khapra beetle (Coleoptera: Dermestidae) and the warehouse beetle (Coleoptera: Dermestidae) on wheat and rice. J. Econ. Entomol. 115, 344–352 (2021).Article 

Google Scholar 
Athanassiou, C. G., Phillips, T. W. & Wakil, W. Biology and control of the khapra beetle, Trogoderma granarium, a major quarantine threat to global food security. Ann. Rev. Entomol. 64, 131–148 (2019).Article 
CAS 

Google Scholar 
Stibick, J. New pest response guidelines: khapra beetle. APHIS– PPQ–Emergency and Domestic Programs. (U.S Department of Agriculture, 2009).Myers, S. W. & Hagstrum, D. W. Quarantine, In Stored stored product protection, (ed. Hagstrum D.W. Phillips T.W. & Cuperus G.) 297–304 (Kansas State University, 2012).Day, C. & White, B. Khapra beetle, Trogoderma granarium interceptions and eradications in Australia and around the world. In SARE working papers 1609. (Crawley: School of Agricul. Res. Econ. 2016).Burges, H. D. Diapause, pest status and control of the Khapra beetle. Trogoderma Granar. Everts Ann. Appl. Biol. 50, 614–617 (1962).Article 

Google Scholar 
Nair, K. & Desai, A. The termination of diapause in Trogoderma granarium Everts (Coleoptera, Dermestidae). J. Stored. Prod. Res. 8, 275–290 (1973).Article 

Google Scholar 
Burges, H. D. Studies on the Dermestid beetle Trogoderma granarium Everts—IV. Feeding, growth, and respiration with particular reference to diapause larvae. J. Insect. Physiol. 5, 317–334 (1960).Article 
CAS 

Google Scholar 
Wilches, D., Laird, R. A., Floate, K. & Fields, P. G. A review of diapause and tolerance to extreme temperatures in dermestids (Coleoptera). J. Stored Prod. Res. 68, 50–62 (2016).Article 

Google Scholar 
Vick, K. W., Drummond, P. C. & Coffelt, J. A. Trogoderma inclusum and T. glabrum: Effects of time of day on production of female pheromone, male responsiveness and mating. Ann. Entomol. Soc. Am. 66, 1001–1004 (1973).Article 

Google Scholar 
Partida, G. J. & Strong, R. G. Distribution and relative abundance of Trogoderma spp. in relation to climate zones of California. J. Econ. Entomol. 63, 1553–1560 (1970).Article 

Google Scholar 
Hagstrum, D. W. Seasonal variation of stored wheat environment and insect populations. J. Econ. Entomol. 16, 77–83 (1987).
Google Scholar 
Mullen, M. A. & Arbogast, R. T. Insect succession in a stored-corn ecosystem in southeast Georgia. J. Econ. Entomol. 81, 899–912 (1988).
Google Scholar 
Partida, G. J. & Strong, R. G. Comparative studies on the biologies of six species of Trogoderma: T. inclusum. Ann. Entomol. Soc. Am. 68, 91–103 (1975).Article 

Google Scholar 
Beal, R. S. Biology and taxonomy of the nearctic species of Trogoderma. Univ. Calif. Misc. Publ. Entomol. 10, 35–102 (1954).
Google Scholar 
Castañé, C., Agustí, N., del Estal, P. & Riudavets, J. Survey of Trogoderma spp in Spanish mills and warehouses. J. Stored. Prod. Res. 88, 1061 (2020).Article 

Google Scholar 
Levinson, H. Z. & Mori, K. The pheromone activity of chiral isomers of trogodermal for male khapra beetles. Naturwissenschaften 67, 148–149 (1980).Article 
CAS 

Google Scholar 
Silverstein, R. M. et al. Perception by Trogoderma species of chirality and methyl branching at a site far removed from a functional group in a pheromone component. J. Chem. Ecol. 6, 911–917 (1980).Article 
CAS 

Google Scholar 
Vick, K. W. Effects of interspecific matings of Trogoderma glabrum and T. inclusum on oviposition and re-mating. Ann. Entomol. Soc. Am. 66, 237–239 (1973).Article 
MathSciNet 

Google Scholar 
Drijfhout, S. et al. Catalogue of abrupt shifts in intergovernmental panel on climate change climate models. Proc. Natl. Acad. Sci. USA 112, E5777–E5786 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Phillips, T. W., Pfannenstiel, L. & Hagstrum, D. Survey of Trogoderma species (Coleoptera: Dermestidae) associated with international trade of dried distiller’s grains and solubles in the USA. Julius-Kühn-Archiv 1, 233–238 (2018).
Google Scholar 
Hadaway, A. The biology of the beetles, Trogoderma granarium Everts and Trogoderma versicolor (Creutz). Bull. Entomol. Res. 46, 781–796 (1956).Article 
CAS 

Google Scholar 
Gorham, J. R. Insect and Mite Pests in Food: An Illustrated Key. Vols. 1 and 2, (U.S Department of Agriculture, 1991).Furui, S., Miyanoshita, A., Imamura, T., Minegishi, Y. & Kokutani, R. Qualitative real-time PCR identification of the khapra beetle, Trogoderma granarium (Coleoptera: Dermestidae). Appl. Entomol. Zool. 54, 101–107 (2019).Article 
CAS 

Google Scholar 
Olson, R. L., Farris, R. E., Barr, N. B. & Cognato, A. I. Molecular identification of Trogoderma granarium (Coleoptera: Dermestidae) using the 16s gene. J Pest Sci 87, 701–710 (2014).Article 

Google Scholar 
Wu, Y. et al. Development of an array of molecular tools for the identification of khapra beetle (Trogoderma granarium), a destructive beetle of stored food products. Sci. Rep. 13, 3327 (2023).Article 
PubMed 
PubMed Central 

Google Scholar 
Lampiri, E., Athanassiou, C. & Arthur, F. H. Population growth and development of the khapra beetle (Coleoptera: Dermestidae), on different sorghum fractions. J. Econ. Entomol. 114, 424–429 (2021).Article 
CAS 
PubMed 

Google Scholar 
Athanassiou, C. G., Kavallieratos, N. G. & Boukouvala, M. C. Population growth of the khapra beetle, Trogoderma granarium Everts (Coleoptera: Dermestidae) on different commodities. J. Stored. Prod. Res. 69, 72–77 (2016).Article 

Google Scholar 
Karnavar, G. K. Mating behaviour and fecundity in Trogoderma granarium (Coleoptera: Dermestidae). J. Stored. Prod. Res. 8, 65–69 (1972).Article 

Google Scholar 
Pray, L. A. & Goodnight, C. J. Genetic variation in inbreeding depression in the red flour beetle Tribolium castaneum. Evolution 49, 176–188 (1995).Article 
PubMed 

Google Scholar 
Barzin, S., Naseri, B., Fathi, S. A. A., Razmjou, J. & Aeinehchi, P. Feeding efficiency and digestive physiology of Trogoderma granarium Everts (Coleoptera: Dermestidae) on different rice cultivars. J. Stored. Prod. Res. 84, 101511 (2019).Article 

Google Scholar 
Naseri, B., Aeinehchi, P. & Ashjerdi, A. R. Nutritional responses and digestive enzymatic profile of Trogoderma granarium Everts (Coleoptera: Dermestidae) on 10 commercial rice cultivars. J. Stored. Prod. Res. 87, 101591 (2020).Article 

Google Scholar 
Sarwar, M. & Sattar, M. Varietals assessment of different wheat varieties for their resistance response to Khapra beetle Trogoderma granarium. Pak. J. Seed. Technol. 1(10), 1–7 (2007).
Google Scholar 
Wilches, D., Laird, R., Floate, K. & Fields, P. Effects of acclimation and diapause on the cold tolerance of Trogoderma granarium. Entomol. Exp. Appl. 165, 169–178 (2017).Article 
CAS 

Google Scholar 
Paini, D. R. & Yemshanov, D. Modelling the arrival of invasive organisms via the international marine shipping network: a Khapra beetle study. PLoS ONE 7(9), e44589 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Morrison, W. R., Grosdidier, R. F., Arthur, F. H., Myers, S. W. & Domingue, M. J. Attraction, arrestment, and preference by immature Trogoderma variabile and Trogoderma granarium to food and pheromonal stimuli. J. Pest Sci. 93, 135–147 (2020).Article 

Google Scholar 
Arthur, F. H. & Morrison, W. M. Methodology for assessing progeny production and grain damage on commodities treated with insecticides. Agronomy 10(6), 804 (2020).Article 
CAS 

Google Scholar  More

Post, D. M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 703–718 (2002).Article 

Google Scholar 
Fry, B. Stable Isotope Ecology (Springer, 2007).
Google Scholar 
Boon, P. I. & Bunn, S. E. Variations in the stable isotope composition of aquatic plants and their implications for food web analysis. Aquat. Bot. 48, 99–108 (1994).Article 

Google Scholar 
Kling, G. W., Fry, B. & O’Brien, W. J. Stable isotopes and planktonic trophic structure in arctic lakes. Ecology 73, 561–566 (1992).Article 

Google Scholar 
Nielsen, J. M., Clare, E. L., Hayden, B., Brett, M. T. & Kratina, P. Diet tracing in ecology: Method comparison and selection. Methods Ecol. Evol. 9, 278–291 (2018).Article 

Google Scholar 
Coulter, A. A., Swanson, H. K. & Goforth, R. R. Seasonal variation in resource overlap of invasive and native fishes revealed by stable isotopes. Biol. Invasions 21, 315–321 (2019).Article 

Google Scholar 
Jung, A. S., Van Der Veer, H. W., Van Der Meer, M. T. & Philippart, C. J. Seasonal variation in the diet of estuarine bivalves. PLoS One 14, e0217003 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Devlin, S. P., Vander Zanden, M. J. & Vadeboncoeur, Y. Depth-specific variation in carbon isotopes demonstrates resource partitioning among the littoral zoobenthos. Freshw. Biol. 58, 2389–2400 (2013).CAS 

Google Scholar 
Possamai, B., Vieira, J. P., Grimm, A. M. & Garcia, A. M. Temporal variability (1997–2015) of trophic fish guilds and its relationships with El Niño events in a subtropical estuary. Estuar. Coast. Shelf Sci. 202, 145–154 (2018).Article 
ADS 

Google Scholar 
Syvaranta, J., Hamalainen, H. & Jones, R. I. Within-lake variability in carbon and nitrogen stable isotope signatures. Freshw. Biol. 51, 1090–1102 (2006).Article 
CAS 

Google Scholar 
Janbu, A. D., Paasche, Ø. & Talbot, M. R. Paleoclimate changes inferred from stable isotopes and magnetic properties of organic-rich lake sediments in Arctic Norway. J. Paleolimnol. 46, 29 (2011).Article 
ADS 

Google Scholar 
Leng, M. et al. Late quaternary palaeoenvironmental reconstruction from Lakes Ohrid and Prespa (Macedonia/Albania border) using stable isotopes. Biogeosciences 7, 3109–3122 (2010).Article 
ADS 
CAS 

Google Scholar 
Jiang, Q., Shen, J., Liu, X., Zhang, E. & Xiao, X. A high-resolution climatic change since holocene inferred from multi-proxy of lake sediment in westerly area of China. Chin. Sci. Bull. 52, 1970–1979 (2007).Article 

Google Scholar 
Finlay, J. C. & Kendall, C. Stable isotope tracing of temporal and spatial variability in organic matter sources to freshwater ecosystems. Stable Isot. Ecol. Environ. Sci. 2, 283–333 (2007).Article 

Google Scholar 
Harvey, C. J. & Kitchell, J. F. A stable isotope evaluation of the structure and spatial heterogeneity of a Lake Superior food web. Can. J. Fish. Aquat. Sci. 57, 1395–1403 (2000).Article 
CAS 

Google Scholar 
Xu, D. et al. Spatial heterogeneity of food web structure in a large shallow eutrophic lake (Lake Taihu, China): Implications for eutrophication process and management. J. Freshw. Ecol. 34, 229–245 (2019).Article 
CAS 

Google Scholar 
Ruokonen, T., Kiljunen, M., Karjalainen, J. & Hämäläinen, H. Invasive crayfish increase habitat connectivity: A case study in a large boreal lake. Knowl. Manag. Aquat. Ecosyst. https://doi.org/10.1051/kmae/2013034 (2012).Article 

Google Scholar 
Veselý, L. et al. The crayfish distribution, feeding plasticity, seasonal isotopic variation and trophic role across ontogeny and habitat in a canyon-shaped reservoir. Aquat. Ecol. 54, 1169–1183 (2020).Article 

Google Scholar 
Kalff, J. Limnology: Inland Water Ecosystems Vol. 592 (Prentice Hall, 2002).
Google Scholar 
Polačik, M., Harrod, C., Blažek, R. & Reichard, M. Trophic niche partitioning in communities of African annual fish: Evidence from stable isotopes. Hydrobiologia 721, 99–106 (2014).Article 

Google Scholar 
Costalago, D., Navarro, J., Álvarez-Calleja, I. & Palomera, I. Ontogenetic and seasonal changes in the feeding habits and trophic levels of two small pelagic fish species. Mar. Ecol. Prog. Ser. 460, 169–181 (2012).Article 
ADS 

Google Scholar 
Matthews, B. & Mazumder, A. Consequences of large temporal variability of zooplankton δ15N for modeling fish trophic position and variation. Limnol. Oceanogr. 50, 1404–1414 (2005).Article 
ADS 
CAS 

Google Scholar 
Taipale, S., Kankaala, P., Tiirola, M. & Jones, R. I. Whole-lake dissolved inorganic 13C additions reveal seasonal shifts in zooplankton diet. Ecology 89, 463–474 (2008).Article 
PubMed 

Google Scholar 
Zohary, T., Erez, J., Gophen, M., Berman-Frank, I. & Stiller, M. Seasonality of stable carbon isotopes within the pelagic food web of Lake Kinneret. Limnol. Oceanogr. 39, 1030–1043 (1994).Article 
ADS 
CAS 

Google Scholar 
Stenroth, P. et al. Stable isotopes as an indicator of diet in omnivorous crayfish (Pacifastacus leniusculus): The influence of tissue, sample treatment, and season. Can. J. Fish. Aquat. Sci. 63, 821–831 (2006).Article 
CAS 

Google Scholar 
R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ (2021).Moore, J. W. & Semmens, B. X. Incorporating uncertainty and prior information into stable isotope mixing models. Ecol. Lett. 11, 470–480 (2008).Article 
PubMed 

Google Scholar 
Stock, B. C. & Semmens, B. X. Unifying error structures in commonly used biotracer mixing models. Ecology 97, 2562–2569 (2016).Article 
PubMed 

Google Scholar 
Irz, P., Laurent, A., Messad, S., Pronier, O. & Argillier, C. Influence of site characteristics on fish community patterns in French reservoirs. Ecol. Freshw. Fish 11, 123–136 (2002).Article 

Google Scholar 
Sutela, T., Aroviita, J. & Keto, A. Assessing ecological status of regulated lakes with littoral macrophyte, macroinvertebrate and fish assemblages. Ecol. Indic. 24, 185–192 (2013).Article 

Google Scholar 
Hunt, P. & Jones, J. The effect of water level fluctuations on a littoral fauna. J. Fish Biol. 4, 385–394 (1972).Article 

Google Scholar 
Kaster, J. & Jacobi, G. Benthic macroinvertebrates of a fluctuating reservoir. Freshw. Biol. 8, 283–290 (1978).Article 

Google Scholar 
Kraft, K. The effect of unnatural water level fluctuations on benthic invertebrates in Voyageurs National Park. Research⁄Resources Management Report MWR-12. US Department of the Interior, National Park Service. International Falls, Minnesota (1988).Glon, M., Larson, E. R. & Pangle, K. Comparison of 13C and 15N discrimination factors and turnover rates between congeneric crayfish Orconectes rusticus and O. virilis (Decapoda, Cambaridae). Hydrobiologia 768, 51–61 (2016).Article 
CAS 

Google Scholar 
Hesslein, R. H., Hallard, K. & Ramlal, P. Replacement of sulfur, carbon, and nitrogen in tissue of growing broad whitefish (Coregonus nasus) in response to a change in diet traced by δ34S, δ13C, and δ15N. Can. J. Fish. Aquat. Sci. 50, 2071–2076 (1993).Article 
CAS 

Google Scholar  More

Teale, S. A. & Castello, J. D. The past as key to the future: a new perspective on forest health. In Forest Health: An Integrated Perspective (eds Castello, J. D. & Teale, S. A.) 3–16 (Cambridge University Press, 2011). https://doi.org/10.1017/CBO9780511974977.002.Chapter 

Google Scholar 
Jactel, H., Koricheva, J. & Castagneyrol, B. Responses of forest insect pests to climate change: Not so simple. Curr. Opin. Insect Sci. 35, 103–108 (2019).Article 
PubMed 

Google Scholar 
Trumbore, S., Brando, P. & Hartmann, H. Forest health and global change. Science 349, 814–818 (2015).Article 
ADS 
CAS 
PubMed 

Google Scholar 
North, M. P. et al. Operational resilience in western US frequent-fire forests. For. Ecol. Manag. 507, 120004 (2022).Article 

Google Scholar 
Raffa, K. F. et al. A literal use of “forest health” safeguards against misuse and misapplication. J. For. 107, 276–277 (2009).
Google Scholar 
Kolb, T. E., Wagner, M. R. & Covington, W. W. Concepts of forest health: Utilitarian and ecosystem perspectives. J. For. 92, 10–15 (1994).
Google Scholar 
Cale, J. A. et al. A quantitative index of forest structural sustainability. Forests 5, 1618–1634 (2014).Article 

Google Scholar 
Lintz, H. E. et al. Quantifying density-independent mortality of temperate tree species. Ecol. Indic. 66, 1–9 (2016).Article 

Google Scholar 
Stanke, H., Finley, A. O., Domke, G. M., Weed, A. S. & MacFarlane, D. W. Over half of western United States’ most abundant tree species in decline. Nat. Commun. 12, 451 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bettinger, P., Boston, K., Siry, J. P. & Grebner, D. L. Chapter 2—Valuing and Characterizing Forest Conditions. In Forest Management and Planning (eds Bettinger, P. et al.) 21–63 (Academic Press, 2017). https://doi.org/10.1016/B978-0-12-809476-1.00002-3.Chapter 

Google Scholar 
Crowther, T. W. et al. Mapping tree density at a global scale. Nature 525, 201–205 (2015).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Fettig, C. J. et al. The effectiveness of vegetation management practices for prevention and control of bark beetle infestations in coniferous forests of the western and southern United States. For. Ecol. Manag. 238, 24–53 (2007).Article 

Google Scholar 
Morin, R. S. & Liebhold, A. M. Invasions by two non-native insects alter regional forest species composition and successional trajectories. For. Ecol. Manag. 341, 67–74 (2015).Article 

Google Scholar 
Nowak, J. T., Meeker, J. R., Coyle, D. R., Steiner, C. A. & Brownie, C. Southern pine beetle infestations in relation to forest stand conditions, previous thinning, and prescribed burning: Evaluation of the southern pine beetle prevention program. J. For. 113, 454–462 (2015).
Google Scholar 
Asaro, C. & Chamberlin, L. A. Outbreak history (1953–2014) of spring defoliators impacting oak-dominated forests in Virginia, with emphasis on gypsy moth (Lymantria dispar L.) and fall cankerworm (Alsophila pometaria Harris). Am. Entomol. 61, 174–185 (2015).Article 

Google Scholar 
Negrón, J. F. Probability of infestation and extent of mortality associated with the Douglas-fir beetle in the Colorado Front Range. For. Ecol. Manag. 107, 71–85 (1998).Article 

Google Scholar 
Negrón, J. F. & Popp, J. B. Probability of ponderosa pine infestation by mountain pine beetle in the Colorado Front Range. For. Ecol. Manag. 191, 17–27 (2004).Article 

Google Scholar 
Schmid, J. M. & Frye, R. H. Spruce Beetle in the Rockies. Gen. Tech. Rep. RM-49 (US Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, 1977).
Google Scholar 
Krivak-Tetley, F. E. et al. Aggressive tree killer or natural thinning agent? Assessing the impacts of a globally important forest insect. For. Ecol. Manag. 483, 118728 (2021).Article 

Google Scholar 
Bradford, J. B. et al. Tree mortality response to drought-density interactions suggests opportunities to enhance drought resistance. J. Appl. Ecol. 59, 549–559 (2022).Article 

Google Scholar 
Young, D. J. N. et al. Long-term climate and competition explain forest mortality patterns under extreme drought. Ecol. Lett. 20, 78–86 (2017).Article 
PubMed 

Google Scholar 
Furniss, T. J., Das, A. J., van Mantgem, P. J., Stephenson, N. L. & Lutz, J. A. Crowding, climate, and the case for social distancing among trees. Ecol. Appl. 32, e2507 (2022).Article 
PubMed 

Google Scholar 
Woodall, C. W. & Weiskittel, A. R. Relative density of United States forests has shifted to higher levels over last two decades with important implications for future dynamics. Sci. Rep. 11, 1–12 (2021).Article 

Google Scholar 
Gandhi, K. J. K., Campbell, F. & Abrams, J. Current status of forest health policy in the United States. Insects 10, 1–14 (2019).Article 

Google Scholar 
Ciesla, W. M. The role of human activities on forest insect outbreaks worldwide. Int. For. Rev. 17, 269–281 (2015).
Google Scholar 
Jactel, H. & Brockerhoff, E. G. Tree diversity reduces herbivory by forest insects. Ecol. Lett. 10, 835–848 (2007).Article 
PubMed 

Google Scholar 
Marini, L., Ayres, M. P. & Jactel, H. Impact of stand and landscape management on forest pest damage. Annu. Rev. Entomol. 67, 181–199 (2022).Article 
PubMed 

Google Scholar 
Guyot, V., Castagneyrol, B., Vialatte, A., Deconchat, M. & Jactel, H. Tree diversity reduces pest damage in mature forests across Europe. Biol. Lett. 12, 20151037 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Kneeshaw, D. D. et al. The vision of managing for pest-resistant landscapes: Realistic or utopic? Curr. For. Rep. 7, 97–113 (2021).PubMed 
PubMed Central 

Google Scholar 
Chisholm, P. J., Stevens-Rumann, C. S. & Davis, T. S. Interactions between climate and stand conditions predict pine mortality during a bark beetle outbreak. Forests 12, 360 (2021).Article 

Google Scholar 
Ferrell, G. T., Otrosina, W. J. & Demars, C. J. Predicting susceptibility of white fir during a drought-associated outbreak of the fir engraver, Scolytus ventralis in California. Can. J. For. Res. 24, 302–305 (1994).Article 

Google Scholar 
Asaro, C., Nowak, J. T. & Elledge, A. Why have southern pine beetle outbreaks declined in the southeastern U.S. with the expansion of intensive pine silviculture? A brief review of hypotheses. For. Ecol. Manag. 391, 338–348 (2017).Article 

Google Scholar 
Nowak, J. T., Klepzig, K. D., Coyle, D. R., Carothers, W. A. & Gandhi, K. J. K. Southern pine beetles in central hardwood forests: Frequency, spatial extent, and changes to forest structure. In Managing Forest Ecosystems Volume 32: Natural Disturbances and Historic Range of Variation (eds Greenberg, C. H. & Collins, B. S.) 73–88 (Springer International Publishing, 2016). https://doi.org/10.1007/978-3-319-21527-3_4.Chapter 

Google Scholar 
Crocker, S. J., Liknes, G. C., McKee, F. R., Albers, J. S. & Aukema, B. H. Stand-level factors associated with resurging mortality from eastern larch beetle (Dendroctonus simplex LeConte). For. Ecol. Manag. 375, 27–34 (2016).Article 

Google Scholar 
Mattson, W. J. & Addy, N. D. Phytophagous insects as regulators of forest primary production. Science 190, 515–522 (1975).Article 
ADS 

Google Scholar 
Thom, D. & Seidl, R. Natural disturbance impacts on ecosystem services and biodiversity in temperate and boreal forests. Biol. Rev. 91, 760–781 (2016).Article 
PubMed 

Google Scholar 
Grégoire, J. C., Raffa, K. F. & Lindgren, B. S. Economics and politics of bark beetles. In Bark Beetles: Biology and Ecology of Native and Invasive Species (eds Vega, F. E. & Hofstetter, R. W.) 585–613 (Academic Press, 2015). https://doi.org/10.1016/B978-0-12-417156-5.00015-0.Chapter 

Google Scholar 
Kolb, T. E. et al. Observed and anticipated impacts of drought on forest insects and diseases in the United States. For. Ecol. Manag. 380, 321–334 (2016).Article 

Google Scholar 
Fettig, C. J. et al. Changing climates, changing forests: A western North American perspective. J. For. 111, 214–228 (2013).
Google Scholar 
Liebhold, A. M. et al. A highly aggregated geographical distribution of forest pest invasions in the USA. Divers. Distrib. 19, 1208–1216 (2013).Article 

Google Scholar 
Siegert, N. W., Mccullough, D. G., Liebhold, A. M. & Telewski, F. W. Dendrochronological reconstruction of the epicentre and early spread of emerald ash borer in North America. Divers. Distrib. 20, 847–858 (2014).Article 

Google Scholar 
Smith, A., Herms, D. A., Long, R. P. & Gandhi, K. J. K. Community composition and structure had no effect on forest susceptibility to invasion by the emerald ash borer (Coleoptera: Buprestidae). Can. Entomol. 147, 318–328 (2015).Article 

Google Scholar 
Aukema, J. E. et al. Historical accumulation of nonindigenous forest pests in the continental United States. Bioscience 60, 886–897 (2010).Article 

Google Scholar 
Hicke, J. A. et al. Effects of biotic disturbances on forest carbon cycling in the United States and Canada. Glob. Chang. Biol. 18, 7–34 (2012).Article 
ADS 

Google Scholar 
Feeny, P. Seasonal changes in oak leaf tannins and nutrients as a cause of spring feeding by winter moth caterpillars. Ecology 51, 565–581 (1970).Article 

Google Scholar 
Schowalter, T. D., Hargrove, W. W. & Crossley, D. A. Herbivory in forested ecosystems. Annu. Rev. Entomol. 31, 177–196 (1986).Article 

Google Scholar 
Seidl, R. et al. Forest disturbances under climate change. Nat. Clim. Chang. 7, 395–402 (2017).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Colautti, R. I., Ricciardi, A., Grigorovich, I. A. & MacIsaac, H. J. Is invasion success explained by the enemy release hypothesis? Ecol. Lett. 7, 721–733 (2004).Article 

Google Scholar 
Catford, J. A., Jansson, R. & Nilsson, C. Reducing redundancy in invasion ecology by integrating hypotheses into a single theoretical framework. Divers. Distrib. 15, 22–40 (2009).Article 

Google Scholar 
Guyot, V. et al. Tree diversity limits the impact of an invasive forest pest. PLoS One 10, 1–16 (2015).Article 

Google Scholar 
Root, R. B. Organization of a plant-arthropod association in simple and diverse habitats: The fauna of collards (Brassica oleracea). Ecol. Monogr. 43, 95–124 (1973).Article 

Google Scholar 
Acker, S. A., Boetsch, J. R., Fallon, B. & Denn, M. Stable background tree mortality in mature and old-growth forests in western Washington (NW USA). For. Ecol. Manag. 532, 120817 (2023).Article 

Google Scholar 
Shive, K. L. et al. Ancient trees and modern wildfires: Declining resilience to wildfire in the highly fire-adapted giant sequoia. For. Ecol. Manag. 511, 120110 (2022).Article 

Google Scholar 
Searle, E. B., Chen, H. Y. H. & Paquette, A. Higher tree diversity is linked to higher tree mortality. Proc. Natl. Acad. Sci. U.S.A. 119, 1–7 (2022).Article 

Google Scholar 
Hart, S. J., Veblen, T. T., Eisenhart, K. S., Jarvis, D. & Kulakowski, D. Drought induces spruce beetle (Dendroctonus rufipennis) outbreaks across northwestern Colorado. Ecology 95, 930–939 (2014).Article 
PubMed 

Google Scholar 
Hart, S. J., Veblen, T. T. & Kulakowski, D. Do tree and stand-level attributes determine susceptibility of spruce-fir forests to spruce beetle outbreaks in the early 21st century? For. Ecol. Manag. 318, 44–53 (2014).Article 

Google Scholar 
Temperli, C. et al. Are density reduction treatments effective at managing for resistance or resilience to spruce beetle disturbance in the southern Rocky Mountains? For. Ecol. Manag. 334, 53–63 (2014).Article 

Google Scholar 
Six, D. L., Biber, E. & Long, E. Management for mountain pine beetle outbreak suppression: Does relevant science support current policy? Forests 5, 103–133 (2014).Article 

Google Scholar 
Black, S. H., Kulakowski, D., Noon, B. R. & Dellasala, D. A. Do bark beetle outbreaks increase wildfire risks in the central U.S. rocky mountains? Implications from recent research. Nat. Areas J. 33, 59–65 (2013).Article 

Google Scholar 
Oswalt, S. N., Smith, W. B., Miles, P. D. & Pugh, S. A. Forest Resources of the United States, 2017: A Technical Document Supporting the Forest Service 2020 RPA Assessment. Gen. Tech. Rep. WO-97 (US Department of Agriculture, Forest Service, 2019). https://doi.org/10.2737/WO-GTR-97.Book 

Google Scholar 
Cleland, D. et al. Terrestrial condition assessment for national forests of the USDA Forest Service in the continental US. Sustainability 9, 1–19 (2017).Article 

Google Scholar 
USDA Forest Service Forest Health Protection. Insect and Disease Detection Survey (IDS) data downloads. https://www.fs.usda.gov/foresthealth/applied-sciences/mapping-reporting/detection-surveys.shtml (2021). Accessed on 9 October 2021.Spruce, J. P. et al. Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks. Remote Sens. Environ. 115, 427–437 (2011).Article 
ADS 

Google Scholar 
Gomez, D. F., Ritger, H. M. W., Pearce, C., Eickwort, J. & Hulcr, J. Ability of remote sensing systems to detect bark beetle spots in the southeastern US. Forests 11, 1–10 (2020).Article 

Google Scholar 
Hanavan, R. P. et al. Supplementing the forest health national aerial survey program with remote sensing during the COVID-19 pandemic: Lessons learned from a collaborative approach. J. For. 120, 125–132 (2021).
Google Scholar 
Johnson, E. W. & Wittwer, D. Aerial detection surveys in the United States. Aust. For. 71, 212–215 (2008).Article 

Google Scholar 
Bright, B. C. et al. Using satellite imagery to evaluate bark beetle-caused tree mortality reported in aerial surveys in a mixed conifer forest in Northern Idaho, USA. Forests 11, 1–19 (2020).Article 

Google Scholar 
Coleman, T. W. et al. Accuracy of aerial detection surveys for mapping insect and disease disturbances in the United States. For. Ecol. Manag. 430, 321–336 (2018).Article 

Google Scholar 
Hicke, J. A., Xu, B., Meddens, A. J. H. & Egan, J. M. Characterizing recent bark beetle-caused tree mortality in the western United States from aerial surveys. For. Ecol. Manag. 475, 118402 (2020).Article 

Google Scholar 
Kosiba, A. M. et al. Spatiotemporal patterns of forest damage and disturbance in the northeastern United States: 2000–2016. For. Ecol. Manag. 430, 94–104 (2018).Article 

Google Scholar 
Meigs, G. W., Kennedy, R. E., Gray, A. N. & Gregory, M. J. Spatiotemporal dynamics of recent mountain pine beetle and western spruce budworm outbreaks across the Pacific Northwest Region USA. For. Ecol. Manag. 339, 71–86 (2015).Article 

Google Scholar 
Bechtold, W. A. & Patterson, P. L. The Enhanced Forest Inventory and Analysis Program—National Sampling Design and Estimation Procedures. Gen. Tech. Rep. SRS-80 (US Department of Agriculture, Forest Service, Southern Research Station, 2005). https://doi.org/10.2737/SRS-GTR-80.Book 

Google Scholar 
Randolph, K. D. C. et al. Past and present individual-tree damage assessments of the US national forest inventory. Environ. Monit. Assess. 193, 116 (2021).Article 
PubMed 

Google Scholar 
Kromroy, K. W., Juzwik, J., Castillo, P. & Hansen, M. H. Using forest service forest inventory and analysis data to estimate regional oak decline and oak mortality. North. J. Appl. For. 25, 17–24 (2008).Article 

Google Scholar 
Coulston, J. W., Edgar, C. B., Westfall, J. A. & Taylor, M. E. Estimation of forest disturbance from retrospective observations in a broad-scale inventory. Forests 11, 1298 (2020).Article 

Google Scholar 
Wilson, B. T., Lister, A. J. & Riemann, R. I. A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data. For. Ecol. Manag. 271, 182–198 (2012).Article 

Google Scholar 
Blackard, J. A. et al. Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information. Remote Sens. Environ. 112, 1658–1677 (2008).Article 
ADS 

Google Scholar 
Brosofske, K. D., Froese, R. E., Falkowski, M. J. & Banskota, A. A review of methods for mapping and prediction of inventory attributes for operational forest management. For. Sci. 60, 733–756 (2014).Article 

Google Scholar 
Lister, A. J. et al. Use of remote sensing data to improve the efficiency of national forest inventories: A case study from the United States national forest inventory. Forests 11, 1–41 (2020).Article 

Google Scholar 
USDA Forest Service Forest Health Protection. Individual Tree Species Parameter (ITSP) maps – GIS data downloads. https://www.fs.usda.gov/foresthealth/applied-sciences/mapping-reporting/indiv-tree-parameter-maps.shtml (2021). Accessed on 9 October 2021.Ellenwood, J. R., Krist, F. J. & Romero, S. A. National Individual Tree Species Atlas. FHTET-15-01 (US Department of Agriculture, Forest Service, Forest Health Technology Enterprise Team, 2015).
Google Scholar 
Krist, F. J. et al. National Insect and Disease Forest Risk Assessment. FHTET-14-01 (US Department of Agriculture, Forest Service, Forest Health Technology Enterprise Team, 2014).
Google Scholar 
Rulequest Inc. Cubist, release 2.07. https://www.rulequest.com/cubist-info.html (2011). Accessed on 15 July 2022.R Core Team. R: A language and environment for statistical computing. https://www.r-project.org (2021). Accessed on 4 March 2022.Esri Inc. ArcGIS Pro 2.8.0. https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview (2021). Accessed on 4 March 2022. More

Cohan FM. Bacterial species and speciation. Syst Biol. 2001;50:513–24.Article 
CAS 
PubMed 

Google Scholar 
Delmont TO, Kiefl E, Kilinc O, Esen OC, Uysal I, Rappe MS, et al. Single-amino acid variants reveal evolutionary processes that shape the biogeography of a global SAR11 subclade. eLife 2019;8:e46497.Article 
PubMed 
PubMed Central 

Google Scholar 
Hunt DE, David LA, Gevers D, Preheim SP, Alm EJ, Polz MF. Resource partitioning and sympatric differentiation among closely related bacterioplankton. Science 2008;320:1081–5.Article 
CAS 
PubMed 

Google Scholar 
Moore LR, Rocap G, Chisholm SW. Physiology and molecular phylogeny of coexisting Prochlorococcus ecotypes. Nature 1998;393:464–7.Article 
CAS 
PubMed 

Google Scholar 
Rivas LR. A reinterpretation of the concepts “sympatric” and “allopatric” with proposal for the additional terms “syntopic” and “allotopic”. Syst Zool. 1964;13:42–3.Article 

Google Scholar 
Friedman J, Alm EJ, Shapiro BJ. Sympatric speciation: when is it possible in bacteria? PLoS One. 2013;8:e53539.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kiene RP, Nowinski B, Esson K, Preston C, Marin R III, Birch J, et al. Unprecedented DMSP concentrations in a massive dinoflagellate bloom in Monterey Bay. Ca Geophys Res Lett. 2019;46:12279–88.Article 

Google Scholar 
Scholin CA, Birch J, Jensen S, Marin R, Massion E, Pargett D, et al. The quest to develop ecogenomic sensors a 25-year history of the environmental sample processor (ESP) as a case study. Oceanography. 2017;30:100–13.Article 

Google Scholar 
Nowinski B, Smith CB, Thomas CM, Esson K, Marin R, Preston CM, et al. Microbial metagenomes and metatranscriptomes during a coastal phytoplankton bloom. Sci Data. 2019;6:1–7.Article 
CAS 

Google Scholar 
Luo H, Löytynoja A, Moran MA. Genome content of uncultivated marine Roseobacters in the surface ocean. Environ Microbiol. 2012;14:41–51.Article 
CAS 
PubMed 

Google Scholar 
Connon SA, Giovannoni SJ. High-throughput methods for culturing microorganisms in very-low-nutrient media yield diverse new marine isolates. Appl Environ Microbiol. 2002;68:3878–85.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Feng X, Chu X, Qian Y, Henson MW, Lanclos VC, Qin F, et al. Mechanisms driving genome reduction of a novel Roseobacter lineage. ISME J. 2021;15:3576–86.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Moran MA, Belas R, Schell M, González J, Sun F, Sun S, et al. Ecological genomics of marine roseobacters. Appl Environ Microbiol. 2007;73:4559–69.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Newton RJ, Griffin LE, Bowles KM, Meile C, Gifford S, Givens CE, et al. Genome characteristics of a generalist marine bacterial lineage. ISME J. 2010;4:784–98.Article 
CAS 
PubMed 

Google Scholar 
Suzuki MT, Preston CM, Béjà O, De La Torre J, Steward G, DeLong EF. Phylogenetic screening of ribosomal RNA gene-containing clones in bacterial artificial chromosome (BAC) libraries from different depths in Monterey Bay. Micro Ecol. 2004;48:473–88.Article 
CAS 

Google Scholar 
Buchan A, González JM, Moran MA. Overview of the marine Roseobacter lineage. Appl Environ Microbiol. 2005;71:5665–77.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Giebel H-A, Kalhoefer D, Lemke A, Thole S, Gahl-Janssen R, Simon M, et al. Distribution of Roseobacter RCA and SAR11 lineages in the North Sea and characteristics of an abundant RCA isolate. ISME J. 2011;5:8–19.Article 
PubMed 

Google Scholar 
Ottesen EA, Marin R, Preston CM, Young CR, Ryan JP, Scholin CA, et al. Metatranscriptomic analysis of autonomously collected and preserved marine bacterioplankton. ISME J. 2011;5:1881–95.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang Y, Sun Y, Jiao N, Stepanauskas R, Luo H. Ecological genomics of the uncultivated marine Roseobacter lineage CHAB-I-5. Appl Environ Microbiol. 2016;82:2100–11.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Aylward FO, Eppley JM, Smith JM, Chavez FP, Scholin CA, DeLong EF. Microbial community transcriptional networks are conserved in three domains at ocean basin scales. Proc Nat Acad Sci. 2015;112:5443–8.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ottesen EA, Young CR, Eppley JM, Ryan JP, Chavez FP, Scholin CA, et al. Pattern and synchrony of gene expression among sympatric marine microbial populations. Proc Nat Acad Sci. 2013;110:E488–E97.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nowinski B, Motard‐Côté J, Landa M, Preston CM, Scholin CA, Birch JM, et al. Microdiversity and temporal dynamics of marine bacterial dimethylsulfoniopropionate genes. Environ Microbiol. 2019;21:1687–701.Article 
CAS 
PubMed 

Google Scholar 
Mukherjee S, Stamatis D, Bertsch J, Ovchinnikova G, Sundaramurthi JC, Lee J, et al. Genomes OnLine Database (GOLD) v. 8: overview and updates. Nucleic Acids Res. 2021;49:D723–D33.Article 
CAS 
PubMed 

Google Scholar 
Chen I-MA, Chu K, Palaniappan K, Pillay M, Ratner A, Huang J, et al. IMG/M v. 5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res. 2019;47:D666–D77.Article 
CAS 
PubMed 

Google Scholar 
Satinsky BM, Gifford SM, Crump BC, Moran MA. Use of internal standards for quantitative metatranscriptome and metagenome analysis. In: DeLong EF, editor. Methods in Enzymology 531: Elsevier; 2013. p. 237–50.Satinsky BM, Gifford SM, Crump BC, Smith C.Moran MA, Internal genomic DNA standard for quantitative metagenome analysis V3. protocols io 2017; https://doi.org/10.17504/protocols.io.jxdcpi6p.Satinsky BM, Gifford SM, Crump BC, Smith C.Moran MA, Preparation of custom synthesized RNAtranscript standard V3. protocols io. 2017; https://doi.org/10.17504/protocols.io.jxccpiwp.Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 2015;3:e1165.Article 
PubMed 
PubMed Central 

Google Scholar 
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rodriguez-R LM, Konstantinidis KT. The enveomics collection: a toolbox for specialized analyses of microbial genomes and metagenomes. PeerJ Prepr. 2016. Report No.: 2167–9843Lee K, Choo Y-J, Giovannoni SJ, Cho J-C. Maritimibacter alkaliphilus gen. nov., sp. nov., a genome-sequenced marine bacterium of the Roseobacter clade in the order Rhodobacterales. Int J Syst Evol Microbiol. 2007;57:1653–8.Article 
PubMed 

Google Scholar 
Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ 2015;3:e1319.Article 
PubMed 
PubMed Central 

Google Scholar 
Tatusov RL, Galperin MY, Natale DA, Koonin EV. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 2000;28:33–6.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, Von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017;34:2115–22.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, et al. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 2020;36:2251–2.Article 
CAS 
PubMed 

Google Scholar 
Bushnell B. BBMap: a fast, accurate, splice-aware aligner. No. LBNL-7065E. Lawrence Berkeley National Laboratory, Berkeley, CA (United States); 2014.Markowitz VM, Chen I-MA, Palaniappan K, Chu K, Szeto E, Grechkin Y, et al. The integrated microbial genomes system: an expanding comparative analysis resource. Nucleic Acids Res. 2010;38:D382–D90.Article 
CAS 
PubMed 

Google Scholar 
Sun Y, Luo H. Homologous recombination in core genomes facilitates marine bacterial adaptation. Appl Environ Microbiol. 2018;84:e02545–17.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pfaffel O. ClustImpute: An R package for K-means clustering with build-in missing data imputation. https://www.researchgate.net/publication/341881683.Moran MA, Satinsky B, Gifford SM, Luo H, Rivers A, Chan L-K, et al. Sizing up metatranscriptomics. ISME J 2013;7:237–43.Article 
CAS 
PubMed 

Google Scholar 
Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Structure and function of the global ocean microbiome. Science. 2015;348:1261359.Article 
PubMed 

Google Scholar 
Gifford SM, Zhao L, Stemple B, DeLong K, Medeiros PM, Seim H, et al. Microbial niche diversification in the Galápagos Archipelago and its response to El Niño. Front Microbiol. 2020;11:575194.Article 
PubMed 
PubMed Central 

Google Scholar 
Rich VI, Pham VD, Eppley J, Shi Y, DeLong EF. Time‐series analyses of Monterey Bay coastal microbial picoplankton using a ‘genome proxy’microarray. Environ Microbiol. 2011;13:116–34.Article 
CAS 
PubMed 

Google Scholar 
Riedel T, Tomasch J, Buchholz I, Jacobs J, Kollenberg M, Gerdts G, et al. Constitutive expression of the proteorhodopsin gene by a flavobacterium strain representative of the proteorhodopsin-producing microbial community in the North Sea. Appl Environ Microbiol. 2010;76:3187–97.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Iverson V, Morris RM, Frazar CD, Berthiaume CT, Morales RL, Armbrust EV. Untangling genomes from metagenomes: revealing an uncultured class of marine Euryarchaeota. Science 2012;335:587–90.Article 
CAS 
PubMed 

Google Scholar 
Yooseph S, Nealson KH, Rusch DB, McCrow JP, Dupont CL, Kim M, et al. Genomic and functional adaptation in surface ocean planktonic prokaryotes. Nature 2010;468:60–6.Article 
CAS 
PubMed 

Google Scholar 
Wagner-Döbler I, Biebl H. Environmental biology of the marine Roseobacter lineage. Annu Rev Microbiol. 2006;60:255–80.Article 
PubMed 

Google Scholar 
West NJ, Obernosterer I, Zemb O, Lebaron P. Major differences of bacterial diversity and activity inside and outside of a natural iron‐fertilized phytoplankton bloom in the Southern Ocean. Environ Microbiol. 2008;10:738–56.Article 
CAS 
PubMed 

Google Scholar 
Luo H, Moran MA. Evolutionary ecology of the marine Roseobacter clade. Microbiol Mol Biol Rev. 2014;78:573–87.Article 
PubMed 
PubMed Central 

Google Scholar 
Simon M, Scheuner C, Meier-Kolthoff JP, Brinkhoff T, Wagner-Döbler I, Ulbrich M, et al. Phylogenomics of Rhodobacteraceae reveals evolutionary adaptation to marine and non-marine habitats. ISME J 2017;11:1483–99.Article 
PubMed 
PubMed Central 

Google Scholar 
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Comm. 2018;9:1–8.Article 

Google Scholar 
Caro‐Quintero A, Konstantinidis KT. Bacterial species may exist, metagenomics reveal. Environ Microbiol. 2012;14:347–55.Article 
PubMed 

Google Scholar 
Tindall BJ, Rosselló-Móra R, Busse H-J, Ludwig W, Kämpfer P. Notes on the characterization of prokaryote strains for taxonomic purposes. Int J Syst Evol Microbiol. 2010;60:249–66.Article 
CAS 
PubMed 

Google Scholar 
Cohan FM. What are bacterial species? Ann Rev Microbiol. 2002;56:457–87.Article 
CAS 

Google Scholar 
Mende DR, Sunagawa S, Zeller G, Bork P. Accurate and universal delineation of prokaryotic species. Nat Meth. 2013;10:881–4.Article 
CAS 

Google Scholar 
Olm MR, Crits-Christoph A, Diamond S, Lavy A, Matheus Carnevali PB, Banfield JF. Consistent metagenome-derived metrics verify and delineate bacterial species boundaries. mSystems 2020;5:e00731–19.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Konstantinidis KT, Tiedje JM. Prokaryotic taxonomy and phylogeny in the genomic era: advancements and challenges ahead. Curr Opin Microbiol. 2007;10:504–9.Article 
CAS 
PubMed 

Google Scholar 
Delmont TO, Eren EM. Linking pangenomes and metagenomes: The Prochlorococcus metapangenome. PeerJ 2018;2018:e4320–e.Article 

Google Scholar 
Neidhardt F, Umbarger H Chemical composition of Escherichia coli. In: FC N, Curtiss R III, JL I, ECC L, KB L, B M, et al., editors. Escherichia coli and Salmonella typhimurium: Cellular and Molecular Biology. Washington DC: ASM Press; 1996. p. 13-6.Taniguchi Y, Choi PJ, Li G-W, Chen H, Babu M, Hearn J, et al. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science. 2010;329:533–8.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rodríguez-Gijón A, Nuy JK, Mehrshad M, Buck M, Schulz F, Woyke T, et al. A genomic perspective across Earth’s microbiomes reveals that genome size in Archaea and Bacteria is linked to ecosystem type and trophic strategy. Front Microbiol. 2022;12:761869.Article 
PubMed 
PubMed Central 

Google Scholar 
Ryu K-S, Kim C, Kim I, Yoo S, Choi B-S, Park C. NMR application probes a novel and ubiquitous family of enzymes that alter monosaccharide configuration. J Biol Chem. 2004;279:25544–8.Article 
CAS 
PubMed 

Google Scholar 
Giachino A, Waldron KJ. Copper tolerance in bacteria requires the activation of multiple accessory pathways. Mol Microbiol. 2020;114:377–90.Article 
CAS 
PubMed 

Google Scholar 
Wang X, Zhang Y, Ren M, Xia T, Chu X, Liu C, et al. Cryptic speciation of a pelagic Roseobacter population varying at a few thousand nucleotide sites. ISME J. 2020;14:3106–19.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Uchimiya M, Schroer W, Olofsson M, Edison AS, Moran MA. Diel investments in metabolite production and consumption in a model microbial system. ISME J. 2022;16:1306–17.Article 
CAS 
PubMed 

Google Scholar 
Cordero OX, Wildschutte H, Kirkup B, Proehl S, Ngo L, Hussain F, et al. Ecological populations of bacteria act as socially cohesive units of antibiotic production and resistance. Science 2012;337:1228–31.Article 
CAS 
PubMed 

Google Scholar 
Morris JJ, Lenski RE, Zinser ER. The Black Queen Hypothesis: evolution of dependencies through adaptive gene loss. mBio 2012;3:e00036–12.Article 
PubMed 
PubMed Central 

Google Scholar 
Environmental data from CTD during the Fall 2016 ESP deployment in Monterey Bay, CA. Biological and Chemical Oceanography Data Management Office (BCO-DMO). 2019. Available from: https://doi.org/10.1575/1912/bco-dmo.756376.1.Environmental data from Niskin bottle sampling during the Fall 2016 ESP deployment in Monterey Bay. Biological and Chemical Oceanography Data Management Office (BCO-DMO). 2019. Available from: https://doi.org/10.1575/1912/bco-dmo.756413.1. More