Philippa Kaur

Schendel, V., Rash, L. D., Jenner, R. A. & Undheim, E. A. The diversity of venom: The importance of behavior and venom system morphology in understanding its ecology and evolution. Toxins 11(11), 666 (2019).Article 
CAS 

Google Scholar 
Casewell, N. R., Wüster, W., Vonk, F. J., Harrison, R. A. & Fry, B. G. Complex cocktails: The evolutionary novelty of venoms. Trends Ecol. Evol. 28(4), 219–229 (2013).Article 

Google Scholar 
Pineda, S. S. et al. Structural venomics reveals evolution of a complex venom by duplication and diversification of an ancient peptide-encoding gene. Proc. Natl. Acad. Sci. USA 117(21), 11399–11408 (2020).Article 
ADS 
CAS 

Google Scholar 
Chippaux, J. P., Williams, V. & White, J. Snake venom variability: Methods of study, results and interpretation. Toxicon 29(11), 1279–1303 (1991).Article 
CAS 

Google Scholar 
Lyons, K., Dugon, M. M. & Healy, K. Diet breadth mediates the prey specificity of venom potency in snakes. Toxins 12(2), 74 (2020).Article 

Google Scholar 
Pekár, S. et al. Venom gland size and venom complexity—essential trophic adaptations of venomous predators: A case study using spiders. Mol. Ecol. 27(21), 4257–4269 (2018).Article 

Google Scholar 
Phuong, M. A., Mahardika, G. N. & Alfaro, M. E. Dietary breadth is positively correlated with venom complexity in cone snails. BMC Genom. 17(1), 401 (2016).Article 

Google Scholar 
Holding, M. L., Biardi, J. E. & Gibbs, H. L. Coevolution of venom function and venom resistance in a rattlesnake predator and its squirrel prey. Proc. R. Soc. B. 283(1829), 20152841 (2016).Article 

Google Scholar 
Pekár, S., Líznarová, E., Bočánek, O. & Zdráhal, Z. Venom of prey-specialized spiders is more toxic to their preferred prey: A result of prey-specific toxins. J. Anim. Ecol. 87(6), 1639–1652 (2018).Article 

Google Scholar 
Pekár, S., Coddington, J. A. & Blackledge, T. A. Evolution of stenophagy in spiders (Araneae): Evidence based on the comparative analysis of spider diets. Evolution 66(3), 776–806 (2012).Article 

Google Scholar 
Herzig, V., King, G. F. & Undheim, E. A. Can we resolve the taxonomic bias in spider venom research?. Toxicon: X 1, 100005 (2019).Article 
CAS 

Google Scholar 
Platnick, N. A relimitation and revision of the Australasian ground spider family Lamponidae (Araneae: Gnaphosoidea). Bull. Am. Mus. Nat. Hist. 2000(245), 1–328 (2000).Article 

Google Scholar 
World Spider Catalog. Version 22.0. Natural History Museum Bern. http://wsc.nmbe.ch. Accessed 15 Mar 2021 (2021).White, J. & Weinstein, S. A. A phoenix of clinical toxinology: White-tailed spider (Lampona spp.) bites. A case report and review of medical significance. Toxicon 87, 76–80 (2014).Article 
CAS 

Google Scholar 
Rash, L. D., King, R. G. & Hodgson, W. C. Sex differences in the pharmacological activity of venom from the white-tailed spider (Lampona cylindrata). Toxicon 38, 1111–1127 (2000).Article 
CAS 

Google Scholar 
Young, A. R. & Pincus, S. J. Comparison of enzymatic activity from three species of necrotising arachnids in Australia: Loxosceles rufescens, Badumna insignis and Lampona cylindrata. Toxicon 39, 391–400 (2001).Article 
CAS 

Google Scholar 
Michálek, O., Petráková, L. & Pekár, S. Capture efficiency and trophic adaptations of a specialist and generalist predator: A comparison. Ecol. Evol. 7(8), 2756–2766 (2017).Article 

Google Scholar 
Klint, J. K. et al. Spider-venom peptides that target voltage-gated sodium channels: Pharmacological tools and potential therapeutic leads. Toxicon 60(4), 478–491 (2012).Article 
CAS 

Google Scholar 
Diniz, M. R. et al. An overview of Phoneutria nigriventer spider venom using combined transcriptomic and proteomic approaches. PLoS ONE 13(8), e0200628 (2018).Article 

Google Scholar 
Wilson, D. et al. The aromatic head group of spider toxin polyamines influences toxicity to cancer cells. Toxins 9(11), 346 (2017).Article 

Google Scholar 
Herzig, V. & King, G. F. The cystine knot is responsible for the exceptional stability of the insecticidal spider toxin ω-hexatoxin-Hv1a. Toxins 7(10), 4366–4380 (2015).Article 
CAS 

Google Scholar 
Wang, X. H. et al. Discovery and characterization of a family of insecticidal neurotoxins with a rare vicinal disulfide bridge. Nat. Struct. Biol. 7(6), 505–513 (2000).Article 
CAS 

Google Scholar 
Yuan, C. H. et al. Discovery of a distinct superfamily of Kunitz-type toxin (KTT) from tarantulas. PLoS ONE 3(10), e3414 (2008).Article 
ADS 

Google Scholar 
Luo, J. et al. Molecular diversity and evolutionary trends of cysteine-rich peptides from the venom glands of Chinese spider Heteropoda venatoria. Sci. Rep. 11, 3211 (2021).Article 
ADS 
CAS 

Google Scholar 
Cole, J., Buszka, P. A., Mobley, J. A. & Hataway, R. A. Characterization of the venom proteome for the wandering spider, Ctenus hibernalis (Aranea: Ctenidae). J. Proteom. Bioinform. 9, 196–199 (2016).Article 

Google Scholar 
Korolkova, Y. et al. New Insectotoxin from Tibellus Oblongus Spider venom presents novel daptation of ICK Fold. Toxins 13(1), 29 (2021).Article 
CAS 

Google Scholar 
Koua, D. et al. Proteotranscriptomic insights into the venom composition of the wolf spider Lycosa tarantula. Toxins 12(8), 501 (2020).Article 
CAS 

Google Scholar 
Liberato, T., Troncone, L. R. P., Yamashiro, E. T., Serrano, S. M. & Zelanis, A. High-resolution proteomic profiling of spider venom: Expanding the toxin diversity of Phoneutria nigriventer venom. Amino Acids 48(3), 901–906 (2016).Article 
CAS 

Google Scholar 
Oldrati, V. et al. Peptidomic and transcriptomic profiling of four distinct spider venoms. PLoS ONE 12(3), e0172966 (2017).Article 

Google Scholar 
King, G. F. & Hardy, M. C. Spider-venom peptides: Structure, pharmacology, and potential for control of insect pests. Annu. Rev. Entomol. 58, 475–496 (2013).Article 
CAS 

Google Scholar 
Turner, A. J., Isaac, R. E. & Coates, D. The neprilysin (NEP) family of zinc metalloendopeptidases: Genomics and function. BioEssays 23(3), 261–269 (2001).Article 
CAS 

Google Scholar 
Casewell, N. R., Harrison, R. A., Wüster, W. & Wagstaff, S. C. Comparative venom gland transcriptome surveys of the saw-scaled vipers (Viperidae: Echis) reveal substantial intra-family gene diversity and novel venom transcripts. BMC Genom. 10(1), 1–12 (2009).Article 

Google Scholar 
Tan, C. H., Tan, K. Y., Fung, S. Y. & Tan, N. H. Venom-gland transcriptome and venom proteome of the Malaysian king cobra (Ophiophagus hannah). BMC Genom. 16(1), 1–21 (2015).Article 

Google Scholar 
Tan, K. Y., Tan, C. H., Chanhome, L. & Tan, N. H. Comparative venom gland transcriptomics of Naja kaouthia (monocled cobra) from Malaysia and Thailand: Elucidating geographical venom variation and insights into sequence novelty. PeerJ 5, e3142 (2017).Article 

Google Scholar 
Undheim, E. A. et al. A proteomics and transcriptomics investigation of the venom from the barychelid spider Trittame loki (brush-foot trapdoor). Toxins. 5(12), 2488–2503 (2013).Article 
CAS 

Google Scholar 
do Nascimento, S. M., de Oliveira, U. C., Nishiyama-Jr, M. Y., Tashima, A. K. & Silva Junior, P. I. D. Presence of a neprilysin on Avicularia juruensis (Mygalomorphae: Theraphosidae) venom. Toxin Rev. 41(2), 370–379 (2021).Article 

Google Scholar 
Zobel-Thropp, P. A. et al. Not so dangerous after all? Venom composition and potency of the Pholcid (daddy long-leg) spider Physocyclus mexicanus. Front. Ecol. Evol. 7, 256 (2019).Article 

Google Scholar 
Diniz, M. R. et al. An overview of Phoneutria nigriventer spider venom using combined transcriptomic and proteomic approaches. PLoS ONE 13(8), e0200628 (2018).Article 

Google Scholar 
He, Q. et al. The venom gland transcriptome of Latrodectus tredecimguttatus revealed by deep sequencing and cDNA library analysis. PLoS ONE 8(11), e81357 (2013).Article 
ADS 

Google Scholar 
Haney, R. A., Ayoub, N. A., Clarke, T. H., Hayashi, C. Y. & Garb, J. E. Dramatic expansion of the black widow toxin arsenal uncovered by multi-tissue transcriptomics and venom proteomics. BMC Genom. 15(1), 1–18 (2014).Article 

Google Scholar 
Haney, R. A., Matte, T., Forsyth, F. S. & Garb, J. E. Alternative transcription at venom genes and its role as a complementary mechanism for the generation of venom complexity in the common house spider. Front. Ecol. Evol. 7, 85 (2019).Article 

Google Scholar 
Lüddecke, T. et al. An economic dilemma between molecular weapon systems may explain an arachno-atypical venom in wasp spiders (Argiope bruennichi). Biomolecules 10(7), 978 (2020).Article 

Google Scholar 
Fainzilber, M., Gordon, D., Hasson, A., Spira, M. E. & Zlotkin, E. Mollusc-specific toxins from the venom of Conus textile neovicarius. Eur. J. Biochem. 202(2), 589–595 (1991).Article 
CAS 

Google Scholar 
Pawlak, J. et al. Denmotoxin, a three-finger toxin from the colubrid snake Boiga dendrophila (Mangrove Catsnake) with bird-specific activity. J. Biol. Chem. 281(39), 29030–29041 (2006).Article 
CAS 

Google Scholar 
Krasnoperov, V. G., Shamotienko, O. G. & Grishin, E. V. Isolation and properties of insect and crustacean-specific neurotoxins from the venom of the black widow spider (Latrodectus mactans tredecimguttatus). J. Nat. Toxins 1, 17–23 (1992).CAS 

Google Scholar 
Xu, X. et al. A comparative analysis of the venom gland transcriptomes of the fishing spiders Dolomedes mizhoanus and Dolomedes sulfurous. PLoS ONE 10(10), e0139908 (2015).Article 

Google Scholar 
Kuzmenkov, A. I., Sachkova, M. Y., Kovalchuk, S. I., Grishin, E. V. & Vassilevski, A. A. Lachesana tarabaevi, an expert in membrane-active toxins. Biochem. J. 473(16), 2495–2506 (2016).Article 
CAS 

Google Scholar 
Pekár, S. & Toft, S. Trophic specialisation in a predatory group: The case of prey-specialised spiders (Araneae). Biol. Rev. 90(3), 744–761 (2015).Article 

Google Scholar 
Nyffeler, M. & Pusey, B. J. Fish predation by semi-aquatic spiders: A global pattern. PLoS ONE 9(6), e99459 (2014).Article 
ADS 

Google Scholar 
Pekár, S. & Lubin, Y. Prey and predatory behavior of two zodariid species (Araneae, Zodariidae). J. Arachnol. 37(1), 118–121 (2009).Article 

Google Scholar 
Michálek, O., Kuhn-Nentwig, L. & Pekár, S. High specific efficiency of venom of two prey-specialized spiders. Toxins 11(12), 687 (2019).Article 

Google Scholar 
Modahl, C. M., Mrinalini, Frietze, S. & Mackessy, S. P. Adaptive evolution of distinct prey-specific toxin genes in rear-fanged snake venom. Proc. R. Soc. B. 285(1884), 20181003 (2018).Article 

Google Scholar 
Harris, R. J., Zdenek, C. N., Harrich, D., Frank, N. & Fry, B. G. An appetite for destruction: Detecting prey-selective binding of α-neurotoxins in the venom of Afro-Asian elapids. Toxins 12(3), 205 (2020).Article 
CAS 

Google Scholar 
Duran, L. H., Rymer, T. L. & Wilson, D. T. Variation in venom composition in the Australian funnel-web spiders Hadronyche valida. Toxicon: X 8, 100063 (2020).Article 
CAS 

Google Scholar 
Kuhn-Nentwig, L., Schaller, J. & Nentwig, W. Purification of toxic peptides and the amino acid sequence of CSTX-1 from the multicomponent venom of Cupiennius salei (Araneae: Ctenidae). Toxicon 32(3), 287–302 (1994).Article 
CAS 

Google Scholar 
Friedel, T. & Nentwig, W. Immobilizing and lethal effects of spider venoms on the cockroach and the common mealbeetle. Toxicon 27(3), 305–316 (1989).Article 
CAS 

Google Scholar 
Eggs, B., Wolff, J. O., Kuhn-Nentwig, L., Gorb, S. N. & Nentwig, W. Hunting without a web: How lycosoid spiders subdue their prey. Ethology 121(12), 1166–1177 (2015).Article 

Google Scholar 
Andrews, S. FastQC: A quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc (2015).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30(15), 2114–2120 (2014).Article 
CAS 

Google Scholar 
Song, L. & Florea, L. Rcorrector: Efficient and accurate error correction for Illumina RNA-seq reads. GigaScience 4(1), s13742–s14015 (2015).Article 

Google Scholar 
Grabherr, M. G. et al. Trinity: Reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nat. Biotechnol. 29(7), 644 (2011).Article 
CAS 

Google Scholar 
Gilbert, D. EvidentialGene: Evidence directed gene predictions for eukaryotes. Available online at: http://arthropods.eugenes.org/EvidentialGene/ (2010).Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10(3), 1–10 (2009).Article 

Google Scholar 
Seppey, M., Manni, M. & Zdobnov, E. M. BUSCO: Assessing genome assembly and annotation completeness. In Gene Prediction (ed. Kollmar, M.) 227–245 (Humana, 2019).
Google Scholar 
Haas, B. TransDecoder. Available online at: https://github.com/TransDecoder/TransDecoder (2015).Petersen, T. N., Brunak, S., Von Heijne, G. & Nielsen, H. SignalP 4.0: Discriminating signal peptides from transmembrane regions. Nat. Methods 8(10), 785–786 (2011).Article 
CAS 

Google Scholar 
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 25(17), 3389–3402 (1997).Article 
CAS 

Google Scholar 
UniProt. The universal protein knowledgebase in 2021. Nucleic Acids Res. 49(1), 480–489 (2021).
Google Scholar 
Eddy, S. R. A probabilistic model of local sequence alignment that simplifies statistical significance estimation. PLoS Comput. Biol. 4(5), e1000069 (2008).Article 
ADS 
MathSciNet 

Google Scholar 
Finn, R. D. et al. Pfam: The protein families database. Nucleic Acids Res. 42(1), 222–230 (2014).Article 

Google Scholar 
Wong, E. S., Hardy, M. C., Wood, D., Bailey, T. & King, G. F. SVM-based prediction of propeptide cleavage sites in spider toxins identifies toxin innovation in an Australian tarantula. PLoS ONE 8(7), e66279 (2013).Article 
ADS 
CAS 

Google Scholar 
King, G. F., Gentz, M. C., Escoubas, P. & Nicholson, G. M. A rational nomenclature for naming peptide toxins from spiders and other venomous animals. Toxicon 52(2), 264–276 (2008).Article 
CAS 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available online at: https://www.R-project.org/ (2019).Venables, W. N. & Ripley, B. D. Random and mixed effects in Modern Applied Statistics with S 271–300 (Springer, New York, 2002).Pekár, S. & Brabec, M. Modern Analysis of Biological Data: Generalized Linear Models in R (Masaryk University Press, 2016).
Google Scholar 
Halekoh, U., Højsgaard, S. & Yan, J. The R package geepack for generalized estimating equations. J. Stat. Softw. 15(2), 1–11 (2006).Article 

Google Scholar 
Pekár, S. & Brabec, M. Generalized estimating equations: A pragmatic and flexible approach to the marginal GLM modelling of correlated data in the behavioural sciences. Ethology 124(2), 86–93 (2018).Article 

Google Scholar  More

Sampling strategies and dataThe inner Oslofjorden phytoplankton dataset is a compilation of data mostly assembled from the monitoring program, financed since 1978 by a cooperation between the municipalities around the fjord, united in the counsel for technical water and sewage cooperation called “Fagrådet for Vann- og avløpsteknisk samarbeid i Indre Oslofjord”. The monitoring program started in 1973 and is ongoing. The program has sampled environmental parameters and chlorophyll since 1973, but for the first 25 years, phytoplankton data is only reported for the years 1973, 1974, 1988/9, 1990, 1994 and 1995. Since 1998, yearly sampling has been conducted, and from 2006 to 2019, the sampling frequency was approximately monthly. In addition, we have compiled research and monitoring data from researchers at the University of Oslo from 1896 and 1916, 1933–34 and 1962–1965.The records from 1896 and 1897 were collected using zoo-plankton net13. The phytoplankton collection in 1916–1917 used buckets or Nansen flasks for sampling. From 1933 to 1984, phytoplankton samples were collected using Nansen bottles and then from 1985–2020 with Niskin bottles from research vessels. The exception is the period from 2006 to 2018 when samples were also collected with FerryBox- equipped ships of opportunity14 with refrigerated autosamplers (Table 2).Since the 1990s, quantitative phytoplankton samples have mostly been preserved in Lugol’s solution, except for spring and autumn samples in the period 1990–2000 that were preserved in formalin. The records from 1896, 1897 and 1916 were preserved in ethanol, and between 1933 and 1990, samples were preserved in formalin. Sampling strategies and methods are listed in Table 2.The records from 1896 and 1897 were quantified by weight, and taxon abundance is categorised as “rare” (r), “rather common” (+), “common” (c) and “very common” (cc)13. In 1916 and 1917, Grans filtration method15 was used, and the number was given in cell counts per litre. From 1916 to 1993, the data is reported only as phytoplankton abundance (N, number of cells per litre). For most years after 1994, the dataset includes both abundance and biomass (μg C per litre), except for 2003, 2004, 2017 and 2018. Phytoplankton was identified and quantified using the sedimentation method of Utermöhl (1958)16. Biovolume for each species is calculated according to HELCOM 200617 and converted to biomass (μg C) following Menden-Deuer & Lessards (2000)18.Data inventoryThe inner Oslofjorden Phytoplankton dataset was compiled in 2020, comprising quantitative phytoplankton cell counts from inner Oslofjorden since 1896. Previously, parts of the data have been available as handwritten or printed tables in reports and published sources19,20,21 (Fig. 2). All sources are digitally available from the University of Oslo Library, the website for “Fagrådet” (http://www.indre-oslofjord.no/) or the NIVA online report database (https://www.niva.no/rapporter). Data from 1994 and onwards have been accessed digitally from the NIVA’s databases. They are also available from client reports from the monitoring project for inner Oslofjorden from the online sites listed above.The first known, published investigation of hydrography and plankton in the upper water column of the inner Oslofjorden was by Hjort & Gran (1900)13. Samples were collected during a hydrographical and biological investigation covering both the Skagerrak and Oslofjorden. There is only one sampling event from Steilene (Dk 1), but some phytoplankton data were obtained at Drøbak, just south of the shallow sill separating the inner and outer Oslofjorden, from winter 1896 to autumn 1897. Twenty years later, Gran and Gaarder (1927)22 conducted a study that included culture experiments at Drøbak field station (at the border between the inner and outer Oslofjorden) in March – April 1916 and August – September 1917. A higher frequency investigation was carried out from June 1933 to May 1934, covering 12 stations in inner and outer Oslofjorden where phytoplankton was analysed by microscopic examination23. The extensive program (the Oslofjord Project) conducted from 1962–1964 covered many parameters, and we have extracted the data for phytoplankton. From 1973 and onward, the research vessel-based monitoring program was financed by the municipalities around the fjord, and since 2006 NIVA has supplemented the monitoring program using FerryBox ships of opportunity. Samples from 4 m depth were collected using a refrigerated autosampler system (Teledyne ISCO) connected to a FerryBox system on M/S Color Festival and M/S Color Fantasy through cooperation between NIVA and Color Line A/S. Since 2018, the FerryBox has been part of the Norwegian Ships of Opportunity Program research infrastructure funded by the Research Council of Norway.The indicated depth of 3.5–4 m is an estimated average, as the actual sampling depth depends on shipload and sea conditions.Several other research projects have sampled from inner Oslofjorden between 1886 and 2000 with different aims. Data from relevant projects reporting on the whole phytoplankton community have also been included in this database.Data compilationThe data already digitalised were compiled from MS Excel files, and other data were manually entered into the standard format in MS Excel files. All collected data were then integrated into one MS Excel database, and this file was used for upload into GBIF. Data can be downloaded from GBIF in different formats and be linked together by the measurementsorfacts table.Quality control and standardisationAfter compilation, the data were checked for errors that could occur during manual digitalisation or just the compilation process. Duplicates and zero values were removed (Fig. 2). The major quantitative unit is phytoplankton abundance in cells per litre. Due to varying scopes of sampling and the development of gear and instruments, the number of species identified may vary between projects. Some of the earliest records were registered as “present”, indicating the amount in comments.Metadata, such as geographical reference, depth and methodology accessed from papers and reports, were accessible from the data source. When data was accessed from the NIVA internal databases, the metadata information was provided by the database owners/researchers.TaxonomyThe taxonomy of microalgae is in constant revision as new knowledge and techniques for identification are developing. Several historical species names recorded in this database are synonyms of accepted names in 2021. We have used the original names in our database and matched them to accepted names and Aphia ID using the taxon match tool available in the open-access reference system; World Register of Marine Species (Worms)24. The taxon match was conducted in March 2021.The nomenclature in Worms is quality assured by a wide range of taxonomic specialists. The Aphia ID is a unique and stable identifier for each available name in the database24. We also cross-checked the last updated nomenclature in Algaebase25 (March 2022) to assign species to a valid taxon name. When Algaebase and Worms were not in accordance, Algaebase taxonomy was usually chosen except in the case of Class Bacillariophyceae.Before matching the species list, the original species names were cleaned from spelling mistakes or just spelling mismatches like spaces, commas, etc. The original name is, however, left in one column in the database. For registrations where a species identification is uncertain, e.g. Alexandrium cf. tamarense, we used only Alexandrium. For registrations where the full name is uncertain, e.g. cf. Alexandrium tamarense, we used the name and Aphia ID for higher taxa, in this case, order. For others, e.g. “pennate diatoms” or “centric diatoms“, we used the name and Aphia ID for class. When names for, e.g. order and class were not recognised automatically by the matching tool in World Register of Marine Species (WoRMS), these were matched manually. Only very few records, mostly “cysts” and “unidentified monads”, could not be matched neither automatically nor manually but were assigned to general “protists” with affiliated ID. More

The phototrophic species Rhodovastum atsumiense G2-11 acquired MGCs from an unknown alphaproteobacterial MTB by recent HGTIn a systematic database search for novel MGCs, we identified several orthologs of known magnetosome genes in the recently released draft genome sequence of the culturable anoxygenic phototroph Rhodovastum atsumiense G2-11 [25]. This finding was unexpected as, after isolation of G2-11 from a paddy field more than 20 years ago, no magnetosome formation has been reported [26]. Furthermore, no MTB has been identified so far among phototrophs or within the Acetobacteraceae family to which G2-11 belongs [26] (Fig. 1a).Fig. 1: Phylogeny, chromosome, and MGCs organization of G2-11.a The maximum likelihood phylogenetic tree based on ribosomal proteins demonstrates the position of G2-11 (highlighted in red) within family Acetobacteraceae (highlighted in the yellow box). The Azospirillaceae family was used as an outgroup based on the latest Alphaproteobacteria phylogeny. Branch length represents the number of base substitutions per site. Values at nodes indicate branch support calculated from 500 replicates using non-parametric bootstrap analysis. Bootstrap values 20 genes with no homology to known magnetosome genes (Fig. 1c). In contrast, the compact MGCs in G2-11 include only a few genes that could not be associated with magnetosome biosynthesis.Tetranucleotide usage patterns are frequently employed as a complementary tool to group organisms since they bear a reliable phylogenetic signal [32]. Likewise, deviations of tetranucleotide usage in a certain fragment from the flanking genome regions can indicate HGT [21]. Comparison of the z-normalized tetranucleotide frequencies of the MGCs (27.5 kb) with the flanking upstream (117.7 kb) and downstream (79.5 kb) fragments showed a considerably lower correlation between them (Pearson’s r = 0.88 with both flanking fragments) than between the flanking fragments themselves (Pearson’s r = 0.97, Fig. 1e). This indicates a significant difference in the tetranucleotide composition of the MGCs compared to the flanking genomic regions and supports a foreign origin of the magnetosome genes in G2-11 suggested by the phylogenetic analysis. Besides, the presence of a mobile element (transposase) and position of the MGCs directly downstream of a tRNA gene, a common hotspot for integration of genomic islands [33,34,35], suggests that the MGCs of G2-11 are indeed located on a genomic island, i.e., represent MAI, like in many other MTB [20, 21]. Unfortunately, the lack of other representatives of the genus Rhodovastum makes it impossible to infer whether the MAI was transferred directly to G2-11 or the last common ancestor of the genus. Nonetheless, its compact organization and conspicuous tetranucleotide usage suggest a relatively recent HGT event.G2-11 does not form magnetosomes under laboratory conditionsAlthough magnetosome genes discovered in G2-11 comply with the minimal set required for magnetosome biomineralization in MSR-1 [36], no magnetosomes have been detected in this organism. It might have several explanations: (i) the strain might switch to the magnetotactic lifestyle only under very specific, yet not tested, conditions; (ii) it once was able to synthesize magnetosomes in its natural environment but lost this ability upon subcultivation due to mutations before its characterization; (iii) the strain might naturally not exploit magnetotaxis as its genes might be non-functional or not actively expressed. To clarify which of these explanations is most likely, we first tested whether G2-11 can form magnetosomes under different laboratory conditions. To this end, the strain was cultivated photoheterotrophically, anoxic or microoxic, in a complex medium with potassium lactate and soybean peptone, as commonly used for MSR-1 (FSM) [37], as well as in minimal media with different C-sources previously shown to support growth in G2-11 (glucose, pyruvate, L-glutamine, and ethanol) [26]. All media were supplied with 50 μM ferric citrate to provide sufficient iron for magnetite biomineralization. Since magnetosome biosynthesis is possible only under low oxygen tension, aerobic chemoheterotrophic growth of G2-11 was not tested. The best growth was observed in the complex FSM medium and a minimal medium with glucose or pyruvate, whereas L-glutamine and ethanol supported only weak growth (Supplementary Fig. S3). Irrespective of the growth stage, none of the tested cultures demonstrated magnetic response as measured by a magnetically induced differential light scattering assay (Cmag) [38]. Consistently, micrographs of cells collected from stationary phase cultures did not show any magnetosome-like particles (Supplementary Fig. S3). This confirmed that G2-11 indeed cannot biosynthesize magnetosomes, at least under the conditions available for the laboratory tests. During cultivation, we also noticed that G2-11 cells did not move at any growth stage despite the initial description of this organism as motile using a single polar flagellum [26], and containing several flagellum synthesis operons and other motility-related genes. Moreover, the cells tended to adhere to glass surfaces under all tested conditions and formed a dense clumpy biofilm immersed in a thick extracellular matrix (Supplementary Fig. S3a-ii).Considering that G2-11 generally lacks magnetosomes and appears to have a stationary lifestyle, which is not consistent with magnetotaxis, we assessed whether the maintenance of MGCs comes at fitness costs for the organism. To this end, we deleted the entire region containing the magnetosome genes (in the following, referred to as the MAI region) using the genetic tools we established for G2-11 in this work (Supplementary Fig. S4a, see Materials and Methods for details). After PCR screening, replica plating test, and genome re-sequencing, two of G2-11 ΔMAI mutants were selected for further analysis (Supplementary Fig. S5). These mutants showed no significant differences in the growth behavior compared to the wildtype (WT) when incubated in minimal media supplied with acetate or pyruvate as a sole carbon source (Supplementary Fig. S4b). This finding suggests that the presence of the magnetosome genes neither provides benefits nor poses any substantial metabolic burden for G2-11, at least under the given experimental conditions.RNAseq reveals poor expression levels and antisense transcription in the MGCs of G2-11We set on to determine whether the magnetosome genes are transcribed in G2-11. To this end, we analyzed its whole transcriptome for the photoheterotrophic conditions, under which the best growth was observed, in two biological replicates. The expression levels of all the encoded genes calculated as TPM (transcripts per million) demonstrated a high correlation between the two replicates (Pearson’s r = 0.98). Most genes of the (mms6-like1)(mmsF-like1)mamH1IEKLMOH2 cluster were only poorly or not transcribed at all (Fig. 2a, Supplementary dataset). Transcription of mms6-like1, mamF-like1, mamL, mamH1, mamI, and mamK, for example, did not pass the noise background threshold (TPM ≤ 2) in both replicates and were unlikely to be expressed, whereas mamE, mamM, mamH2, feoAm, and feoBm slightly exceeded the threshold in at least one replicate and might be weakly transcribed (Fig. 2a). Although the TPM of mamO (TPM = 5.67–6.10, Supplementary dataset) exceeded the background threshold, the coverage plot reveals that the number of mapped reads sharply rises at its 3’-end, whereas the 5’-end has low read coverage (Fig. 2b). This indicates the presence of an internal transcription start site (TSS) and its associated promoter within the coding sequence of mamO instead of the full transcription of the gene. Localization of an active promoter within mamO was recently described in MSR-1, suggesting that the transcriptional organization of MGCs may be more broadly conserved across MTB than assumed previously [39].Fig. 2: Transcription of the magnetosome genes in G2-11.a Log10 of the transcript abundances for all genes in the G2-11 genome presented as TPM (transcripts per million). Red dots represent the magnetosome genes. Red rectangle shows genes with TPM below the threshold, and blue rectangle shows genes with expression levels above median. R1 and R2: biological replicates. Pearson’s r and the p value is presented on the graph. b RNAseq coverage of reads mapped on the positive (red) and negative (blue) strands of the genome in the MAI region. The gray balk shows the gene map: genes encoded on the negative strand are colored in black, on the positive – in green. Red arrows indicate the anti-sense transcription in the mamPAQRBST operon. Green arrows indicate the intragenic TSS within mamO. TSS are indicated with dashed lines and black arrowheads that show the direction of transcription.Full size imageTranscription of genes within the mag123, (mms6-like2)(mmsF-like2), and mamAPQRBST clusters significantly exceeded the threshold, with the expression levels of mag1, mamT, and mamS being above the overall median. At the same time, antisense transcription was detected in the mamAPQRBST region, with the coverage considerably exceeding the sense transcription (Fig. 2b). This antisense RNA (asRNA) likely originated from a promoter controlling the tRNA gene positioned on the negative strand downstream of mamT. Such long asRNAs have the potential to interfere with sense transcripts, thereby significantly decreasing the expression of genes encoded on the opposite strand [40].In summary, the RNAseq data revealed extremely low or lack of transcription of several genes that are known to be essential for magnetosome biosynthesis (mamL, mamI, mamM, mamE, and mamO) [27, 41]. Additionally, the detected antisense transcription can potentially attenuate expression of the mamAPQRBST cluster that also comprises essential genes, i.e., mamQ and mamB. Although other factors, like the absence of several accessory genes mentioned above and the potential accumulation of point mutations, might also be involved, the lack or insufficient transcription of the essential magnetosome genes appears to be the primary reason for the absence of magnetosome biosynthesis in G2-11.Magnetosome proteins from G2-11 are functional in a model magnetotactic bacteriumAlthough visual inspection of the G2-11 magnetosome genes did not reveal any frameshifts or other apparent mutations, accumulation of non-obvious functionally deleterious point substitutions in the essential genes could not be excluded. Therefore, we next tested whether at least some of the magnetosome genes from G2-11 still encode functional proteins that can complement isogenic mutants of the model magnetotactic bacterium MSR-1. In addition, we analyzed the intracellular localization of their products in both MSR-1 and G2-11 by fluorescent labeling.One of the key proteins for magnetosome biosynthesis in MSR-1 is MamB, as its deletion mutant is severely impaired in magnetosome vesicle formation and is entirely devoid of magnetite crystals [42, 43]. Here, we observed that expression of MamB[G2-11] partially restored magnetosome chain formation in MSR-1 ΔmamB (Fig. 3a, b-i, b-ii). Consistently, MamB[G2-11] tagged with mNeonGreen (MamB[G2-11]-mNG) was predominantly localized to magnetosome chains in MSR-1, suggesting that the magnetosome vesicle formation was likely restored to the WT levels (Fig. 3b-iii).Fig. 3: Genetic complementation and intracellular localization of magnetosome proteins from G2-11 in MSR-1 isogenic mutants.a TEM micrograph of MSR-1 wildtype (WT). b MSR-1 ΔmamB::mamB[G2-11]. b-i TEM micrograph and b-ii magnetosome chain close-up; b-iii) 3D-SIM Z-stack maximum intensity projection of MSR-1 ΔmamB::mamB[G2-11]-mNG. c MSR-1ΔmamQ::mamQ[G2-11]. c-i TEM micrograph and c-ii close-up of the particles; c-iii 3D-SIM Z-stack maximum intensity projection. d MSR-1 ΔmamK::mamK[G2-11]. d-i TEM micrograph of MSR-1 ΔmamK; d-ii TEM micrograph of MSR-1 ΔmamK::mamK[G2-11]; d-iii 3D-SIM Z-stack maximum intensity projection of MSR-1 ΔmamK::mNG-mamK[G2-11]. e MSR-1 ΔmamKY::mamK[G2-11]. e-i-ii Representative cells of MSR-1 ΔmamKY mutant showing examples of a short chain, cluster (e-i), and ring-shaped chain (e-ii); (e-iii) TEM micrograph of MSR ΔmamKY::mamK[G2-11] mutant showing the complemented phenotype; e-iv distribution of cells with different phenotypes in the populations of MSR-1 ΔmamKY and MSR-1 ΔmamKY::mamK[G2-11] mutants (N  > 50 cells for each strain population); e-v 3D-SIM Z-stack maximum intensity projection of MSR-1 ΔmamKY::mNG-mamK[G2-11]. f MSR-1 ΔmamJ::mamJ-like[G2-11]. f-i TEM micrograph of MSR-1 ΔmamJ; f-ii TEM micrograph of MSR-1 ΔmamJ::mamJ-like[G2-11]; f-iii 3D-SIM Z-stack maximum intensity projection of MSR-1 ΔmamJ::mamJ-like[G2-11]-gfp. g MSR-1 ΔF3::mmsF-like1[G2-11] and ΔF3::mmsF-like2[G2-11]. g-i TEM micrograph of MSR-1 ΔF3; g-ii TEM micrograph of MSR-1 ΔF3::mmsF-like1[G2-11]; g-iii TEM micrograph of MSR-1 ΔF3::mmsF-like2[G2-11]; g-iv magnetosome diameter distribution in MSR-1 ΔF3 and the mutants complemented with mmsF-like1/mmsF-like2. Asterisks indicate points of significance calculated using Kruskal–Wallis test (****p 50 cells for each of two randomly selected insertion mutants MSR-1 ΔmamKY::mamK[G2-11] revealed that the long magnetosome chains were restored in 35-40% of the population (Fig. 3e-iv). Of note, mNG-MamK[G2-11] formed slightly shorter filaments in MSR-1 ΔmamKY than in ΔmamK, which were also characteristically displaced to the outer cell curvature due to the lack of mamY [46] (Fig. 3e-v).MamJ attaches magnetosomes to the MamK filament in MSR-1, mediating their chain-like arrangement. Elimination of mamJ disrupts this linkage, causing magnetosomes to aggregate owing to magnetic interactions [47] (Fig. 3f-i). In MSR-1, MamJ is encoded within the mamAB operon, between mamE and mamK. Within the (mms6-like1)(mmsF-like1)mamH1IEKLMOH2 cluster of G2-11, there is an open reading frame (ORF) encoding a hypothetical protein that is located in a syntenic locus (Fig. 1c). Although the hypothetical protein from G2-11 and MamJ from MSR-1 differ considerably in length (563 vs. 426 aa), share only a low overall sequence similarity (31%), and are not identified as orthologues by reciprocal blast analyses, multiple sequence alignments revealed a few conserved amino acids at their N- and C-termini (Supplementary Fig. S6). Moreover, in both proteins, these conserved residues are separated by a large region rich in acidic residues (pI 3.3 and 3.2) suggesting that the G2-11 protein might be a distant MamJ homolog. To test if it implements the same function as MamJ, we transferred this gene to MSR-1 ΔmamJ. Interestingly, it indeed restored chain-like magnetosome arrangement, which, however, often appeared as closed rings rather than linear chains (Fig. 3f-ii). Despite this difference, it indicated the ability of the hypothetical protein (hereafter referred to as MamJ-like[G2-11]) to attach magnetosomes to MamK, suggesting that in the native context, it can have a function identical to MamJ. Consistently, its fluorescently labeled version was often observed in ring-like structures within the cytoplasm of MSR-1 ΔmamJ, suggesting that it is indeed localized to magnetosomes (Fig. 3f-iii).In magnetospirilla, magnetosome proteins MmsF, MamF, and MmxF share an extensive similarity. Their individual and collective elimination gradually reduces the magnetite crystal size and disrupts the chain formation in MSR-1 (Fig. 3g-i; Paulus, manuscript in preparation). The MAI of G2-11 includes two genes, whose products have high similarity to these proteins, designated here as MmsF-like1[G2-11] and MmsF-like2[G2-11]. Expression of each of them in the MSR-1 ΔmmsFΔmamFΔmmxF triple mutant (ΔF3) partially restored the magnetosome size and led to the formation of short magnetosome chains in MSR ΔF3::mmsF-like1[G2-11] (Fig. 3g-ii) or clusters in MSR-1 ΔF3::mmsF-like2[G2-11] (Fig. 3g-iii, iv). Consistently, fluorescently tagged mNG-MmsF-like1[G2-11] and mNG-MmsF-like2[G2-11] localized to magnetosomes in the pattern resembling that in the TEM micrographs of the complemented corresponding mutants (Fig. 3g-v, vii), or were perfectly targeted to the magnetosome chains in MSR-1 WT (Fig. 3g-vi, viii).In G2-11, MamB[G2-11]-mNG, mNG-MamQ[G2-11], MamJ-like[G2-11]-GFP, mNG-MmsF-like1[G2-11], and mNG-MmsF-like2[G2-11] were patchy-like or evenly distributed in the inner and intracellular membranes (Supplementary Fig. S7). No linear structures that would indicate the formation of aligned magnetosome vesicles were observed in these mutants. As expected, mNG-MamK[G2-11] formed filaments in G2-11 (Supplementary Fig. S7c).Expression of MamM, MamO, MamE, and MamL failed to complement the corresponding deletion mutants of MSR-1 (not shown). Although detrimental mutations in the genes cannot be excluded, this result can be attributed to the lack of their native, cognate interaction partners, likely due to the large phylogenetic distances between the respective orthologues.Transfer of MGCs from MSR-1 endows G2-11 with magnetosome biosynthesis that is rapidly lost upon subcultivationHaving demonstrated the functionality of several G2-11 magnetosome genes in the MSR-1 background, we wondered whether, conversely, the G2-11 background is permissive for magnetosome biosynthesis. To this end, we transferred the well-studied MGCs from MSR-1 into G2-11, thereby mimicking an HGT event under laboratory conditions. The magnetosome genes from MSR-1 were previously cloned on a single vector pTpsMAG1 to enable the one-step transfer and random insertion into the genomes of foreign organisms [23]. Three G2-11 mutants with different positions of the integrated magnetosome cassette were incubated under anoxic phototrophic conditions with iron concentrations (50 μM) sufficient for biomineralization in the donor organism MSR-1. The obtained transgenic strains indeed demonstrated a detectable magnetic response (Cmag = 0.38 ± 0.11) [38], and TEM confirmed the presence of numerous electron-dense particles within the cells (Fig. 4), which, however, were significantly smaller than magnetosome crystals of MSR-1 (ranging 18.5 ± 4.3 nm to 19.9 ± 5.0 nm in three G2-11 MAG insertion mutants vs 35.4 ± 11.5 nm in MSR-1 WT, Fig. 4b) and formed only short chains or were scattered throughout the cells (Fig. 4a, c-i). Mapping of the particle elemental compositions with energy-dispersive X-ray spectroscopy (EDS) in STEM mode revealed iron- and oxygen-dominated compositions, suggesting they were iron oxides. High-resolution TEM (HRTEM) images and their FFT (Fast Fourier Transform) patterns were consistent with the structure of magnetite (Fig. 4c). Thus, G2-11 was capable of genuine magnetosome formation after acquisition of the MGCs from MSR-1.Fig. 4: Magnetosome biosynthesis by G2-11 upon transfer of the MGCs from MSR-1.a A cell with magnetosomes (i) and a close-up of the area with magnetosome chains (ii). Scale bars: 1 µm. b Violin plots displaying magnetosome diameter in three MAG insertion mutants of G2-11 in comparison to MSR-1. Asterisks indicate points of significance calculated using the Kruskal–Wallis test (**** designates p  More

Human-modified forests, such as selectively logged forests, are often characterized as degraded ecosystems because of their altered structure and low biomass. The concept of ecosystem degradation can be a double-edged sword. It rightly draws attention to the conservation value of old-growth systems and the importance of ecosystem restoration. However, it can also suggest that human-modified ecosystems are of low ecological value and therefore, in some cases, suitable for conversion to agriculture (such as oil palm plantations) and other land uses3,4,5.Selectively logged and other forms of structurally altered forests are becoming the prevailing vegetation cover in much of the tropical forest biome2. Such disturbance frequently leads to a decline in old-growth specialist species1, and also in non-specialist species in some contexts6,7,8. However, species-focused biodiversity metrics are only one measure of ecosystem vitality and functionality, and rarely consider the collective role that suites of species play in maintaining ecological functions9.An alternative approach is to focus on the energetics of key taxonomic groups, and the number and relative dominance of species contributing to each energetic pathway. Energetic approaches to examining ecosystem structure and function have a long history in ecosystem ecology10. Virtually all ecosystems are powered by a cascade of captured sunlight through an array of autotroph tissues and into hierarchical assemblages of herbivores, carnivores and detritivores. Energetic approaches shine light on the relative significance of energy flows among key taxa and provide insight into the processes that shape biodiversity and ecosystem function. The common currency of energy enables diverse guilds and taxa to be compared in a unified and physically meaningful manner: dominant energetic pathways can be identified, and the resilience of each pathway to the loss of individual species can be assessed. Quantitative links can then be made between animal communities and the plant-based ecosystem productivity on which they depend. The magnitude of energetic pathways in particular animal groups can often be indicators of key associated ecosystem processes, such as nutrient cycling, seed dispersal and pollination, or trophic factors such as intensity of predation pressure or availability of resource supply, all unified under the common metric of energy flux11,12.Energetics approaches have rarely been applied in biodiverse tropical ecosystems because of the range of observations they require11,12,13. Such analyses rely on: population density estimates for a very large number of species; understanding of the diet and feeding behaviour of the species; and reliable estimation of net primary productivity (NPP). Here we take advantage of uniquely rich datasets to apply an energetics lens to examine and quantify aspects of the ecological function and vitality of habitats in Sabah, Malaysia, that comprise old-growth forests, logged forest and oil palm plantation (Fig. 1 and Extended Data Fig. 1). Our approach is to calculate the short-term equilibrium production or consumption rates of food energy by specific species, guilds or taxonomic groups. We focus on three taxonomic groups (plants, birds and mammals) that are frequently used indicators of biodiversity and are relatively well understood ecologically.Fig. 1: Maps of the study sites in Sabah, Borneo.a–d, Maps showing locations of NPP plots and biodiversity surveys in old-growth forest, logged forest and oil palm plantations in the Stability of Altered Forest Ecosystems Project landscape (a), Maliau Basin (b), Danum Valley (c) and Sepilok (d). The inset in a shows the location of the four sites in Sabah. The shade of green indicates old-growth (dark green), twice-logged (intermediate green) or heavily logged (light green) forests. The camera and trap grid includes cameras and small mammal traps. White areas indicate oil palm plantations.Full size imageWe are interested in the fraction of primary productivity consumed by birds and mammals, and how it varies along the disturbance gradient, and how and why various food energetic pathways in mammals and birds, and the diversity of species contributing to those pathways, vary along the disturbance gradient. To estimate the density of 104 mammal and 144 bird species in each of the three habitat types, we aggregated data from 882 camera sampling locations (a total of 42,877 camera trap nights), 508 bird point count locations, 1,488 small terrestrial mammal trap locations (34,058 live-trap nights) and 336 bat trap locations (Fig. 1 and Extended Data Fig. 1). We then calculated daily energetic expenditure for each species based on their body mass, assigned each species to a dietary group and calculated total food consumption in energy units. For primary productivity, we relied on 34 plot-years (summation of plots multiplied by the number of years each plot is monitored) of measurements of the key components of NPP (canopy litterfall, woody growth, fine root production) using the protocols of the Global Ecosystem Monitoring Network14,15,16 across old-growth (n = 4), logged (n = 5) and oil palm (n = 1) plots. This dataset encompasses more than 14,000 measurements of litterfall, 20,000 tree diameter measurements and 2,700 fine root samples.Overall bird species diversity is maintained across the disturbance gradient and peaks in the logged forest; for mammals, there is also a slight increase in the logged forest, followed by rapid decline in the oil palm (Fig. 2b,c). Strikingly, both bird and mammal biomass increases substantially (144% and 231%, respectively) in the logged forest compared to the old-growth forest, with mammals contributing about 75% of total (bird plus mammal) biomass in both habitat types (Fig. 2b,c).Fig. 2: Variation of ecosystem energetics along the disturbance gradient from old-growth forest through logged forest to oil palm.a, Total NPP along the gradient (mean of intensive 1-ha plots; n = 4 for old growth (OG), n = 5 for logged and n = 1 for oil palm (OP); error bars are 95% confidence intervals derived from propagated uncertainty in the individually measured NPP components), with individual plot data points overlaid. b,c, Total body mass (bars, left axis) and number of species counted (blue dots and line, right axis) of birds (b) and mammals (c). d,e, Total direct energetic food intake by birds (d) and mammals (e). f,g, Percentage of NPP directly consumed by birds (f) and mammals (g). In b–e, body mass and energetics were estimated for individual bird and mammal species, with the bars showing the sum. Error bars denote 95% confidence intervals derived from 10,000 Monte Carlo simulation estimates incorporating uncertainty in body mass, population density, the daily energy expenditure equation, assimilation efficiency of the different food types, composition of the diet of each species and NPP. In f,g, the grey bars indicate direct consumption of NPP, white bars denote the percentage of NPP indirectly supporting bird and mammal food intake when the mean trophic level of consumed invertebrates is assumed to be 2.5, with the error bars denoting assumed mean trophic levels of 2.4 and 2.6. Note the log scale of the y axis in f,g. Numbers for d,e provided in Supplementary Data Tables 1, 2.Full size imageThe total flow of energy through consumption is amplified across all energetic pathways by a factor of 2.5 (2.2–3.0; all ranges reported are 95% confidence intervals) in logged forest relative to old-growth forest. In all three habitat types, total energy intake by birds is much greater than by mammals (Fig. 2d,e and Extended Data Table 1). Birds account for 67%, 68% and 90% of the total direct consumption by birds and mammals combined in old-growth forests, logged forests and oil palm, respectively. Although mammal biomass is higher than bird biomass in the old-growth and logged forests, the metabolism per unit mass is much higher in birds because of their small body size; hence, in terms of the energetics and consumption rates, the bird community dominates. The total energy intake by birds alone increases by a factor of 2.6 (2.1–3.2) in the logged forest relative to old-growth forest. This is mainly driven by a 2.5-fold (1.7–2.8) increase in foliage-gleaning insectivory (the dominant energetic pathway), and most other feeding guilds also show an even larger increase (Figs. 2d and 3). However, total bird energy intake in the oil palm drops back to levels similar to those in the old-growth forest, with a collapse in multiple guilds. For mammals, there is a similar 2.4-fold (1.9–3.2) increase in total consumption when going from old-growth to logged forest, but this declines sharply in oil palm plantation. Most notable is the 5.7-fold (3.2–10.2) increase in the importance of terrestrial mammal herbivores in the logged relative to old-growth forests. All four individual old-growth forest sites show consistently lower bird and mammal energetics than the logged forests (Extended Data Fig. 5).Fig. 3: Magnitude and species diversity of energetic pathways in old-growth forest, logged forest and oil palm.The size of the circles indicates the magnitude of energy flow, and the colour indicates birds or mammals. S, number of species; E, ESWI, an index of species redundancy and, therefore, resilience (high values indicate high redundancy; see main text). For clarity, guilds with small energetic flows are not shown, but are listed in Supplementary Data 4. Images created by J. Bentley.Full size imageThe fraction of NPP flowing through the bird and mammal communities increases by a factor of 2.1 (1.5–3.0) in logged forest relative to old-growth forest. There is very little increase in NPP in logged relative to old-growth forests (Fig. 2a) because increased NPP in patches of relatively intact logged forest is offset by very low productivity in more structurally degraded areas such as former logging platforms14,15. In oil palm plantations, oil palm fruits account for a large proportion of NPP, although a large fraction of these is harvested and removed from the ecosystem17. As a proportion of NPP, 1.62% (1.35–2.13%) is directly consumed by birds and mammals in the old-growth forest; this rises to 3.36% (2.57–5.07%) in the logged forest but drops to 0.89% (0.57–1.44%) in oil palm (Fig. 2f,g and Extended Data Table 2).If all invertebrates consumed are herbivores or detritivores (that is, at a trophic level of 2.0), and trophic efficiency is 10% (ref. 10), the total amount of NPP supporting the combined bird and mammal food intake would be 9%, 16% and 5% for old-growth forest, logged forest and oil palm, respectively. However, if the mean trophic level of consumed invertebrates is 2.5 (that is, a mix of herbivores and predators), the corresponding proportions would be 27%, 51% and 17% (Fig. 2f,g). As insectivory is the dominant feeding mode for the avian community, these numbers are dominated by bird diets. For birds in the old-growth forests, 0.35% of NPP supports direct herbivory and frugivory, but around 22% of NPP (assumed invertebrate trophic level 2.5) is indirectly required to support insectivory. The equivalent numbers for birds in logged forest are 0.83% and 46%. Hence, birds account for a much larger indirect consumption of NPP. Bird diet studies in old-growth and logged forest in the region suggest that consumed invertebrates have a mean trophic level of 2.5 (ref. 18; K. Sam, personal communication), indicating that the higher-end estimates of indirect NPP consumption (that is, around 50% in logged forests) are plausible.It is interesting to compare such high fractions of NPP to direct estimates of invertebrate herbivory. Scans of tree leaf litter from these forests suggest that just 7.0% of tree canopy leaf area (1–3% of total NPP) is removed by tree leaf herbivory14,16, but such estimates do not include other pathways available to invertebrates, including herbivory of the understorey, aboveground and belowground sap-sucking, leaf-mining, fruit- and wood-feeding, and canopy, litter and ground-layer detritivory. An increase in invertebrate biomass and herbivory in logged forest compared to old-growth forest has previously been reported in fogging studies in this landscape19. Such high levels of consumption of NPP by invertebrates could have implications on ecosystem vegetation biomass production, suggesting, first, that invertebrate herbivory has a substantial influence on recovery from logging and, second, that insectivorous bird densities may exert substantial indirect controls on ecosystem recovery.The distributions of energy flows among feeding guilds are remarkably stable among habitat types (Fig. 3), indicating that the amplified energy flows in the logged forests do not distort the overall trophic structure of vertebrate communities. Overall bird diet energetics are dominated by insectivory, which accounts for a strikingly invariant 66%, 63% and 66% of bird energetic consumption in old-growth forest, logged forest and oil palm, respectively. Foliage-gleaning dominates as a mode of invertebrate consumption in all three habitat types, with frugivory being the second most energetically important feeding mode (26%, 27% and 19%, respectively). Mammal diet is more evenly distributed across feeding guilds, but frugivory (31%, 30%, 30%) and folivory (24%, 38%, 26%) dominate. Small mammal insectivores are probably under-sampled (see Methods) so the contribution of mammal insectivory may be slightly greater than that estimated here. The apparent constancy of relative magnitude of feeding pathways across the intact and disturbed ecosystems is noteworthy and not sensitive to plausible shifts in feeding behaviour between habitat types (see Supplementary Discussion). There is no evidence of a substantial shift in dominant feeding guild: the principal feeding pathways present in the old-growth forest are maintained in the logged forest.When examining change at species level in the logged forests, the largest absolute increases in bird food consumption were in arboreal insectivores and omnivores (Fig. 4a and Extended Data Fig. 2a). In particular, this change was characterized by large increases in the abundance of bulbul species (Pycnonotus spp.). No bird species showed a significant or substantial reduction in overall energy consumption. In the oil palm plantation, total food consumption by birds was less than in logged forests, but similar to that in old-growth forests. However, this was driven by very high abundance of a handful of species, notably a single arboreal omnivore (yellow-vented bulbul Pycnonotus goiavier) and three arboreal insectivores (Mixornis bornensis, Rhipidura javanica, Copsychus saularis), whereas energy flows through most other bird species were greatly reduced (Fig. 4b and Extended Data Fig. 2b).Fig. 4: Changes in energy consumption by species in logged forest and oil palm relative to old-growth forest.a,b, Changes in energy consumption by species in logged forest relative to old-growth forest (a) and in oil palm relative to old-growth forest (b). The 20 species experiencing the largest increase (red) and decrease (blue) in both habitat types are shown. Bird species are shown in a lighter tone and mammal species are shown in a darker tone. The error bars denote 95% confidence intervals, derived from 10,000 Monte Carlo simulation estimates incorporating uncertainty in body mass, population density, the daily energy expenditure equation, assimilation efficiency of the different food types and composition of the diet of each species.Full size imageFor mammals, the increase in consumption in logged forests is dominated by consumption by large terrestrial herbivores increasing by a factor of 5.7 (3.2–10.2), particularly sambar deer (Rusa unicolor) and Asian elephant (Elephas maximus; Fig. 4a and Extended Data Figs. 2b and 3), along with that by small omnivores, predominantly rodents (native spiny rats, non-native black rat; Fig. 4). A few rainforest species show a strong decline (for example, greater mouse-deer Tragulus napu and brown spiny rat Maxomys rajah). In the oil palm, most mammal species collapse (Fig. 4b) and the limited consumption is dominated by a few disturbance-tolerant habitat generalists (for example, red muntjac Muntiacus muntjak, black rat Rattus rattus, civets), albeit these species are at lower densities than observed in old-growth forest (Extended Data Fig. 2).With very few exceptions, the amplified energy flows in logged forest seem to retain the same level of resilience as in old-growth forest. The diversity and dominance of species within any pathway can be a measure of the resilience of that pathway to loss of species. We assessed energetic dominance within individual pathways by defining an energetic Shannon–Wiener index (ESWI) to examine distribution of energy flow across species; low ESWI indicates a pathway with high dependence on a few species and hence potential vulnerability (Fig. 3). The overall ESWI across guilds does not differ between the old-growth and logged forest (t2,34 = −0.363, P = 0.930), but does decline substantially from old-growth forest to oil palm (t2,34 = −3.826, P = 0.0015), and from logged forest to oil palm (t2,34 = −3.639, P = 0.0025; linear mixed-effects models, with habitat type as fixed effect and guild as random effect; for model coefficients see Supplementary Table 3).Hence, for birds, the diversity of species contributing to dominant energetic pathways is maintained in the transition from old-growth to logged forests but declines substantially in oil palm. Mammals generally show lower diversity and ESWI than birds, but six out of ten feeding guilds maintain or increase ESWI in logged forest relative to the old-growth forests but collapse in oil palm (Fig. 3). Terrestrial herbivory is the largest mammal pathway in the logged forest but is dependent on only four species and is probably the most vulnerable of the larger pathways: a few large mammals (especially sambar deer) play a dominant terrestrial herbivory role in the logged forest. In parallel, bearded pigs (Sus barbatus), the only wild suid in Borneo, form an important and functionally unique component of the terrestrial omnivory pathway. These larger animals are particularly sensitive to anthropogenic pressures such as hunting, or associated pathogenic pressures as evidenced by the recent precipitous decline of the bearded pig in Sabah due to an outbreak of Asian swine fever (after our data were collected)20.Vertebrate populations across the tropics are particularly sensitive to hunting pressure21. Our study site has little hunting, but as a sensitivity analysis we explored the energetic consequences of 50% reduction in population density of those species potentially affected by targeted and/or indiscriminate hunting (Extended Data Fig. 4). Targeted hunted species include commercially valuable birds, and gun-hunted mammals (bearded pig, ungulates, banteng and mammals with medicinal value). Indiscriminately hunted species include birds and mammals likely to be trapped with nets and snares. Hunting in the logged forests lowers both bird and mammal energy flows but still leaves them at levels higher than in faunally intact old-growth forests. Such hunting brings bird energetics levels close to (but still above) those of old-growth forests. For mammals, however, even intensively hunted logged forests seem to maintain higher energetic flows than the old-growth forests. Hence, only very heavy hunting is likely to ‘offset’ the amplified energetics in the logged forest.The amplified energetic pathways in our logged forest probably arise as a result of bottom-up trophic factors including increased resource supply, palatability and accessibility. The more open forest structure in logged forest results in more vegetation being near ground level22,23 and hence more accessible to large generalist mammal herbivores, which show the most striking increase of the mammal guilds. The increased prioritization by plants of competition for light and therefore rapid vegetation growth strategies in logged forests results in higher leaf nutrient content and reduced leaf chemical defences against herbivory24,25, along with higher fruiting and flowering rates19 and greater clumping in resource supply9. This increased resource availability and palatability probably supports high invertebrate and vertebrate herbivore densities25. The act of disturbance displaces the ecosystem from a conservative chemically defended state to a more dynamic state with amplified energy and nutrient flow, but not to an extent that causes heavy disruption in animal community composition. Top-down trophic factors might also play a role in amplifying the energy flows in intermediate trophic levels, through mechanisms such as increased protection of ground-dwelling or nesting mammals and birds from aerial predators in the dense vegetation ground layer. This might partially explain the increased abundance of rodents, but there is little evidence of trophic release at this site because of the persisting high density of mammal carnivores26. Overall, the larger number of bottom-up mechanisms and surge in invertebrate consumption suggest that increased resource supply and palatability largely explains the amplification of consumption pathways in the logged forest. An alternative possibility is that the amplified vertebrate energetics do not indicate amplified overall animal energetics but rather a large diversion of energy from unmeasured invertebrate predation pathways (for example, parasitoids); this seems unlikely but warrants further exploration.Oil palm plantations show a large decline in the proportion of NPP consumed by mammals and birds compared to logged forests12. Mammal populations collapse because they are more vulnerable and avoid humans, and there is no suite of mammal generalists that can step in27,28. Birds show a more modest decline, to levels similar to those observed in old-growth forests, as there is a broad suite of generalist species that are able to adapt to and exploit the habitat types across the disturbance gradient, and because their small size and mobility render them less sensitive to human activity29. There is a consistent decline in the oil palm in ESWI for birds and especially for mammals, indicating a substantial increase in ecosystem vulnerability in many pathways.In conclusion, our analysis demonstrates the tremendously dynamic and ecologically vibrant nature of the studied logged forests, even heavily and repeatedly logged forests such as those found across Borneo. It is likely that the patterns, mechanisms and basic ecological energetics we describe are general to most tropical forests; amplification of multiple ecosystem processes after logging has also been reported for logged forests in Kenya9, but similar detailed analyses are needed for a range of tropical forests to elucidate the importance of biogeographic, climatic or other factors. We stress that our findings do not diminish the importance of protecting structurally intact old-growth forests, but rather question the meaning of degradation by shining a new light on the ecological value of logged and other structurally ‘degraded’ forests, reinforcing their significance to the conservation agenda30. We have shown that a wide diversity of species not only persist but thrive in the logged forest environment. Moreover, such ecological vibrancy probably enhances the prospects for ecosystem structural recovery. In terms of faunal intactness, our study landscape is close to a best-case scenario because hunting pressures were low. If logged forests can be protected from heavy defaunation, our analysis demonstrates that they can be vibrant ecosystems, providing many key ecosystem functions at levels much higher than in old-growth forests. Conservation of logged forest landscapes has an essential role to play in the in the protection of global biodiversity and biosphere function. More

Gruninger, R. J., Ribeiro, G. O., Cameron, A. & McAllister, T. A. Invited review: Application of meta-omics to understand the dynamic nature of the rumen microbiome and how it responds to diet in ruminants. Animal 13, 1843–1854 (2019).CAS 

Google Scholar 
Morgavi, D. P., Kelly, W. J., Janssen, P. H. & Attwood, G. T. Rumen microbial (meta)genomics and its application to ruminant production. Animal 7, 184–201 (2013).CAS 

Google Scholar 
Bergman, E. N. Energy contributions of volatile fatty acids from the gastrointestinal tract in various species. Physiol. Rev. 70, 567–590 (1990).CAS 

Google Scholar 
Flint, H. J. The rumen microbial ecosystem—Some recent developments. Trends Microbiol. 5, 483–488 (1997).CAS 

Google Scholar 
Hobson, P. N. & Stewart, C. S. The Rumen Microbial Ecosystem. (Springer, 2012).Moraïs, S. & Mizrahi, I. The road not taken: The rumen microbiome, functional groups, and community states. Trends Microbiol. 27, 538–549 (2019).
Google Scholar 
Cheng, K. J., Forsberg, C. W., Minato, H. & Costerton, J. W. in Physiological Aspects of Digestion and Metabolism in Ruminants (eds T. Tsuda, Y. Sasaki, & R. Kawashima) 595–624 (Academic Press, 1991).McSweeney, C. S., Palmer, B., McNeill, D. M. & Krause, D. O. Microbial interactions with tannins: Nutritional consequences for ruminants. Anim. Feed Sci. Technol. 91, 83–93 (2001).CAS 

Google Scholar 
Skene, I. K. & Brooker, J. D. Characterization of tannin acylhydrolase activity in the ruminal bacterium Selenomonas ruminantium. Anaerobe 1, 321–327 (1995).CAS 

Google Scholar 
Khanbabaee, K. & van Ree, T. Tannins: Classification and definition. Nat. Prod. Rep. 18, 641–649 (2001).CAS 

Google Scholar 
Makkar, H. P. S. & Becker, K. Isolation of tannins from leaves of some trees and shrubs and their properties. J. Agric. Food Chem. 42, 731–734 (1994).CAS 

Google Scholar 
Bhat, T. K., Kannan, A., Singh, B. & Sharma, O. P. Value addition of feed and fodder by alleviating the antinutritional effects of tannins. Agr. Res. 2, 189–206 (2013).CAS 

Google Scholar 
Shimada, T. Salivary proteins as a defense against dietary tannins. J. Chem. Ecol. 32, 1149–1163 (2006).CAS 

Google Scholar 
Zhu, J., Filippich, L. J. & Alsalami, M. T. Tannic acid intoxication in sheep and mice. Res. Vet. Sci. 53, 280–292 (1992).CAS 

Google Scholar 
Kohl, K. D., Stengel, A. & Dearing, M. D. Inoculation of tannin-degrading bacteria into novel hosts increases performance on tannin-rich diets. Environ. Microbiol. 18, 1720–1729 (2016).CAS 

Google Scholar 
Kumar, K., Chaudhary, L. C., Agarwal, N. & Kamra, D. N. Isolation and characterization of tannin-degrading bacteria from the rumen of goats fed oak (Quercus semicarpifolia) leaves. Agr. Res. 3, 377–385 (2014).
Google Scholar 
Odenyo, A. A. et al. Characterization of tannin-tolerant bacterial isolates from East African ruminants. Anaerobe 7, 5–15 (2001).CAS 

Google Scholar 
Grilli, D. J. et al. Analysis of the rumen bacterial diversity of goats during shift from forage to concentrate diet. Anaerobe 42, 17–26 (2016).
Google Scholar 
Tong, J. et al. Illumina sequencing analysis of the ruminal microbiota in high-yield and low-yield lactating dairy cows. PLoS ONE 13, e0198225 (2018).
Google Scholar 
Pope, P. B. et al. Metagenomics of the Svalbard reindeer rumen microbiome reveals abundance of polysaccharide utilization loci. PLoS ONE 7, e38571 (2012).ADS 
CAS 

Google Scholar 
Østbye, K., Wilson, R. & Rudi, K. Rumen microbiota for wild boreal cervids living in the same habitat. FEMS Microbiol. Lett. 363, fnw233 (2016).
Google Scholar 
Gruninger, R. J., Sensen, C. W., McAllister, T. A. & Forster, R. J. Diversity of rumen bacteria in Canadian cervids. PLoS ONE 9, e89682 (2014).ADS 

Google Scholar 
Henderson, G. et al. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci. Rep. 5, 14567 (2015).CAS 

Google Scholar 
Reese, A. T. & Kearney, S. M. Incorporating functional trade-offs into studies of the gut microbiota. Curr. Opin. Microbiol. 50, 20–27 (2019).CAS 

Google Scholar 
Moeller, A. H. et al. Social behavior shapes the chimpanzee pan-microbiome. Sci. Adv. 2, e1500997 (2016).ADS 

Google Scholar 
Okano, T. & Matsuda, H. Biocultural diversity of Yakushima Island: Mountains, beaches, and sea. J. Mar. Isl. Cult. 2, 69–77 (2013).
Google Scholar 
Agetsuma, N., Agetsuma-Yanagihara, Y. & Takafumi, H. Food habits of Japanese deer in an evergreen forest: Litter-feeding deer. Mamm. Biol. 76, 201–207 (2011).
Google Scholar 
Higashi, Y., Hirota, S. K., Suyama, Y. & Yahara, T. Geographical and seasonal variation of plant taxa detected in faces of Cervus nippon yakushimae based on plant DNA analysis in Yakushima Island. Ecol. Res. 37, 582–597 (2022).CAS 

Google Scholar 
Kuroiwa, A. Nutritional ecology of the Yakushika (Cervus nippon yakushimae) population under high density Ph.D. thesis, Kyushu University, (2017).Koda, R., Agetsuma, N., Agetsuma-Yanagihara, Y., Tsujino, R. & Fujita, N. A proposal of the method of deer density estimate without fecal decomposition rate: A case study of fecal accumulation rate technique in Japan. Ecol. Res. 26, 227–231 (2011).
Google Scholar 
Yahara, T. in Deer eats world heritages: Ecology of deer and forets (eds T. Yumoto & H. Matsuda) 168–187 (Bunichi-Sogo-Shuppan, 2006).Onoda, Y. & Yahara, T. in Challenges for Conservation Ecology in Space and Time. (eds T. Miyashita & J. Nishihiro) 126–149 (University of Tokyo Press, 2015).Kagoshima Prefecture Nature Conservation Division. The current status of Yakusika in FY 2020, available at https://www.rinya.maff.go.jp/kyusyu/fukyu/shika/attach/pdf/yakushikaWG_R3_2-23.pdf (2020).Kuroiwa, A., Kuroe, M. & Yahara, T. Effects of density, season, and food intake on sika deer nutrition on Yakushima Island, Japan. Ecol. Res. 32, 369–378 (2017).
Google Scholar 
Hiura, T., Hashidoko, Y., Kobayashi, Y. & Tahara, S. Effective degradation of tannic acid by immobilized rumen microbes of a sika deer (Cervus nippon yesoensis) in winter. Anim. Feed Sci. Technol. 155, 1–8 (2010).CAS 

Google Scholar 
Kawarai, S. et al. Seasonal and geographical differences in the ruminal microbial and chloroplast composition of sika deer (Cervus nippon) in Japan. Sci. Rep. 12, 6356 (2022).ADS 
CAS 

Google Scholar 
Li, Z. et al. Response of the rumen microbiota of sika deer Cervus nippon fed different concentrations of tannin rich plants. PLoS ONE 10, e0123481 (2015).
Google Scholar 
McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012).CAS 

Google Scholar 
Kim, M., Morrison, M. & Yu, Z. Status of the phylogenetic diversity census of ruminal microbiomes. FEMS Microbiol. Ecol. 76, 49–63 (2011).CAS 

Google Scholar 
Weimer, P. J. Redundancy, resilience, and host specificity of the ruminal microbiota: Implications for engineering improved ruminal fermentations. Front. Microbiol. 6, 296 (2015).
Google Scholar 
Scott, K. P., Gratz, S. W., Sheridan, P. O., Flint, H. J. & Duncan, S. H. The influence of diet on the gut microbiota. Pharmacol. Res. 69, 52–60 (2013).CAS 

Google Scholar 
Tapio, I. et al. Taxon abundance, diversity, co-occurrence and network analysis of the ruminal microbiota in response to dietary changes in dairy cows. PLoS ONE 12, e0180260 (2017).
Google Scholar 
Ohara, M. in Agriculture in Hokkaido v2 (ed K. Iwama, Ohara, M., Araki, H., Yamada, T., Nakatsuji, H., Kataoka, T., Yamamoto, Y.) 1–18(Faculty of Agriculture, Hokkaido Univ., 2009).Igota, H., Sakuragi, M. & Uno, H. in Sika Deer: Biology and Management of Native and Introduced Populations (eds. Dale R. McCullough, Seiki Takatsuki, & Koichi Kaji) 251–272 (Springer Japan, 2009).Fernando, S. C. et al. Rumen microbial population dynamics during adaptation to a high-grain diet. Appl. Environ. Microbiol. 76, 7482–7490 (2010).ADS 
CAS 

Google Scholar 
Hu, X. et al. High-throughput analysis reveals seasonal variation of the gut microbiota composition within forest musk deer (Moschus berezovskii). Front. Microbiol. 9, (2018).Artzi, L., Morag, E., Shamshoum, M. & Bayer, E. A. Cellulosomal expansion: Functionality and incorporation into the complex. Biotechnol. Biofuels 9, 61 (2016).
Google Scholar 
Biddle, A., Stewart, L., Blanchard, J. & Leschine, S. Untangling the genetic basis of fibrolytic specialization by Lachnospiraceae and Ruminococcaceae in diverse gut communities. Diversity 5, (2013).Eisenhauer, N., Scheu, S. & Jousset, A. Bacterial diversity stabilizes community productivity. PLoS ONE 7, e34517 (2012).ADS 
CAS 

Google Scholar 
Miller, A. W., Oakeson, K. F., Dale, C. & Dearing, M. D. Effect of dietary oxalate on the gut microbiota of the mammalian herbivore Neotoma albigula. Appl. Environ. Microbiol. 82, 2669–2675 (2016).ADS 
CAS 

Google Scholar 
Adams, J. M., Rehill, B., Zhang, Y. & Gower, J. A test of the latitudinal defense hypothesis: Herbivory, tannins and total phenolics in four North American tree species. Ecol. Res. 24, 697–704 (2009).CAS 

Google Scholar 
Nabeshima, E., Murakami, M. & Hiura, T. Effects of herbivory and light conditions on induced defense in Quercus crispula. J. Plant Res. 114, 403–409 (2001).
Google Scholar 
Yang, C.-M., Yang, M.-M., Hsu, J.-M. & Jane, W.-N. Herbivorous insect causes deficiency of pigment–protein complexes in an oval-pointed cecidomyiid gall of Machilus thunbergii leaf. Bot. Bull. Acad. Sin. 44, 315–321 (2003).
Google Scholar 
Agetsuma, N., Agetsuma-Yanagihara, Y., Takafumi, H. & Nakaji, T. Plant constituents affecting food selection by sika deer. J. Wildl. Manag. 83, 669–678 (2019).
Google Scholar 
Couch, C. E. et al. Diet and gut microbiome enterotype are associated at the population level in African buffalo. Nat. Commun. 12, 2267 (2021).ADS 
CAS 

Google Scholar 
Goel, G., Puniya, A. K. & Singh, K. Tannic acid resistance in ruminal streptococcal isolates. J. Basic Microbiol. 45, 243–245 (2005).CAS 

Google Scholar 
Jiménez, N. et al. Genetic and biochemical approaches towards unravelling the degradation of gallotannins by Streptococcus gallolyticus. Microb. Cell Fact. 13, 154 (2014).
Google Scholar 
Nelson, K. E., Thonney, M. L., Woolston, T. K., Zinder, S. H. & Pell, A. N. Phenotypic and phylogenetic characterization of ruminal tannin-tolerant bacteria. Appl. Environ. Microbiol. 64, 3824–3830 (1998).ADS 
CAS 

Google Scholar 
Selwal, M. K. et al. Optimization of cultural conditions for tannase production by Pseudomonas aeruginosa IIIB 8914 under submerged fermentation. World J. Microbiol. Biotechnol. 26, 599–605 (2010).CAS 

Google Scholar 
Kohl, K. D., Weiss, R. B., Cox, J., Dale, C. & Denise Dearing, M. Gut microbes of mammalian herbivores facilitate intake of plant toxins. Ecol. Lett. 17, 1238–1246 (2014).
Google Scholar 
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Method 7, 335–336 (2010).CAS 

Google Scholar 
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).CAS 

Google Scholar 
Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2009).
Google Scholar 
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).ADS 

Google Scholar 
R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2020).Osawa, R. Formation of a clear zone on tannin-treated brain heart infusion agar by a Streptococcus sp. isolated from feces of koalas. Appl. Environ. Microbiol. 56, 829–831 (1990).ADS 
CAS 

Google Scholar 
Hamamura, N., Olson, S. H., Ward, D. M. & Inskeep, W. P. Diversity and functional analysis of bacterial communities associated with natural hydrocarbon seeps in acidic soils at Rainbow Springs, Yellowstone National Park. Appl. Environ. Microbiol. 71, 5943–5950 (2005).ADS 
CAS 

Google Scholar 
Benson, D. A. et al. GenBank. Nucleic Acids Res. 41, D36–D42 (2012).ADS 

Google Scholar 
Chen, I.-M. A. et al. The IMG/M data management and analysis system v.6.0: new tools and advanced capabilities. Nucleic Acids Res. 49, D751–D763 (2020)Suzuki, M. T., Taylor, L. T. & Delong, E. F. Quantitative analysis of small-subunit rRNA genes in mixed microbial populations via 5 ’-nuclease assays. Appl. Environ. Microbiol. 66, 4605–4614 (2000).ADS 
CAS 

Google Scholar  More

Jackson, R. B. et al. The Ecology of soil carbon: Pools, vulnerabilities, and biotic and abiotic controls. Annu. Rev. Ecol. Evol. Syst. 48, 419–445. https://doi.org/10.1146/annurev-ecolsys-112414-054234 (2017).Article 

Google Scholar 
Sanderman, J., Hengl, T. & Fiske, G. J. Soil carbon debt of 12,000 years of human land use. PNAS 114, 9575–9580 (2017).Article 
ADS 
CAS 

Google Scholar 
Amelung, W. et al. Towards a global-scale soil climate mitigation strategy. Nat. Commun. 11, 5427. https://doi.org/10.1038/s41467-020-18887-7 (2020).Article 
ADS 
CAS 

Google Scholar 
Hopkins, A. & Holz, B. Grassland for agriculture and nature conservation: Production, quality and multi-functionality. Agron 4, 3–20 (2006).
Google Scholar 
van Veen, J. A., Liljeroth, E., Lekkerkerk, L. J. A. & van de Geijn, S. C. Carbon fluxes in plant-soil systems at elevated atmospheric CO2 levels. Ecol. Appl. 1, 175–181. https://doi.org/10.2307/1941810 (1991).Article 

Google Scholar 
Jones, M. B. & Donnelly, A. Carbon sequestration in temperate grassland ecosystems and the influence of management, climate and elevated CO2. New Phytol. 164, 423–439. https://doi.org/10.1111/j.1469-8137.2004.01201.x (2004).Article 

Google Scholar 
Ward, S. E. et al. Legacy effects of grassland management on soil carbon to depth. Glob. Change Biol. 22, 2929–2938. https://doi.org/10.1111/gcb.13246 (2016).Article 
ADS 

Google Scholar 
Hungate, B. A. et al. The fate of carbon in grasslands under carbon dioxide enrichment. Nature 388, 576–579. https://doi.org/10.1038/41550 (1997).Article 
ADS 
CAS 

Google Scholar 
Six, J., Conant, R. T., Paul, E. A. & Paustian, K. Stabilization mechanisms of soil organic matter: Implications for C-saturation of soils. Plant Soil 241, 155–176. https://doi.org/10.1023/A:1016125726789 (2002).Article 
CAS 

Google Scholar 
Chang, J. et al. Climate warming from managed grasslands cancels the cooling effect of carbon sinks in sparsely grazed and natural grasslands. Nat. Commun. 12, 118. https://doi.org/10.1038/s41467-020-20406-7 (2021).Article 
ADS 
CAS 

Google Scholar 
IPCC. 2001. Climate change 2001: The scientific basis contribution of working group 1 to the third assessment report of the intergovernmental panel on climate change In (eds Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., Van Der Linden, P. J., Dai, X., Maskell, K. & Johnson, C. A.) (Cambridge University Press).Gwin, L. Scaling-up sustainable livestock production: Innovation and challenges for grass-fed beef in the U.S. J. Sustain. Agric. 33, 189–209. https://doi.org/10.1080/10440040802660095 (2009).Article 

Google Scholar 
Iqbal, J., Siegrist, J. A., Nelson, J. A. & McCulley, R. L. Fungal endophyte infection increases carbon sequestration potential of southeastern USA tall fescue stands. Soil Biol. Biochem. 44, 81–92. https://doi.org/10.1016/j.soilbio.2011.09.010 (2012).Article 
CAS 

Google Scholar 
Robinson, R. A. & Sutherland, W. J. Post-war changes in arable farming and biodiversity in Great Britain. J. Appl. Ecol. 39, 157–176. https://doi.org/10.1046/j.1365-2664.2002.00695.x (2002).Article 

Google Scholar 
Law, Q. D., Bigelow, C. A. & Patton, A. J. Selecting turfgrasses and mowing practices that reduce mowing requirements. Crop Sci. 56, 3318–3327. https://doi.org/10.2135/cropsci2015.09.0595 (2016).Article 

Google Scholar 
White, L. M. Carbohydrate reserves of grasses: A review. Rangel Ecol. Manag. 26(1), 13–18 (1973).Article 
CAS 

Google Scholar 
Virkajarvi, P. Effects of defoliation height on regrowth of timothy and meadow fescue in the generative and vegetative phases of growth. Agric. Food Sci. 12, 177–193 (2003).Article 

Google Scholar 
Reicher, Z., Patton, A. J., Bigelow, C. A. & Voigt, T. Mowing, Thatching, Aerifying, and Rolling Turf (Turf Grass Sci. Purdue Univ, 2006).
Google Scholar 
Kaatz, P. Cutting management for cool-season forage grasses. Michigan State University Extension, https://www.canr.msu.edu/news/cutting_management_for_cool_season_forage_grasses (2011).Briske, D. D. Strategies of plant survival in grazed systems: A functional interpretation. Ecol. Manag. Graz. Syst. 37–67 (1996).Crider, F. J. Root-growth stoppage resulting from defoliation of grass (No. 156759). United States Department of Agriculture, Economic Research Service (1995).Lal, R., Negassa, W. & Lorenz, K. Carbon sequestration in soil. Curr. Opin. Environ. Sustain. 15, 79–86. https://doi.org/10.1016/j.cosust.2015.09.002 (2015).Article 

Google Scholar 
Coughenour, M. B., McNaughton, S. J. & Wallace, L. L. Modelling primary production of perennial graminoids – uniting physiological processes and morphometric traits. Ecol. Modell. 23, 101–134. https://doi.org/10.1016/0304-3800(84)90121-2 (1984).Article 
CAS 

Google Scholar 
Whipps, J. M. & Lynch, J. M. Energy losses by the plant in rhizodeposition. Plant products and the new technology / edited by K.W. Fuller and J.R. Gallon (1985).Johansson, G. Release of organic C from growing roots of meadow fescue (Festuca pratensis L.). Soil Biol. Biochem. 24, 427–433. https://doi.org/10.1016/0038-0717(92)90205-C (1992).Article 

Google Scholar 
Woodburn, A. T. Glyphosate: Production, pricing and use worldwide. Pest Manag. Sci. 56, 309–312. https://doi.org/10.1002/(SICI)1526-4998(200004)56:4%3c309::AID-PS143%3e3.0.CO;2-C (2000).Article 
CAS 

Google Scholar 
Duke, S. O. & Powles, S. B. Glyphosate: A once-in-a-century herbicide. Pest Manag. Sci. 64, 319–325. https://doi.org/10.1002/ps.1518 (2008).Article 
CAS 

Google Scholar 
Helander, M., Saloniemi, I. & Saikkonen, K. Glyphosate in northern ecosystems. Trends Plant Sci. 17, 569–574. https://doi.org/10.1016/j.tplants.2012.05.008 (2012).Article 
CAS 

Google Scholar 
Benbrook, C. M. Trends in glyphosate herbicide use in the United States and globally. Environ. Sci. Eur. 28, 3. https://doi.org/10.1186/s12302-016-0070-0 (2016).Article 
CAS 

Google Scholar 
Helander, M. et al. Glyphosate decreases mycorrhizal colonization and affects plant-soil feedback. Sci. Total Environ. 642, 285–291. https://doi.org/10.1016/j.scitotenv.2018.05.377 (2018).Article 
ADS 
CAS 

Google Scholar 
Helander, M., Pauna, A., Saikkonen, K. & Saloniemi, I. Glyphosate residues in soil affect crop plant germination and growth. Sci. Rep. 9, 19653. https://doi.org/10.1038/s41598-019-56195-3 (2019).Article 
ADS 
CAS 

Google Scholar 
Zaller, J. G. & Brühl, C. A. Editorial: Non-target effects of pesticides on organisms inhabiting agroecosystems. Front Environ. Sci. 7, 75. https://doi.org/10.3389/fenvs.2019.00075 (2019).Article 

Google Scholar 
Muola, A. et al. Risk in the circular food economy: Glyphosate-based herbicide residues in manure fertilizers decrease crop yield. Sci. Total Environ. 750, 141422. https://doi.org/10.1016/j.scitotenv.2020.141422 (2021).Article 
ADS 
CAS 

Google Scholar 
Fuchs, B., Saikkonen, K. & Helander, M. Glyphosate-modulated biosynthesis driving plant defense and species interactions. Trends Plant Sci. 26, 312–323. https://doi.org/10.1016/j.tplants.2020.11.004 (2021).Article 
CAS 

Google Scholar 
Fuchs, B. et al. A Glyphosate-based herbicide in soil differentially affects hormonal homeostasis and performance of non-target crop plants. Front Plant Sci. 12, 787958 (2022).Article 

Google Scholar 
Borggaard, O. K. & Gimsing, A. L. Fate of glyphosate in soil and the possibility of leaching to ground and surface waters: A review. Pest Manag. Sci. 64, 441–456. https://doi.org/10.1002/ps.1512 (2008).Article 
CAS 

Google Scholar 
Rueppel, M. L., Brightwell, B. B., Schaefer, J. & Marvel, J. T. Metabolism and degradation of glyphosate in soil and water. J. Agric. Food Chem. 25, 517–528. https://doi.org/10.1021/jf60211a018 (1977).Article 
CAS 

Google Scholar 
Carlisle, S. M. & Trevors, J. T. Glyphosate in the environment. Wat Air Soil Poll 39, 409–420 (1988).Article 
ADS 
CAS 

Google Scholar 
Torstensson, N. T. L., Lundgren, L. N. & Stenström, J. Influence of climatic and edaphic factors on persistence of glyphosate and 2,4-D in forest soils. Ecotoxicol. Environ. Saf. 18, 230–239. https://doi.org/10.1016/0147-6513(89)90084-5 (1989).Article 
CAS 

Google Scholar 
Stenrød, M., Eklo, O. M., Charnay, M.-P. & Benoit, P. Effect of freezing and thawing on microbial activity and glyphosate degradation in two Norwegian soils. Pest Manag. Sci. 61, 887–898. https://doi.org/10.1002/ps.1107 (2005).Article 
CAS 

Google Scholar 
Antier, C. et al. Glyphosate use in the European agricultural sector and a framework for its further monitoring. Sustainability 12, 5682. https://doi.org/10.3390/su12145682 (2020).Article 
CAS 

Google Scholar 
Jones, R. J. Effect of an associate grass, cutting interval, and cutting height on yield and botanical composition of Siratro pastures in a sub-tropical environment. Aust. J. Exp. Agric. 14, 334–342. https://doi.org/10.1071/ea9740334 (1974).Article 

Google Scholar 
Volenec, J. J. & Nelson, C. J. Responses of Tall Fescue leaf meristems to N fertilization and harvest frequency. Crop Sci. 23(4), 720–724. https://doi.org/10.2135/cropsci1983.0011183X002300040028x (1983).Article 

Google Scholar 
Saikkonen, K. et al. Fungal endophytes help prevent weed invasions. Agric. Ecosyst. Environ. 165, 1–5. https://doi.org/10.1016/j.agee.2012.12.002 (2013).Article 

Google Scholar 
Scavo, A. & Mauromicale, G. Integrated weed management in herbaceous field crops. Agronomy 10, 466. https://doi.org/10.3390/agronomy10040466 (2020).Article 

Google Scholar 
Clay, K. & Holah, J. Fungal endophyte symbiosis and plant diversity in successional fields. Science 285, 1742–1744. https://doi.org/10.1126/science.285.5434.1742 (1999).Article 
CAS 

Google Scholar 
Gundel, P. E., Pérez, L. I., Helander, M. & Saikkonen, K. Symbiotically modified organisms: Nontoxic fungal endophytes in grasses. Trends Plant Sci. 18, 420–427. https://doi.org/10.1016/j.tplants.2013.03.003 (2013).Article 
CAS 

Google Scholar 
Kauppinen, M., Saikkonen, K., Helander, M., Pirttilä, A. M. & Wäli, P. R. Epichloë grass endophytes in sustainable agriculture. Nat. Plants 2, 15224 (2016).Article 

Google Scholar 
Clay, K. Fungal endophytes of grasses. Annu. Rev. Ecol. Syst. 21, 275–297 (1990).Article 

Google Scholar 
Saikkonen, K., Young, C. A., Helander, M. & Schardl, C. L. Endophytic Epichloë species and their grass hosts: From evolution to applications. Plant Mol. Biol. 90, 665–675. https://doi.org/10.1007/s11103-015-0399-6 (2016).Article 
CAS 

Google Scholar 
Ahlholm, J. U., Helander, M., Lehtimäki, S., Wäli, P. & Saikkonen, K. Vertically transmitted fungal endophytes: Different responses of host-parasite systems to environmental conditions. Oikos 99, 173–183. https://doi.org/10.1034/j.1600-0706.2002.990118.x (2002).Article 

Google Scholar 
Easton, H. S. & Fletcher, L. R. in Proc. 6th International Symposium Fungal Endophytes of Grasses (eds Popay, A. J. & Thom, E. R.) 11–18 (New Zealand Grassland Association, 2007).Saari, S., Lehtonen, P., Helander, M. & Saikkonen, K. High variation in frequency of infection by endophytes in cultivars of meadow fescue in Finland. Grass Forage Sci. 64, 169–176. https://doi.org/10.1111/j.1365-2494.2009.00680.x (2009).Article 

Google Scholar 
König, J., Fuchs, B., Krischke, M., Mueller, M. J. & Krauss, J. Hide and seek: Infection rates and alkaloid concentrations of Epichloë festucae var. lolii in Lolium perenne along a land-use gradient in Germany. Grass Forage Sci. 73, 510–516. https://doi.org/10.1111/gfs.12330 (2018).Article 
CAS 

Google Scholar 
Krauss, J. et al. Epichloë endophyte infection rates and alkaloid content in commercially available grass seed mixtures in Europe. Microorganisms 8, 498. https://doi.org/10.3390/microorganisms8040498 (2020).Article 
CAS 

Google Scholar 
Brink, G. E., Casler, M. D. & Martin, N. P. Meadow Fescue, Tall Fescue, and Orchardgrass response to defoliation management. Agronomy J 102, 667–674. https://doi.org/10.2134/agronj2009.0376 (2010).Article 

Google Scholar 
Conant, R. T., Cerri, C. E. P., Osborne, B. B. & Paustian, K. Grassland management impacts on soil carbon stocks: A new synthesis. Ecol. Appl. 27, 662–668. https://doi.org/10.1002/eap.1473 (2017).Article 

Google Scholar 
Trlica, M. J. Distribution and utilization of carbohydrate reserves in range plants. In (ed Sosebee, R. E.) 73–96 (Rangeland Plant Physiology, 1977).Faeth, S. H. & Sullivan, T. J. Mutualistic asexual endophytes in a native grass are usually parasitic. Am. Nat. 161, 310–325. https://doi.org/10.1086/345937 (2003).Article 

Google Scholar 
Saikkonen, K., Saari, S. & Helander, M. Defensive mutualism between plants and endophytic fungi?. Fungal Divers. 41, 101–113. https://doi.org/10.1007/s13225-010-0023-7 (2010).Article 

Google Scholar 
Clay, K. & Schardl, C. Evolutionary origins and ecological consequences of endophyte symbiosis with grasses. Am. Nat. 160, 99–127. https://doi.org/10.1086/342161 (2002).Article 

Google Scholar 
Rozpądek, P. et al. The fungal endophyte Epichloë typhina improves photosynthesis efficiency of its host orchard grass (Dactylis glomerata). Planta 242, 1025–1035. https://doi.org/10.1007/s00425-015-2337-x (2015).Article 
CAS 

Google Scholar 
Xia, C. et al. An Epichloë endophyte improves photosynthetic ability and dry matter production of its host Achnatherum inebrians infected by Blumeria graminis under various soil water conditions. Fungal Ecol. 22, 26–34. https://doi.org/10.1016/j.funeco.2016.04.002 (2016).Article 

Google Scholar 
Malinowski, D., Leuchtmann, A., Schmidt, D. & Nosberger, J. Symbiosis with Neotyphodium uncinatum endophyte may increase the competitive ability of meadow fescue. Agron. J. 89, 833–839 (1997).Article 

Google Scholar 
Schardl, C. L., Leuchtmann, A. & Spiering, M. J. Symbioses of grasses with seedborne fungal endophytes. Ann. Rev. Plant Biol. 55, 315–340. https://doi.org/10.1146/annurev.arplant.55.031903.141735 (2004).Article 
CAS 

Google Scholar 
Chen, Z. et al. Fungal endophyte improves survival of Lolium perenne in low fertility soils by increasing root growth, metabolic activity and absorption of nutrients. Plant Soil 452, 185–206. https://doi.org/10.1007/s11104-020-04556-7 (2020).Article 
CAS 

Google Scholar 
Franz, J. E., Mao, M.K. and Sikorski, J.A. (1997). Uptake, transport and metabolism of glyphosate in plants, in Glyphosate: A unique global herbicide, ed by Franz JE, ACS Monograph No 189, American Chemical Society, Washington, DC, pp 143–181.Pline, W. A., Wilcut, J. W., Edmisten, K. L. & Wells, R. Physiological and morphological response of glyphosate-resistant and non-glyphosate-resistant cotton seedlings to root-absorbed glyphosate. Pestic. Biochem. Phys. 73, 48–58. https://doi.org/10.1016/S0048-3575(02)00014-7 (2002).Article 
CAS 

Google Scholar 
Johansson, G. Carbon distribution in grass (Festuca pratensis L.) during regrowth after cutting—utilization of stored and newly assimilated carbon. Plant Soil 151, 11–20. https://doi.org/10.1007/BF00010781 (1993).Article 
ADS 
CAS 

Google Scholar 
Ergon, Å. et al. How can forage production in Nordic and Mediterranean Europe adapt to the challenges and opportunities arising from climate change?. Euro J. Agron. 92, 97–106. https://doi.org/10.1016/j.eja.2017.09.016 (2018).Article 

Google Scholar 
Niemelainen, O. et al. Increase in perennial forage yields driven by climate change, at Apukka Research Station, Rovaniemi, 1980–2017. Agric. Food Sci. 29, 139–153 (2020).Article 

Google Scholar 
Anwar, M. R., Liu, D. L., Macadam, I. & Kelly, G. Adapting agriculture to climate change: A review. Theor. Appl. Climatol. 113, 225–245. https://doi.org/10.1007/s00704-012-0780-1 (2013).Article 
ADS 

Google Scholar 
Farmit. Nurmea yli kymppitonni hehtaarilta. Farmit.net. (accessed 28 June 2022); https://www.farmit.net/nurmikasvit-lypsylehma/2016/05/24/nurmea-yli-kymppitonni-hehtaarilta (2016).Peltonen, S., Aalto, K., Hennola, I. & Anttila, S. (Eds.). Peltojen kunnostus. (Tieto Tuottamaan; No. 145), (ProAgria Keskusten Liiton julkaisuja; No. 1163). ProAgria maaseutukeskusten liitto (2019).Laihonen, M., Saikkonen, K., Helander, M. & Tammaru, T. Insect oviposition preference between Epichloë-symbiotic and Epichloë-free grasses does not necessarily reflect larval performance. Ecol. Evol. 10, 7242–7249. https://doi.org/10.1002/ece3.6450 (2020).Article 

Google Scholar  More

Semple, K. T., Morriss, A. W. J. & Paton, G. I. Bioavailability of hydrophobic organic contaminants in soils: Fundamental concepts and techniques for analysis. Eur. J. Soil Sci. 54, 809–818 (2003).Article 
CAS 

Google Scholar 
Ivshina, I. et al. Removal of polycyclic aromatic hydrocarbons in soil spiked with model mixtures of petroleum hydrocarbons and heterocycles using biosurfactants from Rhodococcus ruber IEGM 231. J. Hazard. Mater. 312, 8–17 (2016).Article 
CAS 

Google Scholar 
Varjani, S. J. Microbial degradation of petroleum hydrocarbons. Bioresour. Technol. 223, 277–286 (2017).Article 
CAS 

Google Scholar 
Chen, J. et al. Long-chain n-alkane biodegradation coupling to methane production in an enriched culture from production water of a high-temperature oil reservoir. AMB Express 10, 63 (2020).Article 
CAS 

Google Scholar 
Li, Y. & Xiong, Y. Identification and quantification of mixed sources of oil spills based on distributions and isotope profiles of long-chain n-alkanes. Mar. Pollut. Bull. 58, 1868–1873 (2009).Article 
CAS 

Google Scholar 
Stout, S. A., Payne, J. R., Emsbo-Mattingly, S. D. & Baker, G. Weathering of field-collected floating and stranded Macondo oils during and shortly after the Deepwater Horizon oil spill. Mar. Pollut. Bull. 105, 7–22 (2016).Article 
CAS 

Google Scholar 
Wang, X. et al. Polycyclic aromatic hydrocarbons, polychlorinated biphenyls and legacy and current pesticides in indoor environment in Australia—occurrence, sources and exposure risks. Sci. Total Environ. 693, 133588 (2019).Article 
ADS 
CAS 

Google Scholar 
Qiao, M., Qi, W., Liu, H. & Qu, J. Oxygenated polycyclic aromatic hydrocarbons in the surface water environment: Occurrence, ecotoxicity, and sources. Environ. Int. 163, 107232 (2022).Article 

Google Scholar 
Abbasnezhad, H., Foght, J. M. & Gray, M. R. Adhesion to the hydrocarbon phase increases phenanthrene degradation by Pseudomonas fluorescens LP6a. Biodegradation 22, 485–496 (2011).Article 
CAS 

Google Scholar 
Abbasnezhad, H., Gray, M. & Foght, J. M. Influence of adhesion on aerobic biodegradation and bioremediation of liquid hydrocarbons. Appl. Microbiol. Biotechnol. 92, 653–675 (2011).Article 
CAS 

Google Scholar 
Dewangan, N. K. & Conrad, J. C. Bacterial motility enhances adhesion to oil droplets. Soft Matter 16, 8237–8244 (2020).Article 
ADS 
CAS 

Google Scholar 
Rodrigues, E. M., Cesar, D. E., Santos de Oliveira, R., de Paula Siqueira, T. & Tótola, M. R. Hydrocarbonoclastic bacterial species growing on hexadecane: Implications for bioaugmentation in marine ecosystems. Environ. Pollut. 267, (2020).Wang, J. D., Qu, C. T. & Song, S. F. Temperature-induced changes in the proteome of Pseudomonas aeruginosa during petroleum hydrocarbon degradation. Arch. Microbiol. 203, 2463–2473 (2021).Article 
CAS 

Google Scholar 
Bastiaens, L. et al. Isolation of adherent polycyclic aromatic hydrocarbon (PAH)-degrading bacteria using PAH-sorbing carriers. Appl. Environ. Microbiol. 66, 1834–1843 (2000).Article 
ADS 
CAS 

Google Scholar 
Tao, K., Zhao, S., Gao, P., Wang, L. & Jia, H. Impacts of Pantoea agglomerans strain and cation-modified clay minerals on the adsorption and biodegradation of phenanthrene. Ecotoxicol. Environ. Saf. 161, 237–244 (2018).Article 
CAS 

Google Scholar 
Xu, X. et al. Biodegradation potential of polycyclic aromatic hydrocarbons by immobilized Klebsiella sp. in soil washing effluent. Chemosphere 223, 140–147 (2019).Wang, H. et al. Transmembrane transport of polycyclic aromatic hydrocarbons by bacteria and functional regulation of membrane proteins. Front. Environ. Sci. Eng. 14, 1–21 (2020).Article 

Google Scholar 
Tarasova, E. V., Grishko, V. V. & Ivshina, I. B. Cell adaptations of Rhodococcus rhodochrous IEGM 66 to betulin biotransformation. Process Biochem. 52, 1–9 (2017).Article 
CAS 

Google Scholar 
Bohinc, K. et al. Available surface dictates microbial adhesion capacity. Int. J. Adhes. Adhes. 50, 265–272 (2014).Article 
CAS 

Google Scholar 
Carniello, V., Peterson, B. W., van der Mei, H. C. & Busscher, H. J. Physico-chemistry from initial bacterial adhesion to surface-programmed biofilm growth. Adv. Colloid Interface Sci. 261, 1–14 (2018).Article 
CAS 

Google Scholar 
Dorobantu, L. S., Bhattacharjee, S., Foght, J. M. & Gray, M. R. Analysis of force interactions between AFM tips and hydrophobic bacteria using DLVO theory. Langmuir 25, 6968–6976 (2009).Article 
CAS 

Google Scholar 
Lehocký, M. et al. Adhesion of Rhodococcus sp. S3E2 and Rhodococcus sp. S3E3 to plasma prepared Teflon-like and organosilicon surfaces. J. Mater. Process. Technol. 209, 2871–2875 (2009).Hori, K. & Matsumoto, S. Bacterial adhesion: From mechanism to control. Biochem. Eng. J. 48, 424–434 (2010).Article 
CAS 

Google Scholar 
Ivshina, I. B. et al. Biosurfactant-enhanced immobilization of hydrocarbon-oxidizing Rhodococcus ruber on sawdust. Appl. Microbiol. Biotechnol. 97, 5315–5327 (2013).Article 
CAS 

Google Scholar 
Pen, Y. et al. Effect of extracellular polymeric substances on the mechanical properties of Rhodococcus. Biochim. Biophys. Acta – Biomembr. 1848, 518–526 (2015).Article 
CAS 

Google Scholar 
De Cesare, F., Di Mattia, E., Zussman, E. & Macagnano, A. A study on the dependence of bacteria adhesion on the polymer nanofibre diameter. Environ. Sci. Nano 6, 778–797 (2019).Article 

Google Scholar 
Bergeau, D. et al. Unusual extracellular appendages deployed by the model strain Pseudomonas fluorescens C7R12. PLoS ONE 14, 1–20 (2019).Article 

Google Scholar 
Jin, X. & Marshall, J. S. Mechanics of biofilms formed of bacteria with fimbriae appendages. PLoS ONE 15, 1–22 (2020).Article 

Google Scholar 
Tarafdar, A., Sarkar, T. K., Chakraborty, S., Sinha, A. & Masto, R. E. Biofilm development of Bacillus thuringiensis on MWCNT buckypaper: Adsorption-synergic biodegradation of phenanthrene. Ecotoxicol. Environ. Saf. 157, 327–334 (2018).Article 
CAS 

Google Scholar 
Rodrigues, A. C., Wuertz, S., Brito, A. G. & Melo, L. F. Fluorene and phenanthrene uptake by Pseudomonas putida ATCC 17514: Kinetics and physiological aspects. Biotechnol. Bioeng. 90, 281–289 (2005).Article 
CAS 

Google Scholar 
Yang, H. Y., Jia, R. B., Chen, B. & Li, L. Degradation of recalcitrant aliphatic and aromatic hydrocarbons by a dioxin-degrader Rhodococcus sp. strain p52. Environ. Sci. Pollut. Res. 21, 11086–11093 (2014).Auffret, M. D., Yergeau, E., Labbé, D., Fayolle-Guichard, F. & Greer, C. W. Importance of Rhodococcus strains in a bacterial consortium degrading a mixture of hydrocarbons, gasoline, and diesel oil additives revealed by metatranscriptomic analysis. Appl. Microbiol. Biotechnol. 99, 2419–2430 (2015).Article 
CAS 

Google Scholar 
Ahmed, R. Z. & Ahmed, N. Isolation of Rhodococcus sp. CMGCZ capable to degrade high concentration of fluoranthene. Water. Air. Soil Pollut. 227, 162 (2016).Ivshina, I. B., Kuyukina, M. S. & Krivoruchko, A. V. Hydrocarbon-oxidizing bacteria and their potential in eco-biotechnology and bioremediation. in Microbial Resources (ed. Kurtboke, I.) 121–148 (Elsevier Inc., 2017). https://doi.org/10.1016/B978-0-12-804765-1.00006-0.Pi, Y. et al. Microbial degradation of four crude oil by biosurfactant producing strain Rhodococcus sp. Bioresour. Technol. 232, 263–269 (2017).Article 
CAS 

Google Scholar 
Cappelletti, M., Fedi, S. & Zannoni, D. Degradation of alkanes in Rhodococcus. in Biology of Rhodococcus, Microbiology Monographs 16 (ed. Alvarez, H. M.) 137–171 (Springer Nature Switzerland AG, 2019). https://doi.org/10.1007/978-3-030-11461-9_6.Kuyukina, M. S. & Ivshina, I. B. Application of Rhodococcus in bioremediation of contaminated environments. in Biology of Rhodococcus, Microbiology Monographs 16 (ed. Alvarez, H. M.) 231–262 (Springer Nature Switzerland, 2019). https://doi.org/10.1007/978-3-642-12937-7_9.Krivoruchko, A. V. et al. Adhesion of Rhodococcus ruber IEGM 342 to polystyrene studied using contact and non-contact temperature measurement techniques. Appl. Microbiol. Biotechnol. 102, 8525–8536 (2018).Article 
CAS 

Google Scholar 
Rubtsova, E. V., Kuyukina, M. S. & Ivshina, I. B. Effect of cultivation conditions on the adhesive activity of Rhodococcus cells towards n-hexadecane. Appl. Biochem. Microbiol. 48, 452–459 (2012).Article 
CAS 

Google Scholar 
Pearlman, R. S., Yalkowsky, S. H. & Banerjee, S. Water solubilities of polynuclear aromatic and heteroaromatic compounds. J. Phys. Chem. Ref. Data 13, 555–562 (1984).Article 
ADS 
CAS 

Google Scholar 
Wrenn, B. A. & Venosa, A. D. Selective enumeration of aromatic and aliphatic hydrocarbon degrading bacteria by a most-probable-number procedure. Can. J. Microbiol. 42, 252–258 (1996).Article 
CAS 

Google Scholar 
Christofi, N., Ivshina, I. B., Kuyukina, M. S. & Philp, J. C. Biological treatment of crude oil contaminated soil in Russia. Geol. Soc. Eng. Geol. Spec. Publ. 14, 45–51 (1998).
Google Scholar 
Sorongon, M. L., Bloodgood, R. A. & Burchard, R. P. Hydrophobicity, adhesion, and surface-exposed proteins of gliding bacteria. Appl. Environ. Microbiol. 57, 3193–3199 (1991).Article 
ADS 
CAS 

Google Scholar 
Bellon-Fontaine, M.-N., Rault, J. & van Ossb, C. J. Microbial adhesion to solvents : a novel method to determine the electron-donor/electron-acceptor or Lewis acid-base properties of microbial cells. Colloids Surf. B Biointerfaces 7, 47–53 (1996).Article 
CAS 

Google Scholar 
Mattos-Guaraldi, A. L., Formiga, L. C. D. & Andrade, A. F. B. Cell surface hydrophobicity of sucrose fermenting and nonfermenting Corynebacterium diphtheriae strains evaluated by different methods. Curr. Microbiol. 38, 37–42 (1999).Article 
CAS 

Google Scholar 
Nikiyan, H., Vasilchenko, A. & Deryabin, D. Humidity-dependent bacterial cells functional morphometry investigations using atomic forcemicroscope. Int. J. Microbiol. 2010, 704170 (2010).Article 

Google Scholar 
Xu, J. L. et al. Rhodococcus qingshengii sp. nov., a carbendazim-degrading bacterium. Int. J. Syst. Evol. Microbiol. 57, 2754–2757 (2007).Lee, S. D. & Kim, I. S. Rhodococcus spelaei sp. nov., isolated from a cave, and proposals that Rhodococcus biphenylivorans is a later synonym of Rhodococcus pyridinivorans, Rhodococcus qingshengii and Rhodococcus baikonurensis are later synonym. Int. J. Syst. Evol. Microbiol. 71, (2021).Korshunova, I. O., Pistsova, O. N., Kuyukina, M. S. & Ivshina, I. B. The effect of organic solvents on the viability and morphofunctional properties of Rhodococcus. Appl. Biochem. Microbiol. 52, 53–61 (2016).Article 

Google Scholar 
de Carvalho, C. C. C. R., Wick, L. Y. & Heipieper, H. J. Cell wall adaptations of planktonic and biofilm Rhodococcus erythropolis cells to growth on C5 to C16 n-alkane hydrocarbons. Appl. Microbiol. Biotechnol. 82, 311–320 (2009).Article 
CAS 

Google Scholar 
Kuyukina, M. S. et al. Oilfield wastewater biotreatment in a fluidized-bed bioreactor using co-immobilized Rhodococcus cultures. J. Environ. Chem. Eng. 5, 1252–1260 (2017).Article 
CAS 

Google Scholar 
Abdel-Shafy, H. I. & Mansour, M. S. M. A review on polycyclic aromatic hydrocarbons: Source, environmental impact, effect on human health and remediation. Egypt. J. Pet. 25, 107–123 (2016).Article 

Google Scholar 
He, J. et al. Subchronic exposure of benzo(a)pyrene interferes with the expression of Bcl-2, Ki-67, C-myc and p53, Bax, Caspase-3 in sub-regions of cerebral cortex and hippocampus. Exp. Toxicol. Pathol. 68, 149–156 (2016).Article 
CAS 

Google Scholar 
Boente, C., Baragaño, D. & Gallego, J. R. Benzo[a]pyrene sourcing and abundance in a coal region in transition reveals historical pollution, rendering soil screening levels impractical. Environ. Pollut. 266, (2020).Cao, Y. et al. Interfacial interaction between benzo[a]pyrene and pulmonary surfactant: Adverse effects on lung health. Environ. Pollut. 287, 117669 (2021).Article 
CAS 

Google Scholar 
Gallardo-Moreno, A. M. et al. Thermodynamic analysis of growth temperature dependence in the adhesion of Candida parapsilosis to polystyrene. Appl. Environ. Microbiol. 68, 2610–2613 (2002).Article 
ADS 
CAS 

Google Scholar 
Kuyukina, M. S., Ivshina, I. B., Korshunova, I. O., Stukova, G. I. & Krivoruchko, A. V. Diverse effects of a biosurfactant from Rhodococcus ruber IEGM 231 on the adhesion of resting and growing bacteria to polystyrene. AMB Express 6, 1–12 (2016).Article 
CAS 

Google Scholar 
Letek, M. et al. The genome of a pathogenic Rhodococcus: Cooptive virulence underpinned by key gene acquisitions. PLoS Genet. 6, 1–17 (2010).Article 

Google Scholar 
Dayan, A. et al. The involvement of coordinative interactions in the binding of dihydrolipoamide dehydrogenase to titanium dioxide – Localization of a putative binding site. J. Mol. Recognit. 30, 1–11 (2017).Article 
ADS 

Google Scholar 
Choi, E. J. & Dimitriadis, E. K. Cytochrome c adsorption to supported, anionic lipid bilayers studied via atomic force microscopy. Biophys. J. 87, 3234–3241 (2004).Article 
ADS 
CAS 

Google Scholar 
Wright, C. J. & Armstrong, I. The application of atomic force microscopy force measurements to the characterisation of microbial surfaces. Surf. Interface Anal. 38, 1419–1428 (2006).Article 
CAS 

Google Scholar 
Salerno, M., Dante, S., Patra, N. & Diaspro, A. AFM measurement of the stiffness of layers of agarose gel patterned with polylysine. Microsc. Res. Tech. 73, 982–990 (2010).CAS 

Google Scholar 
Campbell, J. E., Yang, J. & Day, G. M. Predicted energy-structure-function maps for the evaluation of small molecule organic semiconductors. J. Mater. Chem. C 5, 7574–7584 (2017).Article 
CAS 

Google Scholar 
Wang, N. et al. Molecular elucidating of an unusual growth mechanism for polycyclic aromatic hydrocarbons in confined space. Nat. Commun. 11, 1079 (2020).Article 
ADS 
CAS 

Google Scholar  More

Whale entanglements in fishing gear threaten whale populations, seafood production and long-term sustainability of commercial fisheries. While multiple mitigation strategies to reduce entanglements exist, there has been minimal consideration of the economic impact of these strategies. Here, we estimated retrospective losses to ex-vessel revenues for one of California’s most lucrative fisheries. Overall, we found fishery closures decreased ex-vessel revenue, with results showing some uncertainty due to large model prediction error. Regional differences in losses revealed interesting trends in the capacity for the fishery to recoup costs. For example, in the NMA, relatively small losses at the fishery level were predicted ($0.3 million in total) for the 2019 season despite an early closure to the season due to whale entanglement risk.NMA fishers collectively were able to meet predicted revenue for the season despite a shortening of the fishing 2019 season. In the 2020 season however, the NMA did not experience disturbances due to whale entanglements but larger ex-vessel losses (of $3.9 million) were predicted. This suggests that other disturbances such as a delay to the season due to crab meat quality, lost fishing opportunity related to the COVID-19 pandemic, or other unknown factors, had an influence on ex-vessel revenue during the 2020 season. While most of the 2020 season landings in the NMA occurred before COVID-19 arrived in the US, there is evidence that prices in latter part of the season may have been depressed due to loss of export markets for live crab47.In the CMA however, despite landing the majority of crab available during the 2019 season (see Fig. 2c), losses of $9.4 million were experienced across the fishery. While total fishery catch was not greatly reduced, closure to the fishery in the spring may be responsible for revenue losses through other mechanisms (e.g. price). In the 2020 season, whale entanglement risk substantially shortened the fishing season in the CMA, through a delay at the beginning of the season and an early closure in the spring. Estimated losses were largest ($14.4 million) during this season. It is likely that the COVID-19 pandemic was also responsible for some of this estimated loss in the CMA in the 2020 season47. Our model did not control for impacts of the COVID-19 pandemic. However, price trends suggest that that price of Dungeness Crab in California was not affected until mid-March 2020, at which point the fishery had caught 92% of the seasons catch (see Supplementary File S2). Prices then returned to normal levels in mid-May. If we apply extrapolated prices between mid-March and mid-May by replacing observed prices with linearly increasing prices by week, revenues would have been $753,754 higher in total across the fishery. This rough estimate suggests we can attribute 4.1% of overall estimated revenue losses during the 2020 season to COVID-19 impacts, with the caveat that we do not know what prices would have been in the absence of the pandemic. A counterfactual approach has been used to disentangle multiple stressors to infer causal impacts of management interventions elsewhere48, however as these closures, and the COVID pandemic, potentially impacted all fishers in the California Dungeness crab fishery, there are no control groups available for comparison and therefore this approach would not be appropriate.Closures and other disturbances appear to have been less impactful in the NMA and high price for Dungeness crab may have contributed to the ability of vessels operating in the NMA to withstand disturbances (Supplementary Fig. S2). Prices were particularly high during the summer portion of the season in 2020 during which time the CMA was closed to Dungeness crab fishing (Supplementary Fig. S2). The NMA did not experience closures due to whale entanglement during 2020 and was predicted to have lower than average pre-season abundance (lower catch potential) during 2020 (see Fig. 2.b), while the CMA was predicted to have high catch potential for 2020 (Fig. 2.c), therefore differences in management measures implemented, and seasons’ catch potential, also contributed to differences in losses estimated.The CMA also experienced high prices, including decadal high prices for crab during the November–December of the 2019 fishing season (Supplementary Fig. S2). However, losses observed overall across the two seasons suggest the fishery, unlike the NMA, did not get much overall benefit from the high price in 2019 or the high pre-season abundance of crab (i.e. catch potential) estimated for the 2020 season in the CMA. A number of factors may have contributed to a poor season in the CMA including catchability or biology of Dungeness crab as well as external factors such as the COVID-19 pandemic behavioral choice factors, for example deciding not to fish45. Temporally shifting or reducing the opportunity for participation through closed periods due to whale entanglement risk may have exacerbated other impacts on revenues in the CMA which were not as impactful on revenues in the NMA.The high variability in estimated economic impacts per vessel reported here demonstrates that closures did not affect all vessels equally, similarly to impacts observed following a climate related harmful algal bloom in the 2016 season which were variable by vessel size and between communities45. The estimated losses we present at the fishery level in the NMA and CMA may therefore be underestimated, or overestimated, for particular groups of vessels within those management areas. This reflects the diverse nature of the Dungeness Crab fishery in behaviour and fishing strategy and highlights the importance of capturing impacts at finer scales than the fishery level alone.Limitations to the estimation of closure impactsA limitation of the hurdle model is that there are other latent factors influencing fishery participation and revenues that our model does not incorporate, particularly those determining fisher behavior such as fuel price, shipyard backlogs and market demand. A behavioral choice model, for example one that incorporates location or fishing alternative choice given a closure50,51,52 would be a potential method to better understand how spatial management strategies affect fisher behavior and is recommended as a future analysis to assess trade-offs involving socio-economic risk. Our results, reporting losses from Dungeness crab fishing revenue only, also do not account for the ability of some fishers to mitigate revenue losses by participating in other fisheries. Dungeness crab fishing is highly connected within west coast fishery participation networks44,45. Thus, it is important to note that our results for the 2019 and 2020 seasons present only losses from Dungeness crab fishing and may overestimate total annual revenue losses by some vessels that are able to mitigate impacts with participation in other fisheries.The model, predicting out-of-sample, over-estimated revenues in recent years suggesting that our predictions of revenues may also be over-predicted. An improved estimation at the vessel level, given some over-estimation of vessels that did not fish, could be investigated through a selection model approach rather than a two-part model approach54. However, two-part models are most appropriate for estimation of conditional (actual) outcomes as was intended here rather than unconditional (potential) outcomes and they do not require separate drivers for the selection and estimation model, which we did not have available54. When the impacts of policy interventions are difficult to disentangle from other impacts, approaches such as a counterfactual synthetic control48 approach could be used to separate the impacts of the policy alone. In this context, however, it is useful to report the cumulative impact of disturbances given that these disturbances (e.g., delays due to crab quality, harmful algal blooms) happen frequently and therefore the closures will rarely happen in isolation.Whilst there are limitations to our approach, revenue predictions presented here offer more insight compared to predicting revenues based only on a 5-year average of total fishery revenues (Supplementary Table S3) as is commonly conducted to calculate disaster assistance requirement, as our analysis includes an estimation of crab abundance as well as historical vessel level data in its estimation. Accounting for the influence of crab abundance is critical in this fishery given abundance is highly variable and the majority of fishable biomass is taken each year. Estimation of revenue at the individual vessel level allows for consideration of fishery heterogeneity (e.g., by vessel size). Revenues calculated on a 5-year average would suggest total California Commercial Dungeness crab fishery revenues would have been $10.62 million higher than observed in 2019 and $12.73 million higher than observed in 2020 (Supplementary Table S3). Thus, revenues estimated on the 5-year average suggest that losses would have been $0.97 million higher than our model prediction across the fishery for 2019 and $5.56 million lower than our model prediction for 2020. Our predictions suggest that delays and closures due to whale entanglement mitigation and other disturbances in to the 2019 and 2020 seasons were similar to the impact of closures due to the HAB in the 2016 season, which were estimated at $13.6 million in losses from Dungeness Crab revenues across the fishery38.Economic cost of mitigationMany strategies that prevent fishery interactions with marine mammals exist, including gear reductions or modifications, depth limitations and dynamic or seasonal time-area closures13,14,22,23,24,25,26,55. Whilst the fishery does implement pro-active gear modification measures set out in the best practices guide34, only two management intervention options were enacted in the 2019 and 2020 seasons to mitigate against entanglements of marine life with Dungeness crab gear; delays to the start of the crab season in the winter and early closures in spring due to overlap with whale distribution in fishing grounds. These delays and closures can have differential impacts on the fishery as the fishing season is not heterogeneously prosperous. An example is that closures during the holiday season (Nov–Dec) when Dungeness crab is traditionally consumed can cause substantial lost revenue opportunity for fishers at a time when price and demand are highest35,49. The fishery operates as a derby in which the majority of revenues are made in the first month of the fishery being open. The strong seasonal dynamics of the Dungeness crab fishery, largely driven by rapid depletion of legal sized crab, mean that the timing of management actions can have important impacts on fishing revenues. Across the fishery, based on observed vessel level revenues during the 2011–2018 baseline period, vessels earned an average of 62.33% (SD 24.04) of annual ex-vessel revenue during the first month of the season (15th Nov–15th Dec for the CMA/1st Dec–31st Dec for the NMA). After April 1st, vessels on average earn 10.54% (SD 18.98) of annual ex-vessel revenue. This average, based only on vessels that historically have actively participate past April 1st, (283 vessels in the NMA, 346 vessels in the CMA) rises to 20.36% (SD 13.37) of ex-vessel revenue. Thus, while the majority of the overall fisheries revenue is taken at the start of the season, an April 1st closure could still have a substantial impact on the revenues of active fishing vessels in the spring. Determination of economic risk for the fishery, at a minimum, should consider timing of closures in addition to total revenue losses, in order to quantify losses that will be felt at the individual vessel level. We suggest further research to investigate how closures affect different groups of fishers through stakeholder participation.Socio-economic impacts from whale mitigation measures could permeate into communities further than our analysis (based on ex-vessel revenue only) conveys35,36,37,49, and further investigation into these community level impacts is necessary to understand and sustain an equitable fishery supply chain even where there is no absolute revenue loss. Some of the communities influenced by whale entanglement mitigation in California rely heavily on ocean resources for employment, through fishing occupations but also through hospitality and tourism. Managing this issue in a way that minimizes the burden on resource dependent communities is strongly in line with the objectives set out in the UN Sustainable Development Goals (SDG’s), especially SDG 14 (life below water) but also related goals such as human well-being, reducing inequality and reducing the impacts of climate change56.Management ImplicationsBalancing socio-economic impacts against whale entanglement risk is challenging given the legally protected status of whale populations. However, potential economic losses reported here should motivate the development of mitigation measures (through cooperative innovation between industry, researchers and managers) that allow fishery production to be optimized whilst ensuring successful whale protection. At present, entire management areas, which constitute large regions of the coast, are closed in response to whale entanglement risk in California. Investigating how to minimize the spatiotemporal footprint of closures, such as by defining high risk zones dynamically based on fine-scale information of whale density and fishing effort, could provide an alternative mitigation structure. This could better consider the economic and conservation trade-offs while still being sensitive to changing environmental conditions. The introduction of dynamic zone closures, often broadly referred to as dynamic ocean management, has been demonstrated to reduce risk whilst minimizing lost fishing opportunities12,26,57,58, especially when environmental variability is high or species have a dynamic distribution59. Moreover, analysis of policy instruments to reduce whale entanglements with the American lobster fishery on the US Northeast coast found that economic costs of risk reduction could be 20% lower when mitigation decisions considered fishing opportunity costs alongside non-monetary benefits (biological risk), compared to non-monetary benefits alone12. This is promising for the implementation of such strategies in the California Current System.The caveat of this strategy is that dynamic zone closures require spatially and temporally explicit information on whale density and fishing effort which can be costly to attain. The use of ropeless gear has also been suggested as an alternative whale entanglement mitigation measure that requires further research and development before being initiated as an alternative regulatory tool60. The costs of monitoring or technical advancements however may outweigh the financial and societal cost of fishery closures. Revenue losses for Dungeness crab estimated here for the 2019 and 2020 seasons are on par with losses experienced during the HAB period. During the delays to the 2016 fishing season an estimated $26.1 million was lost from ex-vessel revenues from all species that crab fishers target, including $13.6 million from Dungeness crab alone38, requiring $25 million in government aid. Whale mitigation under the RAMP regulation will potentially delay or close the fishery year after year with uncertain economic impact that cannot be sustainably resolved with government aid. Development of tools to mitigate against economic loss while achieving whale protection will be necessary to come to a sustainable solution. This can only be achieved by first including economic loss in risk assessments. Doing so may also provide balance to partnerships between fishery managers and fishers.Regulators are obligated to protect Humpback whales, blue whales and Leatherback turtles using the best available science33. In this fishery, current triggers to open and close are based on a range of factors, but thus ultimately depend on the number of whales present within a management region33. Regulators have a number of alternative regulatory options available to them, which include depth restrictions, gear restrictions or modifications and fleet advisories, if they can offer the same level of whale protection33. Yet, the RAMP process lacks the socio-economic information needed to consider the socio-economic risk of regulatory actions, and that of the alternatives, to the fishing community. Results presented here highlight that the economic effects and that risk to fishing communities should be considered when designing whale entanglement mitigation programs33. Having this economic information will facilitate the ability of managers, as set out in the RAMP regulation (subsection d4)33, to consider the socio-economic impact if deciding between management measures that equivalently reduce entanglement risk.We have used two fishing seasons as an example of the economic impacts of these new whale entanglement regulations which will be implemented each year going forward. Synthesis of ex-vessel revenues is not a complete picture of the socio-economic impacts of regulations, but it provides a starting point for protecting both whales and fishing communities. While reported whale entanglements remain higher than pre-2014 totals, reported whale entanglements in California have declined markedly in the years following the 2014–2016 large marine heatwave (Fig. 1b). This is a success for this fishery and attributed to increased awareness, development of best practices for fishing gear and the mitigation program to protect whales. We now need to be successful at protecting and mitigating the socio-economic impacts to fishery participants and the fishing communities they support. More