Philippa Kaur

Semin, G. R., Scandurra, A., Baragli, P., Lanatà, A. & D’Aniello, B. Inter- and intra-species communication of emotion: Chemosignals as the neglected medium. Animals 9, 887 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Désiré, L., Boissy, A. & Veissier, I. Emotions in farm animals: A new approach to animal welfare in applied ethology. Behav. Processes 60, 165–180 (2002).Article 
PubMed 

Google Scholar 
Briefer, E. F. & Le Comber, S. Vocal expression of emotions in mammals: mechanisms of production and evidence. J. Zool. 288, 1–20 (2012).Article 

Google Scholar 
Jardat, P. & Lansade, L. Cognition and the human–animal relationship: a review of the sociocognitive skills of domestic mammals toward humans. Anim. Cogn. 25, 369–384 (2022).Article 
PubMed 

Google Scholar 
Galvan, M. & Vonk, J. Man’s other best friend: domestic cats (F. silvestris catus) and their discrimination of human emotion cues. Anim. Cogn. 19, 193–205 (2016).Nawroth, C., Albuquerque, N., Savalli, C., Single, M. S. & McElligott, A. G. Goats prefer positive human emotional facial expressions. R. Soc. Open Sci. 5, 180491 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Proops, L., Grounds, K., Smith, A. V. & McComb, K. Animals remember previous facial expressions that specific humans have exhibited. Curr. Biol. 28, 1428-1432.e4 (2018).Article 
CAS 
PubMed 

Google Scholar 
Smith, A. V., Proops, L., Grounds, K., Wathan, J. & McComb, K. Functionally relevant responses to human facial expressions of emotion in the domestic horse (Equus caballus). Biol. Lett. 12, 20150907 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Siniscalchi, M., D’Ingeo, S., Fornelli, S. & Quaranta, A. Lateralized behavior and cardiac activity of dogs in response to human emotional vocalizations. Sci. Rep. 8, 77 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Siniscalchi, M., D’Ingeo, S. & Quaranta, A. Orienting asymmetries and physiological reactivity in dogs’ response to human emotional faces. Learn. Behav. 46, 574–585 (2018).Article 
PubMed 

Google Scholar 
Smith, A. V. et al. Domestic horses (Equus caballus) discriminate between negative and positive human nonverbal vocalisations. Sci. Rep. 8, 13052 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Trösch, M. et al. Horses categorize human emotions cross-modally based on facial expression and non-verbal vocalizations. Animals 9, 862 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Quaranta, A., D’ingeo, S., Amoruso, R. & Siniscalchi, M. Emotion recognition in cats. Animals 10, 1107 (2020).Nakamura, K., Takimoto-Inose, A. & Hasegawa, T. Cross-modal perception of human emotion in domestic horses (Equus caballus). Sci. Rep. 8, 8660 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Albuquerque, N. et al. Dogs recognize dog and human emotions. Biol. Lett. 12, 20150883 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Briefer, E. F. Vocal contagion of emotions in non-human animals. Proc. R. Soc. B Biol. Sci. 285 (2018).Sabiniewicz, A., Tarnowska, K., Świątek, R., Sorokowski, P. & Laska, M. Olfactory-based interspecific recognition of human emotions: Horses (Equus ferus caballus) can recognize fear and happiness body odour from humans (Homo sapiens). Appl. Anim. Behav. Sci. 230, 105072 (2020).Article 

Google Scholar 
Baba, C., Kawai, M. & Takimoto-Inose, A. Are horses (Equus caballus) sensitive to human emotional cues?. Animals 9, 630 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Brennan, P. A. & Kendrick, K. M. Mammalian social odours: attraction and individual recognition. Philos. Trans. R. Soc. B Biol. Sci. 361, 2061–2078 (2006).Saslow, C. A. Understanding the perceptual world of horses. Appl. Anim. Behav. Sci. 78, 209–224 (2002).Article 

Google Scholar 
Péron, F., Ward, R. & Burman, O. Horses (Equus caballus) discriminate body odour cues from conspecifics. Anim. Cogn. 17, 1007–1011 (2014).Article 
PubMed 

Google Scholar 
Krueger, K. & Flauger, B. Olfactory recognition of individual competitors by means of faeces in horse (Equus caballus). Anim. Cogn. 14, 245–257 (2011).Article 
PubMed 

Google Scholar 
Boissy, A., Terlouw, C. & Le Neindre, P. Presence of cues from stressed conspecifics increases reactivity to aversive events in cattle: evidence for the existence of alarm substances in urine. Physiol. Behav. 63, 489–495 (1998).Article 
CAS 
PubMed 

Google Scholar 
Vieuille-Thomas, C. & Signoret, J. P. Pheromonal transmission of an aversive experience in domestic pig. J. Chem. Ecol. 18, 1551–1557 (1992).Article 
CAS 
PubMed 

Google Scholar 
Siniscalchi, M., D’Ingeo, S. & Quaranta, A. The dog nose ‘KNOWS’ fear: Asymmetric nostril use during sniffing at canine and human emotional stimuli. Behav. Brain Res. 304, 34–41 (2016).Article 
PubMed 

Google Scholar 
Calvi, E. et al. The scent of emotions: A systematic review of human intra- and interspecific chemical communication of emotions. Brain Behav. 10 (2020).de Groot, J. H. B., Semin, G. R. & Smeets, M. A. M. On the communicative function of body odors: A theoretical integration and review. Perspect. Psychol. Sci. 12, 306–324 (2017).Article 
PubMed 

Google Scholar 
de Groot, J. H. B. et al. A sniff of happiness. Psychol. Sci. 26, 684–700 (2015).Article 
PubMed 

Google Scholar 
Destrez, A. et al. Male mice and cows perceive human emotional chemosignals: A preliminary study. Anim. Cogn. 24, 1205–1214 (2021).Article 
PubMed 

Google Scholar 
Wilson, Id. Dogs can discriminate between human baseline and psychological stress condition odours. PLoS ONE 17, e0274143 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
D’Aniello, B., Semin, G. R., Alterisio, A., Aria, M. & Scandurra, A. Interspecies transmission of emotional information via chemosignals: from humans to dogs (Canis lupus familiaris). Anim. Cogn. 21, 67–78 (2018).Article 
PubMed 

Google Scholar 
D’Aniello, B. et al. Sex differences in the behavioral responses of dogs exposed to human chemosignals of fear and happiness. Anim. Cogn. 24, 299–309 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Siniscalchi, M., d’Ingeo, S., Quaranta, A., D’Ingeo, S. & Quaranta, A. Lateralized emotional functioning in domestic animals. Appl. Anim. Behav. Sci. 237, 105282 (2021).Article 

Google Scholar 
Rogers, L. & Vallortigara, G. Lateralized Brain Functions: Methods in Human and Non-Human Species. vol. 122 (2017).D’Ingeo, S. et al. Horses associate individual human voices with the valence of past interactions: A behavioural and electrophysiological study. Sci. Rep. 9, 11568 (2019).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Siniscalchi, M., Padalino, B., Aubé, L. & Quaranta, A. Right-nostril use during sniffing at arousing stimuli produces higher cardiac activity in jumper horses. Laterality 20, (2015).De Boyer Des Roches, A., Richard-Yris, M.-A. A., Henry, S., Ezzaouïa, M. & Hausberger, M. Laterality and emotions: Visual laterality in the domestic horse (Equus caballus) differs with objects’ emotional value. Physiol. Behav. 94, 487–490 (2008).Albrecht, J. et al. Smelling chemosensory signals of males in anxious versus nonanxious condition increases state anxiety of female subjects. Chem. Senses 36, 19–27 (2011).Article 
CAS 
PubMed 

Google Scholar 
Derrickson, S. Sinister (VF)—YouTube. (Wild Bunch SA, 2001).Friard, O. & Gamba, M. BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evol. 7, 1325–1330 (2016).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing (2021).Wickham, H. Ggplot2: Elegant graphics for data analysis. (2016).Hothorn, T., Winell, H., Hornik, K., van de Wiel, M. A. & Zeileis, A. coin: Conditional inference procedures in a permutation test framework (2021).Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models (2021).Lenth, R. V. emmeans: Estimated Marginal Means, aka Least-Squares Means. (2022).Hothersall, B., Harris, P., Sörtoft, L. & Nicol, C. J. Discrimination between conspecific odour samples in the horse (Equus caballus). Appl. Anim. Behav. Sci. 126, 37–44 (2010).Article 

Google Scholar 
Smeets, M. A. M. et al. Chemical fingerprints of emotional body odor. Metabolites 10, (2020).Sabiniewicz, A. et al. A preliminary investigation of interspecific chemosensory communication of emotions: Can Humans (Homo sapiens) recognise fear- and non-fear body odour from horses (Equus ferus caballus). Animal 11, 3499 (2021).Article 

Google Scholar 
Zhou, W. & Chen, D. Entangled chemosensory emotion and identity: Familiarity enhances detection of chemosensorily encoded emotion. Soc. Neurosci. 6, 270–276 (2011).Article 
PubMed 

Google Scholar 
Starling, M., McLean, A. & McGreevy, P. The contribution of equitation science to minimising horse-related risks to humans. Animal 6, 15 (2016).Article 

Google Scholar 
Basile, M. et al. Socially dependent auditory laterality in domestic horses (Equus caballus). Anim. Cogn. 12, 611–619 (2009).Article 
PubMed 

Google Scholar 
Hatfield, E., Cacioppo, J. T. & Rapson, R. L. Emotional contagion. Curr. Dir. Psychol. Sci. 2, 96–100 (1993).Article 

Google Scholar 
Austin, N. P. & Rogers, L. J. Limb preferences and lateralization of aggression, reactivity and vigilance in feral horses Equus caballus. Anim. Behav. 83, 239–247 (2012).Article 

Google Scholar 
Larose, C., Richard-Yris, M.-A., Hausberger, M. & Rogers, L. J. Laterality of horses associated with emotionality in novel situations Laterality Asymmetries Body. Brain Cogn. 11, 355–367 (2006).
Google Scholar 
Farmer, K., Krüger, K., Byrne, R. W. & Marr, I. Sensory laterality in affiliative interactions in domestic horses and ponies (Equus caballus). Anim. Cogn. 21, 631–637 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Chen, D. & Haviland-Jones, J. Human olfactory communication of emotion. Percept. Mot. Skills 91, 771–781 (2000).Article 
CAS 
PubMed 

Google Scholar 
de Groot, J. H. B., Semin, G. R. & Smeets, M. A. M. Chemical communication of fear: A case of male-female asymmetry. J. Exp. Psychol. Gen. 143, 1515–1525 (2014).Article 
PubMed 

Google Scholar 
Marinier, S. L., Alexander, A. J. & Waring, G. H. Flehmen behaviour in the domestic horse: Discrimination of conspecific odours. Appl. Anim. Behav. Sci. 19, 227–237 (1988).Article 

Google Scholar 
Lansade, L., Pichard, G. & Leconte, M. Sensory sensitivities: Components of a horse’s temperament dimension. Appl. Anim. Behav. Sci. 114, 534–553 (2008).Article 

Google Scholar 
Lansade, L., Bouissou, M. F. & Erhard, H. W. Fearfulness in horses: A temperament trait stable across time and situations. Appl. Anim. Behav. Sci. 115, 182–200 (2008).Article 

Google Scholar 
Hoenen, M., Wolf, O. T. & Pause, B. M. The impact of stress on odor perception. https://doi.org/10.1177/030100661668870746,366-376 (2017).Article 
PubMed 

Google Scholar 
Rørvang, M. V., Nicova, K. & Yngvesson, J. Horse odor exploration behavior is influenced by pregnancy and age. Front. Behav. Neurosci. 16, 295 (2022).Article 

Google Scholar 
Doty, R. L. & Cameron, E. L. Sex differences and reproductive hormone influences on human odor perception. Physiol. Behav. 97, 213–228 (2009).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar  More

Different picocyanobacterial communities exhibit distinct gene repertoiresTo analyze the distribution of Prochlorococcus and Synechococcus reads along the Tara Oceans transect, metagenomic reads corresponding to the bacterial size fraction were recruited against 256 picocyanobacterial reference genomes, including SAGs and MAGs representative of uncultured lineages (e.g., Prochlorococcus HLIII-IV, Synechococcus EnvA or EnvB). This yielded a total of 1.07 billion recruited reads, of which 87.7% mapped onto Prochlorococcus genomes and 12.3% onto Synechococcus genomes, which were then functionally assigned by mapping them onto the manually curated Cyanorak v2.1 CLOG database [19]. In order to identify picocyanobacterial genes potentially involved in niche adaptation, we analyzed the distribution across the oceans of flexible (i.e. non-core) genes. Clustering of Tara Oceans stations according to the relative abundance of flexible genes resulted in three well-defined clusters for Prochlorococcus (Fig. 1A), which matched those obtained when stations were clustered according to the relative abundance of Prochlorococcus ESTUs, as assessed using the high-resolution marker gene petB, encoding cytochrome b6 (Fig. 1A; [24]). Only a few discrepancies can be observed between the two trees, including stations TARA-070 that displayed one of the most disparate ESTU compositions and TARA-094, dominated by the rare HLID ESTU (Fig. 1A). Similarly, for Synechococcus, most of the eight assemblages of stations discriminated based on the relative abundance of ESTUs (Fig. 1B) were also retrieved in the clustering based on flexible gene abundance, except for a few intra-assemblage switches between stations, notably those dominated by ESTU IIA (Fig. 1B). Despite these few variations, four major clusters can be clearly delineated in both Synechococcus trees, corresponding to four broadly defined ecological niches, namely (i) cold, nutrient-rich, pelagic or coastal environments (blue and light red in Fig. 1B), (ii) Fe-limited environments (purple and grey), (iii) temperate, P-depleted, Fe-replete areas (yellow) and (iv) warm, N-depleted, Fe-replete regions (dark red). This correspondence between taxonomic and functional information was also confirmed by the high congruence between distance matrices based on ESTU relative abundance and on CLOG relative abundance (p-value  0.01) are marked by a cross. Φsat: index of iron limitation derived from satellite data. PAR30: satellite-derived photosynthetically available radiation at the surface, averaged on 30 days. DCM: depth of the deep chlorophyll maximum.Full size imageIdentification of individual genes potentially involved in niche partitioningTo identify genes relevant for adaptation to a specific set of environmental conditions and enriched in specific ESTU assemblages, we selected the most representative genes from each module (Dataset 5; Figs. 3, S2). Most genes retrieved this way encode proteins of unknown or hypothetical function (85.7% of 7,485 genes). However, among the genes with a functional annotation (Dataset 6), a large fraction seems to have a function related to their realized environmental niche (Figs. 3, S2). For instance, many genes involved in the transport and assimilation of nitrite and nitrate (nirA, nirX, moaA-C, moaE, mobA, moeA, narB, M, nrtP; [6]) as well as cyanate, an organic form of nitrogen (cynA, B, D, S), are enriched in the Prochlorococcus blue module, which is correlated with the HLIIA-D ESTU and to low inorganic N, P, and silica levels and anti-correlated with Fe availability (Fig. 2A–C). This is consistent with previous studies showing that while only a few Prochlorococcus strains in culture possess the nirA gene and even less the narB gene, natural Prochlorococcus populations inhabiting N-poor areas do possess one or both of these genes [40,41,42]. Similarly, numerous genes amongst the most representative of Prochlorococcus brown, red and turquoise modules are related to adaptation of HLIIIA/IVA, HLIA and LLIA ESTUs to Fe-limited, cold P-limited, and cold, mixed waters, respectively (Fig. 3). Comparable results were obtained for Synechococcus, although the niche delineation was less clear than for Prochlorococcus since genes within each module exhibited lower correlations with the module eigenvalue (Fig. S2). These results therefore constitute a proof of concept that this network analysis was able to retrieve niche-related genes from metagenomics data.Fig. 3: Violin plots highlighting the most representative genes of each Prochlorococcus module.For each module, each gene is represented as a dot positioned according to its correlation with the eigengene for each module, the most representative genes being localized on top of each violin plot. Genes mentioned in the text and/or in Dataset 6 have been colored according to the color of the corresponding module, indicated by a colored bar above each module. The text above violin plots indicates the most significant environmental parameter(s) and/or ESTU(s) for each module, as derived from Fig. 2.Full size imageIdentification of eCAGs potentially involved in niche partitioningIn order to better understand the function of niche-related genes, notably of the numerous unknowns, we then integrated global distribution data with gene synteny in reference genomes using a network approach (Datasets 7, 8). This led us to identify clusters of adjacent genes in reference genomes, and thus potentially involved in the same metabolic pathway (Figs. 4, S3, S4; Dataset 6). These clusters were defined within each module and thus encompass genes with similar distribution and abundance in situ. Hereafter, these environmental clusters of adjacent genes will be called “eCAGs”.Fig. 4: Delineation of Prochlorococcus eCAGs, defined as a set of genes that are both adjacent in reference genomes and share a similar in situ distribution.Nodes correspond to individual genes with their gene name (or significant numbers of the CK number, e.g. 1234 for CK_00001234) and are colored according to their WGCNA module. A link between two nodes indicates that these two genes are less than five genes apart in at least one genome. The bottom insert shows the most significant environmental parameter(s) and/or ESTU(s) for each module, as derived from Fig. 2.Full size imageeCAGs related to nitrogen metabolismThe well-known nitrate/nitrite gene cluster involved in uptake and assimilation of inorganic forms of N (see above), which is present in most Synechococcus genomes (Dataset 6), was expectedly not restricted to a particular niche in natural Synechococcus populations, as shown by its quasi-absence from WGCNA modules. In Prochlorococcus, this cluster is separated into two eCAGs enriched in low-N areas (Fig. S5A, B), most genes being included in Pro-eCAG_002, present in only 13 out of 118 Prochlorococcus genomes, while nirA and nirX form an independent eCAG (Pro-eCAG_001) due to their presence in many more genomes. The quasi-core ureA-G/urtB-E genomic region was also found to form a Prochlorococcus eCAG (Pro-eCAG_003) that was impoverished in low-Fe compared to other regions (Fig. S5C, D), in agreement with its presence in only two out of six HLIII/IV genomes. We also uncovered several other Prochlorococcus and Synechococcus eCAGs that seem to be involved in the transport and/or assimilation of more unusual and/or complex forms of nitrogen, which might either be degraded into elementary N molecules or possibly directly used by cells for e.g. the biosynthesis of proteins or DNA. Indeed, we detected in both genera an eCAG (Pro-eCAG_004 and Syn-eCAG_001; Fig. S6A, B; Dataset 6) that encompasses speB2, an ortholog of Synechocystis PCC 6803 sll1077, previously annotated as encoding an agmatinase [29, 43] and which was recently characterized as a guanidinase that degrades guanidine rather than agmatine to urea and ammonium [44]. E. coli produces guanidine under nutrient-poor conditions, suggesting that guanidine metabolism is biologically significant and potentially prevalent in natural environments [44, 45]. Furthermore, the ykkC riboswitch candidate, which was shown to specifically sense guanidine and to control the expression of a variety of genes involved in either guanidine metabolism or nitrate, sulfate, or bicarbonate transport, is located immediately upstream of this eCAG in Synechococcus reference genomes, all genes of this cluster being predicted by RegPrecise 3.0 to be regulated by this riboswitch (Fig. S6C; [45, 46]). The presence of hypA and B homologs within this eCAG furthermore suggests that, in the presence of guanidine, these homologs could be involved in the insertion of Ni2+, or another metal cofactor, in the active site of guanidinase. The next three genes of this eCAG, which encode an ABC transporter similar to the TauABC taurine transporter in E. coli (Fig. S6C), could be involved in guanidine transport in low-N areas. Of note, the presence in most Synechococcus/Cyanobium genomes possessing this eCAG of a gene encoding a putative Rieske Fe-sulfur protein (CK_00002251) downstream of this gene cluster, seems to constitute a specificity compared to the homologous gene cluster in Synechocystis sp. PCC 6803. The presence of this Fe-S protein suggests that Fe is used as a cofactor in this system and might explain why this gene cluster is absent from picocyanobacteria thriving in low-Fe areas, while it is present in a large proportion of the population in most other oceanic areas (Fig. S6A, B).Another example of the use of organic N forms concerns compounds containing a cyano radical (C ≡ N). The cyanate transporter genes (cynABD) were indeed found in a Prochlorococcus eCAG (Pro-eCAG_005, also including the conserved hypothetical gene CK_00055128; Fig. S7A, B). While only a small proportion of the Prochlorococcus community possesses this eCAG in warm, Fe-replete waters, it is absent from other oceanic areas in accordance with its low frequency in Prochlorococcus genomes (present in only two HLI and five HLII genomes). In Synechococcus these genes were not included in a module, and thus are not in an eCAG (Dataset 6; Fig. S7C), but seem widely distributed despite their presence in only a few Synechococcus genomes (mostly in clade III strains; [6, 47, 48]). Interestingly, we also uncovered a 7-gene eCAG (Pro-eCAG_006 and Syn-eCAG_002), encompassing a putative nitrilase gene (nitC), which also suggests that most Synechococcus cells and a more variable fraction of the Prochlorococcus population could use nitriles or cyanides in warm, Fe-replete waters and more particularly in low-N areas such as the Indian Ocean (Fig. 5A, B). The whole operon (nitHBCDEFG; Fig. 5C), called Nit1C, was shown to be upregulated in the presence of cyanide and to trigger an increase in the rate of ammonia accumulation in the heterotrophic bacterium Pseudomonas fluorescens [49], suggesting that like cyanate, cyanide could constitute an alternative nitrogen source in marine picocyanobacteria as well. However, given the potential toxicity of these C ≡ N-containing compounds [50], we cannot exclude that these eCAGs could also be devoted to cell detoxification [45, 47]. Such an example of detoxification has been described for arsenate and chromate that, as analogs of phosphate and sulfate respectively, are toxic to marine phytoplankton and must be actively exported out of the cells [51, 52].Fig. 5: Global distribution map of the eCAG involved in nitrile or cyanide transport and assimilation.A Prochlorococcus Pro-eCAG_006. B Synechococcus Syn-eCAG_002. C The genomic region in Prochlorococcus marinus MIT9301. The size of the circle is proportional to relative abundance of each genus as estimated based on the single-copy core gene petB and this gene was also used to estimate the relative abundance of other genes in the population. Black dots represent Tara Oceans stations for which Prochlorococcus or Synechococcus read abundance was too low to reach the threshold limit.Full size imageWe detected the presence of an eCAG encompassing asnB, pyrB2, and pydC (Pro-eCAG_007, Syn-eCAG_003, Fig. S8), which could contribute to an alternative pyrimidine biosynthesis pathway and thus provide another way for cells to recycle complex nitrogen forms. While this eCAG is found in only one fifth of HLII genomes and in quite specific locations for Prochlorococcus, notably in the Red Sea, it is found in most Synechococcus cells in warm, Fe-replete, N and P-depleted niches, consistent with its phyletic pattern showing its absence only from most clade I, IV, CRD1, and EnvB genomes (Fig. S8; Dataset 6). More generally, most N-uptake and assimilation genes in both genera were specifically absent from Fe-depleted areas, including the nirA/narB eCAG for Prochlorococcus, as mentioned by Kent et al. [36] as well as guanidinase and nitrilase eCAGs. In contrast, picocyanobacterial populations present in low-Fe areas possess, in addition to the core ammonium transporter amt1, a second transporter amt2, also present in cold areas for Synechococcus (Fig. S9). Additionally, Prochlorococcus populations thriving in HNLC areas also possess two amino acid-related eCAGs that are present in most Synechococcus genomes, the first one involved in polar amino acid N-II transport (Pro-eCAG_008; natF-G-H-bgtA; [53]; Fig. S10A, B) and the second one (leuDH-soxA-CK_00001744, Pro-eCAG_009, Fig. S10C, D) that notably encompasses a leucine dehydrogenase, able to produce ammonium from branched-chain amino acids. This highlights the profound difference in N acquisition mechanisms between HNLC regions and Fe-replete, N-deprived areas: the primary nitrogen sources for picocyanobacterial populations dwelling in HNLC areas seem to be ammonium and amino acids, while N acquisition mechanisms are more diverse in N-limited, Fe-replete regions.eCAGs related to phosphorus metabolismAdaptation to P depletion has been well documented in marine picocyanobacteria showing that while in P-replete waters Prochlorococcus and Synechococcus essentially rely on inorganic phosphate acquired by core transporters (PstSABC), strains isolated from low-P regions and natural populations thriving in these areas additionally contain a number of accessory genes related to P metabolism, located in specific genomic islands [6, 14, 30,31,32, 54]. Here, we indeed found in Prochlorococcus an eCAG containing the phoBR operon (Pro-eCAG_010) that encodes a two-component system response regulator, as well as an eCAG including the alkaline phosphatase phoA (Pro-eCAG_011), both present in virtually the whole Prochlorococcus population from the Mediterranean Sea, the Gulf of Mexico and the Western North Atlantic Ocean, which are known to be P-limited [30, 55] (Fig. S11A, B). By comparison, in Synechococcus, we only identified the phoBR eCAG (Syn-eCAG_005, Fig. S11C) that is systematically present in warm waters whatever the limiting nutrient, in agreement with its phyletic pattern in reference genomes showing its specific absence from cold thermotypes (clades I and IV, Dataset 6). Furthermore, although our analysis did not retrieve them within eCAGs due to the variability of gene content and synteny in this genomic region, even within each genus, several other P-related genes were enriched in low-P areas but partially differed between Prochlorococcus and Synechococcus (Figs. 3, S2, S11; Dataset 6). While the genes putatively encoding a chromate transporter (ChrA) and an arsenate efflux pump ArsB were present in both genera in different proportions, a putative transcriptional phosphate regulator related to PtrA (CK_00056804; [56]) was specific to Prochlorococcus. Synechococcus in contrast harbors a large variety of alkaline phosphatases (PhoX, CK_00005263 and CK_00040198) as well as the phosphate transporter SphX (Fig. S11).Phosphonates, i.e. reduced organophosphorus compounds containing C–P bonds that represent up to 25% of the high-molecular-weight dissolved organic P pool in the open ocean, constitute an alternative P form for marine picocyanobacteria [57]. We indeed identified, in addition to the core phosphonate ABC transporter (phnD1-C1-E1), a second previously unreported putative phosphonate transporter phnC2-D2-E2-E3 (Pro-eCAG_012; Fig. 6A). Most of the Prochlorococcus population in strongly P-limited areas of the ocean harbored these genes, while they were absent from other areas, consistent with their presence in only a few Prochlorococcus and no Synechococcus genomes. Furthermore, as previously described [58,59,60], we found a Prochlorococcus eCAG encompassing the phnYZ operon involved in C-P bond cleavage, the putative phosphite dehydrogenase ptxD, and the phosphite and methylphosphonate transporter ptxABC (Pro-eCAG_0013, Dataset 6; Fig. 6B, [60,61,62]). Compared to these previous studies that mainly reported the presence of these genes in Prochlorococcus cells from the North Atlantic Ocean, here we show that they actually occur in a much larger geographic area, including the Mediterranean Sea, the Gulf of Mexico, and the ALOHA station (TARA_132) in the North Pacific, even though they were present in a fairly low fraction of Prochlorococcus cells. These genes occurred in an even larger proportion of the Synechococcus population, although not found in an eCAG for this genus (Fig. S12; Dataset 6). Synechococcus cells from the Mediterranean Sea, a P-limited area dominated by clade III [24], seem to lack phnYZ, in agreement with the phyletic pattern of these genes in reference genomes, showing the absence of this two-gene operon in the sole clade III strain that possesses the ptxABDC gene cluster. In contrast, the presence of the complete gene set (ptxABDC-phnYZ) in the North Atlantic, at the entrance of the Mediterranean Sea, and in several clade II reference genomes rather suggests that it is primarily attributable to this clade. Altogether, our data indicate that part of the natural populations of both Prochlorococcus and Synechococcus would be able to assimilate phosphonate and phosphite as alternative P-sources in low-P areas using the ptxABDC-phnYZ operon. Yet, the fact that no picocyanobacterial genome except P. marinus RS01 (Fig. 6C) possesses both phnC2-D2-E2-E3 and phnYZ, suggests that the phosphonate taken up by the phnC2-D2-E2-E3 transporter could be incorporated into cell surface phosphonoglycoproteins that may act to mitigate cell mortality by grazing and viral lysis, as recently suggested [63].Fig. 6: Global distribution map of eCAGs putatively involved in phosphonate and phosphite transport and assimilation.A Prochlorococcus Pro-eCAG_012 putatively involved in phosphonate transport. B Prochlorococcus Pro-eCAG_013, involved in phosphonate/phosphite uptake and assimilation and phosphonate C-P bond cleavage. C The genomic region encompassing both phnC2-D2-E2-E3 and ptxABDC-phnYZ specific to P. marinus RS01. The size of the circle is proportional to relative abundance of Prochlorococcus as estimated based on the single-copy core gene petB and this gene was also used to estimate the relative abundance of other genes in the population. Black dots represent Tara Oceans stations for which Prochlorococcus read abundance was too low to reach the threshold limit.Full size imageeCAGs related to iron metabolismAs for macronutrients, it has been hypothesized that the survival of marine picocyanobacteria in low-Fe regions was made possible through several strategies, including the loss of genes encoding proteins that contain Fe as a cofactor, the replacement of Fe by another metal cofactor, and the acquisition of genes involved in Fe uptake and storage [14, 15, 36, 39, 64]. Accordingly, several eCAGs encompassing genes encoding proteins interacting with Fe were found in modules anti-correlated to HNLC regions in both genera. These include three subunits of the (photo)respiratory complex succinate dehydrogenase (SdhABC, Pro-eCAG_014, Syn-eCAG_006, Fig. S13; [65]) and Fe-containing proteins encoded in most abovementioned eCAGs involved in N or P metabolism, such as the guanidinase (Fig. S6), the NitC1 (Fig. 5), the pyrB2 (Fig. S8), the phosphonate (Fig. 6, S12), and the urea and inorganic nitrogen eCAGs (Fig. S5). Most Synechococcus cells thriving in Fe-replete areas also possess the sodT/sodX eCAG (Syn-eCAG_007, Fig. S14A, B) involved in nickel transport and maturation of the Ni-superoxide dismutase (SodN), these three genes being in contrast core in Prochlorococcus. Additionally, Synechococcus from Fe-replete areas, notably from the Mediterranean Sea and the Indian Ocean, specifically possess two eCAGs (Syn-eCAG_008 and 009; Fig. S14C, D), involved in the biosynthesis of a polysaccharide capsule that appear to be most similar to the E. coli groups 2 and 3 kps loci [66]. These extracellular structures, known to provide protection against biotic or abiotic stress, were recently shown in Klebsiella to provide a clear fitness advantage in nutrient-poor conditions since they were associated with increased growth rates and population yields [67]. However, while these authors suggested that capsules may play a role in Fe uptake, the significant reduction in the relative abundance of kps genes in low-Fe compared to Fe-replete areas (t-test p-value  More

Spribille, T. et al. Basidiomycete yeasts in the cortex of ascomycete macrolichens. Science 353, 488–492 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hawksworth, D. L. & Grube, M. Lichens redefined as complex ecosystems. New Phytol. 227, 1281 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Jung, P. et al. Lichens bite the dust-a bioweathering scenario in the atacama desert. iScience 23, 101647 (2020).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Seneviratne, G. & Indrasena, I. Nitrogen fixation in lichens is important for improved rock weathering. J. Biosci. 31, 639–643 (2006).Article 
PubMed 

Google Scholar 
Nybakken, L., Solhaug, K. A., Bilger, W. & Gauslaa, Y. The lichens Xanthoria elegans and Cetraria islandica maintain a high protection against uv-b radiation in arctic habitats. Oecologia 140, 211–216 (2004).Article 
ADS 
PubMed 

Google Scholar 
Peksa, O. & Škaloud, P. Do photobionts influence the ecology of lichens? A case study of environmental preferences in symbiotic green alga asterochloris (trebouxiophyceae). Mol. Ecol. 20, 3936–3948 (2011).Article 
PubMed 

Google Scholar 
Friedmann, E. I. & Galun, M. Desert algae, lichens and fungi. Desert Biol. 2, 165–212 (1974).Article 

Google Scholar 
Conti, M. E. & Cecchetti, G. Biological monitoring: Lichens as bioindicators of air pollution assessment—a review. Environ. Pollut. 114, 471–492 (2001).Article 
CAS 
PubMed 

Google Scholar 
Van Herk, C., Mathijssen-Spiekman, E. & De Zwart, D. Long distance nitrogen air pollution effects on lichens in Europe. Lichenologist 35, 347–359 (2003).Article 

Google Scholar 
Osyczka, P., Lenart-Boroń, A., Boroń, P. & Rola, K. Lichen-forming fungi in postindustrial habitats involve alternative photobionts. Mycologia 113, 43–55 (2021).Article 
CAS 
PubMed 

Google Scholar 
Margulis, L. & Fester, R. Symbiosis as a Source of Evolutionary Innovation: Speciation and Morphogenesis (MIT press, 1991).
Google Scholar 
Solé, R. et al. Synthetic collective intelligence. Biosystems 148, 47–61 (2016).Article 
PubMed 

Google Scholar 
Dal Forno, M. et al. Extensive photobiont sharing in a rapidly radiating cyanolichen clade. Mol. Ecol. 30, 1755–1776 (2021).Article 

Google Scholar 
Nishiguchi, M. K. Cospeciation between hosts and symbionts. In Symbiosis 757–774 (Springer, 2001).Hill, D. J. Asymmetric co-evolution in the lichen symbiosis caused by a limited capacity for adaptation in the photobiont. Bot. Rev. 75, 326–338 (2009).Article 

Google Scholar 
Muggia, L., Pérez-Ortega, S., Fryday, A., Spribille, T. & Grube, M. Global assessment of genetic variation and phenotypic plasticity in the lichen-forming species Tephromela atra. Fungal Divers. 64, 233–251 (2014).Article 

Google Scholar 
Vančurová, L., Muggia, L., Peksa, O., Řídká, T. & Škaloud, P. The complexity of symbiotic interactions influences the ecological amplitude of the host: A case study in stereocaulon (lichenized ascomycota). Mol. Ecol. 27, 3016–3033 (2018).Article 
PubMed 

Google Scholar 
Peksa, O., Gebouská, T., Škvorová, Z., Vančurová, L. & Škaloud, P. The guilds in green algal lichens-an insight into the life of terrestrial symbiotic communities. FEMS Microbiol. Ecol. 98, fiac008 (2022).Article 
PubMed 

Google Scholar 
Wagner, M. et al. Macroclimatic conditions as main drivers for symbiotic association patterns in lecideoid lichens along the transantarctic mountains, ross sea region, antarctica. Sci. Rep. 11, 1–15 (2021).Article 
MathSciNet 

Google Scholar 
Nascimbene, J. & Marini, L. Epiphytic lichen diversity along elevational gradients: Biological traits reveal a complex response to water and energy. J. Biogeogr. 42, 1222–1232 (2015).Article 

Google Scholar 
Galloway, D. Lichen biogeography. Lichen Biol. 2, 315–35 (1996).
Google Scholar 
Vančurová, L., Malíček, J., Steinová, J. & Škaloud, P. Choosing the right life partner: Ecological drivers of lichen symbiosis. Front. Microbiol. 12, 769304 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Škvorová, Z. et al. Promiscuity in lichens follows clear rules: Partner switching in cladonia is regulated by climatic factors and soil chemistry. Front. Microbiol. 12, 56 (2021).
Google Scholar 
Medeiros, I. D. et al. Turnover of lecanoroid mycobionts and their trebouxia photobionts along an elevation gradient in bolivia highlights the role of environment in structuring the lichen symbiosis. Front. Microbiol. 2021, 3859 (2021).
Google Scholar 
Marini, L., Nascimbene, J. & Nimis, P. L. Large-scale patterns of epiphytic lichen species richness: Photobiont-dependent response to climate and forest structure. Sci. Total Environ. 409, 4381–4386 (2011).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Saini, K. C., Nayaka, S. & Bast, F. Diversity of lichen photobionts: Their coevolution and bioprospecting potential. Microb. Divers. Ecosyst. Sustain. Biotechnol. Appl. 2019, 307–323 (2019).
Google Scholar 
Ivens, A. B., von Beeren, C., Blüthgen, N. & Kronauer, D. J. Studying the complex communities of ants and their symbionts using ecological network analysis. Annu. Rev. Entomol. 61, 353–371 (2016).Article 
CAS 
PubMed 

Google Scholar 
Ziegler, M., Eguíluz, V. M., Duarte, C. M. & Voolstra, C. R. Rare symbionts may contribute to the resilience of coral-algal assemblages. ISME J. 12, 161–172 (2018).Article 
PubMed 

Google Scholar 
Rikkinen, J. et al. Ecological and evolutionary role of photobiont-mediated guilds in lichens. Symbiosis 2003, 256 (2003).
Google Scholar 
Belinchón, R., Yahr, R. & Ellis, C. J. Interactions among species with contrasting dispersal modes explain distributions for epiphytic lichens. Ecography 38, 762–768 (2015).Article 

Google Scholar 
Muggia, L. et al. The symbiotic playground of lichen thalli-a highly flexible photobiont association in rock-inhabiting lichens. FEMS Microbiol. Ecol. 85, 313–323 (2013).Article 
CAS 
PubMed 

Google Scholar 
Rikkinen, J., Oksanen, I. & Lohtander, K. Lichen guilds share related cyanobacterial symbionts. Science 297, 357 (2002).Article 
CAS 
PubMed 

Google Scholar 
Kaasalainen, U., Tuovinen, V., Mwachala, G., Pellikka, P. & Rikkinen, J. Complex interaction networks among cyanolichens of a tropical biodiversity hotspot. Front. Microbiol. 12, 1246 (2021).Article 

Google Scholar 
Werth, S. Fungal-algal interactions in Ramalina menziesii and its associated epiphytic lichen community. Lichenologist 44, 543–560 (2012).Article 

Google Scholar 
O’Brien, H. E., Miadlikowska, J. & Lutzoni, F. Assessing population structure and host specialization in lichenized cyanobacteria. New Phytol. 198, 557–566 (2013).Article 
PubMed 

Google Scholar 
Pino-Bodas, R. & Stenroos, S. Global biodiversity patterns of the photobionts associated with the genus cladonia (lecanorales, ascomycota). Microb. Ecol. 82, 173–187 (2021).Article 
CAS 
PubMed 

Google Scholar 
Miadlikowska, J. et al. New insights into classification and evolution of the lecanoromycetes (pezizomycotina, ascomycota) from phylogenetic analyses of three ribosomal rna-and two protein-coding genes. Mycologia 98, 1088–1103 (2006).Article 
CAS 
PubMed 

Google Scholar 
Chagnon, P.-L., Magain, N., Miadlikowska, J. & Lutzoni, F. Species diversification and phylogenetically constrained symbiont switching generated high modularity in the lichen genus peltigera. J. Ecol. 107, 1645–1661 (2019).Article 

Google Scholar 
Bascompte, J. & Jordano, P. Plant-animal mutualistic networks: The architecture of biodiversity. Annu. Rev. Ecol. Evol. Syst. 38, 567–593 (2007).Article 
MATH 

Google Scholar 
Olesen, J. M., Bascompte, J., Dupont, Y. L. & Jordano, P. The modularity of pollination networks. Proc. Natl. Acad. Sci. 104, 19891–19896 (2007).Article 
ADS 
CAS 
PubMed 
PubMed Central 
MATH 

Google Scholar 
Weitz, J. S. et al. Phage-bacteria infection networks. Trends Microbiol. 21, 82–91 (2013).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Maliet, O., Loeuille, N. & Morlon, H. An individual-based model for the eco-evolutionary emergence of bipartite interaction networks. Ecol. Lett. 23, 1623–1634 (2020).Article 
PubMed 

Google Scholar 
Fortuna, M. A. et al. Nestedness versus modularity in ecological networks: Two sides of the same coin?. J. Anim. Ecol. 2010, 811–817 (2010).
Google Scholar 
Mariani, M. S., Ren, Z.-M., Bascompte, J. & Tessone, C. J. Nestedness in complex networks: Observation, emergence, and implications. Phys. Rep. 813, 1–90 (2019).Article 
ADS 
MathSciNet 

Google Scholar 
Almeida-Neto, M., Guimaraes, P., Guimaraes, P. R. Jr., Loyola, R. D. & Ulrich, W. A consistent metric for nestedness analysis in ecological systems: Reconciling concept and measurement. Oikos 117, 1227–1239 (2008).Article 

Google Scholar 
Flores, C. O., Valverde, S. & Weitz, J. S. Multi-scale structure and geographic drivers of cross-infection within marine bacteria and phages. ISME J. 7, 520–532 (2013).Article 
PubMed 

Google Scholar 
Sanders, W. B. & Masumoto, H. Lichen algae: The photosynthetic partners in lichen symbioses. Lichenologist 53, 347–393 (2021).Article 

Google Scholar 
Duran-Nebreda, S. & Bassel, G. W. Bridging scales in plant biology using network science. Trends Plant Sci. 22, 1001–1003 (2017).Article 
CAS 
PubMed 

Google Scholar 
Galiana, N. et al. Ecological network complexity scales with area. Nature Ecol. Evol. 2022, 1–8 (2022).
Google Scholar 
Solé, R. V. & Valverde, S. Spontaneous emergence of modularity in cellular networks. J. R. Soc. Interface 5, 129–133 (2008).Article 
PubMed 

Google Scholar 
Jackson, M. D., Duran-Nebreda, S. & Bassel, G. W. Network-based approaches to quantify multicellular development. J. R. Soc. Interface 14, 20170484 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Jackson, M. D., Xu, H., Duran-Nebreda, S., Stamm, P. & Bassel, G. W. Topological analysis of multicellular complexity in the plant hypocotyl. Elife 6, e26023 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Jackson, M. D. et al. Global topological order emerges through local mechanical control of cell divisions in the arabidopsis shoot apical meristem. Cell Syst. 8, 53–65 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Miadlikowska, J. et al. A multigene phylogenetic synthesis for the class lecanoromycetes (ascomycota): 1307 fungi representing 1139 infrageneric taxa, 317 genera and 66 families. Mol. Phylogenet. Evol. 79, 132–168 (2014).Article 
PubMed 

Google Scholar 
Perez-Lamarque, B., Selosse, M.-A., Öpik, M., Morlon, H. & Martos, F. Cheating in arbuscular mycorrhizal mutualism: A network and phylogenetic analysis of mycoheterotrophy. New Phytol. 226, 1822–1835 (2020).Article 
PubMed 

Google Scholar 
Jaccard, P. Étude comparative de la distribution florale dans une portion des alpes et des jura. Bull. Soc. Vaudoise Sci. Naturelles 37, 547–579 (1901).
Google Scholar 
Müllner, D. fastcluster: Fast hierarchical, agglomerative clustering routines for r and python. J. Stat. Softw. 53, 1–18 (2013).Article 

Google Scholar 
Sole, R. V. & Montoya, M. Complexity and fragility in ecological networks. Proc. R. Soc. Lond. Ser. B Biol. Sci. 268, 2039–2045 (2001).Article 
CAS 

Google Scholar 
Guimaraes, P. R. Jr. The structure of ecological networks across levels of organization. Annu. Rev. Ecol. Evol. Syst. 51, 433–460 (2020).Article 

Google Scholar 
Nash, T. H. Lichen Biology (Cambridge University Press, 1996).
Google Scholar 
Hawksworth, D. The variety of fungal-algal symbioses, their evolutionary significance, and the nature of lichens. Bot. J. Linn. Soc. 96, 3–20 (1988).Article 

Google Scholar 
Richardson, D. H. War in the world of lichens: Parasitism and symbiosis as exemplified by lichens and lichenicolous fungi. Mycol. Res. 103, 641–650 (1999).Article 
ADS 

Google Scholar 
Lücking, R. et al. Do lichens domesticate photobionts like farmers domesticate crops? Evidence from a previously unrecognized lineage of filamentous cyanobacteria. Am. J. Bot. 96, 1409–1418 (2009).Article 
PubMed 

Google Scholar 
Kaasalainen, U., Schmidt, A. R. & Rikkinen, J. Diversity and ecological adaptations in palaeogene lichens. Nature Plants 3, 1–8 (2017).Article 

Google Scholar 
Piercey-Normore, M. D. The lichen-forming ascomycete evernia mesomorpha associates with multiple genotypes of Trebouxia jamesii. New Phytol. 169, 331–344 (2006).Article 
CAS 
PubMed 

Google Scholar 
Rudgers, J. A. & Strauss, S. Y. A selection mosaic in the facultative mutualism between ants and wild cotton. Proc. R. Soc. Lond. Ser. B Biol. Sci. 271, 2481–2488 (2004).Article 

Google Scholar 
Spribille, T., Resl, P., Stanton, D. E. & Tagirdzhanova, G. Evolutionary biology of lichen symbioses. New Phytol. 2022, 25 (2022).
Google Scholar 
Thébault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856 (2010).Article 
ADS 
PubMed 

Google Scholar 
Stouffer, D. B. & Bascompte, J. Compartmentalization increases food-web persistence. Proc. Natl. Acad. Sci. 108, 3648–3652 (2011).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Guimaraes, P. R. Jr. et al. Interaction intimacy affects structure and coevolutionary dynamics in mutualistic networks. Curr. Biol. 17, 1797–1803 (2007).Article 
CAS 
PubMed 

Google Scholar 
Valverde, S. et al. The architecture of mutualistic networks as an evolutionary spandrel. Nature Ecol. Evol. 2, 94–99 (2018).Article 

Google Scholar 
Staniczenko, P. P., Kopp, J. C. & Allesina, S. The ghost of nestedness in ecological networks. Nat. Commun. 4, 1–6 (2013).Article 

Google Scholar 
Vázquez, D. P., Blüthgen, N., Cagnolo, L. & Chacoff, N. P. Uniting pattern and process in plant-animal mutualistic networks: A review. Ann. Bot. 103, 1445–1457 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Mello, M. A. et al. Insights into the assembly rules of a continent-wide multilayer network. Nature Ecol. Evol. 3, 1525–1532 (2019).Article 

Google Scholar 
Felix, G. M., Pinheiro, R. B., Jorge, L. R. & Lewinsohn, T. M. A framework for hierarchical compound topologies in species interaction networks. Oikos 2022, 9538 (2022).Article 

Google Scholar 
Valverde, S. et al. Coexistence of nestedness and modularity in host-pathogen infection networks. Nature Ecol. Evol. 4, 568–577 (2020).Article 

Google Scholar 
Hui, C. & Richardson, D. M. How to invade an ecological network. Trends Ecol. Evol. 34, 121–131 (2019).Article 
PubMed 

Google Scholar 
Layman, C. A., Quattrochi, J. P., Peyer, C. M. & Allgeier, J. E. Niche width collapse in a resilient top predator following ecosystem fragmentation. Ecol. Lett. 10, 937–944 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Vidiella, B., Fontich, E., Valverde, S. & Sardanyés, J. Habitat loss causes long extinction transients in small trophic chains. Thyroid Res. 14, 641–661 (2021).
Google Scholar 
Donohue, I. et al. Navigating the complexity of ecological stability. Ecol. Lett. 19, 1172–1185 (2016).Article 
PubMed 

Google Scholar 
Krause, A. E., Frank, K. A., Mason, D. M., Ulanowicz, R. E. & Taylor, W. W. Compartments revealed in food-web structure. Nature 426, 282–285 (2003).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Hagberg, A., Swart, P. & S Chult, D. Exploring network structure, dynamics, and function using networkx. In Tech. Rep., Los Alamos National Lab.(LANL), Los Alamos (2008).Chimani, M. et al. The open graph drawing framework (ogdf). Handb. Graph Draw. Visual. 2011, 543–569 (2013).
Google Scholar 
Hachul, S. & Jünger, M. Drawing large graphs with a potential-field-based multilevel algorithm. In Graph Drawing: 12th International Symposium, GD 2004, New York, NY, USA, September 29-October 2, 2004, Revised Selected Papers 12, 285–295 (Springer, 2005).Raghavan, U. N., Albert, R. & Kumara, S. Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 036106 (2007).Article 
ADS 

Google Scholar 
Newman, M. E. & Girvan, M. Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004).Article 
ADS 
CAS 

Google Scholar 
Barber, M. J. Modularity and community detection in bipartite networks. Phys. Rev. E 76, 066102 (2007).Article 
ADS 
MathSciNet 

Google Scholar 
Pesántez-Cabrera, P. & Kalyanaraman, A. Efficient detection of communities in biological bipartite networks. IEEE/ACM Trans. Comput. Biol. Bioinf. 16, 258–271 (2017).Article 

Google Scholar 
Almeida-Neto, M. & Ulrich, W. A straightforward computational approach for measuring nestedness using quantitative matrices. Environ. Model. Softw. 26, 173–178 (2011).Article 

Google Scholar 
Latora, V. & Marchiori, M. Efficient behavior of small-world networks. Phys. Rev. Lett. 87, 198701 (2001).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Borgatti, S. P. & Halgin, D. S. Analyzing affiliation networks. Sage Handb. Soc. Netw. Anal. 1, 417–433 (2011).
Google Scholar 
Roopnarine, P. D. Extinction cascades and catastrophe in ancient food webs. Paleobiology 32, 1–19 (2006).Article 

Google Scholar 
Pires, M. M. et al. The indirect paths to cascading effects of extinctions in mutualistic networks. Ecology 101(7), e03080 https://doi.org/10.1002/ecy.3080 (2020).Article 
PubMed 

Google Scholar 
Mestres, J., Gregori-Puigjane, E., Valverde, S. & Sole, R. V. Data completeness-the achilles heel of drug-target networks. Nat. Biotechnol. 26, 983–984 (2008).Article 
CAS 
PubMed 

Google Scholar 
Milo, R. et al. Network motifs: Simple building blocks of complex networks. Science 298, 824–827 (2002).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Sarzynska, M., Leicht, E. A., Chowell, G. & Porter, M. A. Null models for community detection in spatially embedded, temporal networks. J. Complex Netw. 4, 363–406 (2016).Article 
MathSciNet 

Google Scholar  More

Lal R, Bruce JP. The potential of world cropland soils to sequester C and mitigate the greenhouse effect. Environ Sci Policy. 1999;2:177–85.Article 
CAS 

Google Scholar 
Wang J, Feng L, Palmer PI, Liu Y, Fang S, Bosch H, et al. Large Chinese land carbon sink estimated from atmospheric carbon dioxide data. Nature. 2020;586:720–3.Article 
CAS 
PubMed 

Google Scholar 
Lal R. Managing soils and ecosystems for mitigating anthropogenic carbon emissions and advancing global food security. Bioscience. 2010;60:708–21.Article 

Google Scholar 
Rumpel C, Lehmann J, Chabbi A. ‘4 per 1,000’ initiative will boost soil carbon for climate and food security. Nature. 2018;553:27–27.Article 
CAS 
PubMed 

Google Scholar 
Zhao Y, Wang M, Hu S, Zhang X, Ouyang Z, Zhang G, et al. Economics- and policy-driven organic carbon input enhancement dominates soil organic carbon accumulation in Chinese croplands. Proc Natl Acad Sci USA. 2018;115:4045–50.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yang F, Xu Y, Cui Y, Meng Y, Dong Y, Li R, et al. Variation of soil organic matter content in croplands of china over the last three decades (in Chinese). Acta Pedol Sin. 2017;5:1047–56.
Google Scholar 
Lehmann J, Hansel CM, Kaiser C, Kleber M, Maher K, Manzoni S, et al. Persistence of soil organic carbon caused by functional complexity. Nat Geosci. 2020;13:529–34.Article 
CAS 

Google Scholar 
Schmidt M, Torn MS, Abiven S, Dittmar T, Guggenberger G, Janssens IA. et al. Persistence of soil organic matter as an ecosystem property. Nature. 2011;478:49–56.Article 
CAS 
PubMed 

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

Google Scholar 
Cotrufo MF, Soong JL, Horton AJ, Campbell EE, Haddix ML, Wall DH, et al. Formation of soil organic matter via biochemical and physical pathways of litter mass loss. Nat Geosci. 2015;8:776–9.Article 
CAS 

Google Scholar 
Schnitzer M, Monreal CM. Quo vadis soil organic matter research? A biological link to the chemistry of humification. Adv Agron. 2011;113:139–213.
Google Scholar 
Wang X, Sun B, Mao J, Sui Y, Cao X. Structural convergence of maize and wheat straw during two-year decomposition under different climate conditions. Environ Sci Technol. 2012;46:7159–65.Article 
CAS 
PubMed 

Google Scholar 
Liang C, Schimel JP, Jastrow JD. The importance of anabolism in microbial control over soil carbon storage. Nat Microbiol. 2017;2:17105.Article 
CAS 
PubMed 

Google Scholar 
Wickings K, Grandy AS, Reed SC, Cleveland CC. The origin of litter chemical complexity during decomposition. Ecol Lett. 2012;15:1180–8.Article 
PubMed 

Google Scholar 
Grandy AS, Neff JC. Molecular C dynamics downstream: the biochemical decomposition sequence and its impact on soil organic matter structure and function. Sci Total Environ. 2008;404:297–307.Article 
CAS 
PubMed 

Google Scholar 
Jenkinson DS, Ayanaba A. Decomposition of 14C labeled plant material under tropical conditions. Soil Sci Soc Am J. 1977;41:912–5.Article 
CAS 

Google Scholar 
Li Y, Chen N, Harmon ME, Li Y, Cao X, Chappell MA, et al. Plant species rather than climate greatly alters the temporal pattern of litter chemical composition during long-term decomposition. Sci Rep. 2015;5:15783.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Preston CM, Nault JR, Trofymow JA, Smyth C, Grp CW. Chemical changes during 6 years of decomposition of 11 litters in some Canadian forest sites. Part 1. Elemental composition, tannins, phenolics, and proximate fractions. Ecosystems. 2009;12:1053–77.Article 
CAS 

Google Scholar 
Kallenbach CM, Frey SD, Grandy AS. Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls. Nat Commun. 2016;7:13630.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wickings K, Stuart Grandy A, Reed S, Cleveland C. Management intensity alters decomposition via biological pathways. Biogeochemistry. 2011;104:365–79.Article 

Google Scholar 
Schimel JP, Schaeffer SM. Microbial control over carbon cycling in soil. Front Microbiol. 2012;3:348.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sun B, Wang X, Wang F, Jiang Y, Zhang X-X. Assessing the relative effects of geographic location and soil type on microbial communities associated with straw decomposition. Appl Environ Microbiol. 2013;79:3327.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Balser TC, Firestone MK. Linking microbial community composition and soil processes in a California annual grassland and mixed-conifer forest. Biogeochemistry. 2005;73:395–415.Article 
CAS 

Google Scholar 
Grandy AS, Neff JC, Weintrau MN. Carbon structure and enzyme activities in alpine and forest ecosystems. Soil Biol Biochem. 2007;39:2701–11.Article 
CAS 

Google Scholar 
Maynard DS, Crowther TW, Bradford MA. Competitive network determines the direction of the diversity-function relationship. Proc Natl Acad Sci USA. 2017;114:11464–9.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wagg C, Bender SF, Widmer F, van der Heijden MGA. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc Natl Acad Sci USA. 2014;111:5266–70.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Snajdr J, Cajthaml T, Valaskova V, Merhautova V, Petrankova M, Spetz P, et al. Transformation of Quercus petraea litter: successive changes in litter chemistry are reflected in differential enzyme activity and changes in the microbial community composition. FEMS Microbiol Ecol. 2011;75:291–303.Article 
CAS 
PubMed 

Google Scholar 
Banerjee S, Kirkby CA, Schmutter D, Bissett A, Kirkegaard JA, Richardson AE. Network analysis reveals functional redundancy and keystone taxa amongst bacterial and fungal communities during organic matter decomposition in an arable soil. Soil Biol Biochem. 2016;97:188–98.Article 
CAS 

Google Scholar 
Banerjee S, Schlaeppi K, van der Heijden MGA. Keystone taxa as drivers of microbiome structure and functioning. Nat Rev Microbiol. 2018;16:567–76.Article 
CAS 
PubMed 

Google Scholar 
Carrias J-F, Gerphagnon M, Rodríguez-Pérez H, Borrel G, Loiseau C, Corbara B, et al. Resource availability drives bacterial succession during leaf-litter decomposition in a bromeliad ecosystem. FEMS Microbiol Ecol. 2020;96:fiaa045.Article 
CAS 
PubMed 

Google Scholar 
Zhan P, Liu Y, Wang H, Wang C, Xia M, Wang N, et al. Plant litter decomposition in wetlands is closely associated with phyllospheric fungi as revealed by microbial community dynamics and co-occurrence network. Sci Total Environ. 2021;753:142194.Article 
CAS 
PubMed 

Google Scholar 
Panettieri M, Knicker H, Murillo JM, Madejon E, Hatcher PG. Soil organic matter degradation in an agricultural chronosequence under different tillage regimes evaluated by organic matter pools, enzymatic activities and CPMAS C-13 NMR. Soil Biol Biochem. 2014;78:170–81.Article 
CAS 

Google Scholar 
Skjemstad JO, Clarke P, Taylor JA, Oades JM, Newman RH. The removal of magnetic-materials from surface soils – a solid state 13C CP/MAS NMR study. Aust J Soil Res. 1994;32:1215–29.Article 
CAS 

Google Scholar 
Sokolenko S, Jézéquel T, Hajjar G, Farjon J, Akoka S, Giraudeau P. Robust 1D NMR lineshape fitting using real and imaginary data in the frequency domain. J Magn Reson. 2019;298:91–100.Article 
CAS 
PubMed 

Google Scholar 
Grandy AS, Strickland MS, Lauber CL, Bradford MA, Fierer N. The influence of microbial communities, management, and soil texture on soil organic matter chemistry. Geoderma.2009;150:278–86.Article 
CAS 

Google Scholar 
Saiya-Cork KR, Sinsabaugh RL, Zak DR. The effects of long term nitrogen deposition on extracellular enzyme activity in an Acer saccharum forest soil. Soil Biol Biochem. 2002;34:1309–15.Article 
CAS 

Google Scholar 
Allison SD, Jastrow JD. Activities of extracellular enzymes in physically isolated fractions of restored grassland soils. Soil Biol Biochem. 2006;38:3245–56.Article 
CAS 

Google Scholar 
Zhang XD, Amelung W. Gas chromatographic determination of muramic acid, glucosamine, mannosamine, and galactosamine in soils. Soil Biol Biochem. 1996;28:1201–6.Article 
CAS 

Google Scholar 
Lee CK, Barbier BA, Bottos EM, McDonald IR, Cary SC. The inter-valley soil comparative survey: the ecology of dry valley edaphic microbial communities. ISME J. 2012;6:1046–57.Article 
CAS 
PubMed 

Google Scholar 
Degnan PH, Ochman H. Illumina-based analysis of microbial community diversity. ISME J. 2012;6:183–94.Article 
CAS 
PubMed 

Google Scholar 
Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 2007;35:7188–96.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kõljalg U, Nilsson RH, Abarenkov K, Tedersoo L, Taylor AFS, Bahram M, et al. Towards a unified paradigm for sequence-based identification of fungi. Mol Ecol. 2013;22:5271–7.Article 
PubMed 

Google Scholar 
Faust K, Sathirapongsasuti JF, Izard J, Segata N, Gevers D, Raes J, et al. Microbial co-occurrence relationships in the human microbiome. PLoS Comp Biol. 2012;8:e1002606.Article 
CAS 

Google Scholar 
Chong IG, Jun CH. Performance of some variable selection methods when multicollinearity is present. Chemometrics Intell Lab Syst. 2005;78:103–12.Article 
CAS 

Google Scholar 
Strukelj M, Brais S, Mazerolle MJ, Pare D, Drapeau P. Decomposition patterns of foliar litter and deadwood in managed and unmanaged stands: A 13-year experiment in boreal mixedwoods. Ecosystems. 2018;21:68–84.Article 
CAS 

Google Scholar 
Manzoni S, Piñeiro G, Jackson RB, Jobbágy EG, Kim JH, Porporato A. Analytical models of soil and litter decomposition: Solutions for mass loss and time-dependent decay rates. Soil Biol Biochem. 2012;50:66–76.Article 
CAS 

Google Scholar 
Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.Article 

Google Scholar 
Grace JB (ed). Structural Equation Modeling and Natural Systems. Cambridge University Press, Cambridge, 2006.Shen Y, Cheng R, Xiao W, Yang S, Guo Y, Wang N, et al. Labile organic carbon pools and enzyme activities of Pinus massoniana plantation soil as affected by understory vegetation removal and thinning. Sci Rep. 2018;8:573.Article 
PubMed 
PubMed Central 

Google Scholar 
Gallo ME, Lauber CL, Cabaniss SE, Waldrop MP, Sinsabaugh RL, Zak DR. Soil organic matter and litter chemistry response to experimental N deposition in northern temperate deciduous forest ecosystems. Glob Change Biol. 2005;11:1514–21.Article 

Google Scholar 
Wilhelm RC, Singh R, Eltis LD, Mohn WW. Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. ISME J. 2019;13:413–29.Article 
CAS 
PubMed 

Google Scholar 
Sahay H, Yadav AN, Singh AK, Singh S, Kaushik R, Saxena AK. Hot springs of Indian Himalayas: potential sources of microbial diversity and thermostable hydrolytic enzymes. 3 Biotech. 2017;7:118.Article 
PubMed 
PubMed Central 

Google Scholar 
Robledo M, Rivera L, Jimenez-Zurdo JI, Rivas R, Dazzo F, Velazquez E, et al. Role of Rhizobium endoglucanase CelC2 in cellulose biosynthesis and biofilm formation on plant roots and abiotic surfaces. Micro Cell Factories. 2012;11:125.Article 
CAS 

Google Scholar 
Wang X, Bian Q, Jiang Y, Zhu L, Chen Y, Liang Y, et al. Organic amendments drive shifts in microbial community structure and keystone taxa which increase C mineralization across aggregate size classes. Soil Biol Biochem. 2021;153:108062.Article 
CAS 

Google Scholar 
Joergensen RG. Amino sugars as specific indices for fungal and bacterial residues in soil. Biol Fert Soils. 2018;54:559–68.Article 
CAS 

Google Scholar 
Chen Y, Sun R, Sun T, Chen P, Yu Z, Ding L, et al. Evidence for involvement of keystone fungal taxa in organic phosphorus mineralization in subtropical soil and the impact of labile carbon. Soil Biol Biochem. 2020;148:107900.Article 
CAS 

Google Scholar 
Puentes-Tellez PE, Salles JF. Construction of effective minimal active microbial consortia for lignocellulose degradation. Micro Ecol. 2018;76:419–29.Article 
CAS 

Google Scholar 
Zark M, Dittmar T. Universal molecular structures in natural dissolved organic matter. Nat Commun. 2018;9:3178.Article 
PubMed 
PubMed Central 

Google Scholar 
Lynch LM, Sutfin NA, Fegel TS, Boot CM, Covino TP, Wallenstein MD. River channel connectivity shifts metabolite composition and dissolved organic matter chemistry. Nat Commun. 2019;10:459.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Filley TR, Boutton TW, Liao JD, Jastrow JD, Gamblin DE. Chemical changes to nonaggregated particulate soil organic matter following grassland-to-woodland transition in a subtropical savanna. J Geophys Res Biogeosci. 2008;113:G03009.Article 

Google Scholar 
Stewart CE, Neff JC, Amatangelo KL, Vitousek PM. Vegetation effects on soil organic matter chemistry of aggregate fractions in a Hawaiian forest. Ecosystems. 2011;14:382–97.Article 
CAS 

Google Scholar  More

Bindoff, N. L. et al. Changing ocean, marine ecosystems, and dependent communities. IPCC Special Report on the Ocean Cryosphere in a Changing Climate 477–587 (2019).Atkinson, A., Siegel, V., Pakhomov, E. & Rothery, P. Long-term decline in krill stock and increase in salps within the Southern Ocean. Nature 432, 100–103 (2004).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Chang. 9, 142–147 (2019).Article 
ADS 

Google Scholar 
Lowther, A. D., Staniland, I., Lydersen, C. & Kovacs, K. M. Male Antarctic fur seals: Neglected food competitors of bioindicator species in the context of an increasing Antarctic krill fishery. Sci. Rep. 10, 1–12 (2020).Article 

Google Scholar 
Barbosa, A., Benzal, J., de León, A. & Moreno, J. Population decline of chinstrap penguins (Pygoscelis antarctica) on Deception Island, South Shetlands, Antarctica. Polar Biol. 35, 1453–1457 (2012).Article 

Google Scholar 
Forcada, J., Trathan, P. N., Reid, K. & Murphy, E. J. The effects of global climate variability in pop production of Antarctic fur seals. Ecology 86, 2408–2417 (2005).Article 

Google Scholar 
Forcada, J. & Trathan, P. N. Penguin responses to climate change in the Southern Ocean. Glob. Change Biol. https://doi.org/10.1111/j.1365-2486.2009.01909.x (2009).Article 

Google Scholar 
Fraser, W. & Hofmann, E. A predator’s perspective on causal links between climate change, physical forcing and ecosystem response. Mar. Ecol. Prog. Ser. 265, 1–15 (2003).Article 
ADS 

Google Scholar 
Tulloch, V. J. D., Plagányi, É. E., Brown, C., Richardson, A. J. & Matear, R. Future recovery of baleen whales is imperiled by climate change. Glob. Change Biol. 25, 1263–1281 (2019).Article 
ADS 

Google Scholar 
Gosler, A. G. Environmental and social determinants of winter fat storage in the great tit Parus major. J. Anim. Ecol. 65, 1–17 (1996).Article 

Google Scholar 
Green, A. J. Mass/Length residuals: Measures of body condition or generators of spurious results?. Ecology 13, 1473–1483 (2001).Article 

Google Scholar 
Schulte-Hostedde, A. I., Zinner, B., Millar, J. S. & Hickling, G. J. Restitution of mass-size residuals: Validating body condition indices. Ecology 86, 155–163 (2005).Article 

Google Scholar 
Arnbom, T., Fedak, M. A. & Boyd, I. L. Factors affecting maternal expenditure in southern elephant seals during lactation. Ecology 78, 471–483 (1997).Article 

Google Scholar 
Boltnev, A. I. & York, A. E. Maternal investment in northern fur seals (Callorhinus ursinus): Interrelationships among mothers’ age, size, parturition date, offspring size and sex ratios. J. Zool. 254, 219–228 (2001).Article 

Google Scholar 
Tollefson, T. N., Shipley, L. A., Myers, W. L., Keisler, D. H. & Dasgupta, N. Influence of summer and autumn nutrition on body condition and reproduction in lactating mule deer. J. Wildl. Manag. 74, 974–986 (2010).Article 

Google Scholar 
Wheatley, K. E., Bradshaw, C. J. A., Davis, L. S., Harcourt, R. G. & Hindell, M. A. Influence of maternal mass and condition on energy transfer in Weddell seals. J. Anim. Ecol. 75, 724–733 (2006).Article 
PubMed 

Google Scholar 
Miller, C. A. et al. Blubber thickness in right whales Eubalaena glacialis and Eubalaena australis related with reproduction, life history status and prey abundance. Mar. Ecol. Prog. Ser. 438, 267–283 (2011).Article 
ADS 

Google Scholar 
Christiansen, F., Víkingsson, G. A., Rasmussen, M. H. & Lusseau, D. Female body condition affects foetal growth in a capital breeding mysticete. Funct. Ecol. 28, 579–588 (2014).Article 

Google Scholar 
Christiansen, F. et al. Maternal body size and condition determine calf growth rates in southern right whales. Mar. Ecol. Prog. Ser. 592, 267–282 (2018).Article 
ADS 

Google Scholar 
Norris, K. S. Some observations on the migration and orientation of marine mammals. Anim. Orientat. Migr. 101, 125 (1967).
Google Scholar 
Corkeron, P. J. & Connor, R. C. Why do baleen whales migrate?. Mar. Mammal Sci. 15, 1228–1245 (1999).Article 

Google Scholar 
Frazer, J. F. D. & Huggett, A. S. G. Specific foetal growth rates of cetaceans. J. Zool. 169, 111–126 (1973).Article 

Google Scholar 
Stearns, S. C. Trade-offs in life-history evolution. Funct. Ecol. 3, 259–268 (1989).Article 

Google Scholar 
Castrillon, J. & Bengtson Nash, S. Evaluating cetacean body condition; A review of traditional approaches and new developments. Ecol. Evol. 10, 6144–6162 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Leaper, R. et al. Global climate drives southern right whale (Eubalaena australis) population dynamics. Biol. Lett. 2, 289–292 (2006).Article 
PubMed 
PubMed Central 

Google Scholar 
Lockyer, C. All creatures great and smaller: A study in cetacean life history energetics. J. Mar. Biol. Assoc. UK 87, 1035–1045 (2007).Article 

Google Scholar 
Seyboth, E. et al. Southern right whale (Eubalaena australis) reproductive success is influenced by Krill (Euphausia superba) density and climate. Sci. Rep. 6, 28205 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Williams, R. et al. Evidence for density-dependent changes in body condition and pregnancy rate of North Atlantic fin whales over four decades of varying environmental conditions. ICES J. Mar. Sci. 70, 1273–1280 (2013).Article 

Google Scholar 
Best, P. B. Trends in the inshore right whale population off South Africa, 1969–1987. Mar. Mammal Sci. 6, 93–108 (1990).Article 

Google Scholar 
Best, P. B. Seasonality of reproduction and the length of gestation in southern right whales Eubalaena australis. J. Zool. 232, 175–189 (1994).Article 

Google Scholar 
Best, P. B., Brandão, A. & Butterworth, D. S. Demographic parameters of southern right whales off South Africa. J. Cetacean Res. Manag. https://doi.org/10.47536/jcrm.vi.296 (2001).Article 

Google Scholar 
Vermeulen, E., Wilkinson, C. & Thornton, M. Report of the 2018 South African Southern Right. Paper SC/68A/SH/01 Presented to IWC Scientific Committee, 2019 (unpublished). 25 pp. (Available from Off. this Journal) (2019).Knowlton, A. R., Kraus, S. D. & Kenney, R. D. Reproduction in North Atlantic right whales (Eubalaena glacialis). Can. J. Zool. 72, 1297–1305 (1994).Article 

Google Scholar 
van den Berg, G. L. et al. Decadal shift in foraging strategy of a migratory Southern Ocean predator. Glob. Change Biol. https://doi.org/10.1111/gcb.15465 (2021).Article 

Google Scholar 
Carroll, E. L. et al. First Direct evidence for natal wintering ground fidelity and estimate of Juvenile Survival in the New Zealand southern right whale Eubalaena australis. PLoS ONE 11, e0146590 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Valenzuela, L. O., Sironi, M., Rowntree, V. J. & Seger, J. Isotopic and genetic evidence for culturally inherited site fidelity to feeding grounds in southern right whales (Eubalaena australis). Mol. Ecol. 18, 782–791 (2009).Article 
CAS 
PubMed 

Google Scholar 
Best, P. B. & Ruther, H. Aerial photogrammetry of southern right whales, Eubalaena australis. J. Zool. 228, 595–614 (1992).Article 

Google Scholar 
Mate, B., Best, P., Lagerquist, B. A. & Winsor, M. H. Coastal, offshore, and migratory movements of South African right whales revealed by satellite telemetry. Mar. Mammal Sci. 27, 455–476 (2011).Article 

Google Scholar 
Christiansen, F., Dujon, A. M., Sprogis, K. R., Arnould, J. P. Y. & Bejder, L. Non-invasive unmanned aerial vehicle provides estimates of the energetic cost of reproduction in humpback whales. Ecosphere 7, 1–7 (2016).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (2020).Lockyer, C. Growth and energy budgets of large baleen whales from the Southern Hemisphere. Mamm. Seas, vol. 3, (FAO Fisheries Series No. 5) 379–487 (1981).Christiansen, F. et al. Estimating body mass of free-living whales using aerial photogrammetry and 3D volumetrics. Methods Ecol. Evol. 10, 2034–2044 (2019).Article 

Google Scholar 
Christiansen, F. et al. Population comparison of right whale body condition reveals poor state of the North Atlantic right whale. Mar. Ecol. Prog. Ser. 640, 1–16 (2020).Article 
ADS 

Google Scholar 
Braithwaite, J. E., Meeuwig, J. J., Letessier, T. B., Jenner, K. C. S. & Brierley, A. S. From sea ice to blubber: Linking whale condition to krill abundance using historical whaling records. Polar Biol. 38, 1195–1202 (2015).Article 

Google Scholar 
Loeb, V. J., Hofmann, E. E., Klinck, J. M., Holm-Hansen, O. & White, W. B. ENSO and variability of the antarctic peninsula pelagic marine ecosystem. Antarct. Sci. 21, 135–148 (2009).Article 
ADS 

Google Scholar 
Reid, K. & Croxall, J. P. Environmental response of upper trophic-level predators reveals a system change in an Antarctic marine ecosystem. Proc. R. Soc. B Biol. Sci. 268, 377–384 (2001).Article 
CAS 

Google Scholar 
Trathan, P. N., Forcada, J. & Murphy, E. J. Environmental forcing and Southern Ocean marine predator populations: Effects of climate change and variability. Philos. Trans. R. Soc. Biol. Sci. https://doi.org/10.1098/rstb.2006.1953 (2007).Article 

Google Scholar 
Bost, C. A. et al. The importance of oceanographic fronts to marine birds and mammals of the Southern Oceans. J. Mar. Syst. 78, 363–376 (2009).Article 

Google Scholar 
Crocker, D. E., Costa, D. P., Le Boeuf, B. J., Webb, P. M. & Houser, D. S. Impact of El Niño on the foraging behavior of female northern elephant seals. Mar. Ecol. Prog. Ser. 309, 1–10 (2006).Article 
ADS 

Google Scholar 
Flores, H. et al. Impact of climate change on Antarctic krill. Mar. Ecol. Prog. Ser. 458, 1–19 (2012).Article 
ADS 

Google Scholar 
Forcada, J. et al. Responses of Antarctic pack-ice seals to environmental change and increasing krill fishing. Biol. Conserv. 149, 40–50 (2012).Article 

Google Scholar 
Garcia-Rojas, M. I. et al. Environmental evidence for a pygmy blue whale aggregation area in the Subtropical Convergence Zone south of Australia. Mar. Mammal Sci. 34, 901–923 (2018).Article 

Google Scholar 
Tormosov, D. et al. Soviet catches of southern right whales Eubalaena australis, 1951–1971: Biological data and conservation implications. Biol. Conserv. 86, 185–197 (1998).Article 

Google Scholar 
Trathan, P. N. et al. Foraging dynamics of macaroni penguins Eudyptes chrysolophus at South Georgia during brood-guard. Mar. Ecol. Prog. Ser. 323, 239–251 (2006).Article 
ADS 

Google Scholar 
Murphy, E. J. et al. Climatically driven fluctuations in Southern Ocean ecosystems. Proc. R. Soc. B Biol. Sci. 274, 3057–3067 (2007).Article 

Google Scholar 
Nicol, S. Krill, currents, and sea ice: Euphausia superba and its changing environment. Bioscience 56, 111–120 (2006).Article 

Google Scholar 
Atkinson, A. et al. Oceanic circumpolar habitats of Antarctic krill. Mar. Ecol. Prog. Ser. 362, 1–23 (2008).Article 
ADS 
CAS 

Google Scholar 
Atkinson, A. et al. South Georgia, Antarctica: A productive, cold water, pelagic ecosystem. Mar. Ecol. Prog. Ser. 216, 279–308 (2001).Article 
ADS 
CAS 

Google Scholar 
Atkinson, A., Ward, P., Hill, A., Brierley, A. S. & Cripps, G. C. Krill-copepod interactions at South Georgia, Antarctica, II. Euphausia superba as a major control on copepod abundance. Mar. Ecol. Prog. Ser. 176, 63–79 (1999).Article 
ADS 

Google Scholar 
DeLorenzo Costa, A., Durbin, E. G. & Mayo, C. A. Variability in the nutritional value of the major copepods in Cape Cod Bay (Massachusetts, USA) with implications for right whales. Mar. Ecol. 27, 109–123 (2006).Article 
ADS 

Google Scholar 
Linder, M., Belhaj, N., Sautot, P. & Tehrany, E. A. From krill to whale: An overview of marine fatty acids and lipid compositions. Oleagineux Corps Gras Lipides: OCL 17, 194–204 (2010).Article 

Google Scholar 
McKinstry, C. A. E., Westgate, A. J. & Koopman, H. N. Annual variation in the nutritional value of stage V Calanus finmarchicus: Implications for right whales and other copepod predators. Endanger. Species Res. 20, 195–204 (2013).Article 

Google Scholar 
Maron, C. F. et al. Fatty acids and stable isotopes (13C, 15N) in southern right whale Eubalaena australis calves in relation toage and mortality at Peninsula Valdes, Argentina. Mar. Ecol. Prog. Ser. 646, 189–200 (2020).Article 
ADS 
CAS 

Google Scholar 
Thomas, V. G. Control of reproduction in animal species with high and low body fat reserves. Prog. Reprod. Biol. Med. 14, 27–41 (1990).
Google Scholar 
Ford, J. K. B., Ellis, G. M., Olesiuk, P. F. & Balcomb, K. C. Linking killer whale survival and prey abundance: Food limitation in the oceans’ apex predator?. Biol. Lett. 6, 139–142 (2010).Article 
PubMed 

Google Scholar 
Greene, C. H., Pershing, A. J., Kenney, R. D. & Jossi, J. W. Impact of climate variability on the recovery of endangered North Atlantic right whales. Oceanography 16, 98–103 (2003).Article 

Google Scholar 
Vermeulen, E., Wilkinson, C. & Van Den Berg, G. Report of the Southern Right Whale Aerial Surveys—2019. Paper SC/68B/SH/02 Presented to IWC Scientific Committee, 2020 (unpublished). 25 pp. (Available from Off. this Journal) (2020).Douhard, F., Gaillard, J. M., Pellerin, M., Jacob, L. & Lemaître, J. F. The cost of growing large: Costs of post-weaning growth on body mass senescence in a wild mammal. Oikos 126, 1329–1338 (2017).Article 

Google Scholar 
Sigurjónsson, J., Halldórsson, S. D. & Konráðsson, A. New Information on Age and Reproduction in Minke Whales (Balaenoptera acutorostrata) in Icelandic Waters. Page Doc. SC/42/NHMi27 Scientific Communication International Whaling Commission. Noordwijkerhout, Netherlands (1990).Charlton, C. et al. Demographic Parameters of Southern Right Whales (Eubalaena australis) off Australia. Paper SC/67B/INFO/22 Presented to IWC Scientific Committee, 2018 (Unpublished). 28 pp. (Available from Off. This Journal) (2018).Marón, C. F. et al. Increased wounding of southern right Whale (Eubalaena australis) calves by Kelp Gulls (Larus dominicanus) at Península Valdés, Argentina. PLoS ONE 10, 1–20 (2015).
Google Scholar 
Rowntree, V. J. et al. Unexplained recurring high mortality of southern right whale Eubalaena australis calves at Península Valdés, Argentina. Mar. Ecol. Prog. Ser. 493, 275–289 (2013).Article 
ADS 

Google Scholar 
Brandão, A., Vermeulen, E., Ross-gillespie, A., Findlay, K. & Butterworth, D. S. Updated Application of a Photo-Identification Based Assessment Model to Southern Right Whales in South African Waters , Focussing on Inferences to be Drawn from a Series of Appreciably Lower Counts of Calving Females Over 2015 to 2017. Paper SC/67B/SH2 Presented to IWC Scientific Committee, 2018 (unpublished). 18 pp. (Available from Off. this Journal) (2018).Crespo, E. A. et al. The southwestern Atlantic southern right whale, Eubalaena australis, population is growing but at a decelerated rate. Mar. Mammal Sci. 35, 93–107 (2019).Article 

Google Scholar 
Agrelo, M. et al. Ocean warming threatens southern right whale population recovery. Sci. Adv. 7, eabh2823 (2021).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Stenseth, N. C. et al. Ecological effects of climate fluctuations. Science (80-) 297, 1292–1296 (2002).Article 
ADS 
CAS 

Google Scholar 
Nicol, S., Worby, A. & Leaper, R. Changes in the Antarctic sea ice ecosystem: Potential effects on krill and baleen whales. Mar. Freshw. Res. 59, 361–382 (2008).Article 

Google Scholar 
Meredith, M. P. & King, J. C. Rapid climate change in the ocean west of the Antarctic Peninsula during the second half of the 20th century. Geophys. Res. Lett. 32, 1–5 (2005).Article 

Google Scholar 
Croxall, J., Reid, K. & Prince, P. Diet, provisioning and productivity responses of marine predators to differences in availability of Antarctic krill. Mar. Ecol. Prog. Ser. 177, 115–131 (1999).Article 
ADS 

Google Scholar 
Tulloch, V. J. D., Plagányi, É. E., Brown, C., Richardson, A. J. & Matear, R. Future recovery of baleen whales is imperiled by climate change. Glob. Change Biol. 25, 1263–1281 (2019).Article 
ADS 

Google Scholar 
Hoegh-Guldberg, O. & Bruno, J. F. The impact of climate change on the world’s marine ecosystems. Science (80-) 328, 1523–1528 (2010).Article 
ADS 
CAS 

Google Scholar 
González Carman, V., Piola, A., O’Brien, T. D., Tormosov, D. D. & Acha, E. M. Circumpolar frontal systems as potential feeding grounds of Southern Right whales. Prog. Oceanogr. 176, 102123 (2019).Article 

Google Scholar  More

Extraction of total active proteomes from sediment samplesWe sampled 14 sediments along the coastlines of the Irish Sea, the Mediterranean Sea, and the Red Sea (from 16°N to 53°N), applying uniform sampling and storage procedures. Location details and sediment temperature fluctuations are summarized in Supplementary Table S1. We collected sediments (5 Kg) in triplicate and extracted the total proteins using a well-established microbial detachment procedure67, with some modifications. We mixed 100 g of sediment with 300 ml of sterilized saline solution (5 mM sodium pyrophosphate and 35 g L−1 of NaCl) containing 150 mg L−1 of Tween 80 (from Merck Life Science S.L.U., Madrid, Spain) in an ice water bath. After re-suspension, samples were kept in a water bath ultra-sonicator (Bandelin SONOREX, Berlin, Germany) on ice and sonicated (60 W output) for 120 min. We repeated this procedure twice, with an ice water bath incubation of 60 min between each cycle. We then centrifuged the samples at 500 g for 15 min at 4 °C to remove the sediments in a centrifuge 5810 R (Eppendorf AG, Hamburg, Germany). Supernatants were carefully transferred to a new tube, minimizing disruption of the sediments, and the resulting supernatants were centrifuged at 13,000 g for 15 min at 4 °C to produce microbial cell pellets. We used the resulting cell mix to extract the total protein by mixing the cells with 1.2 ml BugBuster® Protein Extraction Reagent (Novagen, Darmstadt, Germany) for 30 min with shaking (250 rpm). Subsequently, samples were disrupted by sonication using a pin Sonicator® 3000 (Misonix, New Highway Farmingdale, NY, USA) for a total time of 2 min (10 watts) on ice (4 cycles × 0.5 min with 1 min ice-cooling between each cycle). Extracts were centrifuged for 10 min at 12,000 g at 4 °C to separate cellular debris and intact cells. Supernatants were carefully aspirated (to avoid disturbing the pellet), transferred to new tubes, and stored at –80 °C until use. The protein solution was filtered at 15 °C for 7 h using Vivaspin filters (Sartorius, Goettingen, Germany) with a molecular weight (MW) cut-off of 3,000 Da to concentrate the proteins up to a final concentration of 10 mg ml−1, according to the Bradford Protein Assay (Bio-Rad Laboratories, S.A., Madrid, Spain)68. The average total amount of proteins extracted per each 100 g of sediment was 612 µg (interquartile range, 31 µg, see details in Supplementary Fig. S2). In all cases, extensive dialysis of protein solutions against 40 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) buffer was performed using a Pur-A-LyzerTM Maxi 1200 dialysis kit (Merck Life Science S.L.U., Madrid, Spain)69, and active proteins stored at a concentration of 10 mg ml−1at –86 °C until use. As reported previously70, 2DE was performed using GE Healthcare reagents and equipment, 11 cm IPG strips in the pH range of 3–10 and molecular weight ranging from 10 to 250 kDa (Precision Plus Protein Dual Color Standards #1610374, Bio-Rad Laboratories, S.A., Madrid, Spain). The 2-DE was performed using a validated pooling strategy71, in which proteins extracted from three independent biological replicates (i.e., sediments) were mixed in equal amounts and a total of 150 µg of protein were further loaded per gel. Staining was performed with SYPRO Ruby Protein Gel Stain (Invitrogen, Waltham, MA, USA). The two-dimensional SDS-PAGE (12% acrylamide) gels of extracted proteins are reported in Supplementary Fig. S2 (original gels in Source Data). The same protocol was applied to extract and analyse by SDS-PAGE the total active proteins extracted from sediment samples with different temperature variability levels (HTV, ITV, and LTV) collected in the Red Sea (Supplementary Table S4). The total amount of protein extracted per each 100 g of sediment is given in Supplementary Table S8. Coomassie-stained one-dimension SDS-PAGE (1-DE) gels of extracted proteins are shown in Supplementary Fig. S9 (original gel in Source Data).Source, expression and purification of esterases and EXDOs from a wide geographical rangeWe recovered 83 enzymes (78 esterases and 5 EXDO) from microbial communities inhabiting marine sediments across ten distinct locations from the latitudinal transect described above: Ancona harbour (Anc), Priolo Gargallo (Pri), Gulf of Genoa, Messina harbour (Mes), Milazo harbour (Mil), Mar Chica lagoon (MCh), Bizerte lagoon (Biz), El-Max site (ElMax), Gulf of Aqaba (Aq), and Menai Strait (MS); further details are provided in Supplementary Data S3. Sources of the enzymes were the corresponding shotgun metagenomes (see Supplementary Table S3) and the metagenome clone libraries generated from the extracted DNA71. The sediment sample from the Gulf of Genoa was not used for activity tests and metaproteome analysis because no raw sample material was available; however, because of the possibility to access its shotgun metagenome (see Supplementary Table S3) and a metagenome clone library72, we used the sample for screening esterases to incorporate an additional latitude in our transect. In the case of Menai Strait (Irish Sea), five additional esterases were retrieved from a metagenome obtained from enriched cultures prepared with samples collected on 22nd June 2019 from Menai Strait (School of Ocean Sciences, Bangor University, St. George’s Pier, Menai Bridge, N53°13′31.3″; W4°09′33.3”). The water temperature was 14 °C and the salinity was 32 p.s.u. Two enrichment cultures were set up at 20 °C: (i) SW: seawater enrichment with 0.1% lignin; the enrichment was set up using 50 ml of the sample as inoculum with the addition of 0.1% lignin (Sigma-Aldrich, Gillingham, United Kingdom) (w/v); (ii) AW: algal surface wash-off in seawater, enriched with 0.1% lignin; the enrichment was set up using 50 ml of surface wash-off after mixing of ca. 10 g of Fucus (brown algae) in the seawater and removal of plant tissue, 0.1% lignin (w/v) was added. After 92 days of incubation, 5 ml of each enrichment cultures were transferred into the new flask containing 45 ml autoclaved and filtered seawater with 0.1% lignin. This procedure was repeated on days 185 and 260, and the incubation was stopped on day 365. The DNA was extracted using 12 months using MetaGnome extraction kit (EpiCentre, Biotechnologies, Madison, WI, USA), sequenced on Illumina MiSeq™ platform (Illumina Inc., San Diego, CA, USA) using paired-end 250 bp reads at the Centre for Environmental Biotechnology (Bangor, UK), and sequencing reads were processed and analysed as described previously73.The screening, cloning and activity of a subset of 35 identified esterases have been reported previously72. The remaining 48 enzymes are reported for the first time in this study and were identified using naive and in silico metagenomic approaches, as detailed below. The environmental site from which each enzyme originated and the method employed for its identification are detailed in Supplementary Data S3. For naive screens addressing the recovery of new sequences encoding esterases and EXDO, the large-insert pCCFOS1 fosmid libraries made using the corresponding DNA samples, the CopyControl Fosmid Library Kit (Epicentre Biotechnologies, Madison, WI, USA) and the Escherichia coli EPI300-T1R strain were used. The nucleic acid extraction, construction and the functional screens of such libraries have been previously described72. In brief, fosmid clones were plated onto large (22.5 × 22.5 cm) Petri plates with Luria Bertani (LB) agar containing chloramphenicol (12.5 µg ml−1) and induction solution (Epicentre Biotechnologies; WI, USA), at a quantity recommended by the supplier to induce a high fosmid copy number. Clones were scored by the ability to hydrolyze α-naphthyl acetate and tributyrin (for esterase activity), and catechol (for EXDO activity)72,74. Positive clones presumed to contain esterases and EXDOs were selected, and their DNA inserts were sequenced using a MiSeq Sequencing System (Illumina, San Diego, USA) with a 2 × 150-bp sequencing v2 kit at Lifesequencing S.L. (Valencia, Spain). After sequencing, the reads were quality-filtered and assembled to generate nonredundant meta-sequences, and genes were predicted and annotated via BLASTP and the PSI-BLAST tool72. For in silico screens, addressing the recovery of new sequences encoding esterases, the predicted protein-coding genes, obtained after the sequencing of DNA material from resident microbial communities in each of the samples, were used. The meta-sequences are available from the National Center for Biotechnology Information (NCBI) nonredundant public database (accession numbers reported in Supplementary Data S3). Protein-coding genes identified from the DNA inserts of positive clones (naive screen) or from the meta-sequences were screened for enzymes of interest using the Blastp algorithm via the DIAMOND v2.0.9 program with default parameters (percentage of identity ≥60%; alignment length ≥70; e-value ≤1e−5)29, against the Lipase Engineering sequence databases (to screen for esterases) and AromaDeg database (for EXDO)74. Since the collection of sediments across locations experiencing different MATs was limited by our sampling capacity, to expand our range of exploration at a global scale and to validate our dataset, we added our single enzyme analysis to the seawater metagenomes retrieved from the Tara Ocean Expedition database (accession number in Supplementary Data S4). Due to the volume of sequences generated, this database provides access to a large number of enzymes, including those studied here through homology search. Esterases were selected as target sequences, and the following pipeline was used. First, we selected a sequence encoding an esterase reported as one of the most substrate-ambiguous esterases out of 145 tested (EH1, Protein Data Bank acc. nr. 5JD4) and well-distributed in the marine environment72. Second, we performed a homology search of this sequence against the Tara Ocean metagenome21 to retrieve similar sequences, using the Blastp algorithm via the DIAMOND v2.0.9 program30 (e-value 98% using SDS-PAGE analysis in a Mini PROTEAN electrophoresis system (Bio-Rad Laboratories, S.A., Madrid, Spain). Purified protein was stored at –86 °C until use at a concentration of 10 mg ml−1 in 40 mM HEPES buffer (pH 7.0). A total of approximately 5–40 mg of total purified recombinant protein was obtained from 1 L of culture. Supplementary Fig. S1 illustrates a schematic representation of the pipeline implemented in this work to investigate enzyme activities in a large set of marine samples, starting from samples collected (sediments) and available metagenomes.Enzyme activity assessmentsAll substrates used for activity tests were of the highest purity and, if not indicated otherwise, were obtained from Merck Life Science S.L.U. (Madrid, Spain): 4-nitrophenyl-propionate (ref. MFCD00024664), 4-nitrophenyl phosphate (ref. 487663), 4-nitrophenyl β-D-galactose (ref. N1252), bis(p-nitrophenyl) phosphate (ref. 123943), benzaldehyde (ref. B1334), 2-(4-nitrophenyl)ethan-1-amine (ref. 184802-5G), pyridoxal phosphate (ref. P9255), acetophenone (ref. A10701), NADPH (ref. N5130) and catechol (ref. PHL82372). We directly tested total protein extracts for esterase, phosphatase, beta-galactosidase, and nuclease activity using 4-nitrophenyl-propionate, 4-nitrophenyl phosphate, 4-nitrophenyl β-D-galactose, and bis(p-nitrophenyl) phosphate, respectively, by following the production of 4-nitrophenol at 348 nm (extinction coefficient [ε], 4147 M−1 cm−1), as previously described69. For determination: [total protein]: 5 μg ml−1; [substrate]: 0.8 mM; reaction volume: 200 μl; T: 4–85 °C; and pH: 8.0 (50 mM Tris-HCl buffer). The hydrolysis of 4-nitrophenyl-propionate was used to determine, under these standard conditions, the effects of temperature on the purified esterase. Transaminase activity was determined using benzaldehyde as amine acceptor, 2-(4-nitrophenyl)ethan-1-amine as amine donor, and pyridoxal phosphate as a cofactor, by following the production of a colour amine at 600 nm (extinction coefficient, 537 M−1 cm−1), as previously described75. For determination, [total protein]: 5 μg ml−1; [substrates]: 25 mM; [pyridoxal phosphate]: 1 mM; reaction volume: 200 μL; T: 4-85 °C; and pH: 8.0 (50 mM Tris-HCl buffer). Aldo-keto reductase activity was determined using acetophenone as a substrate and NADPH as a cofactor, by following the consumption of NADPH at 340 nm (extinction coefficient, 6220 M−1 cm−1), as described76. For determination, [total protein]: 5 μg ml−1; [substrate]: 1 mM; [cofactor]: 1 mM; reaction volume: 200 μL; T: 4–85 °C; and pH: 8.0 (50 mM Tris-HCl buffer). We determined EXDO activity using catechol as substrate, by following the increase of absorbance at 375 nm of the ring fission products (extinction coefficient, 36000 M−1 cm−1), as previously described74. For determination, [protein]: 5 μg ml−1; [catechol]: 0.5 mM; reaction volume: 200 μL; T: 4–85 °C; and pH: 8.0 (50 mM Tris-HCl buffer). The hydrolysis of catechol was used to determine, under these standard conditions, the effects of temperature on the purified EXDOs. All measurements were performed in 96-well plates (ref. 655801, Greiner Bio-One GmbH, Kremsmünster, Austria), in biological triplicates over 180 min in a Synergy HT Multi-Mode Microplate Reader (Biotek Instruments, Winooski, VT, USA) in continuous mode (measurements every 30 s) and determining the absorbance per minute from the slopes generated and applying the formula (1). All values were corrected for nonenzymatic transformation.$${Rate}left(frac{mu {mol}}{{{min }}{mg},{protein}}right)= frac{frac{triangle {{{{{rm{Abs}}}}}}}{{{min }}}}{{{{{{rm{varepsilon }}}}}},{{{{{rm{M}}}}}}-1{{{{{rm{cm}}}}}}-1}*frac{1}{0.4,{cm}}*frac{{10}^{6},mu M}{1{{{{{rm{M}}}}}}}\ *0.0002,L*frac{1}{{mg},{protein}}$$
(1)
Shotgun proteomicsProteomics was performed by using total active proteins (extracted as above), which were then subjected to protein precipitation, protein digestion and Liquid Chromatography-Electrospray Ionization Tandem Mass Spectrometric (LC-ESI-MS/MS) analysis, as previously described77. High-quality reference metagenomes corresponding to each sample (BioProject number in Supplementary Table S3) were used for protein calling, with a threshold of only one identified peptide per protein identification because False Discovery Rates (FDR) controlled experiments counter-intuitively suffer from the two-peptide rule. The confidence interval for protein identification was set to ≥95% (p  50 °C for which the second phase transition was chosen to focus on the decomposition of the core. It is important to note that applying CNA to MD simulations at room temperature may lead to an evening out of Tp values for esterases that transition around this temperature, i.e., systems with a Tp at or below room temperature might all be influenced similarly by loosening their bonding network. By contrast, systems with a transition temperature at or above room temperature would still be discriminated against. The data generated in this study for analyzing Tp values have been deposited at researchdata.hhu.de under accession code DOI: 10.25838/d5p-42101 [https://doi.org/10.25838/d5p-42].Relationship of temperature-induced changes in enzymeRelationship between MAT and enzyme response to temperature (i.e., Topt, Td and Tp) were evaluated by performing linear regression in R. In the case of enzymes retrieved from the Tara ocean dataset we calculated first the break point (flexus) using the package segmented in R102 and then we computed separately the linear model describing the two linear regressions before and after the breakpoint. To evaluate the possible relation between enzyme thermal response and other environmental parameters, salinity and pH data were retrieved from Bio-ORACLE52 using GPS coordinates of each location.Environmental characterization and sediment collection from different temperature variability levels in the Red SeaWe recorded the temperatures of surface sediments from March 2015 to September 2016 along the coast of the Red Sea using HOBO data loggers (Onset, USA) in nine stations located at 3, 25, and 50 m depth. Details on the location, depth and temperature fluctuations of the studied sediments are reported in Supplementary Table S4 and Source Data. We first assess the differences in the homogeneity of the temperature variance in the three types of sediments to evaluate the magnitude of thermal variation and then we test the difference among their MATs using a non-parametric ANOVA (Dunnett’s multiple comparisons tests). We identified three different levels of temperature variability (Fig. 3a–c; Supplementary Table S5): high, intermediate, and low thermal variability (HTV, ITV, and LTV, respectively), where sediments experienced temperature variations of 12.8 °C, 8.8 °C, and 6.7 °C, respectively. From each station, we sampled 200 g of surface sediment (0–5 cm depth) in triplicate in August and December 2015 with a Van der Venn grab (1 dm3) equipped with a MicroCat 250 Seabird CTD (Conductivity, Temperature, Depth), which was assembled on board the research vessel R/V Explorer (KAUST). During sampling, we measured the temperature of the sediments and the water layer covering the sediments using a digital thermometer and the CTD, respectively. We conducted all sampling in compliance with the guidelines specified by KAUST and Saudi Arabian authorities.Sediment processing for analysis of bacterial communitiesFrom each sample (in triplicate), we immediately removed subsamples of sediment (n = 54, ~10 g) and stored them at –20 °C for molecular analysis. Separately, sediment 25 ± 1 g was transferred to 50 ml tubes and added 30 ml of filtered (0.2 µm) water from the Red Sea. The tubes were shaken at 500 rpm for one hour and then centrifuged them at 300 g for 15 min to detach the microbial cells in the sediments without affecting their vitality103,104. The supernatant containing the extracted cells was collected in sterile tubes and was immediately used to measure microbial growth rates.Evaluation of bacterial growth in sediments at different temperaturesWe evaluated the microbial growth rate of the heterotrophic community extracted from the sediments under HTV, ITV, and LTV at 10 °C, 20 °C, 30 °C, 40 °C and 50 °C, using Marine Broth as the cultivation medium (Zobell Marine Broth 2216) supplemented with 0.1 g/L cycloheximide; a rich-medium was selected to avoid the nutrient limitation effect that can affect bacterial physiology63,105. We inoculated 96-well plates with 200 µl of cultivation medium and 25 µl of the cell suspension extracted from the sediments. We inoculated the three biological replicates from each station and each level of temperature variability in eight wells, giving a total of 72 wells for each plate, with 24 wells used as a negative control inoculated with water. We assembled a total of three plates for each incubation temperature from August and December. Plates were spectrophotometrically measured at 3 h intervals using an optical density of 600 nm (Spectramax® M5) for 72 h. Wells with optical density 90%) for further analysis (Supplementary Tables S9 and S10). We calculated the compositional similarity matrix (Bray-Curtis of the log-transformed OTU table) with Primer 6109. Using the same software, canonical analysis of principal coordinates (CAP)110 was used to compare the temperature variability samples (temperature variability levels: HTV, ITV, and LTV; season levels: August and December) based on the compositional similarity matrix. We applied permutational multivariate analyses of variance to the matrix (PERMANOVA; main and multiple comparison tests). We tested the occurrence of thermal-decay patterns in sediments with different temperature variability levels using linear regression (Prism 9.2 software, La Jolla California USA, www.graphpad.com) between the bacterial community similarities (Bray-Curtis) and the temperature differences among sediments (∆T°C) at the time of sampling. We calculated alphadiversity indices (richness and evenness) using the paleontological statistics (PAST) software, and their correlation with temperature was modelled using linear regression in Prism 9.2. Spearman correlation among temperature and relative abundance of OTUs within each sediment sample was evaluated; OTUs were classified based on their positive (enriched) and negative (depleted) correlation with sediment temperature.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

Céréghino, R., Biggs, J., Oertli, B. & Declerck, S. The ecology of European ponds: Defining the characteristics of a neglected freshwater habitat. In Pond Conservation in Europe (eds Oertli, B. et al.) 1–6 (Springer Netherlands, 2007).
Google Scholar 
Olmo, C. et al. The environmental framework of temporary ponds: A tropical-Mediterranean comparison. CATENA 210, 105845 (2022).CAS 

Google Scholar 
Griffiths, R. A. Temporary ponds as amphibian habitats. Aquat. Conserv. Mar. Freshw. Ecosyst. 7, 119–126 (1997).
Google Scholar 
Boix, D. et al. Conservation of temporary wetlands. In Encyclopedia of the World’s Biomes 279–294 (Elsevier, 2020). https://doi.org/10.1016/B978-0-12-409548-9.12003-2.Chapter 

Google Scholar 
Fritz, K. A. & Whiles, M. R. Reciprocal subsidies between temporary ponds and riparian forests. Limnol. Oceanogr. 66, 3149–3161 (2021).ADS 

Google Scholar 
Jeffries, M. The spatial and temporal heterogeneity of macrophyte communities in thirty small, temporary ponds over a period of ten years. Ecography 31, 765–775 (2008).
Google Scholar 
Hassall, C. The ecology and biodiversity of urban ponds. WIREs Water 1, 187–206 (2014).
Google Scholar 
Lukács, B. A. et al. Macrophyte diversity of lakes in the Pannon Ecoregion (Hungary). Limnologica 53, 74–83 (2015).
Google Scholar 
Florencio, M., Díaz-Paniagua, C., Gómez-Rodríguez, C. & Serrano, L. Biodiversity patterns in a macroinvertebrate community of a temporary pond network. Insect Conserv. Divers. 7, 4–21 (2014).
Google Scholar 
Meland, S., Sun, Z., Sokolova, E., Rauch, S. & Brittain, J. E. A comparative study of macroinvertebrate biodiversity in highway stormwater ponds and natural ponds. Sci. Total Environ. 740, 140029 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Hahn, M. W. The microbial diversity of inland waters. Curr. Opin. Biotechnol. 17, 256–261 (2006).CAS 
PubMed 

Google Scholar 
Felföldi, T. Microbial communities of soda lakes and pans in the Carpathian Basin: A review. Biol. Futura 71, 393–404 (2020).
Google Scholar 
Grossart, H., Massana, R., McMahon, K. D. & Walsh, D. A. Linking metagenomics to aquatic microbial ecology and biogeochemical cycles. Limnol. Oceanogr. 65, S2–S20 (2020).CAS 

Google Scholar 
Marrone, F., Fontaneto, D. & Naselli-Flores, L. Cryptic diversity, niche displacement and our poor understanding of taxonomy and ecology of aquatic microorganisms. Hydrobiologia https://doi.org/10.1007/s10750-022-04904-x (2022).Article 

Google Scholar 
Ducklow, H. Microbial services: Challenges for microbial ecologists in a changing world. Aquat. Microb. Ecol. 53, 13–19 (2008).ADS 

Google Scholar 
Bodelier, P. L. E. Toward understanding, managing, and protecting microbial ecosystems. Front. Microbiol. 2, 80 (2011).PubMed 
PubMed Central 

Google Scholar 
Bell, T., Newman, J. A., Silverman, B. W., Turner, S. L. & Lilley, A. K. The contribution of species richness and composition to bacterial services. Nature 436, 1157–1160 (2005).ADS 
CAS 
PubMed 

Google Scholar 
Trivedi, C. et al. Losses in microbial functional diversity reduce the rate of key soil processes. Soil Biol. Biochem. 135, 267–274 (2019).CAS 

Google Scholar 
Wellborn, G. A., Skelly, D. K. & Werner, E. E. Mechanisms creating community structure across freshwater habitat gradient. Annu. Rev. Ecol. Syst. 27, 337–363 (1996).
Google Scholar 
Chase, J. M. Drought mediates the importance of stochastic community assembly. Proc. Natl. Acad. Sci. 104, 17430–17434 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tweed, S., Grace, M., Leblanc, M., Cartwright, I. & Smithyman, D. The individual response of saline lakes to a severe drought. Sci. Total Environ. 409, 3919–3933 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Aguilar, P., Acosta, E., Dorador, C. & Sommaruga, R. Large differences in bacterial community composition among three nearby extreme waterbodies of the High Andean Plateau. Front. Microbiol. 7, 976 (2016).PubMed 
PubMed Central 

Google Scholar 
Boros, E., Balogh, K., Vörös, L. & Horváth, Z. Multiple extreme environmental conditions of intermittent soda pans in the Carpathian Basin (Central Europe). Limnologica 62, 38–46 (2017).CAS 
PubMed 

Google Scholar 
Lengyel, E., Pálmai, T., Padisák, J. & Stenger-Kovács, C. Annual hydrological cycle of environmental variables in astatic soda pans (Hungary). J. Hydrol. 575, 1188–1199 (2019).ADS 
CAS 

Google Scholar 
Vieira-Silva, S. & Rocha, E. P. C. The Systemic imprint of growth and its uses in ecological (Meta)genomics. PLoS Genet. 6, e1000808 (2010).PubMed 
PubMed Central 

Google Scholar 
Cunillera-Montcusí, D. et al. Freshwater salinisation: A research agenda for a saltier world. Trends Ecol. Evol. 37, 440–453 (2022).PubMed 

Google Scholar 
Šolić, M. et al. Structure of microbial communities in phosphorus-limited estuaries along the eastern Adriatic coast. J. Mar. Biol. Assoc. U.K. 95, 1565–1578 (2015).
Google Scholar 
Traving, S. J. et al. The Effect of increased loads of dissolved organic matter on estuarine microbial community composition and function. Front. Microbiol. 8, 351 (2017).PubMed 
PubMed Central 

Google Scholar 
Zhang, G. et al. Salinity controls soil microbial community structure and function in coastal estuarine wetlands. Environ. Microbiol. 23, 1020–1037 (2021).CAS 
PubMed 

Google Scholar 
Tkavc, R. et al. Bacterial communities in the ‘petola’ microbial mat from the Sečovlje salterns (Slovenia): Bacterial communities in the ‘petola’. FEMS Microbiol. Ecol. 75, 48–62 (2011).CAS 
PubMed 

Google Scholar 
Ali, I. et al. Comparative study of physical factors and microbial diversity of four man-made extreme ecosystems. Proc. Natl. Acad. Sci. India Sect. B Biol. Sci. 86, 767–778 (2016).
Google Scholar 
Paul, V., Banerjee, Y., Ghosh, P. & Busi, S. B. Depthwise microbiome and isotopic profiling of a moderately saline microbial mat in a solar saltern. Sci. Rep. 10, 20686 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Stenger-Kovács, C. et al. Vanishing world: Alkaline, saline lakes in Central Europe and their diatom assemblages. Inland Waters 4, 383–396 (2014).
Google Scholar 
Stenger-Kovács, C., Hajnal, É., Lengyel, E., Buczkó, K. & Padisák, J. A test of traditional diversity measures and taxonomic distinctness indices on benthic diatoms of soda pans in the Carpathian basin. Ecol. Indic. 64, 1–8 (2016).
Google Scholar 
Szabó, B., Lengyel, E., Padisák, J., Vass, M. & Stenger-Kovács, C. Structuring forces and β-diversity of benthic diatom metacommunities in soda pans of the Carpathian Basin. Eur. J. Phycol. 53, 219–229 (2018).
Google Scholar 
Szabó, A. et al. Soda pans of the Pannonian steppe harbor unique bacterial communities adapted to multiple extreme conditions. Extremophiles 21, 639–649 (2017).PubMed 

Google Scholar 
Szabó, A. et al. Grazing pressure-induced shift in planktonic bacterial communities with the dominance of acIII-A1 actinobacterial lineage in soda pans. Sci. Rep. 10, 19871 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Benlloch, S. et al. Prokaryotic genetic diversity throughout the salinity gradient of a coastal solar saltern. Environ. Microbiol. 4, 349–360 (2002).PubMed 

Google Scholar 
Horváth, Z. et al. Opposing patterns of zooplankton diversity and functioning along a natural stress gradient: When the going gets tough, the tough get going. Oikos 123, 461–471 (2014).
Google Scholar 
Mo, Y. et al. Low shifts in salinity determined assembly processes and network stability of microeukaryotic plankton communities in a subtropical urban reservoir. Microbiome 9, 128 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Gómez-Rodríguez, C., Bustamante, J. & Díaz-Paniagua, C. Evidence of hydroperiod shortening in a preserved system of temporary ponds. Remote Sens. 2, 1439–1462 (2010).ADS 

Google Scholar 
Finger Higgens, R. A. et al. Changing lake dynamics indicate a drier arctic in western greenland. J. Geophys. Res. Biogeosciences 124, 870–883 (2019).ADS 

Google Scholar 
Zacharias, I. & Zamparas, M. Mediterranean temporary ponds. A disappearing ecosystem. Biodivers. Conserv. 19, 3827–3834 (2010).
Google Scholar 
Horváth, Z., Ptacnik, R., Vad, C. F. & Chase, J. M. Habitat loss over six decades accelerates regional and local biodiversity loss via changing landscape connectance. Ecol. Lett. 22, 1019–1027 (2019).PubMed 
PubMed Central 

Google Scholar 
Grillas, P., Rhazi, L., Lefebvre, G., El Madihi, M. & Poulin, B. Foreseen impact of climate change on temporary ponds located along a latitudinal gradient in Morocco. Inland Waters 11, 492–507 (2021).CAS 

Google Scholar 
Xi, Y., Peng, S., Ciais, P. & Chen, Y. Future impacts of climate change on inland Ramsar wetlands. Nat. Clim. Change 11, 45–51 (2021).ADS 

Google Scholar 
Zhong, Y. et al. Shrinking habitats and native species loss under climate change: a multifactorial risk Assessment of China’s inland wetlands. 28 (2022).Atkinson, S. T. et al. Substantial long-term loss of alpha and gamma diversity of lake invertebrates in a landscape exposed to a drying climate. Glob. Change Biol. 27, 6263–6279 (2021).CAS 

Google Scholar 
Whiting, G. J. & Chanton, J. P. Greenhouse carbon balance of wetlands: Methane emission versus carbon sequestration: Greenhouse carbon balance of wetlands. Tellus B 53, 521–528 (2001).ADS 

Google Scholar 
Mitsch, W. J. et al. Wetlands, carbon, and climate change. Landsc. Ecol. 28, 583–597 (2013).
Google Scholar 
Ardón, M., Helton, A. M. & Bernhardt, E. S. Salinity effects on greenhouse gas emissions from wetland soils are contingent upon hydrologic setting: A microcosm experiment. Biogeochemistry 140, 217–232 (2018).
Google Scholar 
Jeppesen, E., Beklioğlu, M., Özkan, K. & Akyürek, Z. Salinization increase due to climate change will have substantial negative effects on inland waters: A call for multifaceted research at the local and global scale. Innovation 1, 100030 (2020).PubMed 
PubMed Central 

Google Scholar 
Boros, E., Horváth, Z., Wolfram, G. & Vörös, L. Salinity and ionic composition of the shallow astatic soda pans in the Carpathian Basin. Ann. Limnol. Int. J. Limnol. 50, 59–69 (2014).
Google Scholar 
Sorokin, D. Y. et al. Microbial diversity and biogeochemical cycling in soda lakes. Extremophiles 18, 791–809 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Horváth, Z., Vad, C. F., Vörös, L. & Boros, E. The keystone role of anostracans and copepods in European soda pans during the spring migration of waterbirds: The keystone trophic role of crustaceans in European soda pans. Freshw. Biol. 58, 430–440 (2013).
Google Scholar 
Stenger-Kovács, C. & Lengyel, E. Taxonomical and distribution guide of diatoms in soda pans of Central Europe. Stud. Bot. Hung. 46, 3–203 (2015).
Google Scholar 
Szabó, B. et al. Microbial stowaways: Waterbirds as dispersal vectors of aquatic pro- and microeukaryotic communities. J. Biogeogr. 49, 1286–1298 (2022).
Google Scholar 
Williams, D. D. The Ecology of Temporary Waters (Springer Netherlands, 1987).
Google Scholar 
Hammer, U. T. The effects of climate change on the salinity, water levels and biota of Canadian prairie saline lakes. SIL Proc. 1922–2010(24), 321–326 (1990).
Google Scholar 
Schallenberg, M., Hall, C. & Burns, C. Consequences of climate-induced salinity increases on zooplankton abundance and diversity in coastal lakes. Mar. Ecol. Prog. Ser. 251, 181–189 (2003).ADS 

Google Scholar 
Felföldi, T., Somogyi, B., Márialigeti, K. & Vörös, L. Characterization of photoautotrophic picoplankton assemblages in turbid, alkaline lakes of the Carpathian Basin (Central Europe). J. Limnol. 68, 385 (2009).
Google Scholar 
Somogyi, B. et al. Winter bloom of picoeukaryotes in Hungarian shallow turbid soda pans and the role of light and temperature. Aquat. Ecol. 43, 735–744 (2009).CAS 

Google Scholar 
Pálffy, K. et al. Unique picoeukaryotic algal community under multiple environmental stress conditions in a shallow, alkaline pan. Extremophiles 18, 111–119 (2014).PubMed 

Google Scholar 
Padisák, J. & Naselli-Flores, L. Phytoplankton in extreme environments: Importance and consequences of habitat permanency. Hydrobiologia 848, 157–176 (2021).
Google Scholar 
Olli, K., Ptacnik, R., Klais, R. & Tamminen, T. Phytoplankton species richness along coastal and estuarine salinity continua. Am. Nat. 194, E41–E51 (2019).PubMed 

Google Scholar 
Olli, K., Tamminen, T. & Ptacnik, R. Predictable shifts in diversity and ecosystem function in phytoplankton communities along coastal salinity continua. Limnol. Oceanogr. Lett. https://doi.org/10.1002/lol2.10242 (2022).Article 

Google Scholar 
Tikhonenkov, D. V., Burkovsky, I. V. & Mazei, Y. A. Is there a relation between the distribution of heterotrophic flagellates and the zonation of a marine intertidal flat?. Oceanology 55, 13 (2015).
Google Scholar 
Arndt, H. et al. Functional diversity of heterotrophic flagellates in aquatic ecosystems. In Flagellates 252–280 (CRC Press, 2000). https://doi.org/10.1201/9781482268225-18.Chapter 

Google Scholar 
JeLee, W. & Patterson, D. J. Diversity and geographic distribution of free-living heterotrophic flagellates—Analysis by PRIMER. Protist 149, 229–244 (1998).CAS 

Google Scholar 
Azovsky, A. I., Tikhonenkov, D. V. & Mazei, Y. A. An estimation of the global diversity and distribution of the smallest eukaryotes: Biogeography of marine benthic heterotrophic flagellates. Protist 167, 411–424 (2016).PubMed 

Google Scholar 
Tikhonenkov, D. V., Mazei, Y. A. & Mylnikov, A. P. Species diversity of heterotrophic flagellates in White Sea littoral sites. Eur. J. Protistol. 42, 191–200 (2006).PubMed 

Google Scholar 
Van der Gucht, K. et al. The power of species sorting: Local factors drive bacterial community composition over a wide range of spatial scales. Proc. Natl. Acad. Sci. 104, 20404–20409 (2007).PubMed 
PubMed Central 

Google Scholar 
Vanschoenwinkel, B. et al. Species sorting in space and time—The impact of disturbance regime on community assembly in a temporary pool metacommunity. J. North Am. Benthol. Soc. 29, 1267–1278 (2010).
Google Scholar 
Datry, T. et al. Metacommunity patterns across three neotropical catchments with varying environmental harshness. Freshw. Biol. 61, 277–292 (2016).
Google Scholar 
Hansen, H. P. & Koroleff, F. Determination of nutrients. In Methods of Seawater Analysis (eds Grasshoff, K. et al.) 159–228 (Wiley-VCH Verlag GmbH, 1999).
Google Scholar 
Clesceri, L. S., Greenberg, A. E. & Eaton, A. D. Standard methods for examination of water and wastewater. 20th ed. http://ipkosar.ir/jspui/handle/961944/280820 (1999).Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples: Primers for marine microbiome studies. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
PubMed 

Google Scholar 
Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).
Google Scholar 
Ray, J. L. et al. Metabarcoding and metabolome analyses of copepod grazing reveal feeding preference and linkage to metabolite classes in dynamic microbial plankton communities. Mol. Ecol. 25, 5585–5602 (2016).CAS 
PubMed 

Google Scholar 
Hadziavdic, K. et al. Characterization of the 18S rRNA gene for designing universal eukaryote specific primers. PLoS One 9, e87624 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence sata on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kunin, V., Engelbrektson, A., Ochman, H. & Hugenholtz, P. Wrinkles in the rare biosphere: Pyrosequencing errors can lead to artificial inflation of diversity estimates. Environ. Microbiol. 12, 118–123 (2010).CAS 
PubMed 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).PubMed 
PubMed Central 

Google Scholar 
Rohwer, R. R., Hamilton, J. J., Newton, R. J. & McMahon, K. D. TaxAss: Leveraging a custom freshwater database achieves fine-scale taxonomic resolution. mSphere 3, e00327-18 (2018).PubMed 
PubMed Central 

Google Scholar 
Guillou, L. et al. The Protist Ribosomal Reference database (PR2): A catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41, D597–D604 (2012).PubMed 
PubMed Central 

Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7. https://CRAN.R-project.org/package=vegan (2020).Martinez Arbizu, P. pairwiseAdonis: Pairwise multilevel comparison using Adonis. Pairwise Adonis R package version 0.4. R package. https://cran.r-project.org/web/packages/pairwise/index.html (2017).Kassambara, A. ggpubr: ‘ggplot2’ based publication ready plots. ggpubr R package version 0.4.0. https://CRAN.R-project.org/package=ggpubr (2020).Burian, A. et al. Predation increases multiple components of microbial diversity in activated sludge communities. ISME J. 16, 1086–1094 (2022).CAS 
PubMed 

Google Scholar 
Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology, picante R package version 1.8.2. Bioinformatics 26, 1463–1464. https://cran.r-project.org/web/packages/picante/index.html (2010).Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S. 4th ed. MASS R package version 7.3-54 (Springer, 2002). https://cran.r-project.org/web/packages/MASS/index.html. ISBN 0-387-95457-0.Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. mgcv R package version 1.8-38. J. R. Stat. Soc. B 73(1), 3–36. https://cran.r-project.org/web/packages/mgcv/index.html (2011).Gu, Z. Complex heatmap visualization. iMeta 1 (2022).R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2022). https://www.R-project.org/. More

Raubenheimer, D., Simpson, S. J. & Mayntz, D. Nutrition, ecology and nutritional ecology: Toward an integrated framework. Funct. Ecol. 23, 4–16 (2009).Article 

Google Scholar 
Behmer, S. T. & Joern, A. Coexisting generalist herbivores occupy unique nutritional feeding niches. Proc. Natl. Acad. Sci. U. S. A. 105, 1977–1982. https://doi.org/10.1073/pnas.0711870105 (2008).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Lihoreau, M. et al. Nutritional ecology beyond the individual: A conceptual framework for integrating nutrition and social interactions. Ecol. Lett. 18, 273–286 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Raubenheimer, D., Simpson, S. J. & Tait, A. H. Match and mismatch: conservation physiology, nutritional ecology and the timescales of biological adaptation. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 367, 1628–1646. https://doi.org/10.1098/rstb.2012.0007 (2012).Article 
CAS 

Google Scholar 
von Liebig, J. Die organische Chemie in ihrer Anwendung auf Agricultur und Physiologie. (Vieweg, 1841).Simpson, C., Simpson, S. & Abisgold, J. In Symposium Biologica Hungarica. 39–46.Boersma, M. & Elser, J. Too much of a good thing: On stoichiometrically balanced diets and maximal growth. Ecology 87, 1325–1330 (2006).Article 
PubMed 

Google Scholar 
Simpson, S. J. & Raubenheimer, D. A multi-level analysis of feeding behaviour: The geometry of nutritional decisions. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 342, 381–402. https://doi.org/10.1098/rstb.1993.0166 (1993).Article 
ADS 

Google Scholar 
Zanotto, F. P., Raubenheimer, D. & Simpson, S. J. Haemolymph amino acid and sugar levels in locust fed nutritionally unbalanced diets. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 166, 223–229 (1996).Article 
CAS 

Google Scholar 
Kohl, K. D., Coogan, S. C. & Raubenheimer, D. Do wild carnivores forage for prey or for nutrients? Evidence for nutrient-specific foraging in vertebrate predators. BioEssays 37, 701–709. https://doi.org/10.1002/bies.201400171 (2015).Article 
PubMed 

Google Scholar 
Remonti, L., Balestrieri, A., Raubenheimer, D. & Saino, N. Functional implications of omnivory for dietary nutrient balance. Oikos 125, 1233–1240 (2016).Article 
CAS 

Google Scholar 
McIntyre, P. B. & Flecker, A. S. In Community Ecology of Stream Fishes: Concepts, Approaches, and Techniques. American Fisheries Society, Symposium. 539–558 (Citeseer).DeGabriel, J. L. et al. Translating nutritional ecology from the laboratory to the field: Milestones in linking plant chemistry to population regulation in mammalian browsers. Oikos 123, 298–308 (2014).Article 

Google Scholar 
Nielsen, S. E., Larsen, T. A., Stenhouse, G. B. & Coogan, S. C. P. Complementary food resources of carnivory and frugivory affect local abundance of an omnivorous carnivore. Oikos 126, 369–380. https://doi.org/10.1111/oik.03144 (2017).Article 
CAS 

Google Scholar 
Mayntz, D., Raubenheimer, D., Salomon, M., Toft, S. & Simpson, S. J. Nutrient-specific foraging in invertebrate predators. Science 307, 111–113 (2005).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Anderson, T. R., Boersma, M. & Raubenheimer, D. Stoichiometry: Linking elements to biochemicals. Ecology 85, 1193–1202 (2004).Article 

Google Scholar 
McManamay, R. A., Webster, J. R., Valett, H. M. & Dolloff, C. A. Does diet influence consumer nutrient cycling? Macroinvertebrate and fish excretion in streams. J. N. Am. Benthol. Soc. 30, 84–102. https://doi.org/10.1899/09-152.1 (2011).Article 

Google Scholar 
Vivas, M., Sánchez-Vázquez, F., García García, B. & Madrid, J. Macronutrient self-selection in European sea bass in response to dietary protein or fat restriction. Aquac. Res. 34, 271–280 (2003).Article 

Google Scholar 
Rubio, V., Navarro, D. B., Madrid, J. & Sánchez-Vázquez, F. Macronutrient self-selection in Solea senegalensis fed macronutrient diets and challenged with dietary protein dilutions. Aquaculture 291, 95–100 (2009).Article 
CAS 

Google Scholar 
Mayntz, D. et al. Balancing of protein and lipid intake by a mammalian carnivore, the mink, Mustela vison. Anim. Behav. 77, 349–355 (2009).Article 

Google Scholar 
Al Shareefi, E. & Cotter, S. C. The nutritional ecology of maturation in a carnivorous insect. Behav. Ecol. 30, 256–266 (2019).Article 

Google Scholar 
Jensen, K. et al. Nutrient-specific compensatory feeding in a mammalian carnivore, the mink, Neovison vison. Br. J. Nutr. 112, 1226–1233. https://doi.org/10.1017/S0007114514001664 (2014).Article 
CAS 
PubMed 

Google Scholar 
Hayward, M., Jędrzejewski, W. & Jedrzejewska, B. Prey preferences of the tiger Panthera tigris. J. Zool. 286, 221–231 (2012).Article 

Google Scholar 
Whitney, T. D., Sitvarin, M. I., Roualdes, E. A., Bonner, S. J. & Harwood, J. D. Selectivity underlies the dissociation between seasonal prey availability and prey consumption in a generalist predator. Mol. Ecol. 27, 1739–1748 (2018).Article 
PubMed 

Google Scholar 
Potter, T. I., Stannard, H. J., Greenville, A. C. & Dickman, C. R. Understanding selective predation: Are energy and nutrients important?. PLoS One 13, e0201300 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Machovsky-Capuska, G. E. et al. Sex-specific macronutrient foraging strategies in a highly successful marine predator: The Australasian gannet. Mar. Biol. 163, 75 (2016).Article 

Google Scholar 
Remonti, L., Balestrieri, A. & Prigioni, C. Percentage of protein, lipids, and carbohydrates in the diet of badger (Meles meles) populations across Europe. Ecol. Res. 26, 487–495 (2011).Article 
CAS 

Google Scholar 
Wilder, S. M. et al. Three-dimensional diet regulation and the consequences of choice for weight and activity level of a marsupial carnivore. J. Mammal. 97, 1645–1651 (2016).Article 

Google Scholar 
Yu, D.-H. et al. Effect of partial replacement of fish meal with soybean meal and feeding frequency on growth, feed utilization and body composition of juvenile Chinese sucker, Myxocyprinus asiaticus (Bleeker). Aquac. Res. 44, 388–394. https://doi.org/10.1111/j.1365-2109.2011.03043.x (2013).Article 
CAS 

Google Scholar 
Kaushik, S. J. & Seiliez, I. Protein and amino acid nutrition and metabolism in fish: current knowledge and future needs. Aquac. Res. 41, 322–332. https://doi.org/10.1111/j.1365-2109.2009.02174.x (2010).Article 
CAS 

Google Scholar 
Gaye-Siessegger, J., McCullagh, J. S. & Focken, U. The effect of dietary amino acid abundance and isotopic composition on the growth rate, metabolism and tissue delta13C of rainbow trout. Br. J. Nutr. 105, 1764–1771. https://doi.org/10.1017/S0007114510005696 (2011).Article 
CAS 
PubMed 

Google Scholar 
McCullagh, J., Gaye-Siessegger, J. & Focken, U. Determination of underivatized amino acid delta(13)C by liquid chromatography/isotope ratio mass spectrometry for nutritional studies: The effect of dietary non-essential amino acid profile on the isotopic signature of individual amino acids in fish. Rapid Commun. Mass Spectrom. RCM 22, 1817–1822. https://doi.org/10.1002/rcm.3554 (2008).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Gao, K. et al. Dietary L-arginine supplementation enhances placental growth and reproductive performance in sows. Amino Acids 42, 2207–2214 (2012).Article 
CAS 
PubMed 

Google Scholar 
Wu, G. et al. Amino acid nutrition in animals: Protein synthesis and beyond. Annu. Rev. Anim. Biosci. 2, 387–417. https://doi.org/10.1146/annurev-animal-022513-114113 (2014).Article 
CAS 
PubMed 

Google Scholar 
Dwyer, G. K., Stoffels, R. J., Silvester, E. & Rees, G. N. Prey amino acid composition affects rates of protein synthesis and N wastage of a freshwater carnivore. Mar. Freshw. Res. 71, 229–237. https://doi.org/10.1071/MF18410 (2020).Article 
CAS 

Google Scholar 
Kremen, N. et al. Body composition and amino acid concentrations of select birds and mammals consumed by cats in northern and central California. J. Anim. Sci. 91, 1270–1276 (2013).Article 
CAS 
PubMed 

Google Scholar 
Goodman-Lowe, G., Carpenter, J., Atkinson, S. & Ako, H. Nutrient, fatty acid, amino acid and mineral analysis of natural prey of the Hawaiian monk seal, Monachus schauinslandi. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 123, 137–146 (1999).Article 
CAS 

Google Scholar 
Dwyer, G. K., Stoffels, R. J., Rees, G. N., Shackleton, M. & Silvester, E. A predicted change in the amino acid landscapes available to freshwater carnivores. Freshw. Sci. 37, 000–000 (2018).Article 

Google Scholar 
Kolmakova, A. A. et al. Amino acid composition of epilithic biofilm and benthic animals in a large Siberian river. Freshw. Biol. 58, 2180–2195. https://doi.org/10.1111/fwb.12200 (2013).Article 
CAS 

Google Scholar 
Thera, J. C., Kidd, K. A. & Bertolo, R. F. Amino acids in freshwater food webs: Assessing their variability among taxa, trophic levels, and systems. Freshw. Biol. 65, 1101–1113 (2020).Article 
CAS 

Google Scholar 
Fargallo, J. A., Navarro-López, J., Palma-Granados, P. & Nieto, R. M. Foraging strategy of a carnivorous-insectivorous raptor species based on prey size, capturability and nutritional components. Sci. Rep. 10, 1–12 (2020).Article 

Google Scholar 
Shakya, M., Silvester, E., Holland, A. & Rees, G. Taxonomic, seasonal and spatial variation in the amino acid profile of freshwater macroinvertebrates. Aquat. Sci. 83, 1–15 (2021).Article 

Google Scholar 
Martinez, J. B., Chatzifotis, S., Divanach, P. & Takeuchi, T. Effect of dietary taurine supplementation on growth performance and feed selection of sea bass Dicentrarchus labrax fry fed with demand-feeders. Fish. Sci. 70, 74–79 (2004).Article 

Google Scholar 
Yamamoto, T. et al. Self-selection and feed consumption of diets with a complete amino acid composition and a composition deficient in either methionine or lysine by rainbow trout, Oncorhynchus mykiss (Walbaum). Aquac. Res. 32, 83–91 (2001).Article 
CAS 

Google Scholar 
Dabrowski, K., Arslan, M., Terjesen, B. F. & Zhang, Y. The effect of dietary indispensable amino acid imbalances on feed intake: Is there a sensing of deficiency and neural signaling present in fish?. Aquaculture 268, 136–142. https://doi.org/10.1016/j.aquaculture.2007.04.065 (2007).Article 
CAS 

Google Scholar 
Caprio, J. Olfaction and taste in the channel catfish: An electrophysiological study of the responses to amino acids and derivatives. J. Comp. Physiol. 123, 357–371 (1978).Article 

Google Scholar 
Hazlett, B. A. Crayfish feeding responses to zebra mussels depend on microorganisms and learning. J. Chem. Ecol. 20, 2623–2630. https://doi.org/10.1007/bf02036196 (1994).Article 
CAS 
PubMed 

Google Scholar 
Gietzen, D. W. & Aja, S. M. The brain’s response to an essential amino acid-deficient diet and the circuitous route to a better meal. Mol. Neurobiol. 46, 332–348. https://doi.org/10.1007/s12035-012-8283-8 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rees, G. N., Shackleton, M. E., Watson, G. O., Dwyer, G. K. & Stoffels, R. J. Metabarcoding demonstrates dietary niche partitioning in two coexisting blackfish species. Mar. Freshw. Res. 71, 512–517 (2020).Article 
CAS 

Google Scholar 
Antoine, F., Wei, C., Littell, R. & Marshall, M. HPLC method for analysis of free amino acids in fish using o-phthaldialdehyde precolumn derivatization. J. Agric. Food Chem. 47, 5100–5107 (1999).Article 
CAS 
PubMed 

Google Scholar 
Anderson, M. J. & Santana-Garcon, J. Measures of precision for dissimilarity-based multivariate analysis of ecological communities. Ecol. Lett. 18, 66–73 (2015).Article 
PubMed 

Google Scholar 
Fountoulakis, M. & Lahm, H.-W. Hydrolysis and amino acid composition analysis of proteins. J. Chromatogr. A 826, 109–134 (1998).Article 
CAS 
PubMed 

Google Scholar 
McArdle, B. H. When are rare species not there?. Oikos 57, 276–277 (1990).Article 

Google Scholar 
Machovsky-Capuska, G. E., Coogan, S. C., Simpson, S. J. & Raubenheimer, D. Motive for killing: What drives prey choice in wild predators?. Ethology 122, 703–711 (2016).Article 

Google Scholar 
Tait, A. H., Raubenheimer, D., Stockin, K. A., Merriman, M. & Machovsky-Capuska, G. E. Nutritional geometry and macronutrient variation in the diets of gannets: The challenges in marine field studies. Mar. Biol. 161, 2791–2801 (2014).Article 
CAS 

Google Scholar 
Bosch, G., Hagen-Plantinga, E. A. & Hendriks, W. H. Dietary nutrient profiles of wild wolves: Insights for optimal dog nutrition?. Br. J. Nutr. 113, S40–S54 (2015).Article 
CAS 
PubMed 

Google Scholar 
Machovsky-Capuska, G. E., Senior, A. M., Simpson, S. J. & Raubenheimer, D. The multidimensional nutritional niche. Trends Ecol. Evol. 31, 355–365 (2016).Article 
PubMed 

Google Scholar 
Jensen, K. et al. Optimal foraging for specific nutrients in predatory beetles. Proc. R. Soc. B 279, 2212–2218. https://doi.org/10.1098/rspb.2011.2410 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Machovsky-Capuska, G. E. & Raubenheimer, D. The nutritional ecology of marine apex predators. Ann. Rev. Mar. Sci. 12, 361–387 (2020).Article 
PubMed 

Google Scholar 
Schindler, D. E. & Eby, L. A. Stoichiometry of fishes and their prey: Implications for nutrient recycling. Ecology 78, 1816–1831 (1997).Article 

Google Scholar 
Morosinotto, C., Villers, A., Varjonen, R. & Korpimäki, E. Food supplementation and predation risk in harsh climate: Interactive effects on abundance and body condition of tit species. Oikos 126, 863–873. https://doi.org/10.1111/oik.03476 (2017).Article 

Google Scholar 
Österblom, H., Olsson, O., Blenckner, T. & Furness, R. W. Junk-food in marine ecosystems. Oikos 117, 967–977 (2008).Article 

Google Scholar 
Dwyer, G. K., Stoffels, R. J. & Pridmore, P. A. Morphology, metabolism and behaviour: responses of three fishes with different lifestyles to acute hypoxia. Freshw. Biol. 59, 819–831. https://doi.org/10.1111/fwb.12306 (2014).Article 
CAS 

Google Scholar 
Hubel, T. Y. et al. Energy cost and return for hunting in African wild dogs and cheetahs. Nat. Commun. 7, 11034 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ip, Y. K., Lim, C. K., Lee, S. L., Wong, W. P. & Chew, S. F. Postprandial increases in nitrogenous excretion and urea synthesis in the giant mudskipper Periophthalmodon schlosseri. J. Exp. Biol. 207, 3015–3023 (2004).Article 
CAS 
PubMed 

Google Scholar 
Wilkie, M. P. Mechanisms of ammonia excretion across fish gills. Comp. Biochem. Physiol. A Physiol. 118, 39–50 (1997).Article 

Google Scholar 
Yamamoto, T. et al. Self-selection of diets with different amino acid profiles by rainbow trout (Oncorhynchus mykiss). Aquaculture 187, 375–386 (2000).Article 
CAS 

Google Scholar 
Kilkenny, C., Browne, W. J., Cuthill, I. C., Emerson, M. & Altman, D. G. J. P. B. Improving bioscience research reporting: The ARRIVE guidelines for reporting animal research. J. Pharmacol. Pharmacother. 8, e1000412 (2010).
Google Scholar  More