Ecology

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

Study populations and sequencing strategyDNA libraries were prepared for 1261 D. sylvestris individuals from 115 populations (5–20 individuals per population) under a modified protocol49 of the Illumina Nextera DNA library preparation kit (Supplementary Methods S1.1, Supplementary Data 1). Individuals were indexed with unique dual-indexes (IDT Illumina Nextera 10nt UDI – 384 set) from Integrated DNA Technologies Co, to avoid index-hopping50. Libraries were sequenced (150 bp paired-end sequencing) in four lanes of an Illumina NovaSeq 6000 machine at Novogene Co. This resulted in an average coverage of ca. 2x per individual. Sequenced individuals were trimmed for adapter sequences (Trimmomatic version 0.3551), mapped (BWA-MEM version 0.7.1752,53) against a reference assembly54 (ca. 440 Mb), had duplicates marked and removed (Picard Toolkit version 2.0.1; http://broadinstitute.github.io/picard), locally realigned around indels (GATK version 3.555), recalibrated for base quality scores (ATLAS version 0.956) and had overlapping read pairs clipped (bamUtil version 1.0.1457) (Supplementary Methods S1.1). Population genetic analyses were performed on the resultant BAM files via genotype likelihoods (ANGSD version 0.93358 and ATLAS versions 0.9–1.056), to accommodate the propagation of uncertainty from the raw sequence data to population genetic inference.Population genetic structure and biogeographic barriersTo investigate the genetic structure of our samples (Fig. 2A, Supplementary Fig. S2), we performed principal component analyses (PCA) on all 1261 samples (“full” dataset) via PCAngsd version 0.9859, following conversion of the mapped sequence data to ANGSD genotype likelihoods in Beagle format (Supplementary Methods S1.2). To visualise PCA results in space (Supplementary Fig. S4), individuals’ principal components were projected on a map, spatially interpolated (linear interpolation, akima R package version 0.6.260) and had the first two principal components represented as green and blue colour channels. Given that uneven sampling can bias the inference of structure in PCA, PCA was also performed on a balanced dataset comprising a common, down-sampled size of 125 individuals per geographic region (“balanced” dataset; Fig. 2B, Supplementary Fig. S3; Supplementary Methods S1.2; Supplementary Data 1). Individual admixture proportions and ancestral allele frequencies were estimated using PCAngsd (-admix model) for K = 2–6, using the balanced dataset to avoid potential biases related to imbalanced sampling22,23 and an automatic search for the optimal sparseness regularisation parameter (alpha) soft-capped to 10,000 (Supplementary Methods S1.2). To visualise ancestry proportions in space, population ancestry proportions were spatially interpolated (kriging) via code modified from Ref. 61 (Supplementary Fig. S5).To test if between-lineage admixture underlies admixture patterns inferred by PCAngsd or if the data is better explained by alternative scenarios such as recent bottlenecks, we used chromosome painting and patterns of allele sharing to construct painting palettes via the programmes MixPainter and badMIXTURE (unlinked model)28 and compared this to the PCAngsd-inferred palettes (Fig. 2B, C; Supplementary Methods S1.2). We referred to patterns of residuals between these palettes to inform of the most likely underlying demographic scenario. For assessing Alpine–Balkan palette residuals (and hence admixture), 65 individuals each from the French Alps (inferred as pure Alpine ancestry in PCAngsd), Monte Baldo (inferred with both Alpine and Balkan ancestries in PCAngsd) and Julian Alps (inferred as pure Balkan ancestry in PCAngsd) were analysed under K = 2 in PCAngsd and badMIXTURE (Fig. 2C). For assessing Apennine–Balkan admixture, 22 individuals each from the French pre-Alps (inferred as pure Apennine ancestry in PCAngsd), Tuscany (inferred with both Apennine and Balkan ancestries in PCAngsd) and Julian Alps (inferred as pure Balkan ancestry in PCAngsd) were analysed under K = 2 in PCAngsd and badMIXTURE.To construct a genetic distance tree (Supplementary Fig. S1), we first calculated pairwise genetic distances between 549 individuals (5 individuals per population for all populations) using ATLAS, employing a distance measure (weight) reflective of the number of alleles differing between the genotypes (Supplementary Methods S1.2; Supplementary Data 1). A tree was constructed from the resultant distance matrix via an initial topology defined by the BioNJ algorithm with subsequent topological moves performed via Subtree Pruning and Regrafting (SPR) in FastME version 2.1.6.162. This matrix of pairwise genetic distances was also used as input for analyses of effective migration and effective diversity surfaces in EEMS25. EEMS was run setting the number of modelled demes to 1000 (Fig. 2A, Supplementary Fig. S8). For each case, ten independent Markov chain Monte Carlo (MCMC) chains comprising 5 million iterations each were run, with a 1 million iteration burn-in, retaining every 10,000th iteration. Biogeographic barriers (Fig. 2A, Supplementary Fig. S7) were further identified via applying Monmonier’s algorithm24 on a valuated graph constructed via Delauney triangulation of population geographic coordinates, with edge values reflecting population pairwise FST; via the adegenet R package version 2.1.163. FST between all population pairs were calculated via ANGSD, employing a common sample size of 5 individuals per population (Supplementary Fig. S6; Supplementary Methods S1.2; Supplementary Data 1). 100 bootstrap runs were performed to generate a heatmap of genetic boundaries in space, from which a weighted mean line was drawn (Supplementary Fig. S7). All analyses in ANGSD were performed with the GATK (-GL 2) model, as we noticed irregularities in the site frequency spectra (SFS) with the SAMtools (-GL 1) model similar to that reported in Ref. 58 with particular BAM files. All analyses described above were performed on the full genome.Ancestral sequence reconstructionTo acquire ancestral states and polarise site-frequency spectra for use in the directionality index ψ and demographic inference, we reconstructed ancestral genome sequences at each node of the phylogenetic tree of 9 Dianthus species: D. carthusianorum, D. deltoides, D. glacialis, D. sylvestris (Apennine lineage), D. lusitanus, D. pungens, D. superbus alpestris, D. superbus superbus, and D. sylvestris (Alpine lineage). This tree topology was extracted from a detailed reconstruction of Dianthus phylogeny based on 30 taxa by Fior et al. (Fior, Luqman, Scharmann, Zemp, Zoller, Pålsson, Gargano, Wegmann & Widmer; paper in preparation) (Supplementary Methods S1.3). For ancestral sequence reconstruction, one individual per species was sequenced at medium coverage (ca. 10x), trimmed (Trimmomatic), mapped against the D. sylvestris reference assembly (BWA-MEM) and had overlapping read pairs clipped (bamUtil) (Supplementary Methods S1.3). For each species, we then generated a species-specific FASTA using GATK FastaAlternateReferenceMaker. This was achieved by replacing the reference bases at polymorphic sites with species-specific variants as identified by freebayes64 (version 1.3.1; default parameters), while masking (i.e., setting as “N”) sites (i) with zero depth and (ii) that didn’t pass the applied variant filtering criteria (i.e., that are not confidently called as polymorphic; Supplementary Methods S1.3). Species FASTA files were then combined into a multi-sample FASTA. Using this, we probabilistically reconstructed ancestral sequences at each node of the tree via PHAST (version 1.4) prequel65, using a tree model produced by PHAST phylofit under a REV substitution model and the specified tree topology (Supplementary Methods S1.3). Ancestral sequence FASTA files were then generated from the prequel results using a custom script.Expansion signalTo calculate the population pairwise directionality index ψ for the Alpine lineage, we utilised equation 1b from Peter and Slatkin (2013)31, which defines ψ in terms of the two-population site frequency spectrum (2D-SFS) (Supplementary Methods S1.4). 2D-SFS between all population pairs (10 individuals per population; Supplementary Data 1) were estimated via ANGSD and realSFS66 (Supplementary Methods S1.4), for unfolded spectra. Unfolding of spectra was achieved via polarisation with respect to the ancestral state of sites defined at the D. sylvestris (Apennine lineage) – D. sylvestris (Alpine lineage) ancestral node. Correlation of pairwise ψ and (great-circle) distance matrices was tested via a Mantel test (10,000 permutations). To infer the geographic origin of the expansion (Fig. 3), we employed a time difference of arrival (TDOA) algorithm following Peter and Slatkin (2013);31 performed via the rangeExpansion R package version 0.0.0.900031,67. We further estimated the strength of the founder of this expansion using the same package.Demographic inferenceTo evaluate the demographic history of D. sylvestris, a set of candidate demographic models was formulated. To constrain the topology of tested models, we first inferred the phylogenetic tree of the three identified evolutionary lineages of D. sylvestris (Alpine, Apennine and Balkan) as embedded within the larger phylogeny of the Eurasian Dianthus clade (note that the phylogeny from Fior et al. (Fior, Luqman, Scharmann, Zemp, Zoller, Pålsson, Gargano, Wegmann & Widmer; paper in preparation) excludes Balkan representatives of D. sylvestris). Trees were inferred based on low-coverage whole-genome sequence data of 1–2 representatives from each D. sylvestris lineage, together with whole-genome sequence data of 7 other Dianthus species, namely D. carthusianorum, D. deltoides, D. glacialis, D. lusitanus, D. pungens, D. superbus alpestris and D. superbus superbus, that were used to root the D. sylvestris clade (Supplementary Methods S1.5). We estimated distance-based phylogenies using ngsDist68 that accommodates genotype likelihoods in the estimation of genetic distances (Supplementary Methods S1.5). Genetic distances were calculated via two approaches: (i) genome-wide and (ii) along 10 kb windows. For the former, 110 bootstrap replicates were calculated by re-sampling over similar-sized genomic blocks. For the alternative strategy based on 10 kb windows, window trees were combined using ASTRAL-III version 5.6.369 to generate a genome-wide consensus tree accounting for potential gene tree discordance (Supplementary Methods S1.5). Trees were constructed from matrices of genetic distances from initial topologies defined by the BioNJ algorithm with subsequent topological moves performed via Subtree Pruning and Regrafting (SPR) in FastME version 2.1.6.162. We rooted all resultant phylogenetic trees with D. deltoides as the outgroup70. Both approaches recovered a topology with the Balkan lineage diverging prior to the Apennine and Alpine lineages (Supplementary Fig. S9). This taxon topology for D. sylvestris was supported by high ASTRAL-III posterior probabilities ( >99%), ASTRAL-III quartet scores ( >0.5) and bootstrap values ( >99%). Topologies deeper in the tree were less well-resolved (with quartet scores More

Modular components of the DFTe energy functionalThe central ingredient of DFTe is an energy functional E, assembled according to Eq. (1). The methodology of DFTe can be understood by inspecting the dispersal and environmental energies in Eqs. (2) and (3) without interactions. In our first case study, illustrated in Fig. 2 and Supplementary Fig. 2, we demonstrate that equation (3), in conjunction with Eq. (2), can realistically describe the influence of the environment on species’ distributions. Mechanisms that alter the trade-off between dispersal and environment can be introduced as part of Eint. For instance, back reactions on the environment could be modelled with a bifunctional Ebr[Venv, n] that yields the equilibrated modified environment ({V}_{s}^{{{{{{{{rm{env}}}}}}}}}+delta {E}_{{{{{{{{rm{br}}}}}}}}}[{{{{{{{{bf{V}}}}}}}}}^{{{{{{{{rm{env}}}}}}}}},{{{{{{{bf{n}}}}}}}}]/delta {n}_{s}({{{{{{{bf{r}}}}}}}})), cf. Eq. (5).In the following we make explicit the interaction and resource energies that enter Eq. (1) and are used in our case studies of Figs. 2–7. We let Eint[n] include all possible bipartite interactions$${E}_{gamma }[{{{{{{{bf{n}}}}}}}}]=mathop{sum }limits_{{s,{s}^{{prime} }!=!1}atop {{s}^{{prime} }ne s}}^{S}{int}_{A}({{{{{{{rm{d}}}}}}}}{{{{{{{bf{r}}}}}}}})({{{{{{{rm{d}}}}}}}}{{{{{{{{bf{r}}}}}}}}}^{{prime} }){n}_{s}{({{{{{{{bf{r}}}}}}}})}^{{alpha }_{s}},{gamma }_{s{s}^{{prime} }}({{{{{{{bf{r}}}}}}}},, {{{{{{{{bf{r}}}}}}}}}^{{prime} }){n}_{{s}^{{prime} }}{({{{{{{{{bf{r}}}}}}}}}^{{prime} })}^{{beta }_{{s}^{{prime} }}},$$
(6)
which include amensalism, commensalism, mutualism, and so forth. Here, ({alpha }_{s},, {beta }_{{s}^{{prime} }}ge 0), and the interaction kernels ({gamma }_{s{s}^{{prime} }}) are assembled from fitness proxies of species s and ({s}^{{prime} }) (Supplementary Table 1). Higher-order interactions can be introduced, for example, through (i) terms like ({n}_{s},{gamma }_{s{s}^{{prime} }},{n}_{{s}^{{prime} }},{gamma }_{{s}^{{prime} }{s}^{{primeprime} }}^{{prime} },{n}_{{s}^{{primeprime} }}) that build on pairwise interactions or (ii) genuinely multipartite expressions like ({gamma }_{s{s}^{{prime} }{s}^{{primeprime} }}{n}_{s},,{n}_{{s}^{{prime} }},{n}_{{s}^{{primeprime} }}). Multi-partite interactions based on bipartite interactions do not seem to be an uncommon scenario48. However, there may be systems where nonzero coefficients ({gamma }_{s{s}^{{prime} }{s}^{{primeprime} }}) couple all species. This poses a challenge for mechanistic theories in general. Then, ‘simpler subsystems’ that have to be included in the DFTe workflow of Fig. 1a can only refer to situations where other energy components are absent, such as resource terms or complex environments. For example, the coefficients ({gamma }_{s{s}^{{prime} }{s}^{{primeprime} }}) could be extracted in an experiment with a controlled simple environment and then used to model the interacting species in a real-world setting. For (({alpha }_{s},, {beta }_{{s}^{{prime} }})=(1,1)) we identify the contact interaction in physics as ({gamma }_{s{s}^{{prime} }}propto delta ({{{{{{{bf{r}}}}}}}}-{{{{{{{{bf{r}}}}}}}}}^{{prime} })) with the two-dimensional delta function δ( ), while the Coulomb interaction amounts to setting ({gamma }_{s{s}^{{prime} }}propto 1/|{{{{{{{bf{r}}}}}}}}-{{{{{{{{bf{r}}}}}}}}}^{{prime} }|). The mechanistic effect of these interaction kernels on the density distributions is the same in ecology as it is in physics—a mathematical insight that inspired us to build ecological analogues to the phenomenology of quantum gases, which feature functionals of the kind in Eq. (6). Note that we do not introduce any quantum effects into ecology despite the fact that the mathematical structure of DFTe is borrowed in part from quantum physics. While the contact interaction is a suitable candidate for plants and especially microbes52, we expect long-range interactions (for example, repulsion of Coulomb type) to be more appropriate for species with long-range sensors, such as eyes. Both types of interactions feature in describing the ecosystems addressed in this work.In a natural setting the equilibrium abundances are ultimately constrained by the accessible resources. It is within these limits of resource availability that environment as well as intra- and inter-specific interactions can shape the density distributions. An energy term for penalising over- and underconsumption of resources is thus of central importance. Each species consumes resources from some of the K provided resources, indexed by k. A subset of species consumes the locally available resource density ρk(r) according to the resource requirements νks, which represent the absolute amount of resource k consumed by one individual (or aggregated constituent) of species s. The simple quadratic functional$${E}_{{{{{{{{rm{Res}}}}}}}}}[{{{{{{{bf{n}}}}}}}}]={int}_{A}({{{{{{{rm{d}}}}}}}}{{{{{{{bf{r}}}}}}}})mathop{sum }limits_{k=1}^{K}{{{{{{{{mathcal{L}}}}}}}}}_{k}left({{{{{{{bf{n}}}}}}}},, {rho }_{k}right)equiv zeta {int}_{A}({{{{{{{rm{d}}}}}}}}{{{{{{{bf{r}}}}}}}})mathop{sum }limits_{k=1}^{K}{w}_{k}({{{{{{{bf{r}}}}}}}}){left[mathop{sum }limits_{s=1}^{S}{nu }_{ks}{n}_{s}({{{{{{{bf{r}}}}}}}})-{rho }_{k}({{{{{{{bf{r}}}}}}}})right]}^{2}$$
(7)
proves appropriate. Here, νksns is the portion of resource density ρk that is consumed by species s. That is, νks  > 0 indicates that s requires resource k. If Eq. (7) is the total energy functional, then a single-species system with a single resource equilibrates with density n1(r) = ρ1(r)/ν11 at every position r, and additional DFTe energy components would modify this equilibrium. Predator–prey relationships are introduced by making species k a resource ({rho }_{k}=left]{n}_{k}right[), where (left]nright[) declares n a constant w.r.t. the functional differentiation of E, that is, the predator tends to align with the prey, not the prey with the predator. In view of the energy minimisation, the quadratic term in Eq. (7) entails that regions of low resource density ρk are less important than regions of high ρk. The different resources k have the same ability to limit the abundances, such that the limiting resource k = l at r has to come with the largest of weights wl(r), irrespective of the absolute amounts of resources at r. For example, the weights wk have to ensure that an essential but scarce mineral has (a priori) the same ability to limit the abundances as a resource like water, which might be abundant in absolute terms. To that end, we specify the weights$${w}_{k}({{{{{{{bf{r}}}}}}}})=frac{1}{{bar{rho }}_{k}^{2}}mathop{sum}limits_{s}eta ({nu }_{ks})exp left[sigma left(frac{{lambda }_{ks}}{{lambda }_{ls}}-1right)right],$$
(8)
which are inspired by the smooth minimum function, where σ  λls irrelevant at r. Using ({E}_{{{{{{{{rm{Res}}}}}}}}}), we show that an analytically solvable minimal example of two amensalistically interacting species already exhibits a plethora of resource-dependent equilibrium states (see Supplementary Notes and Supplementary Fig. 1).We specify the DFTe energy functional in Eq. (1) by summing Eqs. (2), (3), (6), and (7) and by (optionally) constraining the abundances to N via Lagrange multipliers μ:$$E[{{{{{{{bf{n}}}}}}}},, {{{{{{{boldsymbol{mu }}}}}}}}]({{{{{{{bf{N}}}}}}}}) equiv E[{{{{{{{bf{n}}}}}}}}]+{E}_{{{{{{{{boldsymbol{mu }}}}}}}}}[{{{{{{{bf{n}}}}}}}}]({{{{{{{bf{N}}}}}}}})\ equiv {E}_{{{{{{{{rm{dis}}}}}}}}}[{{{{{{{bf{n}}}}}}}}]+{E}_{{{{{{{{rm{env}}}}}}}}}[{{{{{{{bf{n}}}}}}}}]+{E}_{gamma }[{{{{{{{bf{n}}}}}}}}]+{E}_{{{{{{{{rm{Res}}}}}}}}}[{{{{{{{bf{n}}}}}}}}]+mathop{sum }limits_{s=1}^{S}{mu }_{s}left({N}_{s}-{int}_{A}({{{{{{{rm{d}}}}}}}}{{{{{{{bf{r}}}}}}}}),{n}_{s}right).$$
(9)
Uniform situations are characterised by spatially constant ingredients ns = Ns/A, ρk = Rk/A, coefficients τs, etc. for the DFTe energy, such that Eq. (9) reduces to a function E(N) with building blocks$${E}_{{{{{{{{rm{dis}}}}}}}}}longrightarrow frac{1}{2,A}mathop{sum }limits_{s=1}^{S}{tau }_{s},{N}_{s}^{2},$$
(10)
$${E}_{{{{{{{{rm{env}}}}}}}}}longrightarrow mathop{sum }limits_{s=1}^{S}{V}_{s}^{{{{{{{{rm{env}}}}}}}}},{N}_{s},$$
(11)
$${E}_{gamma }longrightarrow mathop{sum }limits_{{s,{s}^{{prime} }!=!1}atop {{s}^{{prime} }ne s}}^{S}frac{{N}_{s}^{{alpha }_{s}},{gamma }_{s{s}^{{prime} }},{N}_{{s}^{{prime} }}^{{beta }_{{s}^{{prime} }}}}{{A}^{{alpha }_{s}+{beta }_{{s}^{{prime} }}-1}},$$
(12)
$${E}_{{{{{{{{rm{Res}}}}}}}}}longrightarrow Amathop{sum }limits_{k=1}^{K}{{{{{{{{mathcal{L}}}}}}}}}_{k}left({{{{{{{bf{N}}}}}}}}/A,, {R}_{k}/Aright).$$
(13)
Ecosystem equilibria from the DFTe energy functionalThe general form of Eq. (9) gives rise to two types of minimisers (viz., equilibria): First, we term$${{{{{{{mathcal{H}}}}}}}}({{{{{{{bf{N}}}}}}}})equiv E[tilde{{{{{{{{bf{n}}}}}}}}}]equiv mathop{min }limits_{{{{{{{{bf{n}}}}}}}}}left{E[{{{{{{{bf{n}}}}}}}}],left|,{int}_{A}({{{{{{{rm{d}}}}}}}}{{{{{{{bf{r}}}}}}}}),{{{{{{{bf{n}}}}}}}}({{{{{{{bf{r}}}}}}}})={{{{{{{bf{N}}}}}}}},{{{{{{{rm{(fixed)}}}}}}}}right.right}$$
(14)
the ‘DFTe hypersurface’, with (tilde{{{{{{{{bf{n}}}}}}}}}) the energy-minimising spatial density profiles for given (fixed) N. Second, the ecosystem equilibrium is attained at the equilibrium abundances (hat{{{{{{{{bf{N}}}}}}}}}={int}_{A}({{{{{{{rm{d}}}}}}}}{{{{{{{bf{r}}}}}}}}),hat{{{{{{{{bf{n}}}}}}}}}({{{{{{{bf{r}}}}}}}})), which yield the global energy minimum$${{{{{{{mathcal{H}}}}}}}}(hat{{{{{{{{bf{N}}}}}}}}})=mathop{min }limits_{{{{{{{{bf{N}}}}}}}}},{{{{{{{mathcal{H}}}}}}}}({{{{{{{bf{N}}}}}}}}),$$
(15)
where the minimisation samples all admissible abundances, that is, ({{{{{{{bf{N}}}}}}}}in {left({{mathbb{R}}}_{0}^{+}right)}^{times S}) if no further constraints are imposed.The direct minimisation of E[n] is most practical for uniform systems, which only require us to minimise E(N) over an S-dimensional space of abundances. For the general nonuniform case, we adopt a two-step strategy that reflects Eqs. (14) and (15). First, we obtain the equilibrated density distributions on ({{{{{{{mathcal{H}}}}}}}}) for fixed N from the computational DPFT framework26,27,28,29,30,31. Second, a conjugate gradient descent searches ({{{{{{{mathcal{H}}}}}}}}({{{{{{{bf{N}}}}}}}})) for the global minimiser (hat{{{{{{{{bf{N}}}}}}}}}). Technically, we perform the computationally more efficient descent in μ-space. Local minima are frequently encountered, and we identify the best candidate for the global minimum from many individual runs that are initialised with random μ. Note that system realisations with energies close to the global minimum, especially local minima, are likely observable in reality, assuming that the system can equilibrate at all. There is always an equilibrium if the energy functional is bounded from below, together with the fact that the support (abundances/densities) of the energy functional is finite in any practical application. If some DFTe energy components are chosen (too) negative, the system can be unstable, in which case the energy functional has no minimum and is inappropriate for modelling the equilibrium in question. This means that another energy functional has to be considered, or, in the worst case, that DFTe is incapable of simulating this system. We also caution that no numerical optimisation algorithms for non-convex black-box functions can guarantee to find the global minimum, not even approximately. Without analytically available characteristics of the global minimum, all one may hope for are candidates of the minimiser, and those may not even be local minima—there is no way to be certain that an optimum proposed by a numerical optimisation algorithm is stable.Density-potential functional theory (DPFT) in Thomas–Fermi (TF) approximationDefining$${V}_{s}({{{{{{{bf{r}}}}}}}})={mu }_{s}-frac{delta {E}_{{{{{{{{rm{dis}}}}}}}}}[{{{{{{{bf{n}}}}}}}}]}{delta {n}_{s}({{{{{{{bf{r}}}}}}}})}$$
(16)
for all s, we obtain the reversible Legendre transform$${E}_{{{{{{{{rm{dis}}}}}}}}}^{{{{{{{{rm{L}}}}}}}}}[{{{{{{{bf{V}}}}}}}}-{{{{{{{boldsymbol{mu }}}}}}}}]={E}_{{{{{{{{rm{dis}}}}}}}}}[{{{{{{{bf{n}}}}}}}}]+mathop{sum }limits_{s=1}^{S}{int}_{A}({{{{{{{rm{d}}}}}}}}{{{{{{{bf{r}}}}}}}}),({V}_{s}-{mu }_{s}),{n}_{s}$$
(17)
of the dispersal energy and thereby supplement the total energy with the additional variables V:$$E[{{{{{{{bf{V}}}}}}}},, {{{{{{{bf{n}}}}}}}},, {{{{{{{boldsymbol{mu }}}}}}}}]({{{{{{{bf{N}}}}}}}})={E}_{{{{{{{{rm{dis}}}}}}}}}^{{{{{{{{rm{L}}}}}}}}}[{{{{{{{bf{V}}}}}}}}-{{{{{{{boldsymbol{mu }}}}}}}}]-{int}_{A}({{{{{{{rm{d}}}}}}}}{{{{{{{bf{r}}}}}}}}),{{{{{{{bf{n}}}}}}}}cdot ({{{{{{{bf{V}}}}}}}}-{{{{{{{{bf{V}}}}}}}}}^{{{{{{{{rm{env}}}}}}}}})+{E}_{{{{{{{{rm{int}}}}}}}}}[{{{{{{{bf{n}}}}}}}}]+{{{{{{{boldsymbol{mu }}}}}}}}cdot {{{{{{{bf{N}}}}}}}}.$$
(18)
This density-potential functional is equivalent to (but more flexible than) the density-only functional E[n,  μ](N). The minimisers of E[n] are thus among the stationary points of Eq. (18) and are obtained by solving$${n}_{s}[{V}_{s}-{mu }_{s}]({{{{{{{bf{r}}}}}}}})=frac{delta {E}_{{{{{{{{rm{dis}}}}}}}}}^{{{{{{{{rm{L}}}}}}}}}[{V}_{s}-{mu }_{s}]}{delta {V}_{s}({{{{{{{bf{r}}}}}}}})}$$
(19)
and$${V}_{s}[{{{{{{{bf{n}}}}}}}}]({{{{{{{bf{r}}}}}}}})={V}_{s}^{{{{{{{{rm{env}}}}}}}}}({{{{{{{bf{r}}}}}}}})+frac{delta {E}_{{{{{{{{rm{int}}}}}}}}}[{{{{{{{bf{n}}}}}}}}]}{delta {n}_{s}({{{{{{{bf{r}}}}}}}})}$$
(20)
self-consistently for all ns while enforcing ∫A(dr) ns(r) = Ns. Specifically, starting from V(0) = Venv, such that ({n}_{s}^{(0)}={n}_{s}[{V}_{s}^{(0)}-{mu }_{s}^{(0)}]), we iterate$${n}_{s}^{(i)}mathop{longrightarrow }limits^{{{{{{{{rm{equation}}}}}}}},(20)}{V}_{s}^{(i+1)}={V}_{s}[{{{{{{{{bf{n}}}}}}}}}^{(i)}]mathop{longrightarrow }limits^{{{{{{{{rm{equation}}}}}}}},(19)}{n}_{s}^{(i+1)}=(1-{theta }_{s}),{n}_{s}^{(i)}+{theta }_{s},{n}_{s}left[{V}_{s}^{(i+1)}-{mu }_{s}^{(i+1)}right]$$
(21)
until all ns are converged sufficiently. This self-consistent loop establishes a trade-off between dispersal energy and effective environment V by forcing an initial out-of-equilibrium density distribution to equilibrate at fixed N. We adjust ({mu }_{s}^{(i)}) in each iteration i such that ({n}_{s}^{(i)}) integrates to Ns. Small enough density admixtures, with 0  More

Martins, L. C. et al. Poluição atmosférica e atendimentos por pneumonia e gripe em São Paulo, Brasil. Revista de Saúde Pública 36, 88–94 (2002).Article 
PubMed 

Google Scholar 
Goudarzi, G. et al. Health risk assessment on human exposed to heavy metals in the ambient air PM10 in Ahvaz, Southwest Iran. Int. J. Biometeorol. 62, 1075–1083 (2018).Article 
ADS 
PubMed 

Google Scholar 
Makri, A. & Stilianakis, N. I. Vulnerability to air pollution health effects. Int. J. Hygiene Environ. Health 211, 326–336 (2008).Article 

Google Scholar 
Idani, E. et al. Characteristics, sources, and health risks of atmospheric PM10-bound heavy metals in a populated Middle Eastern City. Toxin Rev. 39, 266–274 (2020).Article 

Google Scholar 
Wang, J., Hu, Z., Chen, Y., Chen, Z. & Xu, S. Contamination characteristics and possible sources of PM10 and PM2.5 in different functional areas of Shanghai, China. Atmos. Environ. 68, 221–229 (2013).Article 
ADS 
CAS 

Google Scholar 
Guarnieri, M. & Balmes, J. R. Outdoor air pollution and asthma. Lancet 383, 1581–1592 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Anderson, J. O., Thundiyil, J. G. & Stolbach, A. Clearing the air: A review of the effects of particulate matter air pollution on human health. J. Med. Toxicol. 8, 166–175 (2012).Article 
CAS 
PubMed 

Google Scholar 
Roy, D., Seo, Y.-C., Kim, S. & Oh, J. Human health risks assessment for airborne PM10-bound metals in Seoul, Korea. Environ. Sci. Pollut. Res. 26, 24247–24261 (2019).Article 
CAS 

Google Scholar 
Maesano, C. et al. Impacts on human mortality due to reductions in PM10 concentrations through different traffic scenarios in Paris, France. Sci. The Total. Environ. 698, 134257 (2020).Article 
ADS 
CAS 

Google Scholar 
Maleki, H., Sorooshian, A., Goudarzi, G., Nikfal, A. & Baneshi, M. M. Temporal profile of PM10 and associated health effects in one of the most polluted cities of the world (Ahvaz, Iran) between 2009 and 2014. Aeolian Res. 22, 135–140 (2016).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Medina, S., Le Tertre, A. & Saklad, M. The Apheis project: Air pollution and health—A European information system. Air Qual. Atmos. Heal. 2, 185–198 (2009).Article 

Google Scholar 
Medina, S., Plasencia, A., Ballester, F., Mücke, H. & Schwartz, J. Apheis: Public health impact of PM10 in 19 European cities. J. Epidemiol. Community Heal. 58, 831–836 (2004).Article 
CAS 

Google Scholar 
Pérez-Martínez, P. J., de Fátima Andrade, M. & de Miranda, R. M. Traffic-related air quality trends in São Paulo, Brazil. J. Geophys. Res. Atmos. 120, 6290–6304 (2015).Article 
ADS 

Google Scholar 
Sánchez-Ccoyllo, O. R. et al. Vehicular particulate matter emissions in road tunnels in Sao Paulo, Brazil. Environ. Monitoring Assess. 149, 241–249 (2009).Article 

Google Scholar 
Ribeiro, H. & de Assunção, J. V. Historical overview of air pollution in São Paulo Metropolitan Area, Brazil: Influence of mobile sources and related health effects. WIT Trans. Built Environ. 52,10 (2001).Bravo, M. A. & Bell, M. L. Spatial heterogeneity of PM10 and O3 in São Paulo, Brazil, and implications for human health studies. J. Air Waste Manag. Assoc. 61, 69–77 (2011).Article 
CAS 
PubMed 

Google Scholar 
De Freitas, E. D., Martins, L. D., da Silva Dias, P. L. & de Fátima Andrade, M. A simple photochemical module implemented in rams for tropospheric ozone concentration forecast in the metropolitan area of Sao Paulo, Brazil: Coupling and validation. Atmos. Environ. 39, 6352–6361 (2005).Article 
ADS 

Google Scholar 
Encalada-Malca, A. A., Cochachi-Bustamante, J. D., Rodrigues, P. C., Salas, R. & López-Gonzales, J. L. A spatio-temporal visualization approach of PM10 concentration data in Metropolitan Lima. Atmosphere 12, 609 (2021).Article 
ADS 

Google Scholar 
do Meio Ambiente, C. N. Institutes the national air quality control programee. Tech. Rep., Official Journal of the Federative Republic of Brazil (1989).do Meio Ambiente, C. N. Sets standards of primary and secondary air quality and even the criteria for acute episodes of air pollution. Tech. Rep., Official Journal of the Federative Republic of Brazil (1990).Artaxo, P. O estado da qualidade do ar no brasil. Work. Pap. WRI Brasil 32 (2021).Costa, A. F., Hoek, G., Brunekreef, B. & Ponce de Leon, A. C. Air pollution and deaths among elderly residents of Sao Paulo, Brazil: An analysis of mortality displacement. Environ. Health Perspectives 125, 349–354 (2017).Article 
CAS 

Google Scholar 
Bravo, M. A., Son, J., De Freitas, C. U., Gouveia, N. & Bell, M. L. Air pollution and mortality in São Paulo, Brazil: Effects of multiple pollutants and analysis of susceptible populations. J. Exposure Sci. Environ. Epidemiol. 26, 150–161 (2016).Article 
CAS 

Google Scholar 
Chiarelli, P. S. et al. The association between air pollution and blood pressure in traffic controllers in Santo André, São Paulo, Brazil. Environ. Res. 111, 650–655 (2011).Article 
CAS 

Google Scholar 
Ventura, L. M. B., de Oliveira Pinto, F., Soares, L. M., Luna, A. S. & Gioda, A. Forecast of daily PM2.5 concentrations applying artificial neural networks and holt-winters models. Air Qual. Atmos. Heal. 12, 317–325 (2019).Article 
CAS 

Google Scholar 
Leão, M. L. P., Zhang, L. & da Silva Júnior, F. M. R. Effect of particulate matter (PM2.5 and PM10) on health indicators: Climate change scenarios in a Brazilian Metropolis. Environ. Geochem. Heal. 44, 1–12 (2022).Habermann, M. & Gouveia, N. Application of land use regression to predict the concentration of inhalable particular matter in São Paulo City, Brazil. Engenharia Sanit. e Ambiental 17, 155–162 (2012).Article 

Google Scholar 
Braga, A. L. F., Pereira, L. A. A., Procópio, M., André, P. A. D. & Saldiva, P. H. D. N. Association between air pollution and respiratory and cardiovascular diseases in Itabira, Minas Gerais State. Brazil. Cadernos de Saúde Pública 23, S570–S578 (2007).Article 
PubMed 

Google Scholar 
Pinto, W. D. P., Reisen, V. A. & Monte, E. Z. Previsão da concentração de material particulado inalável, na região da grande vitória, ES, Brasil, utilizando o modelo sarimax. Engenharia Sanitária e Ambiental 23, 307–318 (2018).Article 

Google Scholar 
Schornobay-Lui, E. et al. Prediction of short and medium term PM10 concentration using artificial neural networks. Manag. Environ. Qual. An Int. J. 30, 414–436 (2018).Neto, P. S. D. M. et al. Neural-based ensembles for particulate matter forecasting. IEEE Access 9, 14470–14490 (2021).Article 

Google Scholar 
Albuquerque Filho, F. S. D., Madeiro, F., Fernandes, S. M., de Mattos Neto, P. S. & Ferreira, T. A. Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks. Química Nova 36, 783–789 (2013).Lei, T. M., Siu, S. W., Monjardino, J., Mendes, L. & Ferreira, F. Using machine learning methods to forecast air quality: A case study in Macao. Atmosphere 13, 1412 (2022).Article 
ADS 
CAS 

Google Scholar 
Yu, T. et al. Study on the regional prediction model of PM2.5 concentrations based on multi-source observations. Atmos. Pollut. Res. 13, 101363 (2022).Article 
CAS 

Google Scholar 
Li, J., Xu, G. & Cheng, X. Combining spatial pyramid pooling and long short-term memory network to predict PM2.5 concentration. Atmos. Pollut. Res. 13, 101309 (2022).Article 
CAS 

Google Scholar 
Cordova, C. H. et al. Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru. Sci. Rep. 11, 1–19 (2021).Article 

Google Scholar 
Plocoste, T., Calif, R. & Jacoby-Koaly, S. Temporal multiscaling characteristics of particulate matter PM10 and ground-level ozone O3 concentrations in caribbean region. Atmos. Environ. 169, 22–35 (2017).Article 
ADS 
CAS 

Google Scholar 
Calif, R. & Schmitt, F. G. Multiscaling and joint multiscaling description of the atmospheric wind speed and the aggregate power output from a wind farm. Nonlinear Process. Geophys. 21, 379–392 (2014).Article 
ADS 

Google Scholar 
Hyndman, R. J. & Khandakar, Y. Automatic time series forecasting: The forecast package for r. J. Stat. Softw. 27, 1–22 (2008).Article 

Google Scholar 
Harvey, A. C. Forecasting, structural time series models and the Kalman filter (Cambridge University Press, 1990).Book 
MATH 

Google Scholar 
Zhang, G. P. Time series forecasting using a hybrid arima and neural network model. Neurocomputing 50, 159–175 (2003).Article 
MATH 

Google Scholar 
Liao, T. W. Clustering of time series data—A survey. Pattern Recognit. 38, 1857–1874 (2005).Article 
ADS 
MATH 

Google Scholar 
Bell, M. L., Samet, J. M. & Dominici, F. Time-series studies of particulate matter. Annu. Rev. Public Heal. 25, 247–280 (2004).Article 

Google Scholar 
Hyndman, R. J. & Athanasopoulos, G. Forecasting: Principles and Practice (OTexts, 2018).
Google Scholar 
Box, G. E., Hillmer, S. C. & Tiao, G. C. Analysis and modeling of seasonal time series. in Seasonal analysis of economic time series, 309–344 (NBER, 1978).Sulandari, W., Suhartono, Subanar & Rodrigues, P. C. Exponential smoothing on modeling and forecasting multiple seasonal time series: An overview. Fluctuation Noise Lett. 20, 2130003 (2021).Article 
ADS 

Google Scholar 
Rodrigues, P. C., Awe, O. O., Pimentel, J. S. & Mahmoudvand, R. Modelling the behaviour of currency exchange rates with singular spectrum analysis and artificial neural networks. Stats 3, 137–157 (2020).Article 

Google Scholar 
Sako, K., Mpinda, B. N. & Rodrigues, P. C. Neural networks for financial time series forecasting. Entropy 24, 657 (2022).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Coelho, Leite et al. Statistical and artificial neural networks models for electricity consumption forecasting in the Brazilian industrial sector. Energies 15, 588 (2022).Article 

Google Scholar 
Sulandari, W., Subanar, S., Lee, M. H. & Rodrigues, P. C. Time series forecasting using singular spectrum analysis, fuzzy systems and neural networks. MethodsX 7, 101015 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Sulandari, W. et al. Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks. Energy 190, 116408 (2020).Article 

Google Scholar 
Rodrigues, P. C. & Mahmoudvand, R. The benefits of multivariate singular spectrum analysis over the univariate version. J. Frankl. Inst. 355, 544–564 (2018).Article 
MathSciNet 
MATH 

Google Scholar  More

Global genetic structure of the pathogen tracks the historical spread of wheatWe assessed the evolutionary trajectory of the pathogen in conjunction with the history of global wheat cultivation (Fig. 1a). For this, we assembled a worldwide collection of Z. tritici isolates from naturally infected fields (Fig. 1b). We selected isolates covering most wheat production areas, both in the center of origin of the crop (i.e., the Fertile Crescent in the Middle East), and in areas where wheat was introduced during the last millennia (i.e., Europe and North Africa), or last centuries (i.e., the Americas and Oceania; Fig. 1c). We called variants in a set of 1109 high-quality short-read resequencing datasets (Supplementary Data 1, 2) covering 42 countries and a broad range of climates. Using a joint genotyping approach, we produced raw variant calls mapped to the telomere-to-telomere assembled reference genome IPO323. To assess genotyping accuracy, we used eight isolates with replicate sequencing data to analyze discrepancies. We adjusted quality thresholds targeting specifically the type of genotyping errors observed in our data set (Fig. S1). The improved filtering yielded 8,406,818 high-confidence short variants (short indels and SNPs). The final variant set included 5,578,488 biallelic SNPs corresponding to 14.1% of the genome.Fig. 1: Global sampling of the wheat pathogen Zymoseptoria tritici retracing the historical spread of its host.a Schematic representation of the introduction of wheat across continents. b Septoria tritici blotch symptoms caused by Z. tritici on wheat leaves. Pictures taken by B. A. McDonald, ETH Zurich. c Map of the sampling scheme for the global collection of 1109 isolates for whole-genome sequencing.Full size imageWe tested whether global diversity patterns of pathogen populations are likely a consequence of the history of wheat cultivation. We first performed unsupervised clustering of genotypes and identified eleven well-supported clusters (Fig. 2a, Figs. S2,3). Over 90% of the genotypes were clearly assigned to a single cluster (Fig. 2a, Supplementary Data 3). Two clusters were identified among genotypes originating from the pathogen center of origin, distinguishing collections from Iran and Middle Eastern regions. Genotypes from Africa and Europe split into two distinct genetic clusters without any apparent secondary structure within clusters. This lack of any fine-scale structure is remarkable given the extensive geographic sampling of European genotypes and suggests extensive gene flow within the continent. Genotypes from Oceania grouped into three distinct clusters marked by collections from Tasmania, the Australian mainland, and New Zealand. Genotypes from North America formed two clusters along a North-South separation. Finally, South American genotypes formed two clusters split along the Andes (Chile versus Argentina and Uruguay). Some uncertainty exists in the assessment of regional population structure by low coverage of major wheat-producing countries such as Russia and Ukraine. Septoria tritici blotch is only sporadically reported in China. In complementary analyses, we found that a phylogenetic network accounting for the high frequency of recombination consistently reflected the global population structure (Fig. S4). A principal component analysis of all genotypes confirmed the nested genetic structure with differentiation at the continent level, subdivisions within some continents and the existence of admixed genotypes (Fig. 2b, Fig. S5).Fig. 2: Global genetic structure based on 1109 genomes.a Map of the genetic clustering based on a thinned genome-wide SNP dataset using sNMF. Each color represents a different genetic cluster, and the sizes of the slices represent the average attribution to the cluster across the isolates from each location. Fractions representing less than 10% of all genotypes of a location were colored in grey to improve clarity. The large pie chart outside of the map represents the proportion of isolates assigned clearly (≥75%) to a single genetic cluster (pure; in teal) and isolates identified as hybrids (admixed) between clusters (in yellow). Names of the clusters include an abbreviation of continents and a more precise geographical location (MEA: Middle East and Africa; NA: North America; SA: South America; OC: Oceania). b Principal component analysis, showing the first and second component (PCs) based on a subset of variants. Colors and shapes indicate the genomic clusters identified with the sNMF method (with hybrids in grey). The marginal distributions represent the distribution for each PC. PCs 1 to 8 are shown in Fig S4. c Population tree based on Treemix, rooted using two genomes from the sister species Z. passerinii and Z. ardabiliae. The colors are the same as in the previous panels and only samples which were fully assigned to a cluster were used. d Diversity estimated with using pi per genetic cluster. The boxplots are ordered according to the tree of panel. c. The lower and upper hinges correspond to the first and third quartiles, the whiskers to the largest value are within 1.5 times the inter-quartile range, and the central horizontal line defines the median. e Linkage disequilibrium (r2) between variants per genetic cluster. Colors are identical among panels.Full size imageWe analyzed the history of population splits and admixture using allele frequency information (Fig. 2c). The analyses largely supported a genetic structure shaped by the introduction of wheat across continents. The historical relationships between clusters show an early divergence of the Middle Eastern and North African clusters matching the early introduction of agriculture in these regions. Populations in Europe and the Americas share a similar time point of divergence consistent with extensive contributions of European genotypes to the Western hemisphere. Oceanian groups have diverged as a single branch from genotypes most closely related to extant European populations. Matching the introduction of wheat to Oceania from the European continent, the Australian and New Zealand pathogen populations share a common origin rooted in European genetic diversity. Populations from Australia show also a striking loss of diversity and higher linkage disequilibrium compared to European diversity consistent with a significant founder effect (Fig. 2d, e). Similarly, populations in South and North America have reduced genetic diversity compared to extant European populations as suggested previously based on Sanger sequencing16. The highest diversity was found in populations from Africa and the Middle East closest to the center of origin. Overall, the global genetic structure of the pathogen reveals multiple founder events associated with the introduction of wheat to new continents.Ongoing gene flow among regions should lead to admixed genotypes. We found that nearly 10% of all analyzed genotypes showed contributions from at least two clusters. The most significant recent gene flow was detected between Middle Eastern/North African clusters and European clusters in North Africa (i.e., Algeria and Tunisia) as well as Southern and Eastern Europe (i.e., France, Italy, Hungary, Ukraine, Portugal, and Spain; Supplementary Data 3). We found a particularly high incidence of recent immigration in a durum wheat population in the south of France. The population consisted only of hybrids or atypical genotypes suggesting either recent migration from North Africa or host specialization on durum wheat varieties. Additionally, we found hybrid genotypes with European ancestry in both North America and in Oceania. The relatively balanced ancestry proportions in these hybrids suggest very recent gene flow dating back to only a few generations. We further investigated past gene flow between clusters by allowing Treemix to infer migration events, thus creating a population network (Fig. S6a–d). Three distinct recent migration events were best explaining the data with specific migration routes from the Middle East/African clusters to North America, from an Australian cluster to South America and between two Oceanian clusters (Fig. S6d). However, the migration events did not affect the overall shape of the inferred population tree (Fig. 2c, Fig. S6b–d). To better understand effects of long-distance gene flow, we investigated the relationship between relatedness among genotypes (i.e., identity-by-state) and geographic distance. At the continent level, we observed a negative relationship between identity-by-state and geographic distance (Fig. S7). The wide distribution of identity-by-state values shows that although closely related isolates tend to be found at closer geographic distance, distantly related isolates can be found at both far and close geographic distances. Long-distance migration events are most likely caused by international trade similar as for other crop pathogens17,18,19. In combination, our findings show an important role of long-distance dispersal impacting the genetic make-up of populations from individual fields to continental scale genetic diversity.Relaxation of genomic defenses against transposable elements concurrent with global spreadTransposable elements (TEs) are drivers of genome evolution. In Z. tritici, TE activity created beneficial mutations for fungicide resistance and virulence on the wheat host20,21. Rapid recent adaptation of the pathogen has benefitted from the activity of TEs with consequences for genome size22. Unchecked transposition of TEs can be deleterious and an array of defenses mechanisms has evolved to counteract their activity both at the genomic and epigenetic level including targeted mutations and silencing23. To analyze the effectiveness of genomic defenses against active TEs, we screened all genomes for evidence of TE insertions. We mapped short-read sequencing data on the reference genome and a species-specific TE sequence library. We classified evidence for TEs in each of the analyzed isolates as reference TEs (i.e. also present in the reference genome) and non-reference TE (i.e. absent). Detected TEs among isolates were binned into loci (width 100 bp) to account for uncertainties about the precise mapping of the insertion point. We found that the frequency spectrum of TE insertions is heavily skewed towards low frequencies with 77% of TE insertions being found in single isolates (~0.1% frequency) and 96% of insertions were found in ten or fewer isolates ( 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

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

Screening for viral AMGsViral protein annotation using VIBRANT and DRAM-v software combined with manual proofreading identified the viral AMGs in Napahai plateau wetland, including the viral AMGs phoH, phoU and pstS, which were associated with phosphorus metabolism.Phylogenetic analysis of AMGs associated with phosphorus metabolism in Napahai plateau wetlandThere were 24 amino acid sequences of phoH gene in Napahai plateau wetland (Fig. 1A). They were divided into 5 clusters, the largest of which had 10 sequences, while the smallest cluster had only 1 sequence. The remaining 3 clusters contained 6, 5 and 2 sequences, respectively. The phoH gene was genetically diverse in Napahai plateau wetland, which might be related to the different host origins. A total of 74 sequences of phoU gene could be found in seven clusters (Fig. 1B), with the largest cluster containing 27 sequences and the smallest cluster having two sequences. Similar to phoH, phoU was also genetically diverse, but richer than that of phoH. There were 71 pstS sequences forming 9 clusters, with the largest cluster of 23 sequences and the smallest cluster only 1 sequence (Fig. 1C). It could be seen that the genetic diversity of pstS was better than that of phoH and phoU, which might be related to the unique geographical location. Napahai plateau wetland is located in the Three Parallel Rivers of Yunnan protected areas, which forms a complex landscape, and then controls the evolution and characteristics of organisms, thus showing abundant biodiversity. Li et al. obtained 58 phoH gene sequences from Northeastern wetland sediments of China, which were 22%–99% consistent at the amino acid level, and found that the phoH gene could regulate phosphate uptake and metabolism under the low phosphate or phosphate limitation conditions16. However, the exact function remained unclear. The phoH gene clustered into five clusters in Napahai plateau wetland, indicating high genetic diversity. Additionally, water and soil samples were collected from eight separate sampling sites, and there were differences between samples environments, which might also have an impact on the genetic diversity of the three genes.Figure 1Phylogenetic analysis of phosphorus metabolism AMGs in Napahai plateau wetland, different colors represent different branches. (A) Phylogenetic analysis of phoH genes. (B) Phylogenetic analysis of phoU genes. (C) Phylogenetic analysis of pstS genes.Full size imagePhylogenetic analysis and PCoA analysis of AMGs associated with phosphorus metabolism from different habitats and host originsIn order to understand the genetic diversity of viral AMGs (phoH, phoU, pstS) associated with phosphorus metabolism in Napahai plateau wetland, a phylogenetic tree of phosphorus metabolism AMGs from different habitats was constructed, and PCoA analysis was performed (Fig. 2). The results showed that most sequences of phoH, phoU and pstS genes in Napahai plateau wetland clustered individually, especially phoU and pstS genes, and only a few sequences were closely related to those of other habitats. In Fig. 2A, 14 sequences clustered individually and were relatively far from sequences of other habitats, whereas 7 sequences were close to those from freshwater lakes, and other 3 sequences were close to those from rice fields, oceans and other wetlands, respectively. Therefore, the genetic diversity of phoH in Napahai plateau wetland was independent of the habitat. Moreover, some of the phoH sequences were clustered with those of other habitats and distributed in the fourth quadrants (Fig. 2D). From Fig. 2B, apart from 3 sequences which clustered with those from the marine habitats and freshwater lakes, the rest were clustered separately. Whereas in Fig. 2E, apart from only a few sequences, most sequences of phoU were far away from those of different habitats, which was consistent with Fig. 2B. Thus, the genetic diversity of phoU gene in Napahai wetland was also independent of habitat, where the separately clustered sequences may be unique. From Fig. 2C, we can seen that apart from 8 sequences which more closely related to those from the freshwater lake, ocean, rice field, and other wetlands, all the rest were individually clustered. The result was consistent with that of Fig. 2F. Therefore, the genetic diversity of the pstS gene was also habitat-independent.Figure 2Phylogenetic analysis and PCoA of phosphorus metabolism AMGs in different habitats, different colors represent different habitats. (A) Phylogenetic analysis of phoH genes in different habitats. (B) Phylogenetic analysis of phoU genes in different habitats. (C) Phylogenetic analysis of pstS genes in different habitats. (D) PCoA analysis of phoH genes in different habitats. (E) PCoA analysis of phoU genes in different habitats. (F) PCoA analysis of pstS genes in different habitats.Full size imageTo study whether the genetic diversity was related to host origins, three AMGs associated with phosphorus metabolism were selected for phylogenetic and PCoA analyses with AMGs sequences from different host origins (Fig. 3). It showed that some sequences of all three genes were similar to those from different host origins, while the remaining were separately clustered. In Fig. 3A, apart from 14 sequences which clustered with those from fungi, bacteria, non-culturable phages, phages and viruses, all the rest were clustered separately. Whereas, most sequences were clustered with those from different host origins together, and only six sequences were far from other sequences of different host origins based on PCoA analysis (Fig. 3D). Only three sequences were clustered with those of archaea and uncultured archaea, and the rest were clustered together to form independent clusters (Fig. 3B). A small amount of sequences were gathered with bacteria, uncultured bacteria, archaea and uncultured archaea, and the rest were clustered individually (Fig. 3E). As can be seen in Fig. 3C, six sequences were clustered with those of archaea, fungi, bacteria, while the rest were clustered separately. Some sequences were gathered with bacteria, uncultured bacteria, archaea and uncultured archaea, and others were clustered separately (Fig. 3F). PCoA analysis was largely consistent with phylogenetic analysis. So the genetic diversity of phoH, phoU and pstS genes in Napahai plateau wetland was independent of the host origins.Figure 3Phylogenetic analysis and PCoA of phosphorus metabolism AMGs from different host origins, different colors represent different host origins. (A) Phylogenetic analysis of phoH gene from different host origins. (B) Phylogenetic analysis of phoU gene from different host origins. (C) Phylogenetic analysis of pstS gene from different host origins. (D) PCoA analysis of phoH genes from different host origins. (E) PCoA analysis of phoU genes from different host origins. (F) PCoA analysis of pstS genes from different host origins.Full size imageOverall, the genetic diversity of phoH, phoU and pstS genes associated with phosphorus metabolism in Napahai plateau wetland was independent of both the habitats and host origins based on phylogenetic and PCoA analyses. It suggested that three genes showed relatively rich genetic diversity and were not genetically limited by differences in habitats or host origins. Han et al. showed that phoH sequences were widely distributed in soil, freshwater, and seawater environments in different locations around the world, indicating the genetic diversity independent of the environment17, which corroborated the conclusions in our study. Phylogenetic analysis of the 58 viral phoH gene sequences in Northeastern wetland of China revealed that some sequences were clustered with bacterial sequences and others clustered with phages sequences16. In Napahai plateau wetland, some phoH gene sequences were clustered with fungal, bacterial, phage, uncultured phage, and viruses. Hence, the genetic diversity of phoH gene was independent of the host origins in either Northeastern wetland or Napahai plateau wetland. Compared with Northeastern wetland, the phoH genes in Napahai plateau wetland showed more abundant genetic diversity, which may be related to geographical location and climate. Additionally, compared with sequences from different habitats and host sources, partial sequences from Napahai plateau wetland were clustered individually, thus they were unique, which might be related to the unique geography. Napahai plateau wetland is located in the Three Parallel Rivers with low latitude and high altitude, and shows specific characteristics which not found in other habitats, and then the species very different, thus providing the possibility for the emergence of unique genetic sequences. Of course, it would require further verification by subsequent study.As far as the current studies are concerned, most reports on phosphorus AMGs focused on the function. Wang et al. mentioned that the phoH gene regulated phosphate uptake or metabolism under the low phosphorus or phosphate limitation conditions18. Kelly et al. isolated several phages from oligotrophic water bodies with low phosphorus condition, found that they contained the phosphate binding transporter gene pstS by sequencing, which enhanced the host cell with increasing the infection cycle of phages by increasing phosphate utilization19. Gardner et al. studied the PhoR-PhoB two-component regulatory system in E. coli, which regulated the expression of relevant genes according to environmental phosphate concentration and enabled cells to adapt the phosphate starvation20. The phoU existed in many bacteria and was identified as an auxiliary protein of the phosphate-specific transporter system, regulating phosphate metabolism in the host cell acting as phosphate regulators21. Few studies had been conducted on its genetic diversity, therefore, the information on the genetic diversity was relatively scarce.α diversity analysis of phosphorus metabolism AMGs in different habitats and different host originsChao, Shannon and Simpson diversity indices are common mathematical measure of species alpha diversity in the community. Chao focuses on species richness. Shannon index and Simpson index measure species richness and evenness. Simpson reinforces evenness and Shannon reinforces richness22.Sequences from different habitats, such as Napahai plateau wetland, Pacific Ocean, Lake Baikal, Northeast rice fields, glaciers, and wetlands, were selected for α-diversity analysis (Fig. 4). The genetic diversity indices, such as Chao, Shannon and Simpson, calculated based on the OUT dataset, were used to characterize the alpha diversity. Among them, larger Chao values, smaller Simpson values or larger Shannon values indicate higher genetic diversity. Only at the level of Chao values (Fig. 4A,D,G) and Shannon values (Fig. 4B,E,H), the values of phoH, phoU, and pstS in Napahai plateau wetland were greater than those from other habitats, indicating better heritable, which might be related to the unique geographical location and abundant water resources. The geographical location made it unique and less influenced by external factors, and abundant water resources created a rich biodiversity, thus providing a good genetic environment. From the Simpson values (Fig. 4C,F,I), the values of phoU and pstS genes were smaller than those of other habitats, indicating better inherited. For the phoH gene, the Simpson value was closer in magnitude and lower than those in Antarctic Lake and wetlands, indicating better heritable.Figure 4Plots of genetic diversity indices analysis of phosphorus metabolism AMGs in different habitats, different colors represent different genetic diversity indices. (A, D, G) Represent respectively the Chao values of phoH, phoU, and pstS genes in different habitats. (B, E, H) Represent respectively the Shannon values of phoH, phoU, and pstS genes in different habitats. (C, F, I) Represent respectively the Simpson values of phoH, phoU, and pstS genes in different habitats.Full size imageThree AMGs associated with phosphorus metabolism in Napahai plateau wetland were selected for α-diversity analysis with AMGs sequences from different host origins (Fig. 5). In Fig. 5A, the Chao values of phoH gene from bacteria, phages, uncultured phages and uncultured viruses in Napahai plateau wetland were smaller than those of bacteria, phages, uncultured phages and uncultured viruses, indicating the poor genetic diversity. In addition, compared to the genetic diversity of sequences from other host sources, the genetic diversity of phoH gene from bacteria in Napahai plateau wetland was better. As can be seen in Fig. 5D, G, the Chao values of phoU and pstS genes from bacteria in Napahai plateau wetland were greater than those of other host origins, indicating better genetic diversity, while the Chao values of pstS genes from archaea in Napahai plateau wetland were smaller than those of other host origins, indicating poor genetic diversity.Figure 5Plots of genetic diversity indices analysis of phosphorus metabolism AMGs from different host origins, different colors represent different genetic diversity indices. (A, D, G) Represent respectively the Chao values of phoH, phoU, and pstS genes from different host origins. (B, E, H) Represent respectively the Shannon values of phoH, phoU, and pstS genes from different host origins. (C, F, I) Represent respectively the Simpson values of phoH, phoU, and pstS genes from different host origins.Full size imageThe Shannon value of phoH gene from bacteria in Napahai plateau wetland was smaller than that of bacteria and uncultured viruses, indicating poor diversity, but larger than other host sources, indicating better genetic diversity (Fig. 5B). The Shannon values of phoH gene from phages and uncultured phages in Napahai plateau wetland were lower than those of other host origins, indicating poor diversity. The Shannon value of phoH genes from uncultured viruses in Napahai plateau wetland was 0, probably due to sample size too small to calculate the Shannon value. In Fig. 5E, H, the Chao values of phoU and pstS genes from bacteria in Napahai plateaus wetland were greater than those from other host sources, indicating better diversity, while the Shannon value of pstS gene from archaea in the Napahai plateau wetland was 0, probably small sample size.The Simpson values of phoH genes from phage, uncultured phage and uncultured virus in Napahai plateau wetland were smaller than those of other host origins (except uncultured virus), indicating better diversity. The smaller Simpson values of phoH genes related to fungi, phages, uncultured phages, and viruses indicated better diversity, while the larger Simpson values compared to bacteria, phages, and uncultured viruses indicated poor diversity (Fig. 5C). As can be seen in Figs. 5F,I, the Simpson values of phoU genes from bacteria and pstS genes from bacteria and archaea in Napahai plateau wetland were smaller than those of other host origins, indicating better genetic diversity.Currently, most studies on phosphorus AMGs employed phylogenetic analysis16,23. In contrast, relatively few AMGs associated with phosphorus had been reported based on α-diversity analysis, so it was difficult to obtain specific values of α-diversity indices in other studies.Biogeochemical cycling of AMGs associated with phosphorus metabolism in Napahai plateau wetlandViruses are the gene carriers in susceptible hosts, and AMGs introduced by viruses into new hosts can enhance viral replication and/or influence key microbial metabolic pathways of the biogeochemical cycles24. It is well known that phosphorus is an essential nutrient and plays essential roles in cells25. Phosphorus deficiency leads to restricted cell division, down-regulation of photosynthesis, reduced protein and nitrogen content and chlorophyll synthesis26. To study the effect of AMGs associated with phosphorus metabolism, a phosphorus metabolic pathway containing phoH, phoU and pstS genes was constructed based on metagenomic data (Fig. 6). When phosphorus deficiency occurs in the host, it leads to the expression of phoH, phoU and pstS genes. phoH is a phosphate starvation inducible gene, while pstS acts as a phosphate transport gene and phoU belongs to a phosphate regulatory gene that produces dissolved inorganic phosphorus (DIPs), which then undergoes a series of reactions to produce ATP. The generated ATP becomes PolyP under the action of ppK which encoding polyphosphate kinase, or is used in Calvin cycle to provide energy for Ru5P to produce RuBP, or is used for DNA biosynthesis to provide energy. PolyP is regenerate into DIP with ppX which encoding exopolyphosphatase, and also involves in the biosynthesis process of DNA as Pi to provide phosphate for the nucleic acids synthesis. Thus, phosphorus metabolism of AMGs invoved plays a significant role in the life process of the virus and host. In addition, phoE and ugpQ genes also are identified in Napahai plateau wetland, but their roles in the phosphorus cycling are currently unknown and need further study.Figure 6Biogeochemical cycling of AMGs associated with phosphorus metabolism in Napahai plateau wetland. Red line indicates the process of phosphorus metabolism.Full size imageBased on the phylogenetic and PCoA analyses, we found that the phoH, phoU, and pstS genes all showed unique sequences, which might be drive the microorganisms to produce the phosphorus metabolic pathway in Napahai plateau wetland. Of course, in order to prove this pathway, further validation might be done by metabolomics or metabolic flow method. Furthermore, the phosphorus metabolic pathway was poorly reported, so we could not compare with the phosphorus pathway from other environment to find commonalities and differences. More