Philippa Kaur

Gandhi, K. J. K. & Herms, D. A. Direct and indirect effects of alien insect herbivores on ecological processes and interactions in forests of eastern North America. Biol. Invasions 12, 389–405 (2010).
Google Scholar 
Desurmont, G. A. et al. Alien interference: disruption of infochemical networks by invasive insect herbivores. Plant. Cell Environ. 37, 1854–1865 (2014).PubMed 

Google Scholar 
Kenis, M. et al. Ecological effects of invasive alien insects. Biol. Invasions 11, 21–45 (2009).
Google Scholar 
Paini, D. R. et al. Global threat to agriculture from invasive species. Proc. Natl. Acad. Sci. 113, 7575–7579 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bradshaw, C. J. A. et al. Massive yet grossly underestimated global costs of invasive insects. Nat. Commun. 7, 1–8 (2016).
Google Scholar 
Sherpa, S. et al. Unravelling the invasion history of the Asian tiger mosquito in Europe. Mol. Ecol. 28, 2360–2377 (2019).PubMed 

Google Scholar 
Sherpa, S. et al. Landscape does matter: Disentangling founder effects from natural and human-aided post-introduction dispersal during an ongoing biological invasion. J. Anim. Ecol. 89, 2027–2042 (2020).PubMed 

Google Scholar 
Sherpa, S. & Després, L. The evolutionary dynamics of biological invasions: A multi‐approach perspective. Evol. Appl. (2021).North, H. L., McGaughran, A. & Jiggins, C. Insights into invasive species from whole-genome resequencing. Mol. Ecol. (2021).Ma, L. et al. Rapid and strong population genetic differentiation and genomic signatures of climatic adaptation in an invasive mealybug. Divers. Distrib. 26, 610–622 (2020).
Google Scholar 
Ortego, J., Céspedes, V., Millán, A. & Green, A. J. Genomic data support multiple introductions and explosive demographic expansions in a highly invasive aquatic insect. Mol. Ecol. 30, 4189–4203 (2021).PubMed 

Google Scholar 
Varone, L., Logarzo, G., Briano, J., Hight, S. & Carpenter, J. Cactoblastis cactorum (Berg) (Lepidoptera: Pyralidae) use of Opuntia host species in Argentina. Biol. Invasions 16, 2367–2380 (2014).
Google Scholar 
Singer, M. C., Ng, D. & Moore, R. A. Genetic variation in oviposition preference between butterfly populations. J. Insect Behav. 4, 531–535 (1991).
Google Scholar 
Forister, M. L. Oviposition preference and larval performance within a diverging lineage of lycaenid butterflies. Ecol. Entomol. 29, 264–272 (2004).
Google Scholar 
Wiklund, C. The concept of oligophagy and the natural habitats and host plants of Papilio machaon L. Fennoscandia. Insect Syst. Evol. 5, 151–160 (1974).
Google Scholar 
Courtney, S. P. & Forsberg, J. Host use by two pierid butterflies varies with host density. Funct. Ecol. 2, 67–75 (1988).
Google Scholar 
Franklin, J. Species distribution models in conservation biogeography: developments and challenges. Divers. Distrib. 19, 1217–1223 (2013).
Google Scholar 
Peterson, A. et al. Ecological niches and geographic distributions. Monographs in Population Biology vol. 49 (2011).Alvarado-Serrano, D. F. & Knowles, L. L. Ecological niche models in phylogeographic studies: Applications, advances and precautions. Mol. Ecol. Resour. 14, 233–248 (2014).PubMed 

Google Scholar 
Carrera-Martínez, R., Aponte-Díaz, L. A., Ruiz-Arocho, J., Lorenzo-Ramos, A. & Jenkins, D. A. The effects of the invasive Harrisia cactus mealybug (Hypogeococcus sp.) and exotic lianas (Jasminum fluminense) on Puerto Rican native cacti survival and reproduction. Biol. Invasions 21, 3269–3284 (2019).
Google Scholar 
Acevedo-Rodríguez, P. & Strong, M. T. Catalogue of seed plants of the West Indies. Smithson. Contrib. to Bot. 98, 1–1192 (2012).
Google Scholar 
Carrera-Martínez, R., Aponte-Díaz, L., Ruiz-Arocho, J. & Jenkins, D. A. Symptomatology of infestation by Hypogeococcus pungens: Contrasts between host species. Haseltonia 2015, 14–18 (2015).
Google Scholar 
Aponte-Díaz, L., Ruiz-Arocho, J., Carrera-Martínez, R. & Ee, B. Contrasting effects of the invasive Hypogeococcus sp. (Hemiptera: Pseudococcidae) infestation on seed germination of Pilosocereus royenii (Cactaceae), a Puerto Rican native cactus. Caribb. J. Sci. 50, 212–218 (2020).
Google Scholar 
California Department of Food and Agriculture. Harrisia Cactus Mealybug | Hypogeococcus pungens | Pest rating proposals and final ratings. https://blogs.cdfa.ca.gov/Section3162/?p=5881 (2018).Poveda-Martínez, D. et al. Species complex diversification by host plant use in an herbivorous insect: The source of Puerto Rican cactus mealybug pest and implications for biological control. Ecol. Evol. 10, 10463–10480 (2020).PubMed 
PubMed Central 

Google Scholar 
Segarra-Carmona, A. E., Ramírez-Lluch, A., Cabrera-Asencio, I. & Jiménez-López, A. N. First report of a new invasive mealybug, the Harrisia cactus mealybug Hypogeococcus pungens (Hemiptera: Pseudococcidae). J. Agric. Univ. Puerto Rico 94, 183–187 (2010).
Google Scholar 
Poveda-Martínez, D. et al. Untangling the Hypogeococcus pungens species complex (Hemiptera: Pseudococcidae) for Argentina, Australia, and Puerto Rico based on host plant associations and genetic evidence. PLoS ONE 14, e0220366 (2019).PubMed 
PubMed Central 

Google Scholar 
McKenzie, H. L. Mealybugs of California. (Univ of California Press, 1967).Hamon, A. B. A cactus mealybug, Hypogeococcus festerianus (Lizer y Trelles). Florida (Homoptera Coccoidea Pseudococcidae). Entomol. Circ. Div. Plant Ind. Florida Dep. Agric. Consum. Serv. 263, 2 (1984).Hodges, A. & Hodges, G. Hypogeococcus pungens Granara de Willink (Insecta: Hemiptera: Pseudococcidae), a mealybug. EDIS 2009, (2009).Halbert, S. Entomology section. Triology 35, 2–4 (1996).
Google Scholar 
Aguirre, M. B. et al. Analysis of biological traits of Anagyrus cachamai and Anagyrus lapachosus to assess their potential as biological control candidate agents against Harrisia cactus mealybug pest in Puerto Rico. Biocontrol 64, 539–551 (2019).CAS 

Google Scholar 
Aguirre, M. B. et al. Influence of competition and intraguild predation between two candidate biocontrol parasitoids on their potential impact against Harrisia cactus mealybug, Hypogeococcus sp. (Hemiptera: Pseudococcidae). Sci. Rep. 11, 13377 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Eaton, D. A. R. & Overcast, I. ipyrad: Interactive assembly and analysis of RADseq datasets. Bioinformatics 36, 2592–2594 (2020).CAS 
PubMed 

Google Scholar 
Poveda-Martínez, D., Salinas, N., Aguirre, M. B., Sánchez-Restrepo, A. F. & Hight, S., Diaz-Soltero, H. Dataset generated in Genomic and ecological evidence shed light on the recent demographic history of two related invasive insects. https://doi.org/10.6084/m9.figshare.15167082.v2 (2022).Frichot, E., Mathieu, F., Trouillon, T., Bouchard, G. & François, O. Fast and efficient estimation of individual ancestry coefficients. Genetics 196, 973–983 (2014).PubMed 
PubMed Central 

Google Scholar 
Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).PubMed 
PubMed Central 

Google Scholar 
Gattepaille, L. M., Jakobsson, M. & Blum, M. G. B. Inferring population size changes with sequence and SNP data: Lessons from human bottlenecks. Heredity (Edinb). 110, 409–419 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Born‐Schmidt, G. et al. The implementation of the mexican strategy on invasive species: How far have we come? Invasive Alien Species Obs. Issues from Around World 4, 153–164 (2021).
Google Scholar 
McFadyen, R. E. & Tomley, A. J. Preliminary indications of success in the biological control of Harrisia cactus (Eriocereus martinii Lab.) in Queensland. In Proceedings of the First Conference of the Council of Australian Weed Science Societies held at National Science Centre, Parkville, Victoria, Australia, 12–14 April 1978 108–112 (Council of Australian Weed Science Societies, 1978).McFadyen, R. E. & Tomley, A. J. The successful biological control of Harrisia cactus (Eriocereus martinii) in Queensland. In Proceedings of the Sixth Australian Weeds Conference, Volume 1, City of Gold Coast, Queensland, Australia, 13–18 September, 1981 139–143 (Queensland Weed Society, 1981).Paterson, I. D. et al. Biological control of Cactaceae in South Africa. African Entomol. 19, 230–246 (2011).
Google Scholar 
Sutton, G. F., Klein, H. & Paterson, I. D. Evaluating the efficacy of Hypogeococcus sp. as a biological control agent of the cactaceous weed Cereus jamacaru in South Africa. Biocontrol 63, 493–503 (2018).
Google Scholar 
Paterson, I. D. et al. Biological control of Cactaceae in South Africa. African Entomol. 29, 713–734 (2021).
Google Scholar 
McFadyen, R. E. Harrisia (Eriocereus) martinii (Labour.) Britton—Harrisia cactus Acanthocereus tetragonus (L.) Hummelink—sword pear. (ed. Julien, M., McFadyen, R., & Cullen, J.), Biological control of weeds in Australia 274– 281. (CSIRO Publishing, 2012).Julien, M. H. & Griffiths, M. Biological control of weeds: A world catalogue of agents and their target weeds. (Cab International, 1998).Houston, W. A. & Elder, R. Biocontrol of Harrisia cactus Harrisia martinii by the mealybug Hypogeococcus festerianus (Hemiptera: Pseudococcidae) in salt-influenced habitats in Australia. Austral Entomol. 58, 696–703 (2019).
Google Scholar 
Hofmeister, N., Werner, S. & Lovette, I. Environmental correlates of genetic variation in the invasive European starling in North America. Mol. Ecol. 30, 1251–1263 (2021).PubMed 

Google Scholar 
Driscoe, A. L. et al. Host plant associations and geography interact to shape diversification in a specialist insect herbivore. Mol. Ecol. 28, 4197–4211 (2019).CAS 
PubMed 

Google Scholar 
Vidal, M. C., Quinn, T. W., Stireman, J. O. 3rd., Tinghitella, R. M. & Murphy, S. M. Geography is more important than host plant use for the population genetic structure of a generalist insect herbivore. Mol. Ecol. 28, 4317–4334 (2019).PubMed 

Google Scholar 
Poveda-Martínez, D. et al. Spatial and host related genomic variation in partially sympatric cactophagous moth species. Mol. Ecol. 31, 356–371 (2021).PubMed 

Google Scholar 
Cao, L., Wei, S., Hoffmann, A. A., Wen, J. & Chen, M. Rapid genetic structuring of populations of the invasive fall webworm in relation to spatial expansion and control campaigns. Divers. Distrib. 22, 1276–1287 (2016).
Google Scholar 
Sih, A. et al. Predator–prey naïveté, antipredator behavior, and the ecology of predator invasions. Oikos 119, 610–621 (2010).
Google Scholar 
Yang, Q.-Q. et al. Introgressive hybridization between two non-native apple snails in China: Widespread hybridization and homogenization in egg morphology. Pest Manag. Sci. 76, 4231–4239 (2020).CAS 
PubMed 

Google Scholar 
Cordeiro, E. M. G. et al. Hybridization and introgression between Helicoverpa armigera and H zea: An adaptational bridge. BMC Evol. Biol. 20, 61 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pardo-Diaz, C. et al. Adaptive introgression across species boundaries in Heliconius butterflies. PLOS Genet. 8, e1002752 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Caltagirone, L. E. Landmark examples in classical biological control. Annu. Rev. Entomol. 26, 213–232 (1981).
Google Scholar 
Goldson, S. L., Phillips, C. B. & Barlow, N. D. The value of parasitoids in biological control. New Zeal. J. Zool. 21, 91–96 (1994).
Google Scholar 
Wang, Z., Liu, Y., Shi, M., Huang, J. & Chen, X. Parasitoid wasps as effective biological control agents. J. Integr. Agric. 18, 705–715 (2019).
Google Scholar 
Miller, G., & Lugo. A. E. Guide to the ecological systems of Puerto Rico. IITF-GTR-35 (2009).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Andrews, S. FastQC: A Quality control tool for high throughput sequence data. (2010).Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Purcell, S. et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Foll, M. & Gaggiotti, O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics 180, 977–993 (2008).PubMed 
PubMed Central 

Google Scholar 
Linck, E. & Battey, C. J. Minor allele frequency thresholds strongly affect population structure inference with genomic data sets. Mol. Ecol. Resour. 19, 639–647 (2019).CAS 
PubMed 

Google Scholar 
Keenan, K., McGinnity, P., Cross, T. F., Crozier, W. W. & Prodöhl, P. A. diveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol. 4, 782–788 (2013).
Google Scholar 
Goudet, J. Hierfstat, a package for R to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186 (2005).
Google Scholar 
Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. Poppr: An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281 (2014).PubMed 
PubMed Central 

Google Scholar 
Tajima, F. The effect of change in population size on DNA polymorphism. Genetics 123, 597–601 (1989).CAS 
PubMed 
PubMed Central 

Google Scholar 
Frichot, E. & François, O. LEA: An R package for landscape and ecological association studies. Methods Ecol. Evol. 6, 925–929 (2015).
Google Scholar 
Pembleton, L. W., Cogan, N. O. I. & Forster, J. W. St AMPP: An R package for calculation of genetic differentiation and structure of mixed-ploidy level populations. Mol. Ecol. Resour. 13, 946–952 (2013).CAS 
PubMed 

Google Scholar 
Cockerham, C. C. Drift and mutation with a finite number of allelic states. Proc. Natl. Acad. Sci. 81, 530–534 (1984).ADS 
CAS 
PubMed 
PubMed Central 
MATH 

Google Scholar 
Lynch, M. & Conery, J. S. The origins of genome complexity. Science 302, 1401–1404 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34, 3299–3302 (2017).CAS 
PubMed 

Google Scholar 
Keightley, P. D., Ness, R. W., Halligan, D. L. & Haddrill, P. R. Estimation of the spontaneous mutation rate per nucleotide site in a Drosophila melanogaster full-sib family. Genetics 196, 313–320 (2014).CAS 
PubMed 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2021).Neu, C. W., Byers, C. R. & Peek, J. M. A technique for analysis of utilization-availability data. J. Wildl. Manage. 38, 541–545 (1974).
Google Scholar 
Soberón, J. & Peterson, A. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers. Informatics 2, 1-10 (2005).Jorge, S. & Miguel, N. Niches and distributional areas: Concepts, methods, and assumptions. Proc. Natl. Acad. Sci. 106, 19644–19650 (2009).
Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190, 231–259 (2006).
Google Scholar 
Cobos, M. E., Peterson, A., Barve, N. & Osorio-Olvera, L. Kuenm: An R package for detailed development of ecological niche models using Maxent. PeerJ 7, e6281 (2019).PubMed 
PubMed Central 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
Title, P. O. & Bemmels, J. B. ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography (Cop.) 41, 291–307 (2018).
Google Scholar 
Warren, B. H. et al. Evaluating alternative explanations for an association of extinction risk and evolutionary uniqueness in multiple insular lineages. Evolution 72, 2005–2024 (2018).PubMed 

Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. Environmental niche equivalency versus conservatism: Quantitative approaches to niche evolution. Evol. Int. J. Org. Evol. 62, 2868–2883 (2008).
Google Scholar 
Schoener, T. W. The anolis lizards of Bimini: Resource partitioning in a complex fauna. Ecology 49, 704–726 (1968).
Google Scholar 
Van der Vaart, A. W. Asymptotic Statistics (UK Cam, 1998).MATH 

Google Scholar  More

Subjects and facilityWe observed two groups of Atlantic bottlenose dolphins (six different individuals in total) housed at the marine zoo “Marineland Mallorca”. One of the groups was composed of four individuals (G1) and the other was constituted by five individuals (G2). The two adult males and one of the females were the same in both groups (Table 1). Group composition changed due to the transfer of individuals to another pool of the zoo and due to the arrival of new individuals from another aquatic park.Table 1 Age, sex, group, and identification number in the network of the subject dolphins. M male, F female.Full size tableThe dolphins were kept in three outdoor interconnecting pools: the main performance pool (1.6 million liters of water), a medical pool (37.8 thousand liters of water) and a small pool (636.8 thousand liters of water). During the observational periods, the dolphins had free access to all the pools. Underwater viewing at the main and the small pool was available through the transparent walls around the rim of the pools.Ethics statementThis study was approved by the UIB Committee of Research Ethics and Marineland Mallorca. This research was conducted in compliance with the standards of the European Association of Zoos and Aquaria (EAZA). All subjects tested in this study were housed in Marineland Mallorca following the Directive 1999/22/EC on the keeping of animals in zoos. This study was strictly non-invasive and did not affect the welfare of dolphins.Behavioral observations and data collectionBehavioral data were collected in situ by APM from May to November 2016 for G1 and from November 2017 to February 2018 for G2. All observational periods were also recorded using two waterproof cameras SJCAM SJ4000. Observations were conducted at the main pool between 8:00 a.m. and 11:00 a.m. Due to the schedules and dynamics of the zoo, we were unable to collect data outside this period. Dolphin social behavior was registered and videotaped for 30 min–2 h each day. Only data from sessions that lasted at least 30 min were included in the analysis. We did not collect any data during training or medical procedures and resumed the observational session a few minutes after the end of these events.We recorded all occurrences of affiliative and aggressive interactions, the identities of the involved individuals and the identity of the dolphin initiating the contact. Aggressive contacts were defined by the occurrence of chasing, biting, and hitting, as established in previous studies37,38,39,40,41. Affiliative contacts were defined as contact swimming, synchronous breathing and swimming (at least 30″ of continuous swimming) or flipper-rubbing, as established in previous studies37,39,40,41,43.To assess the strength of the affiliative bonds in both groups, we calculated the index of affiliative relationships (IA) between dolphins following the procedure described in Yamamoto et al. For calculating the IA we recorded the relative frequencies of synchronous swimming since it is a well-defined affiliative behavior in dolphins. Data of synchronous swimming were recorded using group 0–1 sampling44 at 3-min intervals. This method consists of the observation of individuals during short periods and the recording of the occurrence (assigning to that period a 1) or non-occurrence (assigning to that period a 0) of a well-defined behavior44. For calculating the IA for each couple, the number of sampling periods in which synchronous swimming between individuals A and B occurred (XAB) was divided by the number of sampling periods in which individuals A and B were observed (YAB): (IA=frac{{X}_{AB}}{{Y}_{AB}})39,45. Therefore, the IA reflects the level of affiliation for each dolphin dyad based on the pattern of synchronous swimming. This index served to construct the general affiliative social networks of both groups of dolphins.Temporal network constructionTemporal networks can provide insight into social events such as conflicts and post-conflict interactions in which the order of interactions and the timing is crucial. Furthermore, they allow us to calculate the probabilities of the different affiliative and aggressive interactions occurring in the group.We used behavioral observations to construct temporal networks for each group. Each dolphin was treated as a node (N) with their aggressive and affiliative interactions supplying the network links. We divided the daily observations into periods of 3 min. In each period, we assigned a positive (+ 1), negative (− 1) or neutral (0) interaction to each pair of dolphins. That is, if during the period a pair of dolphins displayed affiliative interactions, we assigned a + 1 to the link between that pair of nodes, if they were involved in a conflict, we assigned a − 1, and if the pair did not engage in any interaction, we assigned to that link a 0. If during the same period, the pair displayed both aggressive and affiliative interactions we considered the last observed interaction. Therefore, we obtained an adjacency matrix (an N × N matrix describing the links in the network) for each group of dolphins. Thus, for each day we had a series of different signed networks of the group, each network representing a 3-min period.Social network analysis: time-aggregated networks and network motifsWe collapsed the temporal networks of each day in time-aggregated networks. This procedure consists in aggregating the data collected over time within specific intervals to create weighted networks. The sign and the weight of the links characterize these networks, indicating the valence and duration of the interaction respectively. Thus, they are static representations of the social structure of the group of dolphins. To obtain these time-aggregated networks we proceeded as follows:First, for each day we aggregated the values of each interaction of the temporal networks until one link qualitatively changed. We considered a qualitative change if one interaction passed from being negative (− 1) to positive (+ 1) meaning that the pair of dolphins reconciled after the conflict or vice versa, or if a new affiliation (+ 1) or aggression (− 1) took place, that is the link changed from being neutral (0) to positive or negative. If a link changed from being negative or positive to being neutral, we did not consider that this interaction has changed qualitatively. For example, if dolphins interacted positively during two periods of time, then they ceased to interact (neutral) and finally they engaged in an aggressive interaction, the total weight of the interaction in the resulting time-aggregated network would be of + 2. Therefore, a conflict or an affiliation may extend over multiple periods containing several contacts, and is considered finished when the interaction changes its valence. In this way, we obtained a series of time-aggregated networks for each day, which retain the information on the duration, timing, and ordering of the affiliative and aggressive events in the group.We examined the local-scale structure of the affiliative-aggressive social networks using motif analysis. Thus, for each group, we analyzed the network motif representation of the temporal and time-aggregated networks, identifying and recording the number of occurrences of each motif.Model of affiliative and aggressive interactionsWe built two models (a simple and a complex one) that aim to simulate the dynamics of aggressive and affiliative interactions of a group of four dolphins. These models were created using the observed probabilities of each affiliative or aggressive interaction between individuals in group G1. We only used the data of G1 since we had more hours of video recordings and, thus, more statistics of the pattern of dolphins’ interactions. Both models return affiliative/aggressive temporal networks constituted by four nodes and different aggressive, affiliative, or neutral interactions between the six possible pairs of individuals in the network. We simulated data for 20 periods of 3 min per day for a total of 80 days to mimic the empirical data time structure. We obtained one temporal network for each period (1600 temporal networks in total) and ran 100 realizations of each model.Our models work as follows: At the beginning of the simulations, all the interactions between the four nodes are neutral (0). In each period, we select a pair of nodes randomly and assign to that link a positive (+ 1) or a negative (− 1) interaction with probability p (calculated previously for each type of interaction). These interactions correspond to spontaneous aggressions and affiliations. In the complex model, if in the previous period a conflict took place, before assessing spontaneous interactions we first evaluated the different possible post-conflict contacts that could occur (reconciliation, new aggressions, and affiliations). Therefore, for reconciliations, we change the valence of the interaction from negative to positive with a certain probability. Then, we also randomly choose a pair of nodes including one of the former opponents and assign to that link a positive or negative interaction with the observed probabilities to simulate the occurrence of new affiliations (third party-affiliation) or redirected aggressions arising from the previous conflict. We keep on doing this procedure period by period. Lastly, we obtained the time-aggregated networks for the two models.The simpler model only includes the probability of aggression and affiliation between group members, whereas the complex one also includes the patterns of conflict resolution previously observed. In this way, the complex model serves to assess the influence of post-conflict management mechanisms on the observed pattern of aggressive/affiliative networks. That is, the complex model also keeps track of past actions. Thus, depending on the interaction of the previous step, the probability of the following interaction changes based on the observed pattern of conflict resolution strategies.Calculation of the observed probabilities of affiliative and aggressive interactionsFor the simple model, we calculated the probability of general aggression and affiliation per day without distinguishing between types of positive and negative interactions. Thus, we obtained the number of periods in which an aggressive or affiliative contact took place per day and divided it by the total number of periods of that day (probability of general aggression or affiliation per 3-min period). With these probabilities, we calculated the mean probability of general aggression and affiliation per period.For the complex model, we calculated the probabilities of reconciliation, new affiliations/aggressions, and spontaneous affiliations/aggressions per day. That is, the probability that former opponents exchange affiliative contacts after an aggressive encounter (reconciliation), the probabilities that a conflict may promote new affiliations (third-party affiliation) or new conflicts (redirected aggression) between one of the opponents and a bystander in the same day, and the probability of affiliative or aggressive interactions not derived from a previous conflict (spontaneous interactions). To classify affiliations and aggressions in these categories we used the temporal networks, examining the interactions that took place after a conflict between opponents and between them and bystanders. If the opponents reconciled or affiliated with a bystander after a fight, we assumed that the following affiliative or aggressive interactions were spontaneous and were not a consequence of that conflict. Thus, to calculate the number of spontaneous affiliations, we subtracted the number of reconciliations and new affiliations from the total number of affiliations per day. For spontaneous aggressions, we subtracted the number of new aggressions to the total number of aggressions per day. Then, we obtained the probability of spontaneous affiliation and aggression per period.Using the previous probabilities, we obtained the rate (r) of reconciliation, new aggression and new affiliation per minute with the following formula:({p=1-e}^{-rDelta t}). Using the same formula, we finally calculated the probability of reconciliation, new aggression and affiliation per 3-min period used in the complex model (Supplementary Table 1 for details of probabilities calculation).Network-motif analysisWe also carried out a network-motif analysis. As we did not consider the identities or sex of the nodes in these models, we grouped the obtained motifs into equivalent categories considering the pattern of interactions between nodes. We also classified the motifs obtained from the real data of G1 into those equivalent categories. Finally, we compared the pattern of equivalent network motifs of the observed social network of dolphins and the ones of the two models. To do so we calculated the Spearman’s rank correlation coefficient (rs), defined as a nonparametric measure of the statistical dependence between the rankings of two variables: ({r}_{s}=frac{covleft({rg}_{X}{rg}_{Y}right)}{{sigma }_{{rg}_{X}}}{sigma }_{{rg}_{Y}}); rgX and rgY are the rank variables; cov (rgX rgY) is the covariance of the rank variables, and σrgX and σrgY are the standard deviations of the rank variables. Therefore, this coefficient allows us to assess the statistical dependence between the motif ranking of the real data and the one of each model.Computational implementationsAll the models, network construction, visualization and motif analysis were generated and implemented using MATLAB R2018b. More

The effects of extreme drought on soil biochemical propertiesAs shown in Fig. 1A, the range of SOC during the early, midterm and late extreme drought experiments, were 73.53–251.44 g kg−1, 54.75–256.16 g kg−1, and 66.37–282.16 g kg−1, respectively. Concomitantly, DOC was 171.85–323.74 mg kg−1, 158.15 – 504.62 mg kg−1, and 166.63–418.43 mg kg−1, MBC was 247.80 – 461.69 mg kg−1, 257.90–450.98 mg kg−1, and 264.10–458.15 mg kg−1, respectively (Fig. 1B, C). The variation ranges of soil TN were 3.50–16.60 g kg−1, 4.70–34.5 g kg−1, and 6.70–32.50 g kg−1, respectively (Fig. 1D). Similarly, the variation ranges of NH4+ were 5.96–12.03 g kg−1, 5.39–12.59 g kg−1, and 5.74–13.03 g kg−1, NO3− were 2.27–8.79 mg kg−1, 5.07–9.62 mg kg−1, and 5.09–9.52 mg kg−1, respectively (Fig. 1E, F). The changes of SOC and NH4+ with soil depth were significantly different in different extreme drought periods and decreased significantly with the increase of soil depth (Table 1, P  More

Barbier, E. B. Marine ecosystem services. Curr. Biol. 27, R507–R510 (2017).CAS 
PubMed 

Google Scholar 
Liquete, C., Piroddi, C., Macías, D., Druon, J.-N. & Zulian, G. Ecosystem services sustainability in the Mediterranean Sea: Assessment of status and trends using multiple modelling approaches. Sci. Rep. 6, 34162 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Halpern, B. S. et al. Recent pace of change in human impact on the world’s ocean. Sci. Rep. 9, 1–8 (2019).CAS 

Google Scholar 
Duarte, C. M. et al. Rebuilding marine life. Nature 580, 39–51 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Long, R. D., Charles, A. & Stephenson, R. L. Key principles of marine ecosystem-based management. Mar. Policy 57, 53–60 (2015).
Google Scholar 
Link, J. S. & Browman, H. I. Operationalizing and implementing ecosystem-based management. ICES J. Mar. Sci. 74, 379–381 (2017).
Google Scholar 
EC. A Farm to Fork Strategy for a Fair, Healthy and Environmentally-Friendly Food System. Brussels: European Commission. (2020).EC. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions, “Pathway to a healthy planet for all” with the sub-title “EU action Plan: ’Towards zero pollution for air, water and soil, COM (2021) 400. (2021).EC. The European Green Deal. Communication from the Commission to the European Parliament, the European Council, the European Economic and Social Committee and the Committee of the Regions, COM (2019) 640. (2019).Alexander, K. & Haward, M. The human side of marine ecosystem-based management (EBM): ‘Sectoral interplay’ as a challenge to implementing EBM. Mar. Policy 101, 33–38 (2019).
Google Scholar 
EC. The EU Blue Economy Report 2021. (2021).Ostlaender, N. et al. Modelling Inventory and Knowledge Man-agement System of the European Commission (MIDAS) (Publications Office of the European Union, 2019).
Google Scholar 
Friedland, R. et al. Effects of nutrient management scenarios on marine eutrophication indicators: A Pan-European, multi-model assessment in support of the Marine Strategy Framework Directive. Front. Mar. Sci. 8, 596126 (2021).
Google Scholar 
Piroddi, C. et al. Effects of nutrient management scenarios on marine food webs: A Pan-European Assessment in support of the Marine Strategy Framework Directive. Front. Mar. Sci. 8, 179 (2021).
Google Scholar 
Corrales, X. et al. Multi-zone marine protected areas: Assessment of ecosystem and fisheries benefits using multiple ecosystem models. Ocean Coast. Manag. 193, 105232 (2020).
Google Scholar 
Bentley, J. W. et al. Refining fisheries advice with stock-specific ecosystem information. Front. Mar. Sci. 8, 602072 (2021).
Google Scholar 
Steenbeek, J. et al. Making spatial-temporal marine ecosystem modelling better—A perspective. Environ. Model. Softw. 145, 105209 (2021).PubMed 
PubMed Central 

Google Scholar 
Heymans, J. J. et al. The ocean decade: A true ecosystem modelling challenge. Front. Mar. Sci. 7, 554573 (2020).
Google Scholar 
Hernvann, P.-Y. et al. The Celtic sea through time and space: Ecosystem modeling to unravel fishing and climate change impacts on food-web structure and dynamics. Front. Mar. Sci. 7, 1018 (2020).
Google Scholar 
Christensen, V. & Walters, C. J. Ecopath with Ecosim: Methods, capabilities and limitations. Ecol. Model. 172, 109–139 (2004).
Google Scholar 
Steenbeek, J. et al. Bridging the gap between ecosystem modeling tools and geographic information systems: Driving a food web model with external spatial–temporal data. Ecol. Model. 263, 139–151 (2013).
Google Scholar 
Christensen, V. et al. Representing variable habitat quality in a spatial food web model. Ecosystems 17, 1397–1412 (2014).CAS 

Google Scholar 
de Mutsert, K., Lewis, K., Milroy, S., Buszowski, J. & Steenbeek, J. Using ecosystem modeling to evaluate trade-offs in coastal management: Effects of large-scale river diversions on fish and fisheries. Ecol. Model. 360, 14–26 (2017).
Google Scholar 
Serpetti, N. et al. Modelling small scale impacts of Multi-Purpose Platforms: An ecosystem approach. Front. Mar. Sci. 8, 778 (2021).
Google Scholar 
DFO. Technical review of Roberts Bank Terminal 2 environmental assessment: section 10.3—assessing ecosystem productivity. DFO Can. Sci. Advis. Sec. Sci. Resp. 2016/050 (2016).Coll, M., Pennino, M. G., Steenbeek, J., Solé, J. & Bellido, J. M. Predicting marine species distributions: Complementarity of food-web and Bayesian hierarchical modelling approaches. Ecol. Model. 405, 86–101 (2019).
Google Scholar 
Coll, M. et al. The biodiversity of the Mediterranean Sea: Estimates, patterns, and threats. PLoS One 5, e11842 (2010).ADS 
PubMed 
PubMed Central 

Google Scholar 
Coll, M. et al. The Mediterranean Sea under siege: Spatial overlap between marine biodiversity, cumulative threats and marine reserves. Glob. Ecol. Biogeogr. 21, 465–480 (2012).
Google Scholar 
Micheli, F. et al. Cumulative human impacts on mediterranean and black sea marine ecosystems: Assessing current pressures and opportunities. PLoS One 8, e79889 (2013).ADS 
PubMed 
PubMed Central 

Google Scholar 
Piroddi, C., Colloca, F. & Tsikliras, A. C. The living marine resources in the Mediterranean Sea large marine ecosystem. Environ. Dev. 36, 100555 (2020).PubMed 
PubMed Central 

Google Scholar 
Barale, V. & Gade, M. Remote Sensing of the European Seas. (Springer, 2008).Siokou-Frangou, I. et al. Plankton in the open Mediterranean Sea: A review. Biogeosciences 7, 1543–1586 (2010).ADS 

Google Scholar 
Spalding, M. D. et al. Marine ecoregions of the world: A bioregionalization of coastal and shelf areas. Bioscience 57, 573–583 (2007).
Google Scholar 
Bianchi, C. N. et al. In Life in the Mediterranean Sea: A Look at Habitat Changes, vol. 1 55 (2012).Danovaro, R. et al. Deep-sea biodiversity in the Mediterranean Sea: The known, the unknown, and the unknowable. PLoS One 5, e11832 (2010).ADS 
PubMed 
PubMed Central 

Google Scholar 
Moullec, F. et al. Capturing the big picture of Mediterranean marine biodiversity with an end-to-end model of climate and fishing impacts. Prog. Oceanogr. 178, 102179 (2019).
Google Scholar 
Macias, D., Garcia-Gorriz, E., Piroddi, C. & Stips, A. Biogeochemical control of marine productivity in the Mediterranean Sea during the last 50 years. Glob. Biogeochem. Cycles 28, 897–907 (2014).ADS 
CAS 

Google Scholar 
Piroddi, C. et al. Historical changes of the Mediterranean Sea ecosystem: Modelling the role and impact of primary productivity and fisheries changes over time. Sci. Rep. 7, 44491 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lotze, H. K. et al. Depletion, degradation, and recovery potential of estuaries and coastal seas. Science 312, 1806–1809 (2006).ADS 
CAS 
PubMed 

Google Scholar 
Lotze, H. K., Coll, M. & Dunne, J. A. Historical changes in marine resources, food-web structure and ecosystem functioning in the Adriatic Sea, Mediterranean. Ecosystems 14, 198–222 (2011).
Google Scholar 
Macias, D., Huertas, I. E., Garcia-Gorriz, E. & Stips, A. Non-Redfieldian dynamics driven by phytoplankton phosphate frugality explain nutrient and chlorophyll patterns in model simulations for the Mediterranean Sea. Prog. Oceanogr. 173, 37–50 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Spedicato, M. T. et al. The MEDITS trawl survey specifications in an ecosystem approach to fishery management. Sci. Mar. 83, 9–20 (2019).
Google Scholar 
Corrales, X. et al. Hindcasting the dynamics of an Eastern Mediterranean marine ecosystem under the impacts of multiple stressors. Mar. Ecol. Prog. Ser. 580, 17–36 (2017).ADS 

Google Scholar 
FAO. The State of the Mediterranean and Black Sea fisheries (General Fisheries Commission for the Mediterranean (GFCM), 2020).Ferrà, C. et al. Mapping change in bottom trawling activity in the Mediterranean Sea through AIS data. Mar. Policy 94, 275–281 (2018).
Google Scholar 
Russo, T. et al. Trends in effort and yield of trawl fisheries: A case study from the Mediterranean Sea. Front. Mar. Sci. 6, 153 (2019).ADS 

Google Scholar 
Ramírez, F., Coll, M., Navarro, J., Bustamante, J. & Green, A. J. Spatial congruence between multiple stressors in the Mediterranean Sea may reduce its resilience to climate impacts. Sci. Rep. 8, 1–8 (2018).
Google Scholar 
Coll, M., Steenbeek, J., Ben Rais Lasram, F., Mouillot, D. & Cury, P. ‘Low-hanging fruit’ for conservation of marine vertebrate species at risk in the Mediterranean Sea. Glob. Ecol. Biogeogr. 24, 226–239 (2015).
Google Scholar 
Ruiz, J. et al. “Strengthening regional cooperation in the area of large pelagic fishery data collection (RECOLAPE)”, Annex III “Biological data collection for fisheries on highly migratory species” (2019).Boerder, K., Schiller, L. & Worm, B. Not all who wander are lost: Improving spatial protection for large pelagic fishes. Mar. Policy 105, 80–90 (2019).
Google Scholar 
Giakoumi, S. et al. Conserving European biodiversity across realms. Conserv. Lett. 12, e12586 (2019).
Google Scholar 
Gascuel, D. & Cheung, W. W. In Predicting Future Oceans 79–85 (Elsevier, 2019).Macias, D., Garcia-Gorriz, E. & Stips, A. Major fertilization sources and mechanisms for Mediterranean Sea coastal ecosystems. Limnol. Oceanogr. 63, 897–914 (2018).ADS 
CAS 

Google Scholar 
Alvarez-Berastegui, D., Tugores, M., Ottmann, D., Martín-Quetglas, M. & Reglero, P. Bluefin tuna larval indices in the Western Mediterranean, ecological and analytical sources of uncertainty. Collect. Vol. Sci. Pap. ICCAT. 77, 289–311 (2020).
Google Scholar 
ICCAT. 2020 SCRS Advice to the Commission (Madrid, Spain, 2020).Clavel-Henry, M., Piroddi, C., Quattrocchi, F., Macias, D. & Christensen, V. Spatial distribution and abundance of mesopelagic fish biomass in the Mediterranean Sea. Front. Mar. Sci. 7, 1136 (2020).
Google Scholar 
García-Ruiz, C. et al. Spatio-temporal patterns of macrourid fish species in the northern Mediterranean Sea. Sci. Mar. 83, 117–127 (2019).
Google Scholar 
Ainsworth, C. Quantifying species abundance trends in the Northern Gulf of California using local ecological knowledge. Mar. Coast. Fish. 3, 190–218 (2011).
Google Scholar 
Morris, E. K. et al. Choosing and using diversity indices: Insights for ecological applications from the German Biodiversity Exploratories. Ecol. Evol. 4, 3514–3524 (2014).PubMed 
PubMed Central 

Google Scholar 
Coll, M. et al. Ecological indicators to capture the effects of fishing on biodiversity and conservation status of marine ecosystems. Ecol. Ind. 60, 947–962 (2016).
Google Scholar 
Swartz, W., Sala, E., Tracey, S., Watson, R. & Pauly, D. The spatial expansion and ecological footprint of fisheries (1950 to present). PLoS One 5, e15143 (2010).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Damasio, L. M., Peninno, M. G. & Lopes, P. F. Small changes, big impacts: Geographic expansion in small-scale fisheries. Fish. Res. 226, 105533 (2020).
Google Scholar 
Coll, M. et al. Assessing fishing and marine biodiversity changes using fishers’ perceptions: The Spanish Mediterranean and Gulf of Cadiz case study. PLoS One 9, e85670 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Tsikliras, A. C., Dinouli, A., Tsiros, V.-Z. & Tsalkou, E. The Mediterranean and Black Sea fisheries at risk from overexploitation. PLoS ONE 10, e0121188 (2015).PubMed 
PubMed Central 

Google Scholar 
Pittman, S. et al. Seascape ecology: Identifying research priorities for an emerging ocean sustainability science. Mar. Ecol. Prog. Ser. 663, 1–29 (2021).ADS 

Google Scholar 
Kritzer, J. P. & Liu, O. R. In Stock Identification Methods 29–57 (Elsevier, 2014).Piroddi, C., Heymans, J. J., Macias, D., Gregoire, M. & Townsend, H. Editorial: Using ecological models to support and shape environmental policy decisions. Front. Mar. Sci. https://doi.org/10.3389/fmars.2021.815313 (2021).
Google Scholar 
Macias, D. et al. JRC marine modelling framework in support of the marine strategy framework directive: Inventory of models, basin configurations and datasets. Update 2018. (2018).Piante, C. & Ody, D. Blue Growth in the Mediterranean Sea: The Challenge of Good Environmental Status. 192 (France, 2015).Borja, A. et al. Past and future grand challenges in marine ecosystem ecology. Front. Mar. Sci. 7, 362 (2020).
Google Scholar 
Claudet, J., Loiseau, C., Sostres, M. & Zupan, M. Underprotected marine protected areas in a global biodiversity hotspot. One Earth 2, 380–384 (2020).ADS 

Google Scholar 
Piroddi, C., Coll, M., Steenbeek, J., Moy, D. M. & Christensen, V. Modelling the Mediterranean marine ecosystem as a whole: Addressing the challenge of complexity. Mar. Ecol. Prog. Ser. 533, 47–65 (2015).ADS 

Google Scholar 
Walters, C., Pauly, D. & Christensen, V. Ecospace: Prediction of mesoscale spatial patterns in trophic relationships of exploited ecosystems, with emphasis on the impacts of marine protected areas. Ecosystems 2, 539–554 (1999).
Google Scholar 
Christensen, V., Walters, C., Pauly, D. & Forrest, R. Ecopath with Ecosim 6: A User’s Guide (University of British Columbia, 2008).
Google Scholar 
Kaschner, K. et al. AquaMaps: Predicted range maps for aquatic species. In World Wide Web Electronic Publication, www.aquamaps.org, Version, vol. 8, 2016 (2016).De Mutsert, K., Lewis, K. A., White, E. D. & Buszowski, J. End-to-End modeling reveals species-specific effects of large-scale coastal restoration on living resources facing climate change. Front. Mar. Sci. 8, 104 (2021).
Google Scholar 
Coll, M. et al. Advancing global ecological modeling capabilities to simulate future trajectories of change in marine ecosystems. Front. Mar. Sci. 741, 567877 (2020).
Google Scholar 
Shannon, C. & Weaver, W. (Univ. Illinois Press, 1949).Ainsworth, C. H. & Pitcher, T. J. Modifying Kempton’s species diversity index for use with ecosystem simulation models. Ecol. Ind. 6, 623–630 (2006).
Google Scholar 
Coll, M. & Steenbeek, J. Standardized ecological indicators to assess aquatic food webs: The ECOIND software plug-in for Ecopath with Ecosim models. Environ. Model. Softw. 89, 120–130 (2017).
Google Scholar 
Taconet, M., Kroodsma, D. & Fernandes, J. Global Atlas of AIS-Based Fishing Activity—Challenges and Opportunities (2021). More

Ringø, E. et al. Probiotics, lactic acid bacteria and bacilli: Interesting supplementation for aquaculture. J. Appl. Microbiol. 129, 116–136 (2020).PubMed 

Google Scholar 
Borges, N. et al. Bacteriome structure, function, and probiotics in fish larviculture: The good, the bad, and the gaps. Annu. Rev. Anim. Biosci. 9, 423–452 (2021).CAS 
PubMed 

Google Scholar 
Aguilar-Toalá, J. E. et al. Postbiotics: An evolving term within the functional foods field. Trends Food Sci. Technol. 75, 105–114 (2018).
Google Scholar 
Salminen, S. et al. The International Scientific Association of Probiotics and Prebiotics (ISAPP) consensus statement on the definition and scope of postbiotics. Nat. Rev. Gastroenterol. Hepatol. 18, 649–667 (2021).PubMed 
PubMed Central 

Google Scholar 
Dash, G. et al. Evaluation of paraprobiotic applicability of Lactobacillus plantarum in improving the immune response and disease protection in giant freshwater prawn, Macrobrachium rosenbergii (de Man, 1879). Fish Shellf. Immunol. 43, 167–174 (2015).CAS 

Google Scholar 
Dawood, M. A. O., Koshio, S., Ishikawa, M. & Yokoyama, S. Effects of partial substitution of fish meal by soybean meal with or without heat-killed Lactobacillus plantarum (LP20) on growth performance, digestibility, and immune response of Amberjack, Seriola dumerili Juveniles. Biomed. Res. Int. 2015, 514196 (2015).PubMed 
PubMed Central 

Google Scholar 
Dawood, M. A. O., Koshio, S., Ishikawa, M. & Yokoyama, S. Immune responses and stress resistance in red sea bream, Pagrus major, after oral administration of heat-killed Lactobacillus plantarum and vitamin C. Fish Shellf. Immunol. 54, 266–275 (2016).CAS 

Google Scholar 
Dawood, M. A. O., Koshio, S., Ishikawa, M. & Yokoyama, S. Interaction effects of dietary supplementation of heat-killed Lactobacillus plantarum and β-glucan on growth performance, digestibility and immune response of juvenile red sea bream, Pagrus major. Fish Shellf. Immunol. 45, 33–42 (2015).CAS 

Google Scholar 
Tung, H. T. et al. Effects of heat-killed Lactobacillus plantarum supplemental diets on growth performance, stress resistance and immune response of juvenile Kuruma shrimp Marsupenaeus japonicus bate. Aquac. Sci. 57, 175–184 (2009).CAS 

Google Scholar 
Van Nguyen, N. et al. Evaluation of dietary heat-killed Lactobacillus plantarum strain L-137 supplementation on growth performance, immunity and stress resistance of Nile tilapia (Oreochromis niloticus). Aquaculture 498, 371–379 (2019).
Google Scholar 
Yang, H. et al. Effects of dietary heat-killed Lactobacillus plantarum L-137 (HK L-137) on the growth performance, digestive enzymes and selected non-specific immune responses in sea cucumber, Apostichopus japonicus Selenka. Aquac. Res. 47, 2814–2824 (2016).CAS 

Google Scholar 
Singh, S. T., Kamilya, D., Kheti, B., Bordoloi, B. & Parhi, J. Paraprobiotic preparation from Bacillus amyloliquefaciens FPTB16 modulates immune response and immune relevant gene expression in Catla catla (Hamilton, 1822). Fish Shelf. Immunol. 66, 35–42 (2017).CAS 

Google Scholar 
Kamilya, D., Baruah, A., Sangma, T., Chowdhury, S. & Pal, P. Inactivated probiotic bacteria stimulate cellular immune responses of catla, Catla catla (Hamilton) in vitro. Probiot. Antimicrob. Proteins 7, 101–106 (2015).CAS 

Google Scholar 
Wang, J. et al. Supplementation of heat-inactivated Bacillus clausii DE 5 in diets for grouper, Epinephelus coioides, improves feed utilization, intestinal and systemic immune responses and not growth performance. Aquac. Nutr. 24, 821–831 (2018).CAS 

Google Scholar 
Giri, S. S. et al. Effects of dietary heat-killed Pseudomonas aeruginosa strain VSG2 on immune functions, antioxidant efficacy, and disease resistance in Cyprinus carpio. Aquaculture 514, 734489 (2020).CAS 

Google Scholar 
Shabanzadeh, S. et al. Growth performance, intestinal histology, and biochemical parameters of rainbow trout (Oncorhynchus mykiss) in response to dietary inclusion of heat-killed Gordonia bronchialis. Fish Physiol. Biochem. 42, 65–71 (2016).CAS 
PubMed 

Google Scholar 
Wu, X. et al. Use of a paraprobiotic and postbiotic feed supplement (HWF™) improves the growth performance, composition and function of gut microbiota in hybrid sturgeon (Acipenser baerii × Acipenser schrenckii). Fish Shelf. Immunol. 104, 36–45 (2020).CAS 

Google Scholar 
Mora-Sánchez, B., Balcázar, J. L. & Pérez-Sánchez, T. Effect of a novel postbiotic containing lactic acid bacteria on the intestinal microbiota and disease resistance of rainbow trout (Oncorhynchus mykiss). Biotechnol. Lett. 42, 1957–1962 (2020).PubMed 

Google Scholar 
Xiong, J. et al. The temporal scaling of bacterioplankton composition: High turnover and predictability during shrimp cultivation. Microb. Ecol. 67, 256–264 (2014).PubMed 

Google Scholar 
Martins, P. et al. Seasonal patterns of bacterioplankton composition in a semi-intensive European seabass (Dicentrarchus labrax) aquaculture system. Aquaculture 490, 240–250 (2018).
Google Scholar 
Offret, C. et al. Protective efficacy of a Pseudoalteromonas strain in European abalone, Haliotis tuberculata, infected with Vibrio harveyi ORM4. Probiot. Antimicrob. Proteins 11, 239–247 (2019).
Google Scholar 
Richards, G. P. et al. Mechanisms for Pseudoalteromonas piscicida-induced killing of vibrios and other bacterial pathogens. Appl. Environ. Microbiol. 83, e00175-e217 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lin, S.-R., Chen, Y.-H., Tseng, F.-J. & Weng, C.-F. The production and bioactivity of prodigiosin: Quo vadis?. Drug Discov. Today 25, 828–836 (2020).CAS 
PubMed 

Google Scholar 
Vynne, N. G., Månsson, M., Nielsen, K. F. & Gram, L. Bioactivity, chemical profiling, and 16S rRNA-based phylogeny of Pseudoalteromonas strains collected on a global research cruise. Mar. Biotechnol. 13, 1062–1073 (2011).CAS 

Google Scholar 
Lovejoy, C., Bowman, J. P. & Hallegraeff, G. M. Algicidal effects of a novel marine Pseudoalteromonas isolate (class Proteobacteria, gamma subdivision) on harmful algal bloom species of the genera Chattonella, Gymnodinium, and Heterosigma. Appl. Environ. Microbiol. 64, 2806–2813 (1998).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Franks, A. et al. Inhibition of fungal colonization by Pseudoalteromonas tunicata provides a competitive advantage during surface colonization. Appl. Environ. Microbiol. 72, 6079–6087 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hjelm, M. et al. Selection and identification of autochthonous potential probiotic bacteria from turbot larvae (Scophthalmus maximus) rearing units. Syst. Appl. Microbiol. 27, 360–371 (2004).PubMed 

Google Scholar 
Sorieul, L. et al. Survival improvement conferred by the Pseudoalteromonas sp. NC201 probiotic in Litopenaeus stylirostris exposed to Vibrio nigripulchritudo infection and salinity stress. Aquaculture 495, 888–898 (2018).
Google Scholar 
Pruitt, K. D., Tatusova, T. & Maglott, D. R. NCBI reference sequence (RefSeq): A curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 33, D501–D504 (2005).CAS 
PubMed 

Google Scholar 
Yan, Y.-Y., Xia, H.-Q., Yang, H.-L., Hoseinifar, S. H. & Sun, Y.-Z. Effects of dietary live or heat-inactivated autochthonous Bacillus pumilus SE5 on growth performance, immune responses and immune gene expression in grouper Epinephelus coioides. Aquac. Nutr. 22, 698–707 (2016).CAS 

Google Scholar 
Louvado, A. et al. Humic substances modulate fish bacterial communities in a marine recirculating aquaculture system. Aquaculture 544, 737121 (2021).CAS 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 1, 5 (2019).
Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 1–17 (2018).
Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-6. 2019. (2020).R. Core Team. R: A Language and Environment for Statistical Computing (Version 2120) (R Foundation for Statistical Computing, 2012).
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 
Wemheuer, F. et al. Tax4Fun2: Prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences. Environ. Microbiome 15, 11 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Setiyono, E. et al. An Indonesian marine bacterium, Pseudoalteromonas rubra, produces antimicrobial prodiginine pigments. ACS Omega 5, 4626–4635 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Buchan, A., LeCleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: Features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686–698 (2014).CAS 
PubMed 

Google Scholar 
Bakenhus, I. et al. Composition of total and cell-proliferating bacterioplankton community in early summer in the North Sea—roseobacters are the most active component. Front. Microbiol. 8, 1771 (2017).PubMed 
PubMed Central 

Google Scholar 
Sieber, C. M. K. et al. Unusual metabolism and hypervariation in the genome of a gracilibacterium (BD1-5) from an oil-degrading community. MBio 10, e02128-e2219 (2022).
Google Scholar 
Jaffe, A. L., Castelle, C. J., Matheus Carnevali, P. B., Gribaldo, S. & Banfield, J. F. The rise of diversity in metabolic platforms across the Candidate Phyla Radiation. BMC Biol. 18, 69 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Williams, H. N. & Piñeiro, S. Ecology of the predatory Bdellovibrio and like organisms. In Predatory Prokaryotes 213–248 (Springer, 2006).
Google Scholar 
Johnke, J. et al. Multiple micro-predators controlling bacterial communities in the environment. Curr. Opin. Biotechnol. 27, 185–190 (2014).CAS 
PubMed 

Google Scholar 
Rice, T. D., Williams, H. N. & Turng, B.-F. Susceptibility of bacteria in estuarine environments to autochthonous bdellovibrios. Microb. Ecol. 35, 256–264 (1998).CAS 
PubMed 

Google Scholar 
Feng, S. et al. Predation by Bdellovibrio bacteriovorus significantly reduces viability and alters the microbial community composition of activated sludge flocs and granules. FEMS Microbiol. Ecol. 93, fix020 (2017).
Google Scholar 
Duarte, L. N. et al. Bacterial and microeukaryotic plankton communities in a semi-intensive aquaculture system of sea bass (Dicentrarchus labrax): A seasonal survey. Aquaculture 503, 59–69 (2019).
Google Scholar 
Hu, L. et al. Reclassification of the taxonomic framework of orders cellvibrionales, oceanospirillales, pseudomonadales, and alteromonadales in class gammaproteobacteria through phylogenomic tree analysis. mSystems 5, e00543-e620 (2021).
Google Scholar 
Garrity, G. M., Bell, J. A. & Lilburn, T. Oceanospirillalesord. Nov. in Bergey’s Manual® of Systematic Bacteriology 270–323 (Springer, 2005).
Google Scholar 
Thomas, F., Hehemann, J.-H., Rebuffet, E., Czjzek, M. & Michel, G. Environmental and gut bacteroidetes: The food connection. Front. Microbiol. 2, 93 (2011).PubMed 
PubMed Central 

Google Scholar 
Rajilić-Stojanović, M. & De Vos, W. M. The first 1000 cultured species of the human gastrointestinal microbiota. FEMS Microbiol. Rev. 38, 996–1047 (2014).PubMed 

Google Scholar 
Rosenberg, E. The family Chitinophagaceae. In The Prokaryotes: Other Major Lineages of Bacteria and The Archaea (eds Rosenberg, E. et al.) (Springer, 2014).
Google Scholar 
Beckmann, A., Hüttel, S., Schmitt, V., Müller, R. & Stadler, M. Optimization of the biotechnological production of a novel class of anti-MRSA antibiotics from Chitinophaga sancti. Microb. Cell Fact. 16, 1–10 (2017).
Google Scholar 
Loudon, A. H. et al. Interactions between amphibians’ symbiotic bacteria cause the production of emergent anti-fungal metabolites. Front. Microbiol. 5, 441 (2014).PubMed 
PubMed Central 

Google Scholar 
Taylor, J. D. & Cunliffe, M. Coastal bacterioplankton community response to diatom-derived polysaccharide microgels. Environ. Microbiol. Rep. 9, 151–157 (2017).CAS 
PubMed 

Google Scholar 
González, J. M. & Whitman, W. B. Oceanospirillum and related genera. In The Prokaryotes: A Handbook on the Biology of Bacteria Volume 6: Proteobacteria: Gamma Subclass (eds Dworkin, M. et al.) 887–915 (Springer, 2006).
Google Scholar 
Kleindienst, S., Paul, J. H. & Joye, S. B. Using dispersants after oil spills: Impacts on the composition and activity of microbial communities. Nat. Rev. Microbiol. 13, 388–396 (2015).CAS 
PubMed 

Google Scholar 
Williams, T. J. et al. The role of planktonic Flavobacteria in processing algal organic matter in coastal East Antarctica revealed using metagenomics and metaproteomics. Environ. Microbiol. 15, 1302–1317 (2013).CAS 
PubMed 

Google Scholar 
Gobet, A. et al. Evolutionary evidence of algal polysaccharide degradation acquisition by Pseudoalteromonas carrageenovora 9T to adapt to macroalgal niches. Front. Microbiol. 10, 2740 (2018).
Google Scholar 
Beier, S., Rivers, A. R., Moran, M. A. & Obernosterer, I. The transcriptional response of prokaryotes to phytoplankton-derived dissolved organic matter in seawater. Environ. Microbiol. 17, 3466–3480 (2015).CAS 
PubMed 

Google Scholar 
Müller, O., Seuthe, L., Bratbak, G. & Paulsen, M. L. Bacterial response to permafrost derived organic matter input in an Arctic fjord. Front. Mar. Sci. 5, 263 (2018).
Google Scholar 
Fernández-Álvarez, C. & Santos, Y. Identification and typing of fish pathogenic species of the genus Tenacibaculum. Appl. Microbiol. Biotechnol. 102, 9973–9989 (2018).PubMed 

Google Scholar 
Wirth, J. S. & Whitman, W. B. Phylogenomic analyses of a clade within the roseobacter group suggest taxonomic reassignments of species of the genera Aestuariivita, Citreicella, Loktanella, Nautella, Pelagibaca, Ruegeria, Thalassobius, Thiobacimonas and Tropicibacter, and the proposal. Int. J. Syst. Evol. Microbiol. 68, 2393–2411 (2018).CAS 
PubMed 

Google Scholar 
Luo, H. & Moran, M. A. Evolutionary ecology of the marine Roseobacter clade. Microbiol. Mol. Biol. Rev. 78, 573–587 (2014).PubMed 
PubMed Central 

Google Scholar 
Shahina, M. et al. Luteibaculum oceani gen. nov., sp. Nov., a carotenoid-producing, lipolytic bacterium isolated from surface seawater, and emended description of the genus Owenweeksia Lau et al. 2005. Int. J. Syst. Evol. Microbiol. 63, 4765–4770 (2013).CAS 
PubMed 

Google Scholar 
Davidov, Y. & Jurkevitch, E. Diversity and evolution of Bdellovibrio-and-like organisms (BALOs), reclassification of Bacteriovorax starrii as Peredibacter starrii gen. nov., comb. nov., and description of the Bacteriovorax–Peredibacter clade as Bacteriovoracaceae fam. nov. Int. J. Syst. Evol. Microbiol. 54, 1439–1452 (2004).CAS 
PubMed 

Google Scholar 
Müller, F. D., Beck, S., Strauch, E. & Linscheid, M. W. Bacterial predators possess unique membrane lipid structures. Lipids 46, 1129–1140 (2011).PubMed 

Google Scholar 
Kandel, P. P., Pasternak, Z., van Rijn, J., Nahum, O. & Jurkevitch, E. Abundance, diversity and seasonal dynamics of predatory bacteria in aquaculture zero discharge systems. FEMS Microbiol. Ecol. 89, 149–161 (2014).CAS 
PubMed 

Google Scholar 
Cao, H., Wang, H., Yu, J., An, J. & Chen, J. Encapsulated bdellovibrio powder as a potential bio-disinfectant against whiteleg shrimp-pathogenic vibrios. Microorganisms 7, 25 (2019).
Google Scholar 
Jafarian, N., Sepahi, A. A., Naghavi, N. S., Hosseini, F. & Nowroozi, J. Using autochthonous Bdellovibrio as a predatory bacterium for reduction of Gram-negative pathogenic bacteria in urban wastewater and reuse it. Iran. J. Microbiol. 12, 556–564 (2020).PubMed 
PubMed Central 

Google Scholar 
Cosgrove, L., McGeechan, P. L., Handley, P. S. & Robson, G. D. Effect of biostimulation and bioaugmentation on degradation of polyurethane buried in soil. Appl. Environ. Microbiol. 76, 810–819 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Cruz, A. et al. Microbial remediation of organometals and oil hydrocarbons in the marine environment. In Marine Pollution and Microbial Remediation 41–66 (Springer, 2017).
Google Scholar 
Zhao, J., Chen, M., Quan, C. S. & Fan, S. D. Mechanisms of quorum sensing and strategies for quorum sensing disruption in aquaculture pathogens. J. Fish Dis. 38, 771–786 (2015).CAS 
PubMed 

Google Scholar 
Tyc, O., Song, C., Dickschat, J. S., Vos, M. & Garbeva, P. The ecological role of volatile and soluble secondary metabolites produced by soil bacteria. Trends Microbiol. 25, 280–292 (2017).CAS 
PubMed 

Google Scholar  More

Model validationBased on the time-series validations of water temperature and DO concentration, model accuracy improved gradually, despite several discrepancies at the beginning of the simulation (Supplementary Fig. S1). The model is primarily driven by a set of boundary data, including wind speed, solar radiation, and precipitation data24,25. From this perspective, more high-quality boundary data promotes better numerical reproducibility. However, meteorological data collection was challenging due to the early observation equipment limitations and low observational accuracy compared to current data. The temporal inconsistency of accuracy in observational data has been eliminated to a large extent by fitting a regression curve24. Spatial resolution is the other issue. Possessing spatially constant values for all boundary conditions complicates the numerical reproducibility of variations on finer scales.The relationship between turnovers and the curve shape of water temperature versus DO concentration is theoretically sound27,28. In the last stage of stratification in the lake, water temperature and DO concentration near the bottom are more likely to slightly increase due to thermal diffusion and DO supplies from the upper water. If a turnover occurs, the whole column of water is mixed strongly (Supplementary Fig. S3). Bottom water temperature decreases due to surface water cooling, and DO concentration increases, due to surface water replenishment and increased oxygen solubility. If the turnover fails, only the partial column of water is mixed, causing a delay in the timing of deep-water renewal (Supplementary Fig. S3). However, the upper water in later months, like that in March, has been rapidly warmed, resulting in an increase in the bottom water temperature. For example, in 2007 and 2016, the simulated water temperature and DO concentration fluctuated within a limited range in February and then skyrocketed in March, after mixing with the warmed surface water (blue points in Supplementary Fig. S4). On the other hand, explicit definitions of turnover timing are challenging. The threshold used to judge turnover timing is reliable because the results matched the observation. The turnover timing varied by 36 days in Lake Biwa during the simulation period, which is comparable to that observed in other lakes, such as approximately 21 days in Heiligensee, Germany over a 17-year timespan29, 16 days in Lake Washington over a 40-year timespan30, and 28 days in Blelham Tarn over a 41-year timespan31.Variables affecting the mixing regimeDetermining variables that affect the mixing regime is essential to improve understanding and enable future projections16,17,18. Air temperature, wind speed, cloud cover, precipitation, water density, and lake transparency are all potential variables. We, here, compared the above variables to the turnover timing in Lake Biwa. The meteorological inputs in this study provided data for air temperature, wind speed, cloud cover, and precipitation. Water density and particulate organic carbon (POC) concentration representing lake transparency were the model’s outputs. The annual averages and cold season (November–April) values of the above variables were calculated over the simulation period (Supplementary Fig. S6). Annual averages illustrate general long-term warming trends18, while cold season values particularly determine the timing of turnover17. However, in Lake Biwa, air temperature during the cold season fluctuated greatly compared to the annual averages. A random forest analysis17 has been conducted between the turnover timing and the above two variable sets (cold season values versus annual averages) in Lake Biwa, and the cold season values better explained the turnover timing (35.39% versus 18.48%). The results agree with the conclusion drawn from the previous sensitivity tests, which indicated the relative importance of air temperature and solar radiation during winter based on 40 scenarios32.The importance of variables was estimated based on the random forest analysis using the cold season data (Fig. 4a). Wind speed dominates the timing of turnover, which is consistent with the previous studies17,25. The POC concentration, the difference in water density between the surface and bottom, and cloud cover have moderate effects on the timing of turnover. However, air temperature is less important, which is contrary to the turnover mechanism17,24,32. A re-confirmation was conducted of the relationship between turnover timing and air temperature (Fig. 4b and Supplementary Fig. S7). The cool air generally encourages an early turnover, albeit with several anomalies. The turnover timing between 1976 and 1990 remained constant independent of climate change, and the period coincidently had a substantial nutrient fluctuation (Fig. 3). As a result, it is essential to investigate the nutrient status further.Figure 4Analysis results of the relationship between potential variables and turnover timing: (a) the importance of variables importance using a random forest analysis, and (b) the relationship between the cold season air temperature and the timing of turnover. Variable importance is calculated using the percentage increase in mean square error (MSE) and the increase in node purity. Higher values illustrate the greater importance of the variable. Variables include air temperature (AT), precipitation (pptn.), cloud cover (CC), the difference in density (DD), POC, and wind speed (WS).Full size imageLake nutrient concentrationsBecause phosphorus is the limiting nutrient in Lake Biwa and DIP concentrations can be effectively limited by regulating external loadings as practiced (Fig. 3), DIP concentrations become the focus of this discussion for nutrient status. However, the DIP concentrations disproportionately responded to the external loadings of total phosphorus (TP) in Lake Biwa. Although external TP loading itself fails to determine lake phosphorus concentrations due to the hydrodynamics of lakes33, Lake Biwa exhibited insignificant changes in the inflow rate or the retention time (and see an example of the surface flow in Supplementary Fig. S8). Therefore, it can be assumed that the hydraulic loading remained constant, and the input nutrient concentrations were proportionate to the external nutrient loadings in Lake Biwa. This finding contradicts a recent meta-analysis that highlighted a deterministic relationship between input nutrient concentrations and lake nutrient concentrations, based on steady-state mass balance models6. The possible reason is the dynamics of the lake’s ecosystem22, which have been considered in this study. For example, the surface DIP concentrations were almost nonexistent regardless of the external TP loadings in Lake Biwa, supporting that phosphorus is the limiting nutrient in Lake Biwa34,35. The low DIP concentrations at the surface may be caused by the rapid recycling of phosphorus because the amount of phosphorus available for phytoplankton is easily affected by the feedback mechanism between phytoplankton photosynthesis and the phosphorus released from the water35,36.Hypoxia and strategiesThe variations in DO concentration are the public’s top concern as it relates to hypoxia, a key indicator of water quality. Lake bottom, among all water depths, is more sensitive to small changes in oxygen conditions12. In Lake Biwa, the annual minimum DO concentrations ranged from 2 to 5.5 mg/L over the last 60 years (Supplementary Fig. S9). The decrease in DO concentrations in the early period, typically till the 1980s, was mainly caused by nutrient enrichments (Fig. 3). The nutrient enrichment-induced heavy eutrophication eventually accelerates the rate of DO depletion2. After eutrophication was controlled in the 1980s, climate change became the dominant stressor23. There remains much uncertainty surrounding the relationship between climatic variables-related turnover timing and hypoxia in Lake Biwa12. We, therefore, first investigate the relationship between hypoxia and turnover timing, and then concentrate on nutrients to alleviate hypoxia.Although the relationship between turnover timing and DO concentrations is quite weak (R2 = 0.10), there is a general decrease in DO concentrations with increasing turnover timing (Fig. 5a). On the other hand, a linear relationship has been found between DIP concentrations and DO concentrations, with an R2 of 0.67 (Fig. 5b). The slope of –0.841 μgP/mgDO means an increase in DIP concentrations by approximately 0.841 μgP/L causes a decrease in DO concentrations by 1 mg/L. Note that the simulation results were compared over the whole period, and eutrophication-induced hypoxia differs theoretically from climate-induced hypoxia. Additional testing has been conducted to distinguish the effects of two stressors (eutrophication- and climate-induced hypoxia; Supplementary Fig. S10). Before 1980 when eutrophication progressed, the annual minimum DO concentrations and the DIP concentrations had a stronger linear relationship (R2 = 0.89). Although waste-water treatment has improved conditions in the lake, climate change induced alteration of turnover timing may adversely influence water quality. However, the relationship weakened dramatically with an R2 of 0.10 after 1980, when climate change dominated hypoxia. The lower R2 value indicates that climate-related hypoxia is more complex as concluded previously37,38. The two possibilities are as follows. First, there can be a legacy of hypoxia related to eutrophication. The DO recovery at the bottom of Lake Biwa was complicated by the low DO concentration in 1980 and the delayed timing of turnover; similar phenomena have been observed in the Lake of Zurich22. Second, ecosystem dynamics could help explain the difficulty in predicting hypoxia at the bottom. Phytoplankton fully exploits phosphorus at the surface, as explained above, then the death and sinking of the surface phytoplankton are accompanied by the sedimentation of phosphorus to the bottom as modeled. Bacteria break down the sinking phytoplankton, releasing phosphorus and consuming DO in the process. Additional DO consumption lowers the bottom DO concentration, which in turn encourages phosphorus release from the sediment in a low DO environment22. Such unfavorable feedback between DIP and DO concentrations are strengthened by prolonged stratification and eventually accelerates the development of hypoxia. However, future research is necessary because this numerical model simplified the relationship between water and sediment. The sinking of organic carbon into sediment is integrated in the model, and due to the decomposition of organic carbon in the sediment, nutrients are released into and oxygen is depleted in the water. Despite that, the trends between DO and DIP concentrations stay the same under climate change (Fig. 5b), and thus controlling lake phosphorus is beneficial to the Lake Biwa hypoxia.Figure 5The linear regression results of the relationship: (a) between turnover timing and annual minimum concentration of DO, (b) between the annual minimum concentration of DO and annual average concentration of DIP. The simulation results at the monitoring station were used for analysis.Full size image More

We investigated whether ricefield mosquito larval control and/or rice cultivation practices are associated with malaria vector densities through a systematic review and meta-analysis. Forty-seven experimental studies were eligible for inclusion in the qualitative analysis and thirty-three studies were eligible for the meta-analysis. It was demonstrated that the use of fish, chemical and biological larvicides in rice fields were effective in controlling larval malaria vector densities at all developmental stages. Intermittent irrigation, however, could only significantly reduce late-stage larvae. Based on a limited number of studies, meta-analyses on other forms of larval control such as monomolecular surface films (MSFs), neem, copepods and Azolla failed to demonstrate any consistent reduction in anopheline numbers. Similarly, rice cultivation practices such as plant variety and density, type of levelling and pesticide application were not generally associated with reduced malaria vectors. Nonetheless, in one study, minimal tillage was observed to reduce average numbers of larvae throughout a cropping season. In another study, herbicide application increased larval abundance over a 4-week period, as did one-time drainage in a third study.
Despite their different modes of action, the use of chemical and bacterial larvicides and MSFs were all relatively effective measures of larval control in rice fields, varying between a 57% to 76% reduction in vector abundance compared to no larviciding. Their effects were highest (often reaching 100% reduction) only shortly following application but did not persist for longer than two weeks. These larvicides mostly had short residual half-lives because they were applied to paddy water which was naturally not completely stagnant: there was a small but constant process of water loss (through drainage, evapotranspiration and percolation) and replacement through irrigation. Hence, even with a residual formulation, weekly re-application would be needed for sustained control47,40,41,50. This would be very labour- and cost-intensive to scale-up, to ensure that larvicides are evenly distributed across vast areas (even at plot/sub-plot level) throughout at least one 5-month long rice-growing season per year42,51. Aerial application (including unmanned aerial vehicles), although widely used in the US and Europe, is unlikely to be a feasible delivery system for smallholders in SSA, even in large irrigation schemes26,27,48,49. Furthermore, if synthetic organic chemicals were to be considered for riceland malaria vector control, their management in the current landscape of insecticide resistance across Africa must be considered.Biological control using fish was found to be, in general, slightly more effective than (chemical, bacterial and MSF) larviciding. The degree of effectiveness was dependent on the fish species and their feeding preferences: surface-feeding, larvivorous species provided better anopheline control than bottom-feeding selective feeders4,43. Selecting the most suitable fish for local rice fields is not straightforward; many criteria need to be considered4,52,53. Generally, fish were well-received by rice farmers, perceived to contribute to increased yield by reducing weeds and pests and providing fertiliser through excrement43,44. This was reportedly also observed in Guangxi, China, where a certain proportion of the field had to be deepened into a side-trench where the fish could take shelter when the fields were drained. Even with this reduction in rice production area, carp rearing still increased yields by 10% and farmer’s income per hectare by 70%53. Unfortunately, none of the eligible studies in this review had included yield or water use as an outcome. Future entomological studies need to measure these critical agronomic variables so that studies of vector control in rice can be understood by, and transferred to, agronomists. In SSA, irrigated rice-fish farming can be scaled up provided that an inventory of fish species suitable for specific locations is available and that water is consistently available in fields (an important limiting factor in African irrigation schemes)54. Lessons can be learnt from successful large-scale rice-fish systems in Asia, where they have served as win–win solutions for sustainable food production and malaria control16,55.Overall, there was only limited evidence that intermittent irrigation is effective at reducing late-instar anopheline larvae in rice fields. This finding contrasts with prior reviews, which found mixed results (regardless of larval stage) but emphasised that success was site-specific4,17,56. This contrast is presumably due to the inclusion criteria of our systematic review. These reviews excluded studies in various geographical settings and some older studies that reported successful anopheline control with intermittent irrigation but lacked either a contemporaneous control arm, adequate replication or adequate differentiation between culicines and anophelines16,57,50,51,52,61. It seems, from our review, that intermittent irrigation does not prevent the recruitment of early instars (and in one case, may have encouraged oviposition31) but tends to prevent their development into late-stage immatures. This important conclusion is, however, based only on four studies; more evidence is urgently needed where future trials should consider the basic principles of modern trials with adequate replication, controls and differentiation between larval instars and species.Generally, it is observed that drainage, passive or active, did not reliably reduce overall numbers of mosquito immatures. In India and Kenya, closer inspection revealed that soils were not drying sufficiently, so any stranded larvae were not killed31,46. Highlighted by van der Hoek et al.29 and Keiser et al.17, water management in rice fields is very dependent on the physical characteristics of the soil and the climate and is most suited to places that not only favour rapid drying, but also have a good control of water supply17,56. Moreover, repeated drainage, although directed against mosquitoes, can also kill their aquatic predators62. Since mosquitoes can re-establish themselves in a newly flooded rice field more quickly than their predators, intermittent irrigation with more than a week between successive drying periods can permit repeated cycles of mosquito breeding without any predation pressure. Its efficacy against malaria vectors is therefore highly reliant on the timing of the wetting and drying periods. Further site-specific research on timing, especially with regards to predator–prey interactions within the rice agroecosystem, is required to find the perfect balance.Another limitation in intermittent irrigation is that it cannot be applied during the first two to three weeks following transplanting, because rice plants must remain flooded to recover from transplanting shock. Unfortunately, this time coincides with peak vector breeding. Thus, other methods of larval control would be required to fill this gap. To agronomists, intermittent irrigation provides benefits to farmers, as it does not penalise yield but significantly reduces water consumption. Nonetheless, farmer compliance seems to be variable, especially in areas where water availability is inconsistent and intermittent irrigation would potentially require more labour31,32,39. Importantly, rice farmers doubted their ability to coordinate water distribution evenly amongst themselves, suggesting that there may be sharing issues, as in the “tragedy of the commons”63. Instead, they said that they preferred to have an agreed authority to regulate water46.No general conclusions could be made on the effect on malaria vectors of other rice cultivation practices (apart from water management) because only one study was eligible for each practice. Nevertheless, these experiments on pesticide application, tillage and weed control, as well as another study on plant spacing (not eligible since glass rods were used to simulate rice plants), do illustrate that small changes in agronomic inputs and conditions can have considerable effects on mosquito densities, not just rice yield36,38,64. Moreover, in partially- or shallowly-flooded plots, the larvae are often concentrated in depressions (usually footprints), suggesting that rice operations which leave or remove footprints (e.g. hand-weeding, drum seeders, levelling) will influence vector breeding4.Our study has some important limitations. First, in most trials, the units of intervention were replicate plots of rice, and success was measured as a reduction in larval densities within treated plots. This design focuses on the identification of effective and easy-to-implement ways of growing rice without growing mosquitoes, on the assumption that higher vector densities are harmful. However, from a public health perspective, the need for epidemiological outcomes is often, and reasonably, stressed22,65. Nonetheless, from a farmers’ perspective, it is also important to consider whether the vectors emerging from their rice fields significantly contribute to the local burden of malaria and to determine how this contribution can be minimised. There is evidence that riceland vectors do increase malaria transmission, since human biting rates are much higher in communities living next to rice schemes than their non-rice counterparts66 and that additional riceland vectors may intensify transmission and malaria prevalence in rice communities15. Hence, when investigating how rice-attributed malaria risk can be minimised, mosquito abundance as measured in the experimental rice trials is a useful indicator of potential impact on epidemiological outcomes.Second, larval density was not always separated into larval developmental stages. This can be misleading because some interventions work by reducing larval survival (but not by preventing oviposition) and development to late instars and pupae. Therefore, an intervention could completely eliminate late-stage larvae and pupae but have little effect on the total number of immatures. This was illustrated in our meta-analyses of intermittent irrigation in Table 3 and Supplementary Table 5, and could have been the case for some studies that failed to demonstrate consistent reductions in overall anopheline numbers but did not differentiate between larval instars34,45,67,60,69. We infer that when monitoring mosquito immatures in rice trials, it is important to distinguish between larval instars and pupae. Pupae should always be counted separately since its abundance is the most direct indicator of adult productivity70.Third, experimental trials rarely reported the timing of intervention application or accounted for different rice-growing phases, or “days after transplantation”, in the outcome. Both aspects are important to consider since an intervention may be suited to control larvae during certain growth phases but not others. This is illustrated by Djegbe et al.38, where, compared to deep tillage, minimal tillage could significantly reduce larvae during the early stages of rice cultivation but not during tillering and maturation38. In contrast, other interventions, such as Azolla and predatory copepods, took time to grow and accumulate, and were more effective during the later stages of a rice season45,67,71. This differentiation is important because it can identify components that could potentially form a complementary set of interventions against riceland malaria vectors, each component being effective at different parts of the season. Since rice fields, and hence the dynamics of riceland mosquito populations, vary from place to place, this set of interventions must also be robust. Special attention must be paid to the early stages of rice cultivation, particularly the first few weeks after transplanting (or sowing), since, with many vector species, a large proportion of adult mosquitoes are produced during this time.Fourth, the analysis of entomological counts is often inadequate. Many studies failed to provide the standard deviation (or any other measure of error) for larval counts and could not be included in the quantitative analysis. Often, due to the extreme (and not unexpected) variability of larval numbers, sample sizes were insufficient to calculate statistically significant differences between treatments. Fifth, a high risk of bias was found across both CTS and CITS studies, including high heterogeneity and some publication bias. Study quality was, in general, a shortcoming and limited the number of eligible studies for certain interventions, including intermittent irrigation. Moreover, there are conspicuous a priori reasons for bias in such experimental trials: trial locations are frequently chosen to maximise the probability of success.Finally, few studies were conducted in African countries, where the relationship between rice and malaria is most important because of the efficiency, and the “rice-philic” nature, of the vector An. gambiae s.l.15. In particular, there was a lack of studies on the effectiveness and scalability of biological control and rice cultivation practices. There is also very little information (particularly social science studies) on the views and perspectives of African rice farmers on mosquitoes in rice and interventions to control them72,73.In the future, as malaria declines (particularly across SSA), the contribution of rice production to increased malaria transmission is likely to become more conspicuous15. Unless this problem is addressed, rice growing will probably become an obstacle to malaria elimination. Current default methods of rice production provide near-perfect conditions for the larvae of African malaria vectors. Therefore, we need to develop modified rice-growing methods that are unfavourable to mosquitoes but still favourable for the rice. Although larviciding and biological control may be appropriate, their unsustainable costs remain the biggest barrier to uptake amongst smallholder farmers. Future investigations into riceland vector control should pay more attention to interventions that may be useful to farmers.Supported by medical entomologists, agronomists should lead the research task of identifying cultivation methods that achieve high rice productivity whilst suppressing vector productivity. Rice fields are a major global source of greenhouse gases, and agronomists have responded by successfully developing novel cultivation methods that minimise these emissions while maintaining yield. We need the same kind of response from agronomists, to achieve malaria control co-benefits within rice cultivation. At present, only a few aspects of rice cultivation have been investigated for their effects on mosquitoes, and the potential of many other practices for reducing anopheline numbers are awaiting study. Due to the spatial and temporal heterogeneity of rice agroecosystems, it is likely that no single control method can reduce mosquito numbers throughout an entire cropping season and in all soil types and irrigation methods. Thus, effective overall control is likely to come from a combination of local, site-specific set of complementary methods, each of which is active and effective during a different phase of the rice-growing season. More

Chen, X. L. et al. Effects of Meloidogyne incognitaon the fungal community in tobaccorhizosphere. Rev. Bras. Cienc. Solo. 46, e0210127 (2022).
Google Scholar 
Zhang, S. X. et al. Research progresses on continuous cropping obstacles of tobacco. Soils 47(5), 823–829 (2015).CAS 

Google Scholar 
Luo, J. Y. et al. Effects of soil salinity onrhizosphere soil microbes in transgenic Bt cotton fields. J. Integr. Agric. 16, 1624–1633 (2017).CAS 

Google Scholar 
Chaparro, J. M. et al. Manipulating the soil microbiome to increase soil health and plant fertility. Biol. Fertil. Soils 48, 489–499 (2012).
Google Scholar 
Newton, A., Begg, G. & Swanston, J. Deployment of diversity for enhanced crop function. Ann. Appl. Biol. 154, 309–322 (2009).
Google Scholar 
Li, X. G. et al. Effects of intercropping with Atractylodeslancea and application of bio-organic fertiliser on soil invertebrates, disease control and peanut productivity in continuouspeanut cropping field in subtropical China. Agrofor. Syst. 88, 41–52 (2014).
Google Scholar 
Ahmed, W. et al. Ralstonia solanacearum, a deadly pathogen: Revisiting the bacterial wilt biocontrol practices in tobacco and other Solanaceae. Rhizosphere 21, 100479 (2022).
Google Scholar 
Gómez-Rodrıguez, O., Zavaleta-Mejıa, E., Gonzalez-Hernandez, V., Livera-Munoz, M. & Cárdenas-Soriano, E. Allelopathyand microclimatic modification of intercropping with marigold on tomato early blight disease development. Field Crops Res. 83, 27–34 (2003).
Google Scholar 
Weidenhamer, J. D., Montgomery, T. M., Cipollini, D. F., Weston, P. A. & Mohney, B. K. Plandensity and rhizosphere chemistry: Does marigold root exudate composition respond to intra-and interspecific competition?. J. Chem. Ecol. 45(5–6), 525–533 (2019).CAS 
PubMed 

Google Scholar 
Ploeg, A. T. Effects of selected marigold varieties on root-knot nematodes and tomato and melon yields. Plant Dis. 86(5), 505–508 (2002).PubMed 

Google Scholar 
Hooks, C. R., Wang, K. H., Ploeg, A. & McSorley, R. Using marigold (Tagetes spp.) as a cover crop to protect crops fromplant-parasitic nematodes. Appl. Soil Ecol. 46, 307–320 (2010).
Google Scholar 
Li, W., Xu, J., Chen, H. & Qi, Y. Phytochemicals and their biological activities of plants in tagetes l.-sciencedirect. Chin. Herbal Med. 4(2), 103–117 (2012).
Google Scholar 
Weidenhamer, J. D., Mohney, B. K., Shihada, N. & Rupasinghe, M. Spatial and temporal dynamics of root exudation: How important is heterogeneity in allelopathic interactions?. J. Chem. Ecol. 40(8), 940–952 (2014).CAS 
PubMed 

Google Scholar 
Marotti, I. et al. Thiophene occurrence in different tagetes species: Agricultural biomasses as sources ofbiocidal substances. J. Sci. Food Agric. 90(7), 1210–1217 (2010).CAS 
PubMed 

Google Scholar 
Barto, E. K. et al. The fungal fastlane: Common mycorrhizal networks extendbioactive zones of allelochemicals in soils. PLoS ONE 6, e27195 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Evenhuis, A., Korthals, G. & Molendijk, L. Tagetes patula as an effective catch crop forlong-term control of Pratylenchus penetrans. Nematology 6, 877–881 (2004).
Google Scholar 
Wu, W. T. et al. Effects of marigold-tobacco rotation on soil nematode community composition. Southwest China J. Agric. Sci. 32(2), 342–348 (2019).
Google Scholar 
Reynolds, L. B., Potter, J. W. & Ball-Coelho, B. R. Crop rotation with sp. is an alternative to chemical fumigation for control of root-lesion nematodes. Agron. J. 92(5), 957–966 (2000).
Google Scholar 
El-Hamawi, M., Youssef, M. & Zawam, H. S. Management of Meloidogyne incognita, the root-knot nematode, on soybean asaffected by marigold and sea ambrosia (damsisa) plants. J. Pest Sci. 77, 95–98 (2004).
Google Scholar 
Kumar, N., Krishnappa, K., Reddy, B., Ravichandra, N. & Karuna, K. Intercropping for the management of root-knotnematode, Meloidogyne incognitain vegetable-based cropping systems. Indian J. Nematol. 35, 46–49 (2005).
Google Scholar 
Zhang, J. et al. Crop rotation with marigold promotes soil bacterial structure to assist in mitigating clubroot Incidence in Chinese Cabbage. Plants 11(17), 2295 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Xia, T. Y. et al. Microbial diversity of tobacco rhizospheresoil in different growth stages of marigold-tobacco intercropping system. Southwest China J. Agric. Sci. 31(4), 680–686 (2018).
Google Scholar 
Wei, H. Y. et al. Effects of marigold diversified cropping with angelica on fungal community in soils. Plant Prot. 41(5), 69–74 (2015).MathSciNet 
CAS 

Google Scholar 
Li, Y. et al. Intercropping with marigold promotes soil health and microbialstructure to assist in mitigating tobacco bacterial wilt. J. Plant Pathol. 102, 731–742 (2020).
Google Scholar 
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. 108, 4516–4522 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).CAS 
PubMed 

Google Scholar 
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. MicroBiol. 73, 5261–5267 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
PubMed 

Google Scholar 
Irikiin, I. et al. Rhizobacterial community-level, sole carbon source utilization pattern aff ects the delay in the bacterial wilt of tomato grown in rhizobacterial community model system. Appl. Soil Ecol. 34(1), 27–32 (2006).
Google Scholar 
Wu, M. N. et al. Soil fungistasis and its relations to soil microbial composition and diversity: A case study of a series of soils with different fungistasis. J. Environ. Sci. 20(7), 871–877 (2008).CAS 

Google Scholar 
Mendes, L. W. et al. Soil-Borne microbiome: Linking diversity to function. Microb. Ecol. 70(1), 255–265 (2015).CAS 
PubMed 

Google Scholar 
Jaiswal, A. K. et al. Linking the belowground microbial composition, diversity and activity to soilborne disease suppression and growth promotion of tomato amended with biochar. Sci. Rep. 7, 44382 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Raaijmakers, J. M. & Mazzola, M. Soil immune responses soil microbiomes may be harnessed for plant health. Science 352, 1392–1393 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Kušlienė, G., Rasmussen, J., Kuzyakov, Y. & Eriksen, J. Medium-term response of microbial community to rhizodeposits of white clover and ryegrass and tracing of active processes induced by 13C and 15N labelled exudates. Soil Biol. Biochem. 76, 22–33 (2014).
Google Scholar 
Mohammadi, K. Soil microbial activity and biomass as influenced by tillage and fertilization in wheat production. Am.-Eurasian J. Agric. Environ. Sci. 10, 330–337 (2011).
Google Scholar 
Wang, G. H. et al. Research progress of Acidobacteria ecology in soils. Biotechnol. Bull. 32(2), 14–20 (2016).
Google Scholar 
Wei, H., Wang, L., Hassan, M. & Xie, B. Succession of the functional microbial communities and the metabolic functions in maize straw composting process. Bioresour. Technol. 256, 333–341 (2018).CAS 
PubMed 

Google Scholar 
Wang, Y., Liu, L., Yang, J., Duan, Y. & Zhao, Z. The diversity of microbial community and function varied in response to different agricultural residues composting. Sci. Total Environ. 715, 136983 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Glass, N. L., Schmoll, M., Cate, J. H. & Coradetti, S. Plant cell wall deconstruction by ascomycete fungi. Annu. Rev. Microbiol. 67, 477–498 (2013).CAS 
PubMed 

Google Scholar 
Li, Y. et al. Linking soil fungal community structure and function to soil organic carbon chemical composition in intensively managed subtropical bamboo forests. Soil Biol. Biochem. 107, 19–31 (2017).CAS 

Google Scholar 
Martins, L. F., Kolling, D., Camassola, M., Dillon, A. J. & Ramos, L. P. Comparison of Penicillium echinulatumand Trichoderma reeseicellulases in relation to their activity against various cellulosic substrates. Bioresour. Technol. 99, 1417–1424 (2008).CAS 
PubMed 

Google Scholar  More