Philippa Kaur

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

During recent decades, pathogens that originated in bats have become an increasing public health concern. A major challenge is to identify how those pathogens spill over into human populations to generate a pandemic threat1. Many correlational studies associate spillover with changes in land use or other anthropogenic stressors2,3, although the mechanisms underlying the observed correlations have not been identified4. One limitation is the lack of spatially and temporally explicit data on multiple spillovers, and on the connections among spillovers, reservoir host ecology and behavior, and viral dynamics. We present 25 years of data on land-use change, bat behavior, and spillover of Hendra virus from Pteropodid bats to horses in subtropical Australia. These data show that bats are responding to environmental change by persistently adopting behaviors that were previously transient responses to nutritional stress. Interactions between land-use change and climate now lead to persistent bat residency in agricultural areas, where periodic food shortages drive clusters of spillovers. Pulses of winter flowering of trees in remnant forests appeared to prevent spillover. We developed integrative Bayesian network models based on these phenomena that accurately predicted the presence or absence of clusters of spillovers in each of 25 years. Our long-term study identifies the mechanistic connections among habitat loss, climate, and increased spillover risk. It provides a framework for examining causes of bat virus spillover and for developing ecological countermeasures to prevent pandemics. 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

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

Kazarov, E. The Role of Zoos in Creating a Conservation Ethic in Visitors. SIT Digital Collections (2022). at https://digitalcollections.sit.edu/isp_collection/584.Hosey, G. How does the zoo environment affect the behaviour of captive primates?. Appl. Anim. Behav. Sci. 90, 107–129 (2005).
Google Scholar 
Morgan, K. & Tromborg, C. Sources of stress in captivity. Appl. Anim. Behav. Sci. 102, 262–302 (2007).
Google Scholar 
Sherwen, S. & Hemsworth, P. The visitor effect on zoo animals: Implications and opportunities for zoo animal welfare. Animals 9, 366 (2019).PubMed Central 

Google Scholar 
Chamove, A., Hosey, G. & Schaetzel, P. Visitors excite primates in zoos. Zoo Biol. 7, 359–369 (1988).
Google Scholar 
Tetley, C. L. & O’Hara, S. J. Ratings of animal personality as a tool for improving the breeding, management and welfare of zoo mammals. Anim. Welf. UFAW J. 21(4), 463 (2012).CAS 

Google Scholar 
Stoinski, T. S., Jaicks, H. F. & Drayton, L. A. Visitor effects on the behavior of captive western lowland gorillas: The importance of individual differences in examining welfare. Zoo Biol. 31(5), 586–599 (2012).PubMed 

Google Scholar 
Queiroz, M. B. & Young, R. J. The different physical and behavioural characteristics of zoo mammals that influence their response to visitors. Animals 8(8), 139 (2018).PubMed Central 

Google Scholar 
Fanson, K. V. & Wielebnowski, N. C. Effect of housing and husbandry practices on adrenocortical activity in captive Canada lynx (Lynx canadensis). Anim. Welf. 22, 159–165 (2013).CAS 

Google Scholar 
Pirovino, M. et al. Fecal glucocorticoid measurements and their relation to rearing, behavior, and environmental factors in the population of pileated gibbons (Hylobates pileatus) held in European zoos. Int. J. Primatol. 32(5), 1161–1178 (2011).
Google Scholar 
Williams, I., Hoppitt, W. & Grant, R. The effect of auditory enrichment, rearing method and social environment on the behavior of zoo-housed psittacines (Aves: Psittaciformes); implications for welfare. Appl. Anim. Behav. Sci. 186, 85–92 (2017).
Google Scholar 
Fernandez, E., Tamborski, M., Pickens, S. & Timberlake, W. Animal–visitor interactions in the modern zoo: Conflicts and interventions. Appl. Anim. Behav. Sci. 120, 1–8 (2009).
Google Scholar 
Hosey, G. & Skyner, L. Self-injurious behavior in zoo primates. Int. J. Primatol. 28, 1431–1437 (2007).
Google Scholar 
Mallapur, A., Sinha, A. & Waran, N. Influence of visitor presence on the behaviour of captive lion-tailed macaques (Macaca silenus) housed in Indian zoos. Appl. Anim. Behav. Sci. 94, 341–352 (2005).
Google Scholar 
Davey, G. Visitors’ Effects on the Welfare of Animals in the Zoo: A Review. J. Appl. Anim. Welf. Sci. 10, 169–183 (2007).CAS 
PubMed 

Google Scholar 
Jones, H., McGregor, P., Farmer, H. & Baker, K. The influence of visitor interaction on the behavior of captive crowned lemurs (Eulemur coronatus) and implications for welfare. Zoo Biol. 35, 222–227 (2016).CAS 
PubMed 

Google Scholar 
Cook, S. & Hosey, G. R. Interaction sequences between chimpanzees and human visitors at the zoo. Zoo Biol. 14(5), 431–440 (1995).
Google Scholar 
Baker, K. C. Benefits of positive human interaction for socially-housed chimpanzees. Anim. Welf. (South Mimms, Engl.nd) 13(2), 239 (2004).CAS 

Google Scholar 
Carder, G. & Semple, S. Visitor effects on anxiety in two captive groups of western lowland gorillas. Appl. Anim. Behav. Sci. 115, 211–220 (2008).
Google Scholar 
Wood, W. Interactions among environmental enrichment, viewing crowds, and zoo chimpanzees (Pantroglodytes). Zoo Biol. 17, 211–230 (1998).
Google Scholar 
Todd, P., Macdonald, C. & Coleman, D. Visitor-associated variation in captive Diana monkey (Cercopithecus diana diana) behaviour. Appl. Anim. Behav. Sci. 107, 162–165 (2007).
Google Scholar 
Davis, N., Schaffner, C. & Smith, T. Evidence that zoo visitors influence HPA activity in spider monkeys (Ateles geoffroyii rufiventris). Appl. Anim. Behav. Sci. 90, 131–141 (2005).
Google Scholar 
Sherwen, S. L. et al. Effects of visual contact with zoo visitors on black-capped capuchin welfare. Appl. Anim. Behav. Sci. 167, 65–73 (2015).
Google Scholar 
Choo, Y., Todd, P. & Li, D. Visitor effects on zoo orangutans in two novel, naturalistic enclosures. Appl. Anim. Behav. Sci. 133, 78–86 (2011).
Google Scholar 
Sherwen, S., Magrath, M., Butler, K., Phillips, C. & Hemsworth, P. A multi-enclosure study investigating the behavioural response of meerkats to zoo visitors. Appl. Anim. Behav. Sci. 156, 70–77 (2014).
Google Scholar 
Hosey, G. & Druck, P. The influence of zoo visitors on the behaviour of captive primates. Appl. Anim. Behav. Sci. 18, 19–29 (1987).
Google Scholar 
Mitchell, G. et al. More on the ‘influence’of zoo visitors on the behaviour of captive primates. Appl. Anim. Behav. Sci. 35(2), 189–198 (1992).
Google Scholar 
Sellinger, R. & Ha, J. The effects of visitor density and intensity on the behavior of two captive jaguars (Panthera onca). J. Appl. Anim. Welfare Sci. 8, 233–244 (2005).CAS 

Google Scholar 
Azevedo, C., Lima, M., Silva, V., Young, R. & Rodrigues, M. Visitor Influence on the Behavior of Captive Greater Rheas (Rhea americana, Rheidae Aves). J. Appl. Anim. Welfare Sci. 15, 113–125 (2012).
Google Scholar 
Das Gupta, M., Das, A., Sumy, M. C. & Islam, M. M. An explorative study on visitor’s behaviour and their effect on the behaviour of primates at Chittagong zoo. Bangladesh J. Vet. Anim. Sci. 5(2), 24–32 (2017).
Google Scholar 
Hemsworth, P. Human–animal interactions in livestock production. Appl. Anim. Behav. Sci. 81, 185–198 (2003).
Google Scholar 
Stoinski, T., Czekala, N., Lukas, K. & Maple, T. Urinary androgen and corticoid levels in captive, male Western lowland gorillas (Gorilla g. gorilla): Age- and social group-related differences. Am. J. Primatol. 56, 73–87 (2002).CAS 
PubMed 

Google Scholar 
Stoinski, T., Lukas, K., Kuhar, C. & Maple, T. Factors influencing the formation and maintenance of all-male gorilla groups in captivity. Zoo Biol. 23, 189–203 (2004).
Google Scholar 
Olsson, I. & Westlund, K. More than numbers matter: The effect of social factors on behaviour and welfare of laboratory rodents and non-human primates. Appl. Anim. Behav. Sci. 103, 229–254 (2007).
Google Scholar 
Martin, J. E. Early life experiences: Activity levels and abnormal behaviours in resocialised chimpanzees. Anim Welf. 11(4), 419–436 (2002).CAS 

Google Scholar 
Birkett, L. P. & Newton-Fisher, N. E. How abnormal is the behaviour of captive, zoo-living chimpanzees?. PLoS ONE 6(6), e20101 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ballen, C., Shine, R. & Olsson, M. Effects of early social isolation on the behaviour and performance of juvenile lizards Chamaeleo calyptratus. Anim. Behav. 88, 1–6 (2014).
Google Scholar 
Coe, C., Mendoza, S., Smotherman, W. & Levine, S. Mother-infant attachment in the squirrel monkey: Adrenal response to separation. Behav. Biol. 22, 256–263 (1978).CAS 
PubMed 

Google Scholar 
Mendoza, S., Smotherman, W., Miner, M., Kaplan, J. & Levine, S. Pituitary-adrenal response to separation in mother and infant squirrel monkeys. Dev. Psychobiol. 11, 169–175 (1978).CAS 
PubMed 

Google Scholar 
Gilbert, M. & Baker, K. Social buffering in adult male rhesus macaques (Macaca mulatta): Effects of stressful events in single vs. pair housing. J. Med. Primatol. 40, 71–78 (2010).PubMed 
PubMed Central 

Google Scholar 
Schapiro, S. Effects of social manipulations and environmental enrichment on behavior and cell-mediated immune responses in rhesus macaques. Pharmacol. Biochem. Behav. 73, 271–278 (2002).CAS 
PubMed 

Google Scholar 
Chen, W. et al. Effects of social isolation and re-socialization on cognition and ADAR1 (p110) expression in mice. PeerJ 4, e2306 (2016).PubMed 
PubMed Central 

Google Scholar 
Glatston, A., Geilvoet-Soeteman, E., Hora-Pecek, E. & Van Hooff, J. The influence of the zoo environment on social behavior of groups of cotton-topped tamarins Saguinus oedipus oedipus. Zoo Biol. 3, 241–253 (1984).
Google Scholar 
Mitchell, G. et al. Effects of visitors and cage changes on the behaviors of mangabeys. Zoo Biol. 10, 417–423 (1991).
Google Scholar 
Geissmann, T. & Orgeldinger, M. The relationship between duet songs and pair bonds in siamangs Hylobates syndactylus. Anim. Behav. 60, 805–809 (2000).CAS 
PubMed 

Google Scholar 
Palombit, R. Pair bonds in monogamous apes: A comparison of the siamang hylobates syndactylus and the white-handed gibbon hylobates lar. Behaviour 133, 321–356 (1996).
Google Scholar 
Rutberg, A. The evolution of monogamy in primates. J. Theor. Biol. 104, 93–112 (1983).ADS 
CAS 
PubMed 

Google Scholar 
Giorgi, A., Montebovi, G., Vitale, A. & Alleva, E. A behavioural case study of early social isolation of a subadult white-handed gibbon (Hylobates lar). Folia Primatol. 89, 287–294 (2018).
Google Scholar 
Skynner, L. A., Amory, J. R. & Hosey, G. The effect of visitors on the self-injurious behaviour of a male pileated gibbon (Hylobates pileatus). Zool. Garten 74(1), 38–41 (2004).
Google Scholar 
Smith, K. & Kuhar, C. Siamangs (Hylobates syndactylus) and white-cheeked gibbons (Hylobates leucogenys) show few behavioral differences related to zoo attendance. J. Appl. Anim. Welfare Sci. 13, 154–163 (2010).CAS 

Google Scholar 
Lukas, K. E. et al. Longitudinal study of delayed reproductive success in a pair of white-cheeked gibbons (Hylobates leucogenys). Zoo Biol. 21, 413–434 (2002).
Google Scholar 
Cooke, C. & Schillaci, M. Behavioral responses to the zoo environment by white handed gibbons. Appl. Anim. Behav. Sci. 106, 125–133 (2007).
Google Scholar 
Mootnick, A. & Baker, E. Masturbation in captiveHylobates (gibbons). Zoo Biol. 13, 345–353 (1994).
Google Scholar 
Geissmann, T. Reassessment of age of sexual maturity in gibbons (hylobates spp.). American Journal of Primatology 23, 11–22 (1991).Altmann, J. Observational study of behavior: Sampling methods. Behaviour 49(3–4), 227–266 (1974).CAS 
PubMed 

Google Scholar 
Pomerantz, O. & Terkel, J. Effects of positive reinforcement training techniques on the psychological welfare of zoo-housed chimpanzees (Pan troglodytes). Am. J. Primatol. 71, 687–695 (2009).PubMed 

Google Scholar 
Orgeldinger, M. Protective and territorial behavior in captive siamangs (Hylobates syndactylus). Zoo Biol. 16, 309–325 (1997).
Google Scholar 
Fox, J. et al. Package ‘car’. Vienna: R Foundation for Statistical Computing, 16 https://cran.uni-muenster.de/web/packages/car/car.pdf (2012).Magnusson, A., Skaug, H., Nielsen, A., Berg, C., Kristensen, K., Maechler, M., van Bentham, K., Bolker, B., Brooks, M. & Brooks, M. M. Package ‘glmmtmb’. R Package Version 0.2. 0 (2017).Hartig, F., & Hartig, M. F. Package ‘DHARMa’. Vienna, Austria: R Development Core Team (2017).Troisi, A. Displacement activities as a behavioral measure of stress in nonhuman primates and human subjects. Stress 5, 47–54 (2002).PubMed 

Google Scholar 
Baker, K. & Aureli, F. Behavioural indicators of anxiety: An empirical test in chimpanzees. Behaviour 134, 1031–1050 (1997).
Google Scholar 
Vick, S. J. & Paukner, A. Variation and context of yawns in captive chimpanzees (Pan troglodytes). Am. J. Primatol. Off. J. Am. Soc. Primatol. 72(3), 262–269 (2010).
Google Scholar 
Norscia, I. & Palagi, E. When play is a family business: Adult play, hierarchy, and possible stress reduction in common marmosets. Primates 52, 101–104 (2010).PubMed 

Google Scholar 
Held, S. & Špinka, M. Animal play and animal welfare. Anim. Behav. 81, 891–899 (2011).
Google Scholar 
Davey, G. Visitor behavior in zoos: A review. Anthrozoös 19, 143–157 (2006).
Google Scholar 
Nimon, A. & Dalziel, F. Cross-species interaction and communication: a study method applied to captive siamang (Hylobates syndactylus) and long-billed corella (Cacatua tenuirostris) contacts with humans. Appl. Anim. Behav. Sci. 33, 261–272 (1992).
Google Scholar 
Suomi, S. Early determinants of behaviour: Evidence from primate studies. Br. Med. Bull. 53, 170–184 (1997).CAS 
PubMed 

Google Scholar 
Anderson, J. & Chamove, A. Self-aggression and social aggression in laboratory-reared macaques. J. Abnorm. Psychol. 89, 539–550 (1980).CAS 
PubMed 

Google Scholar 
Mallapur, A. & Choudhury, B. Behavioral abnormalities in captive nonhuman primates. J. Appl. Anim. Welfare Sci. 6, 275–284 (2003).CAS 

Google Scholar 
Barlow, C., Caldwell, C. & Lee, P. Individual differences and response to visitors in zoo-housed diana monkeys (Cercopithecus diana diana). Cabdirect.org (2022). at https://www.cabdirect.org/cabdirect/abstract/20123180753.Gartner, M. & Weiss, A. Studying primate personality in zoos: Implications for the management, welfare and conservation of great apes. International Zoo Yearbook 52, 79–91 (2018).
Google Scholar 
Mitchell, G., Raymond, E., Ruppenthal, G. & Harlow, H. Long-term effects of total social isolation upon behavior of rhesus monkeys. Psychol. Rep. 18, 567–580 (1966).
Google Scholar 
Martín, O., Vinyoles, D., García-Galea, E. & Maté, C. Improving the welfare of a zoo-housed male drill (Mandrillus leucophaeus poensis) aggressive toward visitors. J. Appl. Anim. Welfare Sci. 19, 323–334 (2016).
Google Scholar 
Ross, S., Melber, L., Gillespie, K. & Lukas, K. The impact of a modern, naturalistic exhibit design on visitor behavior: A cross-facility comparison. Visitor Stud. 15, 3–15 (2012).
Google Scholar 
Quadros, S., Goulart, V., Passos, L., Vecci, M. & Young, R. Zoo visitor effect on mammal behaviour: Does noise matter?. Appl. Anim. Behav. Sci. 156, 78–84 (2014).
Google Scholar 
Bonnie, K., Ang, M. & Ross, S. Effects of crowd size on exhibit use by and behavior of chimpanzees (Pan troglodytes) and Western lowland gorillas (Gorilla gorilla) at a zoo. Appl. Anim. Behav. Sci. 178, 102–110 (2016).
Google Scholar  More

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

Bardgett, R. D. et al. Combatting global grassland degradation. Nat. Rev. Earth Environ. 2, 720–735 (2021)Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).ADS 
CAS 
PubMed 

Google Scholar 
Seneviratne, S. I. et al. Weather and climate extreme events in a changing climate. In Climate change 2021: the physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2021).Pretty, J. et al. Global assessment of agricultural system redesign for sustainable intensification. Nat. Sustain 1, 441–446 (2018).
Google Scholar 
Allan, E. et al. Land use intensification alters ecosystem multifunctionality via loss of biodiversity and changes to functional composition. Ecol. Lett. 18, 834–843 (2015).PubMed 
PubMed Central 

Google Scholar 
Bardgett, R. D. & Cook, R. Functional aspects of soil animal diversity in agricultural grasslands. Appl. Soil Ecol. 10, 263–276 (1998).
Google Scholar 
Postma-Blaauw, M. B., de Goede, R. G. M., Bloem, J., Faber, J. H. & Brussaard, L. Soil biota community structure and abundance under agricultural intensification and extensification. Ecology 91, 460–473 (2010).PubMed 

Google Scholar 
Vályi, K., Rillig, M. C. & Hempel, S. Land-use intensity and host plant identity interactively shape communities of arbuscular mycorrhizal fungi in roots of grassland plants. N. Phytologist 205, 1577–1586 (2015).
Google Scholar 
de Vries, F. T., Hoffland, E., van Eekeren, N., Brussaard, L. & Bloem, J. Fungal/bacterial ratios in grasslands with contrasting nitrogen management. Soil Biol. Biochem. 38, 2092–2103 (2006).
Google Scholar 
de Vries, F. T. et al. Extensive Management Promotes Plant and Microbial Nitrogen Retention in Temperate Grassland. PLoS ONE 7, e51201 (2012).ADS 
PubMed 
PubMed Central 

Google Scholar 
de Vries, F. T., van Groenigen, J. W., Hoffland, E. & Bloem, J. Nitrogen losses from two grassland soils with different fungal biomass. Soil Biol. Biochem. 43, 997–1005 (2011).
Google Scholar 
Malik, A. A. et al. Soil fungal: bacterial ratios are linked to altered carbon cycling. Front. Microbiol. 7, 1247 (2016).Bardgett, R. D., Streeter, T. C. & Bol, R. Soil Microbes Compete Effectively with Plants for Organic-Nitrogen Inputs to Temperate Grasslands. Ecology 84, 1277–1287 (2003).
Google Scholar 
Bardgett, R. D. & McAlister, E. The measurement of soil fungal:bacterial biomass ratios as an indicator of ecosystem self-regulation in temperate meadow grasslands. Biol. Fertil. Soils 29, 282–290 (1999).
Google Scholar 
Gordon, H., Haygarth, P. M. & Bardgett, R. D. Drying and rewetting effects on soil microbial community composition and nutrient leaching. Soil Biol. Biochem. 40, 302–311 (2008).CAS 

Google Scholar 
Duffy, J. E. et al. The functional role of biodiversity in ecosystems: incorporating trophic complexity. Ecol. Lett. 10, 522–538 (2007).PubMed 

Google Scholar 
Wang, S. & Brose, U. Biodiversity and ecosystem functioning in food webs: the vertical diversity hypothesis. Ecol. Lett. 21, 9–20 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Ruf, A., Kuzyakov, Y. & Lopatovskaya, O. Carbon fluxes in soil food webs of increasing complexity revealed by C-14 labelling and C-13 natural abundance. Soil Biol. Biochem. 38, 2390–2400 (2006).CAS 

Google Scholar 
Pollierer, M. M., Langel, R., Koerner, C., Maraun, M. & Scheu, S. The underestimated importance of belowground carbon input for forest soil animal food webs. Ecol. Lett. 10, 729–736 (2007).PubMed 

Google Scholar 
Eissfeller, V. et al. Incorporation of plant carbon and microbial nitrogen into the rhizosphere food web of beech and ash. Soil Biol. Biochem. 62, 76–81 (2013).CAS 

Google Scholar 
Gilbert, K. J. et al. Exploring carbon flow through the root channel in a temperate forest soil food web. Soil Biol. Biochem. 76, 45–52 (2014).CAS 

Google Scholar 
Goncharov, A. A., Tsurikov, S. M., Potapov, A. M. & Tiunov, A. V. Short-term incorporation of freshly fixed plant carbon into the soil animal food web: field study in a spruce forest. Ecol. Res. 31, 923–933 (2016).CAS 

Google Scholar 
Chomel, M. et al. Drought decreases incorporation of recent plant photosynthate into soil food webs regardless of their trophic complexity. Glob. Change Biol. 25, 3549–3561 (2019).ADS 

Google Scholar 
Moore, J. C., de Ruiter, P. C. & Hunt, H. W. Influence of productivity on the stability of real and model ecosystems. Science 261, 906–908 (1993).ADS 
CAS 
PubMed 
MATH 

Google Scholar 
de Ruiter, P. C., Neutel, A.-M. & Moore, J. C. Energetics, Patterns of Interaction Strengths, and Stability in Real Ecosystems. Science 269, 1257–1260 (1995).ADS 
PubMed 

Google Scholar 
Rooney, N., McCann, K., Gellner, G. & Moore, J. C. Structural asymmetry and the stability of diverse food webs. Nature 442, 265–269 (2006).ADS 
CAS 
PubMed 

Google Scholar 
Rooney, N. & McCann, K. S. Integrating food web diversity, structure and stability. Trends Ecol. Evolution 27, 40–46 (2012).
Google Scholar 
de Vries, F. T. et al. Land use alters the resistance and resilience of soil food webs to drought. Nat. Clim. Change 2, 276 (2012).ADS 

Google Scholar 
Ingrisch, J. et al. Land Use Alters the Drought Responses of Productivity and CO2 Fluxes in Mountain Grassland. Ecosystems 21, 689–703 (2018).PubMed 

Google Scholar 
Karlowsky, S. et al. Land use in mountain grasslands alters drought response and recovery of carbon allocation and plant‐microbial interactions. J. Ecol. 106, 1230–1243 (2017).PubMed 
PubMed Central 

Google Scholar 
Vilonen, L., Ross, M. & Smith, M. D. What happens after drought ends: synthesizing terms and definitions. N. Phytologist 235, 420–431 (2022).
Google Scholar 
Ingrisch, J., Karlowsky, S., Hasibeder, R., Gleixner, G. & Bahn, M. Drought and recovery effects on belowground respiration dynamics and the partitioning of recent carbon in managed and abandoned grassland. Glob. Change Biol. 26, 4366–4378 (2020).ADS 

Google Scholar 
Ward, S. E. et al. Legacy effects of grassland management on soil carbon to depth. Glob. Change Biol. 22, 2929–2938 (2016).ADS 

Google Scholar 
Henry, C. et al. A stomatal safety-efficiency trade-off constrains responses to leaf dehydration. Nat. Commun. 10, 3398 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Baptist, F. et al. 13C and 15N allocations of two alpine species from early and late snowmelt locations reflect their different growth strategies. J. Exp. Bot. 60, 2725–2735 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bergmann, J. et al. The fungal collaboration gradient dominates the root economics space in plants. Sci. Adv. 6, eaba3756 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Williams, A. et al. Root functional traits explain root exudation rate and composition across a range of grassland species. J. Ecol. 110, 21–33 (2022).
Google Scholar 
Deyn, G. B. D., Quirk, H., Oakley, S., Ostle, N. J. & Bartgett, R. D. Rapid transfer of photosynthetic carbon through the plant-soil system in differently managed species-rich grasslands. Biogeosciences 8, 1131–1139 (2011).Pausch, J. et al. Small but active – pool size does not matter for carbon incorporation in below‐ground food webs.Functional Ecol. 30, 479–489 (2016).
Google Scholar 
Morriën, E. et al. Soil networks become more connected and take up more carbon as nature restoration progresses. Nat. Commun. 8, 14349 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Li, Z. et al. The flux of root-derived carbon via fungi and bacteria into soil microarthropods (Collembola) differs markedly between cropping systems. Soil Biol. Biochem. 160, 108336 (2021).CAS 

Google Scholar 
Joergensen, R. Ergosterol and microbial biomass in the rhizosphere of grassland soils. Soil Biol. Biogeochemistry 32, 647–652 (2000).CAS 

Google Scholar 
Staddon, P. L., Ramsey, C. B., Ostle, N., Ineson, P. & Fitter, A. H. Rapid Turnover of Hyphae of Mycorrhizal Fungi Determined by AMS Microanalysis of 14C. Science 300, 1138–1140 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Johnson, D., Leake, J. R., Ostle, N., Ineson, P. & Read, D. J. In situ 13CO2 pulse-labelling of upland grassland demonstrates a rapid pathway of carbon flux from arbuscular mycorrhizal mycelia to the soil. N. Phytologist 153, 327–334 (2002).CAS 

Google Scholar 
Johnson, D., Leake, J. R. & Read, D. J. Transfer of recent photosynthate into mycorrhizal mycelium of an upland grassland: short-term respiratory losses and accumulation of C-14. Soil Biol. Biochem. 34, 1521–1524 (2002).CAS 

Google Scholar 
Schimel, J., Balser, T. C. & Wallenstein, M. Microbial Stress-Response Physiology and Its Implications for Ecosystem Function. Ecology 88, 1386–1394 (2007).PubMed 

Google Scholar 
Strickland, M. S. & Rousk, J. Considering fungal:bacterial dominance in soils – Methods, controls, and ecosystem implications. Soil Biol. Biochem. 42, 1385–1395 (2010).CAS 

Google Scholar 
Manzoni, S., Schimel, J. P. & Porporato, A. Responses of soil microbial communities to water stress: results from a meta-analysis. Ecology 93, 930–938 (2012).PubMed 

Google Scholar 
Holden, S. R. & Treseder, K. K. A meta-analysis of soil microbial biomass responses to forest disturbances. Front Microbiol 4, 163 (2013).PubMed 
PubMed Central 

Google Scholar 
Guhr, A., Borken, W., Spohn, M. & Matzner, E. Redistribution of soil water by a saprotrophic fungus enhances carbon mineralization. PNAS 112, 14647–14651 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
de Vries, F. T. et al. Soil bacterial networks are less stable under drought than fungal networks. Nat. Commun. 9, 3033 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Allen, M. F. Mycorrhizal Fungi: Highways for Water and Nutrients in Arid Soils. Vadose Zone J. 6, 291–297 (2007).
Google Scholar 
Kakouridis, A. et al. Routes to roots: direct evidence of water transport by arbuscular mycorrhizal fungi to host plants. bioRxiv https://doi.org/10.1101/2020.09.21.305409 (2020).Leake, J. R., Ostle, N. J., Rangel-Castro, J. I. & Johnson, D. Carbon fluxes from plants through soil organisms determined by field 13CO2 pulse-labelling in an upland grassland. Appl. Soil Ecol. 33, 152–175 (2006).
Google Scholar 
Maaß, S., Migliorini, M., Rillig, M. C. & Caruso, T. Disturbance, neutral theory, and patterns of beta diversity in soil communities. Ecol. Evolution 4, 4766–4774 (2014).
Google Scholar 
Barnard, R. L., Osborne, C. A. & Firestone, M. K. Changing precipitation pattern alters soil microbial community response to wet-up under a Mediterranean-type climate. ISME J. 9, 946–957 (2015).CAS 
PubMed 

Google Scholar 
Lennon, J. T. & Jones, S. E. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat. Rev. Microbiol 9, 119–130 (2011).CAS 
PubMed 

Google Scholar 
Meisner, A., Bååth, E. & Rousk, J. Microbial growth responses upon rewetting soil dried for four days or one year. Soil Biol. Biochem. 66, 188–192 (2013).CAS 

Google Scholar 
Meisner, A., Rousk, J. & Bååth, E. Prolonged drought changes the bacterial growth response to rewetting. Soil Biol. Biochem. 88, 314–322 (2015).CAS 

Google Scholar 
Blazewicz, S. J., Schwartz, E. & Firestone, M. K. Growth and death of bacteria and fungi underlie rainfall-induced carbon dioxide pulses from seasonally dried soil. Ecology 95, 1162–1172 (2014).PubMed 

Google Scholar 
Butterbach-Bahl, K., Baggs, E. M., Dannenmann, M., Kiese, R. & Zechmeister-Boltenstern, S. Nitrous oxide emissions from soils: how well do we understand the processes and their controls? Philos. Trans. R. Soc. B: Biol. Sci. 368, 20130122 (2013).
Google Scholar 
Baggs, E. M., Rees, R. M., Smith, K. A. & Vinten, A. J. A. Nitrous oxide emission from soils after incorporating crop residues. Soil Use & Manag. 16, 82–87 (2000).
Google Scholar 
Le Roux, X., Bardy, M., Loiseau, P. & Louault, F. Stimulation of soil nitrification and denitrification by grazing in grasslands: do changes in plant species composition matter? Oecologia 137, 417–425 (2003).ADS 
PubMed 

Google Scholar 
Morley, N. & Baggs, E. M. Carbon and oxygen controls on N2O and N2 production during nitrate reduction. Soil Biol. Biochem. 42, 1864–1871 (2010).CAS 

Google Scholar 
Davidson, E. A. & Kanter, D. Inventories and scenarios of nitrous oxide emissions. Environ. Res. Lett. 9, 105012 (2014).ADS 

Google Scholar 
Bateman, E. J. & Baggs, E. M. Contributions of nitrification and denitrification to N2O emissions from soils at different water-filled pore space. Biol. Fertil. Soils 41, 379–388 (2005).CAS 

Google Scholar 
Lehmann, J., Bossio, D. A., Kögel-Knabner, I. & Rillig, M. C. The concept and future prospects of soil health. Nat. Rev. Earth Environ. 1, 544–553 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Knapp, A. K. et al. Pushing precipitation to the extremes in distributed experiments: recommendations for simulating wet and dry years. Glob. Change Biol. 23, 1774–1782 (2017).ADS 

Google Scholar 
Cole, A. J. et al. Grassland biodiversity restoration increases resistance of carbon fluxes to drought. J. Appl. Ecol. 56, 1806–1816 (2019).CAS 

Google Scholar 
Fuchslueger, L., Bahn, M., Fritz, K., Hasibeder, R. & Richter, A. Experimental drought reduces the transfer of recently fixed plant carbon to soil microbes and alters the bacterial community composition in a mountain meadow. N. Phytologist 201, 916–927 (2014).CAS 

Google Scholar 
Buyer, J. S. & Sasser, M. High throughput phospholipid fatty acid analysis of soils. Appl. Soil Ecol. 61, 127–130 (2012).
Google Scholar 
Frostegård, Å., Bååth, E. & Tunlio, A. Shifts in the structure of soil microbial communities in limed forests as revealed by phospholipid fatty acid analysis. Soil Biol. Biochem. 25, 723–730 (1993).
Google Scholar 
Olsson, P. A., Thingstrup, I., Jakobsen, I. & Bååth, E. Estimation of the biomass of arbuscular mycorrhizal fungi in a linseed field. Soil Biol. Biochem. 31, 1879–1887 (1999).CAS 

Google Scholar 
Hopkin, S. P. A key to the Collembola (springtails) of Britain and Ireland (FSC, 2007).Krantz, G. W. & Walter, D. E. A manual of acarology (Texas Tech Universty Press, 2009).Caruso, T. & Migliorini, M. Euclidean geometry explains why lengths allow precise body mass estimates in terrestrial invertebrates: The case of oribatid mites. J. Theor. Biol. 256, 436–440 (2009).ADS 
MathSciNet 
CAS 
PubMed 
MATH 

Google Scholar 
Ganihar, S. R. Biomass estimates of terrestrial arthropods based on body length. J. Biosci. 22, 219–224 (1997).
Google Scholar 
Johnson, D., Vachon, J., Britton, A. J. & Helliwell, R. C. Drought alters carbon fluxes in alpine snowbed ecosystems through contrasting impacts on graminoids and forbs. N. Phytologist 190, 740–749 (2011).CAS 

Google Scholar 
Legendre, P. & Gallagher, E. D. Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280 (2001).ADS 
PubMed 

Google Scholar 
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2001).
Google Scholar 
Anderson, M. J. Distance-Based Tests for Homogeneity of Multivariate Dispersions. Biometrics 62, 245–253 (2006).MathSciNet 
PubMed 
MATH 

Google Scholar 
Zuur, A., Ieno, E., Walker, N., Saveliev, A. & Smith, G. Mixed effects models and extensions in ecology with R. (Springer, 2009). 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