Philippa Kaur

Bacterial density and growth potential in the rearing water were related to the microbial carrying capacityQuantifying the bacterial density in each tank verified that we obtained a higher bacterial load in the systems with added organic material. The bacterial density was, on average, 7.8× higher in the systems with high compared to low bacterial carrying capacity. This difference was particularly evident at 2 (34.8×, Kruskal–Wallis p = 0.0008) and 9 DPH (9.1×, Kruskal–Wallis p = 0.0007) (Fig. 1). The bacterial density increased throughout the experiment for the tanks with low microbial carrying capacity (treatment group MMS−, FTS−), reflecting increased larval feeding and defecation. Contrastingly, the bacterial density was relatively stable over time in the MMS+ treatment and even decreased over time in the FTS+ treatment. When averaging the densities at 11 and 15 DPH within each rearing treatment, we observed that the ‘MMS+ to FTS+’ had a considerable difference in the bacterial density between incoming and rearing water (24.2×). In contrast, this difference was below 8.2× in all other treatment tanks. Such differences in density indicated that some communities were below the microbial carrying capacity of the systems. We thus investigated the growth potential to determine if carrying capacity was reached in the rearing water.Figure 1Bacterial density (million bacterial cells mL−1) at various days post-hatching (DPH) in incoming and rearing tank water. Note that the y-axis is log scaled. Colours indicate the rearing treatment, and shape signifies rearing (filled circle) and incoming water (filled triangle).Full size imageThe bacterial net growth potential in the intake and rearing water was quantified as the number of cell doublings after incubation for 3 days11. Generally, the FTS− and MMS− rearing water had net growth potential with an average of 0.2 and 0.1, respectively (Supplementary Fig. 2). In contrast, the rearing water of the FTS+ and MMS+ had a negative net growth potential with averages of −0.2 and −0.06, respectively. In the case of negative net growth potential, the bacterial density decreased during the incubation. A negative net growth potential suggested that the rearing water bacterial communities were at the tank’s microbial carrying capacity at the time of sampling. Thus, the bacterial communities were at the carrying capacity of the high (+) carrying capacity systems and below in the low (−) systems. To gain a deeper understanding of the bacterial community characteristics the 16S rRNA gene of the bacterial community was sequenced at 1 and 9 DPH.Initial rearing condition did not leave a legacy effect on bacterial α-diversityThe bacterial α-diversity of the rearing water was investigated at 1 and 12 DPH (Fig. 2). At 1 DPH, the richness was comparable between the FTS−, FTS+ and MMS+ treatments, but on average, 1.5× higher for the MMS− treatment (307 vs 205 ASVs, Tukey’s test p  More

Hobbs, R. J. et al. Novel ecosystems: Theoretical and management aspects of the new ecological world order. Glob. Ecol. Biogeogr. 15, 1–7 (2006).
Google Scholar 
Kraft, N. J. B. et al. Community assembly, coexistence and the environmental filtering metaphor. Funct. Ecol. 29, 592–599 (2015).
Google Scholar 
Mayfield, M. M. et al. What does species richness tell us about functional trait diversity? Predictions and evidence for responses of species and functional trait diversity to land-use change. Glob. Ecol. Biogeogr. 19, 423–431 (2010).
Google Scholar 
Zobel, M. The species pool concept as a framework for studying patterns of plant diversity. J. Veg. Sci. 27, 8–18 (2016).
Google Scholar 
Birkhofer, K. et al. Land-use type and intensity differentially filter traits in above- and below-ground arthropod communities. J. Anim. Ecol. 86, 511–520 (2017).PubMed 

Google Scholar 
Temperton, V. M. Assembly Rules and Restoration Ecology: Bridging the Gap Between Theory and Practice (Island Press, 2004).
Google Scholar 
Flynn, D. F. B. et al. Loss of functional diversity under land use intensification across multiple taxa. Ecol. Lett. 12, 22–33 (2009).PubMed 

Google Scholar 
Gascon, C. et al. Matrix habitat and species richness in tropical forest remnants. Biol. Conserv. 91, 223–229 (1999).
Google Scholar 
Filloy, J., Zurita, G. A., Corbelli, J. M. & Bellocq, M. I. On the similarity among bird communities: Testing the influence of distance and land use. Acta Oecol. 36, 333–338 (2010).ADS 

Google Scholar 
Vaccaro, A., Filloy, J. & Bellocq, M. What land use better preserves the functional and taxonomic diversity of birds in a grassland biome?. Avian Conserv. Ecol. 14, 1 (2019).
Google Scholar 
Vaccaro, A. S. & Bellocq, M. I. Diversidad taxonómica y funcional de aves: Diferencias entre hábitats antrópicos en un bosque subtropical. Ecol. Austral 29, 391–404 (2019).
Google Scholar 
Sekercioglu, C. H. Bird functional diversity and ecosystem services in tropical forests, agroforests and agricultural areas. J. Ornithol. 153, 153–161 (2012).
Google Scholar 
Zurita, G. A. & Bellocq, M. I. Bird assemblages in anthropogenic habitats: Identifying a suitability gradient for native species in the Atlantic Forest. Biotropica 44, 412–419 (2012).
Google Scholar 
Azpiroz, A. B. et al. Ecology and conservation of grassland birds in southeastern South America: A review. J. Field Ornithol. 83, 217–246 (2012).
Google Scholar 
Devictor, V. et al. Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: The need for integrative conservation strategies in a changing world. Ecol. Lett. 13, 1030–1040 (2010).PubMed 

Google Scholar 
Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. 61, 1–10 (1992).
Google Scholar 
Corbelli, J. M. et al. Integrating taxonomic, functional and phylogenetic beta diversities: Interactive effects with the biome and land use across taxa. PLoS ONE 10, 1–17 (2015).
Google Scholar 
Purschke, O. et al. Contrasting changes in taxonomic, phylogenetic and functional diversity during a long-term succession: Insights into assembly processes. J. Ecol. 101, 857–866 (2013).
Google Scholar 
Srivastava, D. S., Cadotte, M. W., Macdonald, A. A. M., Marushia, R. G. & Mirotchnick, N. Phylogenetic diversity and the functioning of ecosystems. Ecol. Lett. 15, 637–648 (2012).PubMed 

Google Scholar 
Cavender-Bares, J., Kozak, K. H., Fine, P. V. A. & Kembel, S. W. The merging of community ecology and phylogenetic biology. Ecol. Lett. 12, 693–715 (2009).PubMed 

Google Scholar 
Mouquet, N. et al. Ecophylogenetics: Advances and perspectives. Biol. Rev. 87, 769–785 (2012).PubMed 

Google Scholar 
Ackerly, D. D., Schwilk, D. W. & Webb, C. O. Niche evolution and adaptive radiation: Testing the order of trait divergence. Ecology 87, S50–S61 (2006).CAS 
PubMed 

Google Scholar 
Cavender-Bares, J., Ackerly, D. D., Baum, D. A. & Bazzaz, F. A. Phylogenetic overdispersion in Floridian oak communities. Am. Nat. 163, 823–843 (2004).CAS 
PubMed 

Google Scholar 
Losos, J. B. et al. Niche lability in the evolution of a Caribbean lizard community. Nature 424, 542–545 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Stevens, R. D., Gavilanez, M. M., Tello, J. S. & Ray, D. A. Phylogenetic structure illuminates the mechanistic role of environmental heterogeneity in community organization. J. Anim. Ecol. 81, 455–462 (2012).PubMed 

Google Scholar 
García-Navas, V. & Thuiller, W. Farmland bird assemblages exhibit higher functional and phylogenetic diversity than forest assemblages in France. J. Biogeogr. 47, 2392–2404 (2020).
Google Scholar 
Henwood, W. D. Toward a strategy for the conservation and protection of the world’s temperate grasslands. Univ. Neb. Press 20, 121–134 (2010).ADS 

Google Scholar 
Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Landi, M., Oesterheld, M. & Deregibus, V. A. Manual de especies forrajeras de los pastizales naturales de Entre Ríos (1987).Viglizzo, E. F. et al. Ecological lessons and applications from one century of low external-input farming in the pampas of Argentina. Agric. Ecosyst. Environ. 83, 65–81 (2001).
Google Scholar 
Galindo Leal, C. & de Gusmão Câmara, I. The Atlantic Forest of South America: Biodiversity Status, Threats and Outlook (Island Press, 2003).
Google Scholar 
Oliveira-Filho, A. T. & Fontes, M. A. L. Patterns of floristic differentiation among Atlantic Forests in Southeastern Brazil and the influence of climate. Biotropica 32, 793–810 (2000).
Google Scholar 
DeGraaf, R. M., Geis, A. D. & Healy, P. A. Bird population and habitat surveys in urban areas. Landsc. Urban Plan. 21, 181–188 (1991).
Google Scholar 
Ralph, C. J. et al. Manual de métodos de campo para el monitoreo de aves terrestres. Pacific Southwest Research Station, Forest Service, U.S. Department of Agriculture, Albany, CA 46 http://www.srs.fs.usda.gov/pubs/31462. https://doi.org/10.3145/epi.2006.jan.15 (1996).Bibby, C., Jones, M. & Marsden, S. Expedition field techniques: Bird surveys. in (ed. Society, R. G.) (1998).Zurita, G. A. & Bellocq, M. I. Spatial patterns of bird community similarity: Bird responses to landscape composition and configuration in the Atlantic forest. Landsc. Ecol. 25, 147–158 (2010).
Google Scholar 
Koper, N. & Schmiegelow, F. K. K. A multi-scaled analysis of avian response to habitat amount and fragmentation in the Canadian dry mixed-grass prairie. Landsc. Ecol. 21, 1045 (2006).
Google Scholar 
Xeno-canto-Foundation. Xeno-canto website. https://www.xeno-canto.org (2018).Petchey, O. L. & Gaston, K. J. Functional diversity (FD), species richness and community composition. Ecol. Lett. 5, 402–411 (2002).
Google Scholar 
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.r-project.org (2018).Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).CAS 
PubMed 

Google Scholar 
Webb, C. O., Ackerly, D. D. & Kembel, S. W. Phylocom: Software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics 24, 2098–2100 (2008).CAS 
PubMed 

Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D. R Core Team. nlme: Linear and Nonlinear mixed effects models. R package version 3.1–117. (2014).Lenth, R. V. Least-Squares Means: The R Package lsmeans. J. Stat. Softw. https://doi.org/10.18637/jss.v069.i01 (2016).Article 

Google Scholar 
Cadotte, M. W. & Tucker, C. M. Should environmental filtering be abandoned?. Trends Ecol. Evol. 32, 429–437 (2017).PubMed 

Google Scholar 
Concepción, E. D. et al. Contrasting trait assembly patterns in plant and bird communities along environmental and human-induced land-use gradients. Ecography 40, 753–763 (2016).
Google Scholar 
Cerezo, A., Conde, M. C. & Poggio, S. L. Pasture area and landscape heterogeneity are key determinants of bird diversity in intensively managed farmland. Biodivers. Conserv. 20, 2649–2667 (2011).
Google Scholar 
Pretelli, M. G., Isacch, J. P. & Cardoni, D. A. Year-round abundance, richness and nesting of the bird assemblage of tall grasslands in the south-east Pampas region, Argentina. Ardeola 60, 327–343 (2013).
Google Scholar 
Molinari, R. L. Biografía de la Pampa: 4 siglos de historia del campo argentino (Fundación Colombina “V Centenario,” 1987).
Google Scholar 
Filloy, J. & Bellocq, M. I. Patterns of bird abundance along the agricultural gradient of the Pampean region. Agric. Ecosyst. Environ. 120, 291–298 (2007).
Google Scholar 
Le Viol, I. et al. More and more generalists: Two decades of changes in the European avifauna. Biol. Lett. 8, 780–782 (2012).PubMed 
PubMed Central 

Google Scholar 
Concepción, E. D., Moretti, M., Altermatt, F., Nobis, M. P. & Obrist, M. K. Impacts of urbanisation on biodiversity: The role of species mobility, degree of specialisation and spatial scale. Oikos 124, 1571–1582 (2015).
Google Scholar 
Emerson, B. C. & Gillespie, R. G. Phylogenetic analysis of community assembly and structure over space and time. Trends Ecol. Evol. 23, 619–630 (2008).PubMed 

Google Scholar 
Morse, N. B. et al. Novel ecosystems in the Anthropocene: A revision of the novel ecosystem concept for pragmatic applications. Ecol. Soc. 19, 12 (2014).
Google Scholar 
Loyn, R. H., McNabb, E. G., Macak, P. & Noble, P. Eucalypt plantations as habitat for birds on previously cleared farmland in south-eastern Australia. Biol. Conserv. 137, 533–548 (2007).
Google Scholar 
Marsden, S., Whiffin, M. & Galetti, M. Bird diversity and abundance in forest fragments and Eucalyptus plantations around an Atlantic forest reserve, Brazil. Biodivers. Conserv. 10, 737–751 (2001).
Google Scholar 
Zurita, G. A., Rey, N., Varela, D. M., Villagra, M. & Bellocq, M. I. Conversion of the Atlantic Forest into native and exotic tree plantations: Effects on bird communities from the local and regional perspectives. For. Ecol. Manag. 235, 164–173 (2006).
Google Scholar 
Flynn, D. F. B., Mirotchnick, N., Jain, M., Palmer, M. I. & Naeem, S. Functional and phylogenetic diversity as predictors of biodiversity ecosystem-function. Ecology 92, 1573–1581 (2011).PubMed 

Google Scholar 
Sol, D. et al. The worldwide impact of urbanisation on avian functional diversity. Ecol. Lett. 23, 962–972 (2020).PubMed 

Google Scholar 
Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).
Google Scholar 
Palacio, F. X., Ibañez, L. M., Maragliano, R. E. & Montalti, D. Urbanization as a driver of taxonomic, functional, and phylogenetic diversity losses in bird communities. Can. J. Zool. 96, 1114–1121 (2018).
Google Scholar 
Sol, D., Bartomeus, I., González-Lagos, C. & Pavoine, S. Urbanisation and the loss of phylogenetic diversity in birds. Ecol. Lett. 20, 721–729 (2017).PubMed 

Google Scholar 
Luck, G. W., Carter, A. & Smallbone, L. Changes in bird functional diversity across multiple land uses: Interpretations of functional redundancy depend on functional group identity. PLoS ONE 8, e63671 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Coetzee, B. W. T. & Chown, S. L. Land-use change promotes avian diversity at the expense of species with unique traits. Ecol. Evol. 6, 7610–7622 (2016).PubMed 
PubMed Central 

Google Scholar  More

Morrison, M. A., Francis, M. P., Hartill, B. W. & Parkinson, D. M. Diurnal and tidal variation in the abundance of the fish fauna of a temperate tidal mudflat. Estuar. Coast. Shelf Sci. 54, 793–807 (2002).Article 
ADS 

Google Scholar 
Ribeiro, J. et al. Seasonal, tidal and diurnal changes in fish assemblages in the Ria Formosa lagoon (Portugal). Estuar. Coast. Shelf Sci. 67, 461–474 (2006).Article 
ADS 

Google Scholar 
Takemura, A., Rahman, M. S. & Park, Y. J. External and internal controls of lunar-related reproductive rhythms in fishes. J. Fish Biol. 76, 7–26 (2010).Article 
CAS 
PubMed 

Google Scholar 
Mendes, S., Turrell, W., Lütkebohle, T. & Thompson, P. Influence of the tidal cycle and a tidal intrusion front on the spatio-temporal distribution of coastal bottlenose dolphins. Mar. Ecol. Prog. Ser. 239, 221–229 (2002).Article 
ADS 

Google Scholar 
Johnston, D. W., Thorne, L. H. & Read, A. J. Fin whales Balaenoptera physalus and minke whales Balaenoptera acutorostrata exploit a tidally driven island wake ecosystem in the Bay of Fundy. Mar. Ecol. Prog. Ser. 305, 287–295 (2005).Article 
ADS 

Google Scholar 
Ichikawa, K. et al. Dugong (Dugong dugon) vocalization patterns recorded by automatic underwater sound monitoring systems. J. Acoust. Soc. Am. 119, 3726–3733 (2006).Article 
ADS 
PubMed 

Google Scholar 
Akamatsu, T. et al. Seasonal and diurnal presence of finless porpoises at a corridor to the ocean from their habitat. Mar. Biol. 157, 1879–1887 (2010).Article 

Google Scholar 
Li, S. et al. Seasonal, lunar and tidal influences on habitat use of indo-pacific humpback dolphins in Beibu gulf, China. Zool. Stud. https://doi.org/10.6620/ZS.2018.57-01 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Zamon, J. E. Seal predation on salmon and forage fish schools as a function of tidal currents in the San Juan Islands, Washington, USA. Fish. Oceanogr. 10, 353–366 (2001).Article 

Google Scholar 
Van Parijs, S. M., Hastie, G. D. & Thompson, P. M. Geographical variation in temporal and spatial vocalization patterns of male harbour seals in the mating season. Anim. Behav. 58, 1231–1239 (1999).Article 
PubMed 

Google Scholar 
Bortolotto, G. A., Danilewicz, D., Hammond, P. S., Thomas, L. & Zerbini, A. N. Whale distribution in a breeding area: Spatial models of habitat use and abundance of western South Atlantic humpback whales. Mar. Ecol. Prog. Ser. 585, 213–227 (2017).Article 
ADS 

Google Scholar 
Johnson, J. H. & Wolman, A. A. The humpback whale, Megaptera novaeangliae. Mar. Fish. Rev. 46, 30–37 (1984).
Google Scholar 
Kobayashi, N. et al. Spatial distribution and habitat use patterns of humpback whales in Okinawa, Japan. Mammal Study 41, 207–214 (2016).Article 

Google Scholar 
Mori, K., Sata, F., Yamaguchi, M., Suganuma, H. & Ueyanagi, S. Distribution, migration and local movements of humpback whale (Megaptera novaeangliae) in the adjacent waters of the Ogasawara (Bonin) Islands Japan. J. Fac. Mar. Sci. Technol. Tokai Univ. 45, 197–213 (1998).
Google Scholar 
Rasmussen, K., Calambokidis, J. & Steiger, G. H. Distribution and migratory destinations of humpback whales off the Pacific coast of Central America during the boreal winters of 1996–2003. Mar. Mammal Sci. 28, 1–13 (2012).Article 

Google Scholar 
Calambokidis, J. et al. SPLASH: structure of populations, levels of abuncance and status of humpback whales in the North Pacific. Final report for Contract AB133F-03-RP-00078, to U.S. Dept. of Comm. Western Administrative Center, Seattle, WA. https://cascadiaresearch.org/files/SPLASH-contract-Report-May08.pdf (2008).Hill, M. et al. Found: A missing breeding ground for endangered western North Pacific humpback whales in the Mariana Archipelago. Endanger. Species Res. 41, 91–103 (2020).Article 

Google Scholar 
Payne, R. S. & McVay, S. Songs of humpback whales. Science 173, 585–597 (1971).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Winn, H. E. & Winn, L. The song of the humpback whale Megaptera novaeangliae in the West Indies. Mar. Biol. 47, 97–114 (1978).Article 

Google Scholar 
Tyack, P. Interactions between singing Hawaiian humpback whales and conspecifics nearby. Behav. Ecol. Sociobiol. 8, 105–116 (1981).Article 

Google Scholar 
Herman, L. M. The multiple functions of male song within the humpback whale (Megaptera novaeangliae) mating system: Review, evaluation, and synthesis. Biol. Rev. 92, 1795–1818 (2017).Article 
PubMed 

Google Scholar 
Au, W. W. L., Mobley, J., Burgess, W. C., Lammers, M. O. & Nachtigall, P. E. Seasonal and diurnal trends of chorusing humpback whales wintering in waters off western Maui. Mar. Mammal Sci. 16, 530–544 (2000).Article 

Google Scholar 
Cerchio, S., Collins, T., Strindberg, S., Bennett, C. & Rosenbaum, H. Humpback whale singing activity off northern Angola: An indication of the migratory cycle, breeding habitat and impact of seismic surveys on singer number in Breeding. Int. Whal. Comm. P. SC/62/SH12 (2010).Kobayashi, N., Okabe, H., Higashi, N., Miyahara, H. & Uchida, S. Diel patterns in singing activity of humpback whales in a winter breeding area in Okinawan (Ryukyuan) waters. Mar. Mammal Sci. 37, 982–992 (2021).Article 

Google Scholar 
Munger, L. M., Lammers, M. O., Fisher-Pool, P. & Wong, K. Humpback whale (Megaptera novaeangliae) song occurrence at American Samoa in long-term passive acoustic recordings, 2008–2009. J. Acoust. Soc. Am. 132, 2265–2272 (2012).Article 
ADS 
PubMed 

Google Scholar 
Barlow, D. R., Fournet, M. & Sharpe, F. Incorporating tides into the acoustic ecology of humpback whales. Mar. Mammal Sci. 35, 234–251 (2019).Article 

Google Scholar 
Chenoweth, E., Gabriele, C. & Hill, D. Tidal influences on humpback whale habitat selection near headlands. Mar. Ecol. Prog. Ser. 423, 279–289 (2011).Article 
ADS 

Google Scholar 
Sousa-Lima, R. S., Clark, C. W. & Road, S. W. Modeling the effect of boat traffic on singing activity of humpback whales (Megaptera novaeangliae) in the abrolhos national marine park, Brazil. Can. Acoust 36, 174–181 (2008).
Google Scholar 
Cerchio, S., Strindberg, S., Collins, T., Bennett, C. & Rosenbaum, H. Seismic surveys negatively affect humpback whale singing activity off Northern Angola. PLoS ONE 9, e86464. https://doi.org/10.1371/journal.pone.0086464 (2014).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Darling, J. D. & Mori, K. Recent observations of humpback whales (Megaptera novaeangliae) in Japanese waters off Ogasawara and Okinawa. Can. J. Zool. 71, 325–333 (1993).Article 

Google Scholar 
Calambokidis, J. et al. Movements and population structure of humpback whales in the North Pacific. Mar. Mammal Sci. 17, 769–794 (2001).Article 

Google Scholar 
Wessel, P., Smith, W. H. F., Scharroo, R., Luis, J. & Wobbe, F. Generic mapping tools: Improved version released. Eos Trans. Am. Geophys. Union 94, 409–410 (2013).Article 
ADS 

Google Scholar 
Helweg, D. A. & Herman, L. M. Diurnal patterns of behaviour and group membership of humpback whales (Megaptera novaeangliae) wintering in Hawaiian waters. Ethology 98, 298–311 (1994).Article 

Google Scholar 
Darling, J. D. & Berube, M. Interactions of singing humpback whales with other males. Mar. Mammal Sci. 17, 570–584 (2001).Article 

Google Scholar 
Whitlow, W. L. et al. Acoustic properties of humpback whale songs. J. Acoust. Soc. Am. 120, 1103–1110 (2006).Article 

Google Scholar 
Japan Coast Guard. Sailing Directions for South and East Coasts of Honshu. (1981).Tsujii, K. et al. Change in singing behavior of humpback whales caused by shipping noise. PLoS ONE 13, e0204112. https://doi.org/10.1371/journal.pone.0204112 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ryan, J. P. et al. Humpback whale song occurrence reflects ecosystem variability in feeding and migratory habitat of the northeast Pacific. PLoS ONE 14, e0222456. https://doi.org/10.1371/journal.pone.0222456 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. 4.0.0 version. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (2020).Wood, S.N. Generalized Additive Models: An Introduction with R 2nd edn, (Chapman and Hall/CRC, 2017). More

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

Meat, Milk and More: Policy Innovations to Shepherd Inclusive and Sustainable Livestock Systems in Africa (Malabo Montpellier Panel, 2020).Value of Agricultural Production (FAO, accessed August 25, 2022); https://www.fao.org/faostat/en/#data/QVJayne, T. & Sanchez, P. A. Agricultural productivity must improve in sub-Saharan Africa. Science 372, 1045–1047 (2021).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Dangal, S. R. S. et al. Methane emission from global livestock sector during 1890–2014: magnitude, trends and spatiotemporal patterns. Glob. Change Biol. 23, 4147–4161 (2017).Article 
ADS 

Google Scholar 
Mottet, A. et al. Climate change mitigation and productivity gains in livestock supply chains: insights from regional case studies. Reg. Env. Change 17, 129–141 (2016).Article 

Google Scholar 
Valin, H. et al. Agricultural productivity and greenhouse gas emissions: trade-offs or synergies between mitigation and food security? Environ. Res. Lett. 8, 035019 (2013).Article 
ADS 
CAS 

Google Scholar 
González-Quintero, R. et al. Yield gap analysis to identify attainable milk and meat productivities and the potential for greenhouse gas emissions mitigation in cattle systems of Colombia. Agric. Syst. 195, 103303 (2022).Article 

Google Scholar 
Crops and Livestock Products (FAO, accessed August 17,2022); https://www.fao.org/faostat/en/#data/QCLLedo, J. et al. Persistent challenges in safety and hygiene control practices in emerging dairy chains: the case of Tanzania. Food Control 105, 164–173 (2019).Article 

Google Scholar 
Häsler, B. et al. Integrated food safety and nutrition assessments in the dairy cattle value chain in Tanzania. Glob. Food Sec. 18, 102–113 (2018).Article 

Google Scholar 
Supply Utilization Accounts (FAO, accessed August 26, 2022); https://www.fao.org/faostat/en/#data/SCLMichael, S. et al. Tanzania Livestock Master Plan (International Livestock Research Institute, 2018).Tanzania Livestock Sector Analysis (2016/2017–2030/2031) (United Republic of Tanzania Ministry of Livestock and Fisheries, 2017); https://www.mifugouvuvi.go.tz/uploads/projects/1553602287-LIVESTOCK%20SECTOR%20ANALYSIS.pdfNicholson, C. et al. Assessment of Investment Priorities for Tanzania’s Dairy Sector: Report on Activities and Accomplishments (International Livestock Research Institute, 2021).Chagunda, M. G. C., Romer, D. A. M. & Roberts, D. J. Effect of genotype and feeding regime on enteric methane, non-milk nitrogen and performance of dairy cows during the winter feeding period. Livest. Sci. 122, 323–332 (2009).Article 

Google Scholar 
Notenbaert, A. et al. Towards environmentally sound intensification pathways for dairy development in the Tanga region of Tanzania. Reg. Environ. Change 20, 138 (2020).Yesuf, G. A. et al. Embedding stakeholders’ priorities into the low-emission development of the East African dairy sector. Env. Res. Lett. 16, 064032 (2021).Article 
CAS 

Google Scholar 
GLS (Greening Livestock Survey) (International Livestock Research Institute, 2019); https://data.ilri.org/portal/dataset/greeninglivestockIntended Nationally Determined Contributions (United Republic of Tanzania, 2021); https://unfccc.int/sites/default/files/NDC/2022-06/TANZANIA_NDC_SUBMISSION_30%20JULY%202021.pdfNdung’u, P. W. et al. Farm-level emission intensities of smallholder cattle (Bos indicus; B. indicus–B. taurus crosses) production systems in highlands and semi-arid regions. Animal 16, 100445 (2022).Article 
PubMed 

Google Scholar 
Goopy, J. P. et al. Severe below-maintenance feed intake increases methane yield from enteric fermentation in cattle. Br. J. Nutr. 123, 1239–1246 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Goopy, J. P. et al. A new approach for improving emission factors for enteric methane emissions of cattle in smallholder systems of East Africa—results for Nyando, Western Kenya. Agric. Syst. 161, 72–80 (2018).Article 

Google Scholar 
Supporting Low Emissions Development in the Tanzanian Dairy Cattle Sector—Reducing Enteric Methane for Food Security and Livelihoods (FAO, 2019).Gerssen-Gondelach, S. J. et al. Intensification pathways for beef and dairy cattle production systems: impacts on GHG emissions, land occupation and land use change. Agric. Ecosyst. Environ. 240, 135–147 (2017).Article 

Google Scholar 
Havlik, P. et al. Climate change mitigation through livestock system transitions. Proc. Natl Acad. Sci. USA 111, 3709–3714 (2014).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Herrero, M. et al. Greenhouse gas mitigation potentials in the livestock sector. Nat. Clim. Change 6, 452–461 (2016).Article 
ADS 

Google Scholar 
Dizyee, K., Baker, D. & Omore, A. Upgrading the smallholder dairy value chain: a system dynamics ex-ante impact assessment in Tanzania’s Kilosa district. J. Dairy Res. 86, 440–449 (2019).Article 
CAS 
PubMed 

Google Scholar 
Simões, A. R. P., Nicholson, C. F., Novakovicc, A. M. & Protil, R. M. Dynamic impacts of farm-level technology adoption on the Brazilian dairy supply chain. Int. Food Agribus. Manag. Rev. 23, 71–84 (2020).Article 

Google Scholar 
Rahimi, J. et al. Heat stress will detrimentally impact future livestock production in East Africa. Nat. Food. 2, 88–96 (2021).Article 

Google Scholar 
Mbululo, Y. & Nyihirani, F. Climate characteristics over southern highlands Tanzania. Atmos. Clim. Sci. 2, 454–463 (2012).
Google Scholar 
Kihoro, E. M., Schoneveld, G. C. & Crane, T. A. Pathways toward inclusive low-emission dairy development in Tanzania: producer heterogeneity and implications for intervention design. Agric. Syst. 190, 103073 (2021).Mruttu, H. et al. Animal Genetics Strategy and Vision for Tanzania (Tanzania Ministry of Agriculture, Livestock and Fisheries and ILRI, 2016).Agricultural Sample Survey 2018/19 Report on Livestock and Livestock Characteristics (Private Peasant Holdings) (Central Statistical Agency, 2019).2019/20 National Sample Census of Agriculture Main Report (Tanzania National Bureau of Statistics, 2022).Robinson, T. P. et al. Global Livestock Production Systems (FAO, 2011).Herrero, M. et al. Biomass use, production, feed efficiencies and greenhouse gas emissions from global livestock systems. Proc. Natl Acad. Sci. USA 110, 20888–20893 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Baseline Study of the Tanzania Dairy Value Chain (United Republic of Tanzania Ministry of Agriculture, Livestock and Fisheries, 2016).Mbwambo, N., Nandonde, S., Ndomba, C. & Desta, S. Assessment of Animal Feed Resources in Tanzania (Tanzania Ministry of Agriculture, Livestock and Fisheries and ILRI, 2016).Hartung, C., Lerer, A., Anokwa, Y., Tseng, C., Brunette, W., & Borriello, G. Open data kit: tools to build information services for developing regions. Proc. 4th ACM/IEEE International Conference on Information and Communication Technologies and Development (Association for Computing Machinery, 2010).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).https://www.r-project.orgRufino, M. C. et al. Lifetime productivity of dairy cows in smallholder farming systems of the central highlands of Kenya. Animal 3, 1044–1056 (2009).Article 
CAS 
PubMed 

Google Scholar 
Hawkins, J. et al. Feeding efficiency gains can increase the greenhouse gas mitigation potential of the Tanzanian dairy sector. Sci. Rep. 11, 4190 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Python Software Foundation (Python Software Foundation, 2019); https://www.python.org/psf/Kashoma, I. P. B. et al. Predicting body weight of Tanzania shorthorn zebu cattle using heart girth measurements. Livest. Res. Rural. Dev. 23, Table 1 (2011).Galukande, E. B., Mahadevan, P. & Black, J. G. Milk production in East African zebu cattle. Anim. Sci. 4, 329–336 (1962).Article 

Google Scholar 
Gillah, K. A., Kifaro, G. C. & Madsen, J. Effects of pre partum supplementation on milk yield, reproduction and milk quality of crossbred dairy cows raised in a peri urban farm of Morogoro town Tanzania. Livest. Res. Rural. Dev. 26 (2014).Njau, F. B. C., Lwelamira, J. & Hyandye, C. Ruminant livestock production and quality of pastures in the communal grazing land of semi-arid central Tanzania. Livest. Res. Rural. Dev. 8, Table 4 (2013).Mwambene, P. L. et al. Selecting indigenous cattle populations for improving dairy production in the Southern Highlands and Eastern Tanzania. Livest. Res. Rural. Dev. 26 (2014).Rege, J. E. O. et al. Cattle of Kenya: Uses, Performance, Farmer Preferences, Measures of Genetic Diversity and Options for Improved Use (International Livestock Research Institute, 2001).Beffa, L. M. Genotype × Environment Interaction in Afrikaner Cattle. PhD thesis, Univ. of the Free State (2005).Meaker, H. J., Coetsee, T. P. N. & Lishman, A. W. The effects of age at 1st calving on the productive and reproductive-performance of beef-cows. S. Afr. J. Anim. Sci. 10, 105–113 (1980).
Google Scholar 
Chenyambuga, S. W. & Mseleko, K. F. Reproductive and lactation performances of Ayrshire and Boran crossbred cattle kept in smallholder farms in Mufindi district, Tanzania. Livest. Res. Rural. Dev. 21, 100 (2009).
Google Scholar 
Ojango, J. M. K. et al. Dairy production systems and the adoption of genetic and breeding technologies in Tanzania, Kenya, India and Nicaragua. Anim. Genet. Resour. 59, 81–95 (2016).Article 

Google Scholar 
Feedipedia—Animal Feed Resources Information System (FAO, accessed 2021); https://www.feedipedia.org/Lukuyu, B. et al. (eds) Feeding Dairy Cattle in East Africa (East Africa Dairy Development Project, 2012).Rubanza, C. D. K. et al. Biomass production and nutritive potential of conserved forages in silvopastoral traditional fodder banks (Ngitiri) of Meatu District of Tanzania. Asian-Aust. J. Anim. Sci. 19, 978–983 (2006).Article 

Google Scholar 
Food Balances (2010-) (FAO, accessed September 29, 2021); http://www.fao.org/faostat/en/#data/FBSCrop Data for the United Republic of Tanzania (FAO, accessed September 22, 2021); http://www.fao.org/faost at/en/#data/QCGilbert, M. et al. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci. Data. 5, 180227 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
2014/15 Annual Agricultural Sample Survey Report (The United Republic of Tanzania, 2016).Basic Data for Livestock and Fisheries (The United Republic of Tanzania Ministry of Livestock and Fisheries, 2013).IPCC Guidelines for National Greenhouse Gas Inventories Vol. 4 Agriculture, Forestry and Other Land Use (IPCC, 2006).2019 Refinement to the IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) (IPCC, 2019).Fertilizers by Nutrient (FAO, accessed July 6, 2022); https://www.fao.org/faostat/en/#data/RFNHutton, M. O. et al. Toward a nitrogen footprint calculator for Tanzania. Env. Res. Lett. 12, 034016 (2017).Article 

Google Scholar 
Tanzania Fertilizer Assessment (International Fertilizer Development Center, 2012); http://tanzania.countrystat.org/fileadmin/user_upload/countrystat_fenix/congo/docs/Tanzania%20Fertilizer%20Assessment%202012.pdfA Common Carbon Footprint Approach for the Dairy Sector: The IDF Guide to Standard Life Cycle Methodology (International Dairy Federation, 2015); https://www.fil-idf.org/wp-content/uploads/2016/09/Bulletin479-2015_A-common-carbon-footprint-approach-for-the-dairy-sector.CAT.pdfBruzzone, L., Bovolo, F. & Arino, O. European Space Agency land cover climate change initiative. ESA LC CCI data: high resolution land cover data via Centre for Environmental Data Analysis; https://climate.esa.int/en/projects/high-resolution-land-cover/ (2021)Characteristics of Markets for Animal Feeds Raw Materials in the East African Community: Focus on Maize Bran and Sunflower Seed Cake (Kilimo Trust, 2017).Ngunga, D. & Mwendia, S. Forage Seed System in Tanzania: A Review Report (Alliance of Biodiversity and CIAT, 2020).Nkombe, B.M. Investigation of the Potential for Forage Species to Enhance the Sustainability of Degraded Rangeland and Cropland Soils. MSc thesis, Ohio State Univ. (2016).Producer Prices (FAO, accessed 2021); http://www.fao.org/faostat/en/#data/PP More

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

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

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