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Forest Ecology and Forest Management Group, Wageningen University, Wageningen, the NetherlandsPieter A. Zuidema & Ute Sass-KlaassenSchool of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USAFlurin BabstLaboratory of Tree-Ring Research, University of Arizona, Tucson, AZ, USAFlurin Babst, Valerie Trouet, Zakia Hassan Khamisi, Paul R. Sheppard & Ramzi TouchanDepartment of Plant Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, BrazilPeter Groenendijk & José Roberto Vieira AragãoWorld Agroforestry Centre (ICRAF), Addis Ababa, EthiopiaAbrham AbiyuDepartment of Microbiology and Parasitology, Universidad Nacional Autónoma de México, Mexico City, MexicoRodolfo Acuña-SotoLaboratory of Protection and Forest Management, Department of Forest Engineering, Universidade Regional de Blumenau, Santa Catarina, BrazilEduardo Adenesky-FilhoDepartment of Biology, Wilfrid Laurier University, Waterloo, Ontario, CanadaRaquel Alfaro-SánchezDepartment of Forest Sciences, Luiz de Queiroz College of Agriculture, University of Sao Paulo, Piracicaba, BrazilGabriel Assis-Pereira, Claudia Fontana & Mario Tomazello-FilhoTree-Ring Laboratory, Forest Science Department, Federal University of Lavras, Lavras, BrazilGabriel Assis-Pereira & Ana Carolina BarbosaCAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, ChinaXue Bai, Ze-Xin Fan, Shankar Panthi & Zhe-Kun ZhouDepartment of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania “L. Vanvitelli”, Caserta, ItalyGiovanna BattipagliaService of Wood Biology, Royal Museum for Central Africa, Tervuren, BelgiumHans Beeckman, Camille Couralet & Benjamin ToirambeBrazilian Agricultural Research Corporation (Embrapa), Embrapa Forestry, Colombo, BrazilPaulo Cesar BotossoU.S. Department of Agriculture, Forest Service, NWCG Member Agency, Washington, DC, USATim BradleyInstitute of Geography, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, GermanyAchim Bräuning, Mahmuda Islam, Mulugeta Mokria & Mizanur RahmanSchool of Geography, University of Leeds, Leeds, UKRoel Brienen & Emanuel GloorLamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USABrendan M. Buckley & Rosanne D’ArrigoInstituto Pirenaico de Ecología (IPE-CSIC), Zaragoza, SpainJ. Julio CamareroCentre for Functional Ecology, Department of Life Sciences, Faculty of Sciences and Technology, University of Coimbra, Coimbra, PortugalAna Carvalho & Cristina NabaisDepartment of Botany, Institute of Biosciences, University of São Paulo, São Paulo, BrazilGregório Ceccantini, Bruno Barçante Ladvocat Cintra & Giuliano Maselli LocosselliInstituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP), Centro Nacional de Investigación Disciplinaría en Relación Agua-Suelo-Planta-Atmósfera (CENID-RASPA), Gómez Palacio, MéxicoLibrado R. Centeno-Erguera, Julián Cerano-Paredes & Jose Villanueva-DiazInstituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP), Campo Experimental Centro – Altos de Jalisco, Tepatitlán de Morelos, MéxicoÁlvaro Agustín Chávez-DuránDepartment of Geosciences, University of Arkansas, Fayetteville, AR, USAMalcolm K. Cleaveland & Daniela Granato-SouzaDepartment of Forest Sciences, Universidad Nacional de Colombia – Sede Medellín, Medellín, ColombiaJorge Ignacio del ValleMaster School for Carpentry and Cabinetmaking, Ebern, GermanyOliver DünischDepartment of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USABrian J. EnquistSanta Fe Institute, Santa Fe, NM, USABrian J. EnquistDepartment of Biological Sciences, University of Joinville Region ‐ UNIVILLE, Joinville, BrazilKarin Esemann-QuadrosPostgraduate Program in Forestry, Regional University of Blumenau – FURB, Blumenau, BrazilKarin Esemann-QuadrosCollege of Life Science, Climate Science Center and Department of Earth Science, Addis Ababa University, Addis Ababa, EthiopiaZewdu EshetuDepartamento de Dendrocronología e Historia Ambiental, IANIGLA, CCT-CONICET-Mendoza, Mendoza, ArgentinaM. Eugenia Ferrero, Lidio Lopez, Fidel Alejandro Roig & Ricardo VillalbaLaboratorio de Dendrocronología, Universidad Continental, Huancayo, PerúM. Eugenia Ferrero, Janet G. Inga & Edilson Jimmy Requena-RojasDepartment of Crop Sciences, Tropical Plant Production and Agricultural Systems Modelling, Göttingen University, Göttingen, GermanyEsther FichtlerInstitute of Pacific Islands Forestry, USDA Forest Service Pacific Southwest Research Station, Hilo, HI, USAKainana S. Francisco & Mulugeta MokriaWorld Agroforestry Centre (ICRAF), Nairobi, KenyaAster GebrekirstosFlanders Heritage Agency, Brussels, BelgiumKristof HanecaDepartment of Geography and Geological Sciences, University of Idaho, Moscow, ID, USAGrant Logan HarleyGerman Archaeological Institute DAI, Berlin, GermanyIngo HeinrichGeography Department, Humboldt University Berlin, Berlin, GermanyIngo HeinrichGFZ German Research Centre for Geosciences, Potsdam, GermanyIngo Heinrich & Gerd HelleDepartment of Forestry and Environmental Science, Shahjalal University of Science and Technology, Sylhet, BangladeshMahmuda Islam & Mizanur RahmanFaculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Prague, Czech RepublicYu-mei JiangUS Fish and Wildlife Service, Albuquerque, NM, USAMark KaibDepartment of Ecology and Biogeography, Faculty of Biological and Veterinary Sciences, Nicolaus Copernicus University, Toruń, PolandMarcin KoprowskiCentre for Climate Change Research, Nicolaus Copernicus University, Toruń, PolandMarcin KoprowskiWater Systems and Global Change Group, Wageningen University and Research, Wageningen, the NetherlandsBart KruijtInstituto Nacional de Innovación Agraria, Programa Nacional de Investigación Forestal, Huancayo, PerúEva LaymeEnvironmental Systems Analysis Group, Wageningen University and Research, Wageningen, the NetherlandsRik LeemansDepartment of Natural Resource Management, South Dakota State University, Brookings, USA, SDA. Joshua LefflerLaboratory of Plant Anatomy and Dendrochronology, Department of Biology, Universidade Federal de Sergipe, Sergipe, BrazilClaudio Sergio Lisi, Mariana Alves Pagotto & Adauto de Souza Ribeiro Department of Geography, Swansea University, Swansea, UKNeil J. Loader & Iain RobertsonDepartamento Forestal, Universidad Autónoma Agraria Antonio Narro, Saltillo, MexicoMaría I. López-HernándezCITAB – Department of Forestry Sciences and Landscape (CIFAP), University of Trás-os-Montes and Alto Douro, Vila Real, PortugalJosé Luís Penetra Cerveira LousadaEscuela de Ciencias Biológicas, Universidad Pedagógica y Tecnológica de Colombia (UPTC), Tunja, ColombiaHooz A. MendivelsoBrazilian Agricultural Research Corporation (Embrapa), Embrapa Amazônia Ocidental, Manaus, BrazilValdinez Ribeiro MontóiaIHE Delft, Delft, the NetherlandsEddy MoorsVU University Amsterdam, Amsterdam, the NetherlandsEddy MoorsDepartment of Biomaterials Science and Technology, School of Natural Resources, The Copperbelt University, Kitwe, ZambiaJustine NgomaLaboratory of Ecology and Dendrology of the Federal Institute of Sergipe, São Cristovão, BrazilFrancisco de Carvalho Nogueira JúniorLaboratory of Plant Ecology, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo, BrazilJuliano Morales Oliveira & Gabriela Morais OlmedoBIOAPLIC, Departamento de Botánica, Universidade de Santiago de Compostela, EPSE, Lugo, SpainGonzalo Pérez-De-LisLaboratorio de Dendrocronología, Carrera de Ingeniería Forestal, Universidad Nacional de Loja, Loja, EcuadorDarwin Pucha-CofrepFaculty of Environment and Resource studies, Mahidol University, Nakhon Pathom, ThailandNathsuda PumijumnongFacultad de Ciencias Agrarias, Universidad del Cauca, Popayán, ColombiaJorge Andres RamirezHémera Centro de Observación de la Tierra, Escuela de Ingeniería Forestal, Facultad de Ciencias, Universidad Mayor, Santiago, ChileFidel Alejandro Roig & Alejandro Venegas-GonzálezInstituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP), Centro de Investigación Regional Pacífico Centro – Campo Experimental, Centro Altos de Jalisco, MéxicoErnesto Alonso Rubio-CamachoNational Institute for Amazon Research, Petrópolis, Manaus, BrazilJochen SchöngartDepartment of Earth Sciences, Freie Universität Berlin, Berlin, GermanyFranziska SlottaDepartment of Earth and Environmental Systems, Indiana State University, Terre Haute, IN, USAJames H. SpeerDepartment of Geography, University of Alabama, Tuscaloosa, AL, USAMatthew D. TherrellDepartment of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH, USAMax C. A. TorbensonDepartment of Geography, Johannes Gutenberg University, Mainz, GermanyMax C. A. TorbensonDepartment of Plant and Environmental Sciences, School of Natural Resources, The Copperbelt University, Kitwe, ZambiaRoyd VinyaForest and Nature Management, Van Hall Larenstein University of Applied Sciences, Velp, the NetherlandsMart VlamSchool of Teacher Training for Secondary Education Tilburg, Fontys University of Applied Sciences, Tilburg, the NetherlandsTommy WilsP.A.Z., P.G. and V.T. initiated the tropical tree-ring network; P.A.Z., F.B., P.G. and V.T. designed the study; all co-authors except F.B. contributed tree-ring data; F.B. and P.G. analysed the data, with important contributions from P.A.Z.; P.A.Z. and V.T. wrote the manuscript, with important contributions from F.B. and P.G. All co-authors read and approved the manuscript. More

Study systemThe cuckoo catfish (Synodontis multipunctatus) belongs to the African catfish family Mochokidae. The genus Synodontis, with 131 species distributed across African freshwaters57, gave rise to a small radiation in Lake Tanganyika, with 10 described endemic species58. The taxonomy of the group is not well established59 and we use the name S. multipunctatus as this species is confirmed as a brood parasite30 and the name was used in previous studies4,30,32,37,42. Cuckoo catfish primarily parasitise mouthbrooding cichlids from the tribe Tropheini30, but species from other lineages can also be parasitised59.Experimental designAll experiments took place between January and August 2020 at the Institute of Vertebrate Biology, Czech Republic. Prior to experimental use, fish were housed in mixed-sex groups in tanks equipped with shelter and internal filtration. Cuckoo catfish were F1 generation of commercially imported wild-caught parents (10 pairs). Host cichlids were descendant of wild fish imported from Kalambo, Zambia. Experimental tanks (420 L; length 150 cm, depth 70 cm, height 40 cm) were equipped with internal filtration, fine gravel (2–4 mm diameter), half a clay pot as a shelter on each side of the tank, and one artificial plant in the centre of each tank. Water temperature was maintained at 27 °C (±1 °C) and the dark – light regime was set to 11 h:13 h. In total, we stocked 18 tanks with 4 males and 12 females of the mouthbrooding cichlid Astatotilapia burtoni and introduced 3 cuckoo catfish pairs of one of three different experience levels. Naïve catfish (n = 36 individuals) had no prior experience with cichlids. Experienced catfish (n = 36) were housed together with reproductive cichlids for 12 months prior to the experiment and were age-matched to naïve catfish (5 years old). Highly experienced catfish (n = 36) were raised, coexisted and reproduced with cichlids for 7 years (and were on average 7–8% larger than both naïve and experienced catfish; mean ± SE, naïve: 116.2 ± 1.9 mm, experienced: 117.1 ± 1.5 mm, highly experienced: 125.6 ± 1.4 mm; Linear Model (LM): experienced vs. highly experienced, estimate ± S.E = 8.44 ± 2.29, t = 3.68, P = 0.0004, experienced vs. naïve, estimate ± S.E = −0.94 ± 2.29, t = −0.41, P = 0.681, n = 108). Additionally, both naïve and experienced cuckoo catfish were bred using in-vitro fertilisation32 to avoid cichlid imprinting (i.e., priming with cichlid cues), while highly experienced catfish were bred under natural conditions within the buccal cavities of their hosts. Each experimental tank contained catfish with the same experience level. Due to space limitations, we split the experiment into two consecutive phases with 3 replicate tanks for each treatment within both phases (in total 9 experimental tanks per phase). Between the two experimental phases, host cichlids were placed together and haphazardly assigned to new experimental tanks. During the second phase, we removed some cichlids from the tanks because of injuries caused by their intraspecific aggression (3 males and 3 females in total), and those hosts were not replaced. Over an experimental phase, cuckoo catfish and cichlids freely interacted for 15–16 weeks. During this period, each tank was checked for mouthbrooding cichlids twice each week (Tuesday and Friday). We caught the mouthbrooding females, gently washed the eggs out of their mouths using a jet of water from a Pasteur pipette, measured their body size to the nearest mm, and released them back to their experimental tank. For each female, we counted the number of cichlid eggs and cuckoo catfish eggs (if present). At the end of each experimental phase, we measured body size of all cuckoo catfish to the nearest mm. There was no significant difference between the number of cichlid spawnings between naïve and experienced catfish treatments (Generalised Linear Models with negative binomial error distribution, estimate ± S.E.: −0.093 ± 0.145, z = −0.644, P = 0.519), nor between naïve and highly experienced catfish (estimate ± S.E.: −0.269 ± 0.148, z = −1.810, P = 0.070).Behavioural recordingOver the experimental period, we successfully recorded 18 videos of spawning events (Lamax x3.1 ATLAS cameras; naïve catfish treatment, n = 9; experienced catfish treatment, n = 6; highly experienced catfish treatment, n = 3). One camera was placed near the spawning site approximately 20 cm away from spawning activity and a second camera was placed outside the experimental tank to obtain an overall view. Nine spawnings were recorded from the start (n = 7 naïve catfish experiments and 2 experienced catfish experiments) and nine spawnings were recorded from the timepoint when we recognised ongoing spawning activity (n = 2 naïve, 4 experienced, and 3 highly experienced catfish experiments). From the video footage taken for each spawning, we scored all overt aggression that host cichlids directed towards cuckoo catfish, counted the number of intruding catfish during each distinct cichlid spawning behaviour (i.e., male and female cichlid interact in a repeated succession of quivering and T-positions), measured the delay of intruding catfish to each distinct spawning behaviour (i.e., the time from the start of spawning behaviour until the first catfish directly approaches the spawning cichlids), and recorded the presence or absence of catfish during each spawning behaviour. Additionally, we recorded whether cichlids used the available shelters for spawning as this might have impeded catfish recognition of the spawning activity. When spawning was recorded from the start, scoring started 100 s before we detected the first egg laid (cichlid or cuckoo catfish). When spawning was already ongoing, the scoring started immediately after the cameras were in place. Mounting of the cameras did not interrupt the normal behaviour of cichlids or catfish. For all video footage, scoring ended 100 s after the last male-female interaction within the spawning site. To estimate the duration of male T-positions during spawnings, we measured the time period from the start of male nuzzling near female genital papilla until the female turned around either to collect eggs or start nuzzling near the male´s genital papilla (n = 115 male T-positions from 12 cichlid spawnings).Statistical analysisWe used R v. 3.5.1 (R Development Core Team, 2018) for all statistical analyses. All statistical tests were two-sided. First, we compared body size among the three cuckoo catfish experience levels using a Linear Model with catfish size (mm) as response variable and ‘treatment’ (naïve, experienced, and highly experienced catfish) as predictor variable. Second, we formally tested whether the number of host spawnings varied between the treatment groups (total numbers: naïve = 191 spawnings, experienced = 174 spawnings, highly experienced = 146 spawnings). To obtain an insight into temporal dynamics of cichlid spawning, we calculated the number of cichlid spawnings for each treatment in each quarter of the duration of the experimental period. We fitted a GLM with a negative binomial error distribution (to account for slightly overdispersed data) with the number of cichlid spawnings as the response variable and our treatment groups as predictors.To test how experience with host spawning (treatment) affected cuckoo catfish ability to place their eggs in the care of the host, we compared (1) the number of parasitised cichlid clutches among the three catfish experience groups (prevalence of parasitism), (2) the mean number of catfish eggs introduced into cichlid clutches among the three treatment levels (mean parasite egg abundance, the mean number of catfish eggs calculated across all cichlid broods, (3) mean parasite clutch size (the number of catfish eggs calculated only across parasitised cichlid broods), and examined (4) temporal dynamics of all three measures of parasite success within each treatment group throughout the duration of the experiment.To test for differences in prevalence of parasitism among different cuckoo catfish experience treatments, we applied a Generalised Linear Mixed-effects Model (GLMM, R package glmmTMB)60 with a binomial error distribution. We fitted the occurrence of ‘catfish parasitism’ (1 = yes, 0 = no) as the binary response variable and ‘treatment effect’ (i.e., ‘catfish experience’), ‘time progress of experiment’ (1–113 days) and ‘host female body size’ (in mm) as predictor variables. We additionally fitted an interaction between treatment (‘catfish experience’) and ‘time progress of experiment’ to the model to test whether parasitism rate changed over time at treatment-specific rates. We included tank identity (‘tank ID’) as a random intercept to account for nonindependence of data obtained from the same tank.Next, we tested whether the mean number of parasite eggs that were accepted by host females during one spawning bout differed between catfish experience treatments. We applied two GLMMs (R package glmmTMB)60 with a negative binomial error distribution (i.e., nbinom1) to account for over-dispersed count data. We applied GLMMs on the mean abundance of catfish eggs (across all host clutches) and on mean clutch size of cuckoo catfish using a subset of clutches that were parasitised. For both GLMMs, we included the ‘number of cuckoo catfish eggs per clutch’ as the response variable and treatment (‘catfish experience’), ‘time progress of experiment’, and their interaction as predictor variables. We additionally fitted ‘host female body size’ as a predictor variable because larger female cichlids are capable of laying more eggs and may appear more attractive hosts to cuckoo catfish. Further, a higher number of host eggs may increase the number of opportunities for cuckoo catfish to deposit their own eggs in the host clutch. ‘Tank ID’ was included as random intercept to account for nonindependence of data.To test whether cuckoo catfish presence affected cichlid spawning activity, we applied a GLMM (R package glmmTMB)60 with Gaussian error distribution (which provided superior model fit compared to Poisson and negative binomial distributions by ‘simulateResiduals’ and ‘testDispersion’ functions in the R package DHARMa)61. We fitted the ‘number of host eggs’ per clutch as the response variable and treatment (‘catfish experience’), ‘host female body size’, ‘time progress of experiment’, and ‘experimental phase’ (1st or 2nd phase) as predictor variables. We also included ‘tank ID’ as random intercept to account for nonindependence of data. The full model further included an interaction between treatment and ‘time progress of experiment’ to accommodate the possibility that host egg numbers may be affected differently across catfish experience treatments over time. As this full model predicted no difference in temporal aspect of host clutch size among treatments (‘catfish experience’: ‘time progress’, experienced: z = 0.92, P = 0.360, highly experienced: z = 1.46, P = 0.143), we subsequently dropped the interaction term from the final model.We used data collected from video footage to investigate whether naïve, experienced and highly experienced cuckoo catfish differed in their response to host spawnings and, additionally, if catfish from the three treatments elicited different host reactions towards them by applying Linear Mixed-effect Models using the R packages lme462 and glmmTMB60. To account for different starting times of recordings, we calculated either the rate of behaviour per minute of observation (i.e., for aggression) or their relative values (i.e., for the number of host courtships that cuckoo catfish missed).First, we tested whether host spawning pairs increased their aggressions towards cuckoo catfish over the experimental period to rule out the presence of host adaptation to cuckoo catfish intrusions, which would interfere with our aim of understanding parasite learning. We fitted a Generalised Linear Mixed-effects Model (GLMM, R package glmmTMB) with a negative binomial error distribution. The number of overt aggressive behaviours that the spawning pair performed towards cuckoo catfish per minute of catfish presence at the spawning site (summed over male and female cichlid) was fitted as the response variable and treatment (‘catfish experience’) as the predictor variable. We further included ‘time progress of experiment’ and ‘experimental phase’ as predictors to account for their possible effect on host aggression. We additionally included ‘tank ID’ as random intercept in the model to account for individual variation in host aggression levels among experimental tanks.To investigate if naïve cuckoo catfish missed more opportunities to parasitise cichlids than experienced and highly experienced catfish, we fitted a GLMM (R package lme4) with a binomial error distribution. We included the proportion of missed spawning behaviours (coded as ‘missed spawnings behaviours’ versus ‘intruded spawning behaviours’, based on count data for each spawning) as the response variable (‘spawnings missed’) and treatment (‘catfish experience’) as a predictor variable. We fitted ‘tank ID’ as a random intercept to the model to account for nonindependence of data within tanks, and we additionally fitted a random intercept based on whether the spawning was covered by a shelter or not (‘sheltered spawn’, yes / no) since spawning in a shelter may have been less apparent to catfish.We tested whether cuckoo catfish experience played a role in the timing of their intrusion to specific spawning behaviours by fitting a GLMM (R package lme4) with a Gamma error distribution to account for a positive skew in the data distribution. We included the mean delay of catfish to the first appearance of cichlid T-position in seconds (‘catfish delay’, see main text and Supplementary Movie 1 for a detailed description of cichlid spawning sequence) as the response variable and ‘catfish experience’ as the predictor variable. We included ‘tank ID’ and ‘sheltered spawn’ as random intercepts.Finally, we fitted a GLMM with a Poisson error distribution to test whether cuckoo catfish learn to synchronise their intrusion behaviour as they gain experience through interactions with their hosts. We included the maximum number of catfish during a specific cichlid spawning behaviour (‘intruder number’, count data) as the response variable and ‘catfish experience’ as the predictor variable. To account for nonindependence of data within experimental tanks and spawnings, we included a random intercept where each spawning was nested within ‘tank ID’ in the model.Ethical complianceResearch adhered to all national and institutional animal care and use guidelines, was administered under permit No. CZ62760203 and was approved by ethical boards of the Institute of Vertebrate Biology and the Czech Academy of Sciences (approval No. 32-2019).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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31 March 2022

Funding battles stymie ambitious plan to protect global biodiversity

Researchers are disappointed with the progress — but haven’t lost hope.

Natasha Gilbert

Natasha Gilbert

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Animals such as this orangutan in Indonesia are endangered because of illegal deforestation.Credit: Jami Tarris/Future Publishing via Getty

Scientists are frustrated with countries’ progress towards inking a new deal to protect the natural world. Government officials from around the globe met in Geneva, Switzerland, on 14–29 March to find common ground on a draft of the deal, known as the post-2020 global biodiversity framework, but discussions stalled, mostly over financing. Negotiators say they will now have to meet again before a highly anticipated United Nations biodiversity summit later this year, where the deal was to be signed.The framework so far sets out 4 broad goals, including slowing species extinction, and 21 mostly quantitative targets, such as protecting at least 30% of the world’s land and seas. It is part of an international treaty known as the UN Convention on Biological Diversity, and aims to address the global biodiversity crisis, which could see one million plant and animal species go extinct in the next few decades because of factors such as climate change, human activity and disease.
China takes centre stage in global biodiversity push
The COVID-19 pandemic has already slowed discussions of the deal. Over the past two years, countries’ negotiators met only virtually; the Geneva meeting was the first in-person gathering since the pandemic began. When it ended, Basile van Havre, one of the chairs of the framework negotiations working group, said that because negotiators couldn’t agree on goals, additional discussions will need to take place in June in Nairobi. The convention’s pivotal summit — its Conference of the Parties (COP15) — is expected to be held in Kunming, China, in August and September, but no firm date has been set.Anne Larigauderie, executive secretary of the Intergovernmental Platform on Biodiversity and Ecosystem Services in Bonn, Germany, who attended the Geneva gathering, told Nature: “We are leaving the meeting with no quantitative elements. I was hoping for more progress.”Robert Watson, a retired environmental scientist at the University of East Anglia, UK, says the quantitative targets are crucial to conserving biodiversity and monitoring progress towards that goal. He calls on governments to “bite the bullet and negotiate an appropriate deal that both protects and restores biodiversity”.Finance fightMany who were at the meeting say that disagreements over funding for biodiversity conservation were the main hold-up to negotiations. For example, the draft deal proposed that US$10 billion of funding per year should flow from developed nations to low- and middle-income countries to help them to implement the biodiversity framework. But many think this is not enough. A group of conservation organizations has called for at least $60 billion per year to flow to poorer nations.
Biodiversity moves beyond counting species
The consumption habits of wealthy nations are among the key drivers of biodiversity loss. And poorer nations are often home to areas rich in biodiversity, but have fewer means to conserve them.“The most challenging aspect is the amount of financing that wealthy nations are committing to developing nations,” says Brian O’Donnell, director of the Campaign for Nature in Washington DC, a partnership of private charities and conservation organizations advocating a deal to safeguard biodiversity. “Nations need to up their level of financing to get progress in the COP.”Other nations, particularly low-income ones, probably don’t want to agree “unless they have assurances of resources to allow them to implement the new framework”, Larigauderie says.Countries including Argentina and Brazil are largely responsible for stalling the deal, several sources close to the negotiations told Nature. They asked to remain anonymous because the negotiations are confidential.
The world’s species are playing musical chairs: how will it end?
No agreement could be reached even on targets with broad international support, such as protecting at least 30% of the world’s land and seas by 2030. O’Donnell says that just one country blocked agreement on this target, questioning its scientific basis.Van Havre downplayed the lack of progress, saying that the brinksmanship at the meeting was part of a “normal negotiating process”. He told reporters: “We are happy with the progress made.” Further delays ‘unacceptable’A bright spot in the negotiations, van Havre said, was a last-minute “major step forward” in discussions on how to fairly and equitably share the benefits of digital sequence information (DSI). DSI consists of genetic data collected from plants, animals and other organisms.
Why deforestation and extinctions make pandemics more likely
When pressed, however, van Havre admitted that the progress was simply an agreement between countries to continue discussing a way forward.Thomas Brooks, chief scientist at the International Union for Conservation of Nature in Gland, Switzerland, says that DSI discussions have actually been fraught. Communities from biodiverse-rich regions where genetic material is collected have little control over the commercialization of the data that come from it, and no way to recoup financial and other benefits, he explains.Like biodiversity financing, DSI rights could hold up negotiations on the overall framework. Low-income countries want a fair and equitable share of the benefits from genetic material that originates in their lands, but rich nations don’t want unnecessary barriers to sharing the information.“We are a long way from a watershed moment, and there remain genuine disagreements,” Brooks says. However, he is optimistic that progress will eventually be made.
The biodiversity leader who is fighting for nature amid a pandemic
Some conservation organizations take hope from new provisional language in the deal that calls for halting all human-caused species extinctions. The previous draft of the deal proposed only a reduction in the rate and risk of extinctions, says Paul Todd, an environmental lawyer at the Natural Resources Defense Council, a non-profit group based in New York City.Given how much work governments must do to reach agreement on the deal, Watson says the extra Nairobi meeting is a “logical” move. But he warns: “Any further delay would be unacceptable.”“This isn’t even the hard work,” Todd says. “Implementing the deal will be the real work.”

doi: https://doi.org/10.1038/d41586-022-00916-8

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Discovery of closely related phage sequences with the conserved genetic context of bS21Multiple phage-related sequences with a conserved genomic context were detected from several freshwater metagenome-assembled datasets (see Methods). Genes for bS21, TerL, PVP, prohead core scaffolding, and protease protein (hereafter prohead protease for short), and MCP are encoded in the genomic region. BLASTp search of the TerL sequences against the ggKbase sequences (ggkbase.berkeley.edu) obtained a total of 47 unique scaffolds with the conserved genomic region (Supplementary Table 1). Two related phages were included as outgroups for comparative analyses. The corresponding samples were collected from freshwater lakes or reservoirs (one from a wastewater treatment plant), and all but three were from the oxic layer (see Methods for details).General features of manually curated genomesAll the 49 phage sequences were manually curated to fill scaffolding gaps and fix the assembly errors, and nine of them (including one outgroup phage) were curated to completion (circular and no gaps or local assembly errors) (Supplementary Table 1). A total of 14 related phage genomes from IMG/VR were also included for further analyses. The eight bS21-encoding complete genomes had genome lengths of 293–331 kbp, GC contents of 31.0–33.7% and encoded 350–413 protein-coding genes (coding density, 91.1–94.9%), with 5–25 (average 17) tRNA genes. No alternative coding signal (i.e., stop codon reassignment) was detected in any genome. In comparison, the outgroup complete genome has a size of 308 kbp (450 protein-coding genes, 6 tRNAs, 94.7% coding density) and GC content of 27.3%.Genomic context of bS21 in phagesGenomic context analyses for bS21 genes showed a highly conserved gene architecture across phage genomes in proximity to the region encoding bS21 (see Fig. 1a for example). Specifically, we found that bS21 was consistently located in between two hypothetical protein families (positions 1 and –1 in Fig. 1b and Supplementary Table 2), with core structural proteins—including the TerL, PVP, prohead protease, and MCP—generally located within five genes in both the upstream and downstream DNA. Other hypothetical proteins were also consistently found in this region, although their positions were more variable upstream (positions –4 through –10, Fig. 1b). Importantly, the bS21 gene was consistently encoded in the reverse strand relative to the conserved hypothetical and structural protein genes (Fig. 1a and Supplementary Fig. 1).Fig. 1: Genetic context of the genes encoding bS21 in the phage genomes.a Examples of genetic context of phage genomes with and without bS21. The annotation of protein-coding genes is the same as indicated in b by different colors. Those in white are genes not shown in subfigure (b). b Summary of genetic context of all phage genomes encoding bS21. The relative position of genes near the bS21 gene is shown, and the size of circles indicates the number of phages with a gene belonging to a given protein family (annotation shown on right) at that relative position. Only the 12 most frequent families are shown. The details of the genetic context are shown in Supplementary Fig. 1.Full size imagePhylogeny of bS21-encoding phagesPhylogenetic analyses based on TerL suggested the phages belonging to several groups, we thus assigned them to clades a–e (Fig. 2 and Supplementary Table 1). Most of the phages belong to clades c, d, and e, and they have a broader environmental distribution than clades a and b. Interestingly, we found that some phages within a single clade were from distant sampling sites. Closer inspection indicated they also shared large genomic fragments with high similarity (82–98% for nucleotide sequences; Supplementary Fig. 2). Comparative genome-wide analyses of the complete genomes from the same site but sampled at different time points showed sequence variations in some genes (Supplementary Fig. 3).Fig. 2: The phylogeny of bS21 phages based on the large terminal (TerL) protein sequences.Two closely related phages without bS21 encoded were included as outgroups (shown at the top of the tree). The genomes are assigned to five clades (a, b, c, d, and e) based on the topology of the phylogenetic tree. The numbers in the brackets following the scaffold names indicate the total counts of the same scaffold detected from the corresponding sampling sites. The genomes that were manually curated to completion (circular and no gap) are indicated by squares, and the genome sizes are shown in brackets.Full size imageTerL phylogeny, constructed using sequences from this study and NCBI RefSeq sequences, indicated the most closely related classified phages belong to Caudovirales of either the Myoviridae or Ackermannviridae (Supplementary Fig. 4). A phage baseplate assembly protein was encoded in most curated genomes. This is an important building block for members of Siphoviridae and Myoviridae [8], so we concluded that the bS21-encoding phages are myoviruses.Predicted bacterial hosts of bS21-encoding phagesTo predict host-phage relationships we first used CRISPR-Cas spacers targeting. While none of the 16.5k unique spacers from the relevant metagenomes targeted any of the curated phage genomes from the same sampling sites, a single cross-site target was detected. Specifically, MIW1_072018_0_1um_scaffold_78 was targeted by a spacer (24 nt and no mismatch) from a MIW2 Flavobacterium genome (affiliation: Bacteroidetes, Flavobacteria). We then predicted the bacterial hosts based on the bacterial taxonomic affiliations of the phage gene inventories as previously described [2] (Supplementary Table 3). The results indicated that all of the phages infect members of Bacteroidetes, which were detected in 43 out of 45 samples (Fig. 3 and Supplementary Table 4). The two metagenomic samples without Bacteroidetes identified were both collected via filtering through 0.2 μm and onto 0.1 μm pore size filters. Bacteroidetes were detected in both of the corresponding 0.2 μm fraction samples (Fig. 3).Fig. 3: The relative abundance of the Bacteroidetes classes in all the analyzed samples in this study.The microbial communities were profiled based on ribosomal protein S3 (rpS3) assigned to the Bacteroidetes classes. The sampling sites were indicated by colored names, and the filter sizes used during sampling are shown by circles. The three pairs of filter samples are indicated by colored stars.Full size imageWe profiled the co-detection of phage clades and Bacteroidetes classes to test for specific connections (Supplementary Fig. 5). However, this was uninformative because most samples contained more than one class. However, phages from clades a and b are unlikely to infect class Bacteroidia members, as they did not co-occur in any sample.Comparison of bacterial and phage-encoded bS21Phylogenetic analyses revealed that bS21 protein sequences from phages (this study) and the bacterial bS21 sequences (from the corresponding samples and NCBI RefSeq) clustered separately (Supplementary Fig. 6). The bacterial bS21 sequences that are most similar to phage bS21 were from Bacteroidetes, mostly from the Flavobacteriia class (Supplementary Table 5). We aligned and compared the Bacteroidetes and phage bS21 sequences and mapped the divergent and non-divergent residues to the model of the ribosome of Flavobacterium johnsoniae (Fig. 4a). Multiple divergent positions are located at the beginning of the bS21 sequences and four residues (Arg21, Phe23, Asp25, and Thr28) were significantly divergent (Fig. 4b).Fig. 4: Conservation and differences between phage and bacterial bS21.a Location of bS21 (blue) within the 16S rRNA (green) and the ASD (magenta) of the F. johnsoniae ribosome (PDB ID: 7JIL) [9]. bS21 is in the neck region of the 16S rRNA, interacting closely with the 3’ end of the 16S rRNA, where the ASD is located. The 16S rRNA is shown from the subunit interface direction. b Zebra2 divergency results from an alignment of phage and bacterial bS21 sequences mapped on F. johnsoniae bS21. Divergent positions between phage and bacterial bS21 are shown with red. c Zebra2 conservation results from the same alignment as in (b) mapped on F. johnsoniae bS21 with conserved residues shown in yellow. The stacking interaction between Tyr54 and Adenine 1534 is indicated. d The sequence logo and consensus sequences of phage and bacterial bS21 alignments and the corresponding position of Tyr54 in F. johnsoniae bS21 in the alignment are highlighted. The C-terminal parts are highlighted with gray backgrounds.Full size imageBacteroidetes usually lack the SD sequences. It was recently reported that the bS21 Tyr54 (numbering in F. johnsoniae) is an important residue for blocking the ASD in the 16S rRNA within the ribosome [9]. Our analyses predict that all the analyzed bacterial and phage bS21 in this study have an amino acid with an aromatic ring (often Tyr54 but in a few cases His54, and in one case Phe54) at the position of Tyr54 in F. johnsoniae (Fig. 4c, d and Supplementary Fig. 6). This conservation of the aromatic property in phage bS21 should ensure stacking interaction with Adenine 1534 (numbering in F. johnsoniae 16S) from the ASD. In that way, phage bS21 mimics Bacteroidetes bS21 in the region where it binds the ribosome but differs from it in the region where the mRNA would bind.In contrast, the C-terminal regions of both the bacterial and phage bS21 sets were highly divergent (Fig. 4d). However, the phage C-terminal regions are generally conserved within the clades defined based on TerL phylogeny (Fig. 2 and Supplementary Fig. 7).Metabolic potentials of bS21-encoding phagesFunctional annotation of the predicted protein-coding genes revealed that in addition to bS21, these phages carry other genes related to protein production and stability (Supplementary Table 6). Examples include protein folding chaperones and Clp protease, suggesting the importance of controlling the proteostasis network of the cell. Interestingly, we also identified many genes involved in sugar-related chemistry and polysaccharide biosynthesis. Many of these genes were predicted to perform chemical transformations related to the biosynthesis of lipopolysaccharide, a major component of the Gram-negative bacterial outer membrane. We interpret this as a potential mechanism to remodel the cell surface and prevent superinfection by competitor phages, a strategy common to the phage lysogenic cycle. These phages lack detectable integration machinery (no gene for integrase or resolvase was detected), suggesting the possibility of a non-integrative long-term infection state such as pseudolysogeny [10].Clustering analyses of 22 phages with a minimum genome size of 100 kbp (including the two outgroup genomes) based on the presence/absence of protein families indicated they shared a total of 16 protein families (Supplementary Fig. 8 and Supplementary Table 7). Phosphate starvation-inducible protein PhoH (“fam582”) was the only predicted protein detected in all 22 phages (excluding the shared predicted proteins in the conserved rpS21-encoding region described above). Other common protein families include those related to DNA replication (e.g., DNA primase/helicase, DNA polymerase, HNH endonuclease, thymidylate synthase (EC:2.1.1.45), deoxyuridine 5’-triphosphate nucleotidohydrolase (EC:3.6.1.23)), those associated with virion assembly (e.g., a phage tail sheath protein, phage baseplate assembly protein W), and those for other functions (e.g., chaperone ATPase, alpha-amylase, DegT/DnrJ/EryC1/StrS aminotransferase).Temporal and spatial distribution and activity of bS21-encoding phages in Lake RotseeTo reveal the spatial and temporal distribution of the bS21-encoding phages, we focused on the Lake Rotsee data and profiled phage occurrence based on the sequencing coverage in the metagenomic datasets. The Lake Rotsee samples were collected from the oxic (7 samples) and anoxic (3 samples) layers of the water column. The bS21-encoding phages were readily detected in oxic samples, especially in the under-ice samples when the whole water column was oxic (Fig. 5a).Fig. 5: The spatial and temporal distribution and activity of bS21 phages at Lake Rotsee.a The sequencing coverage of each phage genome in each metagenomic dataset is shown in the heatmaps. The phages are phylogenetically clustered based on their TerL protein sequences (bootstraps shown in numbers), the colored backgrounds are the same as shown in Fig. 2 for different clades. The sampling time points and depths are shown on the left, and the oxygen conditions are indicated by colored circles on the right. Two replicates were sequenced from the 15 m sample collected in 2018. b The percentage of mapped RNA reads to the phage genomes in the corresponding samples (rows labeled in (a)). The mapped RNA reads had a minimum similarity of 98% to the phage genomes. No RNA data were generated for the three samples collected on October 10, 2017. See the figure legend for each genome in the upper right, the circular genomes have names in bold font.Full size imageRotsee Lake RNA reads were mapped to the phage genomes curated from this site to reveal the transcriptional activities of bS21-encoding phages (Fig. 5b). In general, the phages were likely to be most transcriptionally active in the oxic water columns. A total of 736 genes were transcribed in at least one sample (Supplementary Table 8), those for MCP, an AAA ATPase, tail sheath protein, bS21, FKBP-type peptidyl-prolyl cis-trans isomerase, and a methyltransferase FkbM domain protein are among the top 100 most highly transcribed. The high transcriptional activities of MCP in five phages indicated they were in the late stage of replication at the time of sampling.The transcriptional behavior of phage bS21 genesTo seek evidence of a transcriptional relationship involving bS21 and other genes we focused on the three phages that were most active based on the transcriptional level of their 19 shared single-copy genes (Fig. 6a). bS21 had very similar (but slightly lower) transcriptional activities as a neighboring gene (hereafter, bS21_CN gene) encoded on the opposite strand. The bS21_CN gene encodes a hypothetical protein (protein family: fam498) and was not detected in the two outgroup phages without bS21 (Supplementary Table 6). Interestingly, a comparison of the phylogenies of bS21 and bS21_CN showed a very similar evolutionary pattern (Supplementary Fig. 9), likely suggesting their potential functional relationship in the bS21-encoding phages.Fig. 6: The transcription levels of bS21 and core structural protein genes.a The normalized transcriptional level (NTL) of shared single-copy protein families of three phages (indicated by arrows in Fig. 5b) with ≥1000 RNA reads mapped. Two families (including MCP) are listed on a different scale due to their much higher transcription levels. Refer to Fig. 5 for shape symbols that designate phage genomes and samples. b Examples of RNA mapping profiles indicating the co-transcription of some genes neighboring bS21. Hypothetical protein genes are shown in white.Full size imageInspection of the RNA reads mapping profiles indicated that the conserved region encoding bS21 and core structural proteins was not transcribed as an operon, whereas bS21 and bS21_CN, MCP and its upstream hypothetical protein gene, and prohead protease and its downstream hypothetical protein gene may each be transcribed together (Fig. 6b). Given the observed RNA expression patterns, we conclude that the phage-encoded bS21 genes were actively transcribed during late-stage replication, along with other core structural proteins.Genomic context of bS21 genes in published phage genomesTo determine whether the phage bS21 genes are generally co-located with those for core structural proteins in diverse phages, we profiled the genomic context of bS21 in 900 published bS21-encoding phages [2, 11] (Supplementary Table 9). Functional annotations were performed for the upstream and downstream ten genes of the bS21 genes using pVOG (Supplementary Table 10). Of the 20 most abundant pVOGs, 6 were related to core structural assembly (Fig. 7a), i.e., prohead protease (n = 310), MCP (n = 154), PVP (n = 120), TerL (n = 78), neck protein (n = 70), and a tail sheath protein (n = 29). A total of 388 genomes contained at least one of these genes within ten genes of bS21, and eight had all of these six core structural proteins in close proximity. Three pVOGs were related to DNA processing, i.e., an exonuclease (n = 37), an endonuclease (n = 32), DNA helicase (n = 30). Other pVOGs included Hsp20 heat shock protein (n = 127), two ATP-dependent CLP proteases (n = 50 and 47, respectively), and lysozyme (for lysis; n = 29). Interestingly, the prohead protease and the MCP pVOG genes are very close to the bS21 gene (generally 2–4 genes; Fig. 7b), as in the bS21-encoding phage genomes analyzed in this study (2–6 genes away; Fig. 1 and Supplementary Fig. 1).Fig. 7: Neighboring genes within 10 genes of bS21 in published bS21-encoding phage genomes.a The annotation and corresponding functional category (if assigned) of the 20 most commonly detected pVOG genes and their predicted functions are shown on the left, the total number of genomes with the gene are shown on the right. b The distribution of the distance of each gene to bS21 in the genomes. The position of genes next to bS21 (thus distance = 1) is highlighted using a red dashed line. The average distance of each gene to bS21 is shown on the left. c The predicted hosts of bS21-encoding phages with the top 4 most abundant genes detected within 10 genes of bS21. The total count of hosts is shown on the right.Full size imageWe respectively predicted the hosts of the bS21-encoding phages with the four most dominant pVOGs within ten genes of bS21 (Fig. 7c and Supplementary Table 11). The bacterial hosts are diverse and include Proteobacteria, Bacteroidetes, and Firmicutes. More