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Study origin and designThe MeadoWatch project (MW) is a project run collaboratively between the University of Washington (UW) and the United States National Park Service to monitor the phenology of alpine and subalpine wildflower species across large elevational gradients in Mount Rainier National Park (Fig. 2). MW was established in 2013 with the goal of understanding long-term effects of climate change on Mount Rainier National Park wildflower communities using community-science approaches. The first MW transect was established along Reflection Lakes, Skyline, and Paradise Glacier trail system in 2013 (9–11 plots). In 2015, MW expanded to include a second transect (15–17 plots) along the Glacier Basin trail (Fig. 1a). The MW transects span around 5 km each, over a 400 m altitudinal gradient (Reflection Lakes: 1490m–1889m a.s.l.; Glacier Basin: 1460m–1831m a.s.l.)Fig. 2Alpine meadows, plot extension, and target species. (a) Species-rich alpine meadow in Mount Rainier National Park (Mount Tahoma), showing many of the target species in the foreground. (b) MW volunteer coordinator Anna Wilson at a plot, indicating the arm span that defines the plot area (personal likeness used with confirmed consent). (c) Species composition and proportion of reports per species in each of the transects; species common to both trails are highlighted with striped shadowing. Photographs: A. John (a), L. Felker (b).Full size imagePlots are located along the side of each trail, marked with a colored survey marker. The area of each plot is defined by the arm-span of volunteers when they position themselves over the plot marker looking away from the trail (Fig. 2b). While less accurate than marking the corners of plots, this approach was used to avoid establishing permanent structures in wilderness areas within the National Park. The surveyed area in each plot is, on average, 1.25 m2. Each plot is also equipped with temperature sensors (HOBO Pendant Logger, Onset Computer Corp.) buried approximately 4 cm below the ground. Sensors are placed at the start of each fall season and removed at the beginning of each summer season for data downloading. The HOBO sensors provide an estimate for the date of snow disappearance and in-situ temperature at 3 hours intervals. Once plots are covered in snow, soil temperatures remain at 0 °C and show no diurnal variation, so that daily changes in temperatures above 1 °C can be used to determine the earliest date without snow cover20. We use these approaches to provide dates of snow appearance and disappearance, snow cover duration, and minimum soil temperatures for each year and plot. Occasionally, temperature data during the snow disappearing window were lost due to sensor failure or loss of sensors (which occurs because plots are not permanently marked and/or well-meaning visitors remove sensors). This, and the lack of temperature sensors in the first year of the project, resulted in approx. 20% of cases of missing data. In those cases, we used a data imputation method to estimate the missing values based on data from nearby plots and a parallel temperature data collection with 890 total observations. These estimates were highly reliable in filling the data gaps (see Appendix C in16 for further details).Focal speciesWe originally targeted 16 native wildflower species along each transect, which were chosen based on their abundance, ease of identification, and presence in the plot. Four of those target species were present on both transects. In 2016 we replaced one species with a different one (see further information below), making for a total of 17 species monitored (Fig. 2c). The focal species are: American bistort* (Polygonum bistortoides), avalanche lily (Erythronium montanum), bracted lousewort* (Pedicularis bracteosa), broadleaf arnica (Arnica latifolia), cascade aster (Aster ledophyllus; synonym Eucephalus ledophyllus), glacier lily (Erythronium grandiflorum), Gray’s lovage (Ligusticum grayi), magenta paintbrush (Castilleja parviflora), mountain daisy (Erigenon peregrinus; synonym Erigeron glacialis), northern microseris (Microseris alpestris; synonym Nothocalais alpestris), scarlet paintbrush (Castilleja miniata), sharptooth angelica (Angelica arguta), sitka valerian* (Valeriana sitchensis), subalpine lupine* (Lupinus arcticus; synonym Lupinus latifolius var. subalpinus), tall bluebell (Mertensia paniculata), Canby’s licorice-root (Ligusticum canbyi), and western anemone (Anemone occidentalis). Asterisks denote species monitored along both trails.Due to challenges in species identification, we dropped Canby’s licorice-root (Ligusticum canbyi) as a target species in 2016. Consequently, Ligusticum canbyi has limited replication in the database (Fig. 2c). While we included the phenological records of this species for the sake of completeness, we recommend focusing on the other 16 species, which are both better represented (in terms of data coverage) and are free of any potential misidentification issues.For additional information on the species, methods, identification cues, and image resources see: http://www.meadowatch.org, https://www.youtube.com/channel/UCGBFTKxf8FIWswMDxBavpuQ, and the appendices therein16.Data collection and volunteer trainingDuring the summer months, MW volunteers and scientists collect reproductive phenology data with a frequency between 3 and 9 trail reports per week. Each report records the presence or absence of 4 phenophases for each target species present in each of the plots. The phenophases are ‘budding’, ‘flowering’, ‘ripening fruit’, and ‘releasing seed’. Phenophases were defined as follows:BuddingThe beginning growth of the flower which has not yet opened. A plant is considered budding if buds are present, but the stamen and pistils are not yet visible and available to pollinators.FloweringThe generally “showy” part of the plant that holds the reproductive parts (stamens and pistils). A plant is considered flowering when at least one flower is open, and the stamens and pistils are visible and available for pollination and reproduction.Ripening fruitThe fruit develops from the female part of the flower following successful pollination. In the target species, fruits can take many forms, from hard, fleshy capsules, juicy berries, to a feathery tuft on the end of a seed. A plant is in the ripening fruit stage when reproductive parts on at least one reproductive stalk are non-functional and the formation of the fruit part is clearly ongoing (visible), but seeds are not yet fully mature and not yet being dispersed.Releasing seedAfter the fruit ripens, seeds are released to be dispersed by gravity, wind, or animals. A plant is considered in the releasing seed stage if seeds are actively being released on at least one reproductive stalk (but there are still seeds present).A full description, including illustrations for each species’ phenophase and identification cues is available in http://www.meadowatch.org/volunteer-resources.html, as well as in Annex 1 – Supplementary Documentation. Multiple phenophases can be present simultaneously, depending on the species, and are noted independently. Additionally, volunteers are also asked to record the presence of snow (‘snow covered plot’, ‘partially covered plot’, or ‘snow-free plot’), and, since 2017, the presence of damage by herbivory (‘presence of browsed stems’) on each plot.In years not impacted by the SARS-Cov-2 pandemic MW volunteers attend an in-person 3-hour botanical and phenological training session taught by UW scientists at the beginning of each sampling season. Volunteers were also provided with detailed species-identification guides, including an extensive description of sampling methods and location of the plots. The trainings for the 2020 and 2021 seasons were held virtually via a series of online training videos. In these years, volunteers took a quiz on wildflower phenology, plant identification and data collection methods after viewing these videos and were required to ‘pass’ a certain threshold to volunteer (unlimited attempts were allowed). During these virtual trainings, volunteers were provided with digital copies of the species identification guides, with many returning volunteers using printed guides they had kept from previous years.At the end of their phenological hike, volunteers submit their data sheets either by depositing them in lockboxes located within the park, or by scanning and emailing them directly to mwatch@uw.edu. Data are then entered manually and stored in the UW repositories after being checked for consistency at the end of each sampling season.The parallel data collection by members of UW’s Hille Ris Lambers group (including PI, postdoctoral researchers, graduate students, and trained interns) acted as the following: (i) a quality-control, i.e., allowing us to compare the consistency in phenology assessments between volunteers and scientists, and (ii) a way to increase the temporal resolution and scale of the data, e.g., by reducing early season gaps and ‘weekend bias’17. This parallel expert sampling was carried out around once a week between 2013 and 2020, showing great consistency between the two groups. For detailed comparisons between volunteers and scientists’ assessments see the data validation section (as well as Appendix E in16).Processed dataIn addition to the raw phenological data, we also provide here parameters to construct the year, species, and plot-specific flowering phenology based on the timing of snow disappearance (as in16). Models describe unimodal probability distributions that were fitted with maximum likelihood models to binomial flowering data from each species, year, and plot. These curves have been used to estimate peak flowering dates and diversity and link them to reported visitor experiences16. Here, we provide the 3 parameters defining the unimodal curve of flowering probability per species i, plot j and year k: the duration of flowering (𝛿ijk), the maximum probability of flowering (𝜇ijk), and peak flowering (in DOY – ρijk); following the equations described in16 and https://github.com/ajijohn/MeadoWatch).The parameters of these probability distribution curves are ready-to-use values that can be broadly and easily used to estimate floral compositional change over past seasons due to changing environmental conditions—for example, to inform plant-pollinator interaction networks if combined with pollinator behavioral data (e.g.21). More

Disease data sourceAll analyses were conducted at the administrative level, and the exact list of known zoonotic diseases is recorded in the GIDEON database22. GIDEON is currently the most comprehensive and frequently updated infectious disease outbreak database reporting epidemics of human infectious diseases at the global scale and has been widely used in global zoonosis studies42,43 (Last access date, November 9, 2020). The administrative designations used in our analyses were based on the Global Administrative Areas (GADM) database (www.gadm.org, downloaded on November 8, 2020), which includes very detailed boundary data for global countries and major island groups.Pattern and correlates of zoonosis events worldwideNumber of zoonosis eventsGIDEON defines human infectious disease reservoirs as any animal, plant, or substrate supporting the survival and reproduction of infectious agents and promoting transmission to potential susceptible hosts. Its host category therefore includes all human-specific, zoonotic, multihost, and environmental agents. As our main aim was to test the role of established alien animal species in the emergence of zoonotic diseases, we focused on a total of 161 diseases specified in GIDEON’s host designations and definitions as nonhuman zoonotic (n = 115) and multihost (n = 46) diseases (Supplementary Data 1) and excluded diseases with human-specific hosts that do not need animals to persist or be transmitted. The infectious agents of nonhuman zoonotic diseases complete their entire lifecycle in nonhuman hosts but may have the potential to spillover and infect human populations. Infectious agents of multihost diseases can use both human and animal hosts for their development and reproduction. We measured the number of zoonosis events for each jurisdiction according to five host taxonomic groups: mammals, birds, invertebrates, reptiles and amphibians. These zoonoses were mainly caused by bacteria, viruses, parasitic animals and fungi. We excluded zoonoses from the Algae (3 diseases) due to low sample sizes in GIDEON.Correlates of the number of zoonosis eventsClimatic variablesFollowing a previous study21, we used global environmental stratification (GEnS) as a composite bioclimatic variable generated by stratifying the Earth’s surface into zones with similar climates44. The GEnS database was constructed based on a total of 125 strata across 18 global environmental zones with a spatial resolution of 30 arc seconds (equivalent to approximately 0.86 km2 at the equator). The values in GEnS range from 1 to 18 with a higher value indicating warmer and wetter conditions.Human population densityWe used human population density as one general anthropogenic factor reflecting propagule pressure and human-assisted pathogen movements1,21,45. Human population size data and the land area of each jurisdiction were collected from World Bank Open Data from 2011 to 2020 (available at https://data.worldbank.org/indicator/SP.POP.TOTL, accessed on November 18, 2020). We then calculated the human population density using the human population size divided by the land area.Native potential host richness and biodiversity lossData on the richness of native amphibians, birds, and mammals were derived from the Biodiversity Mapping website (https://biodiversitymapping.org/wordpress/index.php/home/, accessed on August 19, 2020), which were based on studies from Jenkins et al. (2013)’s and Pimm et al. (2014)46,47. The map of reptile diversity is based on an updated database of the global spatial distribution of reptiles48. All diversity maps for each taxon were generated through the calculation of grid-based richness at a spatial resolution of 10 km × 10 km in ArcGIS46. We did not include native invertebrate richness, as global maps for most invertebrate taxa are not yet available. For the loss of native biodiversity, we followed the previous study by first extracting the list of threatened species (NT, EN and VU categories evaluated by the IUCN Red List, access on May 10th, 2021)29, and then calculated the number of threatened species for each taxon distributes in each administrative unite as a proxy of biodiversity loss.Richness of established alien zoonotic host speciesWe quantified the richness of established alien animal species from the five main taxonomic groups (mammals, birds, reptiles, amphibians and invertebrates) based on 4,522 establishment events of 795 alien animals in each of 201 jurisdictions according to various databases. Data on 262 established alien reptiles and amphibians were compiled from multiple publications, including Kraus’s compendium49 and other recent updates50. Data on 337 established alien birds after removing all migratory bird species as vagrants were collected from the Global Avian Invasions Atlas (GAVIA)51, which is a comprehensive database of the global distribution of established alien birds. Data on 119 established alien mammals were obtained from the Introduced Mammals of the World database52 and the more recent update53. Data on 77 terrestrial alien invertebrates (66 insects and 11 other groups) across 7 taxa with native and invaded range information were obtained from the Global Invasive Species Database (GISD, http://www.iucngisd.org/gisd/, accessed on July 1, 2020). We calculated the richness of both zoonotic and non-zoonotic alien host species for each order. We first conducted an intensive literature review for each established alien species of each of the four taxa to determine whether they transmit pathogens to humans (Supplementary Data 2). The identification of zoonotic or non-zoonotic host may be influenced by under-sampling in the literature. We therefore incorporated the latest synthesis of human-infecting pathogens in the ‘CLOVER’ dataset to identify zoonotic and non-zoonotic animal hosts54. The CLOVER dataset compiled GMPD255, EID256, HP323 and Shaw57 databases and is currently the most comprehensive dataset on host-pathogen associations. Based on this information, we then categorized each alien species as a ‘zoonotic host’ or ‘non-zoonotic host’. The records of the established alien species were assigned to GADM jurisdictions, and we calculated the richness of the established alien zoonotic and non-zoonotic host species for each taxonomic group within each jurisdiction. In order to increase the statistical power, we conducted subsequent modeling analyses based on four mammalian orders (i.e., Carnivora, Cetartiodactyla, Lagomorpha, and Rodentia), five avian groups (i.e., waterfowl including five orders: Anseriformes, Gruiformes, Pelecaniformes, Phoenicopteriformes and Suliformes; Columbiformes, Galliformes, Passeriformes, Psittaciformes), the order Diptera of the invertebrates, and herpetofauna as a whole, which have established alien populations in at least 50 administrative units.Climate changeWe extracted historical monthly mean temperature and precipitation data recorded between 1901 and 2009 from the University of East Anglia Climate Research Unit (CRU, https://sites.uea.ac.uk/cru/, accessed on November 30, 2020)58. This database provides historical global-scale yearly climatic data with the finest resolution of 0.5° grids. We generated the temperature and precipitation values for all grids in each jurisdiction, calculated the slope of the temperature and precipitation for the time series of the years 1901 to 2009 for each grid and generated the averages based on all grids within each jurisdiction.Anthropogenic land-use changeWe downloaded global land-use data from the Anthromes v2 Dataset (Anthropogenic Biomes version 2, accessed on October 15, 2020) in ESRI GRID format59. We used the 1900 and 2000 data to calculate the temporal changes in land use. By using the reclassify and raster function in ArcGIS, we calculated the percentage of grids in which the land-use type changed to a more anthropogenically influenced type from 1900 to 2000 for each jurisdiction, including 15 scenarios: Wildlands to Seminatural, Wildlands to Rangelands, Wildlands to Croplands, Wildlands to Villages, Wildlands to Dense Settlements, Seminatural to Rangelands, Seminatural to Croplands, Seminatural to Villages, Seminatural to Dense Settlements, Rangelands to Croplands, Rangelands to Villages, Rangelands to Dense Settlements, Croplands to Villages, Croplands to Dense Settlements, and Villages to Dense Settlements.Sampling effort, reporting bias and incomplete dataA potential issue in quantifying the effects of different predictor variables on the number of zoonosis events is the need to account for the differences in survey effort, reporting bias and incomplete disease data among regions1,21,28. There is a high probability that zoonosis discovery is spatially biased by uneven levels of surveillance across countries, as the global allocation of scientific resources has been focused on rich and developed countries. We thus included the Infectious Disease Vulnerability Index (IDVI), which is a comprehensive metric reflecting the demographic, health care, public health, socioeconomic, and political factors that may have an impact on the capacity of surveillance and detection of infectious diseases in each country60. Second, we followed the methods of a previous study21 to control for reporting biases. We incorporated PubMed citations per disease for each jurisdiction using a Python-based PubCrawler21. In addition, we added the longitude and latitude of the geographic centroid of administrative units to control for spatial autocorrelation as there would be a higher probability of having similar diseases in nearby than distant administrative units61.Statistical analysisThe number of zoonosis events, native potential host richness, established alien animal richness and human population density were log-transformed to improve linearity. A potential issue in our data analysis is that the numbers of zoonosis events and the numbers of native and alien animal species are strongly influenced by geographical area, as larger countries or regions may host more native or alien animal species and more disease events. We therefore calculated the density of native or alien species richness and the number of zoonosis events using the total number divided by the geographical area of each jurisdiction. Furthermore, the number of zoonosis events may also be influenced by the degree of local disease surveillance. We thus obtained the residuals from a regression correlating zoonosis event density and all disease event density, and used them as the dependent variable for further analyses (Fig. 1). As some of our variables may be expected to be nonlinear, we performed generalized additive mixed model (GAMM) analyses following Mollentze & Streicker 2020’s framework25 to quantify the relationships between different predictor variables and the number of zoonosis events. We started with a full model with zoonosis event density controlling for overall disease surveillance as the response variable and 13 smoothed fixed effects (Fig. 1 and Supplementary Data 4): GEnS, human population density, density of native species richness, biodiversity loss, density of alien zoonotic host richness, density of alien non-zoonotic host richness, climate (temperature and precipitation) change, land-use change, IDVI, PubMed citations, longitude and latitude of geographic centroid of administrative units. The reason why we included the density of alien non-zoonotic host richness as a covariate is because this variable can serve as a positive control for propagule pressure, allowing us to more explicitly test whether zoonotic alien hosts contribute to zoonoses beyond propagule pressure associated with non-zoonotic alien hosts, which cannot directly increase zoonotic diseases. These predictor variables were not highly collinear as their correlation coefficients based on Pearson rank correlation analyses were all More

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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

NEWS
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.
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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.
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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.
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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.
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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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