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    Spatial assortment of soil organisms supports the size-plasticity hypothesis

    Geisen S, Wall DH, van der Putten WH. Challenges and opportunities for soil biodiversity in the anthropocene. Curr Biol. 2019;29:R1036–44.Article 
    CAS 
    PubMed 

    Google Scholar 
    Fierer N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol. 2017;15:579–90.Article 
    CAS 
    PubMed 

    Google Scholar 
    Gossner MM, Lewinsohn TM, Kahl T, Grassein F, Boch S, Prati D, et al. Land-use intensification causes multitrophic homogenization of grassland communities. Nature. 2016;540:266–9.Article 
    CAS 
    PubMed 

    Google Scholar 
    Leff JW, Jones SE, Prober SM, Barberán A, Borer ET, Firn JL, et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc Natl Acad Sci USA. 2015;112:10967–72.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Alberti M, Correa C, Marzluff JM, Hendry AP, Palkovacs EP, Gotanda KM, et al. Global urban signatures of phenotypic change in animal and plant populations. Proc Natl Acad Sci USA. 2017;114:8951–6.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    El-Sabaawi R. Trophic structure in a rapidly urbanizing planet. Funct Ecol. 2018;32:1718–28.Article 

    Google Scholar 
    Yu S, Wu Z, Xu G, Li C, Wu Z, Li Z, et al. Inconsistent patterns of soil fauna biodiversity and soil physicochemical characteristic along an urbanization gradient. Front Ecol Evol. 2022;9:824004.Article 

    Google Scholar 
    Zambrano L, Aronson MFJ, Fernandez T. The consequences of landscape fragmentation on socio-ecological patterns in a rapidly developing urban area: a case study of the National Autonomous University of Mexico. Front. Environ Sci. 2019;7:152.
    Google Scholar 
    Wilson MC, Chen XY, Corlett RT, Didham RK, Ding P, Holt RD, et al. Habitat fragmentation and biodiversity conservation: key findings and future challenges. Landsc Ecol. 2016;31:219–27.Article 

    Google Scholar 
    Guilland C, Maron PA, Damas O, Ranjard L. Biodiversity of urban soils for sustainable cities. Environ Chem Lett. 2018;16:1267–82.Article 
    CAS 

    Google Scholar 
    Dou Y, Kuang W. A comparative analysis of urban impervious surface and green space and their dynamics among 318 different size cities in China in the past 25 years. Sci. Total Environ. 2020;706:135828.Article 
    CAS 
    PubMed 

    Google Scholar 
    Francini G, Hui N, Jumpponen A, Kotze D, Romantschuk M, Allen J, et al. Soil biota in boreal urban greenspace: responses to plant type and age. Soil Biol Biochem. 2018;118:145–55.Article 
    CAS 

    Google Scholar 
    Corline NJ, Peek RA, Montgomery J, Katz JVE, Jeffres CA. Understanding community assembly rules in managed floodplain food webs. Ecosphere. 2021;12:e03330.Article 

    Google Scholar 
    Tripathi BM, Stegen JC, Kim M, Dong K, Adams JM, Lee YK. Soil pH mediates the balance between stochastic and deterministic assembly of bacteria. ISME J. 2018;12:1072–83.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Liu W, Graham EB, Dong Y, Zhong L, Zhang J, Qiu C, et al. Balanced stochastic versus deterministic assembly processes benefit diverse yet uneven ecosystem functions in representative agroecosystems. Environ Microbiol. 2021;23:391–404.Article 
    CAS 
    PubMed 

    Google Scholar 
    Thakur MP, Phillips HR, Brose U, De Vries FT, Lavelle P, Loreau M, et al. Towards an integrative understanding of soil biodiversity. Biol Rev. 2020;95:350–64.Article 
    PubMed 

    Google Scholar 
    Bahram M, Kohout P, Anslan S, Harend H, Abarenkov K, Tedersoo L. Stochastic distribution of small soil eukaryotes resulting from high dispersal and drift in a local environment. ISME J. 2016;10:885–96.Article 
    PubMed 

    Google Scholar 
    Luan L, Jiang Y, Cheng M, Dini-Andreote F, Sui Y, Xu Q, et al. Organism body size structures the soil microbial and nematode community assembly at a continental and global scale. Nat Commun. 2020;11:1–11.Article 

    Google Scholar 
    Isabwe A, Yang JR, Wang Y, Wilkinson DM, Graham EB, Chen H, et al. Riverine bacterioplankton and phytoplankton assembly along an environmental gradient induced by urbanization. Limnol Oceanogr. 2022;67:1943–58.Article 
    CAS 

    Google Scholar 
    Nemergut DR, Schmidt SK, Fukami T, O’Neill SP, Bilinski TM, Stanish LF, et al. Patterns and processes of microbial community assembly. Microbiol Mol Biol Rev. 2013;77:342–56.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zinger L, Taberlet P, Schimann H, Bonin A, Boyer F, De Barba M, et al. Body size determines soil community assembly in a tropical forest. Mol Ecol. 2019;28:528–43.Article 
    CAS 
    PubMed 

    Google Scholar 
    Jiao S, Yang Y, Xu Y, Zhang J, Lu Y. Balance between community assembly processes mediates species coexistence in agricultural soil microbiomes across eastern China. ISME J. 2020;14:202–16.Article 
    PubMed 

    Google Scholar 
    Jiao S, Chen W, Wei G. Biogeography and ecological diversity patterns of rare and abundant bacteria in oil‐contaminated soils. Mol Ecol. 2017;26:5305–17.Article 
    CAS 
    PubMed 

    Google Scholar 
    Wu W, Lu H-P, Sastri A, Yeh Y-C, Gong G-C, Chou W-C, et al. Contrasting the relative importance of species sorting and dispersal limitation in shaping marine bacterial versus protist communities. ISME J. 2018;12:485–94.Article 
    PubMed 

    Google Scholar 
    Farjalla VF, Srivastava DS, Marino NA, Azevedo FD, Dib V, Lopes PM, et al. Ecological determinism increases with organism size. Ecology. 2012;93:1752–9.Article 
    PubMed 

    Google Scholar 
    Carscadden KA, Emery NC, Arnillas CA, Cadotte MW, Afkhami ME, Gravel D, et al. Niche breadth: causes and consequences for ecology, evolution, and conservation. Q Rev Biol. 2020;95:179–214.Article 

    Google Scholar 
    Beissinger SR. Ecological mechanisms of extinction. Proc Natl Acad Sci USA. 2000;97:11688–9.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Poiani KA, Richter BD, Anderson MG, Richter HE. Biodiversity conservation at multiple scales: functional sites, landscapes, and networks. Bioscience. 2000;50:133–46.Article 

    Google Scholar 
    Yang J, Zhang X, Jin X, Seymour M, Richter C, Logares R, et al. Recent advances in environmental DNA-based biodiversity assessment and conservation. Divers Distrib. 2021;27:1876–9.Article 

    Google Scholar 
    Breed MF, Harrison PA, Blyth C, Byrne M, Gaget V, Gellie NJC, et al. The potential of genomics for restoring ecosystems and biodiversity. Nat Rev Genet. 2019;20:615–28.Article 
    CAS 
    PubMed 

    Google Scholar 
    Department of Economic and Social Affairs (DESA). World Urbanization Prospects. The 2018 Revision. United Nations. 2019. https://population.un.org/wup/publications/Files/WUP2018-Report.pdf. Accessed 13 Mar 2022.Qiao Z, Wang B, Yao H, Li Z, Scheu S, Zhu Y-G, et al. Urbanization and greenspace type as determinants of species and functional composition of collembola communities. Geoderma. 2022;428:116175.Article 

    Google Scholar 
    Shrestha S, Cui S, Xu L, Wang L, Manandhar B, Ding S. Impact of land use change due to urbanisation on surface runoff using GIS-based SCS–CN Method: a case study of Xiamen City, China. Land. 2021;10:839.Article 

    Google Scholar 
    R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing. 2022. Vienna, Austria. https://www.R-project.org/.Wickham. H ggplot2: elegant graphics for data analysis. Springer-Verlag New York, 2016.Kassambara A. ggpubr: ‘ggplot2’ based publication ready plots. 2020. https://CRAN.R-project.org/package=ggpubr.Morlon H, Chuyong G, Condit R, Hubbell S, Kenfack D, Thomas D, et al. A general framework for the distance–decay of similarity in ecological communities. Ecol Lett. 2008;11:904–17.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Goslee S, D. Urban, Goslee, MS. ecodist: dissimilarity-based functions for rcological analysis. 2020. https://cran.r-project.org/web/packages/ecodist/index.html.Ofiţeru ID, Lunn M, Curtis TP, Wells GF, Criddle CS, Francis CA, et al. Combined niche and neutral effects in a microbial wastewater treatment community. Proc Natl Acad Sci USA. 2010;107:15345–50.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Burns AR, Stephens WZ, Stagaman K, Wong S, Rawls JF, Guillemin K, et al. Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J. 2016;10:655–64.Article 
    CAS 
    PubMed 

    Google Scholar 
    Chen W, Ren K, Isabwe A, Chen H, Liu M, Yang J. Stochastic processes shape microeukaryotic community assembly in a subtropical river across wet and dry seasons. Microbiome. 2019;7:138.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chase JM, Kraft NJ, Smith KG, Vellend M, Inouye BD. Using null models to disentangle variation in community dissimilarity from variation in α‐diversity. Ecosphere. 2011;2:1–11.Article 

    Google Scholar 
    Pandit SN, Kolasa J, Cottenie K. Contrasts between habitat generalists and specialists: an empirical extension to the basic metacommunity framework. Ecology. 2009;90:2253–62.Article 
    PubMed 

    Google Scholar 
    Salazar G. EcolUtils: utilities for community ecology analysis. 2019. https://github.com/GuillemSalazar/EcolUtils.Kraft NJB, Adler PB, Godoy O, James EC, Fuller S, Levine JM. Community assembly, coexistence and the environmental filtering metaphor. Funct Ecol. 2015;29:592–9.Article 

    Google Scholar 
    Cadotte MW, Tucker CM. Should environmental filtering be abandoned? Trends Ecol Evol. 2017;32:429–37.Article 
    PubMed 

    Google Scholar 
    Leibold MA, McPeek MA. Coexistence of the niche and neutral perspectives in community ecology. Ecology. 2006;87:1399–410.Article 
    PubMed 

    Google Scholar 
    Evans S, Martiny JB, Allison SD. Effects of dispersal and selection on stochastic assembly in microbial communities. ISME J. 2017;11:176–85.Article 
    PubMed 

    Google Scholar 
    Jiang Y, Liu M, Zhang J, Chen Y, Chen X, Chen L, et al. Nematode grazing promotes bacterial community dynamics in soil at the aggregate level. ISME J. 2017;11:2705–17.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Douhan GW, Vincenot L, Gryta H, Selosse M-A. Population genetics of ectomycorrhizal fungi: from current knowledge to emerging directions. Fungal Biol. 2011;115:569–97.Article 
    PubMed 

    Google Scholar 
    Granot I, Belmaker J. Niche breadth and species richness: correlation strength, scale and mechanisms. Glob Ecol Biogeogr. 2020;29:159–70.Article 

    Google Scholar 
    Sexton JP, Montiel J, Shay JE, Stephens MR, Slatyer RA. Evolution of ecological niche breadth. Annu Rev Ecol Evol Syst Annu Rev Ecol Evol S. 2017;48:183–206.Article 

    Google Scholar 
    Fraaije RGA, ter Braak CJF, Verduyn B, Verhoeven JTA, Soons MB. Dispersal versus environmental filtering in a dynamic system: drivers of vegetation patterns and diversity along stream riparian gradients. J Ecol. 2015;103:1634–46.Article 

    Google Scholar 
    Soininen J, McDonald R, Hillebrand H. The distance decay of similarity in ecological communities. Ecography. 2007;30:3–12.Article 

    Google Scholar 
    Zhang K, Delgado-Baquerizo M, Zhu Y-G, Chu H. Space is more important than season when shaping soil microbial communities at a large spatial scale. mSystems. 2020;5:e00783–19.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ma B, Dai Z, Wang H, Dsouza M, Liu X, He Y, et al. Distinct biogeographic patterns for archaea, bacteria, and fungi along the vegetation gradient at the continental scale in Eastern China. mSystems. 2017;2:e00174–16.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wang J, Zhang T, Li L, Li J, Feng Y, Lu Q. The patterns and drivers of bacterial and fungal β-diversity in a typical dryland ecosystem of northwest China. Front Microbiol. 2017;8:2126.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kang L, Chen L, Zhang D, Peng Y, Song Y, Kou D, et al. Stochastic processes regulate belowground community assembly in alpine grasslands on the Tibetan Plateau. Environ Microbiol. 2021;24:179–94.Article 
    PubMed 

    Google Scholar 
    Chen Q-L, Hu H-W, Yan Z-Z, Li C-Y, Nguyen B-AT, Sun A-Q, et al. Deterministic selection dominates microbial community assembly in termite mounds. Soil Biol Biochem. 2021;152:108073.Article 
    CAS 

    Google Scholar 
    Huang S, Tucker MA, Hertel AG, Eyres A, Albrecht J. Scale-dependent effects of niche specialisation: the disconnect between individual and species ranges. Ecol Lett. 2021;24:1408–19.Article 
    PubMed 

    Google Scholar 
    Rapacciuolo G, Blois JL. Understanding ecological change across large spatial, temporal and taxonomic scales: integrating data and methods in light of theory. Ecography. 2019;42:1247–66.
    Google Scholar 
    van der Gast CJ. Microbial biogeography: the end of the ubiquitous dispersal hypothesis? Environ Microbiol. 2015;17:544–6.Article 
    PubMed 

    Google Scholar 
    Levy-Booth DJ, Giesbrecht IJW, Kellogg CTE, Heger TJ, D’Amore DV, Keeling PJ, et al. Seasonal and ecohydrological regulation of active microbial populations involved in DOC, CO2, and CH4 fluxes in temperate rainforest soil. ISME J. 2019;13:950–63.Article 
    CAS 
    PubMed 

    Google Scholar 
    De Gannes V, Bekele I, Dipchansingh D, Wuddivira MN, De Cairies S, Boman M, et al. Microbial community structure and function of soil following ecosystem conversion from native forests to teak plantation forests. Front Microbiol. 2016;7:1976.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Männistö M, Vuosku J, Stark S, Saravesi K, Suokas M, Markkola A, et al. Bacterial and fungal communities in boreal forest soil are insensitive to changes in snow cover conditions. FEMS Microbiol. 2018;94:fiy123.
    Google Scholar 
    Sakarika M, Spanoghe J, Sui Y, Wambacq E, Grunert O, Haesaert G, et al. Purple non‐sulphur bacteria and plant production: benefits for fertilization, stress resistance and the environment. Microb Biotechnol. 2020;13:1336–65.Article 
    CAS 
    PubMed 

    Google Scholar 
    Kernaghan G, Patriquin G. Diversity and host preference of fungi co-inhabiting Cenococcum mycorrhizae. Fungal Ecol. 2015;17:84–95.Article 

    Google Scholar 
    Lumibao CY, Kimbrough ER, Day RH, Conner WH, Krauss KW, Van Bael SA. Divergent biotic and abiotic filtering of root endosphere and rhizosphere soil fungal communities along ecological gradients. FEMS Microbiol. 2020;96:fiaa124.Article 
    CAS 

    Google Scholar 
    Rueckert S, Betts EL, Tsaousis AD. The symbiotic spectrum: where do the gregarines fit? Trends Parasitol. 2019;35:687–94.Article 
    PubMed 

    Google Scholar 
    Butaeva F, Paskerova G, Entzeroth R. Ditrypanocystis sp.(Apicomplexa, Gregarinia, Selenidiidae): the mode of survival in the gut of Enchytraeus albidus (Annelida, Oligochaeta, Enchytraeidae) is close to that of the coccidian genus Cryptosporidium. Tsitologiia. 2006;48:695–704.CAS 
    PubMed 

    Google Scholar 
    Pavao-Zuckerman MA, Coleman DC. Urbanization alters the functional composition, but not taxonomic diversity, of the soil nematode community. Appl Soil Ecol. 2007;35:329–39.Article 

    Google Scholar 
    Gaspar C, Borges PA, Gaston KJ. Diversity and distribution of arthropods in native forests of the Azores archipelago. Arquipelago: Life Mar Sci. 2008;25:1–30.
    Google Scholar 
    Suter RB, Doyle G, Shane CM. Oviposition site selection by Frontinella pyramitela (Araneae, Linyphiidae). J Arachnol. 1987;15:349–54.Tian T, Ren Q, Fan J, Haseeb M, Zhang R. Too dry or too wet soils have a negative impact on larval pupation of fall armyworm. J Appl Entomol. 2022;146:196–202.Article 

    Google Scholar 
    Marczylo EL, Macchiarulo S, Gant TW. Metabarcoding of soil fungi from different urban greenspaces around Bournemouth in the UK. EcoHealth. 2021;18:315–30.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Corline NJ, Peek RA, Montgomery J, Katz JVE, Jeffres CA. Understanding community assembly rules in managed floodplain food webs. Ecosphere. 2021;12:e03330.Article 

    Google Scholar 
    Schlägel UE, Grimm V, Blaum N, Colangeli P, Dammhahn M, Eccard JA, et al. Movement-mediated community assembly and coexistence. Biol Rev Camb Philos Soc. 2020;95:1073–96.Article 
    PubMed 

    Google Scholar 
    Stubner S. Enumeration of 16S rDNA of desulfotomaculum lineage 1 in rice field soil by real-time PCR with SybrGreen™ detection. J Microbiol Methods. 2002;50:155–64.Article 
    CAS 
    PubMed 

    Google Scholar 
    DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Toju H, Tanabe AS, Yamamoto S, Sato H. High-coverage ITS primers for the DNA-based identification of ascomycetes and basidiomycetes in environmental samples. PloS One. 2012;7:e40863.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Abarenkov K, Henrik Nilsson R, Larsson KH, Alexander IJ, Eberhardt U, Erland S, et al. The UNITE database for molecular identification of fungi–recent updates and future perspectives. New Phytol. 2010;186:281–5.Article 
    PubMed 

    Google Scholar 
    Stoeck T, Bass D, Nebel M, Christen R, Jones MD, Breiner H-W, et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol Ecol. 2010;19:21–31.Article 
    CAS 
    PubMed 

    Google Scholar 
    Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 2012;41:D597–604.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Porazinska DL, Giblin‐Davis RM, Faller L, Farmerie W, Kanzaki N, Morris K, et al. Evaluating high‐throughput sequencing as a method for metagenomic analysis of nematode diversity. Mol Ecol Res. 2009;9:1439–50.Article 
    CAS 

    Google Scholar 
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–96.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Leray M, Yang JY, Meyer CP, Mills SC, Agudelo N, Ranwez V, et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front Zool. 2013;10:1–14.Article 

    Google Scholar 
    Porter TM, Hajibabaei M. Over 2.5 million COI sequences in GenBank and growing. PloS One. 2018;13:e0200177.Article 
    PubMed 
    PubMed Central 

    Google Scholar  More

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    Asian elephants mostly roam outside protected areas — and it’s a problem

    Asian elephants spend most of their time outside protected areas because they prefer the food they find there, an international team of scientists reports. But this behaviour is putting the animals and people in harm’s way, say researchers.The finding has important implications for the long-term survival of the animals because protected areas are a cornerstone of global conservation strategies to protect threatened species, say researchers.If protected areas do not contain animals’ preferred habitats, they will wander out, says Ahimsa Campos-Arceiz, who studies Asian elephants (Elephas maximus) at the Chinese Academy of Sciences’ Xishuangbanna Tropical Botanical Garden in Menglun, China. “It’s a good intention, but doesn’t always work out that way.”Human–elephant conflict is the biggest threat for Asian elephants. Over the past few decades, animals in protected areas have increasingly wandered into villages. They often cause destruction, damaging crops and infrastructure and injuring and even killing people.Wandering elephantsTo understand how effective protected areas are for conserving Asian elephants, Campos-Arceiz and his colleagues set out to get a precise picture of Asian-elephant movements. They collared 102 individuals in Peninsular Malaysia and Borneo, recording 600,000 GPS locations over a decade. They found that most elephants spent most of their time in habitats outside the protected areas, at the forest edge and in areas of regrowth. The findings were published in the Journal of Applied Ecology1 on 18 October.The researchers suspect that the elephants venture out because they like to eat grasses, bamboo, palms and fast-growing trees, which are common in disturbed forests and relatively scarce under the canopy of old-growth forests.Philip Nyhus, a conservation biologist who specializes in human–wildlife conflict at Colby College in Waterville, Maine, says Asian elephants live deep in dense forest and so are much more difficult to study than African elephants, which roam open savannahs. “The sample size is impressive,” he says.The finding is not unexpected given past anecdotal observations of elephant behaviour, says Nyhus. But now the data show that this is a common strategy for the survival of these animals, and not just something seen in a subset of the population. The research provides strong evidence for how to set up suitable protected areas that reduce the risk of elephants wandering out, he says.‘There will be conflict’The results do not diminish the importance of protected areas, which provide long-term safety for the animals, says Campos-Arceiz, who did the field work while at the University of Nottingham Malaysia in Selangor. “But they are clearly not enough.”The study suggests that “there will be conflict between humans and elephants”, says Guo Xianming, director of the Research Institute of Xishuangbanna National Nature Reserve in Jinghong.Asian elephants wander into villages owing to a combination of reasons: an increase in elephant populations, forests in many reserves have grown denser and have become unsuitable for the animals, and increasing habitat loss and degradation outside.Last year, two herds of elephants made global headlines as they wandered out of the Xishuangbanna National Nature Reserve and travelled for hundreds of kilometers, wreaking havoc along the way. One herd spent five weeks at the botanical garden where Campos-Arceiz works. “It was intense,” he says.There is an urgent need to understand how people and elephants can better share the landscape, says Guo. And the first step is by better protecting people’s lives and livelihoods. “It’s the only way of peaceful co-existence.”
    The reporting of the story was supported by International Women’s Media Foundation’s Howard G. Buffett Fund for Women Journalists. More

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    How monkeypox is spreading, and more — this week’s best science graphics

    Adolescents losing sleepEpidemiological studies in US school students aged 14–18 have shown that declines in mental health mirror reductions in the amount of sleep they are getting. Although it is hard to show a causal link between these changes, the authors of this Comment article argue that ensuring that young people get enough sleep is crucial for them to thrive. Various factors could be contributing to this drop-off in sleep, they say, including the use of digital media before bed, schoolwork pressures and extracurricular activities late in the evening or early in the morning.

    Sources: J. M. Twenge et al. Sleep Med. 39, 47–53 (2017)/US CDC YRBSS

    Monkeypox trajectoryAlmost six months after the monkeypox virus started to spread globally, vaccination efforts and behavioural changes seem to be containing the current strain — at least in the United States and Europe. The number of cases in these regions peaked in August and is now falling. But the situation could still play out in several ways, as this News story reports. At best, the outbreak might fizzle out over the next few months or years. At worst, the virus could become endemic outside Africa.

    Source: WHO

    The most valuable soilsThis map shows the regions of the world where the conservation of soil should be prioritized. Soils contain a wealth of biodiversity, such as bacteria, fungi, nematode worms and earthworms. These organisms have important roles in ecosystem processes, such as carbon and nutrient cycling, water storage and supporting plant growth. The authors of a paper in Nature set out to identify global hotspots for conservation by surveying soil biodiversity and ecosystem functions at 615 sites around the world. They found hotspots of biodiversity in temperate and Mediterranean regions and in alpine tundra, whereas hotspots of species uniqueness occurred in the tropics and drylands. More than 70% of the hotspots are not adequately covered by protected areas. More

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    ReSurveyGermany: Vegetation-plot time-series over the past hundred years in Germany

    Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).Chase, J. M. et al. Species richness change across spatial scales. Oikos 128, 1079–1091 (2019).Article 

    Google Scholar 
    Vellend, M. et al. Global meta-analysis reveals no net change in local-scale plant biodiversity over time. Proceedings of the National Academy of Sciences 110, 19456–19459 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Riedel, T., Polley, H. & Klatt, S. Germany. in National Forest Inventories (eds. Vidal, C., Alberdi, I. A., Hernández Mateo, L. & Redmond, J. J.) 405–421, https://doi.org/10.1007/978-3-319-44015-6 (Springer International Publishing, 2016).Braun-Blanquet, J. Pflanzensoziologie. Grundzüge der Vegetationskunde. vol. Seite: (Julius Springer, 1928).Bernhardt-Römermann, M. et al. Drivers of temporal changes in temperate forest plant diversity vary across spatial scales. Glob Change Biol 21, 3726–3737 (2015).Article 
    ADS 

    Google Scholar 
    Ahrns, C. & Hofmann, G. Vegetationsdynamik und Florenwandel im ehemaligen mitteldeutschen Waldschutzgebiet ‘Hainich’ im Intervall 1963–1995. Hercynia N.F. 31, 33–64 (1998).
    Google Scholar 
    Dittmann, T., Heinken, T. & Schmidt, M. Die Wälder von Magdeburgerforth (Fläming, Sachsen-Anhalt) – eine Wiederholungsuntersuchung nach sechs Jahrzehnten, https://doi.org/10.14471/2018.38.009 (2018).Günther, K., Schmidt, M., Quitt, H. & Heinken, T. Veränderungen der Waldvegetation im Elbe-Havelwinkel von 1960 bis 2015. Tuexenia 41, 53–85 (2021).
    Google Scholar 
    Janiesch, P. Vegetationsökologische Untersuchungen in einem Erlenbruchwald im nördlichen Münsterland. 25 Jahre im Vergleich. Abhandlungen aus dem Westfälischen Museum für Naturkunde 71–80 (2003).Naaf, T. & Wulf, M. Habitat specialists and generalists drive homogenization and differentiation of temperate forest plant communities at the regional scale. Biological Conservation 143, 848–855 (2010).Article 

    Google Scholar 
    Reinecke, J., Klemm, G. & Heinken, T. Vegetation change and homogenization of species composition in temperate nutrient deficient Scots pine forests after 45 yr. J Veg Sci 25, 113–121 (2014).Article 

    Google Scholar 
    Mölder, A., Streit, M. & Schmidt, W. When beech strikes back: How strict nature conservation reduces herb-layer diversity and productivity in Central European deciduous forests. Forest Ecology and Management 319, 51–61 (2014).Article 

    Google Scholar 
    Fischer, C., Parth, A. & Schmidt, W. Vegetationsdynamik in Buchen-Naturwäldern. Ein Vergleich aus Süd-Niedersachsen. Hercynia N.F. 45–68 (2009).Schmidt, W. Die Naturschutzgebiete Hainholz und Staufenberg am Harzrand – Sukzessionsforschung in Buchenwäldern ohne Bewirtschaftung (Exkursion E). Tuexenia 22, 151–213 (2002).
    Google Scholar 
    Strubelt, I., Diekmann, M. & Zacharias, D. Changes in species composition and richness in an alluvial hardwood forest over 52 yrs. J Veg Sci 28, 401–412 (2017).Article 

    Google Scholar 
    Strubelt, I., Diekmann, M., Peppler-Lisbach, C., Gerken, A. & Zacharias, D. Vegetation changes in the Hasbruch forest nature reserve (NW Germany) depend on management and habitat type. Forest Ecology and Management 444, 78–88 (2019).Article 

    Google Scholar 
    Wilmanns, O. & Bogenrieder, A. Veränderungen der Buchenwälder des Kaiserstuhls im Laufe von vier Jahrzehnten und ihre Interpretation – pflanzensoziologische Tabellen als Dokumente. Abh. Landesmus. Naturk. Münster Westfalen 48, 55–79 (1986).
    Google Scholar 
    Huwer, A. & Wittig, R. Changes in the species composition of hedgerows in the Westphalian Basin over a thirty-five-year period. Tuexenia 32, 31–53 (2012).
    Google Scholar 
    Immoor, A., Zacharias, D., Müller, J. & Diekmann, M. A re-visitation study (1948–2015) of wet grassland vegetation in the Stedinger Land near Bremen, North-western Germany, https://doi.org/10.14471/2017.37.013 (2017).Rosenthal, G. Erhaltung und Regeneration von Feuchtwiesen. Vegetationsökologische Untersuchungen auf Dauerflächen. Diss. Bot. 182, 1–283 (1992).
    Google Scholar 
    Poptcheva, K., Schwartze, P., Vogel, A., Kleinebecker, T. & Hölzel, N. Changes in wet meadow vegetation after 20 years of different management in a field experiment (North-West Germany). Agriculture, Ecosystems & Environment 134, 108–114 (2009).Article 

    Google Scholar 
    Diekmann, M. et al. Patterns of long‐term vegetation change vary between different types of semi‐natural grasslands in Western and Central Europe. J Veg Sci 30, 187–202 (2019).Article 

    Google Scholar 
    Hundt, R. Ökologisch‐geobotanische Untersuchungen an den mitteldeutschen Wiesengesellschaften unter besonderer Berücksichtigung ihres Wasserhaushaltes und ihrer Veränderung durch die Intensivbewirtschaftung. (Wehry-Druck OHG, 2001).Kuhn, G., Heinz, S. & Mayer, F. Grünlandmonitoring Bayern. Ersterhebung der Vegetation 2002–2008. Schriftenreihe LfL Bayerische Landesanstalt für Landwirtschaft 3, 1–161 (2011).
    Google Scholar 
    Raehse, S. Veränderungen der hessischen Grünlandvegetation seit Beginn der 50er Jahre am Beispiel ausgewählter Tal- und Bergregionen Nord- und Mittelhessens. (University Press GmbH, 2001).Scheidel, U. & Bruelheide, H. Versuche zur Beweidung von Bergwiesen im Harz. Hercynia N.F 37, 87–101 (2004).
    Google Scholar 
    Sommer, S. & Hachmöller, B. Auswertung der Vegetationsaufnahmen von Dauerbeobachtungenflächen auf Bergwiesen im NSG Oelsen bei variierter Mahd im Vergleich zur Brache. Ber. Arbeitsgem. Sächs. Bot. N.F. 18, 99–135 (2001).
    Google Scholar 
    Wegener, U. Vegetationswandel des Berggrünlands nach Untersuchungen von 1954 bis 2016 – Wege zur Erhaltung der Bergwiesen. Mountain grasslands vegetation change after research from 1954 to 2016 – ways to preserve mountain meadows. Abhandlungen und Berichte aus dem Museum Heineanum 11, 35–101 (2018).
    Google Scholar 
    Wittig, B., Müller, J. & Mahnke-Ritoff, A. Talauen-Glatthaferwiesen im Verdener Wesertal (Niedersachsen). Tuexenia 39, 249–265 (2019).
    Google Scholar 
    Heinrich, W., Marstaller, R. & Voigt, W. Eine Langzeitstudie zur Sukzession in Halbtrockenrasen – Strukturwandlungen in einer Dauerbeobachtungsfläche im Naturschutzgebiet “Leutratal und Cospoth” bei Jena (Thüringen). Artenschutzreport Jena 30, 1–80 (2012).
    Google Scholar 
    Hüllbusch, E., Brand, L. M., Ende, P. & Dengler, J. Little vegetation change during two decades in a dry grassland complex in the Biosphere Reserve Schorfheide-Chorin (NE Germany). Tuexenia 36, 395–412 (2016).
    Google Scholar 
    Knapp, R. Dauerflächen-Untersuchungen über die Einwirkung von Haustieren und Wild während trockener und feuchter Zeiten in Mesobromion-Halbtrockenrasen in Hessen. Mitt. Florist.-Soziol. Arbeitsgem. N.F. 19/20, 269–274 (1977).
    Google Scholar 
    Matesanz, S., Brooker, R. W., Valladares, F. & Klotz, S. Temporal dynamics of marginal steppic vegetation over a 26-year period of substantial environmental change: Temporal dynamics of marginal steppic vegetation over a 26-year period. Journal of Vegetation Science 20, 299–310 (2009).Article 

    Google Scholar 
    Schwabe, A., Zehm, A., Nobis, M., Storm, C. & Süß, K. Auswirkungen von Schaf-Erstbeweidung auf die Vegetation primär basenreicher Sand-Ökosysteme. NNA Berichte 1/2004, 39–54 (2004).
    Google Scholar 
    Schwabe, A., Süss, K. & Storm, C. What are the long-term effects of livestock grazing in steppic sandy grassland with high conservation value? Results from a 12-year field study. Tuexenia 33, 189–212 (2013).
    Google Scholar 
    Peppler‐Lisbach, C., Stanik, N., Könitz, N. & Rosenthal, G. Long‐term vegetation changes in Nardus grasslands indicate eutrophication, recovery from acidification, and management change as the main drivers. Appl Veg Sci 23, 508–521 (2020).Article 

    Google Scholar 
    Peppler-Lisbach, C. & Könitz, N. Vegetationsveränderungen in Borstgrasrasen des Werra-Meißner-Gebietes (Hessen, Niedersachsen) nach 25 Jahren. Tuexenia 37, 201–228 (2017).
    Google Scholar 
    Wittig, B., Müller, J., Quast, R. & Miehlich, H. Arnica montana in Calluna-Heiden auf dem Schießplatz Unterlüß (Niedersachsen). Tuexenia 40, 131–146 (2020).
    Google Scholar 
    Rumpf, S. B. et al. Range dynamics of mountain plants decrease with elevation. Proc Natl Acad Sci USA 115, 1848–1853 (2018).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kudernatsch, T. et al. Vegetationsveränderungen alpiner Kalk-Magerrasen im Nationalpark Berchtesgaden während der letzten drei Jahrzehnte. Tuexenia 36, 205–221 (2016).
    Google Scholar 
    Poschlod, P. et al. Long‐term monitoring in rivers of south Germany since the 1970ies. Macrophytes as indicators for the assessment of water quality. in Long‐term ecological research. Between Theory and Application (eds. Müller, F., Baessler, C., Schubert, H. & Klotz, S.) 189–199 (Springer, 2006).Dierschke, H. Dynamik und Konstanz an naturnahen Flussufern. 27 Jahre Dauerflächenuntersuchungen am Oderufer (Harzvorland). Braunschweiger Geobotanische Arbeiten 9, 119–138 (2008).
    Google Scholar 
    Kreyling, J. et al. Rewetting does not return drained fen peatlands to their old selves. Nat Commun 12, 5693 (2021).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bohn, U. & Schniotalle, S. Hochmoor-, Grünland- und Waldrenaturierung im Naturschutzgebiet ‘Rotes Moor’/Hohe Rhön 1981–2001. (Landwirtschaftsverlag, 2008).Koch, M. & Jurasinski, G. Four decades of vegetation development in a percolation mire complex following intensive drainage and abandonment. Plant Ecology & Diversity 8, 49–60 (2015).Article 

    Google Scholar 
    Walther, K. Die Vegetation des Maujahn 1984. Wiederholung der vegetationskundlichen Untersuchung eines wendländischen Moores. Tuexenia 6, 145–193 (1986).
    Google Scholar 
    Berg, C. & Mahn, E.-G. Anthropogene Vegetationsveränderungen der Straßenrandvegetation in den letzten 30 Jahren – die Glatthaferwiesen des Raumes Halle/Saale. Tuexenia 10, 185–195 (1990).
    Google Scholar 
    Meyer, S., Wesche, K., Krause, B. & Leuschner, C. Dramatic losses of specialist arable plants in Central Germany since the 1950s/60s – a cross-regional analysis. Diversity Distribution 19, 1175–1187 (2013).Article 

    Google Scholar 
    Meyer, S., Wesche, K., Krause, B. & Leuschner, C. Veränderungen in der Segetalflora in den letzten Jahrzehnten und mögliche Konsequenzen für Agrarvögel. Julius-Kühn-Archiv 442, 64–78 (2013).
    Google Scholar 
    Kutzelnigg, H. Veränderungen der Ackerwildkrautflora im Gebiet um Moers/Niederrhein seit 1950 und ihre Ursachen. Tuexenia 4, 81–102 (1984).
    Google Scholar 
    Milligan, G., Rose, R. J. & Marrs, R. H. Winners and losers in a long-term study of vegetation change at Moor House NNR: Effects of sheep-grazing and its removal on British upland vegetation. Ecological Indicators 68, 89–101 (2016).Article 

    Google Scholar 
    Wittig, B., Waldman, T. & Diekmann, M. Veränderungen der Grünlandvegetation im Holtumer Moor über vier Jahrzehnte. Hercynia N.F 40, 285–300 (2007).
    Google Scholar 
    Henning, K., Lorenz, A., von Oheimb, G., Härdtle, W. & Tischew, S. Year-round cattle and horse grazing supports the restoration of abandoned, dry sandy grassland and heathland communities by supressing Calamagrostis epigejos and enhancing species richness. Journal for Nature Conservation 40, 120–130 (2017).Article 

    Google Scholar 
    Blüml, V. Langfristige Veränderungen von Flora und Vegetation des Grünlandes in der Dümmerniederung (Niedersachsen) unter dem Einfluss von Naturschutzmaßnahmen. (Bremen, 2011).Von Oheimb, G. et al. Halboffene Weidelandschaft Höltigbaum. Perspektiven für den Erhalt und die naturverträgliche Nutzung von Offenlandlebensräumen. (Landwirschaftsverlag, 2006).Dornelas, M. et al. BioTIME: A database of biodiversity time series for the Anthropocene. Global Ecol Biogeogr 27, 760–786 (2018).Article 

    Google Scholar 
    Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752–1246752 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57 (2011).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Vellend, M. The Biodiversity Conservation Paradox. Am. Sci. 105, 94 (2017).Article 

    Google Scholar 
    Cardinale, B. J., Gonzalez, A., Allington, G. R. H. & Loreau, M. Is local biodiversity declining or not? A summary of the debate over analysis of species richness time trends. Biological Conservation 219, 175–183 (2018).Article 

    Google Scholar 
    Perring, M. P. et al. Understanding context dependency in the response of forest understorey plant communities to nitrogen deposition. Environmental Pollution 242, 1787–1799 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Steinbauer, M. J. et al. Accelerated increase in plant species richness on mountain summits is linked to warming. Nature 556, 231–234 (2018).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Braun-Blanquet, J. Prinzipien einer Systematik der Pflanzengesellschaften auf floristischer Grundlage. Jahrb. St. Gallischen Naturwiss. Ges. 57, 305–351 (1921).
    Google Scholar 
    Becking, R. W. The Zürich-Montpellier school of phytosociology. Bot. Rev. 23, 411–488 (1957).Article 

    Google Scholar 
    Bruelheide, H. et al. sPlot – A new tool for global vegetation analyses. J Veg Sci 30, 161–186 (2019).Article 

    Google Scholar 
    O L Pescott, T A Humphrey & K J Walker. A short guide to using British and Irish plant occurrence data for research, https://doi.org/10.13140/RG.2.2.33746.86720 (2018).Eichenberg, D. et al. Widespread decline in Central European plant diversity across six decades. Global Change Biology 27, 1097–1110 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Chytrý, M. et al. European Vegetation Archive (EVA): an integrated database of European vegetation plots. Appl Veg Sci 19, 173–180 (2016).Article 

    Google Scholar 
    Van der Maarel, E. Transformation of cover-abundance values in phytosociology and its effects on community similarity. Vegetatio 39, 97–114 (1979).Article 

    Google Scholar 
    Tichý, L. et al. Optimal transformation of species cover for vegetation classification. Appl Veg Sci 23, 710–717 (2020).Article 

    Google Scholar 
    Podani, J. Braun-Blanquet’s legacy and data analysis in vegetation science. Journal of Vegetation Science 17, 113–117 (2006).Article 

    Google Scholar 
    Londo, G. Dezimalskala für die vegetationskundliche Aufnahme von Dauerquadraten. in Sukzessionsforschung (ed. Schmidt, W.). Ber. Int. Symp. Int. Vereinig. Vegetationsk. Rinteln vol. 1973, 613–617 (Cramer, 1975).Bruelheide, H. & Luginbühl, U. Peeking at ecosystem stability: making use of a natural disturbance experiment to analyze resistance and resilience. Ecology 90, 1314–1325 (2009).Article 
    PubMed 

    Google Scholar 
    Hennekens, S. M. & Schaminée, J. H. J. TURBOVEG, a comprehensive data base management system for vegetation data. J. Veg. Sc. 12, 589–591 (2001).Article 

    Google Scholar 
    Gaston, K. J. & Curnutt, J. L. The dynamics of abundance-range size relationships. Oikos 81, 38 (1998).Article 

    Google Scholar 
    Gaston, K. J. et al. Abundance-occupancy relationships. J Appl Ecology 37, 39–59 (2000).Article 

    Google Scholar 
    Sporbert, M. et al. Testing macroecological abundance patterns: The relationship between local abundance and range size, range position and climatic suitability among European vascular plants. J Biogeogr jbi.13926, https://doi.org/10.1111/jbi.13926 (2020).European Commission. Report on the Conservation Status of Habitat Types and Species as required under Article 17 of the Habitats Directive. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52009DC0358 (2009).Poschlod, P. Geschichte der Kulturlandschaft. (Ulmer, 2017).Mcgill, B., Enquist, B., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends in Ecology & Evolution 21, 178–185 (2006).Article 

    Google Scholar 
    Jandt, U. et al. More losses than gains during one century of plant biodiversity change in Germany. Nature https://doi.org/10.1038/s41586-022-05320-w (2022).Schaminée, J. H. J., Hennekens, S. M., Chytrý, M. & Rodwell, J. S. Vegetation-plot data and databases in Europe: an overview. Preslia 81, 173–185 (2009).
    Google Scholar 
    ESA. Land Cover CCI product user guide ver. 2. Tech. Rep. https://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (2017).Kadmon, R., Farber, O. & Danin, A. Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models. Ecological Applications 14, 401–413 (2004).Article 

    Google Scholar 
    Davies, C. E., Moss, D. & Hill, M. O. EUNIS Habitat Classification Revised 2004. 310 https://www.eea.europa.eu/data-and-maps/data/eunis-habitat-classification/documentation/eunis-2004-report.pdf/download (2004).Chytrý, M. et al. EUNIS Habitat Classification: Expert system, characteristic species combinations and distribution maps of European habitats. Appl Veg Sci 23, 648–675 (2020).Article 

    Google Scholar 
    Bruelheide, H., Tichý, L., Chytrý, M. & Jansen, F. Implementing the formal language of the vegetation classification expert systems (ESy) in the statistical computing environment R. Appl Veg Sci, https://doi.org/10.1111/avsc.12562 (2021).Jandt, U., Bruelheide, H. & ReSurveyGermany Consortium. ReSurvey Germany: vegetation-plot resurvey data from Germany. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig https://doi.org/10.25829/idiv.3514-0qsq70 (2022).Jansen, F. & Dengler, J. GermanSL – eine universelle taxonomische Referenzliste für Vegetationsdatenbanken. Tuexenia 28, 239–253 (2008).
    Google Scholar 
    Wisskirchen, R. & Haeupler, H. Standardliste der Farn-und Blütenpflanzen Deutschlands. (Ulmer, 1998).Jansen, F. & Dengler, J. Plant names in vegetation databases–a neglected source of bias. Journal of Vegetation Science 21, 1179–1186 (2010).Article 

    Google Scholar 
    Fischer, H. S. On the combination of species cover values from different vegetation layers. Applied Vegetation Science 18, 169–170 (2015).Article 

    Google Scholar 
    Schwabe, A. & Kratochwil, A. Pflanzensoziologische Dauerflächen-Untersuchungen im Bannwald ‘Flüh’ (Südschwarzwald) unter besonderer Berücksichtigung der Weidfeld-Sukzession. Standort.Wald 49, 5–49 (2015).
    Google Scholar 
    Poschlod, P., Schreiber, K.-F., Mitlacher, K., Römermann, C. & Bernhardt-Römermann, M. Entwicklung der Vegetation und ihre naturschutzfachliche Bewertung. in Landschaftspflege und Naturschutz im Extensivgrünland. 30 Jahre Offenhaltungsversuche Baden-Württemberg (eds. Schreiber, K.-F., Brauckmann, H.-J., Broll, G., Krebs, S. & Poschlod, P.) vol. 97 243–288 (2009).Gonzalez, A. et al. Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity. Ecology 97, 1949–1960 (2016).Article 
    PubMed 

    Google Scholar  More

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    Determining the potential distribution of Oryctes monoceros and Oryctes rhinoceros by combining machine-learning with high-dimensional multidisciplinary environmental variables

    Manjeri, G., Muhamad, R. & Tan, S. G. Oryctes rhinoceros beetles, an oil palm pest in Malaysia. Annu. Res. Rev. Biol. 4, 3429–3439 (2014).Article 

    Google Scholar 
    Allou, K., Morin, J. P., Kouassi, P., Nklo, F. H. & Rochat, D. Oryctes monoceros trapping with synthetic pheromone and palm material in Ivory Coast. J. Chem. Ecol. 32, 1743–1754 (2006).Article 
    CAS 
    PubMed 

    Google Scholar 
    Alibert, H. Study on the insect pests of oil palm in Dahomey. Rev. Botan. Appl. 18, 745–773 (1936).
    Google Scholar 
    Catley, A. The coconut rhinoceros beetle Oryctes rhinoceros (L) [Coleoptera: Scarabaeidae: Dynastinae]. PANS Pest Articles News Summar. 15, 18–30 (1969).Article 

    Google Scholar 
    Fauzana, H., Sutikno, A. & Salbiah, D. Population fluctuations Oryctes rhinoceros L. beetle in plant oil palm (Elaeis guineensis Jacq.) given mulching oil palm empty bunch. Cropsaver Int. J. Trop. Insect Sci. 1, 42–47 (2018).
    Google Scholar 
    Paudel, S., Mansfield, S., Villamizar, L. F., Jackson, T. A. & Marshall, S. D. Can biological control overcome the threat from newly invasive coconut rhinoceros beetle populations (Coleoptera: Scarabaeidae)? A review. Ann. Entomol. Soc. Am. 114, 247–256 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Molet, T. In CPHST Pest Datasheet for Oryctes rhinoceros. USDA-APHIS-PPQCPHST. Revised July 2014 (2013).Hinckley, A. D. Ecology of the coconut rhinoceros beetle, Oryctes rhinoceros (L.) (Coleoptera: Dynastidae). Biotropica 1973, 111–116 (1973).Article 

    Google Scholar 
    Sitepu, D., Kharie, S., Waroka, JS & Motulo, HFJ. Methods for the production and use of Marhizium anisopliae against Oryctes rhinoceros. In Integrated Coconut Pest Control Project—Annual report of Coconut Research Institute—Manado, North Sulawesi, Indonesia 104–111 (1988).Philippe, R. & Dery, S. K. Coconut research and development. CORD 20, 43–51 (2004).
    Google Scholar 
    Purrini, K. Baculovirus oryctes release into Oryctes monoceros population in Tanzania, with special reference to the interaction of virus isolates used in our laboratory infection experiments. J. Invertebr. Pathol. 53, 285–300 (1989).Article 

    Google Scholar 
    Ukeh, D. A., Usua, E. J. & Umoetok, S. B. A. Notes on the biology of Oryctes monoceros (OLIV.) A pest of palms in Nigeria. World J. Agric. Res. 2, 33–36 (2003).
    Google Scholar 
    Dry, F. W. Notes on the coconut beetle (Oryctes monoceros, Ol.) in Kenya Colony. Bull. Entomol. Res. 13, 103–107 (1922).Article 

    Google Scholar 
    Bedford, G. O. Biology, ecology, and control of palm rhinoceros beetles. Annu. Rev. Entomol. 25, 309–339 (1980).Article 

    Google Scholar 
    Khoo, K. C., Yusoff, M. N. M. & Lee, T. W. Pulp and paper of oil palm trunk. In Research Pamphlet No.107: Oil Palm Stem Utilisation, Kuala Lumpur, Malaysia, FRIM 51–65 (1991).Giblin-Davis, R. M. Borers of palms. In Insects on Palms (eds Moore, D. et al.) (CABI Publishing, Wallingford, 2001).
    Google Scholar 
    Drumoni, A. & Ponchel, Y. Première capture au Yémen d’ Oryctes (Rykanoryctes) monoceros (Olivier, 1789) et confirmation de la présence de cette espèce africaine dans la Péninsule Arabique (Coleoptera, Dynastidae). Entomol. Afr. 15, 25–29 (2010).
    Google Scholar 
    Lever, R. J. A. W. Pests of the Coconut Palm (Food and Agriculture Organization of the United Nations, Rome, 1969).Moore, A. Rhinoceros beetle pest found in Guam and Saipan. In Pest Alert, Suva, Fiji: Plant Protection Service, Secretariat of the Pacific Community (2007).Zhang, K., Yao, L., Meng, J. & Tao, J. Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Sci. Total Environ. Sci. 634, 1326–1334 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Ding, F., Fu, J., Jiang, D., Hao, M. & Lin, G. Mapping the spatial distribution of Aedes aegypti and Aedes albopictus. Acta Trop. 178, 155–162 (2018).Article 
    PubMed 

    Google Scholar 
    Valencia-Rodríguez, D., Jiménez-Segura, L., Rogéliz, C. A. & Parra, J. L. Ecological niche modeling as an effective tool to predict the distribution of freshwater organisms: The case of the Sabaleta Brycon henni (Eigenmann, 1913). PLoS ONE 16, e0247876 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Escobar, L. E., Qiao, H., Cabello, J. & Peterson, A. T. Ecological niche modeling re-examined: A case study with the Darwin’s fox. Ecol. Evol. 8, 4757–4770 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: The importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342 (2011).Article 
    PubMed 

    Google Scholar 
    Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

    Google Scholar 
    Phillips, S. J. Transferability, sample selection bias and background data in presence-only modelling: A response to Peterson et al. (2007). Ecography 31, 272–278 (2008).Article 

    Google Scholar 
    Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).Article 

    Google Scholar 
    Phillips, S. J. & Dudík, M. Modeling of species distributions with MaxEnt: New extensions and a comprehensive evaluation. Ecography 31, 161–175 (2008).Article 

    Google Scholar 
    Arnold, J. D., Brewer, S. C. & Dennison, P. E. Modeling climate-fire connections within the Great basin and Upper Colorado River Basin. Fire Ecol. 10, 64–75 (2014).Article 

    Google Scholar 
    Phillips, J. S. & Elith, J. On estimating probability of presence from use-availability or presence-background data. Ecology 94, 1409–1419 (2013).Article 
    PubMed 

    Google Scholar 
    Santana, P. A. Jr., Kumar, L., Da Silva, R. S., Pereira, J. L. & Picanço, M. C. Assessing the impact of climate change on the worldwide distribution of Dalbulus maidis (DeLong) using MaxEnt. Pest. Manag. Sci. 75, 2706–2715 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Li, et al. Predicting the current and future distributions of Brontispa longissima (Coleoptera: Chrysomelidae) under climate change in China. Glob. Ecol. Conserv. 25, e01444 (2021).Article 

    Google Scholar 
    Li, T. et al. Direct and indirect effects of environmental factors, spatial constraints, and functional traits on shaping the plant diversity of montane forests. Ecol. Evol. 10, 557–568 (2020).Article 
    PubMed 

    Google Scholar 
    Namgung, H., Kim, M. J., Baek, S., Lee, J. H. & Kim, H. Predicting potential current distribution of Lycorma delicatula (Hemiptera: Fulgoridae) using MaxEnt model in South Korea. J. Asia Pac. Entomol. 23, 291–297 (2020).Article 

    Google Scholar 
    Ji, W., Gao, G. & Wei, J. Potential global distribution of Daktulosphaira vitifoliae under climate change based on MaxEnt. Insects. 12, 347 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ji, W., Han, K., Lu, Y. & Wei, J. Predicting the potential distribution of the vine mealybug, Planococcus ficus under climate change by MaxEnt. J. Crop. Prot. 137, 105268 (2020).Article 

    Google Scholar 
    Sharma, HC & Prabhakar, CS. Impact of climate change on pest management and food security. In Integrated Pest Management 23–36 (Academic Press, Cambridge, 2014).Skendžić, S., Zovko, M., Živković, I. P., Lešic, V. & Lemić, D. The impact of climate change on agricultural insect pests. Insects. 12, 440 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ward, N. L. & Masters, G. J. Linking climate change and species invasion: An illustration using insect herbivores. Glob. Change Biol. 13, 1605–1615 (2007).Article 
    ADS 

    Google Scholar 
    De Queiroz, D. L., Burckhardt, D. & Majer, J. Integrated pest management of eucalypt psyllids (Insecta, Hemiptera, Psylloidea). In Integrated pest management and pest control-current and future tactics. INTECH 2012, 385–412 (2012).
    Google Scholar 
    Hochberg, M. E. & Waage, J. K. A model for the biological control of Oryctes rhinoceros (Coleoptera: Scarabaeidae) by means of pathogens. J. Appl. Ecol. 28, 514–531 (1991).Article 

    Google Scholar 
    Liu, Y. et al. MaxEnt modelling for predicting the potential distribution of a near threatened rosewood species (Dalbergia cultrata Graham ex Benth). Ecol. Eng. 141, 105612 (2019).Article 

    Google Scholar 
    Wang, R. et al. Predictions of potential geographical distribution of Diaphorina citri (Kuwayama) in China under climate change scenarios. Sci. Rep. 10, 1–9 (2020).CAS 

    Google Scholar 
    Wood, B. J. Studies on the effect of ground vegetation on infestations of Oryctes rhinoceros (L.) (Col., Dynastidae) in young oil palm replantings in Malaysia. Bull Entomol. Res. 59, 85–96 (1969).Article 

    Google Scholar 
    Mittal, I. C. Survey of scarabaeid (Coleoptera) fauna of Himachal Pradesh (India). J. Entomol. Res. 24, 259–269 (2000).
    Google Scholar 
    Zheng, C., Jiang, D., Ding, F., Fu, J. & Hao, M. Spatiotemporal patterns and risk factors for scrub typhus from 2007 to 2017 in southern China. Clin. Infect. Dis. 69, 1205–1211 (2019).Article 
    PubMed 

    Google Scholar 
    Chen, S., Ding, F., Hao, M. & Jiang, D. Mapping the potential global distribution of red imported fire ant (Solenopsis invicta Buren) based on a machine learning method. Sustainability. 12, 10182 (2020).Article 

    Google Scholar 
    Ding, F. et al. Infection and risk factors of human and avian influenza in pigs in south China. Prev. Vet. Med. 190, 105317 (2021).Article 
    PubMed 

    Google Scholar 
    Jiang, D. et al. Spatiotemporal patterns and spatial risk factors for Visceral leishmaniasis from 2007 to 2017 in Western and Central China: A modelling analysis. Sci. Total Environ Sci. 764, 144275 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Méndez-Rojas, D. M., Cultid-Medina, C. & Escobar, F. Influence of land use change on rove beetle diversity: A systematic review and global meta-analysis of a mega-diverse insect group. Ecol. Indic. 122, 107239 (2021).Article 

    Google Scholar 
    Oke, T. R. City size and the urban heat island. Atmos. Environ. 7, 769–779 (1973).Article 
    ADS 

    Google Scholar 
    Briere, J. F., Pracros, P., Le Roux, A. Y. & Pierre, J. S. A novel rate model of temperature-dependent development for arthropods. Environ. Entomol. 28, 22–29 (1999).Article 

    Google Scholar 
    Zeng, Y., Low, B. W. & Yeo, D. C. Novel methods to select environmental variables in MaxEnt: A case study using invasive crayfish. Eco. Model. 341, 5–13 (2016).Article 

    Google Scholar 
    Fand, B. B. et al. Invasion risk of the South American tomato pinworm Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) in India: Predictions based on MaxEnt ecological niche modelling. Int. J. Trop. Insect Sci. 40, 1–11 (2020).Article 

    Google Scholar 
    Li, W. J. et al. Potential distribution prediction of natural Pseudotsuga sinensis forest in Guizhou based on Maxent model. J. For. Res. 48, 47–52 (2019).
    Google Scholar 
    McIntyre, S., Rangel, E. F., Ready, P. D. & Carvalho, B. M. Species-specific ecological niche modelling predicts different range contractions for Lutzomyia intermedia and a related vector of Leishmania braziliensis following climate change in South America. Parasit. Vectors 10, 1–15 (2017).Article 

    Google Scholar 
    Hao, M. et al. Global potential distribution of Oryctes rhinoceros, as predicted by boosted regression tree model. Glob. Ecol. Conserv. 37, e02175 (2022).Article 

    Google Scholar 
    Aidoo, O. F. et al. The impact of climate change on potential invasion risk of Oryctes monoceros worldwide. Front. Ecol. Evol. 10, 633 (2022).Article 

    Google Scholar 
    Aidoo, O. F. et al. Lethal yellowing disease: Insights from predicting potential distribution under different climate change scenarios. J. Plant Dis. Prot. 2021, 1–13 (2021).
    Google Scholar 
    Ruheili, A. M. A., Boluwade, A. & Subhi, A. M. A. Assessing the Impact of Climate Change on the Distribution of Lime (16srii-B) and Alfalfa (16srii-D) Phytoplasma Disease Using MaxEnt. Plants. 10, 460 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wang, R. et al. Predicting the potential distribution of the Asian citrus psyllid, Diaphorina citri (Kuwayama), in China using the MaxEnt model. PeerJ 7, e7323 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    He, S. T. & Jing, P. F. Prediction of potential distribution areas of Salvia bowleyana Dunn. in China based on MaxEnt and suitability analysis. J Anhui Agri. Sci. 8, 2311–2314 (2014).
    Google Scholar 
    Chahouki, M. A. Z. & Sahragard, H. P. Maxent modelling for distribution of plant species habitats of rangelands (Iran). Pol. J. Ecol. 64, 453–467 (2016).
    Google Scholar 
    Shabani, F., Kumar, L. & Ahmadi, M. Assessing accuracy methods of species distribution models: AUC, specificity, sensitivity and the true skill statistic. Glob. Int. J. Hum. Soc. Sci. 18, 6–18 (2018).
    Google Scholar 
    Baloch, M. N., Fan, J., Haseeb, M. & Zhang, R. Mapping potential distribution of Spodoptera frugiperda (Lepidoptera: Noctuidae) in central Asia. Insects. 11, 172 (2020).Article 
    PubMed Central 

    Google Scholar 
    Wang, N., Li, Z., Wu, J., Rajotte, E. G., Wan, F & Wang, Z. The potential geographical distribution of Bactrocera dorsalis (Diptera: Tephrididae) in China based on emergence rate model and ArcGIS. In International Conference on Computer and Computing Technologies in Agriculture 399–411. (Springer, Boston, 2008).Manrique, V., Cuda, J. P., Overholt, W. A. & Diaz, R. Temperature-dependent development and potential distribution of Episimus utilis (Lepidoptera: Tortricidae), a candidate biological control agent of Brazilian peppertree (Sapindales: Anacardiaceae) in Florida. Environ. Entomol. 37, 862–870 (2008).Article 
    PubMed 

    Google Scholar 
    Das, D. K., Singh, J. & Vennila, S. Emerging crop pest scenario under the impact of climate change–a brief review. AgroPhysics. 11, 13–20 (2011).CAS 

    Google Scholar 
    Porter, J. H., Parry, M. L. & Carter, T. R. The potential effects of climatic change on agricultural insect pests. Agric. For. Meteorol. 57, 221–240 (1991).Article 
    ADS 

    Google Scholar 
    Trenberth, K. E. Climate change caused by human activities is happening and it already has major consequences. J. Energy Nat. Resour. Law. 36, 463–481 (2018).Article 

    Google Scholar 
    Xu, D., Zhuo, Z., Li, X. & Wang, R. Distribution and invasion risk assessment of Oryctes rhinoceros (L.) in China under changing climate. J. Appl. Entomol. 146, 385–395 (2022).Article 

    Google Scholar 
    Sushil, K. & Mukhtar, A. Effect of temperature and humidity on biology of rhinoceros beetle, Oryctes rhinoceros Linn. on oil palm. J. Appl. Anim. Res. 18, 108–112 (2007).
    Google Scholar 
    Sabidin, N. N. E. The effect of climate change to the population of rhinoceros beetle (Oryctes rhinoceros) at selected oil palm plantation. In Bachelor of Science Thesis Dissertation. Universiti Teknologi MARA. https://ir.uitm.edu.my/id/eprint/22754. (2018).Yadav, R. & Chang, N. T. Effects of temperature on the development and population growth of the melon thrips, Thrips palmi, on eggplant, Solanum melongena. J. Insect Sci. 14, 78 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ju, R. T., Wang, F. & Li, B. Effects of temperature on the development and population growth of the sycamore lace bug, Corythucha ciliata. J. Insect Sci. 11, 1–12 (2011).Article 

    Google Scholar 
    Zheng, F. S., Du, Y. Z., Wang, Z. J. & Xu, J. J. Effect of temperature on the demography of Galerucella birmanica (Coleoptera: Chrysomelidae). Insect Sci. 15, 375–380 (2008).Article 

    Google Scholar 
    Azrag, A. G. et al. Modelling the effect of temperature on the biology and demographic parameters of the African coffee white stem borer, Monochamus leuconotus (Pascoe) (Coleoptera: Cerambycidae). J. Therm. Biol. 89, 102534 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Aidoo, O. F. et al. The African citrus triozid Trioza erytreae Del Guercio (Hemiptera: Triozidae): Temporal dynamics and susceptibility to entomopathogenic fungi in East Africa. Int. J. Trop. Insect Sci. 41, 563–573 (2021).Article 

    Google Scholar 
    Leonard, A. et al. Predicting the current and future distribution of the edible long-horned grasshopper Ruspolia differens (Serville) using temperature-dependent phenology models. J. Therm. Biol. 95, 102786 (2021).Article 
    PubMed 

    Google Scholar 
    Roy, B. A. et al. Increasing forest loss worldwide from invasive pests requires new trade regulations. Front. Ecol. Environ. 12, 457–465 (2014).Article 

    Google Scholar 
    Shabani, F., Kumar, L. & Ahmadi, M. A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area. Ecol. Evol. 6, 5973–5986 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cianci, D., Hartemink, N. & Ibáñez-Justicia, A. Modelling the potential spatial distribution of mosquito species using three different techniques. Int. J. Health Geogr. 14, 10 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zelazny, B. & Alfiler, A. Oryctes rhinoceros (Coleoptera: Scarabaeidae) larva abundance and mortality factors in the Philippines. Environ. Entomol. 15, 84–87 (1986).Article 

    Google Scholar 
    Wood, B.J. Studies on the effect of ground vegetation on infestations of Oryctes rhinoceros (L.)(Col., Dynastidae) in young oil palm replantings in Malaysia. Bull. Entomol. Res. 59, 85–96 (1969). More

  • in

    More losses than gains during one century of plant biodiversity change in Germany

    Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Vellend, M. et al. Global meta-analysis reveals no net change in local-scale plant biodiversity over time. Proc. Natl Acad. Sci. USA 110, 19456–19459 (2013).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Elahi, R. et al. Recent trends in local-scale marine biodiversity reflect community structure and human impacts. Curr. Biol. 25, 1938–1943 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Crossley, M. S. et al. No net insect abundance and diversity declines across US long term ecological research sites. Nat. Ecol. Evol. 4, 1368–1376 (2020).Article 
    PubMed 

    Google Scholar 
    Dirzo, R. & Raven, P. H. Global state of biodiversity and loss. Annu. Rev. Environ. Resour. 28, 137–167 (2003).Article 

    Google Scholar 
    Ceballos, G. et al. Accelerated modern human–induced species losses: entering the sixth mass extinction. Sci. Adv. 1, e1400253 (2015).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).Article 
    PubMed 

    Google Scholar 
    Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57 (2011).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752–1246752 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Primack, R. B. et al. Biodiversity gains? The debate on changes in local- vs global-scale species richness. Biol. Conserv. 219, A1–A3 (2018).Article 

    Google Scholar 
    Vellend, M. The biodiversity conservation paradox. Am. Sci. 105, 94 (2017).Article 

    Google Scholar 
    Cardinale, B. J., Gonzalez, A., Allington, G. R. H. & Loreau, M. Is local biodiversity declining or not? A summary of the debate over analysis of species richness time trends. Biol. Conserv. 219, 175–183 (2018).Article 

    Google Scholar 
    Chase, J. M. et al. Species richness change across spatial scales. Oikos 128, 1079–1091 (2019).Article 

    Google Scholar 
    Ellis, E. C., Antill, E. C. & Kreft, H. All is not loss: plant biodiversity in the anthropocene. PLoS ONE 7, e30535 (2012).Hillebrand, H. et al. Biodiversity change is uncoupled from species richness trends: consequences for conservation and monitoring. J. Appl. Ecol. 55, 169–184 (2018).Staude, I. R. et al. Replacements of small- by large-ranged species scale up to diversity loss in Europe’s temperate forest biome. Nat. Ecol. Evol. 4, 802–808 (2020).Article 
    PubMed 

    Google Scholar 
    Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Finderup Nielsen, T., Sand‐Jensen, K., Dornelas, M. & Bruun, H. H. More is less: net gain in species richness, but biotic homogenization over 140 years. Ecol. Lett. 22, 1650–1657 (2019).Article 
    PubMed 

    Google Scholar 
    Eichenberg, D. et al. Widespread decline in Central European plant diversity across six decades. Glob. Change Biol. 27, 1097–1110 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Beck, J. J., Larget, B. & Waller, D. M. Phantom species: adjusting estimates of colonization and extinction for pseudo-turnover. Oikos 127, 1605–1618 (2018).Article 

    Google Scholar 
    Bruelheide, H. et al. sPlot—a new tool for global vegetation analyses. J. Veg. Sci. 30, 161–186 (2019).Article 

    Google Scholar 
    Avolio, M. L. et al. A comprehensive approach to analyzing community dynamics using rank abundance curves. Ecosphere 10, e02881 (2019).Article 

    Google Scholar 
    Diekmann, M. et al. Patterns of long‐term vegetation change vary between different types of semi‐natural grasslands in Western and Central Europe. J. Veg. Sci. 30, 187–202 (2019).Article 

    Google Scholar 
    Newbold, T. et al. Widespread winners and narrow-ranged losers: land use homogenizes biodiversity in local assemblages worldwide. PLoS Biol. 16, e2006841 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gini, C. Il diverso accrescimento delle classi sociali e la concentrazione della ricchezza. Giornale degli Economisti38, 27–83 (1909).Rumpf, S. B. et al. Range dynamics of mountain plants decrease with elevation. Proc. Natl Acad. Sci. USA 115, 1848–1853 (2018).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gonzalez, A. et al. Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity. Ecology 97, 1949–1960 (2016).Article 
    PubMed 

    Google Scholar 
    Hundt, R. Ökologisch‐geobotanische Untersuchungen an den mitteldeutschen Wiesengesellschaften unter besonderer Berücksichtigung ihres Wasserhaushaltes und ihrer Veränderung durch die Intensivbewirtschaftung (Wehry-Druck OHG, 2001).Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Jansen, F., Bonn, A., Bowler, D. E., Bruelheide, H. & Eichenberg, D. Moderately common plants show highest relative losses. Conserv. Lett. 13, e12674 (2020).Article 

    Google Scholar 
    Bruelheide, H. et al. Using incomplete floristic monitoring data from habitat mapping programmes to detect species trends. Divers. Distrib. 26, 782–794 (2020).Article 

    Google Scholar 
    Sperle, T. & Bruelheide, H. Climate change aggravates bog species extinctions in the Black Forest (Germany). Divers. Distrib. 27, 282–295 (2020).Article 

    Google Scholar 
    McKinney, M. L. & Lockwood, J. L. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453 (1999).Article 
    CAS 
    PubMed 

    Google Scholar 
    Timmermann, A., Damgaard, C., Strandberg, M. T. & Svenning, J.-C. Pervasive early 21st-century vegetation changes across Danish semi-natural ecosystems: more losers than winners and a shift towards competitive, tall-growing species. J. Appl. Ecol. 52, 21–30 (2015).Article 

    Google Scholar 
    Milligan, G., Rose, R. J. & Marrs, R. H. Winners and losers in a long-term study of vegetation change at Moor House NNR: effects of sheep-grazing and its removal on British upland vegetation. Ecol. Indic. 68, 89–101 (2016).Baskin, Y. Winners and losers in a changing world. BioScience 48, 788–792 (1998).Article 

    Google Scholar 
    Pereira, H. M., Navarro, L. M. & Martins, I. S. Global biodiversity change: the bad, the good, and the unknown. Annu. Rev. Environ. Resour. 37, 25–50 (2012).Article 

    Google Scholar 
    Naaf, T. & Wulf, M. Habitat specialists and generalists drive homogenization and differentiation of temperate forest plant communities at the regional scale. Biol. Conserv. 143, 848–855 (2010).Article 

    Google Scholar 
    Heinrichs, S. & Schmidt, W. Biotic homogenization of herb layer composition between two contrasting beech forest communities on limestone over 50 years. Appl. Veg. Sci. 20, 271–281 (2017).Article 

    Google Scholar 
    Reinecke, J., Klemm, G. & Heinken, T. Vegetation change and homogenization of species composition in temperate nutrient deficient Scots pine forests after 45 yr. J. Veg. Sci. 25, 113–121 (2014).Article 

    Google Scholar 
    Metzing, D. et al. Rote Liste und Gesamtartenliste der Farn- und Blütenpflanzen (Trachaeophyta) Deutschlands (Landwirtschaftsverlag, 2018).Poschlod, P. Geschichte der Kulturlandschaft (Ulmer, 2017).Sukopp, H. ‘Rote Liste’ der in der Bundesrepublik Deutschland gefährdeten Arten von Farn- und Blütenpflanzen. (1. Fassung). Nat. Landsch. 49, 315–322 (1974).
    Google Scholar 
    Kuussaari, M. et al. Extinction debt: a challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571 (2009).Article 
    PubMed 

    Google Scholar 
    Dornelas, M. et al. BioTIME: a database of biodiversity time series for the Anthropocene. Glob. Ecol. Biogeogr. 27, 760–786 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jandt, U., von Wehrden, H. & Bruelheide, H. Exploring large vegetation databases to detect temporal trends in species occurrences. J. Veg. Sci. 22, 957–972 (2011).Article 

    Google Scholar 
    Jones, F. A. M. & Magurran, A. E. Dominance structure of assemblages is regulated over a period of rapid environmental change. Biol. Lett. 14, 20180187 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chytrý, M., Tichý, L., Hennekens, S. M. & Schaminée, J. H. J. Assessing vegetation change using vegetation-plot databases: a risky business. Appl. Veg. Sci. 17, 32–41 (2014).Article 

    Google Scholar 
    Jandt, U. et al. ReSurveyGermany: Vegetation-plot time-series over the past hundred years in Germany. Sci. Data, https://doi.org/10.1038/s41597-022-01688-6 (2022)Bohn, U. & Schniotalle, S. Hochmoor-, Grünland- und Waldrenaturierung im Naturschutzgebiet ‘Rotes Moor’/Hohe Rhön 1981–2001 (Landwirtschaftsverlag, 2008).Rosenthal, G. Erhaltung und Regeneration von Feuchtwiesen. Vegetationsökologische Untersuchungen auf Dauerflächen. Diss. Bot. 182, 1–283 (1992).
    Google Scholar 
    Schwabe, A. & Kratochwil, A. Pflanzensoziologische Dauerflächen-Untersuchungen im Bannwald ‘Flüh’ (Südschwarzwald) unter besonderer Berücksichtigung der Weidfeld-Sukzession. Standort Wald 49, 5–49 (2015).
    Google Scholar 
    Poschlod, P., Schreiber, K.-F., Mitlacher, K., Römermann, C. & Bernhardt-Römermann, M. in Landschaftspflege und Naturschutz im Extensivgrünland. 30 Jahre Offenhaltungsversuche Baden-Württemberg Vol. 97 (eds. Schreiber, K.-F. et al.) 243–288 (2009).Hennekens, S. M. & Schaminée, J. H. J. TURBOVEG, a comprehensive data base management system for vegetation data. J. Veg. Sci. 12, 589–591 (2001).Article 

    Google Scholar 
    Chytrý, M. et al. EUNIS Habitat Classification: expert system, characteristic species combinations and distribution maps of European habitats. Appl. Veg. Sci. 23, 648–675 (2020).Article 

    Google Scholar 
    Bruelheide, H., Tichý, L., Chytrý, M. & Jansen, F. Implementing the formal language of the vegetation classification expert systems (ESy) in the statistical computing environment R. Appl. Veg. Sci. 12, e12562 (2021).Jansen, F. & Dengler, J. GermanSL—eine universelle taxonomische Referenzliste für Vegetationsdatenbanken. Tuexenia 28, 239–253 (2008).
    Google Scholar 
    Wisskirchen, R. & Haeupler, H. Standardliste der Farn-und Blütenpflanzen Deutschlands (Ulmer, 1998).Jansen, F. & Dengler, J. Plant names in vegetation databases–a neglected source of bias. J. Veg. Sci. 21, 1179–1186 (2010).Article 

    Google Scholar 
    Wegener, U. Vegetationswandel des Berggrünlands nach Untersuchungen von 1954 bis 2016—Wege zur Erhaltung der Bergwiesen (Mountain grasslands vegetation change after research from 1954 to 2016—ways to preserve mountain meadows). Abh. Berichte Aus Dem Mus. Heine. 11, 35–101 (2018).
    Google Scholar 
    Makowski, D., Ben-Shachar, M. & Lüdecke, D. bayestestR: describing effects and their uncertainty, existence and significance within the Bayesian framework. J. Open Source Softw. 4, 1541 (2019).Article 
    ADS 

    Google Scholar 
    Weiner, J. & Solbrig, O. T. The meaning and measurement of size hierarchies in plant populations. Oecologia 61, 334–336 (1984).Article 
    ADS 
    PubMed 

    Google Scholar 
    Signorell, A. et al. DescTools: tools for descriptive statistics. R version 0.99.32 https://CRAN.R-project.org/package=DescTools (2020).BiolFlor—a new plant-trait database as a tool for plant invasion ecology. Divers. Distrib. 10, 363–365 (2004).INSPIRE. D2.8.III.18 Data Specification on Habitats and Biotopes—Technical Guidelines https://inspire.ec.europa.eu/documents/Data_Specifications/INSPIRE_DataSpecification_HB_v3.0rc2.pdf (2013).Jandt, U. & Bruelheide, H. German Vegetation Reference Database (GVRD). Biodivers. Ecol. 4, 355–355 (2012).Article 

    Google Scholar 
    Sokal, R. R. & Rohlf, F. J. Biometry (Freeman, 1995).Chytrý, M., Tichý, L., Holt, J. & Botta‐Dukát, Z. Determination of diagnostic species with statistical fidelity measures. J. Veg. Sci. 13, 79–90 (2002).Article 

    Google Scholar 
    Gotelli, N. J. Null model analysis of species co‐occurrence patterns. Ecology 81, 2606–2621 (2000).Article 

    Google Scholar 
    Pillar, V. D., Sabatini, F. M., Jandt, U., Camiz, S. & Bruelheide, H. Revealing the functional traits linked to hidden environmental factors in community assembly. J. Veg. Sci. 32, e12976 (2021).Sabatini, F. M., Jiménez‐Alfaro, B., Burrascano, S., Lora, A. & Chytrý, M. Beta‐diversity of central European forests decreases along an elevational gradient due to the variation in local community assembly processes. Ecography 41, 1038–1048 (2018).Article 

    Google Scholar 
    MacArthur, R. On the relative abundance of species. Am. Nat. 94, 25–36 (1960).Article 

    Google Scholar 
    Prado, P. I., Miranda, M. D. & Chalom, A. sads: maximum likelihood models for species abundance distributions. R version 0.4.2. https://CRAN.R-project.org/package=sads (2018).Kuhn, G., Heinz, S. & Mayer, F. Grünlandmonitoring Bayern. Ersterhebung der Vegetation 2002–2008. Schriftenreihe LfL Bayer. Landesanst. Für Landwirtsch. 3, 1–161 (2011).
    Google Scholar  More

  • in

    Introducing African cheetahs to India is an ill-advised conservation attempt

    Jhala, Y. V. et al. Action Plan for Introduction of Cheetah in India (Wildlife Insititute of India, National Tiger Conservation Authority and Madhya Pradesh Forest Department, 2021).Durant, S. M. et al. Proc. Natl Acad. Sci. USA 114, 528–533 (2017).Article 
    CAS 

    Google Scholar 
    Broekhuis, F. et al. Ecography 44, 358–369 (2021).Article 

    Google Scholar 
    Lindsey, P. et al. (eds) Cheetah (Acinonyx jubatus) Population Habitat Viability Assessment Workshop Report. Conservation Breeding Specialist Group (SSC / IUCN) / CBSG Southern Africa (Endangered Wildlife Trust, 2009)Mills, M. G. L. & Mills, M. E. J. Kalahari Cheetahs: Adaptation to an Arid Region (Oxford Univ. Press, 2017).Weise, F. J. et al. PeerJ 5, e4096 (2017).Article 

    Google Scholar 
    Clavel, J., Robert, A., Devictor, V. & Juilliard, R. J. Wildl. Mgmt. 72, 1203–1210 (2008).Article 

    Google Scholar 
    Cheetah Conservation Fund. Project Cheetah: Mission Fact Sheet (Cheetah Conservation Fund, 2022).Boast, L. K. et al. in Cheetahs: Biology and Conservation (eds Marker, L. et al.) 275–289 (Elsevier Science, 2018).PTI. Have to be realistic about losses; not easy to bring back animal from extinction: cheetah expert. thehindu.com, https://www.thehindu.com/sci-tech/energy-and-environment/have-to-be-realistic-about-losses-not-easy-to-bring-back-animal-from-extinction-cheetah-expert/article65909157.ece (September 2022).Dasgupta, P. The Economics of Biodiversity: The Dasgupta Review (HM Treasury, 2021).Melzheimer, J. et al. Proc. Natl Acad. Sci. USA 117, 33325–33333 (2020).Article 
    CAS 

    Google Scholar 
    Khalatbari, L. et al. Science 362, 1255 (2018).Article 
    CAS 

    Google Scholar 
    Gopalaswamy, A. M. et al. Proc. Natl Acad. Sci. USA 119, e2203244119 (2022).Article 
    CAS 

    Google Scholar 
    Madhusudan, M. D. & Vanak, A. T. J. Biogeography https://doi.org/10.1111/jbi.14471 (2022).Article 

    Google Scholar  More

  • in

    Contrafreeloading in kea (Nestor notabilis) in comparison to Grey parrots (Psittacus erithacus)

    This study aimed to compare the extent of contrafreeloading in kea to that in Grey parrots, given that the two species exhibit very different levels of play: specifically, kea exhibit complex and frequent play29,30,35,36, whereas Greys exhibit considerably less play than several parrot species29. We found that, at the group level, although the overall amounts of kea classic contrafreeloading were nonsignificant, as a percentage of behaviour, kea generally contrafreeloaded more than Grey parrots in Experiment 1, whereas the opposite was true for Experiment 2. We compare the various behaviour patterns in detail, and propose explanations for our results below.The most interesting comparisons for Smith et al.’s hypothesis are the results from classic contrafreeloading. In Experiment 1, kea performed this behaviour at non-negligible levels, given the supposed rarity of the behaviour5 (two birds at 50%; the others varying between 39 and 47%). In contrast, although one Grey did classically contrafreeload at a statistically significant level, the other three were at ≤ 36%. These data suggest that the kea may have found the task more engaging than did the Greys. However, given that only two kea chose to pop the lid of an empty cup in control trials significantly above chance, whereas three of the four Greys did so significantly above chance and one at chance, we doubt that the kea found the task inherently rewarding. We note that this comparison between both species must be interpreted cautiously due to differences in methodology: For the Greys, the control trials were performed at the end of the study, by which point they may have learnt to associate lid-popping with reward. However, the data from experimental trials in Smith et al.13 are such that their birds would have been primed in the opposite direction: For example, three of those four birds rarely chose the empty lidded cup when free food was available, nor did they classically or super contrafreeload to any significant extent13; an association-driven explanation is therefore unlikely. In contrast, the kea experienced this control condition at the start of the experiment, allowing them 20 trials to become acquainted with the affordances of both options that would be available throughout the study (lid-popping versus not lid-popping). This opportunity was important for kea, as this species has been previously shown to learn about object properties through extensive object manipulation37. That kea popped lids at or above chance in these first 20 control trials suggested two possibilities: (1) After these 20 trials, the task may have been familiar enough to no longer be of much interest (i.e., no longer novel and worthy of consideration) by the time rewarded trials began (recall nonsignificant downward trends for Harley Quinn and Blofeld). (2) They acquired some interest in popping the lids. This latter case seems more likely, as the lid-popping task still likely provided some added value. Kea engaged in non-negligible levels of classic contrafreeloading, such that the chance to pop a lid and eat could be considered more interesting than simply eating an identical but freely available reward. Furthermore, three kea chose a lidded, empty cup over a free, least-preferred reward at least half the time, again suggesting that the activity held some appeal of its own.In Experiment 2 (which corresponds to classic contrafreeloading), all kea preferred freeloading for the walnut without a shell; two Greys, in contrast, nut contrafreeloaded at a statistically significant extent. This variability in behaviour at both the individual and species levels reveals the significance of a task’s proximate and potentially ultimate values in parrots’ choice to contrafreeload. Interestingly, although species like kea are hypothesized to prefer food items requiring high manipulation38,39, nut-cracking—chosen as an activity to provide direct comparison with the Greys13—is not prevalent in kea diet40, and that activity thus may not have been appropriate as an ethologically relevant one for kea. Greys, in contrast, are known to crack nuts in nature41. Future research could use a more ecologically relevant task for the kea, such as working to access food via digging or scraping32.As with Smith et al.’s Greys13, kea in Experiment 1 performed calculated contrafreeloading to a statistically significant extent. All kea did so on over 83% of trials; for the Greys, three birds were close to 90% but one was at only 67%. Kea consistently selected their preferred food out of the two options provided, suggesting that the lid-popping action did not deter kea from selecting their preferred reward. In related trials, where the lid-status of food paired with an empty cup varied, kea, like some Greys13, preferred lidded food over an empty lidless cup, again showing that lid-popping for food was an acceptable task.When examining situations in which food was discarded after contrafreeloading, we found that this choice in Experiment 1 was most common for Bruce. Notably, Bruce lacks a top mandible, making many of the manipulative behaviours more difficult to execute42. Bruce demonstrated consistent food preferences throughout the experiment, however, indicating that the reason some foods were discarded was, indeed, because they were too difficult for him to manipulate. In Experiment 2, Harley Quinn was the most likely to discard the nut, and did so exclusively in trials in which she chose the walnut without the shell (freeloaded). In these occasions, Harley Quinn was observed choosing the nut by tapping on it or the cup.Like the Greys, the kea failed to super contrafreeload to a statistically significant extent. Furthermore, contrafreeloading trials in which a lid was popped but the food underneath was not consumed occurred most often with the least-preferred food. Given kea’s performance on control trials, the super contrafreeloading results are not surprising. Interestingly, when lid-status of food paired with an empty cup varied, some Greys very rarely—and depending on food desirability—preferred to pop the empty cup’s lid rather than consume the free food; as noted earlier, three of eight kea did so on at least half the trials when the food in the lidless cup was their least preferred option (sultanas). Both kea and Greys thus likely placed the appeal of the task along some “value scale” along with that of the available food rewards, the combination influencing their behaviour when the two variables were presented in various permutations. Notably, even in control trials, where no food was involved, no bird of either species found the task aversive, engaging in the behaviour at least 50% of the time. Future research could investigate how a different, more rewarding task would influence this balance and thus contrafreeloading for both species.One possible alternative explanation for kea’s higher rates of contrafreeloading relative to those of Greys could be their natural tendency to probe and manipulate objects, thus causing them to pry off cup lids rather than manipulate lidless (open) cups. Were this action exploratory in nature, we would have observed significant decreases in behaviour as the experiment progressed, but note that we found no significant changes in any bird. Were they consistently drawn to lids and this behaviour were hard-wired, then we should have observed lid-popping appear significantly above chance across all three types of contrafreeloading. However, as discussed previously, kea did not significantly contrafreeload in the classic condition and actively freeloaded in super contrafreeloading conditions, suggesting that they were not simply interacting with lidded cups preferentially, but rather attending to the contents in the two cups and avoiding the additional manipulation of the lid when it led to a less (or, more often than not, equally) preferred food reward.Another potential explanation for the differences observed between kea and Greys might be found in the theoretical overlap between contrafreeloading and play, and how individuals might view the contrafreeloading action as a type of play. As a seemingly nonfunctional, intrinsically motivating behaviour occurring in low-stress environments, incurring a positive mood, varying between conspecifics, and often incomplete and/or repeated14,15, play shares many proximate-level attributes with contrafreeloading13. Our results demonstrate that kea subjects inhabiting a low-stress, captive environment repeatedly chose to engage in classic contrafreeloading to a non-negligible extent and calculated contrafreeloading to a significant extent, varied in their behaviour between individuals, and at times, left the task incomplete (e.g., left food uneaten). Furthermore, evidence for intrinsic motivation to perform a given task is suggested by the kea’s overall differential behaviour between the two experiments, as well as inter-individual differences.Importantly, this study serves only as a first step into determining whether play manifests as a form of contrafreeloading, but cannot ascertain that this is the only possible explanation for the presence or degree of contrafreeloading in the two species. Several alternative explanatory theories regarding the occurrence of contrafreeloading are enumerated in the discussion of Smith et al. (e.g., work ethic; information gathering; relief from boredom)13, and various other potential explanations (beyond playfulness) may reside at the species-level. Grey parrots (Psittacidae) and kea (Strigopidae) are separated by 50–80 million years of evolution43 and differ in their neurobiology (i.e., the size of the shell region related to vocal and possible cognitive abilities44). Differing ecological evolutionary pressures are also likely relevant: an island-based habitat39, a lack of natural predators30,45, and generalist diets40,46,47 are thought to have shaped the playfulness and cognitive abilities of kea30,40,46,47. Greys, in contrast, evolved predominantly on a continent (i.e., although they can be found on islands such as Principe, the Congo Grey is endemic to central Africa48,49), are subject to considerable predation48,50,51,52, and have a relatively less generalist diet (diverse but almost exclusively vegetarian and in which nuts play a significant role; see review in50). Such disparate evolutionary trajectories may offer other potential explanations for the differences in contrafreeloading observed between the two species, and future research could examine differences at genetic and/or neurological levels.The varying rates of contrafreeloading observed between the species could have also been influenced by other factors. For example, although both parrot groups studied here inhabit enriched environments, are habituated to participating in experimental trials, and have access to food ad libitum, their habitats are markedly different. Notably, the Grey subjects live in “man-made” settings (i.e., Griffin and Athena in a lab; Pepper, Franco, and Lucci in private homes), whereas the kea inhabit a naturalistic zoo enclosure. Physical enrichment, although somewhat different in kind, is unlikely to have differed in quantity, as all birds are provided routine naturalistic foraging, and Lucci lives in a free-flight aviary. More likely is the difference in sociality: Relatively more subjects reside together in the kea group (15) compared to the Greys (two groups of two Greys and one Grey living with two birds of differing species), and thus variables such as social stimulation and flock-based foraging techniques could have contributed to the expression of contrafreeloading (note that subadult male kea are known to obtain food through kleptoparasitism32). In order to elucidate the role of habitat on contrafreeloading, future studies could examine the behaviour of species residing in more comparable captive conditions.Future work should aim not only to apply these same methodologies to a broader range of parrot species, but also objectively quantify frequency and complexity of play across a wide range of parrots to allow a direct correlation between play and contrafreeloading over phylogeny in the parrot order. The apparent link between play behaviour and encephalisation in parrots53 offers another possible avenue for cross-species comparisons on contrafreeloading. Future research could also employ cognitive bias tests to quantify the mood of birds before and following contrafreeloading54, directly manipulate subjects’ participation in play behaviours or other control behaviours and observe whether engaging in play can increase contrafreeloading rates at the individual level, or perform behavioural coding of playfulness and/or arousal before and after contrafreeloading. Future research could incorporate more ecologically relevant contrafreeloading tasks to examine this behaviour at both the individual and species level, and approach the phenomenon by using both genetic and neuroscience techniques.In sum, contrafreeloading is, by its very nature, an enigma whose study presents many difficulties. It varies across the diverse contexts within which it is studied, and given that it is rarely exhibited to a statistically significant extent, analyses that require comparing nonsignificant behaviour patterns across individuals and/or species is a challenging undertaking. Many explanations have been proposed, but contrafreeloading is still poorly understood, and its correlation with play is likely only one of several logical rationales. Nevertheless, our findings suggest that interest in play should not be discounted as a contributing factor. More