Ecology

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

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

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

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

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

Basic characteristics of river sedimentsA considerable variation was found in the distribution of clay (81 to 48.4 g kg−1), silt (145 to 656 g kg−1), and sand (38 to 821 g kg−1) particles among sediment materials. The associated coefficient of variations (CV) was 57, 59.5, and 41%, respectively. Statistical data related to the physicochemical properties of sediments and their main elements are reported in Table 2. The variations in particle size distribution located sediment material in seven textural classes ranging from loamy sand to silty clay. The high variability in particle size distribution suggests that different sets of geogenic and anthropogenic processes are enacted in the development and distribution of sediments in the rivers. The pH and CCE ranged from 7.4 to 8.2 and 31 to 251 g kg−1, respectively, indicating the dominancy of alkaline-calcareous condition. None of the sediment samples exhibited salinity conditions (EC  > 4 dS m−1) with EC in the range of 0.3 to 1.4 dS m−1. A relatively low range of OM was found in all samples ranging from 7 to 61 g kg−1 with a mean value of 19 g kg−1. This range of OM coincides with the corresponding values in regional soils47. Except for pH, other sediments properties demonstrated above 35% of CV illustrating a wide range of variability in sediments’ physicochemical properties across the study rivers.Table 2 Summary statistics of sediment properties.Full size tableThe highest concentration among major elements was observed in SiO2, varying between 37.5 and 55.2%, with a mean percentage of 44.9%. This element followed in magnitude by Al2O3 (8.9–15.9%), CaO (5–14.3%), Fe2O3 (4.8–10%), MgO (2.4–17.2%), K2O (1.2–3.1%), Na2O (0.68–2.7%), SO3 (0.01–4.8% g kg−1) (Table 2). Considering the semi-arid climatic condition of the study region, higher levels of SiO2 and lower levels of Al2O3 may indicate that the silicate minerals forming the sediments of the area have not been subjected to severe weathering processes. Likewise, the Na2/K2O ratio was greater than 1 in the majority of sediment samples, implying an enrichment of potassium feldspar and the relatively intense weathering of Na-bearing minerals in the region48,49. The CIA value was in the range of 64.9 to 85.7% with a mean percentage of 72.9%, representing a moderate chemical weathering intensity of lithological materials (65%  Pb  > Cu  > Cd which varied largely among the sampling points. The level of Zn, Cu, Cd, Pb, and Ni varied in the ranges of 32.6–87.5, 14.2–33.3, 0.42–4.8, 14.5–69.5, and 20.1–183.5 mg kg-1, respectively, for winter, and 35.3–92.5, 15.6–35.1, 0.47–5.1, 15.5–73.1, 23.2–188.3 mg kg−1 for summer. The obtained ranges are comparable with data found in previous studies in Asia4,54,55,54.Figure 2The comparison of the mean concentration of Zn, Cu, Cd, Pb, and Ni elements in the study rivers’ sediments during summer and winter. Different letters show significant differences in metal content among rivers pooled over seasons at P  More

Dorning, M. & Cipollini, D. Leaf and root extracts of the invasive shrub, Lonicera maackii, inhibit seed germination of three herbs with no autotoxic effects. Plant Ecol. 184, 287–296 (2006).Article 

Google Scholar 
Greer, M. J., Wilson, G. W., Hickman, K. R. & Wilson, S. M. Experimental evidence that invasive grasses use allelopathic biochemicals as a potential mechanism for invasion: Chemical warfare in nature. Plant Soil 385, 165–179 (2014).Article 
CAS 

Google Scholar 
Möhler, H., Diekötter, T., Herrmann, J. D. & Donath, T. W. Allelopathic vs. autotoxic potential of a grassland weed-evidence from a seed germination experiment. Plant Ecol. Divers. 11, 539–549 (2018).Article 

Google Scholar 
Callaway, R. M. & Aschehoug, E. T. Invasive plants versus their new and old neighbors: A mechanism for exotic invasion. Science 290, 521–523 (2000).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Niu, H. B., Liu, W. X., Wan, F. H. & Liu, B. An invasive aster (Ageratina adenophora) invades and dominates forest understories in China: Altered soil microbial communities facilitate the invader and inhibit natives. Plant Soil 294, 73–85 (2007).Article 
CAS 

Google Scholar 
Wardle, D. A., Karban, R. & Callaway, R. M. The ecosystem and evolutionary contexts of allelopathy. Trends Ecol. Evol. 26, 655–662 (2011).Article 
PubMed 

Google Scholar 
Meiners, S. J., Kong, C. H., Ladwig, L. M., Pisula, N. L. & Lang, K. A. Developing an ecological context for allelopathy. Plant Ecol. 213, 1221–1227 (2012).Article 

Google Scholar 
Liebhold, A. M., Brockerhoff, E. G., Kalisz, S., Nunez, M. A. & Wardle, D. A. Biological invasions in forest ecosystems. Biol. Invasions 19, 3437–3458 (2017).Article 

Google Scholar 
Liao, H. X. et al. Soil microbes regulate forest succession in a subtropical ecosystem in China: Evidence from a mesocosm experiment. Plant Soil 430, 277–289 (2018).Article 
CAS 

Google Scholar 
Wardle, D. A., Nilsson, M. C., Gallet, C. & Zackrisson, O. An ecosystem-level perspective of allelopathy. Biol. Rev. 73, 305–319 (2010).Article 

Google Scholar 
Hierro, J. L. & Callaway, R. M. Allelopathy and exotic plant invasion. Plant Soil 256, 29–39 (2003).Article 
CAS 

Google Scholar 
Uddin, M. N., Robinson, R. W., Buultjens, A., Harun, M. A. & Shampa, S. H. Role of allelopathy of Phragmites australis in its invasion processes. J. Exp. Mar. Biol. Ecol. 486, 237–244 (2017).Article 

Google Scholar 
Thiébaut, G., Tarayre, M. & Rodríguez-Pérez, H. Allelopathic effects of native versus invasive plants on one major invader. Front. Plant Sci. 2, 854 (2019).Article 

Google Scholar 
Smith, M., Cecchi, L., Skjøth, C. A., Karrer, G. & Šikoparijae, B. Common ragweed: A threat to environmental health in Europe. Environ. Int. 61, 115–126 (2013).Article 
CAS 
PubMed 

Google Scholar 
Montagnani, C., Gentili, R., Smith, M., Guarino, M. F. & Citterio, S. The worldwide spread, success, and impact of ragweed (Ambrosia spp.). Crit. Rev. Plant Sci. 36, 1–40 (2017).Article 

Google Scholar 
Zeng, K., Zhu, Y. Q. & Liu, J. X. Research progress on ragweed (Ambrosia). Acta Prataculturae Sin. 19, 212–219 (2010).
Google Scholar 
Jacobs, R. L. et al. Responses to ragweed pollen in a pollen challenge chamber versus seasonal exposure identify allergic rhinoconjunctivitis endotypes. J. Allergy Clin. Immun. 130, 122-127.e8 (2012).Article 
PubMed 

Google Scholar 
Lake, R. I. et al. Climate change and future pollen allergy in Europe. Environ. Health Perspect. 125, 385–391 (2017).Article 
PubMed 

Google Scholar 
Wang, J. J., Zhao, B. Y., Li, M. T. & Li, R. Ecological invasion plant-bitter weed (Ambrosia artemisiifolia) and integrated control strategy. Pratacultural Sci. 023, 71–75 (2006).CAS 

Google Scholar 
Deng, Z. Z., Bai, J. D., Zhao, C. Y. & Li, J. S. Advance in invasion mechanisms of Ambrosia artemisiifolia. Pratacultural Sci. 32, 54–63 (2015).
Google Scholar 
Dong, H. G. et al. Diffusion and intrusion features of Ambrosia artemisiifolia and Ambrosia trifida in Yili River Valley. J. Arid Land Resour. Environ. 31, 175–180 (2017).
Google Scholar 
Vink, J. P. et al. Glyphosate-resistant giant ragweed (Ambrosia trifida) control in dicamba-tolerant soybean. Weed Technol. 26, 422–428 (2012).Article 
CAS 

Google Scholar 
Simard, M. J. & Benoit, D. L. Effect of repetitive mowing on common ragweed (Ambrosia artemisiifolia L.) pollen and seed production. Ann. Agric. Environ. Med. 18, 55–62 (2011).PubMed 

Google Scholar 
Goplen, J. J. et al. Seedbank depletion and emergence patterns of giant ragweed (Ambrosia trifida) in Minnesota cropping systems. Weed Sci. 65, 52–60 (2017).Article 

Google Scholar 
Jurik, T. W. Population distributions of plant size and light environment of giant ragweed (Ambrosia trifida L.) at three densities. Oecologia 87, 539–550 (1991).Article 
ADS 
PubMed 

Google Scholar 
Patracchini, C., Vidotto, F. & Ferrero, A. Common ragweed (Ambrosia artemisiifolia) growth as affected by plant density and clipping. Weed Technol. 25, 268–276 (2011).Article 

Google Scholar 
Kazinczi, G. Ragweed seed bank in the soils of arable fields of Transdanubia, Hungary. Hung. Weed Res. Technol. 19(1), 21–36 (2018).
Google Scholar 
Essl, F. et al. Biological flora of the British Isles: Ambrosia artemisiifolia. J. Ecol. 103, 1069–1098 (2015).Article 

Google Scholar 
Goplen, J. J. Giant Ragweed (Ambrosia trifida) Seed Bank Dynamics and Management. (Master’s dissertation, University of Minnesota.) Retrieved from https://hdl.handle.net11299174767 (2015).Yoda, K. Self-thinning in overcrowded pure stands under cultivated and natural conditions. J. Biol. 14, 107–129 (1963).
Google Scholar 
Friedman, J. & Waller, G. R. Allelopathy and autotoxicity. Trends Biochem. Sci. 10, 47–50 (1985).Article 
CAS 

Google Scholar 
Weller, D. E. The interspecific size-density relationship among crowded plant stands and its implications for the −3/2 power rule of self-thinning. Am. Nat. 133, 20–41 (1989).Article 

Google Scholar 
Deng, J. et al. Autotoxicity of phthalate esters in tobacco root exudates: Effects on seed germination and seedling growth. Pedosphere 27, 1073–1082 (2017).Article 
CAS 

Google Scholar 
Sudatti, D. B., Duarte, H. M., Soares, A. R., Salgado, L. T. & Pereira, R. C. New ecological role of seaweed secondary metabolites as autotoxic and allelopathic. Front. Plant Sci. 11, 347 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Singh, H. P., Batish, D. & Kohil, R. Autotoxicity: Concepts, organisms, and ecological significance. Plant Sci. 18, 757–772 (1999).CAS 

Google Scholar 
Chon, S. U. et al. Effects of alfalfa leaf extracts and phenolic allelochemicals on early seedling growth and root morphology of alfalfa and barnyard grass. Crop Prot. 21, 1077–1082 (2002).Article 
CAS 

Google Scholar 
Chen, B. M., D’Antonio, C. M., Molinari, N. & Peng, S. L. Mechanisms of influence of invasive grass litter on germination and growth of coexisting species in California. Biol. Invasions 20, 1881–1897 (2018).Article 

Google Scholar 
Chen, L. C., Wang, S. L., Wang, P. & Kong, C. H. Autoinhibition and soil allelochemical (cyclic dipeptide) levels in replanted Chinese fir (Cunninghamia lanceolata) plantations. Plant Soil 374, 793–801 (2014).Article 
CAS 

Google Scholar 
Perry, L. G. et al. Retracted: Dual role for an allelochemical: catechin from Centaurea maculosa root exudates regulates conspecific seedling establishment. J. Ecol. 93, 1126–1135 (2005).Article 
CAS 

Google Scholar 
Yu, J. Q., Ye, S. F., Zhang, M. F. & Hu, W. H. Effects of root exudates and aqueous root extracts of cucumber (Cucumis sativus) and allelochemicals, on photosynthesis and antioxidant enzymes in cucumber. Biochem. Syst. Ecol. 31, 129–139 (2003).Article 
CAS 

Google Scholar 
Kong, C. H., Wang, P. & Xu, X. H. Allelopathic interference of Ambrosia trifida with wheat (Triticum aestivum). Agric. Ecosyst. Environ. 119, 416–420 (2007).Article 
CAS 

Google Scholar 
Béres, I., Kazinczi, G. & Narwal, S. S. Allellopathic plants. 4. Common ragweed (Ambrosia elatior L. syn. A. artemisiifolia). Allelopathy J. 9, 27–34 (2002).
Google Scholar 
Bauer, J. T., Shannon, S. M., Stoops, R. E. & Reynolds, H. L. Context dependency of the allelopathic effects of Lonicera maackii on seed germination. Plant Ecol. 213, 1907–1916 (2012).Article 

Google Scholar 
Renne, I. J., Sinn, B. T., Shook, G. W., Sedlacko, D. M. & Hierro, J. L. Eavesdropping in plants: Delayed germination via biochemical recognition. J. Ecol. 102, 86–94 (2014).Article 

Google Scholar 
Loydi, A., Donath, T. W., Eckstein, R. L. & Otte, A. Non-native species litter reduces germination and growth of resident forbs and grasses: Allelopathic, osmotic or mechanical effects?. Biol. Invasions 17, 581–595 (2014).Article 

Google Scholar 
Bais, H. P., Weir, T. L., Perry, L. G., Gilroy, S. & Vivanco, J. M. The role of root exudates in rhizosphere interactions with plants and other organisms. Annu. Rev. Plant Biol. 57, 233–266 (2006).Article 
CAS 
PubMed 

Google Scholar 
Bonea, D., Bonciu, E., Niculescu, M. & Olaru, A. L. The allelopathic, cytotoxic and genotoxic effect of Ambrosia artemisiifolia on the germination and root meristems of Zea mays. Caryologia 71, 24–28 (2017).Article 

Google Scholar 
Dadkhah, A. Allelopathic effect of sugar beet (Beta vulgaris) and eucalyptus (Eucalyptus camaldulensis) on seed germination and growth of Portulaca oleracea. Russ. Agric. Sci. 39, 117–123 (2013).Article 

Google Scholar 
Zheng, L. & Feng, Y. L. Allelopathic effects of Eupatorium adenophorum Spreng on. seed germination and seedling growth in ten herbaceous species. Acta Ecol. Sin. 25, 2782–2787 (2005).CAS 

Google Scholar 
Brückner, D. J. The allelopathic effect of ragweed (Ambrosia artemisiifolia L.) on the germination of cultivated plants. Novenytermeles 47, 635–644 (1998).
Google Scholar 
Qin, R. M. et al. The evolution of increased competitive ability, innate competitive advantages, and novel biochemical weapons act in concert for a tropical invader. New Phytol. 197, 979–988 (2012).Article 
PubMed 

Google Scholar 
Zheng, Y. L. et al. Integrating novel chemical weapons and evolutionarily increased competitive ability in success of a tropical invader. New Phytol. 205, 1350–1359 (2015).Article 
PubMed 

Google Scholar 
Kaushal, R., Verma, K. S. & Singh, K. N. Effect of Grewia optiva and Populus deltoides leachatesv on field crops. Allelopathy J. 11, 229–234 (2003).
Google Scholar 
Kumari, A. & Kohli, R. Autotoxicity of ragweed parthenium (Parthenium hysterophorus). Weed Sci. 35, 629–632 (1987).Article 

Google Scholar 
Einhellig, F. A. Allelopathy: Current status and future goals. In Allelopathy: Organisms, processes and applications (ed. Inderjit Dakshini, K. M. M.) 1–24 (Am Chem. Soc, Washington, 1995).
Google Scholar 
Hadack, F. Secondary metabolites as plant traits: Current assessment and future perspectives. Crit. Rev. Plant Sci. 21, 273–322 (2002).Article 

Google Scholar 
Rice, E. L. Biological Control of Weeds and Plant Diseases (Oklahomka Press, 1995).
Google Scholar 
Choi, B. et al. Common ragweed-derived phenolic compounds and their effects on germination and seedling growth of weed species. Weed Turfgrass Sci. 30, 396–404 (2010).
Google Scholar 
Friedman, J. & Waller, G. R. Seeds as allelopathic agents. Chem. Ecol. 9, 1107–1117 (1983).Article 
CAS 

Google Scholar 
Canals, R. M., Emeterio, L. S. & Peralta, J. Autotoxicity in Lolium rigidum: Analyzing the role of chemically mediated interactions in annual plant populations. J. Theor. Biol. 235, 402–407 (2005).Article 
ADS 
CAS 
PubMed 

Google Scholar 
San Emeterio, L., Damgaard, C. & Canals, R. M. Modelling the combined effect of chemical interference and resource competition on the individual growth of two herbaceous populations. Plant Soil 292, 95–103 (2007).Article 
CAS 

Google Scholar 
Dickerson, C. T. Studies on the germination, growth, development and control of Common Ragweed (Ambrosia artemisiifolia L.). PhD thesis, Cornell University, Ann Arbor (1968).Nuutinen, V. & Butt, K. R. Homing ability widens the sphere of influence of the earthworm Lumbricus terrestris L. Soil Biol. Biochem. 37, 805–807 (2005).Article 
CAS 

Google Scholar 
Favaretto, A., Scheffer-basso, S. M. & Perez, N. B. Autotoxicity in tough lovegrass (Eragrostis plana). Planta Daninha 35(35), e017164046 (2017).
Google Scholar 
Sinkkonen, A. Modelling the effect of autotoxicity on density-dependent phytotoxicity. J. Theor. Biol. 244, 218–227 (2007).Article 
ADS 
MathSciNet 
CAS 
PubMed 
MATH 

Google Scholar 
Zhang, S. S., Shi, F. Q., Yang, W. Z., Xiang, Z. Y. & Duan, Z. L. Autotoxicity as a cause for natural regeneration failure in Nyssa yunnanensis and its implications for conservation. Isr. J. Plant Sci. 62, 187–197 (2015).Article 

Google Scholar 
Liu, Y. et al. Relationship between seed germination and invasion of Ambrosia artemisiifolia and A. trifida at different positions. Acta Ecol. Sin. 39, 9079–9088 (2019).

Google Scholar  More

Mistakes in a landmark paper that reported the first orangutan genomes might have implications for breeding programmes.Credit: Fiona Rogers/Nature Picture Library

Susie the Sumatran orangutan was a genetic pioneer — the first of her species to have her genome fully sequenced. Her genetic library, and that of ten other orangutans, appeared in a landmark paper in Nature in 20111 that has underpinned hundreds of subsequent studies.But in August, researchers revealed that eight of the sequences in this paper had mistakenly been assigned to the wrong orangutans2. Nature issued a correction from the authors of the original paper3.The scale of the errors sparked ire on social media, and some scientists have warned that the mistakes could have repercussions for orangutan breeding programmes. “Well that’s a bit of a f&£k up orang-utan genome researchers — only mildly embarrassing guys and girls”, tweeted Michael Sweet, a molecular ecologist at the University of Derby, UK.
Africa: sequence 100,000 species to safeguard biodiversity
It’s not clear how these swapped identities have affected orangutan research. But researchers involved in the new analysis believe the discovery might highlight how issues in the scientific community — including the pressure to publish and a reliance on peer review to catch mistakes — could allow such errors to slip into the scientific record.“I think there are errors like this in many, many published papers,” says Graham Banes, an evolutionary biologist formerly at the University of Wisconsin–Madison who led the reanalysis of the 2011 paper. “In some ways, we’re lucky that this was just orangutans. What if this was a biomedical paper and people were developing therapies based on published data?”“It’s fairly easy for these things to occur,” adds Robert Fulton, a genomic scientist at Washington University School of Medicine in St Louis, Missouri, who was part of the team behind the original paper and is a co-author on the reanalysis. “What’s important is that that the data are now correct.” Devin Locke, who led the preparation of the 2011 paper and was formerly a colleague of Fulton’s at Washington University, did not respond to questions about the work.Hybrid headacheDetailed ‘reference’ genomes, such as those published in the 2011 Nature paper, are a key tool for biologists. In 2017, Banes and his team were using the genomes to study what happens when different species of orangutan interbreed, a process called hybridization.They noticed that the names given to some of the samples didn’t match the animals’ reported sex. For example, the 2011 paper reported that an orangutan named Dolly was male. But according to the orangutan studbook — a record of orangutans living in zoos — Dolly was female. Even stranger, Banes found that some of the genomes marked as male lacked a Y chromosome. “There was just this series of things that didn’t make sense,” he recalls.
Major wildlife report struggles to tally humanity’s exploitation of species
Banes and his colleagues eventually found that the 2011 paper had misidentified all but two of the orangutan genomes. Some mistakes seem to be the result of typos. In one case, a sample from a male orangutan was given an ID number that actually corresponded to a sample from an African pig in a tissue repository. Other samples seem to have had their identities swapped during laboratory work. The 2011 study helped to pin down when Bornean and Sumatran orangutans split into separate species, and compared their genomes with those of other primates. These conclusions are largely uncompromised by the mix-up. But Banes says that the errors could have implications for other research, including his own.Banes uses genetic data to provide zoos with recommendations about their captive breeding programmes. Zoos try to avoid crossbreeding orangutan species, partly to mimic wild populations and also because hybrids can suffer high rates of miscarriage and birth defects, says Banes. While re-examining the samples from the 2011 paper, the team realized that one of the sequences thought to be Sumatran (Pongo abelii) was actually Tapanuli (Pongo tapanuliensis), a third species of orangutan that was only described in 20174.Unfortunately, the 2011 paper had wrongly assigned the Tapanuli genome to Baldy, a male orangutan, rather than its actual owner, a female orangutan named Bubbles (both are now dead). Banes says that his team came “perilously close” to announcing in a paper that Baldy was Tapanuli.Although Baldy has no living descendants, Bubbles has several offspring at zoos around the world, all of which are Sumatran–Tapanuli hybrids. Zookeepers will now have to decide whether to stop breeding Bubbles’ descendants to avoid further hybridization, says Vincent Nijman, an anthropologist at Oxford Brookes University, UK.‘Bigger concerns’However, Nijman also argues that the errors will have little effect on orangutan conservation as a whole. Zoos often bill their animals as a back-up for endangered species, but conservationists are much more focused on the thousands of orangutans in the wild that are threatened by deforestation. “I think we have bigger concerns than some mixed-up samples,” says Erik Meijaard, a conservation scientist at Borneo Futures, a conservation consultancy company based in Bandar Seri Begawan, Brunei.
Scientists warn deal to save biodiversity is in jeopardy
Michael Krützen, an evolutionary geneticist at the University of Zurich in Switzerland, agrees that although the errors are “annoying”, their impact on downstream research is probably minimal. However, he says that the problems might be an example of how academia’s publish-or-perish environment could lead to “sloppy” work, as researchers race to publish their work in high-tier journals.Banes agrees that this kind of pressure — along with an over-reliance on a peer-review system that does not offer its volunteer reviewers tangible financial or professional benefits — could lead to errors slipping into published manuscripts.A spokesperson for Nature declined to comment on why the errors in the 2011 paper were not caught by peer review, citing concerns about confidentiality. (Nature’s news team is editorially independent of its academic publishing operation). “However, we would like to stress that we take our responsibility to maintain the accuracy of the scientific record very seriously,” they wrote in an e-mail. “If issues are raised about any paper we have published, we will look into them carefully and update the literature where appropriate.”Banes says that it’s important not to blame individual scientists for such errors, not least because it could discourage efforts to correct mistakes in future. “I think any scientist could have made these mistakes,” he says. “But if we all jump out and say, ‘oh my god, how could they have been so stupid?’, no one is ever going to correct anything. That shame is detrimental to science.” More