The problem of errors in vegetation monitoring by field methods to detect temporal trends and assign causes of change: a comprehensive bibliography

1 The problem of errors in vegetation monitoring by field methods to detect temporal trends and assign causes of change: a comprehensive bibliography Technical report Virgil Iordache, Aurora Neagoe University of Bucharest Content • Context and objective • Method • Results • Sample of information • Conclusions and potential impact • Appendix 1 Structure bibliography. • Appendix 2 Results of the CiteSpace mapping. Context and objective To understand the causes of plant cover changes and the functional effects of these changes on the production of various ecosystem services via their aggregated functional traits, one needs not only data on the presence or relative abundance of plant species but also to incorporate measurement errors into hypothesis testing. While in measurements of physical and chemical environmental variables, there is a tradition of describing the errors associated with measurements and often a data quality procedure to ensure the reliability of the data, in botany, such an approach is not yet mainstream. In this context, our objective was to gather all available literature on this issue and examine its impact on scientific publications. Method We searched Web of Science for (“vegetation plot” OR “vegetation plots” OR “vegetation survey” OR “vegetation surveys” OR “cover estimates” OR "Braun Blanquet" OR floristic) AND (statistic OR error OR “data quality” OR “accurate data” OR “accurately estimate” OR inaccuracy OR “method comparison” OR precision OR “observer training” OR bias OR “stochastic variation” OR “pseudo-turnover”). We read the abstracts of the 894 resulting documents and selected 193 documents (core set) directly relevant to traditional field methods of vegetation description, providing context or outlining the potential for in-situ replacement with new technologies. We read the core set and categorized the articles into six classes. We supplemented the core set with three additional articles that provided context. Finally, we extracted information on the 4293 articles citing the core set (the extended set) from Web of Science and analyzed the total (core + extended) set using CiteSpace to identify the main clusters of literature. As our purpose here is not to review in detail the information in each category, we did not make a detailed synthesis for each category. Such an effort is no longer useful given the availability of AI tools. However, we provide to the general ecologist a few details in order to stimulate the in-depth investigations of the methodological aspects in the full literature body by careful inspection and critical analysis of each article. 2 Results The bibliographic list (196 articles, Appendix 1) is organized in the following categories: • General methodology (36 articles). These articles provide the background needed, especially for experts in fields other than plant sciences, to understand the context of the error issue. • The error issue in traditional field methods (90 articles). • Comparison between methods (21 articles). These articles do not explicitly address the error problem. Still, they are relevant because they provide the information needed to mix data obtained by different methods, which is often the case in long-term, large-scale analyses. • Detecting vegetation changes in time (22 files). This literature describes attempts to detect vegetation changes at various time scales. Some of them explicitly account for the error problem (such as pseudo turnover), suggesting how future standards might be developed. • Influence of the error problem on understanding other processes depending on it (10 articles). Several articles point out the difficulties of integrating vegetation change into a causal chain, either as a dependent, or independent variable. • New technologies for plant cover description in-situ (17 articles). These articles outline directions for automating in-situ plant monitoring down to the species level. The CiteSpace analysis (Appendix 2) revealed that the citation impact of the core set is organized in 12 main clusters. None of these clusters, as keywords retained by CiteSpace, included a term describing the error issue, suggesting that this topic has not been approached as an organized research program. We report the content (i.e., articles within with hyperlink to the publisher) of all these clusters to provide an additional knowledge resource for those interested in developing the subject. Sample of information A large part of the knowledge in vegetation science is based on non-random, preferentially collected data (Chiarucci, 2007). The reliability of vegetation data is a long-standing issue (Lepš and Hadincová, 2009). The problem is relevant for incorporating measurement error in testing the changes in biodiversity (Mason et al., 2018) and for evaluating the effects in time of external drivers on the vegetation (Begley-Miller et al., 2018). In particular, assessing the vegetation change using vegetation-plot databases is difficult (Chytrý et al., 2014). There are several studies dedicated to the evaluation of data consistency and repeatability in relation to defoliation (Eickenscheidt and Wellbrock, 2014), pollen productivity (Farrell et al., 2016), alpine plant species (Futschik et al., 2019), vegetation classification and mapping (Hearn et al., 2011), wetland vegetation (Johns et al., 2015). Non-detection errors occur especially in the case of rare or small species (Clarke et al., 2012). Explicit quality assurance methods is present, according to our knowledge, only in the case of ICPForests programme (Allegrini et al., 2009) and a few national monitoring schemes (Bussotti et al., 2009; Sanders et al., 2015) Observer-driven errors in vegetation monitoring are among the most important (Boch et al., 2022; Milberg et al., 2008; Vittoz and Guisan, 2007), whether of individuals or of sampling teams (Kercher et al., 2003), although using at least two investigators increases the reliability of vegetation analysis (Nilsson, 1992). Relocation errors also matter in the resurveys of historical vegetation plots (Verheyen et al., 2018). Different sampling methods also lead to some extent to different results (Kercher et al., 2003; Laliberté et al., 2010). Knowing how large are these errors is essential for inferring valid conclusions about the change of vegetation. 3 Morrison and his coworkers dedicated the most substantial effort to review and characterize the errors in vegetation surveys (Morrison, 2016, 2021; Morrison et al., 2020), spatiotemporal differences of the errors (Morrison et al., 2024), as well as their consequences on the diversity indices and speciesabundance relationships (Morrison et al., 2023). The visual estimation can be improved by computer-aided calibration (Gallegos Torell and Glimskär, 2009), and the performance in vegetation records can be compared by efficency graphs derived from rarefaction curves (Seidling et al., 2020). A combination of probabilistic and integrated approaches is probably optimal for detecting changes in plants diversity (Alessi et al., 2023). As resurveying studies and permanent plots are among the essential methodological tools expected to play a critical role in solving the emerging challenges of vegetation science (Yannelli et al., 2022), obtaining quantitative information about the errors associated to these tools is essential for both plant science and ecological research directions. Conclusions and potential impact The literature on errors in vegetation monitoring by field methods is limited, given the vast body of plant science literature, and is not organized as a research program, except for a few authors who investigated the issue in more detail. However, the efforts of these groups were not followed by many other scientists, resulting in a non-detectable cluster of articles compared to other subjects. The problem of establishing generally accepted standards for describing errors in vegetation monitoring by field methods remains open. The impact of this report lies in: • Its potential to catalyze cooperation between botanists/plant scientists and researchers from other environmental fields interested in understanding complex processes involving plants. • Its potential to catalyze the development of new plant cover monitoring standards, allowing a quantitative understanding of trends of the plant cover changes at all time scales. • Its relevance for the development of an integrated monitoring of soil, water, air, and biodiversity able to inform decisions about complex problems under the current institutional setting, given the key role of plants as a hub coupling biological and abiotic processes. 4 Appendix 1 Structure bibliography. General methodology Alessi, N., Bonari, G., Zannini, P., Jiménez‐Alfaro, B., Agrillo, E., Attorre, F., Canullo, R., Casella, L., Cervellini, M., Chelli, S., Di Musciano, M., Guarino, R., Martellos, S., Massimi, M., Venanzoni, R., Zerbe, S., Chiarucci, A., 2023. Probabilistic and preferential sampling approaches offer integrated perspectives of Italian forest diversity. Journal of Vegetation Science 34. Allegrini, M.C., Canullo, R., Campetella, G., 2009. ICP-Forests (International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests): Quality Assurance procedure in plant diversity monitoring. J Environ Monit 11, 782–787. Biurrun, I., Pielech, R., Dembicz, I., Gillet, F., Kozub, Ł., Marcenò, C., Reitalu, T., Van Meerbeek, K., Guarino, R., Chytrý, M., Pakeman, R.J., Preislerová, Z., Axmanová, I., Burrascano, S., Bartha, S., Boch, S., Bruun, H.H., Conradi, T., De Frenne, P., Essl, F., Filibeck, G., Hájek, M., Jiménez‐Alfaro, B., Kuzemko, A., Molnár, Z., Pärtel, M., Pätsch, R., Prentice, H.C., Roleček, J., Sutcliffe, L.M.E., Terzi, M., Winkler, M., Wu, J., Aćić, S., Acosta, A.T.R., Afif, E., Akasaka, M., Alatalo, J.M., Aleffi, M., Aleksanyan, A., Ali, A., Apostolova, I., Ashouri, P., Bátori, Z., Baumann, E., Becker, T., Belonovskaya, E., Benito Alonso, J.L., Berastegi, A., Bergamini, A., Bhatta, K.P., Bonini, I., Büchler, M.O., Budzhak, V., Bueno, Á., Buldrini, F., Campos, J.A., Cancellieri, L., Carboni, M., Ceulemans, T., Chiarucci, A., Chocarro, C., Conti, L., Csergő, A.M., Cykowska‐Marzencka, B., Czarniecka‐Wiera, M., Czarnocka‐Cieciura, M., Czortek, P., Danihelka, J., de Bello, F., Deák, B., Demeter, L., Deng, L., Diekmann, M., Dolezal, J., Dolnik, C., Dřevojan, P., Dupré, C., Ecker, K., Ejtehadi, H., Erschbamer, B., Etayo, J., Etzold, J., Farkas, T., Farzam, M., Fayvush, G., Fernández Calzado, M.R., Finckh, M., Fjellstad, W., Fotiadis, G., García‐Magro, D., García‐Mijangos, I., Gavilán, R.G., Germany, M., Ghafari, S., Giusso del Galdo, G.P., Grytnes, J.A., Güler, B., Gutiérrez‐Girón, A., Helm, A., Herrera, M., Hüllbusch, E.M., Ingerpuu, N., Jägerbrand, A.K., Jandt, U., Janišová, M., Jeanneret, P., Jeltsch, F., Jensen, K., Jentsch, A., Kącki, Z., Kakinuma, K., Kapfer, J., Kargar, M., Kelemen, A., Kiehl, K., Kirschner, P., Koyama, A., Langer, N., Lazzaro, L., Lepš, J., Li, C.F., Li, F.Y., Liendo, D., Lindborg, R., Löbel, S., Lomba, A., Lososová, Z., Lustyk, P., Luzuriaga, A.L., Ma, W., Maccherini, S., Magnes, M., Malicki, M., Manthey, M., Mardari, C., May, F., Mayrhofer, H., Meier, E.S., Memariani, F., Merunková, K., Michelsen, O., Molero Mesa, J., Moradi, H., Moysiyenko, I., Mugnai, M., Naqinezhad, A., Natcheva, R., Ninot, J.M., Nobis, M., Noroozi, J., Nowak, A., Onipchenko, V., Palpurina, S., Pauli, H., Pedashenko, H., Pedersen, C., Peet, R.K., Pérez‐Haase, A., Peters, J., Pipenbaher, N., Pirini, C., Pladevall‐Izard, E., Plesková, Z., Potenza, G., Rahmanian, S., Rodríguez‐Rojo, M.P., Ronkin, V., Rosati, L., Ruprecht, E., Rusina, S., Sabovljević, M., Sanaei, A., Sánchez, A.M., Santi, F., Savchenko, G., Sebastià, M.T., Shyriaieva, D., Silva, V., Škornik, S., Šmerdová, E., Sonkoly, J., Sperandii, M.G., Staniaszek‐Kik, M., Stevens, C., Stifter, S., Suchrow, S., Swacha, G., Świerszcz, S., Talebi, A., Teleki, B., Tichý, L., Tölgyesi, C., Torca, M., Török, P., Tsarevskaya, N., Tsiripidis, I., Turisová, I., Ushimaru, A., Valkó, O., Van Mechelen, C., Vanneste, T., Vasheniak, I., Vassilev, K., Viciani, D., Villar, L., Virtanen, R., Vitasović‐Kosić, I., Vojtkó, A., Vynokurov, D., Waldén, E., Wang, Y., Weiser, F., Wen, L., Wesche, K., White, H., Widmer, S., Wolfrum, S., Wróbel, A., Yuan, Z., Zelený, D., Zhao, L., Dengler, J., Kreft, H., 2021. 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A comparison of stratified-random and preferential sampling. Journal of Vegetation Science 22, 281–291. Milberg, P., Bergstedt, J., Fridman, J., Odell, G., Westerberg, L., 2008. Observer bias and random variation in vegetation monitoring data. Journal of Vegetation Science 19, 633–644. Monzingo, D.S., Shipley, L.A., Cook, R.C., Cook, J.G., 2022. Factors influencing predictions of understory vegetation biomass from visual cover estimates. Wildlife Society Bulletin 46. Morrison, L.W., 2016. Observer error in vegetation surveys: a review. Journal of Plant Ecology 9, 367–379. 16 Appendix 2 Results of the CiteSpace mapping. Appendix 2 Figure 1 Clusters of the total set of articles with citation network in 2015. Appendix 2 Figure 2 Clusters timeline of the total set of articles with citation network in 2025. Appendix 2 Table 1 Clusters summary. Appendix 2 Table 2 Cluster 0. Appendix 2 Table 3 Cluster 1. Appendix 2 Table 4 Cluster 2. Appendix 2 Table 5 Cluster 3. Appendix 2 Table 6 Cluster 4. Appendix 2 Table 7 Cluster 5. Appendix 2 Table 8 Cluster 6. Appendix 2 Table 9 Cluster 7. Appendix 2 Table 10 Cluster 8. Appendix 2 Table 11 Cluster 9. Appendix 2 Table 12 Cluster 10. Appendix 2 Table 13 Cluster 11. Appendix 2 Table 14 Cluster 12. 17 Appendix 2 Figure 1 Clusters of the total set of articles with citation network in 2015. 18 Appendix 2 Figure 2 Clusters timeline of the total set of articles with citation network in 2025. 19 Appendix 2 Table 1 Clusters summary. ClusterID Size Silhouette mean(Year) Label (LSI) Label (LLR) Label (MI) 0 197 0.82 2020 genomic resource; hill number; deriving indicator; endothermic species; iberian dung beetle | tree species; coverage gap; genomic resource; overcoming sampling biases; hill number species occurrence data (1055.9, 1.0E-4); terrestrial fauna (813.32, 1.0E -4); community science (745.98, 1.0E -4); global distribution (648.39, 1.0E -4); big data (648.09, 1.0E-4) northwestern coast (1.76); field study (1.76); hie 1 169 0.877 2004 representative plot network; shrubland communities; actual stage; trebon basin; species pool | urban vegetation; different cultivation type; plant communities; diagnostic species; heterogeneity -constrained random resampling czech republic (2415.44, 1.0E-4); alien plant (1586.01, 1.0E -4); weed vegetation (1067, 1.0E -4); global index (943.51, 1.0E -4); man-made habitat (697.81, 1.0E4) northwestern coast (0.59); field study (0.59); hie 2 127 0.841 2015 reproductive phenology; global inventory; floristic change; plant communities; local extinction | global inventory; floristic change; historic phenology; local extinction; central role phenological research (843.9, 1.0E-4); neotropical biodiversity (819.74, 1.0E -4); extinction risk (795.58, 1.0E -4); digital accessible knowledge (791.56, 1.0E -4); survey effort (755.33, 1.0E-4) northwestern coast (1.22); field study (1.22); hie 3 124 0.879 2009 desert mammal; mapping continuous field; light work -but; plant name; variable environment | land management; recording species number; tree species; forest plant community change; urban ecology citizen science (1460.53, 1.0E-4); bavarian alp (1426.38, 1.0E -4); mountain forest (886.18, 1.0E -4); atlas data (517.3, 1.0E -4); occupancy modelling (455.18, 1.0E-4) investigating recommendation (0.39); northwestern 4 121 0.859 2018 slow decrease; grassland biodiversity hotspot; chiprovska planina; parasitic plant; class alnetea glutinosae | slow decrease; chiprovska planina; nordic -baltic grassland vegetation database; large -extent mapping; hierarchical expert system expert system (2238.17, 1.0E-4); vegetation classification (1128.12, 1.0E - 4); alien plant invasion (952.25, 1.0E -4); fine -grain beta diversity (910.59, 1.0E-4); megadiverse country (862.74, 1.0E -4) northwestern coast (1.72); field study (1.72); hie 5 110 0.845 2011 polish carpathian; agricultural matrix; hybrid ecosystem; spatial pattern; ecological indicator value | lowland heath plant metacommunity; plant communities; dry grassland vegetation (866.53, 1.0E-4); syntaxonomy ecology (803.66, 1.0E -4); vegetation diversity (479.88, 1.0E -4); classic vegetation ecology journal disturbed site (0.47); quantifying species coloniz 20 consistent classification; spatial scale; ecosystem mapping (474.76, 1.0E-4); new profile (474.76, 1.0E-4) 6 108 0.883 2020 dynamic habitat mosaic; directional turnover; farmland bird; terrestrial national park; common agricultural policy | common plant species; floristic change; dynamic habitat mosaic; directional turnover; land use history czech flora (792.68, 1.0E-4); biodiversity loss (758.56, 1.0E -4); modern research (733.47, 1.0E -4); open habitat (721.82, 1.0E -4); directional change (600.9, 1.0E4) northwestern coast (1.56); field study (1.56); hie 7 92 0.866 2014 directional turnover; plant communities; prefire vegetation drive; re - visitation study; using red list species | loamy soil; historical charcoal burning; directional turnover; plant communities; conservation condition tatra mt (1273.55, 1.0E-4); mountain summit (1047.33, 1.0E -4); nitrogen deposition (809.58, 1.0E -4); temporal change (754.79, 1.0E -4); agricultural landscape (740.71, 1.0E-4) nanda devi (0.85); long -term forest vegetation dyn 8 91 0.914 2003 northern great plains vegetation; plant communities; testacean assemblage; vascular plant; regional species pool | northern great plains vegetation; plant communities; vascular plant; regional species pool; natural fir -beech forest fen terminology (265.51, 1.0E-4); habitat diversity (265.51, 1.0E -4); central european fen (265.51, 1.0E -4); testacean assemblage (260.26, 1.0E -4); complete base -richness gradient (260.26, 1.0E-4) regional species pool (0.07); northwestern coast ( 9 52 0.947 2010 habitat requirement; taxonomic challenges for; right target; standardized set; large -scale species distribution dataset | ochrana prirody; vascular plant; czech republic; invasion pattern; checklist update czech republic (438.19, 1.0E-4); plant invasion (213.47, 1.0E -4); taxonomic diversity (213.44, 1.0E -4); invasion pattern (213.44, 1.0E -4); checklist update (213.44, 1.0E-4) field study (0.02); short -term population dynamics 10 46 0.966 2013 niche ecology; floristic richness; pollenassemblage richness; plant diversity; recent development | using decoy rare plant; explicit plant trait database; temporal plant cover; weed distribution; conservation value wetland condition (757.29, 1.0E-4); functional ecology (431.01, 1.0E -4); floristic quality assessment (431.01, 1.0E - 4); assessing among -lineage variability (423.43, 1.0E -4); functional trait dataset (423.43, 1.0E-4) northwestern coast (0.17); ecological quality (0.1 11 39 0.958 2014 plant communities; biotic interaction; fragmented grassland; predicting species establishment; driving community composition | pollen-assemblage richness; dark diversity (720.01, 1.0E-4); species pool (403.7, 1.0E -4); community completeness (366.89, 1.0E -4); biotic fragmented grassland (0.04); patch management isol 21 plant diversity; multiple-study comparison; dark diversity concept; dry grassland resource (262.84, 1.0E-4); empirical assessment (252.69, 1.0E-4) 12 33 0.936 2021 dark host specificity; south africa; chinese native useful vascular plant; diversity hotspot; in -situ conservation | typical forest; potential mechanism; complex environmental stress; alien species success; vegetation survey dark diversity (617.07, 1.0E-4); postindustrial vegetation (279.18, 1.0E -4); same coin (279.18, 1.0E -4); habitat conservation status (279.18, 1.0E -4); drive community completeness (269.84, 1.0E-4) spatial mismatch (0.06); in -situ conservation (0.0 13 25 0.99 2006 forest road cutbank; rangeland mapping; landsat data; scale parameter; southern mongolian mountain range | sage -grouse centrocercus urophasianus; nesting ecology; historic distribution; eastern edge; rangeland mapping sage-grouse centrocercus urophasianus (314.65, 1.0E -4); historic distribution (314.65, 1.0E -4); eastern edge (314.65, 1.0E -4); nesting ecology (314.65, 1.0E4); digital photograph analysis (136.06, 1.0E - 4) hierarchical objectbased classification (0); rang 14 15 0.995 1998 association between lowland grassland plant communities and soil properties association (22.19, 1.0E-4); lowland grassland plant communities (22.19, 1.0E - 4); soil properties (22.19, 1.0E -4); vegetation classification (0.05, 1.0); citizen science (0.05, 1.0) czech republic (0.01); vegetation classification ( 15 13 1 2003 participatory forest monitoring; natural resource use; montane forest; forest quality; miombo woodland | montane forest; natural resource use; participatory forest monitoring; forest quality; miombo woodland monitoring matter (55.61, 1.0E-4); locally -based approaches (55.61, 1.0E-4); miombo woodland (36.95, 1.0E -4); natural resource use (36.95, 1.0E -4); forest quality (36.95, 1.0E-4) czech republic (0.01); vegetation classification ( 16 10 0.994 2004 tallgrass prairie; great lake; floristic quality index; coastal wetland; landscape v | vegetation -based indicator; landscape v; plant community assembly; tallgrass prairie; relative influence vegetation-based indicator (70.31, 1.0E4); wetland restoration progress (70.31, 1.0E -4); local factor (52.61, 1.0E-4); relative influence (52.61, 1.0E -4); landscape v (52.61, 1.0E-4) czech republic (0.01); vegetation classification ( 18 9 0.978 1999 cover assessment; scale-dependent variation; vegetation monitoring; grassland plant cover; absence sampling | long -term forest monitoring program; design concept; cover design concept (57.41, 1.0E-4); long-term forest monitoring program (57.41, 1.0E - 4); scale -dependent variation (38.1, 1.0Eczech republic (0.01); vegetation classification ( 22 assessment; vegetation monitoring; absence sampling 4); grassland plant cover (38.1, 1.0E-4); absence sampling (18.96, 1.0E-4) 19 8 0.977 2008 short-term variation; altitudinal gradient; wetland vegetation; vegetation resurvey; monitoring survey design | weighting abundance; western balkan; semi -natural grassland; elevational gradient; forest species weighting abundance (119.31, 1.0E-4); clonal trait (119.31, 1.0E -4); meadow management (119.31, 1.0E -4); plant traitenvironment relationship (105.24, 1.0E - 4); forest succession chronosequence (105.24, 1.0E-4) czech republic (0.01); vegetation classification ( 21 6 1 2000 conservation of lowland semi-natural grasslands in the uk: a review of botanical monitoring results from agri -environment schemes botanical monitoring result (20.6, 1.0E-4); agri -environment scheme (20.6, 1.0E-4); conservation (20.6, 1.0E -4); lowland (20.6, 1.0E-4); review (20.6, 1.0E-4) czech republic (0.01); vegetation classification ( 23 Appendix 2 Table 2 Cluster 0 Coverage GCS LCS Bibliography 19 66 0 Dengler, J (2023-JAN) Ecological indicator values for europe (eive) 1.0. VEGETATION CLASSIFICATION AND SURVEY DOI 10.3897/VCS.98324 17 140 0 Chytry, M (2021-JAN) Pladias database of the czech flora and vegetation. PRESLIA, V93, P87 DOI 10.23855/preslia.2021.001 16 11 0 Yannelli, FA (2022-JAN) Fifteen emerging challenges and opportunities for vegetation science: a horizon scan by early career researchers. JOURNAL OF VEGETATION SCIENCE, V33, P18 DOI 10.1111/jvs.13119 15 220 0 Heberling, JM (2021-JAN) Data integration enables global biodiversity synthesis. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA DOI 10.1073/pnas.2018093118 15 62 0 Biurrun, I (2021-JAN) Benchmarking plant diversity of palaearctic grasslands and other open habitats. JOURNAL OF VEGETATION SCIENCE, V32, P21 DOI 10.1111/jvs.13050 14 84 0 Sabatini, FM (2021-JAN) Splotopen - an environmentally balanced, openaccess, global dataset of vegetation plots. GLOBAL ECOLOGY AND BIOGEOGRAPHY, V30, P25 DOI 10.1111/geb.13346 14 4 0 Alfaro, E (2023-JAN) Rasgos-cl: a functional trait database of chilean woody plants. GLOBAL ECOLOGY AND BIOGEOGRAPHY, V32, P13 DOI 10.1111/geb.13755 13 21 0 Keppel, G (2021-JAN) Synthesizing tree biodiversity data to understand global patterns and processes of vegetation. JOURNAL OF VEGETATION SCIENCE, V32, P14 DOI 10.1111/jvs.13021 13 8 0 Daru, BH (2025-JAN) Tracking hidden dimensions of plant biogeography from herbaria. NEW PHYTOLOGIST DOI 10.1111/nph.70002 13 8 0 Folk, RA (2021-JAN) Biodiversity at the global scale: the synthesis continues. AMERICAN JOURNAL OF BOTANY, V108, P13 DOI 10.1002/ajb2.1694 12 19 0 Arle, E (2021-JAN) Bracatus: a method to estimate the accuracy and biogeographical status of georeferenced biological data. METHODS IN ECOLOGY AND EVOLUTION, V12, P11 DOI 10.1111/2041210X.13629 12 10 0 Ronquillo, C (2023-JAN) Exploring the impact of data curation criteria on the observed geographical distribution of mosses. ECOLOGY AND EVOLUTION, V13, P14 DOI 10.1002/ece3.10786 12 51 0 Ondo, I (2024-JAN) Plant diversity darkspots for global collection priorities. NEW PHYTOLOGIST, V244, P15 DOI 10.1111/nph.20024 12 2 0 Lopez-tobar, R (2023-JAN) Botanical collection patterns and conservation categories of the most traded timber species from the ecuadorian amazon: the role of protected areas. PLANTS-BASEL, V12, P20 DOI 10.3390/plants12183327 24 11 5 0 Craven, D (2021-JAN) Niche properties constrain occupancy but not abundance patterns of native and alien woody species across hawaiian forests. JOURNAL OF VEGETATION SCIENCE, V32, P15 DOI 10.1111/jvs.13025 11 45 0 Daru, BH (2023-JAN) Mass production of unvouchered records fails to represent global biodiversity patterns. NATURE ECOLOGY & EVOLUTION DOI 10.1038/s41559-023-02047-3 11 118 0 Sabatini, FM (2022-JAN) Global patterns of vascular plant alpha diversity. NATURE COMMUNICATIONS, V13, P16 DOI 10.1038/s41467-02232063-z 11 2 0 Marchetto, E (2024-JAN) Addressing multiple facets of bias and uncertainty in continental-scale biodiversity databases. BIODIVERSITY INFORMATICS, V18, P22 DOI 10.5281/zenodo.12179384 11 0 0 Barreiro, K (2025-JAN) Species richness variation in marine and terrestrial fauna across widespread, fragmented territories: assessing inherent challenges of data scarcity at local and regional scales. SCIENTIFIC REPORTS, V15, P19 DOI 10.1038/s41598-025-06631-4 10 7 0 Essl, F (2021-JAN) New and old invaders in forests in eastern austria: the role of species attributes and invasion history. FLORA DOI 10.1016/j.flora.2021.151922 10 60 0 Albani, rocchetti G (2021-JAN) Reversing extinction trends: new uses of (old) herbarium specimens to accelerate conservation action on threatened species. NEW PHYTOLOGIST, V230, P18 DOI 10.1111/nph.17133 10 26 0 Lenzner, B (2021-JAN) Role of diversification rates and evolutionary history as a driver of plant naturalization success. NEW PHYTOLOGIST, V229, P11 DOI 10.1111/nph.17014 10 2 0 Fernandes, MF (2024-JAN) Knowledge gaps in legume diversity and distribution and prospects for future research . BRAZILIAN JOURNAL OF BOTANY, V48, P14 DOI 10.1007/s40415-024-01051-6 10 3 0 De, santis S (2023-JAN) Geographic range vs. occurrence records in plant distribution mapping: the case of arbutus in the old world . FORESTS, V14, P17 DOI 10.3390/f14051010 10 109 0 Cai, L (2023-JAN) Global models and predictions of plant diversity based on advanced machine learning techniques. NEW PHYTOLOGIST, V237, P14 DOI 10.1111/nph.18533 10 35 0 Axmanova, I (2021-JAN) Neophyte invasions in european grasslands. JOURNAL OF VEGETATION SCIENCE, V32, P17 DOI 10.1111/jvs.12994 10 29 0 Farooq, H (2021-JAN) Mapping africa's biodiversity: more of the same is just not good enough. SYSTEMATIC BIOLOGY, V70, P11 DOI 10.1093/sysbio/syaa090 10 1 0 Szymura, TH (2023-JAN) Vegetation databases augment but do not replace species distribution atlases in species richness assessment. ECOLOGICAL INDICATORS DOI 10.1016/j.ecolind.2023.110876 9 25 0 Sporbert, M (2021-JAN) Different sets of traits explain abundance and distribution patterns of european plants at different spatial scales. 25 JOURNAL OF VEGETATION SCIENCE, V32, P15 DOI 10.1111/jvs.13016 9 2 0 Forti, LR (2024-JAN) The implications of estimating rarity in brazilian reptiles from gbif data based on contributions from citizen science versus research institutions(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) (sic)(sic)(sic)gbif(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(si c)(sic)(sic)(sic)(sic)(sic)(sic)implicações da estimativa de raridade de espécies de répteis brasileiros baseada nas contribuições da ciência cidadã versus instituições de pesquisa. INTEGRATIVE CONSERVATION DOI 10.1002/inc3.53 9 2 0 Golivets, M (2024-JAN) Future changes in key plant traits across central europe vary with biogeographical status, woodiness, and habitat type. SCIENCE OF THE TOTAL ENVIRONMENT, V907, P13 DOI 10.1016/j.scitotenv.2023.167954 9 18 0 Mahecha, MD (2021-JAN) Crowd-sourced plant occurrence data provide a reliable description of macroecological gradients. ECOGRAPHY, V44, P12 DOI 10.1111/ecog.05492 9 10 0 Collart, F (2023-JAN) Ecological and biological indicators of the accuracy of species distribution models: lessons from european bryophytes. ECOGRAPHY, V2023, P13 DOI 10.1111/ecog.06721 9 6 0 Ramirez-barahona, S (2023-JAN) Assessing digital accessible botanical knowledge and priorities for exploration and discovery of plant diversity across mesoamerica. NEW PHYTOLOGIST, V240, P14 DOI 10.1111/nph.19190 9 1 0 Serra-diaz, JM (2024-JAN) Occtest: an integrated approach for quality control of species occurrence data. GLOBAL ECOLOGY AND BIOGEOGRAPHY, V33, P15 DOI 10.1111/geb.13847 9 14 0 Kindt, R (2023-JAN) Treegoer: a database with globally observed environmental ranges for 48,129 tree species. GLOBAL CHANGE BIOLOGY, V29, P16 DOI 10.1111/gcb.16914 9 6 0 Antonelli, A (2025-JAN) The 2030 declaration on scientific plant and fungal collecting. PLANTS PEOPLE PLANET DOI 10.1002/ppp3.10569 9 91 0 Koenig, C (2019-JAN) Biodiversity data integration--the significance of data resolution and domain. PLOS BIOLOGY, V17, P16 DOI 10.1371/journal.pbio.3000183 9 22 0 Pouteau, R (2021-JAN) Climate and socio-economic factors explain differences between observed and expected naturalization patterns of european plants around the world. GLOBAL ECOLOGY AND BIOGEOGRAPHY, V30, P18 DOI 10.1111/geb.13316 9 0 0 Eckert, I (2025-JAN) Comparing the ability of natural history collections and community science observations to estimate plant niches. BOTANY, V103, P11 DOI 10.1139/cjb-2025-0010 9 25 0 Garcia-rosello, E (2023-JAN) Gbif falls short of providing a representative picture of the global distribution of insects. 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