Making them visible and usable — vegetation‐plot observations from Fennoscandia based on historical species‐quantity scales

Abstract

Aims Present‐day large‐scale and plot‐based vegetation analyses contribute to the transnational characterization and interpretation of biodiversity patterns and to habitat typologies which are important for planning, monitoring and decision making in nature conservation. Many historical vegetation surveys applied cover abundance, relative occurrence or density scales (species‐quantity scales) that are nowadays poorly known and consequently disregarded or misinterpreted. Therefore, it is worthwhile to put effort into making them compatible with the datasets sampled using mainstream methods. Within Europe, this especially applies to historical data from Fennoscandia. Here, we aim to propose how to transform the species‐quantity scales frequently used in Fennoscandia into percentage cover scales, based on the conversion of their individual grades. Study area Fennoscandia, including Norway, Sweden, Finland and the Fennoscandian part of Russia (Republic of Karelia, Karelian Isthmus, Murmansk Region). Methods and results We inventoried Fennoscandian vegetation‐plot studies and identified that the most frequently applied species‐quantity scales were those of Norrlin, Hult–Sernander and Drude. We reviewed the definitions and applications of these scales in the literature and, if not available, calculated hypothetical species covers to approximate realistic conversions to the percentage scale. As a result, we propose alternative ways of conversion of the individual scale grades to mid‐percentage cover values. Conclusion Historical vegetation plot data from Fennoscandia can be used as quantitative information for vegetation research if their grades are consistently transformed into percentage cover values using the proposals presented in this paper.

Ellenberg Indicator Values Disclose Complex Environmental Filtering Processes in Plant Communities along an Elevational Gradient

“…For each relevé, the vegetation type is briefly indicated and its syntaxonomic classification at the level of alliance is given, as established by Pirone [72], with nomenclature and higher classification updated according to Prodromo della vegetazione d'Italia [76]. To express species abundances, we converted original scores based on the seven-grade Braun-Blanquet scale [77] to percentage cover as follows [78][79][80]: r = 1%, + = 2%, 1 = 3%, 2 = 13%, 3 = 38%, 4 = 63%, and 5 = 88% (however, no species was ranked as r in the original phytosociological study). Because in the original phytosociological study cover data were recorded separately for different strata, we constructed and analyzed two separate matrices: one including only the shrubby-herbaceous stratum, as already conducted in a previous paper, in which only presence/absences were used ( [74], with corrections), and the other also including the arboreal stratum.…”

Inferring the Potential Geographic Distribution and Reasons for the Endangered Status of the Tree Fern, Sphaeropteris lepifera, in Lingnan, China Using a Small Sample Size

“…To accomplish this, we established 20 m × 20 m plots centered around adult individuals and surveyed all cooccurring vascular plants within four 10 × 10 m quadrats nested within each plot. Within each quadrat, we sought to assign each species to one of the seven levels of the Drude scale [37]: plants of high sociability (soc; plantae sociales), three levels of copious or numerous (cop1-3; copiose intermixtae), sparse-sporadic (sp; sparsae, sporadice intermixtae), solitary (sol; plantae solitariae), and low abundance and/or singular individuals (uni; unicum). Assignments to these groups were determined based on relative-cover (relative cover = species cover/quadrat area), and the specific classification was as follows: soc (rc ≥ 75%); cop3 (75% < rc ≤ 50%); cop2 (50% < rc ≤ 25%); cop1 (15% < rc ≤ 25%); sp (5% < rc ≤ 15%); sol (5% < rc ≤ 3%); uni (rc ≤ 2%), where rc refers to relative cover.…”

Optimal transformation of species cover for vegetation classification

“…The cover-abundance scales used for vegetation recording are often considered as ordinal (Podani, 2005(Podani, , 2006, but their ordinal classes are defined using intervals of percentage covers, perhaps except the lowest classes (Tüxen & Ellenberg, 1937;Braun-Blanquet, 1964;van der Maarel, 1979;Pätsch et al, 2019). Therefore, they are often transformed into midpoints of individual ordinal classes on the percentage scale, and such midpoints are used as a common measure of cover abundance in the databases that contain data recorded on different scales (Hennekens & Schaminée, 2001).…”

“…Vegetation classification is often based on unsupervised numerical classification of vegetation-plot records, also called relevés in the phytosociological tradition (Whittaker, 1978;Peet & Roberts, 2013;De Cáceres et al, 2015). Species quantities in these plots are measured in different ways (Wilson, 2011), but most commonly they are visually estimated in percentages or by using cover-abundance scales that can be transformed into percentage covers, e.g., the Braun-Blanquet, Daubenmire, Domin or Hult-Sernander scale (van der Maarel, 1979;McNellie et al, 2019;Pätsch et al, 2019).…”

“…Vegetation classification is often based on unsupervised numerical classification of vegetation‐plot records, also called relevés in the phytosociological tradition (Whittaker, 1978; Peet & Roberts, 2013; De Cáceres et al, 2015). Species quantities in these plots are measured in different ways (Wilson, 2011), but most commonly they are visually estimated in percentages or by using cover‐abundance scales that can be transformed into percentage covers, e.g., the Braun‐Blanquet, Daubenmire, Domin or Hult–Sernander scale (van der Maarel, 1979; McNellie et al, 2019; Pätsch et al, 2019). In spite of concerns about the quality of visual estimates (Wilson, 2011) and statistical properties of data estimated using cover‐abundance scales (Podani, 2005, 2006), in most cases, cover‐abundance scales reveal the same variation patterns in plant communities as cover estimates obtained using more accurate measurement methods (Ricotta & Feoli, 2013).…”