Distinguishing the Brazilian mangrove species Avicennia germinans and A. schaueriana (Acanthaceae) by elliptic Fourier analysis of leaf shape

Abstract

The diagnostic value of leaf shape for mangrove species Avicennia germinans and A. schaueriana was examined in four areas within the Rio Parnaíba delta, Maranhão, Brazil. Elliptic Fourier analysis of 627 leaves, followed by principal component analysis, provided shape and size data; the first seven principal component shape variables expressed 99.2% total variation. Discrimination between twelve combinations of species, location and disturbance levels was studied using k‐nearest‐neighbour classification. The species differed significantly in leaf shape but determination was reliable only in the tallest undisturbed forest (germinans narrowly elliptic or ovate, base acute, apex acute to acuminate; schaueriana broadly obovate, base attenuate, apex rounded). Symmetric variables (PC1 and PC3, 84.5% total variation) showed strongest differences. Predominant variation between species was in relative width and base shape (PC1), and within species in ovateness (PC3) between conserved and regenerating areas. Leaves were significantly smaller in A. schaueriana. Vernacular taxa siriba branca and siriba preta were inconsistent with taxonomic species but often significantly different in leaf shape. Avicennia germinans varied more under different geographical and ecological conditions. Shape‐size correlation needs further allometric study. Species delimitation needs morphometric study of a broader character range. Vernacular taxa recognition should be studied with ethnobiological techniques.

Morphometrics and Phylogenomics of Coca (Erythroxylum spp.) Illuminate Its Reticulate Evolution, With Implications for Taxonomy

“…Studies of other plants cultivated for their fruit ( Chitwood et al 2013 ; Chitwood and Otoni 2017 ; Klein et al 2017 ) or roots ( Gupta et al 2020 ) have shown that leaf shape, as inferred through an EFA framework, is highly heritable. EFA has been used to demonstrate that morphological groupings are consistent with species boundaries ( Andrade et al 2008 ; Sayıncı et al 2015 ; Klein et al 2017 ), but this is not consistently true across plant groups, and particularly not for domesticates occupying ecosystems more diverse than that of a modern agricultural setting ( Soares et al 2011 ; Nascimento et al 2021 ), which is the case for traditionally cultivated coca.…”

Morphometrics and phylogenomics of coca (Erythroxylumspp.) illuminate its reticulate evolution, with implications for taxonomy

“…Studies of other plants cultivated for their fruit (Chitwood et al, 2013;Chitwood & Otoni, 2017;Klein et al, 2017) or roots (Gupta et al, 2020) have shown that leaf shape, as inferred through an EFA framework, is highly heritable. EFA has been used to demonstrate that morphological groupings are consistent with species boundaries (Andrade et al, 2008;Klein et al, 2017;Sayıncı et al, 2015), but this is not consistently true across plant groups, particularly those occupying ecosystems more diverse than that of a modern agricultural setting (Nascimento et al, 2021;Soares et al, 2011), in line with the situation for Indigenously cultivated coca.…”

Images carried before the fire: The power, promise, and responsibility of latent phenotyping in plants

“…This is especially true when measurements are acquired using methods that rely on abstract concepts, as is the case for latent phenotypes. Strategies to relate latent phenotypes to observable quantities are often as straightforward as a statistical comparison between latent phenotypes and human‐understandable features, e.g., classification based on a categorical scale or regression against a numerical scale (Casanova et al., 2017; Chitwood, 2020; Clark et al., 2015; Ishikawa et al., 2018; Kadir, 2015; Lane et al., 2020; Li, An, et al., 2018; Nascimento et al., 2021; Neto et al., 2006; Stewart et al., 2019; Zingaretti et al., 2021). However, it is also possible that latent phenotypes capture aspects governing a trait that are otherwise difficult to quantify, and therefore have complex, non‐linear relationships with or cannot be directly related back to a familiar concept (Li et al., 2017; Migicovsky, Li, et al., 2017; Rice et al., 2020).…”