Abstract
Morphological identification of planktic crustacean larvae is required in many scientific contexts, such as ecology or taxonomy. Due to a still low availability of genetic sequences for many ingroups of Eumalacostraca, this task is still more feasible by morphological methods. Our understanding of eumalacostracan larval morphology is challenged by phenotypic variability. We investigated four eumalacostracan ingroups: Galatheidae, Hippoidea, Raninidae and Stomatopoda. Representatives of all four groups develop through spine-bearing planktic larval stages. Incorporating dorsal and lateral shield outlines into three-dimensional shape analysis of the shields, we compare specimens from the wild with laboratory-reared specimens. Using graphical and statistical analysis methods, we find that at least the lateral morphology of the shields of Hippoidea and Raninidae seems to be too strongly dependent on phylogeny to show phenotypic variability with our current sample size, but Hippoidea do show phenotypic variability in their dorsal shield morphology. In Galatheidae and Stomatopoda, a clear difference in shield morphology can be found between wild-caught and laboratory-reared specimens. This difference likely represents phenotypic variability. The exact environmental signals causing this phenotypic variability are still unknown, but some candidates are discussed.
Keywords:
Galatheidae; Hippoidea; morphological diversity; Raninidae; Stomatopoda
INTRODUCTION
Identifying crustacean larvae is frequently required in many ecological contexts, such as diversity studies or diet analyses, as they make up a large part of the zooplankton (e.g., Lindley, 1998; Sharma et al., 2017; Sorell et al., 2017; MacLeod et al., 2018; Bar-On and Milo, 2019; da Silva et al., 2019). Despite the advances of genomic methods for species identification in the past years, morphological methods are still easier to apply and cheaper in these contexts (Brown et al., 2015; Bucklin et al., 2016). Furthermore, for some crustacean larvae, widespread genetic identification is still hampered due to incomplete reference libraries (e.g., Heimeier et al., 2010; Tang et al., 2010; Brandão et al., 2016). Therefore, it is still important to have a fundamental understanding of the morphology and development of crustacean larvae to correctly identify species from wild samples. This information is often provided by laboratory-rearing studies supplying information on the developmental series of different species (e.g., Provenzano and Mannig, 1978; Konishi, 1987; Christiansen and Anger, 1990). However, larvae can exhibit differences when reared in the laboratory compared to growing up in the wild (e.g., Knight, 1967; Criales and Anger, 1986; Morgan and Provenzano, 1979; Braig et al., 2021). Environmental conditions impact the life-history, survival rates and development (among others) of crustacean larvae, but studies on the impact on their morphology are scarce (e.g., Anger 2001; 2006; Spitzner et al., 2019). Braig et al. (2021) found that shield configuration and spine length of some decapodan larvae can be influenced by the environment, by applying methods of quantitative morphology. Other authors have mentioned similar observations before (e.g., Knight, 1968; Furigo and Anger, pers. comm.) on a qualitative level.
Here, we investigate the morphological differences of selected eumalacostracan larvae from the laboratory and the wild. We focus on four different groups: Galatheidae (squat lobsters), Hippoidea (sand crabs), Raninidae (frog crabs), and Stomatopoda (mantis shrimps). The first three groups pass through a zoea phase during their development. The plesiomorphic (ancestral) condition for the zoea larva appears to have a shield without prominent spines as seen in most zoea larvae of Caridea (true shrimps) and many clawed lobsters (e.g., Hayashi and Hamano, 1984; Magalhães and Walker, 1988; Thatje et al., 2001; Rötzer and Haug, 2015). However, the three groups considered here all have developed zoea-type larvae with rather large spines (Fig. 1). The larval phase of mantis shrimps (Stomatopoda) is different from that of the other three (all ingroups of Decapoda) due to a more distant relationship, but includes larvae that are at least distantly comparable to zoea stage larvae (see discussion in Gurney, 1942). Mantis shrimp larvae again have spiny shields, like those of the other three groups.
Comparison of unidentified larvae of the four groups of Eumalacostraca, museum specimens under cross-polarized light. A-C: Representative of Galatheidae, specimen MNHN-IU-2014-5513B. A. Ventral view. B. Lateral view. C. Dorsal view. D-F: Representative of Hippoidea, specimen MNHN-IU-2014-5468 (after Rudolf et al., 2016). D. Anterior view. E. Ventral view. F. Posterior view. G-H: Representative of Hippoidea, specimen NMHD-86486 (Old number: ZMUC-CRU-8682; after Rudolf et al., 2016). G. Dorsal view. H. Ventral view. I: Representative of Raninidae, specimen MNHN-IU-2014-5467, lateral view. J: Representative of Raninidae, specimen MNHN-IU-2014-5360, lateral view. K-L: Representative of Stomatopoda, specimen NHMD-916095 (Old number: Stat-3955-II-A). K. Ventral view. L. Dorsal view. M-O: Representative of Stomatopoda, specimen ZMUC-CRU-8660 (after Haug et al., 2016). M. Ventral view. N. Dorsal view. O. Lateral view.
Especially in these spines, we expect to find phenotypic variability (Braig et al., 2021). Therefore, we investigate the morphological diversity (related to “disparity”; Hopkins and Gerber, 2017) and potential phenotypic variability of the shield in dorsal and lateral view of these four eumalacostracan groups using quantitative morphology. We compare these aspects for planktic larvae caught in the wild and larvae reared in laboratory. The obtained results and implications are discussed in an ecological context.
MATERIAL AND METHODS
Material
Material for this study originated in parts from published literature, which provided images and reconstruction drawings of specimens. Additionally, specimens caught in the wild and stored in museum collections were directly investigated.
Literature sources include: Lebour, 1930; 1931; Johnson and Lewis, 1942; Manning and Provenzano, 1963; Knight, 1967; 1968; Fagetti and Campodonico, 1971; Sakai, 1971; Michel and Manning, 1972; Shanbhogue, 1975; Rice and Ingle, 1977; Provenzano and Manning, 1978; Gamô, 1979; Morgan and Provenzano, 1979; Greenwood and Williams, 1984; Stuck and Truesdale, 1986; Morgan and Goy, 1987; Seridji, 1988; Christiansen and Anger, 1990; Minagawa, 1990; Manning, 1991; Diaz, 1998; Konishi and Saito, 2000; Fujita et al., 2001; Fujita and Shokita, 2005; Siddiqi and Ghory, 2006; Fujita, 2007; Fonghoy, 2015; Rudolf et al., 2016; Mujica et al., 2019; Braig et al., 2021. Further material was provided by museums and collections: ‘Museum für Naturkunde’ Berlin, ‘Natural History Museum of Denmark’ Copenhagen, ‘Senckenberg Naturmuseum’ Frankfurt, ‘Muséum national d’Historie naturelle’ Paris and ‘Centrum für Naturkunde’ Hamburg.
In total, 266 larval specimens were documented in dorsal and/or lateral view: 62 specimens of Galatheidae, 49 specimens of Hippoidea, 18 specimens of Raninidae and 137 specimens of Stomatopoda. The composition of the groups considering sample origin (i.e., wild-caught or lab-reared) are given in Tab. 1. For a detailed list of all material used in this study, see App. 1. If an individual was caught in the wild (e.g., plankton sampling) and preserved and then documented either by us or by an author in the literature, it would be classified as “wild-caught”. Specimens that originated from lab-rearing, i.e., when a gravid female was caught in the wild and its eggs were reared in the laboratory, and the consecutive stages were documented in the literature, they were classified as “lab-reared”.
Group composition (wild-caught and laboratory specimens given in numbers) as well as number of species identified in the data set. Current number of accepted species of the group (WoRMS Editorial Board, 2021) and the resulting taxonomic coverage of the group by our data in percent.
The four groups of crustaceans here were chosen firstly because data on specimens of these groups from the wild was available in high quality from multiple collections by our own documentation. This meant that we could ensure multiple images in different orientations of the same specimen, as well as a wider geographic coverage of specimens, which was a necessity for the study. Furthermore, we only chose groups with planktic and spiny larvae, as this was the trait we assumed to show variation from previous studies. Including more groups in the analysis would be desirable, but due to the state of documentation in the literature it is currently not possible. Larvae are rarely depicted in more than one orientation so that creating a sufficient sample size was hardly possible for more than the four groups presented here.
Data generation
For the documentation of specimens provided by museum collections, a macro-photography set-up was used. The specimens were photographed using a Canon Rebel T3i digital camera with a MP-E 65 mm macro lens. To reduce light-reflection induced artefacts, cross-polarized light was used, provided by a Canon Macro Twin Flash MT-24 or a Meike FC 100 LED ring light equipped with polarization filters and a cross-polarized filter in front of the camera lens (for a detailed description see Haug and Haug, 2014; Eiler et al., 2016). The specific components of this setup varied, but the principles and methodology remained the same throughout the documentation process.
In such high-resolution set-ups, specimens were recorded as stacks of images with changing in-focus layers. For larger specimens, multiple images were taken per specimen to cover the entire organism. To create sharp images from the focus stacks, we used the free software CombineZP (Alan Hadley, GNU), which combines the sharp (in-focus) regions of each image of a focus stack into one sharp image (Haug et al., 2008). In cases in which the specimen was documented via multiple stacks, the sharp images resulting from these stacks were then stitched together to full images, using the Photomerge function of Photoshop CS4 or CS6 (Haug et al., 2008).
We used Adobe Illustrator CS2 to manually reconstruct the outline of shields in dorsal and lateral view (see App. 1 for availability of dorsal and lateral outlines per specimen; Fig. 2). The only exception to this was the group Raninidae, for which no dorsal data was available. To eliminate the influence of left-right asymmetry on the data set in dorsal view, we only reconstructed the left or right half of the shield, depending on which one was preserved better, and then duplicated and mirrored it in anterior-posterior axis and stitched it together to form a whole symmetric shield.
Schematic figure of data generation process. s1: Reconstructing one half of the shield of a specimen; s2: duplicating, mirroring, and removing background; s3: chain coding the shield; s4: checking of graphical interpretation of chain code for accuracy; s5: transforming chain code into elliptic Fourier descriptor; s6: principal component analysis of elliptic Fourier descriptors of all specimens.
Morphometric conversion
To analyse the outlines of the specimens, an elliptic Fourier transformation was performed on the scaled reconstruction drawings (Fig. 2). Following Iwata and Ukai (2002) and Braig et al. (2019) we used the SHAPE software (© National Agricultural Research Organization of Japan) to first transform the outlines into vectorised objects, called chain codes. These chain codes consist of numeric values representing the vectorised shape and are then transformed into normalised elliptic Fourier descriptors (EFDs). This step is based on the Fourier transformation of functions, though not applied to functions, but to shapes of natural objects (Iwata and Ukai 2002; Braig et al., 2019). This step includes alignment, normalization, and scaling, which is important to decrease the influence of size difference in specimens on the analysis.
We decided on the outline-based approach in favor of landmarks, as high-quality landmarks were hard to select for shields apart from spine tips. Furthermore, outline approaches have been found to be equally efficient as landmark approaches (Dujardin et al., 2014).
Statistical analysis
The EFDs representing the specimens were then analysed with a principal component analysis (PCA; e.g., Hotelling, 1933; as featured in the SHAPE software package). Component loadings of the PCA are not given numerically using this method, but graphically to understand the change in shield shape. The mean shield shape as well as +/- 2 standard deviations of the mean shield shape in one or the other direction for each principal component are depicted. This imbalance can sometimes create positive/negative shapes which are impossible in nature. Such over-exaggerated shapes occur when some extreme forms are expressed in one direction, but not in the other. Then the extreme form is extrapolated for the other side of the respective component. Therefore, some shapes depicted by the component loadings do not actually exist in the data set, these are always marked as such in the figures. All further investigation of the data set was conducted in the R-statistics environment (ver. 4.1.0; R Core Team, 2021) using the interface R-Studio. Packages used were dispRity (ver. 1.6.0; Guillerme, 2018), ggplot2 (ver. 3.3.5; Wickham, 2016), RColorBrewer (ver. 1.1-2; Neuwirth, 2014), readxl (ver. 1.3.1; Wickham and Bryan, 2019), and vegan (ver.2.5-7; Oksanen et al., 2020). Part of the R-code is after Guillerme et al. (2020), full R-code is provided in App. 2.
The individual component scores for the different PCAs were visualised using their origin (i.e., wild-caught or lab-reared) for color coding and in the cases of Hippoidea and Raninidae using their phylogeny for symbol coding. To enable interpretation of the morphology beyond only one orientation, the first principal component of the dorsal data set was plotted against the first principal component of the lateral data set. This allowed us to graphically interpret a more complete morphological variation of the animals, not just looking at variation of one orientation (i.e., dorsal or lateral) akin to a 3D representation. All resulting plots are used as proxies for the respective morphospaces. Morphospaces are multi-dimensional spaces that describe the morphology and phenotypic configuration of organisms (Ricklefs and Travis, 1980; Gould, 1991; Mitteroecker and Huttegger, 2009). For visual inspection they are reduced to two dimensions, that being the first two principal components. For quantification, all effective components are used. “Effective” in this case means that the proportion of total variation described by each of these principal components had a value larger than 1/(number of total analyzed components), in this case 1/99.
Quantification of morphological diversity between groups in the morphospace was achieved by calculating “average displacement” of groups as outlined by Guillerme et al. (2020). Hereby, the ratio between the position of an observation in relation to the centroid of the observations’ group and the centre of the morphospace is calculated as displacement. This measure is then averaged for all observations of a group. The significance of the grouping variable (i.e. lab vs. wild) was tested using PERMANOVA (multivariate analysis of variance; Anderson, 2001). Advantageous of this approach is that all dimensions or principal components of a data set can be considered simultaneously.
RESULTS
Results of the morphological analysis of Galatheidae
The analysis of Galatheidae resulted in two PCAs, one of the dorsal and one of the lateral data set, with thirteen and twelve effective principal components, respectively, showing the morphological diversity of shield shapes apparent in the data sets. For visual inspection of morphological diversity, we only looked at the first two principal components of every data set due to limitations of graphical representation and the fact that the first two principal components covered most of the variation for all data sets (Galatheidae: dorsal PC1+2 = 80 %, lateral PC1+2 = 75 %; Hippoidea: dorsal PC1+2 = 85 %, lateral PC1+2 = 83 %; Raninidae: lateral PC1+2 = 84 %; Stomatopoda: dorsal PC1+2 = 70 %, lateral PC1+2 = 60 %). Therefore, a precise description is given on the first two components for every data set. The remaining principal components of the data sets will not be explained in detail, but graphical component loadings are given in the appendix (App. 3).
For the morphospace of the dorsal data set, PC1 described the width of the shield and length of spines. Positive values represented rather slim shields with a long rostrum and deep posterior notch, while negative values described wide shields with short rostrum and shallow posterior notch. PC2 described the prominence of the eye notch and posterior spines. Positive values described a wide rostrum base and a deep posterior notch, while negative values described a slim rostrum base and shallow posterior notch, with deep eye notches (Fig. 3A). Specimens from the plankton seemed to plot into the top right of the morphospace, indicating slimmer shields with relatively longer spines. Specimens from laboratory-rearings plotted into the bottom left of the morphospace, indicating bulkier shields with relatively short spines (Fig. 3A).
Plot of principal components from the PCA on the SHAPE analysis of shield outlines of Galatheidae. A: PC1 plotted against PC2, both from the dorsal analysis. Shapes included are from graphical component loadings and depict the mean shape of the principal component and +/-2 standard deviations of the mean shape. B: PC1 plotted against PC2, both from the lateral analysis. Shapes included are from graphical component loadings and depict the mean shape of the principal component and +/-2 standard deviations of the mean shape.
For the morphospace of the lateral data set, PC1 described the height of the shield, length of spines and prominence of the eye notch. Positive values represented a high shield with a short rostrum and a prominent, strongly inclined, eye notch, while negative values described slim shields with a long rostrum and more pronounced posterior spines and a shallow eye notch that almost appears missing. PC2 described the dorsal outline of the shield. Positive values described a weaker eye notch and posterior dorso-ventrally widened shield (dorsally convex outline), while negative values described a larger and pronounced eye notch and posteriorly slimmer shields (dorsally straighter outline; Fig. 3B). Specimens from the wild plotted on the left side of the morphospace, indicating slimmer shields with longer spines. Specimens from laboratory-rearings plotted on the right side of the morphospace, indicating bulkier shields with relatively short spines but more pronounced eye notches (Fig. 3B).
When incorporating both orientations by plotting PC1 of the dorsal data set against PC1 of the lateral data set, this separation became more apparent (Fig. 4A). Wild-caught specimens plotted on the center to bottom right of the morphospace, indicating slim, flat, and spiny shields. Lab-reared specimens plotted on the top left of the morphospace, indicating bulky and less spiny shields.
Plot of principal components from the PCA on the SHAPE analysis of shield outlines of Galatheidae and Hippoidea. A: PC1 of the dorsal analysis plotted against PC1 of the lateral analysis of Galatheidae. Shapes included are from graphical component loadings and depict the mean shape of the principal component and +/-2 standard deviations of the mean shape. Color coding: dark grey: shapes of PC1 from the dorsal analysis; light grey: shapes of PC1 of the lateral analysis. B: PC1 of the dorsal analysis plotted against PC1 of the lateral analysis of Hippoidea. Shapes included are from graphical component loadings and depict the mean shape of the principal component and +/-2 standard deviations of the mean shape. Color coding: dark grey: shapes of PC1 from the dorsal analysis; light grey: shapes of PC1 of the lateral analysis; dark red: impossible shapes of PC1 from the dorsal analysis; light red: impossible shapes of PC1 of the lateral analysis.
The morphological diversity analysis of the shields supported the graphical interpretation. Both the dorsal and lateral data set showed a significant influence of the sample origin on the position of the group within the morphospace (Tab. 2).
Results of permutational multivariate analysis of variance (PERMANOVA) on “average displacement” scores of wild-caught vs. laboratory specimens. Significant p-values are marked in bold. Abb.: F: pseudo F-statistic; R²: proportion of explained variation.
Results of the morphological analysis of Hippoidea
The analysis of Hippoidea resulted in two PCAs, one of the dorsal and one of the lateral data set, with eight and ten effective principal components respectively (for component loadings see App. 4).
For the morphospace of the dorsal data set, PC1 described the width of the shield and direction of posterior spines. Positive values described a wide shield in triangular shape with more laterally protruding posterior spines, while negative values described a slim shield with posteriorly protruding posterior spines. PC2 described the presence of posterior spines. Positive values describe an overly thin shield with triangular shape and steep protruding posterior spines, while negative values described a wider elliptic shield with no posterior spines (Fig. 5A). Specimens from the wild seemed to plot rather on the top right of the morphospace, indicating shields with relatively longer spines which also protrude in steeper angles. Specimens from laboratory-rearings plotted rather on the left and bottom of the morphospace, indicating slimmer shields with relatively short spines, especially considering posterior spines (Fig. 5A).
Plot of principal components from the PCA on the SHAPE analysis of shield outlines of Hippoidea. A: PC1 and PC2 of the dorsal analysis plotted against each other. Shapes included are from graphical component loadings and depict the mean shape of the principal component and +/-2 standard deviations of the mean shape. Color coding: dark grey: possible shapes of dorsal analysis; red: impossible shapes of dorsal analysis. B: PC1 and PC2 of the lateral analysis plotted against each other. Shapes included are from graphical component loadings and depict the mean shape of the principal component and +/-2 standard deviations of the mean shape. Color coding: light grey: possible shapes of lateral analysis; light red: impossible shapes of lateral analysis.
For the morphospace of the lateral data set, PC1 described the overall configuration of the spines. Positive values described a slim shield with a long rostrum and posteriorly protruding posterior spines. Negative values described a higher shield with ventrally protruding posterior spines. PC2 described the dorsal outline of the shield and the rostrum. Positive values described a higher shield (dorsally strongly convex) with shorter rostrum, while negative values described a slimmer shield shape without ventral spines and a longer rostrum (Fig. 5B). Here, no separation between lab-reared and wild-caught specimens was visible. Instead, a separation into the major ingroups of Hippoidea became apparent. Hippidae plotted on the left side of the morphospace, Blepharipodidae in the middle and Albuneidae on the right (Fig. 5B).
When incorporating both orientations, this phylogenetic separation of the three ingroups of Hippoidea was again visible. Larvae of Albuneidae plotted on the top left of the morphospace, larvae of Hippidae on the bottom right and larvae of Blepharipodidae in between (Fig. 4B).
The morphological diversity analysis of the shields showed that only in the dorsal data set, sample origin had a significant influence on the position of the groups within the morphospace (Tab. 2).
Results of the morphological analysis of Raninidae
Due to a lack of dorsal data for specimens, the analysis of Raninidae resulted only in a PCA of the lateral data set with ten effective principal components (for component loadings see App. 5).
PC1 of the lateral data set described the height of the shield. Positive values represented a flat shield with long rostrum and posterior spine, while negative values described a high shield with a ventrally prominent eye notch. PC2 described the bending of the shield. Positive values described a smaller shield with convex spines, while negative values described a larger shield with concave spines (Fig. 6). Here, no separation due to sample origin could be seen. Instead, a clear separation between the two species in the data set could be seen (Fig. 6). Representatives of Ranina ranina (Linnaeus, 1758) and Raninoides benedictiRathbun, 1935 plotted in two discrete clusters. The former plotted on the right of the morphospace due to its smaller shield and longer spines. The latter plotted on the left of the morphospace due to its larger shield and shorter spines.
Plot of principal components from the PCA on the SHAPE analysis of shield outlines of Raninidae. PC1 and PC2 of the lateral analysis plotted against each other. Shapes included are from graphical component loadings and depict the mean shape of the principal component and +/-2 standard deviations of the mean shape. Color coding: dark grey: possible shapes of the lateral analysis; red: impossible shapes of the lateral analysis.
The morphological diversity analysis of the shields showed no significant influence of the sample origin on the position of the groups within the morphospace (Tab. 2).
Results of the morphological analysis of Stomatopoda
The analysis of Stomatopoda resulted in two PCAs, one of the dorsal and one of the lateral data set, with thirteen and fifteen effective principal components, respectively (for component loadings see App. 6).
For the morphospace of the dorsal data set, PC1 described the width of the shield. Positive values described a slim shield with a long rostrum and deep posterior notch, while negative values described a wide shield with short rostrum and short posterior spines. PC2 described the prominence of spines. Positive values described a rectangular shield shape with short spines, while negative values described a more triangular shape with longer spines (Fig. 7A). Wild-caught specimens extend to the bottom-left and top-left corners of the morphospace, indicating wide shields with steep protruding spines and wide shields with additional anterior spines, respectively. Lab-reared specimens mostly plot in the center of the morphospace, indicating medium sized shields with relatively small spines (Fig. 7A).
Plot of principal components (PCs) from the principal component analysis (PCA) on the SHAPE analysis of shield outlines of Stomatopoda. Shapes included are from graphical component loadings and depict the mean shape of the principal component and +/-2 standard deviations of the mean shape. A: PC1 plotted against PC2 from the dorsal analysis. B: PC1 plotted against PC2 from the lateral analysis. Color coding: dark grey: possible shapes of the dorsal analysis; red: impossible shapes of the dorsal analysis; light grey: possible shapes of lateral analysis; light red: impossible shapes of lateral analysis.
For the morphospace of the lateral data set, PC1 described the presence of dorsal and posterior spines. Positive values described a shield with a dorsal spine but lack of posterior spines, while negative values described a shield without a dorsal spine but posterior spines present. PC2 described how flat the shield was. Positive values described a slim shield with long and straight rostrum and posterior spines, while negative values described a higher shield with a convex rostrum and prominent eye notch (Fig. 7B). Here, an imbalance of the sample sizes for wild vs. lab specimens of three to one becomes apparent (Fig. 7B). Lab-reared specimens plot mostly in the center of the morphospace and in the bottom left, indicating flatter shields, with smaller rostrum and dorsal spine to bulky shields with extended posterior spines. Wild-caught specimens plot in the top two quarters of the morphospace and the bottom right, indicating shields with extended posterior and dorsal spines to flat shields with smaller dorsal spines and long rostrums (Fig. 7B).
When incorporating both orientations, sample sizes were more balanced. The group of wild-caught larvae plotted diagonally across the morphospace from the top left of the morphospace to the bottom right (Fig. 8). This indicated wide shields and dorsal spines on the top left and slim shields with long spines on the bottom right. The lab-reared larvae plotted on the bottom right of the morphospace also indicating slim shields with long spines. Some outliers made an exception for the lab-reared specimens, plotting on the bottom middle of the morphospace (Fig. 8).
Plot of principal components from the PCA on the SHAPE analysis of shield outlines of Stomatopoda. PC1 of the dorsal analysis plotted against PC1 of the lateral analysis. Shapes included are from graphical component loadings and depict the mean shape of the principal component and +/-2 standard deviations of the mean shape. Color coding: dark grey: shapes of PC1 from the dorsal analysis; light grey: shapes of PC1 of the lateral analysis; dark red: impossible shapes of PC1 from the dorsal analysis; light red: impossible shapes of PC1 of the lateral analysis.
The morphological diversity analysis of the shields showed a significant influence of the sample origin on the position of the groups within the morphospace, both for the dorsal and lateral data set (Tab. 2).
DISCUSSION
Limitations of the approach
Gathering data for this analysis posed a challenge, as high-quality depictions of larvae in more than one orientation in literature were scarce. Often, only one orientation was available, reconstruction drawings in the literature were of low quality (e.g., Seridji, 1995), or scales were missing, rendering potential material useless for this study. This affected the total sample size of Raninidae the most. The group Stomatopoda was affected, as well. Due to our previous work with the group (e.g., Haug et al., 2016; 2018), we had a large sample size for wild-caught specimens from our own documentation efforts, but a comparatively small sample size for laboratory specimens. A result of not every specimen having a depiction of both orientations, was that the dorsal or lateral data sets included more specimens than the combined one.
Another issue was low taxonomic coverage. Across groups, the taxonomic coverage ranges between 5 to 10 percent, mostly because specimens from museum collections (mostly wild-caught material) could not be identified to species level. Therefore, the number of actual taxonomic diversity covered is likely higher for each group than stated here. In the data set of Stomatopoda, many specimens are wild-caught, originating from museum collections and could not be identified to species level. The inability to associate larvae with adults is a general problem for mantis shrimps (Tang et al., 2010). This taxonomic uncertainty led to a correlation of phylogeny and sample origin for mantis shrimps in our analysis.
Phylogenetic diversity also influenced the data, especially for Hippoidea. To reduce the effect, a smaller ingroup could be chosen, e.g., Hippidae instead of Hippoidea. However, the current availability of data in the literature makes this approach not feasible (e.g., only 13 specimens of Hippidae were available in dorsal and lateral view).
Expressed morphological diversity of larvae
The graphical analysis of the dorsal and lateral data set of Galatheidae showed separation between specimens from the wild and the lab. Wild-caught larvae show slimmer shields with longer rostrums and longer posterior spines (Figs. 3, 4). Laboratory-reared larvae show shorter rostrums and shorter poster spines with wider shields and less variation in morphology (Figs. 3, 4). This expressed phenotypic variability of the group Galatheidae agrees with our expectation. The statistical analysis, incorporating all principal components of a data set into one analysis, also showed that sample origin had a significant effect on the position of groups within the respective morphospaces (Tab. 2).
The graphical analysis of the dorsal and lateral data set of Hippoidea did not show a strong separation between lab-reared and wild-caught larvae. Wild-caught larvae show shields with laterally protruding spines, lab-reared specimens show more posteriorly protruding spines and especially forms with no posterior visible spines in dorsal view (Fig. 5). This difference in morphology of the groups is in line with an earlier observation of Braig et al. (2021). A stronger separation in the data sets is actually caused by the shield configuration of the three ingroups, which becomes apparent when coding for phylogenetic ingroups of Hippoidea (Fig. 4B). Larvae of Albuneidae have two posterior dorsal spines reaching posteriorly, while those of Hippidae have posterior ventral spines reaching ventrally (Knight, 1967; Stuck and Truesdale, 1986). Larvae of Blepharipodidae lastly have no posterior spines (Johnson and Lewis, 1942; Fig. 4B). The statistical analysis showed a significant effect of sample origin on the group position within the morphospace of the dorsal data set, but not the lateral data set.
The graphical analysis of the Raninidae data set did not reveal any phenotypic variability. A lack of dorsal data was added to the fact that only two identified species made up the data set, Ranina ranina and Raninoides benedicti. The morphological differences between those two species were discerned by the analysis of the lateral data set when adding phylogenetic coding (Fig. 6). Representatives of R. benedicti showed prominent shields and short spines, while representatives of R. ranina showed slimmer shields and longer spines. The statistical analysis showed no significant effect of sample origin (Tab. 2).
The graphical analysis of the dorsal and the lateral data set of Stomatopoda showed, respectively, a larger morphological diversity expressed by wild-caught larvae compared to laboratory-reared larvae (Figs. 7, 8). While wild-caught specimens expressed dorsal spines and wider shields as well as slimmer shields and longer spines, lab-reared specimens mostly showed slim shields with small dorsal spines. Therefore, the difference between groups mostly lies within the dorsal and lateral spines that wild-caught larvae express more dominantly than lab-reared ones. The statistical analysis showed a significant effect of sample origin on the group position within the morphospace of the dorsal and lateral data sets (Tab. 2).
Observations from literature
In the literature, phenotypic variability of Galatheidae has been mentioned considering larval development, which has been described to be variable and can lead to a different number of larval stages (e.g., Christiansen and Anger, 1990).
The developmental cycle in Hippoidea has been generally described as variable (Knight, 1967). Especially concerning comparison between wild-caught and lab-reared larvae, the former having been described to be further developed compared to the latter, when looking at equivalent larval stages (Knight, 1967). Yet, this increased maturity of wild-caught larvae concerned the setation on some appendages and general body growth, not the shield specifically.
For Raninidae, phenotypic variability has been reported in the literature. Knight (1968) mentioned that representatives of R. benedicti from the wild have longer spines on the shield than those reared in the laboratory. This observation coincides with our expectation of phenotypic variability between wild-caught and lab-reared larvae. However, we could not replicate this observation quantitatively in our analysis. Laboratory specimens of Raninidae have also been reported to be smaller than wild-caught larvae of the same larval stage (Knight, 1968).
Phenotypic variability in eumalacostracan larvae
The phenotypic variability found in five of the seven data sets does mostly match with our expectations. For Galatheidae, specimens from the wild express longer spines than laboratory-caught specimens. For Hippoidea, at least dorsally, wild-caught specimens show longer, more laterally protruding posterior spines. For Stomatopoda, wild-caught specimens show wider shields with dorsal spines. Therefore, the overall pattern of the groups seems to be indeed a more prominently spiny appearance for plankton specimens.
The driver behind this phenotypic variability is still unclear. It however seems to be the case that larvae become larger in the wild than in the lab (Knight, 1967; 1968). A larger body size makes it easier for visual predators, such as juvenile fishes, to spot the larvae (O’Brien, 1987; Anger, 2001; Kiørboe, 2011). Therefore, larger spines could be a defensive mechanism against predation. Using spines as a defensive mechanism was already described in some crab zoeas (Morgan, 1990). On the other hand, reaching a certain size threshold can protect from predation by different predators (O’Brien, 1979; Leonie, 2017). These size thresholds can be reached faster by increasing relative spine length.
A larger body size also has the effect of increasing body weight and therefore faster sinking rates of the larvae. To counteract this, the larva would either have to spend more energy on locomotion to maintain its position in the water column or increase its hydrostatic updrift. The latter could be achieved by larger spines as well (Anger, 2001). Lastly, the larger spines could be a general side effect of larger body size. However, it was also mentioned that spines in some groups were relatively longer as well, not just absolutely (Knight, 1968). Here, a connection could be seen to the relative development of the larvae. Authors before have observed that larvae from the wild would be further developed than larvae from the lab (Knight, 1967; Christiansen and Anger, 1990). Relatively longer spines could be one expression of this maturity.
A discrepancy in observed morphological differences is that while larvae of Galatheidae from the wild show slim shields rather than wide ones (Fig. 4A), larvae of Stomatopoda from the wild do also show wide shield forms. These wide shields often belong to “extreme types” of mantis shrimp larvae (“balloon shaped” or “flying saucer shaped”; Haug et al., 2016; 2018), of which there are not as many in the lab-reared data set. Such extreme shapes have not been described in Galatheidae so far. We cannot exclude that these “extreme types” of mantis shrimp larvae are representatives of species only found in the wild-caught sample, making it a phylogenetic signal rather than due to sample origin. But the lab-reared data set covers all larger ingroups of Stomatopoda and has larvae from both ‘spearer’ and ‘smasher’-type mantis shrimps (two types of mantis shrimps are usually differentiated due to the morphology of the adult major raptorial appendages; Patek et al., 2004; Patek and Caldwell, 2005). Therefore, this seems insufficient to be the sole explanation of the observed morphological diversity.
CONCLUSION
Some of the investigated eumalacostracan larvae show phenotypic variability in their shield morphology. This variability especially concerns shield spines, which are more prominent in wild-caught larvae than in lab-reared larvae. Possible reasons could be decreasing predation pressure or increasing hydrostatic updrift, but further studies are needed to evaluate these possibilities. In the groups where we could not find such patterns, strong phylogenetic signals could be identified as a reason. Larger sample sizes of smaller phylogenetic groups are needed in these cases to investigate phenotypic variability. However, this approach is so far not possible due to a lack of material in the literature. This unavailability of morphological data is somewhat puzzling, since crustacean larvae make up a large part of the zooplankton and are important components in food webs. Yet, morphological data on its larger representatives is not widely available. We therefore hope that this study emphasizes the importance of careful documentation and consideration of larval material and stimulates the accumulation of more quantifiable data.
ACKNOWLEDGEMENTS
We thank all students from LMU Munich who helped provide data for this study, especially Amir Fotouhi and Victor Posada Zuluaga for the data on Hippoidea, and Ian Haase for the data on Raninidae and Stomatopoda. We are grateful to all museum curators and collection managers for providing access to the material, namely Oliver Coleman (Berlin), Jørgen Olesen, Danny Eibye-Jacobsen, and Tom Schiøtte (all Copenhagen), the curating people from the crustacean collection at the Senckenberg Naturmuseum Frankfurt, Laure Corbari (Paris), Martin Schwentner (now Vienna, formerly Hamburg) and Nancy Mercado Salas (Hamburg). We thank two anonymous reviewers whose comments greatly improved the manuscript. We thank all providers of free software and Open-Access tools. We are grateful to Prof. J. Matthias Starck, LMU Munich, for long-standing support.
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Appendix
Appendix 2.
R-code used in this study.
# R-Code of Braig, F., Haug, C. & Haug, J. T.
# Geometric morphometrics uncover phenotypic variability in the shields of wild-
# vs. lab-reared eumalacostracan larvae
# START WORKFLOW ---------------------------------------------------------------
library(ggplot2)
library(MASS)
library(car)
library(vegan)
library(tidyverse)
library(RColorBrewer)
library(dispRity)
set.seed(1234)
# DATA SETS ------------------------------------------------------------------
# gal_dor <- Dorsal data set of Galatheidae
# gal_lat <- Lateral data set of Galatheidae
# gal_dorlat <- Dorsal and Lateral data set of Galatheidae combined
# hip_dor <- Dorsal data set of Hippoidea
# hip_lat <- Lateral data set of Hippoidea
# hip_dorlat <- Dorsal and Lateral data set of Hippoidea combined
# ran_lat <- Lateral data set of Raninidae
# sto_dor <- Dorsal data set of Stomatopoda
# sto_lat <- Lateral data set of Stomatopoda
# sto_dorlat <- Dorsal and Lateral data set of Stomatopoda combined
# PLOTS ------------------------------------------------------------------------
#Plot 2 PC's as basic as possible
ggplot(gal_dor, aes(x=PC1, y=PC2, color=origin)) +
geom_point()
#Plot 2 PC's accoridng to origin with labels and ellipses
ggplot(gal_lat, aes(x = PC1, y = PC2, color = origin, label = no)) +
geom_point() +
scale_color_manual(values=c("purple", "orange")) +
geom_text(aes(label=no), hjust = 0, vjust = 1, show.legend = FALSE) +
stat_ellipse(geom = 'polygon', alpha = .1, aes(fill = origin)) +
scale_fill_manual(values = c("purple", "orange")) +
coord_fixed (ratio = 1, xlim = NULL, ylim = NULL, expand = TRUE) +
theme_gray()
# MORPHOLOGICAL DIVERSITY ANALYSIS----------------------------------------------
data <- data.frame(gal_dor[, 6:18]) #Only numerical data
rownames(data) <- 1:nrow(data)
galdor_subsets <- custom.subsets(data, group = list("lab" = c(1:35),
"wild" = c(36:61)))
galdor_bootstrapped <- boot.matrix(data = galdor_subsets, bootstraps = 10000)
disparity.metric <- function(matrix) mean(dispRity::displacements(matrix))
galdor_disparity <- dispRity(data = galdor_bootstrapped,
metric = disparity.metric)
summary(galdor_disparity)
plot(galdor_disparity)
plot(galdor_disparity, type = "preview")
test.dispRity(galdor_disparity, test = adonis.dispRity) #Passing on to
# ANOVA function from package package vegan
test.dispRity(galdor_disparity, test = t.test, correction = "bonferroni")
Appendix 3.
Principal components of Galatheidae from principal component analysis on the shield outline and percentage of total variation in the data set explained by each principal component. A: Dorsal data set. B: Lateral data set.
Appendix 4.
Principal components of Hippoidea from principal component analysis on the shield outline and percentage of total variation in the data set explained by each principal component. A: Dorsal data set. B: Lateral data set.
Appendix 5.:
Principal components of Raninidae from principal component analysis on the lateral shield outline and percentage of total variation in the data set explained by each principal component.
Appendix 6.
Principal components of Stomatopoda from principal component analysis on the shield outline and percentage of total variation in the data set explained by each principal component. A: Dorsal data set. B: Lateral data set.
























