|Haddock, Steven H. D.
|Monterey Bay Aquarium Research Institute (MBARI)
|Principal Investigator, Contact
|Dunn, Casey W.
|Woods Hole Oceanographic Institution (WHOI BCO-DMO)
|BCO-DMO Data Manager
We extracted categorical diet data for different siphonophore species from published sources, including seminal papers [@biggs1977field;@purcell1981dietary;@purcell1984functions;@Mackie:1987uy;@pugh1988two;@purcell1984predation;@andersen1981redescription], and ROV observation data [@choy2017deep;@hissmann2005situ] with the assistance of Elizabeth Hetherington and C. Anela Choy (data available in Dryad repository). In order to detect coarse-level patterns in feeding habits, the data were merged into feeding guilds. For more details on how the diet data was curated and summarized into guilds, please see Supplementary Methods.
We also extracted copepod prey length data from [@purcell1984functions]. To calculate specific prey selectivities, we extracted quantitative diet and zooplankton composition data from [@purcell1981dietary], matched each diet assessment to each prey field quantification by site, calculated Ivlev’s electivity indices [@jacobs1974quantitative], and averaged those by species (data available in Dryad repository).
Published May 07, 2021 on Dryad. https://doi.org/10.5061/dryad.p2ngf1vp2
We used a series of phylogenetic comparative methods to test the evolutionary hypotheses presented in this study. We reconstructed ancestral states using ML (R phytools::anc.ML [@revell2012phytools]), and stochastic character mapping (R phytools::make.simmap) for categorical characters. For more details on the data wrangling prior to these analyses, please see the Supplementary Methods. R scripts are available in the Dryad repository.
In order to study the evolution of predatory specialization, we reconstructed components of the diet and prey selectivity on the phylogeny using ML (R phytools::anc.ML). To identify evolutionary associations of diet with tentillum and nematocyst characters, we compared the performance of a neutral evolution model to that of a diet-driven directional selection model. First, we collapsed the diet data into the five feeding guilds mentioned above (fish specialist, small crustacean specialist, large crustacean specialist, gelatinous specialist, generalist), based on which prey types they were observed consuming most frequently. Then, we reconstructed the feeding guild ancestral states using the ML function ace (package ape [@paradis2019package]), removing tips with no feeding data. The ML reconstruction was congruent with the consensus stochastic character mapping (S15). Then, using the package *OUwie* [@beaulieu2012ouwie], we fitted an OU model with multiple optima and rates of evolution (OUm) matched to the reconstructed ancestral diet regimes, a single optimum OU model, and a BM null model, inspired by the analyses in [@cressler2015detecting]. We then ranked the models in order of increasing parametric complexity (BM, OU, OUm), and compared the corrected Akaike Information Criterion (AICc) support scores [@sugiura1978further] to the lowest (best) score, using a cutoff of 2 units to determine significantly better support. When the best fitting model was not significantly better than a less complex alternative, we selected the least complex model (S9). In addition, we calculated and reported the model adequacy scores using the R package *arbutus* [@pennell2015model].
In order to study correlations between the rates of evolution between different characters, we fitted a set of evolutionary variance-covariance matrices [@revell2009phylogenetic] (R phytools::evol.vcv). For more details on the data wrangling preceding these analyses, please see Supplementary Methods. To test whether phenotypic integration changed across selective regimes determined by the reconstructed feeding guilds, we carried out character-pairwise variance-covariance analysis comparing alternative models (R phytools::evolvcv.lite), including those where correlations are the same across the whole tree and models where correlations differ between selective regimes (S19). Number of taxa used in each pairwise comparison is reported in S20. Finally, we compared regime-specific variance-covariance matrices to the general matrix and to their preceding regime matrix to identify the changes in character dependences unique to each regime (S21-22).
We carried out a linear discriminant analysis of principal components (DAPC) using the dapc function (R adegenet::dapc) [@jombart2010discriminant]. This function allowed us to incorporate more predictors than individuals. We generated discriminant functions for feeding guild, and for the presence of copepods, fish, and shrimp (large crustaceans) in the diet (S10-13). From these DAPCs we obtained the highest contributing morphological characters to the discrimination (characters in the top quartile of the weighted sum of the linear discriminant loadings controlling for the eigenvalue of each discriminant). In order to identify the sign of the relationship between the predictor characters and prey type presence in the diet, we then generated generalized logistic regression models (as a type of generalized linear model, or GLM using R stats::glm) and phylogenetic generalized linear models (R phylolm::phyloglm) with the top contributing characters (from the corresponding DAPC) as predictors (S14). We also carried out these GLMs on the Ivlev’s selectivity indices for each prey type calculated from [@purcell1981dietary]. In addition, we ran a series of comparative analyses to address hypotheses of diet-tentillum relationships posed in the literature. Additional details on the DAPC optimization are available in the Supplementary Methods.
Diet data curation: We removed the gelatinous prey observations for *Praya dubia* eating a ctenophore and a hydromedusa, and for *Nanomia* sp. eating *Aegina* since we believe these are rare events that have a much larger probability of being detected by ROV methods than their usual prey, and it is not clear whether the medusae were attempting to prey upon the siphonophores. Personal observations on feeding (from SHDH, Anela Choy, and Philip Pugh) were also included for *Resomia ornicephala*, *Lychnagalma utricularia*, *Bargmannia amoena*, *Erenna richardi*, *Erenna laciniata*, *Erenna sirena*, and *Apolemia rubriversa*. The feeding guilds declared in this study are: small-crustacean specialist (feeding mainly on copepods and ostracods), large crustacean specialist (feeding on large decapods, mysids, or krill), fish specialist (feeding mainly on actinopterygian larvae, juveniles, or adults), gelatinous specialist (feeding mainly on other siphonophores, medusae, ctenophores, salps, and/or doliolids), and generalist (feeding on a combination of the aforementioned taxa, without favoring any one prey group). These were selected to minimize the number of categories while keeping the most different types of prey separate. The gut content observations on *Forskalia* sp. were synonymized to an arbitrary *Forskalia* species on the tree (*F. tholoides*) for comparative analyses.
Data wrangling for comparative analyses:
For comparative analyses, we removed species present in the tree but not represented in the morphology data, and *vice versa*. Although we measured specimens labeled as *Nanomia bijuga* and *Nanomia cara*, we are not confident in some of the species-level identifications, and some specimens were missing diagnostic zooids. Thus, we decided to collapse these into a single taxonomic concept (*Nanomia* sp.). All *Nanomia* sp. observations were matched to the phylogenetic position of *Nanomia bijuga* in the tree. We carried out all phylogenetic comparative statistical analyses in the programming environment R [@team2017r], using the Bayesian ultrametric species tree (S8), and incorporating intraspecific variation estimated from the specimen data as standard error whenever the analysis tool allowed it. R scripts and summarized species-collapsed data available in the Dryad repository. For each character (or character pair) analyzed, we removed species with missing data and reported the number of taxa included. We tested each character for normality using the Shapiro-Wilk test [@shapiro1965analysis], and log-transformed those that were non-normal.
Data wrangling for the variance-covariance analyses:
When fitting all variance-covariance terms simultaneously (S16-18), we selected the largest set of characters that would allow the analysis to run without computational singularities. This excluded many of the morphometric characters which are linearly dependent on other characters. Since the functions do not tolerate missing data, we ran the analyses in two ways: One including all taxa but transforming absent states to zeroes, and another removing the taxa with absent states. These analyses could only be carried out on the subset of taxa for which diet data is available, and only among character pairs that are not computationally singular for that taxonomic subset. Gelatinous specialist correlations could only be estimated for a small subset of characters present in *Apolemia* (S21F, S22E, S23D) and should be interpreted with care.
Comparative tools used to test character associations:
To test for correlated evolution among binary characters, we used Pagel’s test [@pagel1994detecting]. To characterize and evaluate the relationship between continuous characters, we used phylogenetic generalized least squares regressions (PGLS) [@grafen1989phylogenetic]. To compare the evolution of continuous characters with categorical aspects of the diet, we carried out a phylogenetic logistic regression (R nlme::gls using the 'corBrownian' function for the argument ‘correlation’).
Some taxa have inapplicable states for certain absent characters (such as the length of a nematocyst subtype that is not present in a species), which are problematic for DAPC analyses. We tackled this by transforming the absent states to zeroes. This approach allows us to incorporate all the data, but creates an attraction bias between small character states (*e.g.* small tentilla) and absent states (*e.g.* no tentilla). Absent characters are likely to be very biologically relevant to prey capture and we believe they should be accounted for in a predictive approach. We limited the number of linear discriminant functions retained to the number of groupings in each case. We selected the number of principal components retained using the a-score optimization function (R adegenet::optim.a.score) [@jombart2010discriminant] with 100 iterations, which yielded more stable results than the cross validation function (R adegenet::xval). This optimization aims to find the compromise value with highest discrimination power with the least overfitting. The discriminant analysis for feeding guild (7 principal components, 4 discriminants) produced 100% discrimination, and the highest loading contributions were found for the characters (ordered from highest to lowest): Involucrum length, heteroneme volume, heteroneme number, total heteroneme volume, tentacle width, heteroneme length, total nematocyst volume, and heteroneme width (S10).
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Food webs describe who eats whom, tracing the flow of energy from plants up to large animals. While many connections in food webs on land are quite familiar (lions eat antelope and antelope eat grass, for example), there are large gaps in our understanding of ocean food webs. Closing these gaps is critical to understanding how nutrients and energy move through ocean ecosystems, how organisms interact in the ocean, and how best to manage ocean resources. This project will study ocean food web structure with a focus on siphonophores, an abundant group of predators in the open ocean that range in length from less than an inch to more than one hundred feet. Siphonophores are closely related to corals and many jellyfish. They are known to be important predators within ocean food webs, but they are difficult to study because they live across great ocean depths and are gelatinous and fragile. The details of what they eat, as well as many other features of their biology, remain poorly known. This project will combine direct observations of feeding, genetic analysis of siphonophore gut contents, and stable isotope analyses to identify what different species of siphonophores eat. The team will also examine why they eat what they do. This will provide a new understanding of how the structure of food webs arise, aiding in our ability to predict future changes to food webs as the global climate shifts. Siphonophores feed in a very unique manner--they have highly specialized tentacles that are used solely for capturing prey--thus, the prey captured is determined largely by the anatomy and function of these tentacles. The project will describe these tentacles, reconstruct their evolutionary history, and investigate how evolutionary shifts in tentacle structure have led to changes in diet. This project will train one PhD student, one Master's student, a postdoc, and undergraduate students, including individuals of underrepresented groups. This project will support the production of scientifically rigorous yet engaging videos, foster the expansion of a citizen-science program, and create K-12 teaching modules.
This project will advance three scientific aims: First, it will identify the diet of a diverse range of siphonophores using DNA metabarcoding of gut contents and prey field, remotely operated vehicle (ROV) video of prey encounters, and stable isotope analysis. These approaches are highly complementary and allow for extensive cross validation. Second, the project will characterize the selectivity of siphonophore diets by comparing them to the relative prey abundances in the habitats of each of these species. Third, the project will characterize the structure of the siphonophore prey capture apparatus across species through detailed morphological analysis of their tentacles and nematocysts. These data will be integrated in an ecological and evolutionary framework to identify predator features associated with prey specialization. In a larger context, addressing these questions will advance our understanding of oceanic predation by revealing how evolutionary changes in predator selectivity correspond to evolutionary changes in habitat and feeding apparatus and how these changes shape current food web structure in the open ocean. We will test and refine an integrated approach to describing the structure and origin of food web topology, and evaluate the potential for phylogenetic relationships to explain prey selectivity.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.