Carbonate communities: The activity of anaerobic methane oxidizing microbes facilitates precipitation of vast quantities of authigenic carbonate at methane seeps. Here we demonstrate the significant role of carbonate rocks in promoting diversity by providing unique habitat and food resources for macrofaunal assemblages at seeps on the Costa Rica margin (400–1850 m). The attendant fauna is surprisingly similar to that in rocky intertidal shores, with numerous grazing gastropods (limpets and snails) as dominant taxa. However, the community feeds upon seep-associated microbes. Macrofaunal density, composition, and diversity on carbonates vary as a function of seepage activity, biogenic habitat and location. The macrofaunal community of carbonates at non-seeping (inactive) sites is strongly related to the hydrography (depth, temperature, O2) of overlying water, whereas the fauna at sites of active seepage is not. Densities are highest on active rocks from tubeworm bushes and mussel beds, particularly at the Mound 12 location (1000 m). Species diversity is higher on rocks exposed to active seepage, with multiple species of gastropods and polychaetes dominant, while crustaceans, cnidarians, and ophiuroids were better represented on rocks at inactive sites. Macro-infauna (larger than 0.3 mm) from tube cores taken in nearby seep sediments at comparable depths exhibited densities similar to those on carbonate rocks, but had lower diversity and different taxonomic composition. Seep sediments had higher densities of ampharetid, dorvilleid, hesionid, cirratulid and lacydoniid polychaetes, whereas carbonates had more gastropods, as well as syllid, chrysopetalid and polynoid polychaetes. Stable isotope signatures and metrics: The stable isotope signatures of carbonates were heterogeneous, as were the food sources and nutrition used by the animals. Carbonate δ13Cinorg values (mean = -26.98‰) ranged from -53.3‰ to +10.0‰, and were significantly heavier than carbonate δ13Corg (mean = -33.83‰), which ranged from -74.4‰ to -20.6‰. Invertebrates on carbonates had average δ13C (per rock) = -31.0‰ (range -18.5‰ to -46.5‰) and δ15N = 5.7‰ (range -4.5‰ to +13.4‰). Average δ13C values did not differ between active and inactive sites; carbonate fauna from both settings depend on chemosynthesis-based nutrition. Community metrics reflecting trophic diversity (SEAc, total Hull Area, ranges of δ13C and δ15N) and species packing (mean distance to centroid, nearest neighbor distance) also did not vary as a function of seepage activity or site. However, distinct isotopic signatures were observed among related, co-occurring species of gastropods and polychaetes, reflecting intense microbial resource partitioning. Overall, the substrate and nutritional heterogeneity introduced by authigenic seep carbonates act to promote diverse, uniquely adapted assemblages, even after seepage ceases. The macrofauna in these ecosystems remain largely overlooked in most surveys, but are major contributors to biodiversity of chemosynthetic ecosystems and the deep sea in general.

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Biodiversity on the Rocks: Macrofauna Inhabiting Authigenic Carbonate at Costa Rica Methane Seeps.

50 Years of Image Analysis

Summary 1. Many ecological and evolutionary studies seek to explain patterns of shape variation and its covariation with other variables. Geometric morphometrics is often used for this purpose, where a set of shape variables are obtained from landmark coordinates following a Procrustes superimposition. 2. We introduce geomorph: a software package for performing geometric morphometric shape analysis in the R statistical computing environment. 3. Geomorph provides routines for all stages of landmark-based geometric morphometric analyses in two and three-dimensions. It is an open source package to read, manipulate, and digitize landmark data, generate shape variables via Procrustes analysis for points, curves and surfaces, perform statistical analyses of shape variation and covariation, and to provide graphical depictions of shapes and patterns of shape variation. An important contribution of geomorph is the ability to perform Procrustes superimposition on landmark points, as well as semilandmarks from curves and surfaces. 4. A wide range of statistical methods germane to testing ecological and evolutionary hypotheses of shape variation are provided. These include standard multivariate methods such as principal components analysis, and approaches for multivariate regression and group comparison. Methods for more specialized analyses, such as for assessing shape allometry, comparing shape trajectories, examining morphological integration, and for assessing phylogenetic signal, are also included. 5. Several functions are provided to graphically visualize results, including routines for examining variation in shape space, visualizing allometric trajectories, comparing specific shapes to one another and for plotting phylogenetic changes in morphospace. 6. Finally, geomorph participates to make available advanced geometric morphometric analyses through the R statistical computing platform.

https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.12035

geomorph: an r package for the collection and analysis of geometric morphometric shape data

It is clear that the above can lead to confusion when scientists of different countries are trying to communicate with each other. Another example is the burrowing rodent called a gopher found throughout the western United States. In the southeastern United States the term gopher refers to a burrowing turtle very similar to the desert tortoise found in the American southwest. One final example; two North American mammals known as the elk and the caribou are known in Europe as the reindeer and the elk. We never sing “Rudolph the Red-nosed elk”! Confused? This was the reason for creating an internationally recognized system of naming organisms. To avoid confusion, living organisms are assigned a scientific name based on Latin or Latinized words. The English sparrow is Passer domesticus or Passer domesticus (italics or underlining these two names is the official written representation of a scientific name). Using a uniform naming system allows scientists from all over the world to recognize exactly which life form a scientist is referring to. The naming process is called the binomial system of nomenclature. Passer is comparable to a surname and is called the genus, while domesticus is the specific or species name (like your given name) of the English sparrow. Now scientists can give all sparrow-like birds the genus Passer but the species name will vary. All similar genera (plural for genus) can be grouped into another, “higher” category (see below). Study the following for a more through understanding of taxonomy. Taxonomy Analogy Kingdom: Animalia Country

Diversity of life

Stable isotope analysis has emerged as one of the primary means for examining the structure and dynamics of food webs, and numerous analytical approaches are now commonly used in the field. Techniques range from simple, qualitative inferences based on the isotopic niche, to Bayesian mixing models that can be used to characterize food-web structure at multiple hierarchical levels. We provide a comprehensive review of these techniques, and thus a single reference source to help identify the most useful approaches to apply to a given data set. We structure the review around four general questions: (1) what is the trophic position of an organism in a food web?; (2) which resource pools support consumers?; (3) what additional information does relative position of consumers in isotopic space reveal about food-web structure?; and (4) what is the degree of trophic variability at the intrapopulation level? For each general question, we detail different approaches that have been applied, discussing the strengths and weaknesses of each. We conclude with a set of suggestions that transcend individual analytical approaches, and provide guidance for future applications in the field.

/pdf/applying-stable-isotopes-to-examine-food-web-structure-an-1q6l7odjeq.pdf

Applying stable isotopes to examine food‐web structure: an overview of analytical tools

geomorph: Software for geometric morphometric analyses

The ability to analyse multidimensional traits and other multivariate data has become a requisite skill for evolutionary biologists and ecologists over the last few decades. This reality is important, as highdimensional data are not just encumbrance, but often necessary for addressing important research questions. Advanced computing technology has spurred the quick collection of “highdimensional” data (e.g., genomics, imaging, remote sensing) and some characteristics of research subjects just cannot be described with scant information. Highdimensional (continuous) data are multivariateresponse data that require strict attention to using all variables (p) in analyses, even if they exceed the number of observations (n), as they are needed to describe complex multidimensional traits of research subjects. For example, a comparative morphometric analysis might include many anatomical landmarks (large p, relative to n) whose twoor threedimensional Cartesian coordinates comprise a single trait, organism “shape” (Adams, Rohlf, & Slice, 2013). It is not uncommon to have anatomical landmarks per specimen exceed the number of specimens, especially if such information is obtained by 3D laser scanners. The general analytical problem highdimensional data pose is that when p exceeds the error degrees of freedom of linear models (n minus the number of model parameters), parametric analyses Received: 22 January 2018 | Accepted: 30 April 2018 DOI: 10.1111/2041-210X.13029

https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13029

RRPP: An r package for fitting linear models to high‐dimensional data using residual randomization

The analysis of phenotypic change is important for several evolutionary biology disciplines, including phenotypic plasticity, evolutionary developmental biology, morphological evolution, physiological evolution, evolutionary ecology and behavioral evolution. It is common for researchers in these disciplines to work with multivariate phenotypic data. When phenotypic variables exceed the number of research subjects--data called 'high-dimensional data'--researchers are confronted with analytical challenges. Parametric tests that require high observation to variable ratios present a paradox for researchers, as eliminating variables potentially reduces effect sizes for comparative analyses, yet test statistics require more observations than variables. This problem is exacerbated with data that describe 'multidimensional' phenotypes, whereby a description of phenotype requires high-dimensional data. For example, landmark-based geometric morphometric data use the Cartesian coordinates of (potentially) many anatomical landmarks to describe organismal shape. Collectively such shape variables describe organism shape, although the analysis of each variable, independently, offers little benefit for addressing biological questions. Here we present a nonparametric method of evaluating effect size that is not constrained by the number of phenotypic variables, and motivate its use with example analyses of phenotypic change using geometric morphometric data. Our examples contrast different characterizations of body shape for a desert fish species, associated with measuring and comparing sexual dimorphism between two populations. We demonstrate that using more phenotypic variables can increase effect sizes, and allow for stronger inferences.

/pdf/a-method-for-analysis-of-phenotypic-change-for-phenotypes-1eegedmtvc.pdf

A method for analysis of phenotypic change for phenotypes described by high-dimensional data

Many evolutionary studies require an understanding of phenotypic change. However, while analyses of phenotypic variation across pairs of evolutionary levels (populations or time steps) are well established, methods for testing hypotheses that compare evolutionary sequences across multiple levels are less developed. Here we describe a general analytical procedure for quantifying and comparing patterns of phenotypic evolution. The phenotypic evolution of a lineage is defined as a trajectory across a set of evolutionary levels in a multivariate phenotype space. Attributes of these trajectories (their size, direction, and shape), are quantified, and statistically compared across pairs of taxa, and a summary statistic is used to determine the extent to which patterns of phenotypic evolution are concordant across multiple taxa. This approach provides a direct quantitative description of how patterns of phenotypic evolution differ, as well as a statistical assessment of the degree of repeatability in the evolutionary responses to selection among taxa. We describe how this approach can quantify phenotypic trajectories from many ecological and evolutionary processes, whose data encode multivariate characterizations of the phenotype, including: phenotypic plasticity, ecological selection, ontogeny and growth, local adaptation, and biomechanics. We illustrate the approach by examining the phenotypic evolution of several fossil lineages of Globorotalia.

A general framework for the analysis of phenotypic trajectories in evolutionary studies.

We propose a framework for hypothesis-testing of stable isotope ratios in ecological studies. Statistical procedures are based on analysis of nested linear models and a residual permutation procedure (RPP) that is employed to evaluate probabilities associated with test statistics. We used simulated examples and a real data set to illustrate the utility and generality of the method. First, we developed a test for differences in centroid location and dispersion of delta13C and delta15N values within and among groups of isotopic data. Second, we evaluated magnitude and direction of change in centroid position (termed "path") of a pair of isotopic samples separated in space/time relative to paths of other paired sample sets. Third, we compared attributes of path trajectories (size, direction, and shape) over sample sets containing more than two samples to provide a quantitative description of how patterns of isotopic ratios change in response to spatial and temporal gradients. Examples are limited to the bivariate case (delta13C-delta15N biplots), but the statistical method can readily be applied to univariate and multivariate cases.

https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1890/09-1454.1

Michael L. Collyer

Papers

geomorph: Software for geometric morphometric analyses

RRPP: An r package for fitting linear models to high‐dimensional data using residual randomization

A method for analysis of phenotypic change for phenotypes described by high-dimensional data

A general framework for the analysis of phenotypic trajectories in evolutionary studies.

A general hypothesis‐testing framework for stable isotope ratios in ecological studies