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Reef-building corals and their aragonite (CaCO₃) skeletons support entire reef ecosystems, yet their formation mechanism is poorly understood. Here we used synchrotron spectromicroscopy to observe the nanoscale mineralogy of fresh, forming skeletons from six species spanning all reef-forming coral morphologies: Branching, encrusting, massive, and table. In all species, hydrated and anhydrous amorphous calcium carbonate nanoparticles were precursors for skeletal growth, as previously observed in a single species. The amorphous precursors here were observed in tissue, between tissue and skeleton, and at growth fronts of the skeleton, within a low-density nano- or microporous layer varying in thickness from 7 to 20 μm. Brunauer-Emmett-Teller measurements, however, indicated that the mature skeletons at the microscale were space-filling, comparable to single crystals of geologic aragonite. Nanoparticles alone can never fill space completely, thus ion-by-ion filling must be invoked to fill interstitial pores. Such ionby- ion diffusion and attachment may occur from the supersaturated calcifying fluid known to exist in corals, or from a dense liquid precursor, observed in synthetic systems but never in biogenic ones. Concomitant particle attachment and ion-by-ion filling was previously observed in synthetic calcite rhombohedra, but never in aragonite pseudohexagonal prisms, synthetic or biogenic, as observed here. Models for biomineral growth, isotope incorporation, and coral skeletons’ resilience to ocean warming and acidification must take into account the dual formation mechanism, including particle attachment and ion-by-ion space filling.

Beta diversity is an important metric in ecology quantifying differentiation or disparity in composition among communities, ecosystems, or phenotypes. To compare systems with different sizes (N, number of units within a system), beta diversity is often converted to related indices such as turnover or local/regional differentiation. Here we use simulations to demonstrate that these naive measures of dissimilarity depend on sample size and design. We show that when N is the number of sampled units (e.g., quadrats) rather than the \"true\" number of communities in the system (if such exists), these differentiation measures are biased estimators. We propose using average pairwise dissimilarity as an intuitive solution. That is, instead of attempting to estimate an N-community measure, we advocate estimating the expected dissimilarity between any random pair of communities (or sampling units)—especially when the \"true\" N is unknown or undefined. Fortunately, measures of pairwise dissimilarity or overlap have been used in ecology for decades, and their properties are well known. Using the same simulations, we show that average pairwise metrics give consistent and unbiased estimates regardless of the number of survey units sampled. We advocate pairwise dissimilarity as a general standardization to ensure commensurability of different study systems.

Background Genotype by environment interactions (GxE) affect a range of production traits in beef cattle. Quantifying the effect of GxE in commercial and multi-breed herds is challenging due to unknown genetic linkage between animals across environment levels. The primary aim of this study was to use multi-trait models to investigate GxE for three heifer fertility traits, corpus luteum (CL) presence, first pregnancy and second pregnancy, in a large tropical beef multibreed dataset (n = 21,037). Environmental levels were defined by two different descriptors, burden of heat load (temperature humidity index, THI) and nutritional availability (based on mean average daily gain for the herd, ADWG). To separate the effects of genetic linkage and real GxE across the environments, 1000 replicates of a simulated phenotype were generated by simulating QTL effects with no GxE onto real marker genotypes from the population, to determine the genetic correlations that could be expected across environments due to the existing genetic linkage only. Correlations from the real phenotypes were then compared to the empirical distribution under the null hypothesis from the simulated data. By adopting this approach, this study attempted to establish if low genetic correlations between environmental levels were due to GxE or insufficient genetic linkage between animals in each environmental level. Results The correlations (being less than <0.8) for the real phenotypes were indicative of GxE for CL presence between ADWG environmental levels and in pregnancy traits. However, none of the correlations for CL presence or first pregnancy between ADWG levels were below the 5th percentile value for the empirical distribution under the null hypothesis from the simulated data. Only one statistically significant ( P < 0.05) indication of GxE for first pregnancy was found between THI environmental levels, where r g = 0.28 and 5th percentile value = 0.29, and this result was marginal. Conclusions Only one case of statistically significant GxE for fertility traits was detected for first pregnancy between THI environmental levels 2 and 3. Other initial indications of GxE that were observed from the real phenotypes did not prove significant when compared to an empirical null distribution from simulated phenotypes. The lack of compelling evidence of GxE indicates that direct selection for fertility traits can be made accurately, using a single evaluation, regardless of environment.

It is often hoped that population genetics can answer questions about the demographic and geographic dynamics of recent biological invasions. Conversely, invasions with well-known histories are sometimes billed as opportunities to test methods of population genetic inference. In both cases, underappreciated limitations constrain the usefulness of genetic methods. The most significant is that human-caused invasions have occurred on historical timescales that are orders of magnitude smaller than the timescales of mutation and genetic drift for most multicellular organisms. Analyses based on the neutral theory of molecular evolution cannot resolve such rapid dynamics. Invasion histories cannot be reconstructed in the same way as biogeographic changes occurring over millenia. Analyses assuming equilibrium between mutation, drift, gene flow, and selection will rarely be applicable, and even methods designed for explicitly non-equilibrium questions often require longer timescales than the few generations of most invasions of current concern. We identified only a few population genetic questions that are tractable over such short timescales. These include comparison of alternative hypotheses for the geographic origin of an invasion, testing for bottlenecks, and hybridization between native and invasive species. When proposing population genetic analysis of a biological invasion, we recommend that biologists ask (i) whether the questions to be addressed will materially affect management practice or policy, and (ii) whether the proposed analyses can really be expected to address important population genetic questions. Despite our own enthusiasm for population genetic research, we conclude that genetic analysis of biological invasions is justified only under exceptional circumstances.

Recently there have been major theoretical advances in the quantification and partitioning of diversity within and among communities, regions, and ecosystems. However, applying those advances to real data remains a challenge. Ecologists often end up describing their samples rather than estimating the diversity components of an underlying study system, and existing approaches do not easily provide statistical frameworks for testing ecological questions. Here we offer one avenue to do all of the above using a hierarchical Bayesian approach. We estimate posterior distributions of the underlying “true” relative abundances of each species within each unit sampled. These posterior estimates of relative abundance can then be used with existing formulae to estimate and partition diversity. The result is a posterior distribution of diversity metrics describing our knowledge (or beliefs) about the study system. This approach intuitively leads to statistical inferences addressing biologically motivated hypotheses via Bayesian model comparison. Using simulations, we demonstrate that our approach does as well or better at approximating the “true” diversity of a community relative to naïve or ad-hoc bias-corrected estimates. Moreover, model comparison correctly distinguishes between alternative hypotheses about the distribution of diversity within and among samples. Finally, we use an empirical ecological dataset to illustrate how the approach can be used to address questions about the makeup and diversities of assemblages at local and regional scales.

Most components of an organism’s phenotype can be viewed as the expression of multiple traits. Many of these traits operate as complexes, where multiple subsidiary parts function and evolve together. As trait complexity increases, so does the challenge of describing complexity in intuitive, biologically meaningful ways. Traditional multivariate analyses ignore the phenomenon of individual complexity and provide relatively abstract representations of variation among individuals. We suggest adopting well-known diversity indices from community ecology to describe phenotypic complexity as the diversity of distinct subsidiary components of a trait. Using a hierarchical framework, we illustrate how total trait diversity can be partitioned into within-individual complexity (α diversity) and between-individual components (β diversity). This approach complements traditional multivariate analyses. The key innovations are (i) addition of individual complexity within the same framework as between-individual variation and (ii) a group-wise partitioning approach that complements traditional level-wise partitioning of diversity. The complexity-as-diversity approach has potential application in many fields, including physiological ecology, ecological and community genomics, and transcriptomics. We demonstrate the utility of this complexity-as-diversity approach with examples from chemical and microbial ecology. The examples illustrate biologically significant differences in complexity and diversity that standard analyses would not reveal.

Phylogenetic hypotheses are frequently used to examine variation in rates of diversification across the history of a group. Patterns of diversification-rate variation can be used to infer underlying ecological and evolutionary processes responsible for patterns of cladogenesis. Most existing methods examine rate variation through time. Methods for examining differences in diversification among groups are more limited. Here, we present a new method, parametric rate comparison (PRC), that explicitly compares diversification rates among lineages in a tree using a variety of standard statistical distributions. PRC can identify subclades of the tree where diversification rates are at variance with the remainder of the tree. A randomization test can be used to evaluate how often such variance would appear by chance alone. The method also allows for comparison of diversification rate among a priori defined groups. Further, the application of the PRC method is not restricted to monophyletic groups. We examined the performance of PRC using simulated data, which showed that PRC has acceptable false-positive rates and statistical power to detect rate variation. We apply the PRC method to the well-studied radiation of North American Plethodon salamanders, and support the inference that the large-bodied Plethodon glutinosus clade has a higher historical rate of diversification compared to other Plethodon salamanders.

Many studies aimed at reconstructing the invasion history of a species rely, in part, on inferences based on patterns of genetic variation. These inferences warrant careful interpretation, however. In particular, given the time scale of most invasions, the typical demography of invasive species in their invaded range, and the available molecular tools, the underlying assumptions of population genetic models will often be violated. Given this fact, we examined the potential of population genetic data for reconstructing the history of serial introductions of the small Indian mongoose, Herpestes auropunctatus . We used simulations to test the power of existing microsatellite data for testing the credibility of historical introduction records. Although our results are generally consistent with most historical records for H. auropunctatus , the existing data have low power to reject alternative historical hypotheses. Simulations of a wide range of founder population sizes show broadly overlapping results, making rather different historical scenarios of introductions difficult to rule out with typical datasets. We advocate caution in the use of molecular population genetics to infer the history of invasive species, and we suggest extensive simulations as a tool to evaluate, in advance, this approach for addressing important research questions.

Abstract The northern Australia beef cattle industry operates in harsh environmental conditions which consistently suppress female fertility. To better understand the environmental effect on cattle raised extensively in northern Australia, new environmental descriptors were defined for 54 commercial herds located across the region. Three fertility traits, based on the presence of a corpus luteum at 600 d of age, indicating puberty, (CL Presence, n = 25,176), heifer pregnancy (n = 20,989) and first lactation pregnancy (n = 10,072) were recorded. Temperature, humidity, and rainfall were obtained from publicly available data based on herd location. Being pubertal at 600 d (i.e. CL Presence) increased the likelihood of success at heifer pregnancy and first lactation pregnancy (P < 0.05), underscoring the importance of early puberty in reproductive success. A temperature humidity index (THI) of 65–70 had a significant (P < 0.05) negative effect on first lactation pregnancy rate, heifer pregnancy and puberty at 600 d of age. Area under the curve of daily THI was significant (P < 0.05) and reduced the likelihood of pregnancy at first lactation and puberty at 600 days. Deviation from long-term average rainfall was not significant (P < 0.05) for any trait. Average daily weight gain had a significant and positive relationship (P < 0.05) for heifer and first lactation pregnancy. The results indicate that chronic or cumulative heat load is more determinantal to reproductive performance than acute heat stress. The reason for the lack of a clear relationship between acute heat stress and reproductive performance is unclear but may be partially explained by peak THI and peak nutrition coinciding at the same time. Sufficient evidence was found to justify the use of average daily weight gain and chronic heat load as descriptors to define an environmental gradient.

Patterns of diversification rate variation detected in phylogenetic hypotheses are frequently used to infer historical, ecological, and evolutionary processes. The parametric rate comparison (PRC) is a method for detecting rate variation in trees that models branch lengths as random variables drawn from familiar statistical distributions. iteRates is a library of functions for the R statistical computing environment for implementing PRC on phylogenetic trees. Here, we describe some of the functions in iteRates for subtree identification, tree manipulation, applying the PRC and K-clades PRC analyses, and conducting a whole-tree randomization test.