Guidelines for model adaptation: A study of the transferability of a general seagrass ecosystem Dynamic Bayesian Networks model

In general, it is not feasible to collect enough empirical data to capture the entire range of processes that define a complex system, either intrinsically or when viewing the system from a different geographical or temporal perspective. In this context, an alternative approach is to consider model transferability, which is the act of translating a model built for one environment to another less well‐known situation. Model transferability and adaptability may be extremely beneficial—approaches that aid in the reuse and adaption of models, particularly for sites with limited data, would benefit from widespread model uptake. Besides the reduced effort required to develop a model, data collection can be simplified when transferring a model to a different application context. The research presented in this paper focused on a case study to identify and implement guidelines for model adaptation. Our study adapted a general Dynamic Bayesian Networks (DBN) of a seagrass ecosystem to a new location where nodes were similar, but the conditional probability tables varied. We focused on two species of seagrass (Zostera noltei and Zostera marina) located in Arcachon Bay, France. Expert knowledge was used to complement peer‐reviewed literature to identify which components needed adjustment including parameterization and quantification of the model and desired outcomes. We adopted both linguistic labels and scenario‐based elicitation to elicit from experts the conditional probabilities used to quantify the DBN. Following the proposed guidelines, the model structure of the general DBN was retained, but the conditional probability tables were adapted for nodes that characterized the growth dynamics in Zostera spp. population located in Arcachon Bay, as well as the seasonal variation on their reproduction. Particular attention was paid to the light variable as it is a crucial driver of growth and physiology for seagrasses. Our guidelines provide a way to adapt a general DBN to specific ecosystems to maximize model reuse and minimize re‐development effort. Especially important from a transferability perspective are guidelines for ecosystems with limited data, and how simulation and prior predictive approaches can be used in these contexts.     

Assessment of the stream invertebrate β‐diversity along an elevation gradient using a bidimensional null model analysis

β‐Diversity, commonly defined as the compositional variation among localities that links local diversity (α‐diversity) and regional diversity (γ‐diversity), can arise from two different ecological phenomena, namely the spatial species turnover (i.e., species replacement) and the nestedness of assemblages (i.e., species loss). However, any assessment that does not account for stochasticity in community assembly could be biased and misinform conservation management. In this study, we aimed to provide a better understanding of the overall ecological phenomena underlying stream β‐diversity along elevation gradients and to contribute to the rich debate on null model approaches to identify nonrandom patterns in the distribution of taxa. Based on presence‐absence data of 78 stream invertebrate families from 309 sites located in the Swiss Alpine region, we analyzed the effect size of nonrandom spatial distribution of stream invertebrates on the β‐diversity and its two components (i.e., turnover and nestedness). We used a modeling framework that allows exploring the complete range of existing algorithms used in null model analysis and assessing how distribution patterns vary according to an array of possible ecological assumptions. Overall, the turnover of stream invertebrates and the nestedness of assemblages were significantly lower and higher, respectively, than the ones expected by chance. This pattern increased with elevation, and the consistent trend observed along the altitudinal gradient, even in the most conservative analysis, strengthened our findings. Our study suggests that deterministic distribution of stream invertebrates in the Swiss Alpine region is significantly driven by differential dispersal capacity and environmental stress gradients. As long as the ecological assumptions for constructing the null models and their implications are acknowledged, we believe that they still represent useful tools to measure the effect size of nonrandom spatial distribution of taxa on β‐diversity.     

Demographic responses to climate‐driven variation in habitat quality across the annual cycle of a migratory bird species

The demography and dynamics of migratory bird populations depend on patterns of movement and habitat quality across the annual cycle. We leveraged archival GPS‐tagging data, climate data, remote‐sensed vegetation data, and bird‐banding data to better understand the dynamics of black‐headed grosbeak (Pheucticus melanocephalus) populations in two breeding regions, the coast and Central Valley of California (Coastal California) and the Sierra Nevada mountain range (Sierra Nevada), over 28 years (1992–2019). Drought conditions across the annual cycle and rainfall timing on the molting grounds influenced seasonal habitat characteristics, including vegetation greenness and phenology (maturity dates). We developed a novel integrated population model with population state informed by adult capture data, recruitment rates informed by age‐specific capture data and climate covariates, and survival rates informed by adult capture–mark–recapture data and climate covariates. Population size was relatively variable among years for Coastal California, where numbers of recruits and survivors were positively correlated, and years of population increase were largely driven by recruitment. In the Sierra Nevada, population size was more consistent and showed stronger evidence of population regulation (numbers of recruits and survivors negatively correlated). Neither region showed evidence of long‐term population trend. We found only weak support for most climate–demographic rate relationships. However, recruitment rates for the Coastal California region were higher when rainfall was relatively early on the molting grounds and when wintering grounds were relatively cool and wet. We suggest that our approach of integrating movement, climate, and demographic data within a novel modeling framework can provide a useful method for better understanding the dynamics of broadly distributed migratory species.     

Acetic acid and acidification accelerate chronological and replicative aging in yeast

Comment on: Murakami C, et al. Cell Cycle 2012; 11:3087-96.     

The danger of applying the breeder's equation in observational studies of natural populations

The breeder's equation, which predicts evolutionary change when a phenotypic covariance exists between a heritable trait and fitness, has provided a key conceptual framework for studies of adaptive microevolution in nature. However, its application requires strong assumptions to be made about the causation of fitness variation. In its univariate form, the breeder's equation assumes that the trait of interest is not correlated with other traits having causal effects on fitness. In its multivariate form, the validity of predicted change rests on the assumption that all such correlated traits have been measured and incorporated into the analysis. Here, we (i) highlight why these assumptions are likely to be seriously violated in studies of natural, rather than artificial, selection and (ii) advocate wider use of the Robertson–Price identity as a more robust, and less assumption‐laden, alternative to the breeder's equation for applications in evolutionary ecology.     

Technologies for integrating biological data

The process of building a new database relevant to some field of study in biomedicine involves transforming, integrating and cleansing multiple data sources, as well as adding new material and annotations. This paper reviews some of the requirements of a general solution to this data integration problem. Several representative technologies and approaches to data integration in biomedicine are surveyed. Then some interesting features that separate the more general data integration technologies from the more specialised ones are highlighted.     

Synchrony of Seed Dispersal, Hydrology and Local Climate in a Semi-arid River Reach in California

The temporal availability of propagules is a critical factor in sustaining pioneer riparian tree populations along snowmelt-driven rivers because seedling establishment is strongly linked to seasonal hydrology. River regulation in semi-arid regions threatens to decouple seed development and dispersal from the discharge regime to which they evolved. Using the lower Tuolumne River as a model system, we quantified and modeled propagule availability for Populus fremontii (POFR), Salix gooddingii (SAGO), and Salix exigua (SAEX), the tree and shrub species that dominate near-channel riparian stands in the San Joaquin Basin, CA. A degree-day model was fit to field data of seasonal seed density and local temperature from three sites in 2002–2004 to predict the onset of the peak dispersal period. To evaluate historical synchrony of seed dispersal and seasonal river hydrology, we compared peak spring runoff timing to modeled peak seed release periods for the last 75 years. The peak seed release period began on May 15 for POFR (range April 23–June 10), May 30 for SAGO (range May 19–June 11) and May 31 for SAEX (range May 8–June 30). Degree-day models for the onset of seed release reduced prediction error by 40–67% over day-of-year means; the models predicted best the interannual, versus site-to-site, variation in timing. The historical analysis suggests that POFR seed release coincided with peak runoff in almost all years, whereas SAGO and SAEX dispersal occurred during the spring flood recession. The degree-day modeling approach reduce uncertainty in dispersal timing and shows potential for guiding flow releases on regulated rivers to increase riparian tree recruitment at the lowest water cost.     

Development of a seed DNA-based genotyping system for marker-assisted selection in maize

Leaf collection from the field, labeling and tracking back to the source plants after genotyping are rate limiting steps in leaf DNA-based genotyping. In this study, an optimized genotyping method using endosperm DNA sampled from single maize seeds was developed, which can be used to replace leaf DNA-based genotyping for both genetic studies and breeding applications. A similar approach is likely to be suitable for all plants with relatively large seeds. Part of the endosperm was excised from imbibed maize seeds and DNA extracted in 96-tube plates using individuals from eight F₂ populations and seven inbreds. The quality of the resultant DNA was functionally comparable to DNA extracted from leaf tissue. Extraction from 30 mg of endosperm yields 3–10 μg DNA, which is sufficient for analysis of 200–400 agarose-gel PCR-based markers, with the potential for several million chip-based SNP marker analyses. By comparing endosperm DNA and leaf DNA for individuals from an F₂ population, genotyping errors caused by pericarp contamination and hetero-fertilization were found to average 3.8 and 0.6%, respectively. Endosperm sampling did not affect germination rates under controlled conditions, although under normal field conditions the germination rate, seedling establishment, and growth vigor were significantly lower than that of non-sampled controls for some genotypes. However, careful field management can compensate for these effects. Seed DNA-based genotyping lowered costs by 24.6% compared to leaf DNA-based genotyping due to reduced field plantings and labor costs. A substantial advantage of this approach is that it can be used to select desirable genotypes before planting. As such it provides an opportunity for dramatic improvements in the efficiency and selective gain of breeding systems based on optimum combinations of marker-assisted selection and phenotypic selection within and between generations.     

A rapid selection/amplification procedure for high-level expression of recombinant protein in a metal-amplifiable mammalian expression system

We describe a strategy for the selection and amplification of foreign gene expression in Chinese hamster ovary (CHO) cells employing a metallothionein gene-containing expression vector. This report describes an amplification procedure that results in an enrichment of clones exhibiting high levels of recombinant protein production and reduces the labour required for screening recombinant cell lines.