Topography influences diurnal and seasonal microclimate fluctuations in hilly terrain environments of coastal California.

Understanding the topographic basis for microclimatic variation remains fundamental to predicting the site level effects of warming air temperatures. Quantifying diurnal fluctuation and seasonal extremes in relation to topography offers insight into the potential relationship between site level conditions and changes in regional climate. The present study investigated an annual understory temperature regime for 50 sites distributed across a topographically diverse area (>12 km2) comprised of mixed evergreen-deciduous woodland vegetation typical of California coastal ranges. We investigated the effect of topography and tree cover on site-to-site variation in near-surface temperatures using a combination of multiple linear regression and multivariate techniques. Sites in topographically depressed areas (e.g., valley bottoms) exhibited larger seasonal and diurnal variation. Elevation (at 10 m resolution) was found to be the primary driver of daily and seasonal variations, in addition to hillslope position, canopy cover and northness. The elevation effect on seasonal mean temperatures was inverted, reflecting large-scale cold-air pooling in the study region, with elevated minimum and mean temperature at higher elevations. Additionally, several of our sites showed considerable buffering (dampened diurnal and seasonal temperature fluctuations) compared to average regional conditions measured at an on-site weather station. Results from this study help inform efforts to extrapolate temperature records across large landscapes and have the potential to improve our ecological understanding of fine-scale seasonal climate variation in coastal range environments.

MeadoWatch: a long-term community-science database of wildflower phenology in Mount Rainier National Park.

We present a long-term and high-resolution phenological dataset from 17 wildflower species collected in Mt. Rainier National Park, as part of the MeadoWatch (MW) community science project. Since 2013, 457 unique volunteers and scientists have gathered data on the timing of four key reproductive phenophases (budding, flowering, fruiting, and seeding) in 28 plots over two elevational gradients alongside popular park trails. Trained volunteers (87.2%) and University of  Washington scientists (12.8%) collected data 3-9 times/week during the growing season, using a standardized method. Taxonomic assessments were highly consistent between scientists and volunteers, with high accuracy and specificity across phenophases and species. Sensitivity, on the other hand, was lower than accuracy and specificity, suggesting that a few species might be challenging to reliably identify in community-science projects. Up to date, the MW database includes 42,000+ individual phenological observations from 17 species, between 2013 and 2019. However, MW is a living dataset that will be updated through continued contributions by volunteers, and made available for its use by the wider ecological community.

Evaluation of serverless computing for scalable execution of a joint variant calling workflow.

Advances in whole-genome sequencing have greatly reduced the cost and time of obtaining raw genetic information, but the computational requirements of analysis remain a challenge. Serverless computing has emerged as an alternative to using dedicated compute resources, but its utility has not been widely evaluated for standardized genomic workflows. In this study, we define and execute a best-practice joint variant calling workflow using the SWEEP workflow management system. We present an analysis of performance and scalability, and discuss the utility of the serverless paradigm for executing workflows in the field of genomics research. The GATK best-practice short germline joint variant calling pipeline was implemented as a SWEEP workflow comprising 18 tasks. The workflow was executed on Illumina paired-end read samples from the European and African super populations of the 1000 Genomes project phase III. Cost and runtime increased linearly with increasing sample size, although runtime was driven primarily by a single task for larger problem sizes. Execution took a minimum of around 3 hours for 2 samples, up to nearly 13 hours for 62 samples, with costs ranging from $2 to $70.

BAMSI: a multi-cloud service for scalable distributed filtering of massive genome data.

BACKGROUND: The advent of next-generation sequencing (NGS) has made whole-genome sequencing of cohorts of individuals a reality. Primary datasets of raw or aligned reads of this sort can get very large. For scientific questions where curated called variants are not sufficient, the sheer size of the datasets makes analysis prohibitively expensive. In order to make re-analysis of such data feasible without the need to have access to a large-scale computing facility, we have developed a highly scalable, storage-agnostic framework, an associated API and an easy-to-use web user interface to execute custom filters on large genomic datasets. RESULTS: We present BAMSI, a Software as-a Service (SaaS) solution for filtering of the 1000 Genomes phase 3 set of aligned reads, with the possibility of extension and customization to other sets of files. Unique to our solution is the capability of simultaneously utilizing many different mirrors of the data to increase the speed of the analysis. In particular, if the data is available in private or public clouds - an increasingly common scenario for both academic and commercial cloud providers - our framework allows for seamless deployment of filtering workers close to data. We show results indicating that such a setup improves the horizontal scalability of the system, and present a possible use case of the framework by performing an analysis of structural variation in the 1000 Genomes data set. CONCLUSIONS: BAMSI constitutes a framework for efficient filtering of large genomic data sets that is flexible in the use of compute as well as storage resources. The data resulting from the filter is assumed to be greatly reduced in size, and can easily be downloaded or routed into e.g. a Hadoop cluster for subsequent interactive analysis using Hive, Spark or similar tools. In this respect, our framework also suggests a general model for making very large datasets of high scientific value more accessible by offering the possibility for organizations to share the cost of hosting data on hot storage, without compromising the scalability of downstream analysis.

Leveraging Organismal Biology to Forecast the Effects of Climate Change.

Despite the pressing need for accurate forecasts of ecological and evolutionary responses to environmental change, commonly used modeling approaches exhibit mixed performance because they omit many important aspects of how organisms respond to spatially and temporally variable environments. Integrating models based on organismal phenotypes at the physiological, performance, and fitness levels can improve model performance. We summarize current limitations of environmental data and models and discuss potential remedies. The paper reviews emerging techniques for sensing environments at fine spatial and temporal scales, accounting for environmental extremes, and capturing how organisms experience the environment. Intertidal mussel data illustrate biologically important aspects of environmental variability. We then discuss key challenges in translating environmental conditions into organismal performance including accounting for the varied timescales of physiological processes, for responses to environmental fluctuations including the onset of stress and other thresholds, and for how environmental sensitivities vary across lifecycles. We call for the creation of phenotypic databases to parameterize forecasting models and advocate for improved sharing of model code and data for model testing. We conclude with challenges in organismal biology that must be solved to improve forecasts over the next decade.