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Transcriptomes from nontraditional model organisms often harbor a wealth of unexplored data. Examining these data sets can lead to clarity and novel insights in traditional systems, as well as to discoveries across a multitude of fields. Despite significant advances in DNA sequencing technologies and in their adoption, access to genomic and transcriptomic resources for nontraditional model organisms remains limited. Crustaceans, for example, being among the most numerous, diverse, and widely distributed taxa on the planet, often serve as excellent systems to address ecological, evolutionary, and organismal questions. While they are ubiquitously present across environments, and of economic and food security importance, they remain severely underrepresented in publicly available sequence databases. Here, we present CrusTome, a multispecies, multitissue, transcriptome database of 201 assembled mRNA transcriptomes (189 crustaceans, 30 of which were previously unpublished, and 12 ecdysozoans for phylogenetic context) as an evolving and publicly available resource. This database is suitable for evolutionary, ecological, and functional studies that employ genomic/transcriptomic techniques and data sets. CrusTome is presented in BLAST and DIAMOND formats, providing robust data sets for sequence similarity searches, orthology assignments, phylogenetic inference, etc. and thus allowing for straightforward incorporation into existing custom pipelines for high-throughput analyses. In addition, to illustrate the use and potential of CrusTome, we conducted phylogenetic analyses elucidating the identity and evolution of the cryptochrome/photolyase family of proteins across crustaceans. 

Without active galactic nucleus (AGN) feedback, simulated massive, star-forming galaxies become too compact relative to observed galaxies at z ≲ 2. In this paper, we perform high-resolution re-simulations of a massive ($M_{\star }\sim 10^{11}\, \rm {{\rm M}_{\odot }}$) galaxy at z ∼ 2.3, drawn from the Feedback in Realistic Environments (FIRE) project. In the simulation without AGN feedback, the galaxy experiences a rapid starburst and shrinking of its half-mass radius. We experiment with driving mechanical AGN winds, using a state-of-the-art hyper-Lagrangian refinement technique to increase particle resolution. These winds reduce the gas surface density in the inner regions of the galaxy, suppressing the compact starburst and maintaining an approximately constant half-mass radius. Using radiative transfer, we study the impact of AGN feedback on the magnitude and extent of the multiwavelength continuum emission. When AGN winds are included, the suppression of the compact, dusty starburst results in lowered flux at FIR wavelengths (due to decreased star formation) but increased flux at optical-to-near-IR wavelengths (due to decreased dust attenuation, in spite of the lowered star formation rate), relative to the case without AGN winds. The FIR half-light radius decreases from ∼1 to $\sim 0.1\, \rm {kpc}$ in $\lesssim 40\, \rm {Myr}$ when AGN winds are not included, but increases to $\sim 2\, \rm {kpc}$ when they are. Interestingly, the half-light radius at optical-NIR wavelengths remains approximately constant over $35\, \rm {Myr}$, for simulations with and without AGN winds. In the case without winds, this occurs despite the rapid compaction, and is due to heavy dust obscuration in the inner regions of the galaxy. This work highlights the importance of forward-modelling when comparing simulated and observed galaxy populations.

 

Pollen is used to investigate a diverse range of ecological problems, from identifying plant–pollinator relationships to tracking flowering phenology. Pollen types are identified according to a set of distinctive morphological characters which are understood to capture taxonomic differences and phylogenetic relationships among taxa. However, categorizing morphological variation among hyperdiverse pollen samples represents a challenge even for an expert analyst.

We present an automated workflow for pollen analysis, from the automated scanning of pollen sample slides to the automated detection and identification of pollen taxa using convolutional neural networks (CNNs). We analysed aerial pollen samples from lowland Panama and used a microscope slide scanner to capture three‐dimensional representations of 150 sample slides. These pollen sample images were annotated by an expert using a virtual microscope. Metadata were digitally recorded for ~100 pollen grains per slide, including location, identification and the analyst's confidence of the given identification. We used these annotated images to train and test our detection and classification CNN models. Our approach is two‐part. We first compared three methods for training CNN models to detect pollen grains on a palynological slide. We next investigated approaches to training CNN models for pollen identification.

Because the diversity of pollen taxa in environmental and palaeontological samples follows a long‐tailed distribution, we experimented with methods for addressing imbalanced representation using our most abundant 46 taxa. We found that properly weighting pollen taxa in our training objective functions yielded improved accuracy for individual taxa. Our average accuracy for the 46‐way classification problem was 82.3%. We achieved 89.5% accuracy for our 25 most abundant taxa.

Pollen represents a challenging visual classification problem that can serve as a model for other areas of biology that rely on visual identification. Our results add to the body of research demonstrating the potential for a fully automated pollen classification system for environmental and palaeontological samples. Slide imaging, pollen detection and specimen identification can be automated to produce a streamlined workflow.

 

Andean uplift played a fundamental role in shaping South American climate and species distribution, but the relationship between the rise of the Andes, plant composition, and local climatic evolution is poorly known. We investigated the fossil record (pollen, leaves, and wood) from the Neogene of the Central Andean Plateau and documented the earliest evidence of a puna-like ecosystem in the Pliocene and a montane ecosystem without modern analogs in the Miocene. In contrast to regional climate model simulations, our climate inferences based on fossil data suggest wetter than modern precipitation conditions during the Pliocene, when the area was near modern elevations, and even wetter conditions during the Miocene, when the cordillera was around ~1700 meters above sea level. Our empirical data highlight the importance of the plant fossil record in studying past, present, and future climates and underscore the dynamic nature of high elevation ecosystems.