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ABSTRACT Genome and proteome data predict the presence of both the reductive citric acid cycle (rCAC; also called the reductive tricarboxylic acid cycle) and the Calvin-Benson-Bassham cycle (CBB) in “ Candidatus Endoriftia persephonae,” the autotrophic sulfur-oxidizing bacterial endosymbiont from the giant hydrothermal vent tubeworm Riftia pachyptila . We tested whether these cycles were differentially induced by sulfide supply, since the synthesis of biosynthetic intermediates by the rCAC is less energetically expensive than that by the CBB. R. pachyptila was incubated under in situ conditions in high-pressure aquaria under low (28 to 40 μmol · h −1 ) or high (180 to 276 μmol · h −1 ) rates of sulfide supply. Symbiont-bearing trophosome samples excised from R. pachyptila maintained under the two conditions were capable of similar rates of CO 2 fixation. Activities of the rCAC enzyme ATP-dependent citrate lyase (ACL) and the CBB enzyme 1,3-bisphosphate carboxylase/oxygenase (RubisCO) did not differ between the two conditions, although transcript abundances for ATP-dependent citrate lyase were 4- to 5-fold higher under low-sulfide conditions. δ 13 C values of internal dissolved inorganic carbon (DIC) pools were varied and did not correlate with sulfide supply rate. In samples taken from freshly collected R. pachyptila , δ 13 C values of lipids fell between those collected for organisms using either the rCAC or the CBB exclusively. These observations are consistent with cooccurring activities of the rCAC and the CBB in this symbiosis. IMPORTANCE Previous to this study, the activities of the rCAC and CBB in R. pachyptila had largely been inferred from “omics” studies of R. pachyptila without direct assessment of in situ conditions prior to collection. In this study, R. pachyptila was maintained and monitored in high-pressure aquaria prior to measuring its CO 2 fixation parameters. Results suggest that ranges in sulfide concentrations similar to those experienced in situ do not exert a strong influence on the relative activities of the rCAC and the CBB. This observation highlights the importance of further study of this symbiosis and other organisms with multiple CO 2 -fixing pathways, which recent genomics and biochemical studies suggest are likely to be more prevalent than anticipated. 

Abstract Symbioses between invertebrate animals and chemosynthetic bacteria form the basis of hydrothermal vent ecosystems worldwide. In the Lau Basin, deep-sea vent snails of the genus Alviniconcha associate with either Gammaproteobacteria (A. kojimai, A. strummeri) or Campylobacteria (A. boucheti) that use sulfide and/or hydrogen as energy sources. While the A. boucheti host–symbiont combination (holobiont) dominates at vents with higher concentrations of sulfide and hydrogen, the A. kojimai and A. strummeri holobionts are more abundant at sites with lower concentrations of these reductants. We posit that adaptive differences in symbiont physiology and gene regulation might influence the observed niche partitioning between host taxa. To test this hypothesis, we used high-pressure respirometers to measure symbiont metabolic rates and examine changes in gene expression among holobionts exposed to in situ concentrations of hydrogen (H2: ~25 µM) or hydrogen sulfide (H2S: ~120 µM). The campylobacterial symbiont exhibited the lowest rate of H2S oxidation but the highest rate of H2 oxidation, with fewer transcriptional changes and less carbon fixation relative to the gammaproteobacterial symbionts under each experimental condition. These data reveal potential physiological adaptations among symbiont types, which may account for the observed net differences in metabolic activity and contribute to the observed niche segregation among holobionts. 

Abstract Biodiversity is a complex, yet essential, concept for undergraduate students in ecology and other natural sciences to grasp. As beginner scientists, students must learn to recognize, describe, and interpret patterns of biodiversity across various spatial scales and understand their relationships with ecological processes and human influences. It is also increasingly important for undergraduate programs in ecology and related disciplines to provide students with experiences working with large ecological datasets to develop students’ data science skills and their ability to consider how ecological processes that operate at broader spatial scales (macroscale) affect local ecosystems. To support the goals of improving student understanding of macroscale ecology and biodiversity at multiple spatial scales, we formed an interdisciplinary team that included grant personnel, scientists, and faculty from ecology and spatial sciences to design a flexible learning activity to teach macroscale biodiversity concepts using large datasets from the National Ecological Observatory Network (NEON). We piloted this learning activity in six courses enrolling a total of 109 students, ranging from midlevel ecology and GIS/remote sensing courses, to upper‐level conservation biology. Using our classroom experiences and a pre/postassessment framework, we evaluated whether our learning activity resulted in increased student understanding of macroscale ecology and biodiversity concepts and increased familiarity with analysis techniques, software programs, and large spatio‐ecological datasets. Overall, results suggest that our learning activity improved student understanding of biological diversity, biodiversity metrics, and patterns of biodiversity across several spatial scales. Participating faculty reflected on what went well and what would benefit from changes, and we offer suggestions for implementation of the learning activity based on this feedback. This learning activity introduced students to macroscale ecology and built student skills in working with big data (i.e., large datasets) and performing basic quantitative analyses, skills that are essential for the next generation of ecologists. 

Abstract Macrosystem‐scale research is supported by many ecological networks of people, infrastructure, and data. However, no network is sufficient to address all macrosystems ecology research questions, and there is much to be gained by conducting research and sharing resources across multiple networks. Unfortunately, conducting macrosystem research across networks is challenging due to the diversity of expertise and skills required, as well as issues related to data discoverability, veracity, and interoperability. The ecological and environmental science community could substantially benefit from networking existing networks to leverage past research investments and spur new collaborations. Here, we describe the need for a “network of networks” (NoN) approach to macrosystems ecological research and articulate both the challenges and potential benefits associated with such an effort. We describe the challenges brought by rapid increases in the volume, velocity, and variety of “big data” ecology and highlight how a NoN could build on the successes and creativity within component networks, while also recognizing and improving upon past failures. We argue that a NoN approach requires careful planning to ensure that it is accessible and inclusive, incorporates multimodal communications and ways to interact, supports the creation, testing, and promulgation of community standards, and ensures individuals and groups receive appropriate credit for their contributions. Additionally, a NoN must recognize important trade‐offs in network architecture, including how the degree of centralization of people, infrastructure, and data influence network scalability and creativity. If implemented carefully and thoughtfully, a NoN has the potential to substantially advance our understanding of ecological processes, characteristics, and trajectories across broad spatial and temporal scales in an efficient, inclusive, and equitable manner. 

Plant communities are composed of complex phenotypes that not only differ among taxonomic groups and habitats but also change over time within a species. Restoration projects (e.g. translocations and reseeding) can introduce new functional variation in plants, which further diversifies phenotypes and complicates our ability to identify locally adaptive phenotypes for future restoration. Near‐infrared spectroscopy (NIRS) offers one approach to detect the chemical phenotypes that differentiate plant species, populations, and phenological states of individual plants over time. We use sagebrush (Artemisiaspp.) as a case study to test the accuracy by which NIRS can classify variation within taxonomy and phenology of a plant that is extensively managed and restored. Our results demonstrated that NIRS can accurately classify species of sagebrush within a study site (75–96%), populations of sagebrush within a subspecies (99%), annual phenology within a population (>99%), and seasonal phenology within individual plants (>97%). Low classification accuracy by NIRS in some sites may reflect heterogeneity associated with natural hybridization, translocation of nonlocal seed sources from past restoration, or complex gene‐by‐environment interactions. Advances in our ability to detect and interpret spectral signals from plants may improve both the selection of seed sources for targeted conservation and the capacity to monitor long‐term changes in vegetation.