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Flow pulses mobilize particulate organic matter (POM) in streams from the surrounding landscape and streambed. This POM serves as a source of energy and nutrients, as well as a means for organismal dispersal, to downstream communities. In the barren terrestrial landscape of the McMurdo Dry Valleys (MDV) of Antarctica, benthic microbial mats occupying different in-stream habitat types are the dominant POM source in the many glacier-fed streams. Many of these streams experience daily flow peaks that mobilize POM, and diatoms recovered from underlying stream sediments suggest that mat-derived diatoms in the POM are retained there through hyporheic exchange. Yet, ‘how much’ and ‘when’ different in-stream habitat types contribute to POM diatom assemblages is unknown. To quantify the contribution of different in-stream habitat types to POM diatom assemblages, we collected time-integrated POM samples over four diel experiments, which spanned a gradient of flow conditions over three summers. Diatoms from POM samples were identified, quantified, and compared with dominant habitat types (i.e., benthic ‘orange’ mats, marginal ‘black’ mats, and bare sediments). Like bulk POM, diatom cell concentrations followed a clockwise hysteresis pattern with stream discharge over the daily flow cycles, indicating supply limitation. Diatom community analyses showed that different habitat types harbor distinct diatom communities, and mixing models revealed that a substantial proportion of POM diatoms originated from bare sediments during baseflow conditions. Meanwhile, orange and black mats contribute diatoms to POM primarily during daily flow peaks when both cell concentrations and discharge are highest, making mats the most important contributors to POM diatom assemblages at high flows. These observations may help explain the presence of mat-derived diatoms in hyporheic sediments. Our results thus indicate a varying importance of different in-stream habitats to POM generation and export on daily to seasonal timescales, with implications for biogeochemical cycling and the local diatom metacommunity. 

Much of modern science takes place in a computational environment, and, increasingly, that environment is programmed using R, Python, or Julia. Furthermore, most scientific data now live on the cloud, so the first step in many workflows is to query a cloud database and load the response into a computational environment for further analysis. Thus, tools that facilitate programmatic data retrieval represent a critical component in reproducible scientific workflows. Earth science is no different in this regard. To fulfill that basic need, we developed R, Python, and Julia packages providing programmatic access to the U.S. Geological Survey’s National Water Information System database and the multi-agency Water Quality Portal. Together, these packages create a common interface for retrieving hydrologic data in the Jupyter ecosystem, which is widely used in water research, operations, and teaching. Source code, documentation, and tutorials for the packages are available on GitHub. Users can go there to learn, raise issues, or contribute improvements within a single platform, which helps foster better engagement and collaboration between data providers and their users. 

Quantifying the resilience of ecological communities to increasingly frequent and severe environmental disturbance, such as natural disasters, requires long-term and continuous observations and a research community that is itself resilient. Investigators must have reliable access to data, a variety of resources to facilitate response to perturbation, and mechanisms for rapid and efficient return to function and/or adaptation to post-disaster conditions. There are always challenges to meeting these requirements, which may be compounded by multiple, co-occurring incidents. For example, travel restrictions resulting from the COVID-19 pandemic hindered preparations for, and responses to, environmental disasters that are the hallmarks of resilient research communities. During its initial years of data collection, a diversity of disturbances—earthquakes, wildfires, droughts, hurricanes and floods—have impacted sites at which the National Ecological Observatory Network (NEON) intends to measure organisms and environment for at least 30 years. These events strain both the natural and human communities associated with the Observatory, and additional stressors like public health crises only add to the burden. Here, we provide a case-study of how NEON has demonstrated not only internal resilience in the face of the public health crisis of COVID-19, but has also enhanced the resilience of ecological research communities associated with the network and provided crucial information for quantifying the impacts of and responses to disturbance events on natural systems—their ecological resilience. The key components discussed are: 1) NEON’s infrastructure and resources to support its core internal community, to adapt to rapidly changing situations, and to quickly resume operations following disruption, thus enabling the recovery of information flow crucial for data continuity; 2) how NEON data, tools, and materials are foundational in supporting the continuation of research programs in the face of challenges like those of COVID-19, thus enhancing the resilience of the greater ecological research community; and 3) the importance of diverse and consistent data for defining baseline and post-disaster conditions that are required to quantify the effects of natural disasters on ecosystem patterns and processes. 

Abstract We investigate the spatial distribution, spectral properties and temporal variability of primary producers (e.g. communities of microbial mats and mosses) throughout the Fryxell basin of Taylor Valley, Antarctica, using high-resolution multispectral remote-sensing data. Our results suggest that photosynthetic communities can be readily detected throughout the Fryxell basin based on their unique near-infrared spectral signatures. Observed intra- and inter-annual variability in spectral signatures are consistent with short-term variations in mat distribution, hydration and photosynthetic activity. Spectral unmixing is also implemented in order to estimate mat abundance, with the most densely vegetated regions observed from orbit correlating spatially with some of the most productive regions of the Fryxell basin. Our work establishes remote sensing as a valuable tool in the study of these ecological communities in the McMurdo Dry Valleys and demonstrates how future scientific investigations and the management of specially protected areas could benefit from these tools and techniques.