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1. Geographic variation in responses of kelp forest communities of the California Current to recent climatic changes

   https://doi.org/10.1111/gcb.15273  

   Beas‐Luna, Rodrigo ; Micheli, Fiorenza ; Woodson, C. Brock ; Carr, Mark ; Malone, Dan ; Torre, Jorge ; Boch, Charles ; Caselle, Jennifer E. ; Edwards, Matt ; Freiwald, Jan ; et al ( September 2020 , Global Change Biology)

   The changing global climate is having profound effects on coastal marine ecosystems around the world. Structure, functioning, and resilience, however, can vary geographically, depending on species composition, local oceanographic forcing, and other pressures from human activities and use. Understanding ecological responses to environmental change and predicting changes in the structure and functioning of whole ecosystems require large‐scale, long‐term studies, yet most studies trade spatial extent for temporal duration. We address this shortfall by integrating multiple long‐term kelp forest monitoring datasets to evaluate biogeographic patterns and rates of change of key functional groups (FG) along the west coast of North America. Analysis of data from 469 sites spanning Alaska, USA, to Baja California, Mexico, and 373 species (assigned to 18 FG) reveals regional variation in responses to both long‐term (2006–2016) change and a recent marine heatwave (2014–2016) associated with two atmospheric and oceanographic anomalies, the “Blob” and extreme El Niño Southern Oscillation (ENSO). Canopy‐forming kelps appeared most sensitive to warming throughout their range. Other FGs varied in their responses among trophic levels, ecoregions, and in their sensitivity to heatwaves. Changes in community structure were most evident within the southern and northern California ecoregions, while communities in the center of the range were more resilient. We report a poleward shift in abundance of some key FGs. These results reveal major, ongoing region‐wide changes in productive coastal marine ecosystems in response to large‐scale climate variability, and the potential loss of foundation species. In particular, our results suggest that coastal communities that are dependent on kelp forests will be more impacted in the southern portion of the California Current region, highlighting the urgency of implementing adaptive strategies to sustain livelihoods and ensure food security. The results also highlight the value of multiregional integration and coordination of monitoring programs for improving our understanding of marine ecosystems, with the goal of informing policy and resource management in the future.

    

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2. Fire‐regime variability and ecosystem resilience over four millennia in a Rocky Mountain subalpine watershed

   https://doi.org/10.1111/1365-2745.14201  

   Clark‐Wolf, Kyra D. ; Higuera, Philip E. ; McLauchlan, Kendra K. ; Shuman, Bryan N. ; Parish, Meredith C. ( October 2023 , Journal of Ecology)

   Wildfires strongly influence forest ecosystem processes, including carbon and nutrient cycling, and vegetation dynamics. As fire activity increases under changing climate conditions, the ecological and biogeochemical resilience of many forest ecosystems remains unknown.

   To investigate the resilience of forest ecosystems to changing climate and wildfire activity over decades to millennia, we developed a 4800‐year high‐resolution lake‐sediment record from Silver Lake, Montana, USA (47.360° N, 115.566° W). Charcoal particles, pollen grains, element concentrations and stable isotopes of C and N serve as proxies of past changes in fire, vegetation and ecosystem processes such as nitrogen cycling and soil erosion, within a small subalpine forest watershed. A published lake‐level history from Silver Lake provides a local record of palaeohydrology.

   A trend towards increased effective moisture over the late Holocene coincided with a distinct shift in the pollen assemblage c. 1900 yr BP, resulting from increased subalpine conifer abundance. Fire activity, inferred from peaks in macroscopic charcoal, decreased significantly after 1900 yr BP, from one fire event every 126 yr (83–184 yr, 95% CI) from 4800 to 1900 yr BP, to one event every 223 yr (175–280 yr) from 1900 yr BP to present.

   Across the record, individual fire events were followed by two distinct decadal‐scale biogeochemical responses, reflecting differences in ecosystem impacts of fires on watershed processes. These distinct biogeochemical responses were interpreted as reflecting fire severity, highlighting (i) erosion, likely from large or high‐severity fires, and (ii) nutrient transfers and enhanced within‐lake productivity, likely from lower severity or patchier fires. Biogeochemical and vegetation proxies returned to pre‐fire values within decades regardless of the nature of fire effects.

   Synthesis. Palaeorecords of fire and ecosystem responses provide a novel view revealing past variability in fire effects, analogous to spatial variability in fire severity observed within contemporary wildfires. Overall, the palaeorecord highlights ecosystem resilience to fire across long‐term variability in climate and fire activity. Higher fire frequencies in past millennia relative to the 20th and 21st century suggest that northern Rocky Mountain subalpine ecosystems could remain resilient to future increases in fire activity, provided continued ecosystem recovery within decades.

    

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3. Natural experiments and long-term monitoring are critical to understand and predict marine host–microbe ecology and evolution

   https://doi.org/10.1371/journal.pbio.3001322  

   Leray, Matthieu ; Wilkins, Laetitia G. ; Apprill, Amy ; Bik, Holly M. ; Clever, Friederike ; Connolly, Sean R. ; De León, Marina E. ; Duffy, J. Emmett ; Ezzat, Leïla ; Gignoux-Wolfsohn, Sarah ; et al ( August 2021 , PLOS Biology)

   null (Ed.)

   Marine multicellular organisms host a diverse collection of bacteria, archaea, microbial eukaryotes, and viruses that form their microbiome. Such host-associated microbes can significantly influence the host’s physiological capacities; however, the identity and functional role(s) of key members of the microbiome (“core microbiome”) in most marine hosts coexisting in natural settings remain obscure. Also unclear is how dynamic interactions between hosts and the immense standing pool of microbial genetic variation will affect marine ecosystems’ capacity to adjust to environmental changes. Here, we argue that significantly advancing our understanding of how host-associated microbes shape marine hosts’ plastic and adaptive responses to environmental change requires (i) recognizing that individual host–microbe systems do not exist in an ecological or evolutionary vacuum and (ii) expanding the field toward long-term, multidisciplinary research on entire communities of hosts and microbes. Natural experiments, such as time-calibrated geological events associated with well-characterized environmental gradients, provide unique ecological and evolutionary contexts to address this challenge. We focus here particularly on mutualistic interactions between hosts and microbes, but note that many of the same lessons and approaches would apply to other types of interactions. 

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4. A Deep Learning-Based Real-time Seizure Detection System

   https://doi.org/10.1109/SPMB50085.2020.9353623  

   Shawki, N. ; Elseify, T. ; Cap, T. ; Shah, V. ; Obeid, I. ; Picone, J. ( December 2020 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEG channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9. 

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5. Hydrology Controls Dissolved Organic Carbon and Nitrogen Export and Post‐Storm Recovery in Two Arctic Headwaters

   https://doi.org/10.1029/2023JG007583  

   Shogren, Arial J. ; Zarnetske, Jay P. ; Abbott, Benjamin W. ; Grose, Amelia L. ; Rec, Abigail F. ; Nipko, Jansen ; Song, Chao ; O’Donnell, Jonathan A. ; Bowden, William B. ( February 2024 , Journal of Geophysical Research: Biogeosciences)

   Climate change is rapidly altering hydrological processes and consequently the structure and functioning of Arctic ecosystems. Predicting how these alterations will shape biogeochemical responses in rivers remains a major challenge. We measured [C]arbon and [N]itrogen concentrations continuously from two Arctic watersheds capturing a wide range of flow conditions to assess understudied event‐scale C and N concentration‐discharge (C‐Q) behavior and post‐event recovery of stoichiometric conditions. The watersheds represent low‐gradient, tundra landscapes typical of the eastern Brooks Range on the North Slope of Alaska and are part of the Arctic Long‐Term Ecological Research sites: the Kuparuk River and Oksrukuyik Creek. In both watersheds, we deployed high‐frequency optical sensors to measure dissolved organic carbon (DOC), nitrate (), and total dissolved nitrogen (TDN) for five consecutive thaw seasons (2017–2021). Our analyses revealed a lag in DOC: stoichiometric recovery after a hydrologic perturbation: while DOC was consistently elevated after high flows, diluted during rainfall events and consequently, recovery in post‐event concentration was delayed. Conversely, the co‐enrichment of TDN at high flows, even in watersheds with relatively high N‐demand, represents a potential “leak” of hydrologically available organic N to downstream ecosystems. Our use of high‐frequency, long‐term optical sensors provides an improved method to estimate carbon and nutrient budgets and stoichiometric recovery behavior across event and seasonal timescales, enabling new insights and conceptualizations of a changing Arctic, such as assessing ecosystem disturbance and recovery across multiple timescales.

    

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