Browsing by Author "Zwart, Jacob A."
Now showing 1 - 3 of 3
Results Per Page
Sort Options
- Global patterns and drivers of ecosystem functioning in rivers and riparian zonesTiegs, Scott D.; Costello, David M.; Isken, Mark W.; Woodward, Guy; McIntyre, Peter B.; Gessner, Mark O.; Chauvet, Eric; Griffiths, Natalie A.; Flecker, Alex S.; Acuna, Vicenc; Albarino, Ricardo; Allen, Daniel C.; Alonso, Cecilia; Andino, Patricio; Arango, Clay; Aroviita, Jukka; Barbosa, Marcus V. M.; Barmuta, Leon A.; Baxter, Colden V.; Bell, Thomas D. C.; Bellinger, Brent; Boyero, Luz; Brown, Lee E.; Bruder, Andreas; Bruesewitz, Denise A.; Burdon, Francis J.; Callisto, Marcos; Canhoto, Cristina; Capps, Krista A.; Castillo, Maria M.; Clapcott, Joanne; Colas, Fanny; Colon-Gaud, Checo; Cornut, Julien; Crespo-Perez, Veronica; Cross, Wyatt F.; Culp, Joseph M.; Danger, Michael; Dangles, Olivier; de Eyto, Elvira; Derry, Alison M.; Diaz Villanueva, Veronica; Douglas, Michael M.; Elosegi, Arturo; Encalada, Andrea C.; Entrekin, Sally A.; Espinosa, Rodrigo; Ethaiya, Diana; Ferreira, Veronica; Ferriol, Carmen; Flanagan, Kyla M.; Fleituch, Tadeusz; Shah, Jennifer J. Follstad; Frainer, Andre; Friberg, Nikolai; Frost, Paul C.; Garcia, Erica A.; Lago, Liliana Garcia; Garcia Soto, Pavel Ernesto; Ghate, Sudeep; Giling, Darren P.; Gilmer, Alan; Goncalves, Jose Francisco, Jr.; Gonzales, Rosario Karina; Graca, Manuel A. S.; Grace, Mike; Grossart, Hans-Peter; Guerold, Francois; Gulis, Vlad; Hepp, Luiz U.; Higgins, Scott; Hishi, Takuo; Huddart, Joseph; Hudson, John; Imberger, Samantha; Iniguez-Armijos, Carlos; Iwata, Tomoya; Janetski, David J.; Jennings, Eleanor; Kirkwood, Andrea E.; Koning, Aaron A.; Kosten, Sarian; Kuehn, Kevin A.; Laudon, Hjalmar; Leavitt, Peter R.; Lemes da Silva, Aurea L.; Leroux, Shawn J.; Leroy, Carri J.; Lisi, Peter J.; MacKenzie, Richard; Marcarelli, Amy M.; Masese, Frank O.; Mckie, Brendan G.; Oliveira Medeiros, Adriana; Meissner, Kristian; Milisa, Marko; Mishra, Shailendra; Miyake, Yo; Moerke, Ashley; Mombrikotb, Shorok; Mooney, Rob; Moulton, Tim; Muotka, Timo; Negishi, Junjiro N.; Neres-Lima, Vinicius; Nieminen, Mika L.; Nimptsch, Jorge; Ondruch, Jakub; Paavola, Riku; Pardo, Isabel; Patrick, Christopher J.; Peeters, Edwin T. H. M.; Pozo, Jesus; Pringle, Catherine; Prussian, Aaron; Quenta, Estefania; Quesada, Antonio; Reid, Brian; Richardson, John S.; Rigosi, Anna; Rincon, Jose; Risnoveanu, Geta; Robinson, Christopher T.; Rodriguez-Gallego, Lorena; Royer, Todd V.; Rusak, James A.; Santamans, Anna C.; Selmeczy, Geza B.; Simiyu, Gelas; Skuja, Agnija; Smykla, Jerzy; Sridhar, Kandikere R.; Sponseller, Ryan; Stoler, Aaron; Swan, Christopher M.; Szlag, David; Teixeira-de Mello, Franco; Tonkin, Jonathan D.; Uusheimo, Sari; Veach, Allison M.; Vilbaste, Sirje; Vought, Lena B. M.; Wang, Chiao-Ping; Webster, Jackson R.; Wilson, Paul B.; Woelfl, Stefan; Xenopoulos, Marguerite A.; Yates, Adam G.; Yoshimura, Chihiro; Yule, Catherine M.; Zhang, Yixin X.; Zwart, Jacob A. (American Association for the Advancement of Science, 2019-01-09)River ecosystems receive and process vast quantities of terrestrial organic carbon, the fate of which depends strongly on microbial activity. Variation in and controls of processing rates, however, are poorly characterized at the global scale. In response, we used a peer-sourced research network and a highly standardized carbon processing assay to conduct a global-scale field experiment in greater than 1000 river and riparian sites. We found that Earth’s biomes have distinct carbon processing signatures. Slow processing is evident across latitudes, whereas rapid rates are restricted to lower latitudes. Both the mean rate and variability decline with latitude, suggesting temperature constraints toward the poles and greater roles for other environmental drivers (e.g., nutrient loading) toward the equator. These results and data set the stage for unprecedented “next-generation biomonitoring” by establishing baselines to help quantify environmental impacts to the functioning of ecosystems at a global scale.
- Process-Guided Deep Learning Predictions of Lake Water TemperatureRead, Jordan S.; Jia, Xiaowei; Willard, Jared; Appling, Alison P.; Zwart, Jacob A.; Oliver, Samantha K.; Karpatne, Anuj; Hansen, Gretchen J. A.; Hanson, Paul C.; Watkins, William; Steinbach, Michael; Kumar, Vipin (2019-11-08)The rapid growth of data in water resources has created new opportunities to accelerate knowledge discovery with the use of advanced deep learning tools. Hybrid models that integrate theory with state-of-the art empirical techniques have the potential to improve predictions while remaining true to physical laws. This paper evaluates the Process-Guided Deep Learning (PGDL) hybrid modeling framework with a use-case of predicting depth-specific lake water temperatures. The PGDL model has three primary components: a deep learning model with temporal awareness (long short-term memory recurrence), theory-based feedback (model penalties for violating conversation of energy), and model pretraining to initialize the network with synthetic data (water temperature predictions from a process-based model). In situ water temperatures were used to train the PGDL model, a deep learning (DL) model, and a process-based (PB) model. Model performance was evaluated in various conditions, including when training data were sparse and when predictions were made outside of the range in the training data set. The PGDL model performance (as measured by root-mean-square error (RMSE)) was superior to DL and PB for two detailed study lakes, but only when pretraining data included greater variability than the training period. The PGDL model also performed well when extended to 68 lakes, with a median RMSE of 1.65 degrees C during the test period (DL: 1.78 degrees C, PB: 2.03 degrees C; in a small number of lakes PB or DL models were more accurate). This case-study demonstrates that integrating scientific knowledge into deep learning tools shows promise for improving predictions of many important environmental variables.
- Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial densityLofton, Mary E.; Brentrup, Jennifer A.; Beck, Whitney S.; Zwart, Jacob A.; Bhattacharya, Ruchi; Brighenti, Ludmila S.; Burnet, Sarah H.; McCullough, Ian M.; Steele, Bethel G.; Carey, Cayelan C.; Cottingham, Kathryn L.; Dietze, Michael C.; Ewing, Holly A.; Weathers, Kathleen C.; LaDeau, Shannon L. (Wiley, 2022-03)Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state-space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near-term (1- to 4-week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4-week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1-week-ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long-term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.