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Abstract (from ESA): Estimating population size and resource selection functions (RSFs) are common approaches in applied ecology for addressing wildlife conservation and management objectives. Traditionally such approaches have been undertaken separately with different sources of data. Spatial capture–recapture (SCR) provides a hierarchical framework for jointly estimating density and multi‐scale resource selection, and data integration techniques provide opportunities for improving inferences from SCR models. Despite the added benefits, there have been few applications of SCR‐RSF integration, potentially due to complexities of specifying and fitting such models. Here, we extend a previous integrated SCR‐RSF model...
Abstract (from Ecological Society of America): Successful management of natural resources requires local action that adapts to larger‐scale environmental changes in order to maintain populations within the safe operating space (SOS) of acceptable conditions. Here, we identify the boundaries of the SOS for a managed freshwater fishery in the first empirical test of the SOS concept applied to management of harvested resources. Walleye (Sander vitreus) are popular sport fish with declining populations in many North American lakes, and understanding the causes of and responding to these changes is a high priority for fisheries management. We evaluated the role of changing water clarity and temperature in the decline...
Abstract (from Ecological Society of America): Population dynamics are often correlated in space and time due to correlations in environmental drivers as well as synchrony induced by individual dispersal. Many statistical analyses of populations ignore potential autocorrelations and assume that survey methods (distance and time between samples) eliminate these correlations, allowing samples to be treated independently. If these assumptions are incorrect, results and therefore inference may be biased and uncertainty underestimated. We developed a novel statistical method to account for spatiotemporal correlations within dendritic stream networks, while accounting for imperfect detection in the surveys. Through simulations,...
Abstract (from SpringerLink): The resilience of socio-ecological systems to sea level rise, storms and flooding can be enhanced when coastal habitats are used as natural infrastructure. Grey infrastructure has long been used for coastal flood protection but can lead to unintended negative impacts. Natural infrastructure often provides similar services as well as added benefits that support short- and long-term biological, cultural, social, and economic goals. While natural infrastructure is becoming more widespread in practice, it often represents a relatively small fraction within portfolios of coastal risk-reducing strategies compared to more traditional grey infrastructure. This study provides a comprehensive...
Abstract (from arXiv): This paper introduces a novel framework for combining scientific knowledge of physics-based models with neural networks to advance scientific discovery. This framework, termed as physics-guided neural network (PGNN), leverages the output of physics-based model simulations along with observational features to generate predictions using a neural network architecture. Further, this paper presents a novel framework for using physics-based loss functions in the learning objective of neural networks, to ensure that the model predictions not only show lower errors on the training set but are also scientifically consistent with the known physics on the unlabeled set. We illustrate the effectiveness...
Abstract (from AGU Pubs): Land and water surfaces play a critical role in hydroclimate by supplying moisture to the atmosphere, yet the ability of climate models to capture their feedbacks with the atmosphere relative to large‐scale transport is uncertain. To assess these land‐lake‐atmosphere feedbacks, we compare the controls on atmospheric moisture simulated by a regional climate model (RegCM) with observations and reanalysis products for the Great Lakes region. Three 23 year simulations, driven by one reanalysis product and two general circulation models, are performed. RegCM simulates wetter winters and drier summers than observed by up to 31 and 21%, respectively. Moisture advection exhibits similar biases,...
Abstract (from ScienceDirect): Climate change is affecting the benefits society derives from forests. One such forest ecosystem service is maple syrup, which is primarily derived from Acer saccharum(sugar maple), currently an abundant and widespread tree species in eastern North America. Two climate sensitive components of sap affect syrup production: sugar content and sap flow. The sugar in maple sap derives from carbohydrate stores influenced by prior year growing season conditions. Sap flow is tied to freeze/thaw cycles during early spring. Predicting climate effects on syrup production thus requires integrating observations across scales and biological processes. We observed sap at 6 sugar maple stands spanning...
Abstract (from arXiv): This paper proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improve the modeling of physical processes. Specifically, we show that a PGRNN can improve prediction accuracy over that of physical models, while generating outputs consistent with physical laws, and achieving good generalizability. Standard RNNs, even when producing superior prediction accuracy, often produce physically inconsistent results and lack generalizability. We further enhance this approach by using a pre-training method that leverages the simulated data from a physics-based model to address the scarcity of observed...
Abstract: This research investigates how changes to floodplains in the Connecticut River Basin impact flood events. Climate impacted flows and increased development within the floodplain could lead to worsening flood events and less habitat availability for threatened species. Potential future conditions are evaluated through a wide range of scenarios to assess the range of possible impacts using a HEC-RAS 2D model. Three different flood events, 1-yr, 10-yr, and 100-yr, are evaluated for each scenario. Five metrics, Discharge, Depth, Time of Arrival, Flooding Duration, and Number of Buildings Flooded, are tracked for each scenario. These metrics are compared to select the ideal course of action given multiple potential...
Abstract (from Geoscientific Model Development): Biosphere–atmosphere interactions play a critical role in governing atmospheric composition, mediating the concentrations of key species such as ozone and aerosol, thereby influencing air quality and climate. The exchange of reactive trace gases and their oxidation products (both gas and particle phase) is of particular importance in this process. The FORCAsT (FORest Canopy Atmosphere Transfer) 1-D model is developed to study the emission, deposition, chemistry and transport of volatile organic compounds (VOCs) and their oxidation products in the atmosphere within and above the forest canopy. We include an equilibrium partitioning scheme, making FORCAsT one of the...
Abstract (from arXiv): To simultaneously address the rising need of expressing uncertainties in deep learning models along with producing model outputs which are consistent with the known scientific knowledge, we propose a novel physics-guided architecture (PGA) of neural networks in the context of lake temperature modeling where the physical constraints are hard coded in the neural network architecture. This allows us to integrate such models with state of the art uncertainty estimation approaches such as Monte Carlo (MC) Dropout without sacrificing the physical consistency of our results. We demonstrate the effectiveness of our approach in ensuring better generalizability as well as physical consistency in MC...
Abstract (from Fisheries Oceanography): The timing of recurring biological and seasonal environmental events is changing on a global scale relative to temperature and other climate drivers. This study considers the Gulf of Maine ecosystem, a region of high social and ecological importance in the Northwest Atlantic Ocean and synthesizes current knowledge of (a) key seasonal processes, patterns, and events; (b) direct evidence for shifts in timing; (c) implications of phenological responses for linked ecological‐human systems; and (d) potential phenology‐focused adaptation strategies and actions. Twenty studies demonstrated shifts in timing of regional marine organisms and seasonal environmental events. The most common...
Abstract (from ScienceDirect): Foliar emissions of biogenic volatile organic compounds (BVOC)—important precursors of tropospheric ozone and secondary organic aerosols—vary widely by vegetation type. Modeling studies to date typically represent the canopy as a single dominant tree type or a blend of tree types, yet many forests are diverse with trees of varying height. To assess the sensitivity of biogenic emissions to tree height variation, we compare two 1-D canopy model simulations in which BVOC emission potentials are homogeneous or heterogeneous with canopy depth. The heterogeneous canopy emulates the mid-successional forest at the University of Michigan Biological Station (UMBS). In this case, high-isoprene-emitting...
Abstract (from British Ecological Society): A central theme of range‐limit theory (RLT) posits that abiotic factors form high‐latitude/altitude limits, whereas biotic interactions create lower limits. This hypothesis, often credited to Charles Darwin, is a pattern widely assumed to occur in nature. However, abiotic factors can impose constraints on both limits and there is scant evidence to support the latter prediction. Deviations from these predictions may arise from correlations between abiotic factors and biotic interactions, as a lack of data to evaluate the hypothesis, or be an artifact of scale. Combining two tenets of ecology—niche theory and predator–prey theory—provides an opportunity to understand how...
The American sand lance (Ammodytes americanus, Ammodytidae) and the Northern sand lance (A. dubius, Ammodytidae) are small forage fishes that play an important functional role in the Northwest Atlantic Ocean (NWA). The NWA is a highly dynamic ecosystem currently facing increased risks from climate change, fishing and energy development. We need a better understanding of the biology, population dynamics and ecosystem role of Ammodytes to inform relevant management, climate adaptation and conservation efforts. To meet this need, we synthesized available data on the (a) life history, behaviour and distribution; (b) trophic ecology; (c) threats and vulnerabilities; and (d) ecosystem services role of Ammodytes in the...
Abstract (from arXiv): In this paper, we introduce a novel framework for combining scientific knowledge within physics-based models and recurrent neural networks to advance scientific discovery in many dynamical systems. We will first describe the use of outputs from physics-based models in learning a hybrid-physics-data model. Then, we further incorporate physical knowledge in real-world dynamical systems as additional constraints for training recurrent neural networks. We will apply this approach on modeling lake temperature and quality where we take into account the physical constraints along both the depth dimension and time dimension. By using scientific knowledge to guide the construction and learning the...
Abstract: Active geomorphic features of rivers like sandbars provide habitat for endangered and threatened riparian plant and animal species. However, human development has altered flow and sediment regimes, thus impairing formation of sandbars and islands. Large scale mapping of the fluvial geomorphology in river ecosystems like the Connecticut River is are necessary to understand the dynamics of these features and preserve habitat. Orthophotographs from 2012 from United States Department of Agriculture's Farm Service Agency (FSA), National Agriculture Imagery Program (NAIP) were used to develop a model in ArcGIS Pro to identify fluvial geomorphic features in the Connecticut River and 12 of its major tributaries....
Abstract (from EGU): The General Lake Model (GLM) is a onedimensional open-source code designed to simulate the hydrodynamics of lakes, reservoirs, and wetlands. GLM was developed to support the science needs of the Global Lake Ecological Observatory Network (GLEON), a network of researchers using sensors to understand lake functioning and address questions about how lakes around the world respond to climate and land use change. The scale and diversity of lake types, locations, and sizes, and the expanding observational datasets created the need for a robust community model of lake dynamics with sufficient flexibility to accommodate a range of scientific and management questions relevant to the GLEON community....