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Folders: ROOT > ScienceBase Catalog > National and Regional Climate Adaptation Science Centers > Northeast CASC > FY 2017 Projects ( Show direct descendants )

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Although scientists have identified many ways to reduce the negative effects of climate change on wildlife, this information is not readily available to natural resource managers. For successful wildlife adaptation to climate change, natural resource managers should have current, peerreviewed information to guide their decisions. We conducted a review of over 1300 publications for recommendations to manage wildlife in the face of climate change. We then summarized the findings as the wildlife adaptation menu, a tool to inform planning and decision-making in an accessible format.
With global efforts to restore grassland ecosystems, researchers and land management practitioners are working to reconstruct habitat that will persist and withstand stresses associated with climate change. Part of these efforts involve movement of plant material potentially adapted to future climate conditions from native habitat or seed production locations to a new restoration site. Restoration practice often follows this plant-centered, top-down approach. However, we suggest that restoration of belowground interactions, namely between plants and arbuscular mycorrhizal fungi or rhizobia, is important for restoring resilient grasslands. In this synthesis we highlight these interactions and offer insight into how...
Categories: Publication; Types: Citation
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 PLOS ONE): Adequate diversity and abundance of native seed for large-scale grassland restorations often require commercially produced seed from distant sources. However, as sourcing distance increases, the likelihood of inadvertent introduction of multiple novel, non-native weed species as seed contaminants also increases. We created a model to determine an “optimal maximum distance” that would maximize availability of native prairie seed from commercial sources while minimizing the risk of novel invasive weeds via contamination. The model focused on the central portion of the Level II temperate prairie ecoregion in the Midwest US. The median optimal maximum distance from which to source seed was...
Categories: Publication; Types: Citation
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 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 (from Ecosphere): Understanding invasive species spread and projecting how distributions will respond to climate change is a central task for ecologists. Typically, current and projected air temperatures are used to forecast future distributions of invasive species based on climate matching in an ecological niche modeling approach. While this approach was originally developed for terrestrial species, it has also been widely applied to aquatic species even though aquatic species do not experience air temperatures directly. In the case of lakes, species respond to lake thermal regimes, which reflect the interaction of climate and lake attributes such as depth, size, and clarity. The result is that adjacent...
Categories: Publication; Types: Citation
North American prairies are imperiled with only 11% of tallgrass prairie, 24% of mixed grass prairie and 54% of shortgrass prairie remaining intact (Wilsey et al. 2019). Reclaiming and restoring these natural areas can provide climate change mitigation benefits, including carbon sequestration (Brye and Riley 2009; Guzman and Al-Kaisi 2010; Hernández et al. 2013). Of the tools available for prairie restoration, movement of native plant species as seed is most common. Movement of seed may be necessary due to limited availability or to adapt to potential future climates. Because not enough seed is produced on the native prairies that remain, large-scale agriculture-like production of native seed is necessary to keep...
Categories: Publication; Types: Citation
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...
The real-world application of climate change adaptation practices in terrestrial wildlife conservation has been slowed by a lack of practical guidance for wildlife managers. Although there is a rapidly growing body of literature on the topic of climate change adaptation and wildlife management, the literature is weighted towards a narrow range of adaptation actions and administrative or policy recommendations that are typically beyond the decision space and influence of wildlife professionals. We developed a menu of tiered adaptation actions for terrestrial wildlife management to translate broad concepts into actionable approaches to help managers respond to climate change risks and meet desired management goals....
Categories: Publication; Types: Citation
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...
Water temperatures are warming in lakes, resulting in the loss of many native fish. Many Midwestern lakes “thermally stratify” resulting in warmer waters on top of deeper, cooler waters. In order to understand past change in economically valuable fisheries, it is important that managers have access to accurate estimates of the past temperatures experienced by fish. These data are invaluable for making decisions such as whether to continue stocking walleye in certain lakes how to set walleye harvest limits. This project developed new state-of-theart methods to model historical thermal habitat for thousands of lakes in the Midwest US.
Vernal pools of the northeastern United States are important breeding habitat for amphibians. These wetlands typically fill with water from autumn to early spring and dry by summer. Under projections of future climate, some pools may dry earlier than is typical, which in some cases could make it difficult for amphibians to complete metamorphosis successfully. This study evaluated the factors controlling vernal pool inundation (i.e., whether or not a pool has water in it) and generated model predictions of pool-inundation probability under a variety of weather and climate scenarios (e.g., dry, average, and wet weather, and future climate scenarios for the middle and end of the 21st century). Model predictions were...
Categories: Publication; Types: Citation
Abstract(from:https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019WR024922)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 feedbacks (model penalties...
Abstract (from The Journal of Wildlife Management): Global biodiversity is in unprecedented decline and on‐the‐ground solutions are imperative for conservation. Although there is a large volume of evidence related to climate change effects on wildlife, research on climate adaptation strategies is lagging. To assess the current state of knowledge in climate adaptation, we conducted a comprehensive literature review and evaluated 1,346 peer‐reviewed publications for management recommendations designed to address the consequences of climate change on wildlife populations. From 509 publications, we identified 2,306 recommendations and employed both qualitative and quantitative methods for data analysis. Although we...
Categories: Publication; Types: Citation
Abstract (from Ecohydrology): Vernal pools of the northeastern United States provide important breeding habitat for amphibians but may be sensitive to droughts and climate change. These seasonal wetlands typically fill by early spring and dry by mid-to-late summer. Because climate change may produce earlier and stronger growing-season evapotranspiration combined with increasing droughts and shifts in precipitation timing, management concerns include the possibility that some pools will increasingly become dry earlier in the year, potentially interfering with amphibian life-cycle completion. In this context, a subset of pools that continues to provide wetland habitat later into the year under relatively dry conditions...
Categories: Publication; Types: Citation
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....