TY - JOUR T1 - The impacts of increasing drought on forest dynamics, structure, and biodiversity in the United States. JF - Global change biology Y1 - 2016 A1 - James S Clark A1 - Iverson,Louis A1 - Woodall,Christopher W A1 - Allen,Craig D A1 - Bell,David M A1 - Bragg,Don C A1 - D'Amato,Anthony W A1 - Davis,Frank W A1 - Hersh,Michelle H A1 - Ibanez,Ines A1 - Jackson,Stephen T A1 - Matthews,Stephen A1 - Pederson,Neil A1 - Peters,Matthew A1 - Schwartz,Mark W A1 - Waring,Kristen M A1 - Zimmermann,Niklaus E KW - biodiversity KW - Droughts KW - Ecosystem KW - Forests KW - Trees KW - United States AB -

We synthesize insights from current understanding of drought impacts at stand-to-biogeographic scales, including management options, and we identify challenges to be addressed with new research. Large stand-level shifts underway in western forests already are showing the importance of interactions involving drought, insects, and fire. Diebacks, changes in composition and structure, and shifting range limits are widely observed. In the eastern US, the effects of increasing drought are becoming better understood at the level of individual trees, but this knowledge cannot yet be confidently translated to predictions of changing structure and diversity of forest stands. While eastern forests have not experienced the types of changes seen in western forests in recent decades, they too are vulnerable to drought and could experience significant changes with increased severity, frequency, or duration in drought. Throughout the continental United States, the combination of projected large climate-induced shifts in suitable habitat from modeling studies and limited potential for the rapid migration of tree populations suggests that changing tree and forest biogeography could substantially lag habitat shifts already underway. Forest management practices can partially ameliorate drought impacts through reductions in stand density, selection of drought-tolerant species and genotypes, artificial regeneration, and the development of multistructured stands. However, silvicultural treatments also could exacerbate drought impacts unless implemented with careful attention to site and stand characteristics. Gaps in our understanding should motivate new research on the effects of interactions involving climate and other species at the stand scale and how interactions and multiple responses are represented in models. This assessment indicates that, without a stronger empirical basis for drought impacts at the stand scale, more complex models may provide limited guidance.

VL - 22 SN - 1354-1013 UR - http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&DbFrom=pubmed&Cmd=Link&LinkName=pubmed_pubmed&LinkReadableName=Related%20Articles&IdsFromResult=26898361&ordinalpos=3&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSumhttp://www.ncbi. IS - 7 ER - TY - JOUR T1 - Process Modeling for Soil Moisture Using Sensor Network Data . JF - Statistical Methodology (Special issue on modern statistical methods in ecology) Y1 - 2014 A1 - Ghosh,S A1 - Bell,DM A1 - Clark,JS A1 - Gelfand,AE A1 - Flikkema,P VL - 12 N1 - [Original String]:Ghosh S, Bell DM, Clark JS, Gelfand AE, and Flikkema P. 2014. Process Modeling for Soil Moisture Using Sensor Network Data . Statistical Methodology (Special issue on modern statistical methods in ecology)12: 99-112. ER - TY - JOUR T1 - The relative influences of host plant genotype and yearly abiotic variability in determining herbivore abundance. JF - Oecologia Y1 - 2012 A1 - Evans,Luke M A1 - James S Clark A1 - Whipple,Amy V A1 - Whitham,Thomas G KW - Animals KW - Bayes Theorem KW - Genotype KW - Herbivory KW - Mites KW - Population Density KW - Population Dynamics KW - Populus AB -

Both plant genotype and yearly abiotic variation affect herbivore population sizes, but long-term data have rarely been used to contrast the relative contributions of each. Using a hierarchical Bayesian model, we directly compare effects of these two factors on the population size of a common herbivore, Aceria parapopuli, on Populus angustifolia × fremontii F(1) hybrid trees growing in a common garden across 8 years. Several patterns emerged. First, the Bayesian posterior estimates of tree genotype effects on mite gall number ranged from 0.0043 to 229 on a linear scale. Second, year effect sizes across 8 years of study ranged from 0.133 to 1.895. Third, in comparing the magnitudes of genotypic versus yearly variation, we found that genotypic variation was over 130 times greater than variation among years. Fourth, precipitation in the previous year negatively affected gall abundances, but was minimal compared to tree genotype effects. These findings demonstrate the relative importance of tree genotypic variation in determining herbivore population size. However, given the demonstrated sensitivity of cottonwoods to drought, the loss of individual tree genotypes from an altered climate would have catastrophic impacts on mites that are dependent upon these genotypes for their survival.

VL - 168 SN - 0029-8549 UR - http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&DbFrom=pubmed&Cmd=Link&LinkName=pubmed_pubmed&LinkReadableName=Related%20Articles&IdsFromResult=21918874&ordinalpos=3&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSumhttp://www.ncbi. IS - 2 ER - TY - JOUR T1 - Inferential ecosystem models, from network data to prediction. JF - Ecological applications : a publication of the Ecological Society of America Y1 - 2011 A1 - James S Clark A1 - Agarwal,Pankaj A1 - Bell,David M A1 - Flikkema,Paul G A1 - Gelfand,Alan A1 - Nguyen,Xuanlong A1 - Ward,Eric A1 - Yang,Jun KW - Bayes Theorem KW - Data Interpretation, Statistical KW - Ecology KW - Ecosystem KW - Forecasting KW - Models, Biological KW - Models, Statistical KW - Plant Transpiration KW - Plants KW - Time Factors AB -

Recent developments suggest that predictive modeling could begin to play a larger role not only for data analysis, but also for data collection. We address the example of efficient wireless sensor networks, where inferential ecosystem models can be used to weigh the value of an observation against the cost of data collection. Transmission costs make observations "expensive"; networks will typically be deployed in remote locations without access to infrastructure (e.g., power). The capacity to sample intensively makes sensor networks valuable, but high-frequency data are informative only at specific times and locations. Sampling intervals will range from meters and seconds to landscapes and years, depending on the process, the current states of the system, the uncertainty about those states, and the perceived potential for rapid change. Given that intensive sampling is sometimes critical, but more often wasteful, how do we develop tools to control the measurement and transmission processes? We address the potential of data collection controlled and/or supplemented by inferential ecosystem models. In a given model, the value of an observation can be evaluated in terms of its contribution to estimates of state variables and important parameters. There will be more than one model applied to network data that will include as state variables water, carbon, energy balance, biogeochemistry, tree ecophysiology, and forest demographic processes. The value of an observation will depend on the application. Inference is needed to weigh the contributions against transmission cost. Network control must be dynamic and driven by models capable of learning about both the environment and the network. We discuss application of Bayesian inference to model data from a developing sensor network as a basis for controlling the measurement and transmission processes. Our examples involve soil moisture and sap flux, but we discuss broader application of the approach, including its implications for network design.

VL - 21 SN - 1051-0761 UR - http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&DbFrom=pubmed&Cmd=Link&LinkName=pubmed_pubmed&LinkReadableName=Related%20Articles&IdsFromResult=21830699&ordinalpos=3&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSumhttp://www.ncbi. IS - 5 ER - TY - CONF T1 - From Data Reverence to Data Relevance: Model-Mediated Wireless Sensing of the Physical Environment T2 - ICCS 2007, 7th International Conference Y1 - 2007 A1 - PG Flikkema A1 - Agarwal,PK A1 - Clark,JS A1 - Ellis,C A1 - Gelfand,A ED - Albada,G ED - Dongarra,J ED - Sloot,P AB -

Summary: Wireless sensor networks can be viewed as the integration of three subsystems: a low-impact in situ data acquisition and collection system, a system for inference of process models from observed data and a priori information, and a system that controls the observation and collection. Each of these systems is connected by feedforward and feedback signals from the others; moreover, each subsystem is formed from behavioral components that are distributed among the sensors and out-of-network computational resources. Crucially, the overall performance of the system is constrained by the costs of energy, time, and computational complexity. We are addressing these design issues in the context of monitoring forest environments with the objective of inferring ecosystem process models. We describe here our framework of treating data and models jointly, and its application to soil moisture processes.

JF - ICCS 2007, 7th International Conference T3 - ICCS 2007, 7th International Conference PB - Springer Berlin/Heidelberg CY - Beijing, China VL - 4487 UR - http://link.springer.com/10.1007/978-3-540-72584-8_130 ER - TY - CONF T1 - Model-Driven Dynamic Control of Embedded Wireless Sensor Networks T2 - Computational Science - ICCS 2006, Lecture Notes in Computer Science, 6th International Conference Y1 - 2006 A1 - PG Flikkema A1 - Agarwal,PK A1 - Clark,JS A1 - Ellis,C A1 - Gelfand,A ED - Alexandrov,V ED - van Albada,G ED - Sloot,P ED - Dongarra,J AB -

Next-generation wireless sensor networks may revolutionize understanding of environmental change by assimilating heterogeneous data, assessing the relative value and costs of data collection, and sche

JF - Computational Science - ICCS 2006, Lecture Notes in Computer Science, 6th International Conference T3 - Computational Science - ICCS 2006, Lecture Notes in Computer Science, 6th International Conference, PB - Springer Berlin/Heidelberg CY - Reading, UK VL - 3993 UR - http://www.springerlink.com/content/5603gh1252528020 ER -