TY - CONF T1 - Support of distributed ecological experiments via closed-loop environmental control T2 - 2017 IEEE Conference on Technologies for Sustainability (SusTech), Y1 - 2017 A1 - J.D. Knapp A1 - M. Middleton A1 - P.L. Heinrich A1 - A.V. Whipple A1 - P.G. Flikkema KW - closed-loop KW - distributed experiments KW - Ecology KW - environmental control KW - SEGA KW - technology AB -

Improved understanding of the effects of climate and weather patterns on plant survival and growth is critical for improving management of wildland, rangeland, and crop ecosystems. The Southwest Experimental Garden Array (SEGA) is a distributed research instrument comprising of an array of 10 common gardens across an elevational gradient in Northern Arizona. SEGA's cyber infrastructure facilitates monitoring and control of soil moisture at experimental plots using drip irrigation and wireless sensor/actuator nodes. This paper describes development of software-based workflows for the sensing and control of soil moisture conditions across experimental plots and gardens with different temperature and rainfall regimes, and the necessary hardware and software infrastructure to support this capability.

JF - 2017 IEEE Conference on Technologies for Sustainability (SusTech), PB - IEEE SusTech CY - Phoenix, AZ UR - https://ieeexplore.ieee.org/document/8333478/ ER - TY - JOUR T1 - Phylogenetic organization of bacterial activity. JF - The ISME journal Y1 - 2016 A1 - Ember M Morrissey A1 - Mau,Rebecca L A1 - Egbert Schwartz A1 - Caporaso,J Gregory A1 - P Dijkstra A1 - van Gestel,Natasja A1 - BJ Koch A1 - Liu,Cindy M A1 - Hayer,Michaela A1 - McHugh,Theresa A A1 - Jane C Marks A1 - Lance B Price A1 - Hungate,Bruce A KW - Bacteria KW - Biological Evolution KW - Carbon Isotopes KW - Ecology KW - Ecosystem KW - Oxygen Isotopes KW - Phenotype KW - Phylogeny AB -

Phylogeny is an ecologically meaningful way to classify plants and animals, as closely related taxa frequently have similar ecological characteristics, functional traits and effects on ecosystem processes. For bacteria, however, phylogeny has been argued to be an unreliable indicator of an organism's ecology owing to evolutionary processes more common to microbes such as gene loss and lateral gene transfer, as well as convergent evolution. Here we use advanced stable isotope probing with (13)C and (18)O to show that evolutionary history has ecological significance for in situ bacterial activity. Phylogenetic organization in the activity of bacteria sets the stage for characterizing the functional attributes of bacterial taxonomic groups. Connecting identity with function in this way will allow scientists to begin building a mechanistic understanding of how bacterial community composition regulates critical ecosystem functions.

VL - 10 SN - 1751-7362 UR - http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&DbFrom=pubmed&Cmd=Link&LinkName=pubmed_pubmed&LinkReadableName=Related%20Articles&IdsFromResult=26943624&ordinalpos=3&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSumhttp://www.ncbi. IS - 9 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 - JOUR T1 - Repeatability and transparency in ecological research. JF - Ecology Y1 - 2010 A1 - Ellison,Aaron M KW - Ecology KW - Information Dissemination KW - Reproducibility of Results KW - Research KW - Research Design KW - Software VL - 91 SN - 0012-9658 UR - http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&DbFrom=pubmed&Cmd=Link&LinkName=pubmed_pubmed&LinkReadableName=Related%20Articles&IdsFromResult=20957944&ordinalpos=3&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSumhttp://www.ncbi. IS - 9 ER - TY - JOUR T1 - A dense linkage map of hybrid cottonwood (Populus fremontii x P. angustifolia) contributes to long-term ecological research and comparison mapping in a model forest tree. JF - Heredity Y1 - 2008 A1 - Woolbright,S A A1 - Difazio,S P A1 - Yin,T A1 - Martinsen,G D A1 - Zhang,X A1 - Allan,G J A1 - Whitham,T G A1 - Keim,P KW - Chimera KW - Chromosome Mapping KW - Ecology KW - Genetic Linkage KW - Genetic Markers KW - Genetics, Population KW - Genome, Plant KW - Linkage Disequilibrium KW - Models, Biological KW - Polymorphism, Restriction Fragment Length KW - Populus KW - Trees AB -

Cottonwoods are foundation riparian species, and hybridization among species is known to produce ecological effects at levels higher than the population, including effects on dependent species, communities and ecosystems. Because these patterns result from increased genetic variation in key cottonwood traits, novel applications of genetic tools (for example, QTL mapping) could be used to place broad-scale ecological research into a genomic perspective. In addition, linkage maps have been produced for numerous species within the genus, and, coupled with the recent publication of the Populus genome sequence, these maps present a unique opportunity for genome comparisons in a model system. Here, we conducted linkage analyses in order to (1) create a platform for QTL and candidate gene studies of ecologically important traits, (2) create a framework for chromosomal-scale perspectives of introgression in a natural population, and (3) enhance genome-wide comparisons using two previously unmapped species. We produced 246 backcross mapping (BC(1)) progeny by crossing a naturally occurring F(1) hybrid (Populus fremontii x P. angustifolia) to a pure P. angustifolia from the same population. Linkage analysis resulted in a dense linkage map of 541 AFLP and 111 SSR markers distributed across 19 linkage groups. These results compared favorably with other Populus linkage studies, and addition of SSR loci from the poplar genome project provided coarse alignment with the genome sequence. Preliminary applications of the data suggest that our map represents a useful framework for applying genomic research to ecological questions in a well-studied system, and has enhanced genome-wide comparisons in a model tree.

VL - 100 SN - 0018-067X UR - http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&DbFrom=pubmed&Cmd=Link&LinkName=pubmed_pubmed&LinkReadableName=Related%20Articles&IdsFromResult=17895905&ordinalpos=3&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSumhttp://www.ncbi. IS - 1 ER -