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 - A conditional trophic cascade: birds benefit faster growing trees with strong links between predators and plants. JF - Ecology Y1 - 2010 A1 - Bridgeland,William T A1 - Beier,Paul A1 - Kolb,Thomas A1 - Whitham,Thomas G KW - Animals KW - Birds KW - Food Chain KW - Insecta KW - Predatory Behavior KW - Time Factors KW - Trees AB -

Terrestrial systems are thought to be organized predominantly from the bottom-up, but there is a growing literature documenting top-down trophic cascades under certain ecological conditions. We conducted an experiment to examine how arthropod community structure on a foundation riparian tree mediates the ability of insectivorous birds to influence tree growth. We built whole-tree bird exclosures around 35 mature cottonwood (Populus spp.) trees at two sites in northern Utah, USA, to measure the effect of bird predation on arthropod herbivore and predator species richness, abundance, and biomass, and on tree performance. We maintained bird exclosures over two growing seasons and conducted nondestructive arthropod surveys that recorded 63652 arthropods of 689 morphospecies representing 19 orders. Five major patterns emerged: (1) We found a significant trophic cascade (18% reduction in trunk growth when birds were excluded) only at one site in one year. (2) The significant trophic cascade was associated with higher precipitation, tree growth, and arthropod abundance, richness, and biomass than other site-year combinations. (3) The trophic cascade was weak or not evident when tree growth and insect populations were low apparently due to drought. (4) Concurrent with the stronger trophic cascade, bird predation significantly reduced total arthropod abundance, richness, and biomass. Arthropod biomass was 67% greater on trees without bird predation. This pattern was driven largely by two herbivore groups (folivores and non-aphid sap-feeders) suggesting that birds targeted these groups. (5) Three species of folivores (Orthoptera: Melanoplus spp.) were strong links between birds and trees and were only present in the site and the year in which the stronger trophic cascade occurred. Our results suggest that this trophic system is predominately bottom-up driven, but under certain conditions the influence of top predators can stimulate whole tree growth. When the most limiting factor for tree growth switched from water availability to herbivory, the avian predators gained the potential to reduce herbivory. This potential could be realized when strong links between the birds and plant, i.e., species that were both abundant herbivores and preferred prey, were present.

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=20380198&ordinalpos=3&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSumhttp://www.ncbi. IS - 1 ER -