Wireless Sensor Networks for Ecology

Porter, John H.; Arzberger, Peter; Braun, Hans-Werner; Bryant, P. L.; Gage, Stuart H.; Hansen, Todd; Hanson, Paul C.; Lin, Chau-Chin; Lin, Fei; Kratz, Timothy K.; Michener, William K.; Shapiro, Sedra; Williams, Thomas P.

doi:10.1641/0006-3568(2005)055[0561:wsnfe]2.0.co;2

Cited by 198 publications

(123 citation statements)

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“…Emerging sensor technology and networks are improving capabilities for environmental monitoring (6,8). These technologies hold considerable promise for data-intensive and spatially extensive studies of ecosystem processes, such as metabolism, ecosystem stability, resilience, and threshold detection.…”

Section: Resultsmentioning

confidence: 99%

“…The intensity of sampling needed to acquire such datasets can be costly, and this cost is amplified if multiple variates must be monitored. Modern sensor technology permits automated, in situ, high-frequency, long-duration, and realtime data collection that is less labor-intensive than comparable manual methods (6). Although automated sensors are promising for meeting intensive data requirements, it is not known whether variables measured by such sensors are suitable for assessing resilience or thresholds in complex ecosystems under field conditions.…”

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confidence: 99%

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Changes in ecosystem resilience detected in automated measures of ecosystem metabolism during a whole-lake manipulation

Batt

Carpenter

Cole

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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Environmental sensor networks are developing rapidly to assess changes in ecosystems and their services. Some ecosystem changes involve thresholds, and theory suggests that statistical indicators of changing resilience can be detected near thresholds. We examined the capacity of environmental sensors to assess resilience during an experimentally induced transition in a wholelake manipulation. A trophic cascade was induced in a planktivoredominated lake by slowly adding piscivorous bass, whereas a nearby bass-dominated lake remained unmanipulated and served as a reference ecosystem during the 4-y experiment. In both the manipulated and reference lakes, automated sensors were used to measure variables related to ecosystem metabolism (dissolved oxygen, pH, and chlorophyll-a concentration) and to estimate gross primary production, respiration, and net ecosystem production. Thresholds were detected in some automated measurements more than a year before the completion of the transition to piscivore dominance. Directly measured variables (dissolved oxygen, pH, and chlorophyll-a concentration) related to ecosystem metabolism were better indicators of the approaching threshold than were the estimates of rates (gross primary production, respiration, and net ecosystem production); this difference was likely a result of the larger uncertainties in the derived rate estimates. Thus, relatively simple characteristics of ecosystems that were observed directly by the sensors were superior indicators of changing resilience. Models linked to thresholds in variables that are directly observed by sensor networks may provide unique opportunities for evaluating resilience in complex ecosystems.regime shift | alternative stable states | early warning signals | sonde | high-frequency time series

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Changes in ecosystem resilience detected in automated measures of ecosystem metabolism during a whole-lake manipulation

Batt

Carpenter

Cole

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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“…Input parameters include only daily air temperatures, there is no need for further data of daily frequency. Modeling studies face the problems of lack of data and access to them, respectively (Porter et al 2005), which are really true for climate changerelated modeling (Sipkay et al 2009b). On the one hand, data that have been gathered elsewhere are often difficult to obtain, on the other hand, long-term data of different water bodies have often been collected with different methodologies.…”

Section: Discussionmentioning

confidence: 99%

Simulation modeling of phytoplankton dynamics in a large eutrophic river, Hungary — Danubian Phytoplankton Growth Model (DPGM)

et al. 2012

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Ecological models have often been used in order to answer questions that are in the limelight of recent researches such as the possible effects of climate change. The methodology of tactical models is a very useful tool comparison to those complex models requiring relatively large set of input parameters. In this study, a theoretical strategic model (TEGM ) was adapted to the field data on the basis of a 24-year long monitoring database of phytoplankton in the Danube River at the station of Göd, Hungary (at 1669 river kilometer -hereafter referred to as "rkm"). The Danubian Phytoplankton Growth Model (DPGM) is able to describe the seasonal dynamics of phytoplankton biomass (mg L −1 ) based on daily temperature, but takes the availability of light into consideration as well. In order to improve fitting, the 24-year long database was split in two parts in accordance with environmental sustainability. The period of 1979-1990 has a higher level of nutrient excess compared with that of the 1991-2002. The authors assume that, in the above-mentioned periods, phytoplankton responded to temperature in two different ways, thus two submodels were developed, DPGM-sA and DPGMsB. Observed and simulated data correlated quite well. Findings suggest that linear temperature rise brings drastic change to phytoplankton only in case of high nutrient load and it is mostly realized through the increase of yearly total biomass.

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“…The three data sets of interest were initiated several decades ago, and involve capital-and/or labor-intensive data collection at a limited number of pre-defined locations and times. Nowadays, long-term ecological data are increasingly being collected at fine temporal and spatial scales, at many sites, and possibly even at moving sites (e.g., tagged organisms) (e.g., Porter et al 2005). For these data, visual analytics tools will need to accommodate combinations of time, space, and multiple variables.…”

Section: Discussionmentioning

confidence: 99%

Interactive visual analysis promotes exploration of long‐term ecological data

et al. 2013

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Abstract. Long-term ecological data are crucial in helping ecologists understand ecosystem function and environmental change. Nevertheless, these kinds of data sets are difficult to analyze because they are usually large, multivariate, and spatiotemporal. Although existing analysis tools such as statistical methods and spreadsheet software permit rigorous tests of pre-conceived hypotheses and static charts for simple data exploration, they have limited capacity to provide an overview of the data and to enable ecologists to explore data iteratively, and interactively, before committing to statistical analysis. These issues hinder how ecologists gain knowledge and generate hypotheses from long-term data. We present Ecological Distributions and Trends Explorer (EcoDATE), a web-based, visual-analysis tool that facilitates exploratory analysis of long-term ecological data (i.e., generating hypotheses as opposed to confirming hypotheses). The tool, which is publicly available online, was created and refined through a user-centered design process in which our team of ecologists and visualization researchers collaborated closely. The results of our collaboration were (1) a set of visual representation and interaction techniques well suited to communicating distribution patterns and temporal trends in ecological data sets, and (2) an understanding of processes ecologists use to explore data and generate and test hypotheses. We present three case studies to demonstrate the utility of EcoDATE and the exploratory analysis processes using long-term data on cone production, stream chemistry, and forest structure collected as part of the H.J. Andrews Experimental Forest (HJA), Long Term Ecological Research (LTER), and US Forest Service Pacific Northwest Research Station programs. We also present results from a survey of 15 participants of a working group at the 2012 LTER All Scientists Meeting that showed that users appreciated the tool for its ease of use, holistic access to large data sets, and interactivity.

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Wireless Sensor Networks for Ecology

Cited by 198 publications

References 16 publications

Changes in ecosystem resilience detected in automated measures of ecosystem metabolism during a whole-lake manipulation

Changes in ecosystem resilience detected in automated measures of ecosystem metabolism during a whole-lake manipulation

Simulation modeling of phytoplankton dynamics in a large eutrophic river, Hungary — Danubian Phytoplankton Growth Model (DPGM)

Interactive visual analysis promotes exploration of long‐term ecological data

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