Appeared in the proceedings of the 2021 IFAC Workshop on Time-Delay Systems This paper establishes a PIE (Partial Integral Equation)-based technique for the robust stability and H∞ performance analysis of linear systems with interval delays. The delays considered are time-invariant but uncertain, residing within a bounded interval excluding zero. We first propose a structured class of PIE systems with parametric uncertainty, then propose a Linear PI Inequality (LPI) for robust stability and H∞ performance of PIEs with polytopic uncertainty. Next, we consider the problem of robust stability and H∞ performance of multidelay systems with interval uncertainty in the delay parameters and show this problem is equivalent to robust stability and performance of a given PIE with parametric uncertainty. The robust stability and H∞ performance of the uncertain time-delay system are then solved using the LPI solver in the MATLAB PIETOOLS toolbox. Numerical examples are given to prove the effectiveness and accuracy of the method. This paper adds to the expanding field of PIE approach and can be extended to linear partial differential equations.
more »
« less
Descriptive data file for information regarding microbial genetic research in the environs of Plum Island Sound watersheds, PIE LTER, Massachusetts.
This is a descriptive, tabular dataset of publications related to microbial or genomic research conducted within PIE. Assession numbers for genetic sequences generated from PIE samples are provided where available, followed by a very brief description of analysis type and study objectives. Sampling locations within PIE, sampling dates, and habitat type (sea water, fresh water, sediment, marsh) are also given. Environmental data are included in some publications and are listed here (if brief) or availability is described. Links to sequence archives are given in Methods.
more »
« less
- Award ID(s):
- 1902712
- PAR ID:
- 10562451
- Publisher / Repository:
- Environmental Data Initiative
- Date Published:
- Format(s):
- Medium: X
- Institution:
- Woodwell Climate Research Center
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Predators impact prey populations directly through consumption and indirectly via trait-mediated effects like predator-induced emigration (PIE), where prey alter movement due to predation risk. While PIE can significantly influence prey dynamics, its combined effect with direct predation in fragmented habitats is underexplored. Habitat fragmentation reduces viable habitats and isolates populations, necessitating an understanding of these interactions for conservation. In this paper, we present a reaction–diffusion model to investigate prey persistence under both direct predation and PIE in fragmented landscapes. The model considers prey growing logistically within a bounded habitat patch surrounded by a hostile matrix. Prey move via unbiased random walks internally but exhibit biased movement at habitat boundaries influenced by predation risk. Predators are assumed constant, operating on a different timescale. We examine three predation functional responses—constant yield, Holling Type I, and Holling Type III—and three emigration patterns: density-independent, positive density-dependent, and negative density-dependent emigration. Using the method of sub- and supersolutions, we establish conditions for the existence and multiplicity of positive steady-state solutions. Numerical simulations in one-dimensional habitats further elucidate the structure of these solutions. Our findings demonstrate that the interplay between direct predation and PIE crucially affects prey persistence in fragmented habitats. Depending on the functional response and emigration pattern, PIE can either mitigate or amplify the impact of direct predation. This underscores the importance of incorporating both direct and indirect predation effects in ecological models to better predict species dynamics and inform conservation strategies in fragmented landscapes.more » « less
-
The cyto- and genotoxic potencies of disinfection by-products (DBPs) have been evaluated in published literature by measuring the response of exposed Chinese hamster ovary cells. In recent publications, DBP concentrations divided by their individual toxicity indices are summed to predict the relative toxicity of a water sample. We hypothesized that the omission or inclusion of certain DBPs over others is equivalent to statistical sampling bias and may result in biased conclusions. To test this hypothesis, we removed or added actual or simulated DBP measurements to that of published studies which evaluated granular activated carbon as a treatment to reduce the relative toxicity of the effluent. In several examples, it was possible to overturn the conclusions ( i.e. , activated carbon is detrimental or beneficial in reducing toxicity) by preferentially including specific DBPs. In one example, removing measured haloacetaldehydes caused the predicted cytotoxicity of a treated sample to decrease by up to 47%, reversing the initial conclusion that activated carbon increased the toxicity of the water. We also discuss measurements of statistical error, which are rarely included in publications related to predicted toxicity, but strongly influence the outcomes. Finally, we discuss future research needs in the light of these and other concerns.more » « less
-
Climate change is predicted to shift or extend the range of warm-water species poleward. In 2014, the fiddler crab was observed for the first time at the PIE LTER site in Northeast Massachusetts, which is north of its historical range. Beginning in 2014, density estimates were collected.more » « less
-
This dataset provides summaries of temperature (T) and water table depth (WTD) conditions prior to the collection of peat samples from Stordalen Mire, Sweden, in July of 2011-2017. These summaries include the following files: t_wtd_summaries_July2011-2017samplings.csv This file gives summary statistics over various time intervals for the following environmental measurements: AirTemperature: Mean daily air temperature (°C), obtained from automatic sensors at the nearby Abisko Scientific Research Station (ANS) (station ID 188790; the source file [ANS_Daily_Wx_Jul84_Dec17.txt] is not included due to sharing restrictions). WTD: Water table depths (cm), obtained from Manual active layer and and water table depth measurements from the autochamber sites at Stordalen Mire, northern Sweden (2003-2017) (from Patrick Crill et al.). The time intervals for these summaries are defined relative to the peat sampling date at each site (see EMERGE Sample Metadata Sheet for Samples with Microbiomes), which varies by site and year. The specific intervals are defined as follows: 7d: 7 days prior to the sampling date, plus the sampling date itself. 14d: 14 days prior to the sampling date, plus the sampling date itself. 21d: 21 days prior to the sampling date, plus the sampling date itself. 28d: 28 days prior to the sampling date, plus the sampling date itself. growing: Time from beginning of growing season (defined as June 1) until (and including) the sampling date. all_growing: Entire growing season (June 1 – Sept. 30). For clarity, the start and end dates for each time interval (inclusive) are also given under the columns Start_Date and End_Date, where End_Date=Sampling_Date for all intervals except all_growing. Summary statistics for each interval include: measurement count (n), median (median), mean (mean), and standard deviation (sd), and are given under the column names beginning with these statistic labels. IMPORTANT NOTE: For temperature, these statistics are calculated based on the average temperature measured on each day, meaning that the standard deviations do NOT account for within-day temperature variation. To provide short-term (1 day) temperature variation context for each sampling date, the within-day mean, minimum, and maximum air temperatures for the sampling date only (taken directly from the corresponding row & columns in the source ANS data file) are provided in the columns samplingdate_mean_AirTemperature, samplingdate_min_AirTemperature, and samplingdate_max_AirTemperature. wtd_summaries_July2011-2017samples.csv This file gives the percentage of time that each peat sample's depth midpoint (DepthAvg__) was at or below the water table depth (WTD), over each of the longer time intervals (≥21 days) defined above for the temperature & WTD summaries. (Intervals <21 days are not included due to the lower frequency of WTD measurements, which results in low n for shorter intervals.) The first few columns are taken directly from the EMERGE Sample Metadata Sheet for Samples with Microbiomes, for the samples collected in July of 2011-2017 from the MainAutochamber sites. The last set of columns include the following, with the time interval labels (defined as in the above temperature summaries) appended at the end of each column name: n_WTD_*: Number of WTD measurements used in the calculation. pct_time_below_WTD_*: Fraction (relative to 1) of measured WTDs over the given time interval that were at or above the DepthAvg__ for each sample, which equates to the fraction of measurement timepoints during which the given sample was at or below the WTD. This is the same method used for calculating "% Time below water table" in Figure 6 of Singleton et al. (2018). For palsa sites, this value is automatically set to 0 based on the lack of a water table at all timepoints in the analysis.) As above, the WTD values used for these calculations were obtained from Manual active layer and and water table depth measurements from the autochamber sites at Stordalen Mire, northern Sweden (2003-2017) (Patrick Crill et al.). Funding acknowledgments This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070. This research was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440. The temperature summary has been made possible by data provided by Abisko Scientific Research Station and the Swedish Infrastructure for Ecosystem Science (SITES). We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.more » « less
An official website of the United States government
