Dataset Information

A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags.

ABSTRACT: Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59°C), identified a clear warming trend (0.63 °C decade(-1)) and a widening of the synchronized period (29 d decade(-1)). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (?0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (?0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network.

SUBMITTER: Letcher BH

PROVIDER: S-EPMC4782734 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags.

Letcher Benjamin H BH Hocking Daniel J DJ O'Neil Kyle K Whiteley Andrew R AR Nislow Keith H KH O'Donnell Matthew J MJ

PeerJ 20160229

Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, i ...[more]

PMID: 26966662

Similar Datasets

Project description:Architecture of phase relationships among neural oscillations is central for their functional significance but has remained theoretically poorly understood. We use phenomenological model of delay-coupled oscillators with increasing degree of topological complexity to identify underlying principles by which the spatio-temporal structure of the brain governs the phase lags between oscillatory activity at distant regions. Phase relations and their regions of stability are derived and numerically confirmed for two oscillators and for networks with randomly distributed or clustered bimodal delays, as a first approximation for the brain structural connectivity. Besides in-phase, clustered delays can induce anti-phase synchronization for certain frequencies, while the sign of the lags is determined by the natural frequencies and by the inhomogeneous network interactions. For in-phase synchronization faster oscillators always phase lead, while stronger connected nodes lag behind the weaker during frequency depression, which consistently arises for in-silico results. If nodes are in anti-phase regime, then a distance ? is added to the in-phase trends. The statistics of the phases is calculated from the phase locking values (PLV), as in many empirical studies, and we scrutinize the method's impact. The choice of surrogates do not affects the mean of the observed phase lags, but higher significance levels that are generated by some surrogates, cause decreased variance and might fail to detect the generally weaker coherence of the interhemispheric links. These links are also affected by the non-stationary and intermittent synchronization, which causes multimodal phase lags that can be misleading if averaged. Taken together, the results describe quantitatively the impact of the spatio-temporal connectivity of the brain to the synchronization patterns between brain regions, and to uncover mechanisms through which the spatio-temporal structure of the brain renders phases to be distributed around 0 and ?. TRIAL REGISTRATION:South African Clinical Trials Register: http://www.sanctr.gov.za/SAClinicalbrnbspTrials/tabid/169/Default.aspx, then link to respiratory tract then link to tuberculosis, pulmonary; and TASK Applied Sciences Clinical Trials, AP-TB-201-16 (ALOPEXX): https://task.org.za/clinical-trials/.

Project description:STUDY QUESTION:Does ambient air pollution affect fecundability? SUMMARY ANSWER:While cycle-average air pollution exposure was not associated with fecundability, we observed some associations for acute exposure around ovulation and implantation with fecundability. WHAT IS KNOWN ALREADY:Ambient air pollution exposure has been associated with adverse pregnancy outcomes and decrements in semen quality. STUDY DESIGN, SIZE, DURATION:The LIFE study (2005-2009), a prospective time-to-pregnancy study, enrolled 501 couples who were followed for up to one year of attempting pregnancy. PARTICIPANTS/MATERIALS, SETTING, METHODS:Average air pollutant exposure was assessed for the menstrual cycle before and during the proliferative phase of each observed cycle (n = 500 couples; n = 2360 cycles) and daily acute exposure was assessed for sensitive windows of each observed cycle (n = 440 couples; n = 1897 cycles). Discrete-time survival analysis modeled the association between fecundability and an interquartile range increase in each pollutant, adjusting for co-pollutants, site, age, race/ethnicity, parity, body mass index, smoking, income and education. MAIN RESULTS AND THE ROLE OF CHANCE:Cycle-average air pollutant exposure was not associated with fecundability. In acute models, fecundability was diminished with exposure to ozone the day before ovulation and nitrogen oxides 8 days post ovulation (fecundability odds ratio [FOR] 0.83, 95% confidence interval [CI]: 0.72, 0.96 and FOR 0.84, 95% CI: 0.71, 0.99, respectively). However, particulate matter ≤10 microns 6 days post ovulation was associated with greater fecundability (FOR 1.25, 95% CI: 1.01, 1.54). LIMITATIONS, REASONS FOR CAUTION:Although our study was unlikely to be biased due to confounding, misclassification of air pollution exposure and the moderate study size may have limited our ability to detect an association between ambient air pollution and fecundability. WIDER IMPLICATIONS OF THE FINDINGS:While no associations were observed for cycle-average ambient air pollution exposure, consistent with past research in the United States, exposure during critical windows of hormonal variability was associated with prospectively measured couple fecundability, warranting further investigation. STUDY FUNDING/COMPETING INTEREST(S):This work was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (Longitudinal Investigation of Fertility and the Environment study contract nos. #N01-HD-3-3355, NO1-HD-#-3356, N01-HD-3-3358 and the Air Quality and Reproductive Health Study Contract No. HHSN275200800002I, Task Order No. HHSN27500008). We declare no conflict of interest.

Dataset Information

A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags.

Publications

A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets