Long-term ecological trends of flow-dependent ecosystems in a major regulated river basin

2015 ◽  
Vol 66 (11) ◽  
pp. 957 ◽  
Author(s):  
Matthew J. Colloff ◽  
Peter Caley ◽  
Neil Saintilan ◽  
Carmel A. Pollino ◽  
Neville D. Crossman
Keyword(s):  
Time Series ◽  
State Space ◽  
Meta Analysis ◽  
State Space Model ◽  
Regulated River ◽  
Observation Error ◽  
Murray Darling Basin ◽  
Exponential Rates ◽  
Log Linear ◽  
Condition Assessments

The case for restoring water to the environment in the Murray–Darling Basin, Australia, is based mainly on condition assessments, although time series provide valuable information on trends. We assessed trends of 301 ecological time series (mean 23 years, range 1905–2013) in two categories: (1) ‘population’ (abundance, biomass, extent) and (2) ‘non-population’ (condition, occurrence, composition). We analysed trends using log-linear regression, accounting for observation error only, and a state–space model that accounts for observation error and environmental ‘noise’. Of the log-linear series (n=239), 50 (22%) showed statistically significant decline, but 180 (78%) showed no trend. For state–space series (n=197) one increased, but others were stable. Distribution of median exponential rates of increase (r) indicated a small but statistically significant declining trend, though 35–39% of the series were positive. Our analysis only partly supports, though does not refute, prevailing assumptions of recent ecological decline in the Murray–Darling Basin. The pattern is of fluctuating stability, with declines during droughts and recovery after flood. The overall trend from our meta-analysis is consistent with a pattern of historical decline to a hybrid ecosystem followed by slow, recent decline for some components and stability for others, with considerable variation in trends of specific ecological components: in short, there are ecological ‘winners’ and ‘losers’.

Estimating long-term tree mortality rate time series by combining data from periodic inventories and harvest reports in a Bayesian state-space model

2013 ◽  
Vol 292 ◽  
pp. 64-74 ◽  
Author(s):  
Katalin Csilléry ◽  
Maëlle Seignobosc ◽  
Valentine Lafond ◽  
Georges Kunstler ◽  
Benoît Courbaud
Keyword(s):  
Time Series ◽  
Mortality Rate ◽  
State Space ◽  
Tree Mortality ◽  
State Space Model ◽  
Space Model ◽  
Combining Data

scholarly journals LSTM-Guided Coaching Assistant for Table Tennis Practice

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4112 ◽  
Author(s):  
Se-Min Lim ◽  
Hyeong-Cheol Oh ◽  
Jaein Kim ◽  
Juwon Lee ◽  
Jooyoung Park
Keyword(s):  
Time Series ◽  
State Space ◽  
Time Series Data ◽  
State Space Model ◽  
Skill Assessment ◽  
Series Data ◽  
High Dimensional ◽  
Table Tennis ◽  
Space Model ◽  
Low Dimensional

Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching.

scholarly journals A review of Bayesian state-space modelling of capture–recapture–recovery data

2012 ◽  
Vol 2 (2) ◽  
pp. 190-204 ◽  
Author(s):  
Ruth King
Keyword(s):  
State Space ◽  
State Space Model ◽  
Model Fitting ◽  
The State ◽  
State Space Models ◽  
Observation Error ◽  
Space Model ◽  
System Process ◽  
Observation Process ◽  
Capture Recapture

Traditionally, state-space models are fitted to data where there is uncertainty in the observation or measurement of the system. State-space models are partitioned into an underlying system process describing the transitions of the true states of the system over time and the observation process linking the observations of the system to the true states. Open population capture–recapture–recovery data can be modelled in this framework by regarding the system process as the state of each individual observed within the study in terms of being alive or dead, and the observation process the recapture and/or recovery process. The traditional observation error of a state-space model is incorporated via the recapture/recovery probabilities being less than unity. The models can be fitted using a Bayesian data augmentation approach and in standard BUGS packages. Applying this state-space framework to such data permits additional complexities including individual heterogeneity to be fitted to the data at very little additional programming effort. We consider the efficiency of the state-space model fitting approach by considering a random effects model for capture–recapture data relating to dippers and compare different Bayesian model-fitting algorithms within WinBUGS.

scholarly journals Individual differences in motor noise and adaptation rate are optimally related

2017 ◽  
Author(s):  
Rick van der Vliet ◽  
Maarten A. Frens ◽  
Linda de Vreede ◽  
Zeb D. Jonker ◽  
Gerard M. Ribbers ◽  
...  
Keyword(s):  
Optimal Control ◽  
State Space ◽  
Motor System ◽  
Meta Analysis ◽  
State Space Model ◽  
Motor Adaptation ◽  
Visuomotor Adaptation ◽  
Movement Variability ◽  
Optimal Learning ◽  
Motor Noise

ABSTRACTIndividual variations in motor adaptation rate were recently shown to correlate with movement variability or “motor noise” in a forcefield adaptation task. However, this finding could not be replicated in a meta-analysis of visuomotor adaptation experiments. Possibly, this inconsistency stems from noise being composed of distinct components which relate to adaptation rate in different ways. Indeed, previous modeling and electrophysiological studies have suggested that motor noise can be factored into planning noise, originating from the brain, and execution noise, stemming from the periphery. Were the motor system optimally tuned to these noise sources, planning noise would correlate positively with adaptation rate and execution noise would correlate negatively with adaptation rate, a phenomenon familiar in Kalman filters. To test this prediction, we performed a visuomotor adaptation experiment in 69 subjects. Using a novel Bayesian fitting procedure, we succeeded in applying the well-established state-space model of adaptation to individual data. We found that adaptation rate correlates positively with planning noise (r=0.27; 95%HDI=[0.05 0.50]) and negatively with execution noise (r=−0.41; 95%HDI=[−0.63 −0.16]). In addition, the steady-state Kalman gain calculated from state and execution noise correlated positively with adaptation rate (r = 0.31; 95%HDI = [0.09 0.54]). These results suggest that motor adaptation is tuned to approximate optimal learning, consistent with the “optimal control” framework that has been used to explain motor control. Since motor adaptation is thought to be a largely cerebellar process, the results further suggest the sensitivity of the cerebellum to both planning noise and execution noise.SIGNIFICANCE STATEMENTOur study shows that the adaptation rate is optimally tuned to planning noise and execution noise across individuals. This suggests that motor adaptation is tuned to approximate optimal learning, consistent with “optimal control” approaches to understanding the motor system. In addition, our results imply sensitivity of the cerebellum to both planning noise and execution noise, an idea not previously considered. Finally, our Bayesian statistical approach represents a powerful, novel method for fitting the well-established state-space models that could have an influence on the methodology of the field.

scholarly journals Estimation of N*0 for the Two-Scale Gamma Raindrop Size Distribution Model and Its Statistical Properties at Several Locations in Asia

2010 ◽  
Vol 49 (4) ◽  
pp. 676-686 ◽  
Author(s):  
Toshiaki Kozu ◽  
Kazuhiro Masuzawa ◽  
Toyoshi Shimomai ◽  
Nobuhisa Kashiwagi
Keyword(s):  
Time Series ◽  
State Space ◽  
Size Distribution ◽  
State Space Model ◽  
Statistical Properties ◽  
Gaussian State ◽  
Space Model ◽  
The Third ◽  
Raindrop Size Distribution ◽  
Raindrop Size

Abstract An automatic estimation method is developed to detect stepwise changes in the amplitude parameter of the normalized raindrop size distribution (DSD) N*0. To estimate N*0, it is also assumed that the variation of three DSD parameters follows the two-scale gamma DSD model; this is defined as a DSD model in which one DSD parameter is fixed, the second is allowed to vary rapidly, and the third is constant over a certain space or time domain and sometimes exhibits stepwise transitions. For this study, it is assumed that N*0 is the third DSD parameter. To estimate this stepwise-varying parameter automatically, a non-Gaussian state-space model is used for the time series of log10N*0. The smoothed time series of log10N*0 fit well to the stepwise transition of log10N*0 when it was assumed that the state transition probability follows a Cauchy distribution. By analyzing the long-term disdrometer data using this state-space model, statistical properties for log10N*0 are obtained at several Asian locations. It is confirmed that the N*0 thus estimated is useful to improve the rain-rate estimation from the measurement of radar reflectivity factor.

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