Estimation of deer population dynamics using a bayesian state-space model with multiple abundance indices

2013 ◽  
Vol 77 (5) ◽  
pp. 1038-1047 ◽  
Author(s):  
Hayato Iijima ◽  
Takuo Nagaike ◽  
Takeshi Honda
Keyword(s):  
Population Dynamics ◽  
State Space ◽  
State Space Model ◽  
Space Model ◽  
Deer Population ◽  
Abundance Indices

scholarly journals Seasonal and annual fluctuations of deer populations estimated by a Bayesian state–space model

2019 ◽  
Author(s):  
Inoue Mizuki ◽  
Hiroki Itô ◽  
Michimasa Yamasaki ◽  
Shigeru Fukumoto ◽  
Yuuki Okamoto ◽  
...  
Keyword(s):  
Population Dynamics ◽  
State Space ◽  
Lethal Factor ◽  
State Space Model ◽  
Winter Season ◽  
Seasonal Migration ◽  
Annual Fluctuation ◽  
Space Model ◽  
Deer Population ◽  
Count Surveys

AbstractDeer overabundance is a contributing factor in the degradation of plant communities and ecosystems worldwide. The management and conservation of the deer-affected ecosystems requires us to urgently grasp deer population trends and to identify the factors that affect them. In this study, we developed a Bayesian state–space model to estimate the population dynamics of sika deer (Cervus nippon) in a cool-temperate forest in Japan, where wolves (Canis lupus hodophilax) are extinct. The model was based on field data collected from block count surveys, road count surveys by vehicles, mortality surveys during the winter, and nuisance control for 12 years (2007–2018). We clarified the seasonal and annual fluctuation of the deer population. We found two peaks of deer abundance (2007 and 2010) over 12 years. In 2011 the estimated deer abundance decreased drastically and has remained at a low level then. The deer population increased from spring to autumn and decreased from autumn to winter in most years. The seasonal fluctuation we detected could reflect the seasonal migration pattern of deer and the population recruitment through fawn births in early summer. In our model, snowfall accumulation, which can be a lethal factor for deer, may have slightly affected their mortality during the winter. Although we could not detect a direct effect of snow on population dynamics, snowfall decrease due to global warming may decelerate the winter migration of deer; subsequently, deer staying on-site may intensively forage evergreen perennial plants during the winter season. The nuisance control affected population dynamics. Even in wildlife protection areas and national parks where hunting is regulated, nuisance control could be effective in buffering the effect of deer browsing on forest ecosystems.

scholarly journals Mind the gaps: a state-space model for analysing the dynamics of North Sea herring spawning components

2010 ◽  
Vol 67 (9) ◽  
pp. 1939-1947 ◽  
Author(s):  
Mark R. Payne
Keyword(s):  
State Space ◽  
North Sea ◽  
State Space Model ◽  
Missing Observations ◽  
Component Level ◽  
Space Model ◽  
The North ◽  
Abundance Indices ◽  
The North Sea ◽  
The Individual

Abstract Payne, M. R. 2010. Mind the gaps: a state-space model for analysing the dynamics of North Sea herring spawning components. – ICES Journal of Marine Science, 67: 1939–1947. The North Sea autumn-spawning herring (Clupea harengus) stock consists of a set of different spawning components. The dynamics of the entire stock have been well characterized, but although time-series of larval abundance indices are available for the individual components, study of the dynamics at the component level has historically been hampered by missing observations and high sampling noise. A simple state-space statistical model is developed that is robust to these problems, gives a good fit to the data, and proves capable of both handling and predicting missing observations well. Furthermore, the sum of the fitted abundance indices across all components proves an excellent proxy for the biomass of the total stock, even though the model utilizes information at the individual-component level. The Orkney–Shetland component appears to have recovered faster from historic depletion events than the other components, whereas the Downs component has been the slowest. These differences give rise to changes in stock composition, which are shown to vary widely within a relatively short time. The modelling framework provides a valuable tool for studying and monitoring the dynamics of the individual components of the North Sea herring stock.

scholarly journals Fitting state–space models to seal populations with scarce data

2014 ◽  
Vol 72 (5) ◽  
pp. 1462-1469
Author(s):  
Tor Arne Øigård ◽  
Hans J. Skaug
Keyword(s):  
Population Dynamics ◽  
State Space ◽  
Barents Sea ◽  
State Space Model ◽  
The State ◽  
Harp Seal ◽  
Dynamics Model ◽  
Greenland Sea ◽  
Space Model ◽  
Population Dynamics Model

Abstract We estimate temporal variation in fecundity, the reproduction rate, for Barents Sea and Greenland Sea harp seals using a state–space approach. A stochastic process model for fecundity is integrated with an age-structured population dynamics model and fit to available data for these two harp seal populations. Owing to scarceness of data, it is necessary to “borrow strength” from the Northwest Atlantic harp seal population in form of prior distributions on autocorrelation and variance in fecundity. Comparison is made to a simpler deterministic population dynamics model. The state–space model is more flexible and is able to account for the variations in the data. For Barents Sea harp seals, the state–space model gives a higher estimate of current population size but also a much higher associated uncertainty. In the Greenland Sea, the differences between the stochastic and deterministic models are much smaller.

Development of L1-norm sliding mode observer for sensor fault diagnosis of an industrial gas turbine

2021 ◽  
pp. 095965182199617
Author(s):  
Mahyar Akbari ◽  
Abdol Majid Khoshnood ◽  
Saied Irani
Keyword(s):  
Fault Detection ◽  
State Space ◽  
Gas Turbine ◽  
Sliding Mode ◽  
State Space Model ◽  
Sensor Fault ◽  
Space Model ◽  
Decision Logic ◽  
Sensor Fault Detection ◽  
Industrial Gas Turbine

In this article, a novel approach for model-based sensor fault detection and estimation of gas turbine is presented. The proposed method includes driving a state-space model of gas turbine, designing a novel L1-norm Lyapunov-based observer, and a decision logic which is based on bank of observers. The novel observer is designed using multiple Lyapunov functions based on L1-norm, reducing the estimation noise while increasing the accuracy. The L1-norm observer is similar to sliding mode observer in switching time. The proposed observer also acts as a low-pass filter, subsequently reducing estimation chattering. Since a bank of observers is required in model-based sensor fault detection, a bank of L1-norm observers is designed in this article. Corresponding to the use of the bank of observers, a two-step fault detection decision logic is developed. Furthermore, the proposed state-space model is a hybrid data-driven model which is divided into two models for steady-state and transient conditions, according to the nature of the gas turbine. The model is developed by applying a subspace algorithm to the real field data of SGT-600 (an industrial gas turbine). The proposed model was validated by applying to two other similar gas turbines with different ambient and operational conditions. The results of the proposed approach implementation demonstrate precise gas turbine sensor fault detection and estimation.

Modeling of CCLP in koryo extract production

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ji Chol ◽  
Ri Jun Il
Keyword(s):  
Process Control ◽  
Medical Care ◽  
State Space ◽  
State Space Model ◽  
The State ◽  
Space Model ◽  
Effective Components ◽  
Counter Current

Abstract The modeling of counter-current leaching plant (CCLP) in Koryo Extract Production is presented in this paper. Koryo medicine is a natural physic to be used for a diet and the medical care. The counter-current leaching method is mainly used for producing Koryo medicine. The purpose of the modeling in the previous works is to indicate the concentration distributions, and not to describe the model for the process control. In literature, there are no nearly the papers for modeling CCLP and especially not the presence of papers that have described the issue for extracting the effective components from the Koryo medicinal materials. First, this paper presents that CCLP can be shown like the equivalent process consisting of two tanks, where there is a shaking apparatus, respectively. It allows leachate to flow between two tanks. Then, this paper presents the principle model for CCLP and the state space model on based it. The accuracy of the model has been verified from experiments made at CCLP in the Koryo Extract Production at the Gang Gyi Koryo Manufacture Factory.

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