scholarly journals Estimating Density and Temperature Dependence of Juvenile Vital Rates Using a Hidden Markov Model

2016 ◽  
Author(s):  
Robert M. McElderry
Keyword(s):  
Hidden Markov ◽  
Time Lag ◽  
Predation Rate ◽  
Life Stages ◽  
Vital Rates ◽  
Multiple Predators ◽  
In The Wild ◽  
Hidden Markov Process ◽  
Survival And Growth ◽  
Hidden States

Organisms in the wild have cryptic life stages that are sensitive to changing environmental conditions and can be difficult to survey. In this study, I used mark-recapture methods to repeatedly survey Anaea aidea (Nymphalidae) caterpillars in nature, then modeled caterpillar demography as a hidden Markov process to assess if temporal variability in temperature and density influence the survival and growth of A. aidea over time. Individual encounter histories result from the joint likelihood of being alive and observed in a particular stage, and I included hidden states by separating demography and observations into parallel and independent processes. I constructed a demographic matrix containing the probabilities of all possible fates for each stage, including hidden states, e.g., eggs and pupae. I observed both dead and live caterpillars with high probability. Peak caterpillar abundance attracted multiple predators, and survival of fifth instars declined as per capita predation rate increased through spring. A time lag between predator and prey abundance was likely the cause of improved fifth instar survival estimated at high density. Growth rates showed an increase with temperature, but the most likely model did not include temperature. This work illustrates how state-space models can include unobservable stages and hidden state processes to evaluate how environmental factors influence vital rates of cryptic life stages in the wild.

Mechanisms leading to recruitment inhibition of giant kelp Macrocystis pyrifera by an understory alga

2021 ◽  
Vol 657 ◽  
pp. 59-71
Author(s):  
BA Beckley ◽  
MS Edwards
Keyword(s):  
Field Experiments ◽  
Local Community ◽  
Kelp Forest ◽  
Macrocystis Pyrifera ◽  
Forest Recovery ◽  
Kelp Forests ◽  
Giant Kelp ◽  
Combined Effects ◽  
Life Stages ◽  
Survival And Growth

The forest-forming giant kelp Macrocystis pyrifera and the communities it supports have been decreasing across their native ranges in many parts of the world. The sudden removal of giant kelp canopies by storms increases space and light for the colonization by understory macroalgae, such as Desmarestia herbacea, which can inhibit M. pyrifera recovery and alter local community composition. Understanding the mechanisms by which algae such as D. herbacea interact with M. pyrifera can provide insight into patterns of kelp forest recovery following these disturbances and can aid in predicting future community structure. This study experimentally tested the independent and combined effects of two likely competitive mechanisms by which D. herbacea might inhibit recovery of M. pyrifera in the Point Loma kelp forest in San Diego, California (USA). Specifically, we conducted field experiments to study the individual and combined effects of shade and scour by D. herbacea on the survival of M. pyrifera microscopic life stages, and the recruitment, survival, and growth of its young sporophytes. Our results show that scour had the strongest negative effect on the survival of M. pyrifera microscopic life stages and recruitment, but shade and scour both adversely affected survival and growth of these sporophytes as they grew larger. Canopy-removing storms are increasing in frequency and intensity, and this change could facilitate the rise of understory species, like D. herbacea, which might alter community succession and recovery of kelp forests.

scholarly journals Robustness of life histories to environmental variability in complex versus simple life cycles

2021 ◽  
Author(s):  
Annie Jonsson
Keyword(s):  
Growth Rate ◽  
Life Histories ◽  
Environmental Variability ◽  
Life Cycles ◽  
Relative Advantage ◽  
Complex Life Cycle ◽  
Life Stages ◽  
Vital Rates ◽  
Habitat Separation ◽  
Simple Life

AbstractMost animal species have a complex life cycle (CLC) with metamorphosis. It is thus of interest to examine possible benefits of such life histories. The prevailing view is that CLC represents an adaptation for genetic decoupling of juvenile and adult traits, thereby allowing life stages to respond independently to different selective forces. Here I propose an additional potential advantage of CLCs that is, decreased variance in population growth rate due to habitat separation of life stages. Habitat separation of pre- and post-metamorphic stages means that the stages will experience different regimes of environmental variability. This is in contrast to species with simple life cycles (SLC) whose life stages often occupy one and the same habitat. The correlation in the fluctuations of the vital rates of life stages is therefore likely to be weaker in complex than in simple life cycles. By a theoretical framework using an analytical approach, I have (1) derived the relative advantage, in terms of long-run growth rate, of CLC over SLC phenotypes for a broad spectrum of life histories, and (2) explored which life histories that benefit most by a CLC, that is avoid correlation in vital rates between life stages. The direction and magnitude of gain depended on life history type and fluctuating vital rate. One implication of our study is that species with CLCs should, on average, be more robust to increased environmental variability caused by global warming than species with SLCs.

An MEWMA-based segmental multivariate hidden Markov model for degradation assessment and prediction

2021 ◽  
pp. 1748006X2199052
Author(s):  
Yaping Li ◽  
Enrico Zio ◽  
Ershun Pan
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Average Run Length ◽  
Moving Average ◽  
Model Parameters ◽  
Current Time ◽  
Multivariate Processes ◽  
Industrial Systems ◽  
Hidden States ◽  
Multivariate Signals

Degradation is an unavoidable phenomenon in industrial systems. Hidden Markov models (HMMs) have been used for degradation modeling. In particular, segmental HMMs have been developed to model the explicit relationship between degradation signals and hidden states. However, existing segmental HMMs deal only with univariate cases, whereas in real systems, signals from various sensors are collected simultaneously, which makes it necessary to adapt the segmental HMMs to deal with multivariate processes. Also, to make full use of the information from the sensors, it is important to differentiate stable signals from deteriorating ones, but there is no good way for this, especially in multivariate processes. In this paper, the multivariate exponentially weighted moving average (MEWMA) control chart is employed to identify deteriorating multivariate signals. Specifically, the MEWMA statistic is used as a comprehensive indicator for differentiating multivariate observations. Likelihood Maximization is used to estimate the model parameters. To avoid underflow, the forward and backward probabilities are normalized. In order to assess degradation, joint probabilities are defined and derived. Further, the occurrence probability of each degradation state at the current time, as well as in the future, is derived. The Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset of NASA is employed for comparative analysis. In terms of degradation assessment and prediction, the proposed model performs very well in general. By sensitivity analysis, we show that in order to improve further the performance of the method, the weight of the chart should be set relatively small, whereas the method is not sensitive to the change of the in-control average run length (ARL).

scholarly journals On the Dynamical Behavior of Toxic-Phytoplankton-Zooplankton Model with Delay

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Mehbuba Rehim ◽  
Weixin Wu ◽  
Ahmadjan Muhammadhaji
Keyword(s):  
Time Delay ◽  
Phytoplankton Bloom ◽  
Dynamical Behavior ◽  
Time Lag ◽  
Toxic Substance ◽  
Predation Rate ◽  
Dynamical Behaviour ◽  
Toxic Substances ◽  
Toxic Phytoplankton ◽  
Discrete Time Delay

A toxin producing phytoplankton-zooplankton model with inhibitory exponential substrate and time delay has been formulated and analyzed. Since the liberation of toxic substances by phytoplankton species is not an instantaneous process but is mediated by some time lag required for maturity of the species and the zooplankton mortality due to the toxic phytoplankton bloom occurs after some time laps of the bloom of toxic phytoplankton, we induced a discrete time delay to both of the consume response function and distribution of toxic substance term. Furthermore, based on the fact that the predation rate decreases at large toxic-phytoplankton density, the system is modelled via a Tissiet type functional response. We study the dynamical behaviour and investigate the conditions to guarantee the coexistence of two species. Analytical methods and numerical simulations are used to obtain information about the qualitative behaviour of the models.

Remaining Useful Life Estimation Based on a Segmental Hidden Markov Model With Continuous Observations

2017 ◽  
Author(s):  
Zhen Chen ◽  
Tangbin Xia ◽  
Ershun Pan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Goodness Of Fit ◽  
Hidden Markov ◽  
Remaining Useful Life ◽  
Likelihood Method ◽  
Distribution Model ◽  
Unknown Parameters ◽  
Useful Life ◽  
Hidden States

In this paper, a segmental hidden Markov model (SHMM) with continuous observations, is developed to tackle the problem of remaining useful life (RUL) estimation. The proposed approach has the advantage of predicting the RUL and detecting the degradation states simultaneously. As the observation space is discretized into N segments corresponding to N hidden states, the explicit relationship between actual degradation paths and the hidden states can be depicted. The continuous observations are fitted by Gaussian, Gamma and Lognormal distribution, respectively. To select a more suitable distribution, model validation metrics are employed for evaluating the goodness-of-fit of the available models to the observed data. The unknown parameters of the SHMM can be estimated by the maximum likelihood method with the complete data. Then a recursive method is used for RUL estimation. Finally, an illustrate case is analyzed to demonstrate the accuracy and efficiency of the proposed method. The result also suggests that SHMM with observation probability distribution which is closer to the real data behavior may be more suitable for the prediction of RUL.

Detrimental effects of sediment on attachment, survival and growth of the brown alga Sargassum thunbergii in early life stages

2018 ◽  
Vol 67 (1) ◽  
pp. 77-81 ◽  
Author(s):  
Xu Gao ◽  
Jung Rok Lee ◽  
Seo Kyoung Park ◽  
Nam Gil Kim ◽  
Han Gil Choi
Keyword(s):  
Early Life ◽  
Brown Alga ◽  
Life Stages ◽  
Early Life Stages ◽  
Sargassum Thunbergii ◽  
Survival And Growth

Ecology of the Atlantic Forest

Ecology ◽  
2021 ◽  
Author(s):  
Elise Damstra ◽  
Cristina Banks-Leite
Keyword(s):  
Atlantic Forest ◽  
Forest Cover ◽  
Time Lag ◽  
Native Forest ◽  
Important Species ◽  
Habitat Transformation ◽  
Golden Lion Tamarin ◽  
Palm Heart ◽  
In The Wild ◽  
Unique Domain

Extending along the southern coast of Brazil, into Argentina and Paraguay, the Atlantic Forest is a domain that once covered 150 Mha and includes many distinct forest subtypes and ecosystems. Its large latitudinal (29˚) and altitudinal (0–2,800 m above sea level) range, as well as complex topography in the region, has created microclimates within forest subtypes, which has led to biodiversity specifically adapted to narrow ecological ranges. The region is incredibly species-rich and is home to charismatic or economically important species such as the black and golden lion tamarin, the red-browned Amazon parrot, and the highly prized palm heart from Euterpe edulis. Through widespread human-driven change dating back to the arrival of European settlers in 1500, this realm has been extensively reduced, fragmented, and modified. Nowadays, this region is home to about 130 million people (60 percent of the Brazilian population) and is responsible for producing 70 percent of Brazil’s GDP, putting a strain on natural resources and providing challenges to conservation. Due to its high levels of endemic species coupled with a high threat of habitat loss and fragmentation, the Atlantic Forest has been identified as a “biodiversity hotspot.” Numerous studies have assessed the effects of habitat transformation on biodiversity and the consensus is that the majority of species are negatively affected. It is puzzling however that few species have actually gone extinct in the wild, even if some extinctions might have gone undetected. Extinctions do not immediately follow habitat change, there is often a time lag of many decades between habitat transformation and extinction. This may suggest that many species in the Atlantic Forest are “living deads,” as despite their presence the available habitat no longer supports their requirements. It also suggests that there is a window of opportunity to restoring the domain to avert extinctions before they are realized. Current research and policy actions are geared toward optimizing restoration and increasing the extent of native forest cover, bringing hope to the conservation of this unique domain.

scholarly journals Dynamic Transactions Between News Frames and Sociopolitical Events: An Integrative, Hidden Markov Model Approach

2020 ◽  
Vol 70 (3) ◽  
pp. 335-355 ◽  
Author(s):  
Frederic R Hopp ◽  
Jacob T Fisher ◽  
René Weber
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
News Coverage ◽  
News Frames ◽  
Macro Scale ◽  
News Event ◽  
Hidden States ◽  
Research Domains ◽  
Integrated Study ◽  
Model Approach

Abstract A central goal of news research is to understand the interplay between news coverage and sociopolitical events. Although a great deal of work has elucidated how events drive news coverage, and how in turn news coverage influences societal outcomes, integrative systems-level models of the reciprocal interchanges between these two processes are sparse. Herein, we present a macro-scale investigation of the dynamic transactions between news frames and events using Hidden Markov Models (HMMs), focusing on morally charged news frames and sociopolitical events. Using 3,501,141 news records discussing 504,759 unique events, we demonstrate that sequences of frames and events can be characterized in terms of “hidden states” containing distinct moral frame and event relationships, and that these “hidden states” can forecast future news frames and events. This work serves to construct a path toward the integrated study of the news-event cycle across multiple research domains.

scholarly journals Use of Hidden Markov Models to Identify Background States Behind Risks of Cerebral Infarction and Ischemic Heart Disease

2017 ◽  
Vol 9 (1) ◽  
pp. 24
Author(s):  
Hiroshi Morimoto
Keyword(s):  
Heart Disease ◽  
Ischemic Heart Disease ◽  
Human Health ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Meteorological Factors ◽  
Hidden Markov ◽  
Weather Patterns ◽  
Weather And Human Health ◽  
Hidden States

Cold exposure is often said to trigger the incidence of cerebral infarctions and ischemic heart disease. This association between weather and human health has attracted considerable interest, and has been explored using standard statistical techniques such as regression models. Meteorological factors, such as temperature, are controlled by background systems, notably weather patterns. Therefore, it is reasonable to posit that the incidence of diseases is similarly influenced by a background system. The aim of this paper was to identify and construct these respective background systems. Possible background states or "hidden states", behind the incidence of diseases were derived using the EM and Viterbi algorithms with in the framework of hidden Markov models (HMM). A self-organizing map (SOM) enabled identification of weather patterns, considered as background states behind meteorological factors. These background states were then compared, and the hidden states behind the incidence of diseases were identified by six weather patterns. This finding indicates new evidence of the links between weather and human health, shedding light on the association between changes in the weather and the onset of disease. 

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