scholarly journals Latent variable models for multi-species counts modeling in ecology

2018 ◽  
Vol 19 (5) ◽  
pp. 1871-1876 ◽  
Author(s):  
RIKI HERLIANSYAH ◽  
IRMA FITRIA
Keyword(s):  
Endemic Species ◽  
Latent Variable ◽  
Negative Binomial ◽  
Environmental Changes ◽  
Bird Species ◽  
Latent Variable Models ◽  
Primary Forest ◽  
Variable Model ◽  
Ordinary Regression ◽  
Negative Impacts

Herliansyah R, Fitria I. 2018. Latent variable models for multi-species counts modeling in ecology. Biodiversitas 19: 1871-1876. High-dimensional multi-species counts are often collected in ecology to understand the spatial distribution over different locations and to study effects of environmental changes. Modeling multivariate abundance is challenging as we need to consider the possibility of interactions across species. Latent variable models are the recent popular approaches in statistical ecology to address such issue that has a similar framework to ordinary regression models. In this paper, we employed the poisson distribution for modeling count responses and a negative binomial distribution for more frequent zeros in observations. The implementation of a latent variable model, Generalized Linear Latent Variable Models (GLLVMs), was demonstrated on multi-species counts of endemic bird species collected in 37 different sites in Central Kalimantan, Indonesia. The main objectives were to study the effect of logging activities on abundance of endemic species and their interactions and to observe the habitat preference of certain species. Our study found that out of four endemic species, Alophoixus bres and Eurylaimus javanicus species were significantly affected by logging activities. The sign of parameters was negative indicating the logging activities in 1989 and 1993 bring significantly negative impacts on those species. The interaction created among species was strongly negative for major endemic species especially Alophoixus bres and Eurylaimus javanicus that prefer living in primary forest than in logging areas.

scholarly journals Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4290
Author(s):  
Dongmei Zhang ◽  
Yuyang Zhang ◽  
Bohou Jiang ◽  
Xinwei Jiang ◽  
Zhijiang Kang
Keyword(s):  
Sensitivity Analysis ◽  
Gaussian Processes ◽  
Latent Variable ◽  
History Matching ◽  
Latent Variable Models ◽  
High Dimensional ◽  
Model Parameters ◽  
Variable Model ◽  
Assisted History Matching ◽  
Proxy Models

Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data. Gaussian Process (GP) has shown promising performance for assisted history matching due to the efficient nonparametric and nonlinear model with few model parameters to be tuned automatically. Recently introduced Gaussian Processes proxy models and Variogram Analysis of Response Surface-based sensitivity analysis (GP-VARS) uses forward and inverse Gaussian Processes (GP) based proxy models with the VARS-based sensitivity analysis to optimize the high-dimensional reservoir parameters. However, the inverse GP solution (GPIS) in GP-VARS are unsatisfactory especially for enormous reservoir parameters where the mapping from low-dimensional misfits to high-dimensional uncertain reservoir parameters could be poorly modeled by GP. To improve the performance of GP-VARS, in this paper we propose the Gaussian Processes proxy models with Latent Variable Models and VARS-based sensitivity analysis (GPLVM-VARS) where Gaussian Processes Latent Variable Model (GPLVM)-based inverse solution (GPLVMIS) instead of GP-based GPIS is provided with the inputs and outputs of GPIS reversed. The experimental results demonstrate the effectiveness of the proposed GPLVM-VARS in terms of accuracy and complexity. The source code of the proposed GPLVM-VARS is available at https://github.com/XinweiJiang/GPLVM-VARS.

scholarly journals Population sizes and the status of endemic and restricted-range bird species on Sangihe Island, Indonesia

2001 ◽  
Vol 12 (1) ◽  
pp. 53-78 ◽  
Author(s):  
Jon Riley
Keyword(s):  
Endemic Species ◽  
Bird Species ◽  
Critically Endangered ◽  
Primary Forest ◽  
Restricted Range ◽  
Lowland Forest ◽  
Island Endemic ◽  
The Status ◽  
Secondary Habitats ◽  
Primary Habitat

The island of Sangihe, Indonesia, was visited in 1998–99 with the aim of producing population estimates of the island's endemic and other restricted-range bird species, some of which were poorly known and thought to be at risk of extinction due to habitat change. The study focused on the last remaining patch of primary forest, on Mount Sahendaruman in southern Sangihe, home to three critically endangered endemic species: Cerulean Paradise-flycatcher Eutrichomyias rowleyi, Sangihe Shrike-thrush Colluricincla sanghirensis, and Sangihe White-eye Zosterops nehrkorni. Population densities were estimated from primary forest and secondary habitats across Sangihe to assess species dependency on primary habitat. Twenty of 24 restricted-range, globally threatened or endemic taxa were recorded and density estimates were calculated for 15 of these. The endemic Red-and-blue Lory Eos histrio is extinct on Sangihe as a result of lowland forest loss. Six species (Nicobar Pigeon Caloenas nicobarica, Sulawesi Dwarf Kingfisher Ceyx fallax, Golden Bulbul Ixos affinis, E. rowleyi, C. sanghirensis, and Z. nehrkorni) were either not recorded or have very small populations and are critically endangered on Sangihe; two species of Tanygnathus parrot are also facing local extinction on the island. The major threat to all these species is the destruction of primary forest; larger species are also threatened by hunting. The remaining endemic and restricted-range species (Blue-tailed Imperial Pigeon Ducula concinna, Sangihe Hanging-parrot Loriculus catamene, Sangihe Scops-owl Otus collari, Lilac-cheeked Kingfisher Cittura cyanotis and Elegant Sunbird Aethopyga duyvenbodei) were more widespread and occurred in secondary habitats. I recommend that L. catamene and A. duyvenbodei, currently treated as globally endangered, be reclassified as near-threatened and vulnerable respectively because of their large populations and tolerance of disturbed habitats. Species with wide global ranges that are represented by endemic subspecies have the greatest tolerance for disturbed habitat. The widespread deforestation of Sangihe has had serious consequences for many bird species and today the island supports the most threatened assemblage of single-island endemic species in Indonesia. Species-specific research to determine the status and ecology of E. rowleyi, C. sanghirensis and Z. nehrkorni, and monitoring of the Sahendaruman forest are desperately needed as a basis for future conservation efforts.

scholarly journals Weighted Network Density Predicts Range of Latent Variable Model Accuracy

2018 ◽  
Author(s):  
Jeremiah B. Palmerston ◽  
Qi She ◽  
Rosa H. M. Chan
Keyword(s):  
Latent Variable ◽  
Prediction Accuracy ◽  
Strong Relationship ◽  
Latent Variable Models ◽  
Latent Variable Model ◽  
Sampled Data ◽  
Variable Model ◽  
Weighted Network ◽  
Network Measures

AbstractCurrent experimental techniques impose spatial limits on the number of neuronal units that can be recorded in-vivo. To model the neural dynamics utilizing these sampled data, Latent Variable Models (LVMs) have been proposed to study the common unobserved processes within the system that drives neural activities, through an implicit network with hidden states. Yet, relationships between these latent variable models and widely-studied network connectivity measures remained unclear. In this paper, a biologically plausible latent variable model was first fit to neural activity recorded via 2-photon microscopic calcium imaging in the murine primary visual cortex. Graph theoretic measures were then applied to quantify network properties in the recorded sub-regions. Comparison of weighted network measures with LVM prediction accuracy shows some network measures having a strong relationship with LVM prediction accuracy, while other measures do not have a robust relationship with LVM prediction accuracy. Results show LVM will achieve high accuracy in dense networks.

scholarly journals Distribution of Avifauna in Aketajawe Lolobata National Park Based on Zone and Land Cover Typology

Jurnal Wasian ◽  
2016 ◽  
Vol 3 (1) ◽  
pp. 25
Author(s):  
Supratman Tabba ◽  
Lis Nurrani
Keyword(s):  
Species Diversity ◽  
Endemic Species ◽  
National Park ◽  
Bird Species ◽  
Buffer Zone ◽  
Wildlife Habitat ◽  
Primary Forest ◽  
Point System ◽  
Species Variation ◽  
Core Zone

Aketajawe Lolobata National Park (TNAL) is a protected area for paradise birds, parrots and others North Maluku endemic wildlife. As a former of commercial logging concessions, this areas have been damaged and loss of wildlife habitat. The purpose of this research was to determine bird species of TNAL area based on zone category, this research is important in order to validate data related to the birds species diversity. Data collection used the observation point system with purposive sampling by direct observation and audio. The number of observation points were six points in core zone, ten points in both of jungle and traditional zone, and six points in buffer zone. Research showed that there are 39 species found in the buffer zone, 45 species in jungle and traditional zone, and 19 species in core zone. Wallace’s standardwing (Semioptera wallacei) is one of the 15 endemic species of North Maluku was found along drummer rail (Habroptila wallacii) which is Halmahera’s endemic species. Distribution pattern of birds in TNAL is very varied. The jungle and traditional zone have the amount and the highest species diversity while the highest species variation was in the buffer zone. The natural habitat of bird in this area generally located in open area along former road skid as well as forest edge and only a few were found in primary forest of core zone.

Conservation ecology of birds in Mt. Hilong-hilong, a Key Biodiversity Area on Mindanao Island, the Philippines

2021 ◽  
Vol 13 (5) ◽  
pp. 18110-18121
Author(s):  
Arturo G. Gracia Jr. ◽  
Alma B. Mohagan ◽  
Janezel C. Burlat ◽  
Welfredo L. Yu Jr. ◽  
Janine Mondalo ◽  
...  
Keyword(s):  
Species Richness ◽  
Environmental Changes ◽  
Extinction Risk ◽  
Bird Species ◽  
Relevant Information ◽  
The Philippines ◽  
Vital Role ◽  
Combining Data ◽  
Negative Impacts ◽  
High Level

The identification of key areas for conservation and protection according to science-based evidence is an important component to circumvent the negative impacts of environmental changes within geopolitical territories and across the globe.  Priority areas for biodiversity played an important role to ensure the protection of many species particularly those that are unique and threatened.  There are more than 200 Key Biodiversity Areas (KBAs) in the Philippines, yet many important research and biodiversity data are either unpublished or unconsolidated.  Birds are commonly studied indicators for KBA identification due to their high species richness, diversity, and sensitivity to forest ecosystems.  By combining data from past and present surveys, we accounted for a total of 148 bird species of 51 families, with 20 new records from recent field surveys.  Our analysis showed a high level of endemism within Mt. Hilong-hilong with 36% Philippine endemic, 14% restricted to Mindanao faunal region and 11% migrant. In terms of conservation, 8% of the species were considered in threatened categories.  The species richness and endemism were higher in lowland to mid-elevation areas compared to higher elevation areas of the KBA.  Endemism (i.e., Mindanao endemic) and increasing body mass were important determinants of binary extinction risk for bird species in Mt. Hilong-hilong.  The high biodiversity in Mt. Hilong-hilong indicates an example of the vital role of KBAs in preserving nationally and globally important bird species.  Lastly, we emphasise the importance of collaboration and integrating past and present information to synthesise relevant information to complement ongoing conservation efforts in Mt. Hilong-hilong and other key habitats in the Philippines.

scholarly journals Characterizing the nonlinear structure of shared variability in cortical neuron populations using latent variable models

2018 ◽  
Author(s):  
Matthew R Whiteway ◽  
Karolina Socha ◽  
Vincent Bonin ◽  
Daniel A Butts
Keyword(s):  
Neural Activity ◽  
Latent Variables ◽  
Latent Variable ◽  
Large Scale ◽  
Latent Variable Models ◽  
Neural Population ◽  
Variable Model ◽  
Experimental Conditions ◽  
Affine Model ◽  
Nonlinear Latent Variable Models

AbstractSensory neurons often have variable responses to repeated presentations of the same stimulus, which can significantly degrade the information contained in those responses. Such variability is often shared across many neurons, which in principle can allow a decoder to mitigate the effects of such noise, depending on the structure of the shared variability and its relationship to sensory encoding at the population level. Latent variable models offer an approach for characterizing the structure of this shared variability in neural population recordings, although they have thus far typically been used under restrictive mathematical assumptions, such as assuming linear transformations between the latent variables and neural activity. Here we leverage recent advances in machine learning to introduce two nonlinear latent variable models for analyzing large-scale neural recordings. We first present a general nonlinear latent variable model that is agnostic to the stimulus tuning properties of the individual neurons, and is hence well suited for exploring neural populations whose tuning properties are not well characterized. This motivates a second class of model, the Generalized Affine Model, which simultaneously determines each neuron’s stimulus selectivity and a set of latent variables that modulate these stimulus responses both additively and multiplicatively. While these approaches can detect general nonlinear relationships in shared neural variability, we find that neural activity recorded in anesthetized primary visual cortex (V1) is best described by a single additive and single multiplicative latent variable, i.e., an “affine model”. In contrast, application of the same models to recordings in awake macaque prefrontal cortex discover more general nonlinearities to compactly describe the population response variability. These results thus demonstrate how nonlinear latent variable models can be used to describe population variability, and suggest that a range of methods is necessary to study different brain regions under different experimental conditions.

REGRESSION DEPENDENCE IN LATENT VARIABLE MODELS

2006 ◽  
Vol 20 (2) ◽  
pp. 363-379 ◽  
Author(s):  
Taizhong Hu ◽  
Jing Chen ◽  
Chaode Xie
Keyword(s):  
Latent Variable ◽  
Random Variables ◽  
Random Variable ◽  
Latent Variable Models ◽  
Latent Variable Model ◽  
Variable Model ◽  
Positive Dependence ◽  
Regression Dependence ◽  
Left Tail ◽  
Different Types

Three new notions of positive dependence (positive regression dependence, positive left-tail regression dependence, and positive right-tail regression dependence) are studied in this article. Consider a latent variable model where the manifest random variables T1,T2,…,Tn given latent random variable/vector (Θ1,…,Θm) are conditional independent. Conditions are identified under which T1,…,Tn possesses the new dependence notions for different types of latent variable model. Applications of the results are also provided.

scholarly journals Interpreting psychometric models

2019 ◽  
Author(s):  
Riet van Bork
Keyword(s):  
Latent Variable ◽  
Latent Trait ◽  
Network Models ◽  
Latent Variable Models ◽  
Causal Mechanism ◽  
Variable Model ◽  
Psychological Construct ◽  
Psychological Constructs ◽  
Common Causes ◽  
Psychometric Models

The field of psychometrics aims to develop theories on how to measure psychological constructs through observable behavior. This dissertation focuses on two psychometric theories that differ in how the psychological construct is related to observable behaviors. Latent trait theory understands psychological constructs as underlying common causes of observed behavior that explain the associations between certain behaviors. Alternatively, in the psychological network theory, behaviors correlate because they mutually reinforce each other and the psychological construct refers to the resulting cluster of associated behaviors. These different theories about how to conceptualize psychological constructs and how to relate these constructs to observable behavior can be formally defined in a set of equations and assumptions that make up a psychometric model. The chapters in this dissertation focus on two types of psychometric models: Latent variable models and network models. Part I of the dissertation focuses on the interpretation of the latent variable model. Part II of the dissertation makes a comparison between latent variable models and network models. While psychometric models can be interpreted as representations of a theory about the data-generating mechanism, this is not necessary. Psychometric models are often viewed as mere descriptions of data. This dissertation shows the importance of thinking through the choice of interpreting psychometric models either as a representation of a causal mechanism or as a description of the data and provides insights in the implications of that choice.

Right-Wing Authoritarians Aren't Very Funny: RWA, Personality, and Creative Humor Production

2020 ◽  
Author(s):  
Paul Silvia ◽  
Alexander P. Christensen ◽  
Katherine N. Cotter
Keyword(s):  
Young Adults ◽  
Latent Variable ◽  
Latent Variable Models ◽  
Openness To Experience ◽  
Rasch Models ◽  
Right Wing ◽  
Right Wing Authoritarianism ◽  
Humor Appreciation ◽  
Negative Effect

Right-wing authoritarianism (RWA) has well-known links with humor appreciation, such as enjoying jokes that target deviant groups, but less is known about RWA and creative humor production—coming up with funny ideas oneself. A sample of 186 young adults completed a measure of RWA, the HEXACO-100, and 3 humor production tasks that involved writing funny cartoon captions, creating humorous definitions for quirky concepts, and completing joke stems with punchlines. The humor responses were scored by 8 raters and analyzed with many-facet Rasch models. Latent variable models found that RWA had a large, significant effect on humor production (β = -.47 [-.65, -.30], p < .001): responses created by people high in RWA were rated as much less funny. RWA’s negative effect on humor was smaller but still significant (β = -.25 [-.49, -.01], p = .044) after controlling for Openness to Experience (β = .39 [.20, .59], p < .001) and Conscientiousness (β = -.21 [-.41, -.02], p = .029). Taken together, the findings suggest that people high in RWA just aren’t very funny.

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