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114 results for “Hierarchical Modelling”
Example code and data for ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework
<p>This repository contains an R script (grouse_example.R) and data (grouse_data.csv) used to reproduce the grouse abundance analysis described in Kellner, K. F., et al. (2021) ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework. Methods in Ecology and Evolution. The R script requires installation of the ubms R package, which can be obtained from CRAN (https://cran.r-project.org/package=ubms).</p> <p>The repository also contains an additional example occupancy analysis (occupancy_example.R) using the crossbill dataset included with the unmarked R package.</p>
Dataset for "An alternative to market-oriented energy models: nexus patterns across hierarchical levels"
<p>Dataset used for the publication "Di Felice, Louisa Jane, Maddalena Ripa, and Mario Giampietro. "An alternative to market-oriented energy models: Nexus patterns across hierarchical levels." <em>Energy Policy</em> 126 (2019): 431-443.". The dataset follows the distinction across hierarchical levels as specified in the publication.</p> <p>The same dataset was also used for a case study developed for the MAGIC project, available <a href="http://magic-nexus.eu/case_study/electric-grid-catalonia-illustrations-musiasem">here</a>. </p>
Supporting data for "Mammalian species abundance across a gradient of tropical land-use intensity: A hierarchical multi-species modelling approach"
<p>Combined camera trap and live trap dataset underlying the analyses in a Biological Conservation paper (https://doi.org/10.1016/j.biocon.2017.05.007), provided in .csv format. This spatially- and temporally-replicated dataset is suitable for occupancy modelling.</p> <p>The first 3 columns in the dataset are:</p> <p>1) Trap location name – old-growth forest, logged forest and oil palm plantation locations have the prefixes "Old", "Log" and "Palm", respectively</p> <p>2) Sampling occasion number – camera trap and live trap occasions have the prefixes “Lvtrap” and “Ctrap”, respectively, and are defined in the paper</p> <p>3) Calendar year in which sampling took place (most locations were sampled in > 1 calendar years)</p> <p>Following these 3 columns, there are 66 columns for each of the mammal species detected during the study (species common names are used). The values for each species represent the number of independent captures, as defined in the paper. This can be reduced to detection/non-detection data (zeroes and ones), if needed, for occupancy modelling.</p>
Data for: Reintroduced Oriental stork bayesian hierarchical model data
<p>Long-lived territorial bird populations often consist of a few territorial breeding adults and many non-breeding individuals. Some populations are threatened by anthropogenic activities, because of human conflicts for high-quality breeding habitat. Therefore, habitat restoration projects have been widely implemented to improve avian population status. In conjunction with habitat restoration, conservation translocations have been increasingly implemented. Adequate non-breeder survival can be a key factor in the success of these attempts because non-breeding birds may represent reservoirs for the replacement of breeders. The maintenance of breeding pair numbers is also influenced by the transition rate of non-breeders to breeders. The reintroduction of Oriental stork (<em>Ciconia boyciana</em>), a long-lived, territorial, endangered species, was initiated in Japan in 2005 using captive birds in hopes of increasing the population's use of restored habitat. Our objective of this study was to elucidate the factors determining reintroduced stork survival and recruitment to the breeding populations. We estimated the survival rate and breeding participation rate by sex, age, generation, wild-born or not, haplotypes, and breeding status in storks reintroduced during 2005–2022 using Bayesian hierarchical models. There was no significant difference in survival rate between non-breeders and breeders. However, the survival rate was lower in wild-born birds than released birds, which may be related to the longer-distance natal dispersal of new generations. Accelerated habitat restoration around breeding areas and preventive measures for collision with human-built structures should be implemented for the sustained growth of reintroduced populations. A low survival rate was also detected for a specific mtDNA haplotype that accounts for the majority of the reintroduced population. This phenomenon might be explained by mtDNA-encoded mutations. Moreover, captive breeding and release history might contribute to an increase in the proportion of this haplotype in the wild.</p>
Data from: Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies
<p>Ixodid (hard) ticks play important ecosystem roles and have significant impacts on animal and human health via tick-borne diseases and physiological stress from parasitism. Tick occurrence, abundance, behavior, and key life-history traits are highly influenced by host availability, weather, microclimate, and landscape features. As such, changes in the environment can have profound impacts on ticks, their hosts, and the spread of diseases. Researchers interested in enumerating questing ticks attempt to integrate this heterogeneity by conducting replicate sampling bouts spread over the tick questing period as common field methods notoriously underestimate ticks. However, it is unclear how (or if) tick studies account for this heterogeneity in the modeling process. This step is critical as unaccounted variance in detection can lead to biased estimates of occurrence and abundance. We performed a descriptive review to evaluate the extent to which studies account for the detection process while modeling tick data. We also categorized the types of analyses that are commonly used to model tick data. We used hierarchical models (HMs) that account for imperfect detection to analyze simulated and empirical tick data, demonstrating that inference is muddled when detection probability is not accounted for in the modeling process. Our review indicates that only 5 of 412 (1%) papers explicitly accounted for imperfect detection while modeling ticks. By comparing HMs with the most common approaches used for modeling tick data (e.g., ANOVA), we show that population estimates are biased low for simulated and empirical data when using non-HMs, and that confounding occurs due to not explicitly modeling factors that influenced both detection and abundance. Our review and analysis of simulated and empirical data shows that it is important to account for our ability to detect ticks using field methods with imperfect detection. Not doing so leads to biased estimates of occurrence and abundance which could complicate our understanding of parasite-host relationships and the spread of tick-borne diseases. We highlight the resources available for learning HM approaches and applying them to analyzing tick data.</p>
A hierarchical graph-based model for mobility data representation and analysis
<p>Hierarchical representations of transportation networks should provide a better understanding of mobility patterns and the underlying structures at various abstraction levels. A hierarchical graph-based model allows representing moving objects and trajectories according to multiple spatial, temporal and semantic scales. The latter model is implemented here in a Neo4j graph database (version 4.4.0) and experimented with historical maritime data covering Brittany Bay in France.</p>
Data from: A hierarchical model for jointly assessing ecological and anthropogenic impacts on animal demography
<p>1. The management of sustainable harvest of animal populations is of great ecological and conservation importance. Development of formal quantitative tools to estimate and mitigate the impacts of harvest on animal populations has positively impacted conservation efforts.</p> <p>2. The vast majority of existing harvest models, however, do not simultaneously estimate ecological and harvest impacts on demographic parameters and population trends. Given that the impacts of ecological drivers are often equal to or greater than the effects of harvest, and can covary with harvest, this disconnect has the potential to lead to flawed inference.</p> <p>3. In this study, we used Bayesian hierarchical models and a 43-year capture-mark-recovery dataset from 404,241 female mallards (Anas platyrhynchos) released in the North American midcontinent to estimate mallard demographic parameters. Further, we model the dynamics of waterfowl hunters and habitat, and the direct and indirect effects of anthropogenic and ecological processes on mallard demographic parameters.</p> <p>4. We demonstrate that density-dependence, habitat conditions, and harvest can simultaneously impact demographic parameters of female mallards, and discuss implications for existing and future harvest management models.</p> <p>5. Our results demonstrate the importance of controlling for multicollinearity among demographic drivers in harvest management models, and provide evidence for multiple mechanisms that lead to partial compensation of mallard harvest. We provide a novel model structure to assess these relationships that may allow for improved inference and prediction in future iterations of harvest management models across taxa.</p>
Hierarchical Boosting for Regulatory Genomics Modeling
<p>This contains all the information for the Hematopoiesis dataset that was used in the Hierarchical Boosting for Regulatory Genomics Modeling paper. </p>
New Ideas for Brain Modelling 4-Figure 3. Neuron Pairing: an ensemble neuron links with a hierarchal neuron. Also figure 4 in Greer (2016)
<p>The model is also based on the idea of an auto-associative neural network. The Hopfield neural network (Hopfield, 1982), and its stochastic equivalents are auto-associative or memory networks. With the memory networks, information is sent between the input and the output until a stable state is reached, when the information does not then change. These are resonance networks, such as bidirectional associative memory (BAM), or others (Rojas, 1996), but they can only provide a memory recall – they map the input pattern directly to the output pattern. If some of the input pattern is missing however, they can still provide an accurate recall of the whole pattern. They also prefer the data vectors to be orthogonal without overlap. This is however ideal for the binding that only wants to reproduce the base ensemble in the hierarchy.</p>
Data for: Accurate state-of-charge estimation for sodium-ion batteries based on a low-complexity model with hierarchical learning
<p>The dataset accompanies the Journal of Energy Storage publication by Shuquan Wang et al. (2024), Accurate state-of-charge estimation for sodium-ion batteries based on a low-complexity model with hierarchical learning, DOI 10.1016/j.est.2024.112571. </p> <h2><strong>Experimental Description:</strong></h2> <p>The dataset comprises results from two experimental tests: pulse testing and driving cycle testing. These tests were conducted on two types of sodium-ion batteries—one with a capacity of 3.2 Ah (battery numbers: 1, 2, and 5) and another with a capacity of 10 Ah (battery numbers: 3, 4, and 6).</p> <h3><strong>Pulse Testing:</strong></h3> <p>The pulse tests were carried out using a battery test platform, consisting of an Arbin battery testing system, a temperature-controlled chamber, and a computer. The tests were performed on two 3.2 Ah and two 10 Ah sodium-ion batteries from Transimage and HiNa, respectively, with a nominal voltage of 3.0 V. The upper and lower cut-off voltages were set at 3.9 V and 1.5 V.</p> <p>Enhanced pulse tests were conducted at six different temperatures: -5 ℃, 5 °C, 15 ℃, 25 ℃, 35 ℃, and 45 ℃. The state-of-charge (SOC) was varied in 10% intervals, with pulse currents escalating incrementally from 0.25C to 3C at 0.25C intervals. Each pulse lasted for 5 seconds, followed by a 15-second rest. After completing each set of pulses, the current was increased, and the process was repeated with a two-minute pause between sets of pulses.</p> <h3><strong>Driving Cycle Testing:</strong></h3> <p>The driving cycle tests were designed to simulate real-world driving conditions using various standard test methods, including the Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), and Dynamic Stress Test (DST). These tests were performed in a temperature-controlled chamber using both the 3.2 Ah and 10 Ah sodium-ion batteries.</p> <p>As with the pulse tests, driving cycle tests were carried out at temperatures of -5 ℃, 5 °C, 15 ℃, 25 ℃, 35 ℃, and 45 ℃. Before each test, the batteries were charged with a 0.5C constant current-constant voltage (CC-CV) charging protocol up to 3.9 V, with a cut-off current of 0.02C. After a 30-minute rest, the driving cycle protocol was performed for seven iterations.</p> <h2><strong>File Naming Conventions:</strong></h2> <p>The dataset files are named based on the experimental conditions, as follows:</p> <ul> <li><strong>Pulse_data_tempX_batY</strong>: Data from the pulse tests, where X represents the testing temperature and Y denotes the battery number.</li> <li><strong>Driving_cycle_data_tempX_batY</strong>: Data from the driving cycle tests, where X represents the testing temperature and Y denotes the battery number.</li> </ul>
Numerical study of the one-dimensional Holstein model using the momentum-space hierarchical equations of motion method
<p>Data on the finite-temperature current-current correlation function of the one-dimensional Holstein model. Data are obtained using the newly developed momentum-space hierarchical equations of motion (HEOM) method. Details on the method development, as well as on the model parameters, will be given as a supplementary material to a journal publication that will be deposited on arXiv. Folders Regime* contain temporal evolution of the current-current correlation function (j_j_real_time.txt), diffusion constant (diffusion_constant.txt), diffusion exponent (diffusion_exponent.txt), and the electron's spread (delta_x.txt). They also contain frequency profiles of the Fourier transformed current-current correlation function (j_j_real_frequency.txt) and dynamical mobility (dynamical_mobility.txt).</p>
Hierarchical heuristic species delimitation under the multispecies coalescent model with migration
<p>The multispecies coalescent (MSC) model accommodates genealogical fluctuations across the genome and provides a natural framework for comparative analysis of genomic sequence data to infer the history of species divergence and gene flow. Given a set of populations, hypotheses of species delimitation (and species phylogeny) may be formulated as instances of MSC models (e.g., MSC for one species versus MSC for two species) and compared using Bayesian model selection. This approach, implemented in the program bpp, has been found to be prone to over-splitting. Alternatively, heuristic criteria based on population parameters under the MSC model (such as population/species divergence times, population sizes, and migration rates) estimated from genomic sequence data may be used to delimit species. Here we extend the approach of species delimitation using the genealogical divergence index (𝑔𝑑𝑖) to develop hierarchical merge and split algorithms for heuristic species delimitation and implement them in a python pipeline called hhsd. Applied to data simulated under a model of isolation by distance, the approach was able to recover the correct species delimitation, whereas model comparison by bpp failed. Analyses of empirical datasets suggest that the procedure may be less prone to over-splitting. We discuss possible strategies for accommodating paraphyletic species in the procedure, as well as the challenges of species delimitation based on heuristic criteria.</p>
Data from: Large-scale eDNA sampling and hierarchical modeling elucidates the importance of stream habitat for eastern hellbender (<em>Cryptobranchus a. alleganiensis</em>) occupancy and eDNA detection
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Data for: Reintroduced Oriental stork bayesian hierarchical model data
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Hierarchical heuristic species delimitation under the multispecies coalescent model with migration
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Data from: A hierarchical model for jointly assessing ecological and anthropogenic impacts on animal demography
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Data from: Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies
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Developing hierarchical density-structured models to study the national-scale dynamics of an arable weed
<p class="BodyText1">Population dynamics can be highly variable in the face of environmental heterogeneity, and understanding this variation is central in the study of ecology. Robust management decisions require that we understand how populations respond to management at a range of scales, and under a broad suite of conditions. Population models are potentially valuable tools in addressing this challenge. However, without adequate data, models can fail to produce useful results. Populations of arable weeds are particularly problematic in this respect, as they are widespread and their dynamics are extremely variable. Owing to the inherent cost of collecting data, most studies of weed population dynamics are derived from localized experiments under a small range of environmental conditions, limiting the extent to which variance in population dynamics can be measured. Density-structured models provide a route to rapid, large-scale analysis of population dynamics, and can expand the scale of ecological models that are directly tied to data. Here we extend previous density-structured models to include environmental heterogeneity, variation in management, and to account for inter-population variation. We develop, parameterize and test hierarchical density-structured models for a common agricultural weed, black-grass (<i>Alopecurus myosuroides</i>). We model the dynamics of this species in response to crop management, using survey data gathered over 4 years from 364 fields across a network of 45 UK farms. We show that hierarchical density-structured models provide a substantial improvement over their non-hierarchical counterparts. Using these models, we demonstrate that several alternative crop-rotations are effective in reducing weed densities. Rotations with high wheat prevalence exhibit the most severe infestations, and diverse rotations generally have lower weed densities. However, a key outcome is that in many cases the effect of crop rotation is small compared to the high variability arising from spatio-temporal heterogeneity. This result highlights the need to monitor and model population dynamics across large spatial and temporal scales in order to account for variation in the drivers of plant dynamics. Our framework for data collection and modelling provides a means to achieve this.</p>
Long-term research and hierarchical models reveal consistent fitness costs of being the last egg in a clutch
1. Maintenance of phenotypic heterogeneity in the face of strong selection is an important component of evolutionary ecology, as are the consequences of such heterogeneity. Organisms may experience diminishing returns of increased reproductive allocation as clutch or litter size increases, affecting current and residual reproductive success. Given existing uncertainty regarding trade-offs between the quantity and quality of offspring, we sought to examine the potential for diminishing returns on increased reproductive allocation in a long-lived species of goose, with a particular emphasis on the effect of position in the laying sequence on offspring quality. 2. To better understand the effects of maternal allocation on offspring survival and growth, we estimated the effects of egg size, timing of breeding, inter- and intra-annual variation, and position in the laying sequence on gosling survival and growth rates of black brent (Branta bernicla nigricans) breeding in western Alaska from 1987–2007. 3. We found that gosling growth rates and survival decreased with position in the laying sequence, regardless of clutch size. Mean egg volume of the clutch a gosling originated from had a positive effect on gosling survival (β = 0.095, 95% CRI: 0.024, 0.165), and gosling growth rates (β = 0.626, 95% CRI: 0.469, 0.738). Gosling survival (β = -0.146, 95% CRI: -0.214, -0.079) and growth rates (β = -1.286, 95% CRI: -1.435, -1.132) were negatively related to hatching date. 4. These findings indicate substantial heterogeneity in offspring quality associated with their position in the laying sequence. They also potentially suggest a trade-off mechanism for females whose total reproductive investment is governed by pre-breeding state. 20-Mar-2020
Hierarchical multi-grain models improve descriptions of species' environmental associations, distribution, and abundance
<p>The characterization of species' environmental niches and spatial distribution predictions based on them are now central to much of ecology and conservation, but implicitly requires decisions about the appropriate spatial scale (i.e. <i>grain</i>) of analysis. Ecological theory and empirical evidence suggest that range-resident species respond to their environment at two characteristic, hierarchical spatial grains: (i) <i>response grain</i>, the (relatively fine) grain at which an individual uses environmental resources, and (ii) <i>occupancy grain</i>,<i> </i>the (relatively coarse) grain equivalent to a typical home range. We use a multi-grain (MG) occupancy model, aided by fine-grain remotely sensed imagery, to simultaneously estimate species-environment associations at both grains, conduct grain optimization to measure response grain, and apply this analysis framework to an example species: a medium-sized bird (<i>Tockus deckeni</i>) in a heterogeneous East African landscape. Based on home range analysis of movement data, we calculate an occupancy grain of 1km for <i>T. deckeni</i>. Using a grain optimization procedure across 32 grains from 10m to 500m, we identify 60m as the most strongly supported response grain for a suite of environmental variables, slightly coarser than opportunistic behavioral observations would have suggested. Validation confirms that the accuracy of the optimized MG occupancy model substantially exceeds that of equivalent single-grain (SG) occupancy models. We further use a simulation approach to assess the potential impacts of accounting for the multi-scale structure of species' environmental requirements on estimates of population size. We find that the more strongly supported MG approach consistently predicts a minimum population sizes in the study landscape that is much lower than that provided by the SG model. This suggests that SG approaches commonly used in conservation applications could lead to overly optimistic abundance and population estimates and that the MG approach may be more appropriate for supporting species conservation goals. More generally, we conclude that multi-grain approaches of the sort presented, and increasingly enabled by growing high-resolution remotely sensed data, hold great promise for offering a more mechanistic framework for assessing the appropriate grain(s) for population monitoring and management and enable more reliable estimates of abundances and species' distributions.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.