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700 results for “Dynamical model”
Eco‐evolutionary dynamics driven by fishing: from single species models to dynamic evolution within complex food webs
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Estimating spatio-temporal reproductive dynamics of fish populations with passive acoustic monitoring: A state-space model approach
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Predicting soil interpedal macroporosity and hydraulic conductivity dynamics: A model for integrating laser-scanned profile imagery with soil moisture sensor data
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The role of fire in the carbon dynamics of the boreal forest II. - Eurasia model simulations of historical fire disturbance and carbon dynamics (1000-2002).
The boreal forest contains large reserves of carbon, and across this region wildfire is a common occurrence. To improve the understanding of how wildfire influences the carbon dynamics of this region, methods were developed to incorporate the spatial and temporal effects of fire into the Terrestrial ecosystem Model (TEM). The historical role of fire on carbon dynamics of the boreal region was evaluated within the context of ecosystem responses to changing atmospheric CO2 and climate. These results show that the role of historical fire on boreal carbon dynamics resulted in a net carbon sink; however, fire plays a major role in the interannual and decadal scale variation of source/sink relationships. To estimate the effects of future fire on boreal carbondynamics, spatially and temporally explicit empirical relationships between climate andfire were quantified. Fuel moisture, monthly severity rating, and air temperature explained a significant proportion of observed variability in annual area burned. These relationships were used to estimate annual area burned for future scenarios of climate change and were coupled to TEM to evaluate the role of future fire on the carbon dynamics of the North American boreal region for the 21st Century. Simulations with TEM indicate that boreal North America is a carbon sink in response to CO2 fertilization, climate variability, and fire, but an increase in fire leads to a decrease in the sink strength. While this study highlights the importance of fire on carbon dynamics in the boreal region, there are uncertainties in the effects of fire in TEM simulations. These uncertainties are associated with sparse fire data for northern Eurasia, uncertainty in estimating carbon consumption, and difficulty in verifying assumptions about the representation of fires that occurred prior to the start of the historical fire record. Future studies should incorporate the role of dynamic vegetation to more accurately represent post-fire successional pr
The role of fire in the carbon dynamics of the boreal forest III. - North America model simulations of historical fire disturbance and carbon dynamics (1900-2100).
The boreal forest contains large reserves of carbon, and across this region wildfire is a common occurrence. To improve the understanding of how wildfire influences the carbon dynamics of this region, methods were developed to incorporate the spatial and temporal effects of fire into the Terrestrial ecosystem Model (TEM). The historical role of fire on carbon dynamics of the boreal region was evaluated within the context of ecosystem responses to changing atmospheric CO2 and climate. These results show that the role of historical fire on boreal carbon dynamics resulted in a net carbon sink; however, fire plays a major role in the interannual and decadal scale variation of source/sink relationships. To estimate the effects of future fire on boreal carbondynamics, spatially and temporally explicit empirical relationships between climate andfire were quantified. Fuel moisture, monthly severity rating, and air temperature explained a significant proportion of observed variability in annual area burned. These relationships were used to estimate annual area burned for future scenarios of climate change and were coupled to TEM to evaluate the role of future fire on the carbon dynamics of the North American boreal region for the 21st Century. Simulations with TEM indicate that boreal North America is a carbon sink in response to CO2 fertilization, climate variability, and fire, but an increase in fire leads to a decrease in the sink strength. While this study highlights the importance of fire on carbon dynamics in the boreal region, there are uncertainties in the effects of fire in TEM simulations. These uncertainties are associated with sparse fire data for northern Eurasia, uncertainty in estimating carbon consumption, and difficulty in verifying assumptions about the representation of fires that occurred prior to the start of the historical fire record. Future studies should incorporate the role of dynamic vegetation to more accurately represent post-fire successional pr
Supplement to "Dynamic model of photovoltaic module temperature as a function of atmospheric conditions"
<p>This dataset contains data from two measurement campaigns in autumn 2018 and summer 2019 that were part of the BMWi project "MetPVNet", and serve as a supplement to the paper "Dynamic model of photovoltaic module temperature as a function of atmospheric conditions", published in the special edition of "Advances in Science and Research", the proceedings of the 19th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2019.</p> <p>Data are resampled to one minute, and include:</p> <ol> <li>PV module temperature</li> <li>Ambient temperature</li> <li>Plane-of-array irradiance</li> <li>Windspeed</li> <li>Atmospheric thermal emission</li> </ol> <p>The data were used for the dynamic temperature model, as presented in the paper</p>
Dynamic Load Balancing for Predictions of Storm Surge and Coastal Flooding-Model setup and source code
<p>Source code and model setup/inputs for the paper titled "Dynamic Load Balancing for Predictions of Storm Surge and Coastal Flooding" article. Simulations were conducted using a modified version of ADCIRC+DLB (ADCIRC + Dynamic Load Balancing) on unstructured triangular meshes.</p> <p>Contains:</p> <ol> <li>Model input files. <ol> <li>ADCIRC model input files for the ideal channel setup and Hurricane Irene simulation (*.13, *.14, *.15)</li> </ol> </li> <li>Zipped archive of the ADCIRC code (adcirc-cg-DLB.zip) used to produce the simulations for the paper.</li> <li>Step-by-step compilation and usage instructions for ADCIRC+DLB. <ol> <li>Installation.html </li> <li>Usage.html</li> </ol> </li> </ol>
SIM4NEXUS System Dynamics Model of Azerbaijan
<p>The System Dynamics Model covers all five sectors of the Nexus of Azerbaijan. Namely the climate, water, land, food and energy sector and their interlinkages. The SDM has been developed in the H2020 project SIM4NEXUS and is the base for a Serious Game on the Azerbaijani Nexus.</p>
Data from: Postglacial recolonization of North America by spadefoot toads: integrating niche and corridor modeling to study species' range dynamics over geologic time
<p>Understanding the factors that shape species' distributions is a key topic in biogeography. As climates change, species can either cope with these changes through evolution, plasticity or by shifting their ranges to track the optimal climatic conditions. Ecological niche modeling (ENM) is a widespread technique in biogeography that estimates the niche of the organism by using occurrences and environmental data to estimate species' potential distributions. ENMs are often criticized for failing to take species' dispersal abilities into consideration. Here, we attempt to fill this gap by combining ENMs with dispersal and corridor modeling to study the range dynamics of North American spadefoot toads (Scaphiopodidae) over the Holocene. We first estimated the current and past distributions of spadefoot toads and then estimated their past distributions from the Last Glacial Maximum (LGM) to the present day. Then, we estimated how each taxon recolonized North American by using dispersal and corridor modeling. By combining these two modeling approaches we were able to 1) estimate the LGM refugia used by the North American spadefoot toads, 2) further refine these projections by estimating which of the putative LGM refugia contributed to the recolonization of North America via dispersal, and 3) estimate the relative influence of each LGM refugium to the current species' distributions. The models were tested using previously published phylogeographic data, revealing a high degree of congruence between our models and the genetic data. These results suggest that combining ENMs and dispersal modeling over time is a promising approach to investigate both historical and future species' range dynamics.</p>
Model-based analysis of postprandial glycemic response dynamics for different types of food
<p>Background & aims</p> <p>Knowledge of postprandial glycemic response (PPGR) dynamics is important in nutrition management and diabetes research, care and (self)management. In daily life, food intake is the most important factor influencing the occurrence of hyperglycemia. However, the large variability in PPGR dynamics to different types of food is inadequately predicted by existing glycemic measures. The objective of this study was therefore to quantitatively describe PPGR dynamics using a systems approach.</p> <p>Methods</p> <p>Postprandial glucose and insulin data were collected from literature for many different food products and mixed meals. The predictive value of existing measures, such as the Glycemic Index, was evaluated. A physiology-based dynamic model was used to reconstruct the full postprandial response profiles of both glucose and insulin simultaneously.</p> <p>Results</p> <p>We collected a large range of postprandial glucose and insulin dynamics for 53 common food products and mixed meals. Currently available glycemic measures were found to be inadequate to describe the heterogeneity in postprandial dynamics. By estimating model parameters from glucose and insulin data, the physiology-based dynamic model accurately describes the measured data whilst adhering to physiological constraints.</p> <p>Conclusions</p> <p>The physiology-based dynamic model provides a systematic framework to analyze postprandial glucose and insulin profiles. By changing parameter values the model can be adjusted to simulate impaired glucose tolerance and insulin resistance.</p>
Data: Using environmental DNA and occupancy modeling to estimate rangewide metapopulation dynamics
<p>We demonstrate the power of combining two emergent tools for resolving rangewide metapopulation dynamics. First, we employed environmental DNA (eDNA) surveys to efficiently generate multi-season rangewide site occupancy histories. Second, we developed a novel<i> </i>dynamic, spatial multiscale occupancy model to estimate metapopulation dynamics. The model incorporates spatial relationships, explicitly accounts for non-detection bias and allows direct evaluation of the drivers of extinction and colonization. We applied these tools to examine metapopulation dynamics of endangered tidewater goby, a species endemic to California estuarine habitats. We analyzed rangewide eDNA data from 190 geographically isolated sites (813 total water samples) surveyed from two years (2016 and 2017). Rangewide estimates of the proportion of sites that were occupied varied little between 2016 (0.52) and 2017 (0.51). However, there was evidence of extinction and colonization dynamics. The probability of extinction of an occupied site (0.106) and probability of colonization of an unoccupied site (0.085) were nearly equal. Stability in site occupancy proportions combined with nearly equal rates of extinction and colonization suggests a dynamic equilibrium between the two years surveyed. Assessment of covariate effects revealed that colonization probability increased as the number of occupied neighboring sites increased and as distance between occupied sites decreased. We show that eDNA surveys can rapidly provide a snapshot of a species distribution over a broad geographic range, and when these surveys are paired with occupancy modeling, can uncover metapopulation dynamics and their drivers.</p>
Integrating univariate niche dynamics in species distribution models: a step forward for marine research on biological invasions
<p>Aim The development of approaches to predict the distribution and potential expansion of invasive species is still an open challenge. Here our goal is to improve the modelling procedure for marine invaders by coupling Species Distribution Models (SDMs) with an analysis of their univariate niche dynamics. In particular, we tested for the first time whether choosing model predictors among the stable niche dimensions was effective in improving predictions of invasive species expansion.<br> Location Mediterranean Sea<br> Taxon Dusky spinefoot, Siganus luridus.<br> Methods We analysed the univariate niche dynamics for S. luridus across its native and invaded ranges, by applying a standardized framework that allowed the identification of cases of niche stability or shift. We compared inter-range transferability of SDMs fitted with different combinations of labile or stable predictors. Finally, we evaluated interactions in SDM settings (calibration area, model technique and predictors set) on models' predictive ability, using independent data from the most recent phase of invasion.<br> Results We detected a pattern of niche stability for several variables, especially salinity and bathymetry, which positively influenced model inter-ranges transferability: when the models calibrated in the native range include only stable niche axes, predictive ability is improved. We also identified a shift toward lower surface temperatures in the introduced range, which were almost never experienced by the species before invasion. The model calibrated within the combined ranges was the most ecologically congruent. Also, models calibrated in the invaded range allowed a correct prediction of range expansion, with the predicted suitable areas only slightly underestimated.<br> Main conclusions We provide the first evidence that using conserved predictors in SDMs improves inter-range projections of expanding invasive species. Variable selection, calibration area and modelling technique all matter when modelling invasive species, with important interaction effects. We provide guidelines on how to improve SDMs applications in biological invasion research.</p>
Data from: Integrated modeling predicts shifts in waterbird population dynamics under climate change
Climate change has been identified as one of the most important drivers of wildlife population dynamics. The in-depth knowledge of the complex relationships between climate and population sizes through density dependent demographic processes is important for understanding and predicting population shifts under climate change, which requires integrated population models (IPMs) that unify the analyses of demography and abundance data. In this study we developed an IPM based on Gaussian approximation to dynamic N-mixture models for large scale population data. We then analyzed four decades (1972-2013) of Mallard (Anas platyrhynchos) breeding population survey, band-recovery, and climate data covering a large spatial extent from North American prairies through boreal habitat to Alaska. We aimed to test the hypothesis that climate change will cause shifts in population dynamics if climatic effects on demographic parameters that have substantial contribution to population growth vary spatially. More specifically, we examined the spatial variation of climatic effects on density dependent population demography, identified the key demographic parameters that are influential to population growth, and forecasted population responses to climate change. Our results revealed that recruitment, which explained more variance of population growth than survival, was sensitive to the temporal variation of precipitation in the southern portion of the study area but not in the north. Survival, by contrast, was insensitive to climatic variation. We then forecasted a decrease in Mallard breeding population density in the south and an increase in the northwestern portion of the study area, indicating potential shifts in population dynamics under future climate change. Our results implied that different strategies need to be considered across regions to conserve waterfowl populations in the face of climate change. Our modelling approach can be adapted for other species and thus has wide application to understanding and predicting population dynamics in the presence of global change.
Data from: Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data
While large carnivores are recovering in Europe, assessing their distributions can help to predict and mitigate conflicts with human activities. Because they are highly mobile, elusive and live at very low density, modeling their distributions presents several challenges due to i) their imperfect detectability, ii) their dynamic ranges over time and iii) their monitoring at large scales consisting mainly of opportunistic data without a formal measure of the sampling effort. Here, we focused on wolves (Canis lupus) that have been recolonizing France since the early 90's. We evaluated the sampling effort a posteriori as the number of observers present per year in a cell based on their location and professional activities. We then assessed wolf range dynamics from 1994 to 2016, while accounting for species imperfect detection and time- and space-varying sampling effort using dynamic site-occupancy models. Ignoring the effect of sampling effort on species detectability led to underestimating the number of occupied sites by more than 50% on average. Colonization appeared to be negatively influenced by the proportion of a site with an altitude higher than 2500m and positively influenced by the number of observed occupied sites at short and longdistances , forest cover, farmland cover and mean altitude. The expansion rate, defined as the number of occupied sites in a given year divided by the number of occupied sites in the previous year, decreased over the first years of the study, then remained stable from 2000 to 2016. Our work shows that opportunistic data can be analyzed with species distribution models that control for imperfect detection, pending a quantification of sampling effort. Our approach has the potential for being used by decisionmakers to target sites where large carnivores are likely to occur and mitigate conflicts.
Data from: Integrating genetic analysis of mixed populations with a spatially-explicit population dynamics model
Inferring the dynamics of populations in time and space is a central challenge in ecology. Intra-specific structure (for example genetically distinct sub-populations or meta-populations) may require methods that can jointly infer the dynamics of multiple populations. This is of particular importance for harvested species, for which management must balance utilization of productive populations with protection of weak ones. Here we present a novel method for simultaneous learning about the spatio-temporal dynamics of multiple populations that combines genetic data with prior information about abundance and movement in an integrated population modelling approach. We apply the Bayesian genetic mixed stock analysis to 17 wild and 10 hatchery-reared Baltic salmon (S. salar) stocks, quantifying uncertainty in stock composition in time and space, and in population dynamics parameters such as migration timing and speed. Our results indicate that the commonly used "equal prior probabilities" assumption may not be appropriate for all mixed stock analyses. Incorporation of prior information about stock abundance and movement resulted in more precise and plausible estimates of mixture compositions in time and space. Inclusion of a population dynamics model also allowed robust interpolation of expected catch composition at areas and times with no genetic observations. The genetic data were informative about stock-specific movement patterns, updating priors for migration path, timing and speed. The model we present here forms the basis for optimizing the spatial and temporal allocation of harvest to support the management of mixed populations of migratory species.
Data from: Modeling the impact of Plasmodium falciparum sexual stage immunity on the composition and dynamics of the human infectious reservoir for malaria in natural settings
Malaria transmission remains high in Sub-Saharan Africa despite large-scale implementation of malaria control interventions. A comprehensive understanding of the transmissibility of infections to mosquitoes may guide the design of more effective transmission reducing strategies. The impact of P. falciparum sexual stage immunity on the infectious reservoir for malaria has never been studied in natural settings. Repeated measurements were carried out at start-wet, peak-wet and dry season, and provided data on antibody responses against gametocyte/gamete antigens Pfs48/45 and Pfs230 as anti-gametocyte immunity. Data on high and low-density infections and their infectiousness to anopheline mosquitoes were obtained using quantitative molecular methods and mosquito feeding assays, respectively. An event-driven model for P. falciparum sexual stage immunity was developed and fit to data using an agent based malaria model infrastructure. We found that Pfs48/45 and Pfs230 antibody densities increased with increasing concurrent gametocyte densities; associated with 55–70% reduction in oocyst intensity and achieved up to 44% reduction in proportions of infected mosquitoes. We showed that P. falciparum sexual stage immunity significantly reduces transmission of microscopic (p < 0.001) but not submicroscopic (p = 0.937) gametocyte infections to mosquitoes and that incorporating sexual stage immunity into mathematical models had a considerable impact on the contribution of different age groups to the infectious reservoir of malaria. Human antibody responses to gametocyte antigens are likely to be dependent on recent and concurrent high-density gametocyte exposure and have a pronounced impact on the likelihood of onward transmission of microscopic gametocyte densities compared to low density infections. Our mathematical simulations indicate that anti-gametocyte immunity is an important factor for predicting and understanding the composition and dynamics of the human infectious reservoir for malaria.
Data from: A model for non-equilibrium metapopulation dynamics utilizing data on species occupancy, patch ages and landscape history
1. The distribution pattern of many species reflects the past rather than the current structure of landscapes. Consequently, species are most often not in equilibrium with the current landscape structure. Yet this is a well-known fact, there is no appropriate approach to estimate the colonization rate of non-equilibrium species based on only data on the species occurrence pattern in the landscape. 2. We present an approach to estimate the colonization rate of non-equilibrium metapopulations. The approach requires only data on species presence/absence among its patches (occurrence pattern), data on patch ages and data on the historic distribution of the patches in the landscape. By estimating the past occurrence patterns and colonization events leading to the current pattern of occupied and non-occupied patches, we estimate the colonization rate, including the dispersal kernel. We also show how to estimate effects of local patch conditions and how to include an independent estimate of the local extinction rate based on other data. We use nine epiphytic lichen species confined to beech trees to illustrate the method. 3. Five species had restricted dispersal range, between 200 and 4700 m, and their colonization rate decreased with increasing fragmentation. Species colonization rates were related to niche width. Among the demographic parameters, the force of colonization was more important than the dispersal range in explaining the colonization rates. Local patch conditions did not explain the colonization probability of any species. In metapopulation projections that did not account for restricted dispersal range, higher future metapopulation sizes were projected. 4. Synthesis. The presented approach uses data on only species occurrence, patch age and landscape history to estimate the species colonization rate and dispersal kernel. It can also utilize independent data on local extinction rate. Rather than identifying factors explaining the occurrence pattern, the model estimates the rate of change in the occurrence pattern. This dynamic modelling allows testing general and applied questions on the dynamics or viability of metapopulations of sessile species. The approach is applicable for species whose distribution pattern reflects the past rather than the current landscape structure, e.g. certain epiphytes and ground-floor plants.
Data from: Modeling effects of environmental change on wolf population dynamics, trait evolution, and life history
Environmental change has been observed to generate simultaneous responses in population dynamics, life history, gene frequencies, and morphology in a number of species. But how common are such eco-evolutionary responses to environmental change likely to be? Are they inevitable, or do they require a specific type of change? Can we accurately predict eco-evolutionary responses? We address these questions using theory and data from the study of Yellowstone wolves. We show that environmental change is expected to generate eco-evolutionary change, that changes in the average environment will affect wolves to a greater extent than changes in how variable it is, and that accurate prediction of the consequences of environmental change will probably prove elusive.
Data from: Wear, tear and systematic repair: testing models of growth dynamics in conodonts with high-resolution imaging
Conodont elements are the earliest mineralised vertebrate dental tools and the only ones capable of extensive repair. Two models of conodont growth, as well as the presence of a larval stage, have been hypothesised. We analysed normally and pathologically developed elements to test these hypotheses and identified three ontogenetic stages characterised by different anisometric growth and morphology. The distinction of these stages is independently corroborated by differences in tissue strontium content. The onset of the last stage is marked by the appearance of wear resulting from mechanical food digestion. At least five episodes of damage and repair could be identified in the normally developed specimen. In the pathological element, function was compromised by development of abnormal denticles. This development can be reconstructed as addition of new growth centres out of the main growth axis during an episode of renewed growth. Our findings support the model of periodic retraction of elements and addition of new growth centres. Changes in strontium content coincident with distinct morphology and lack of wear in the early life stage indicate that conodonts might have assumed their mature feeding habit of predators or scavengers after an initial larval stage characterised by a different feeding mode.
Data from: Biomechanical evaluation of peak reverse torque (PRT) in a dynamic compression plate-screw construct used in a goat tibia segmental defect model
Background Peak reverse torque (PRT) is a valid method to evaluate implants' secondary stability in the healing bone. The secondary stability is achieved by the implant over time and it has been positively correlated with the implants' osseointegration level. In other words, peak reverse torque is the force required to break the bone-implant interface. The purpose of this study was to compare the peak reverse torque for the self-tapping and non-self-tapping screws used in a dynamic compression plate–screw–bone construct after 60 days of loading when used to stabilize 2.5-cm defects in the tibia of goats. The second objective was to compare the peak removal torque of the screws placed in the different positions to evaluate the impact of construct biomechanics on implants osseointegration. Results In total, 176 non-self-tapping screws and 66 self-tapping screws were used to fix the 8-holes dynamic compression plates to the bones. The screws were placed in the tibiae from proximal (position sites 1,2, 3) to distal (position sites 4,5,6) and were removed 60 days post-implantation. The animals remained weight-bearing throughout the study period. The screws placed in the proximal diaphysis had significantly less peak reverse torque than screws placed in the distal diaphysis in both groups (p < 0.05). The peak reverse torque resistance was also significantly less for the non-self-tapping screws as compared with the self-tapping screws (p < 0.05). The intracortical fractures in the trans-cortex occurred significantly more frequently during the placement of non-self-tapping screws (p < 0.05) as compared with self-tapping screws (p < 0.05). Conclusions Based on these results, we concluded that self-tapping screws may be expected to maintain a more stable bone-implant interface during the first 60 days of loading as compared with non-self-tapping screws. This should be a consideration for orthopedic surgeons and scientists using bone plates to stabilize non-load sharing fractures when a stable plate-screw-bone interface is needed to ensure prolonged stability.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.