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163 results for “temporal variability”
Temporal variability in host availability alters the outcome of competition between two parasitoid species
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Temporal variability in effective size (Ne) identifies potential sources of discrepancies between mark recapture and close kin mark recapture estimates of population abundance
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Data from: Spatio-temporal dynamics in syntopy are driven by variability in rangeland conditions
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Ziphius cavirostris presence relative to vertical and temporal variability of oceanographic conditions in the southern california bight
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Temporal variability and flooding influence the ecological niche of <em>Biomphalaria</em> intermediate hosts for <em>Schistosoma mansoni</em> in rural Uganda
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Temporal correlations among demographic parameters are ubiquitous but highly variable across species
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A matter of scale: Identifying the best spatial and temporal scale of environmental variables to model the distribution of a small cetacean
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Code from: The relative influence of climate extremes and species richness on the temporal variability of bird communities
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Temporal instability of lake charr phenotypes: Synchronicity of growth rates and morphology linked to environmental variables?
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Data from: Submerged macrophytes affect the temporal variability of aquatic ecosystems
<p>1. Submerged macrophytes are important foundation species that can strongly influence the structure and functioning of aquatic ecosystems, but only little is known about the temporal variation and the timescales of these effects (i.e. from hourly, daily, to monthly).</p> <p>2. Here, we conducted an outdoor experiment in replicated mesocosms (1000 L) where we manipulated the presence and absence of macrophytes to investigate the temporal variability of their ecosystem effects. We measured several parameters (chlorophyll-a, phycocyanin, dissolved organic matter [DOM], and oxygen) with high-resolution sensors (15 min intervals) over several months (94 days from spring to fall), and modelled metabolic rates of each replicate ecosystem in a Bayesian framework. We also implemented a simple model to explore competitive interactions between phytoplankton and macrophytes as a driver of variability in chlorophyll-a.</p> <p>3. Over the entire experiment, macrophytes had a positive effect on mean DOM concentration, a negative effect on phytoplankton biomass, and either a weak or no effect on mean metabolic rates, DOM composition, and conductivity. We also found that macrophytes increased the variance of DOC composition and metabolic rates, and, at some times of the observed period, increased the variance of phytoplankton biomass and conductivity. The observation that macrophytes decreased the mean but increased the variance of phytoplankton biomass was consistent with the model that we implemented.</p> <p>4. Our high-resolution time series embedded within a manipulative experiment reveal how a foundation species can affect ecosystem properties and processes that have characteristically different timescales of response to environmental variation. Specifically, our results show how macrophytes can affect short-term dynamics of algal biomass, while also affecting the seasonal buildup of DOM and the variance of ecosystem metabolism.</p>
Temporal variability is key to modelling the climatic niche
<p><strong>Aim</strong><i>:</i> Niche-based species distribution models (SDMs) have become a ubiquitous tool in ecology and biogeography. These models relate species occurrences with the environmental conditions found at these sites. Climatic variables are the most commonly used environmental data, and are usually included in SDMs as averages of a reference period (30-50 years). In this study we analyze the impact of including inter-annual climatic variability on the estimation of species niches and predicted distributions when assessing plant demographic response to extreme climatic episodes.</p> <p><strong>Location</strong><i>:</i> Mediterranean basin, SE Iberian Peninsula.</p> <p><strong>Methods</strong><i>:</i> We first characterized species niches with inter-annual and average climate in the same environmental space. We then compare the respective capacities of climatic suitability obtained from averaged climate-based and from inter-annual variability-based niches to explain population demographic responses to extreme drought. Furthermore, we assessed the relative increase in niche size when including climatic variability for a set of Mediterranean species exhibiting a wide range of distribution areas.</p> <p><strong>Results</strong><i>:</i> We found that climatic suitability obtained from inter-annual variability-based niches showed higher explanatory capacity than average climate-based suitability, especially for populations living in climatically marginal conditions, although both niches quantifications significantly explained species demographic responses. In addition, species with restricted distribution ranges increased relatively more their niche space when considering climatic variability, probably because in widely distributed species spatial variability compensates for temporal variability.</p> <p><strong>Main Conclusions</strong><i>:</i> The common use of climatic averages when characterizing species niches could lead to underestimations of species distribution and misunderstanding of demographic behavior, with implications for conservation plans derived from SDMs, e.g. overestimations of species extinction risk under climate change, or underestimations of alien species invasion' risk. We highlight that including climatic variability in niche modelling can be particularly important when dealing with species with restricted distribution and populations at the margin of their species niche.</p>
Data from: Temporally variable multivariate sexual selection on sexually dimorphic traits in a wild insect population
A widely held view is that the strength and form of natural selection varies in time and space in response to varying ecological forces; however, adequate quantitative evaluations of this are relatively scarce. In this study, we measured the strength and form of sexual selection acting on a suite of male morphological traits in a wild ambush bug (Phymata americana) population at 10 sampling dates over 2 years. We tested the prediction that the strength and direction of sexual selection would be associated with one or more important ecological variables. We found that patterns of multivariate selection varied considerably over time, and even within a season. Yet, for this population, a sexually dimorphic color pattern trait was consistently a target of directional selection. The strength of sexual selection on this trait was related to both sex ratio and density, which is consistent with the idea that ecological factors can play an important role in generating patterns of sexual selection. We also demonstrate that the median strength of linear selection obtained from replicated cross‐sectional methods was qualitatively similar to the estimates obtained from longitudinal methods, providing multiple lines of evidence that the evolution of sexual color dimorphism in this species is attributable to sexual selection.
Data from: Signal architecture: temporal variability and individual consistency of multiple sexually selected signals
1. Multiple signals should be favoured when the benefit of additional signals outweigh their costs. Despite increased attention on multiple-signalling systems, few studies have focused on signal architecture to understand the potential information content of multiple signals. 2. To understand the patterns of signal plasticity and consistency over the lifetime of individuals we conducted a longitudinal study of multiple signals known to be under sexual selection in male lark buntings, Calamospiza melanocorys. 3. Within years, we found extensive among-individual variation in all four plumage ornaments we measured. Surprisingly, there were few correlations among these signals, suggesting that individuals contain a mosaic of signals. Signals were only moderately repeatable across years, which indicates some signal plasticity or age related change. In some years, the direction of change in particular signals relative to the previous year was consistent for most individuals in the population, suggesting that broad scale ecological factors affected the ornament phenotype. Different ornaments were affected by different ecological or social factors because the population-wide shift in a given signal was independent of change in other signals. 4. Our combined results suggest that different signals—including different components of the same color patch in some cases—provide diverse and independent information about the individual to signal receivers in the context of sexual selection.
Data from: Temporally-variable predation risk and fear retention in Trinidadian guppies
<p>Predation fear is a unifying theme across vertebrate taxa. Here, we explored how the frequency and duration of predation risk affects post-risk fear behaviour in Trinidadian guppies. We first exposed individuals to visual cues of potential predators for 3 days, either frequently (6×/day) or infrequently (1×/day). Each exposure lasted for either a relatively brief (5 min) or long (30 min) duration, whereas a control group consisted of no risk exposures. One day later, we quantified guppy behaviour. All background risk treatments induced a fear response toward a novel odour (i.e., neophobia), and individuals previously exposed to frequent bouts of brief risk showed elevated baseline fear. Although neophobic responses were initially similar across risk treatments (1 day later), retention of this response differed. After 8 days, only individuals previously exposed to brief bouts of risk (both frequent and infrequent) maintained neophobic responses, whereas their initially higher level of baseline fear remained elevated but was no longer significantly different from the control. These results increase our understanding of temporal factors that affect the intensity and retention of fear that persists following risk exposure, which may have applications across vertebrates in relation to problems with fearful phenotypes.</p>
Data from: Spatial and temporal patterns of larval dispersal in a coral-reef fish metapopulation: evidence of variable reproductive success
Many marine organisms can be transported hundreds of kilometers during their pelagic larval stage, yet little is known about spatial and temporal patterns of larval dispersal. Although traditional population-genetic tools can be applied to infer movement of larvae on an evolutionary time scale, large effective population sizes and high rates of gene-flow present serious challenges to documenting dispersal patterns over shorter, ecologically-relevant, time scales. Here, we address these challenges by combining direct parentage analysis and indirect genetic analyses over a four-year period to document spatial and temporal patterns of larval dispersal in a common coral-reef fish: the bicolor damselfish (Stegastes partitus). At four island locations surrounding Exuma Sound, Bahamas, including a long-established marine reserve, we collected 3,278 individuals and genotyped them at 10 microsatellite loci. Using Bayesian parentage analysis, we identified eight parent-offspring pairs, thereby directly documenting dispersal distances ranging from 0 km (i.e., self-recruitment) to 129 km (i.e., larval connectivity). Despite documenting substantial dispersal and gene-flow between islands, we observed more self-recruitment events than expected if the larvae were drawn from a common, well-mixed pool (i.e., a completely open population). Additionally, we detected both spatial and temporal variation in signatures of sweepstakes and Wahlund effects. The high variance in reproductive success (i.e., "sweepstakes") we observed may be influenced by seasonal mesoscale gyres present in the Exuma Sound, which play a prominent role in shaping local oceanographic patterns. This study documents the complex nature of larval dispersal in a coral-reef fish, and highlights the importance of sampling multiple cohorts and coupling both direct and indirect genetic methods in order disentangle patterns of dispersal, gene-flow, and variable reproductive success.
Data from: Exploring the temporal variability of a food web using long-term biomonitoring data
Ecological communities are constantly being reshaped in the face of environmental change and anthropogenic pressures. Yet, how food webs change over time remains poorly understood. Food web science is characterized by a trade-off between complexity (in terms of the number of species and feeding links) and dynamics. Topological analysis can use complex, highly resolved empirical food web models to explore the architecture of feeding interactions but is limited to a static view, whereas ecosystem models can be dynamic but use highly aggregated food webs. Here, we explore the temporal dynamics of a highly resolved empirical food web over a time period of 18 years, using the German Bight fish and benthic epifauna community as our case study. We relied on long-term monitoring ecosystem surveys (from 1998 to 2015) to build a metaweb, i.e. the meta food web containing all species recorded over the time span of our study. We then combined time series of species abundances with topological network analysis to construct annual food web snapshots. We developed a new approach, "node-weighted" food web metrics by including species abundances to represent the temporal dynamics of food web structure, focusing on generality and vulnerability. Our results suggest that structural food web properties change through time; however, binary food web structural properties may not be as temporally variable as the underlying changes in species composition. Further, the node-weighted metrics enabled us to detect that food web structure was influenced by changes in species composition during the first half of the time series and more strongly by changes in species dominance during the second half. Our results demonstrate how ecosystem surveys can be used to monitor temporal changes in food web structure, which are important ecosystem indicators for building marine management and conservation plans.
Variability and Temporality of Lithic Production in Epipaleolithic to Early Neolithic Occupations at Cova del Vidre (Catalonia, Spain)
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Dataset for Can we predict kick force based solely on spatial-temporal variables? Applying long short-term memory model for predicting force values of turning and side kick of taekwon-do athletes
<p>This data set is created for a purpose of publication "<span>Can we predict kick force based solely on spatial-temporal variables? Applying long short-term memory model for predicting force values of turning and side kick of taekwon-do athletes". It contains of dataset of kicks and lstm models for predictions a force of kicks upon IMU data. Detailed description of file names are in readme file. Folders are divided into specific kicks - turning or side kick in sport or traditional versions.</span></p>
Code and Data for "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.
<p>This repository contains the code and data for the study of "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.</p> <p>Specifically, this repository contains the following items: </p> <p>(1) The codes needed for assessing the representation and prediction skills of Random Forest (RF), Convolutional Neural Network (CNN) and Spatial Transformer Networks (STN) models. </p> <p>(2) Original and normalized data to run these codes.</p> <p>(3) Code here is built on early work from our laboratory (Jaderberg et al., 2015; Guan et al., 2022; Zhang et al., 2023), though great modifications have been made tailored to our scientific question.</p> <p>[1] Jaderberg, M., Simonyan, K., Zisserman, A., et al. (2015). Spatial transformer networks. Advances in neural information processing systems, 28.</p> <p>[2] Guan, W., Chen, R., Zhang, H., Yang, Y., & Wei, H. (2022). Seasonal surface eddy mixing in the Kuroshio Extension: Estimation and machine learning prediction. Journal of Geophysical Research: Oceans, 127 (3), e2021JC017967.</p> <div>[3] Zhang, G., Chen, R., Li, X., Li, L., Wei, H., & Guan, W. (2023). Temporal variability of global surface eddy diffusivities: Estimates and machine learning prediction. Journal of Physical Oceanography, 53 (7), 1711–1730.</div>
Dittmann S., Kiessling T., Knickmeier K., Schönberg J., Brennecke D., Hinzmann M., Knoblauch D., Thiel M., 2024. Temporal variability of litter pollution of rivers in Germany - a long-term assessment by schoolchildren as citizen scientists.
<p>Research data to the manuscript "Temporal variability of litter pollution of rivers in Germany - a long-term assessment by schoolchildren as citizen scientists" by Dittmann et al. 2024.</p> <p> </p> <div> </div>
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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.