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89 results for “ecological time”
FIGURE 4 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts
FIGURE 4 Spatiotemporal variation in regional HeV spillover events during the flying fox surveillance period (2012–2014) and its relationship with HeV AUC. (a) Maps display the annual distributions of spillovers (coloured by year) in relation to the nine analysed roosts. (b) Modelled relationships between AUC and spillover counts are shown with fitted values and 95% confidence intervals from GAMs for 50, 100, 200, 300, 400 and 500 km buffers of each roost. Raw data are overlaid and scaled by the inverse of the sampling variance for AUC
Predicting daily activity time through ecological niche modeling and microclimatic data
<p><span>1. </span><span>Climate temporality is a phenomenon that affects species' activity and distribution patterns across spatial and temporal scales. Despite the global availability of microclimatic data, their use to predict activity patterns and distributions remains scarce, particularly at fine temporal scales (e.g., < month). Predicting activity patterns based on climatic data may allow us to foresee some of the consequences of climate change, particularly for ectothermic vertebrates. </span></p> <p><span>2. </span><span>The Gila monster exhibits marked daily and seasonal activity patterns linked to physiology and reproduction. Here we evaluate if ecological niche models fitted using microclimate data can predict temporal activity patterns using the Gila monster (<em>Heloderma suspectum</em>) as a study system. Further, we identified if the activity patterns are related to physiological constraints.</span></p> <p><span>3. </span><span>We used dated occurrences from museum specimens and human observations to generate and test ecological niche models using minimum-volume ellipsoids. We generated hourly microclimatic data for each occurrence site for ten years using the NicheMapR package. For ecological niche modeling, we compared the traditional seasonal approach versus a daily activity pattern strategy for model construction. We tested both using the omission rate of independent observations (citizen science data). Finally, we tested if unimodal and bimodal activity patterns for each season could be recreated through ecological niche modeling and if these patterns followed known physiological constraints.</span></p> <p><span>4. </span><span>The unimodal and bimodal activity patterns previously reported directly from tracking individuals across the year were recovered by using niche modeling and microclimate across the species' geographical range. We found that upper thermal tolerances can explain the daily activity patterns of this species. </span></p> <p><span>5. </span><span>We conclude that ecological niche models trained with microclimatic data can be used to predict activity patterns at fine temporal scales, particularly on ectotherm species of arid zones coping with rapid climate modifications. Further, the use of fine temporal scale variables can lead to a better niche delimitation, enhancing the results of any research objective that uses correlative models.</span></p>
Data for: Simulated climate change causes asymmetric responses in insect life history timing potentially disrupting a classic ecological speciation system
<p>Climate change may alter phenology within populations with cascading consequences for community interactions and ongoing evolutionary processes. Here, we measured the response to climate change in two sympatric, recently diverged (~170 years) populations of <em>Rhagoletis</em> <em>pomonella</em> flies specialized on different host fruits (hawthorn and apple) and their parasitoid wasp communities. We tested whether warmer temperatures affect dormancy regulation and its consequences for synchrony across trophic levels and temporal isolation between divergent populations. Under warmer temperatures, both fly populations developed earlier. However, warming significantly increased the proportion of maladaptive pre-winter development in apple, but not hawthorn, flies. Parasitoid phenology was less affected, potentially generating ecological asynchrony. Observed shifts in fly phenology under warming may decrease temporal isolation, potentially limiting ongoing divergence. Our findings of complex sensitivity of life-history timing to changing temperatures predict that coming decades may see multifaceted ecological and evolutionary changes in temporal specialist communities.</p>
Data from: A user-friendly guide to using distance measures to compare time series in ecology
<p>Time series are a critical component of ecological analysis, used to track changes in biotic and abiotic variables. Information can be extracted from the properties of time series for tasks such as classification (e.g. assigning species to individual bird calls); clustering (e.g. clustering similar responses in population dynamics to abrupt changes in the environment or management interventions); prediction (e.g. accuracy of model predictions to original time series data); and anomaly detection (e.g. detecting possible catastrophic events from population time series). These common tasks in ecological research rely on the notion of (dis-) similarity, which can be determined using distance measures. A plethora of distance measures have been described, predominantly in the computer and information sciences, but many have not been introduced to ecologists. Furthermore, little is known about how to select appropriate distance measures for time-series-related tasks. Therefore, many potential applications remain unexplored.</p> <p>Here we describe 16 properties of distance measures that are likely to be of importance to a variety of ecological questions involving time series. We then test 42 distance measures for each property and use the results to develop an objective method to select appropriate distance measures for any task and ecological dataset. We demonstrate our selection method by applying it to a set of real-world data on breeding bird populations in the UK and discuss other potential applications for distance measures, along with associated technical issues common in ecology.</p> <p>Our real-world population trends exhibit a common challenge for time series comparisons: a high level of stochasticity. We demonstrate two different ways of overcoming this challenge, first by selecting distance measures with properties that make them well-suited to comparing noisy time series, and second by applying a smoothing algorithm before selecting appropriate distance measures. In both cases, the distance measures chosen through our selection method are not only fit-for-purpose but are consistent in their rankings of the population trends.</p> <p>The results of our study should lead to an improved understanding of, and greater scope for, the use of distance measures for comparing ecological time series, and help us answer new ecological questions.</p>
Data for: Ecological pathways connecting drought to stream invertebrate community shifts across space and time
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Data from: Ecological and anthropogenic drivers of waterfowl productivity are synchronous across species, space, and time
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Data from: Ecological trait divergence over evolutionary time underlies the origin and maintenance of tropical spider diversity
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Data from: A user-friendly guide to using distance measures to compare time series in ecology
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Data for: Simulated climate change causes asymmetric responses in insect life history timing potentially disrupting a classic ecological speciation system
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Predicting daily activity time through ecological niche modeling and microclimatic data
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Time-varying flow-ecology relationships for an endangered fish population: Longfin Smelt in the San Francisco Estuary
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Ecological network structure in response to community assembly processes over evolutionary time
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Data from: A rapidly evolved shift in life history timing during ecological speciation is driven by the transition between developmental phases
<p>For insect species in temperate environments, seasonal timing is often governed by the regulation of diapause, a complex developmental program that allows insects to weather unfavorable conditions and synchronize their lifecycles with available resources. Diapause development consists of a series of distinct phases including initiation, maintenance, termination, and post-diapause development. The evolution of insect seasonal timing depends in part on how these phases of diapause development and post-diapause development interact to affect variation in phenology. Here, we dissect the physiological basis of a recently evolved phenological shift in Rhagoletis pomonella (Diptera: Tephritidae), a model system for ecological divergence. A recently derived population of R. pomonella shifted from specializing on native hawthorn fruit to earlier fruiting introduced apples, resulting in a 3-4 week shift in adult emergence timing. We tracked metabolic rates of individual flies across post-winter development to test which phases of development may act either independently or in combination to contribute to this recently evolved divergence in timing. Apple and hawthorn flies differed in a number of facets of their post-winter developmental trajectories. However, divergent adaptation in adult emergence phenology in these flies was due almost entirely to the end of the pupal diapause maintenance phase, with post-diapause development having a very small effect. The relatively simple underpinnings of variation in adult emergence phenology suggest that further adaptation to seasonal change in these flies for this trait might be largely due to the timing of diapause termination unhindered by strong covariance among different components of post-diapause development.</p>
In the right place, at the right time: the integration of bacteria into the Plankton Ecology Group model
<p><strong><span>Background</span></strong></p> <p><span>Planktonic microbial communities have critical impacts on the pelagic food web and water quality status in freshwater ecosystems, yet no general model of bacterial community assembly linked to higher trophic levels and hydrodynamics has been assessed. In this study, we utilized a two-year survey of planktonic communities from bacteria to zooplankton on three freshwater reservoirs to investigate their spatiotemporal dynamics.</span></p> <p><strong><span>Result</span></strong></p> <p><span>We observed the site-specific presence and microdiversification of bacteria in lacustrine and riverine environments, as well as in deep hypolimnia. Moreover, we determined recurrent bacterial seasonal patterns driven by both biotic and abiotic conditions, which could be integrated into the well-known Phytoplankton Ecology Group (PEG) model describing primarily the seasonalities of larger plankton groups. Importantly, bacteria with different ecological potentials showed finely coordinated successions affiliated with four seasonal phases, including the spring bloom dominated by fast-growing opportunists, the clear-water phase associated with oligotrophic ultramicrobacteria, the summer phase characterized by phytoplankton bloom-associated bacteria, and the fall/winter phase driven by decay-specialists. </span><span> </span></p> <p><strong><span>Conclusion</span></strong></p> <p><span>Our findings elucidate the principles driving the spatiotemporal microbial community distribution in freshwater ecosystems. We suggest an extension to the original PEG model by integrating recurrent bacterial seasonal trends.</span></p>
Time series methods for cyclical ecological data
<p>Biodiversity monitoring has entered an era of `big data', exemplified by a near-continuous collection of sounds, images, chemical, and other signals from organisms in diverse ecosystems. Such data streams have the potential to help identify new threats, assess the effectiveness of conservation interventions, as well as generate new ecological insights. However, appropriate analytical methods are often still missing, particularly with respect to characterizing cyclical temporal patterns. Here, we present a framework for characterizing and analyzing ecological responses that represent nonstationary, complex temporal patterns and demonstrate the value of using Fourier transforms to decorrelate continuous data points. In our example, we use a framework based on three approaches (spectral analysis, magnitude squared coherence, and principal component analysis) to characterize differences in tropical forest soundscapes within and across sites and seasons in Gabon. By reconstructing the underlying, cyclic behavior of the soundscape for each site, we show how one can identify circadian patterns in acoustic activity. Soundscapes in the dry season had a complex diel cycle, requiring multiple harmonics to represent daily variation, whilst in the wet season there was less variance attributable to the daily cyclic patterns. Our framework can be applied to most continuous, or near-continuous ecological data collected at a fine temporal resolution, allowing ecologists to explore patterns of temporal autocorrelation at multiple levels for biologically meaningful trends. Such methods will become indispensable as biological big data are used to understand the impact of anthropogenic pressures on biodiversity and to inform efforts to mitigate them.</p>
Data from: Among-species variation in six decades of changing migration timings explained through ecology, life-history and abundance
<p>Species utilising seasonal environments must now alter timings of key life-history events in response to large-scale climatic changes, thereby maintaining trophic synchronies. Yet substantial among-species variation in cross-decadal phenological changes is observed. Transitioning from basic description of such variation towards prediction of future phenological responses now requires standardised studies that rigorously quantify and explain variation in the direction, magnitude and form of changing timings across diverse species in relation to key ecological and life-history variables. Accordingly, we fitted multi-quantile regressions to 59 years of high-quality multi-species data on spring and autumn bird migration timings through northern Scotland. We demonstrate substantial variation in cross-decadal changes in timings among 72 species, and quantify the degree to which variation can be explained through differences in species ecology, life-history and population trajectories. Consistent with predictions, species with seasonal diets, narrower breeding habitat breadths, shorter generation lengths and capability to produce multiple offspring broods per year advanced their migration timing in one or both seasons. In contrast, species with less seasonal diets, and that produce single annual offspring broods, showed no change. Meanwhile, contrary to prediction, long-distance migrants advanced their migration timings as much as short-distance migrants. Changes in migration timing also varied with changes in local migratory abundance, such that species with increasing seasonal abundance apparently altered their migration timing, whilst species with decreasing abundance did not. These patterns concur with expectation if changing migration timing is adaptive. However, we demonstrate that similar patterns can be generated through numerical sampling processes given changing abundances, implying that apparent phenology-abundance relationships should be carefully validated and interpreted. Overall, our results show that migrant bird species with differing ecologies and life-histories have shown systematically differing phenological changes over six decades contextualised by large-scale environmental changes, potentially facilitating future predictions and altering temporal dynamics of seasonal species co-occurrences.</p>
Seasonal footprints on ecological time series and jumps in dynamic states of protein configurations from a nonlinear forecasting method characterization (DataSet)
<p>Data from the article: <br>"Seasonal footprints on ecological time series and jumps in dynamic states of protein configurations from a nonlinear forecasting method characterization", L. Reyes, K. Campos, G. D. Avendaño, L. González-Paz, A. Vivas, Y. J. Alvarado, and S. Flores.</p> <p>Data to be used with some implementation of the forecasting method of reference:<br>Sugihara G. and May R. M., Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series, <em>Nature</em> <strong>344</strong>, 734–741 (1990).</p>
Supporting data for: Physical controls and ecological implications of the timing of the spring phytoplankton bloom on the Newfoundland and Labrador shelf
<p>The Newfoundland and Labrador (NL) shelf and the Grand Banks of Newfoundland have been known as iconic fishing areas for centuries. In such areas with seasonal sea ice coverage, the timing of the spring bloom has been linked to sea ice melting, which stratifies the water column and promotes favorable conditions for phytoplankton to grow and accumulate. With sea ice gradually disappearing, we revisited the physical drivers controlling the initiation of the spring bloom in the region. We found that the timing of the phytoplankton bloom on the Grand Banks corresponds to the timing of ocean re-stratification following winter mixing. We also found that large-scale climate indicators are good proxies for the timing of the bloom and the abundance of<em> Calanus finmarchicus</em>, a key zooplankton species for the ecosystem. By revealing links between physical and biological processes, this work paves the way for an improved ecosystem approach to fisheries management.</p>
Data from: Among-species variation in six decades of changing migration timings explained through ecology, life-history and abundance
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Data from: A rapidly evolved shift in life history timing during ecological speciation is driven by the transition between developmental phases
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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.