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164 results for “Top-down”
Data and code from Artificial light at night increases top-down pressure on caterpillars: experimental evidence from a light-naive forest - 2021-2022
This dataset has been prepared in support of a paper to be published in Proceedings of the Royal Society B: Biological Sciences. It includes both data files and R scripts used for the analysis in this publication: Deitch, J.F. and S.A. Kaiser. 2023. Artificial light at night increases top-down pressure on caterpillars: experimental evidence from a light-naive forest. Proceedings of the Royal Society B: Biological Sciences. (https://doi.org/10.1098/rspb.2023.0153) Artificial light at night (ALAN) is a globally widespread and expanding form of anthropogenic change that impacts arthropod biodiversity. ALAN alters interspecific interactions between arthropods, including predation and parasitism. Despite their ecological importance as prey and hosts, the impact of ALAN on larval arthropod stages, such as caterpillars, is poorly understood. We examined the hypothesis that ALAN increases top-down pressure on caterpillars from arthropod predators and parasitoids. We experimentally illuminated study plots with moderate levels (10-15 lux) of LED lighting at light-naive Hubbard Brook Experimental Forest, New Hampshire. We measured and compared between experimental and control plots: 1) predation on clay caterpillars and 2) abundance of arthropod predators and parasitoids. We found that predation rates on clay caterpillars and abundance of arthropod predators and parasitoids were significantly higher on ALAN treatment plots relative to control plots. These results suggest that moderate levels of ALAN increases top-down pressure on caterpillars. We did not test mechanisms, but sampling data indicates that increased abundance of predators near lights may play a role. This study highlights the importance of examining the effects of ALAN on both adult and larval life stages and suggests potential consequences of ALAN on arthropod populations and communities. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubba
Occipital Nerve Stimulation Selectively Modulates Top-down Inhibitory Control
<p><strong>Objective:</strong> Here we investigate the effect of occipital nerve stimulation using low-gamma range alternating current on goal-directed and stimulus-driven attention and inhibitory training and performance. We sought to determine if stimulation modulated performance over a two-day period. <strong>Methods</strong>: We studied this effect in 47 participants recruited in one of two experiments. The goal-directed task used the stop-signal reaction time task (SSRT) during stimulation and stop-change reaction time (SCRT) in a 24-hour follow-up. Stop-signal reaction time (SSRT) and Stop-change reaction time (SCRT) were recorded in seconds, calculated using a non-integration method. SSRT/SCRT and accuracy were used as outcome measures. The stimulus-driven task used a sustained-attention reaction time task (SART), and reaction time and inhibition (NoGo) accuracy were used as outcome measures. <strong>Results</strong>: Compared to the control group, the stimulation group had improved SCRT 24 hours after combined stimulation and training. No difference in accuracy on either day were present. No difference between groups arose in the SART during training or testing. </p>
Data from "Evaluating top-down, bottom-up, and environmental drivers of pelagic food web dynamics along an estuarine gradient"
Synthesized fish, benthic invertebrate, and water quality dataset used for analysis in: Rogers, T., S. Bashevkin, C. Burdi, D. Colombano, P. Dudley, B. Mahardja, L. Mitchell, S. Perry, and P. Saffarinia. 2022. Evaluating top-down, bottom-up, and environmental drivers of pelagic food web dynamics along an estuarine gradient. preprint, EcoEvoRxiv. https://doi.org/10.32942/X2MK5Z
MCR LTER: Coral Reef: Priority effects in coral-macroalgae interactions can drive alternate community paths in the absence of top-down control, data for Adam 2022 Ecology
These data were generated in support of the manuscript: Adam TC, Holbrook SJ, Burkepile DE, Speare KE, Brooks AJ, Ladd MC, Shantz AA, Thurber RLV, and Schmitt RJ, Ecology The outcomes of species interactions can vary greatly in time and space with the outcomes of some interactions determined by priority effects. On coral reefs, benthic algae rapidly colonize the disturbed substrate. In the absence of top-down control from herbivorous fishes, these algae can inhibit the recruitment of reef-building corals, leading to a persistent phase shift to a macroalgae-dominated state. Yet, corals may also inhibit colonization by macroalgae, and thus the effects of herbivores on algal communities may be strongest following disturbances that reduce coral cover. Here, we report results from experiments conducted on the fore reef of Moorea, French Polynesia, where we: 1) tested the ability of macroalgae to invade coral-dominated and coral-depauperate communities under different levels of herbivory, 2) explored the ability of juvenile corals (Pocillopora spp.) to suppress macroalgae, and 3) quantified the direct and indirect effects of fish herbivores and corallivores on juvenile corals. We found that macroalgae proliferated when herbivory was low but only in recently disturbed communities where coral cover was also low. When coral cover was < 10%, macroalgae increased 20-fold within one year under reduced herbivory conditions relative to high herbivory controls. Yet, when coral cover was high (50%), macroalgae were suppressed irrespective of the level of herbivory despite ample space for algal colonization. Once established in communities with low herbivory and low coral cover, macroalgae suppressed recruitment of coral larvae, reducing the capacity for coral replenishment. However, when we experimentally established small juvenile corals (2 cm diameter) following a disturbance, juvenile corals inhibited macroalgae from invading local neighborhoods, even in the absence of herbivore
Datasets to article "Selection history alters attentional filter settings persistently and beyond top-down control"
<p>Single-Subject Behavioral and ERP mean amplitude data for Experiments 1 to 3.</p>
Bottom-up meets top-down: Leaf litter inputs influence predator-prey interactions in wetlands, 2011.
While the common conceptual role of resource subsidies is one of bottom-up nutrient and energy supply, inputs can also alter the structural complexity of environments. This can further impact resource flow by providing refuge for prey and decreasing predation rates. However, the direct influence of different organic subsidies on predator–prey dynamics is rarely examined. In forested wetlands, leaf litter inputs are a dominant energy and nutrient resource and they can also increase benthic surface cover and decrease water clarity, which may provide refugia for prey and subsequently reduce predation rates. In outdoor mesocosms, we investigated how inputs of leaf litter that alter benthic surface cover and water clarity influence the mortality and growth of gray treefrog tadpoles (Hyla versicolor) in the presence of free-swimming adult newts (Notophthalmus viridiscens), which are visual predators. To manipulate surface cover, we added either oak (Quercus spp.) or red pine (Pinus resinosa) litter and crossed these treatments with three levels of red maple (Acer rubrum) litter leachate to manipulate water clarity. In contrast to our predictions, benthic surface cover had no effect on tadpole survival while darkening the water caused lower survival. In addition, individual tadpole mass was lowest in the high maple leachate treatments, suggesting an interaction between bottom-up effects of leaf litter and topdown effects of predation risk that altered mortality and growth of tadpoles. Our results indicate that realistic changes in forest tree composition, which cause concomitant changes in litter inputs to wetlands, can substantially alter community interactions.
Top-down and bottom-up vegetal and animal product metabolic profiles for 29 european countries (EU27 + United Kingdom + Norway).
<p>This repository contains the data needed to reproduce the results in:</p> <p>Cadillo-Benalcazar, J.; Renner, A. Giampietro, M. (2020). A multiscale integrated analysis of the factors characterizing the sustainability of food systems in Europe. Journal of Environmental Management. Volume 271, 1 October 2020, 110944. <a href="https://doi.org/10.1016/j.jenvman.2020.110944">https://doi.org/10.1016/j.jenvman.2020.110944</a></p> <p>Renner, A.; Cadillo-Benalcazar, J.; Benini, L., Giampietro, M. (2020). Environmental pressure of the European agricultural system: Anticipating the biophysical consequences of internalization. Ecosystem services. Special Issue: Agro-futures. Volume 46, December 2020, 101195. <a href="https://doi.org/10.1016/j.ecoser.2020.101195">https://doi.org/10.1016/j.ecoser.2020.101195</a></p>
Data from: warming and top-down control of stage-structured prey: linking theory to patterns in natural systems
<p>Warming has broad and often nonlinear impacts on organismal physiology and traits, allowing it to impact species interactions like predation through a variety of pathways that may be difficult to predict. Predictions are commonly based on short-term experiments and models, and these studies often yield conflicting results depending on the environmental context, spatiotemporal scale, and the predator and prey species considered. Thus, the accuracy of predicted changes in interaction strength, and their importance to the broader ecosystems they take place in, remain unclear. Here, we attempted to link one such set of predictions generated using theory, modeling, and controlled experiments to patterns in the natural abundance of prey across a broad thermal gradient. To do so, we first predicted how warming will impact a stage-structured predator-prey interaction in riverine rock pools between Pantala spp. dragonfly nymph predators and Aedes atropalpus mosquito larval prey. We then described temperature variation across a set of hundreds of riverine rock pools (n = 775) and leveraged this natural gradient to look for evidence for or against our model's predictions. Our model's predictions suggested that warming should weaken predator control of mosquito larval prey by accelerating their development and shrinking the window of time that aquatic dragonfly nymphs could consume them in. This was consistent with data collected in rock pool ecosystems, where the negative effects of dragonfly nymph predators on mosquito larval abundance were weaker in warmer pools. Our findings provide additional evidence to substantiate our model-derived predictions, while emphasizing the importance of assessing similar predictions using natural gradients of temperature whenever possible.</p>
Behavior-relevant top-down cross-modal predictions in mouse neocortex
<p>Simultaneously recorded S1 and PPC neuronal population activity from awake mice during a texture discrimination task. Data acquired with two-photon calcium imaging.</p>
Training and test data, plus saved models for the upcoming paper `Top-down perceptual inference shaping the activity of early visual cortex'
<p>Each .pkl file contains a training or test dataset in the form of a Python dictionary (generated with Python 3.8.5) with the following fields:</p><ul><li>'train_images': 640,000 float32 images used for model training. These are 40px images that contain 1600 pixel intensities each.</li><li>'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li><li>'test_images': 64,000 float32 images used for model testing. These are 40px images that contain 1600 pixel intensities each.</li><li>'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li></ul><p>The .zip file contains a saved model snapshot and various intermediate evaluative data. Details on these are coming soon.</p>
Data from: Constraining biospheric carbon dioxide fluxes by combined top-down and bottom-up approaches
<p> </p> <p> </p> <p><span>Acknowledgements.</span><span> </span><span>We would like to thank Martin Jung, Jakob A. Nelson, Sophia Walther, and the FLUXCOM team for their structural</span><br><span>support, feedback and discussion. The Authors would like to thank the producers of the Inversion data included in this study: Ingrid Luijkx</span><br><span>and Wouter Peters (CTE), Frederic Chevallier and the Copernicus Atmosphere Monitoring Service (CAMS), Christian Roedenbeck (Jena</span><br><span>Carboscope sEXTocNEET), Yosuke Niwa (NISMON-CO2), and Liang Feng and Paul Palmer (UoE). This research was funded by the</span><br><span>European Research Council (ERC) Synergy Grant ’Understanding and modeling the Earth System with Machine Learning (USMILE)’</span><br><span>under the Horizon 2020 research and innovation programme (Grant Agreement No. 855187)</span></p> <p><br><span>This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux</span><br><span>(U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02</span>-<br><span>04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada (supported by CFCAS,</span><br><span>NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowl-</span><br><span>edge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck</span><br><span>Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval and Environment Canada and US Depart-</span><br><span>ment of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory,</span><br><span>Microsoft Research eScience, Oak Ridge National Laboratory, University of California - Berkeley, University of Virginia</span></p>
DeepLabCut network trained to track mouse body parts during open field locomotion (top-down view)
<p>DeepLabCut (https://github.com/DeepLabCut/) (Mathis et al., 2018; Nath et al., 2019) was used for tracking body parts of mice in an open field arena or in the rotarod. DeepLabCut 2.1.8.2 (local version on Windows with CPU, using the GUI) and 2.1.10.2 (google colab to train the network) were used using default parameters and the pretrained resnet50 network with imgaug augmentation. Frames were extracted with the k-means method and outlier frames with the jump method. <em>Open field: </em>20 images from 19 videos (10 or 30 fps) were extracted for a total of 380 labeled pictures. 8 body parts (snout, both ears, body center, both side laterals, tail base and tail end) and the 4 corners of the field arena were manually labeled and linked to each other using skeletons. A neural network was trained using these images for 170K iterations. 20 outlier frames were extracted from each video and relabeled. An additional 20 images from 19 videos with different recording conditions were labeled. The network was then refined for 210K iterations (from scratch), yielding a train error of 3.33 pixels and a test error of 8.83 pixels (with a likelihood p-cutoff of 0.6). This process was repeated a second time (using an additional 20 images from 15 new videos) to improve the pixel error; to a final 400 K iterations (train error: 2.65, test error: 3.71). 67 videos from 5 different experiments were analyzed on the final network.<em> </em></p> <p><em>Used to analyze videos for a publication (Labouesse et al., Nature Communications 2023)</em></p>
Visual stimuli used in fMRI study on top-down feedback across cortical depths
<p>These videos contain samples of visual stimuli used in an fMRI study on top-down feedback across cortical depths in human early visual cortex [in preparation]. There is one video sample for each of the three experimental conditions in the main experiment: ‘Pac-Man dynamic’, ‘Pac-Man static’, and ‘control dynamic’. In addition, there are videos of stimuli used in a control experiment in which the shape of the stimulus (‘Pac-Man’ or square) and the background (texture or uniform) was varied. Please note that these videos are short sample segments from the experiment, and that in the actual experiment the rest blocks surrounding the stimulus presentations were much longer. The stimulus design of the main experiment is adapted from Akin et al. (2014).</p> <p>Akin, B., Ozdem, C., Eroglu, S., Keskin, D. T., Fang, F., Doerschner, K., Kersten, D., Boyaci, H. (2014). Attention modulates neuronal correlates of interhemispheric integration and global motion perception. Journal of Vision, 14(12). https://doi.org/10.1167/14.12.30</p>
Fig. 5 in Top-Down Control Of Phytoplankton By Zooplankton In Tropical Reservoirs In Singapore?
Fig. 5. The correlations of cladocerans with phytoplankton genera with high loadings on PC3; i.e. Anabaena, Dictyosphaerium, Melosira and "other cyanobacteria" (see Table 3). Cladoceran counts were expressed in number per m3 while phytoplankton counts were expressed as number per ml3.
Fig. 4 in Top-Down Control Of Phytoplankton By Zooplankton In Tropical Reservoirs In Singapore?
Fig. 4. The correlations of rotifers with phytoplankton genera with high loadings on PC1 and PC2; i.e. Ankistrodesmus, Cosmarium, Melosira, Peridinium, Planktotrix sp. 1 and 2, Scenedesmus, Synedra and Trachelomonas (see Table 3). Rotifer counts were expressed in number per m3 while phytoplankton counts were expressed as number per ml3.
Fig. 3 in Top-Down Control Of Phytoplankton By Zooplankton In Tropical Reservoirs In Singapore?
Fig. 3. The correlations of cyclopoid copepods with phytoplankton genera with high loadings on PC1 and PC2; i.e. Ankistrodesmus, Cosmarium, Melosira, Peridinium, Planktotrix sp. 1 and 2, Scenedesmus, Synedra and Trachelomonas (see Table 3). Cyclopoid counts were expressed in number per m3 while phytoplankton counts were expressed as number per ml3.
Fig. 2 in Top-Down Control Of Phytoplankton By Zooplankton In Tropical Reservoirs In Singapore?
Fig. 2. The correlations of calanoid copepods with phytoplankton genera with high loadings on PC1 and PC2; i.e. Ankistrodesmus, Cosmarium, Melosira, Peridinium, Planktotrix sp. 1 and 2, Scenedesmus, Synedra and Trachelomonas (see Table 3). Calanoid counts were expressed in number per m3 while phytoplankton counts were expressed as number per ml3.
Fig. 1 in Top-Down Control Of Phytoplankton By Zooplankton In Tropical Reservoirs In Singapore?
Fig. 1. Location of reservoirs within Singapore within which zooplankton and phytoplankton samples were monitored every month between 1992 and 2006. 1. Bedok, 2. Lower Seletar, 3. Upper Seletar, 4. Lower Peirce, 5. Upper Peirce, 6. MacRitchie, 7. Kranji, 8. Pandan, 9. Jurong Lake, 10. Murai, 11. Poyan and 12. Tengeh.
Top-down modulation of shape and roughness discrimination in active touch by covert attention
<p>Due to limitations in perceptual processing, information relevant to momentary task goals is selected from the vast amount of available sensory information by top-down mechanisms (e.g. attention) that can increase perceptual performance. We investigated how covert attention affects perception of 3D objects in active touch. In our experiment, participants simultaneously explored the shape and roughness of two objects in sequence, and were told afterwards to compare the two objects with regard to one of the two features. To direct the focus of covert attention to the different features we manipulated the expectation of a shape or roughness judgment by varying the frequency of trials for each task (20%, 50%, 80%), then we measured discrimination thresholds. We found higher discrimination thresholds for both shape and roughness perception when the task was unexpected, compared to the conditions in which the task was expected (or both tasks were expected equally). Our results suggest that active touch perception is modulated by expectations about the task. This implies that despite fundamental differences, active and passive touch are affected by feature selective covert attention in a similar way.</p> <p> </p> <p>There are zip files for the main experiment and the two pilot experiments, which contain all data relative to the publication. The data of each participant is contained in a separate folder. This folder contains a *.raw file with the participant's answers for each session of the experiment and a "data" folder, which contains movement trajectories (*.trj files) for each session in separate folders.</p> <p>Variables of the main experiment are described in the file VARIABLE_CODES_MainExp.txt and the variables of the pilot experiments are described in the files VARIABLE_CODES_PilotRoughness.txt and VARIABLE_CODES_PilotShape.txt.</p>
From bottom-up to top-down control of invertebrate herbivores in a retrogressive chronosequence
<p>In the long-term absence of disturbance, ecosystems often enter a decline or retrogressive phase which leads to reductions in primary productivity, plant biomass, nutrient cycling and foliar quality. However, the consequences of ecosystem retrogression for higher trophic levels such as herbivores and predators, are less clear. Using a post-fire forested island-chronosequence across which retrogression occurs, we provide evidence that nutrient availability strongly controls invertebrate herbivore biomass when predators are few, but that there is a switch from bottom-up to top-down control when predators are common. This trophic flip in herbivore control probably arises because invertebrate predators respond to alternative energy channels from the adjacent aquatic matrix, which were independent of terrestrial plant biomass. Our results suggest that effects of nutrient limitation resulting from ecosystem retrogression on trophic cascades are modified by nutrient-independent variation in predator abundance, and this calls for a more holistic approach to trophic ecology to better understand herbivore effects on plant communities.</p>
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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.