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526 results for “management intervention”
The impacts of management interventions on the sociality of African lions (Panthera leo): implications for lion conservation
<ol> <li>African lion (<em>Panthera leo</em>) populations normally consist of several neighbouring prides and multiple adult males or groups of males that interact competitively. In large, open systems, cub defence from infanticidal males and territory defence drive group living in lions. However, in smaller (< 1,000 km<sup>2</sup>), fenced wildlife reserves, opportunities for natural immigration and emigration are limited which means that the evolutionary drivers of lion sociality may collapse.</li> <li>Here, we use lion behavioural data collected from 16 wildlife reserves across South Africa to test how management-induced ecological conditions alter lion social dynamics.</li> <li>The number of lionesses observed together was best predicted by pride size, prey biomass and biome. Lionesses were less likely to group together as pride size increased, but more likely to group together as prey biomass and habitat productivity increased. In addition, adult males were observed more frequently with prides that had young (< 12 months) cubs in reserves that had unfamiliar adult males present compared to reserves without any unfamiliar adult males.</li> </ol> <p>Our results demonstrate how intra-specific competition between lions drives their sociality, and this may break down in small, fenced wildlife reserves where lions are actively managed. Although small, fenced reserves in South Africa have made a significant contribution to increasing lion numbers on the continent, our work highlights several important ecological implications of active lion management. For wildlife managers, mimicking the outcomes of different levels of intra-specific competition is likely a critical management tool for the persistence of lions in small reserves.</p>
Sleep Management And Recovery After Traumatic Brain Injury in Kids: Pilot Intervention of Melatonin
ClinicalTrials.gov study NCT04932096. IPD Sharing: YES. Countries: 1. Publications: 3.
Self-Management Interventions for Chronic Pain Relief With Cancer Survivors
ClinicalTrials.gov study NCT03867760. IPD Sharing: YES. Countries: 1. Publications: 1.
Dataset for: Advancing projections of Crown-of-Thorns Starfish to support management interventions
<h3>Abstract</h3> <div> <p>Regular outbreaks of corallivorous Crown of Thorns Starfish (<em>Acanthaster</em> spp; CoTS) occur on the Great Barrier Reef (GBR) and are one of the leading drivers of coral mortality. Understanding the disparities between real-world observations and model predictions of CoTS densities is crucial for refining population modelling and developing effective control strategies. Using a spatially explicit ecosystem model of the Great Barrier Reef (ReefMod-GBR), we compare model predictions of CoTS densities to manta tow survey observations. We also incorporate a new zone-specific CoTS mortality rate to account for differences in predation of CoTS between fished and protected reefs. We found high congruence between predicted CoTS densities and observations: ~81% of categorical reef level CoTS densities were either the same density level or only differed by one level, however underpredictions increased as observed densities increased. The zone-specific CoTS mortality rate reduced severe underpredictions from 7.1% to 5.6%. Underpredictions are a key concern for reef managers as they indicate potential missing outbreaks where targeted culling efforts are necessary and may lead to an underestimation of the coral loss attributed to CoTS outbreaks. Reef protection status was an important driver of prediction accuracy, suggesting it plays a role in determining CoTS densities, emphasising the importance of further research on in situ CoTS mortality rates. The location of a reef inside or outside the “initiation box”, a speculative area of primary outbreaks on the GBR, was also important, with exact predictions more likely to occur outside the box. Accurately modelling initiation box dynamics is challenging owing to limitations of empirical data on CoTS outbreaks, but this highlights the need for focussed research on these dynamics to enhance overall predictive accuracy. Other spatial factors, such as region and shelf position, also contributed to the variance between observations and predictions, underscoring the importance of the spatial-temporal context of each observation. In conclusion, this study validates our CoTS population modelling efforts, showcasing a high congruence between CoTS density predictions and real-world observations. CoTS observations can help refine predictions and guide targeted control against CoTS populations and outbreaks, contributing to effective ecosystem management for long-term resilience of the GBR.</p> </div> <h3>Methods</h3> <div> <p>Data are mean CoTS density (per manta tow) observations and predictions for individual reefs and years on the Great Barrer Reef. Observations come from several sources (CCP, LTMP, FMP) and predictions come from a spatially explicit ecosystem model of the Great Barrier Reef (ReefMod-GBR). </p> </div> <h3>Subject keywords</h3> <p>Earth and related environmental sciences, adaptive management, coral reef, Great Barrier Reef, individual-based model, Marine Invertebrate, pest management, spatial simulations</p> <h3>Funding</h3> <div> <p>CoTS Control Innovation Program</p> </div> <div> <h2>README: Dataset for: Advancing projections of Crown-of-Thorns Starfish to support management interventions</h2> <p><a href="https://doi.org/10.5061/dryad.31zcrjdtq">https://doi.org/10.5061/dryad.31zcrjdtq</a></p> <p>This dataset is for a paper that compares CoTS density observations to predictions for individual reefs on the Great Barrier Reef in individual years. CoTS manta tow observations derive from the CoTS Control Program (CP), Field Management Program (FMP), and the AIMS Long Term Monitoring Program (LTMP). Where multiple observations exist from the same observation source, they are averaged at the reef-level in that year. If multiple observations exist from different sources, they are kept separate. Predictions are generated by a spatially explicit ecosystem model of the Great Barrier Reef (ReefMod-GBR).</p> <h3>Description of the data and file structure</h3> <p>Observed and predicted CoTS densities are compared to determine prediction accuracy of the ecosystem model. Both observed and predicted CoTS per tow values were categorised as follows: 0 = None (Level 1); ≤ 0.1 = No Outbreak (Level 2); 0.11 - 0.22 = Potential Outbreak (Level 3); 0.22 - 1.0 = Established Outbreak (Level 4); 1.0 - 3.0 = Severe Outbreak (Level 5); > 3.0 = Extreme Outbreak (Level 6). The level of each observation was then compared to the level of each prediction and the difference calculated to determine prediction accuracy.</p> <p>Different variables were extracted as potential predictors of CoTS prediction accuracy. As such, the dataset includes the following for each observation/prediction comparison:</p> <p>1) RM_ID = an individual ID for each of the 3806 reefs that we model in our ecosystem model.</p> <p>2) YEAR = year as an integer.</p> <p>3) LAT and LON = the latitude and longitude of the reef.</p> <p>4) GBRMPAID and GBR_NAME = identifiers for each reef from the Great Barrier Reef Marine Park Authority.</p> <p>5) REGION: 1 = North, 2 = Central, 3 = South</p> <p>6) SHELF: 1 = Inner, 2 = Middle, 3 = Outer</p> <p>7) GZ = Whether a reef is protected (i.e., in a green zone = 1) or not (i.e., in a blue zone = 0).</p> <p>8) IB = Whether a reef is inside the CoTS initiation box (1) or not (0).</p> <p>9) GEOM_CH_KM2 = the area (km2) of coral habitat for that reef</p> <p>10) OBS_SOURCE = the source of the CoTS observation (1 = LTMP, 2 = FMP, 3 = CP)</p> <p>11) OBS_n = the number of observations that went into calculating the reef-level mean CoTS per tow for that year</p> <p>12) OBS_COTS_CAT = observed CoTS density as a categorical level from GBRMPA</p> <p>13) OBS_COTS = mean CoTS per tow, and OBS_COTS_1YB = mean COTS per tow at that reef in the preceding year</p> <p>14) OBS_CC, OBS_CC_1YB = mean coral cover at that reef from that OBS_SOURCE, and OBS_CC_1YB = in the year preceding it.</p> <p>15) PRED_COTS_CAT = predicted CoTS density as a categorical level from GBRMPA</p> <p>16) PRED_COTS, PRED_COTS_1YB, PRED_COTS_2YB, PRED_COTS_3YB = predicted CoTS per tow densities at the current year, and in the one, two, and three years preceding it.</p> <p>17) PRED_CC, PRED_CC_1YB, PRED_CC_2YB, PRED_CC_3YB = predicted coral cover (%) at the current year, and in the one, two, and three years preceding it.</p> <p>18) PRED_ACRO, PRED_ACRO_1YB, PRED_ACRO_2YB, PRED_ACRO_3YB = predicted <em>Acropora</em> cover (%) at the current year, and in the one, two, and three years preceding it. <em>Acropora</em> is the preferred food of CoTS.</p> <p>19) PRED_INSTR, PRED_INSTR_CUMU = incoming strength of CoTS larvae calculated as the CoTS larval input multipled by the size of the reef area. PRED_INSTR_CUMU is the sum of this value over the three years previous to the current year.</p> <p>20) TOTAL_OBS = where data exist, the CoTS model ReefMod is forced with manta tow survey observations from the CP, FMP, and LTMP which override model predictions at individual reefs/years. This shows the total number of observations that have been used to force the CoTS predictions for this reef in all preceding years.</p> <p>21) TIME_SINCE_OBS = similar to TOTAL_OBS, but gives the number of years since an observation last forced the CoTS prediction for this reef.</p> <p>22) PRED_ERR_COTS = the difference in categorical CoTS density levels between observations and predictions.</p> <p>23) PRED_ERR_CC = observed and predicted coral cover was categorised into AIMS coral cover categories: 0 = 0%, 1 = 0 - 10%, 2 = 10 - 30%, 3 = 30 - 50%, 4 = 50 - 75%, 5 = 75 - 100%. The difference in coral cover category between observations and predictions was then compared.</p> <p>24) DIFF_CC = the difference in the % coral cover between the observations and predictions.</p> <p>25) DIFF_CC_CUMU, DIFF_CC_CUMU_N = same as DIFF_CC except the cumulative % difference over the three preceding years, and the number of observations that went into calculating the %.</p> </div>
Experimental evidence that novel land management interventions inspired by history enhance biodiversity
<p>To address biodiversity declines within semi-natural habitats, land-management must cater for diverse taxonomic groups. Integrating our understanding of the ecological requirements of priority (rare, scarce or threatened) species through 'biodiversity auditing', with that of the intensity and complexity of historical land-use, encourages novel forms of management. Experimental confirmation is needed to establish whether this enhances biodiversity conservation relative to routine management.</p> <p>Biodiversity auditing and historical land-use of dry-open terrestrial habitats in Breckland (Eastern England) both encourage management incorporating ground-disturbance and spatio-temporal variability. To test biodiversity conservation outcomes, we developed 40 4-ha management complexes over three successive winters, of which 20 were shallow-cultivated (rotovation) and 20 deep-cultivated (ploughing), stratified across 3,850-ha of closed-sward dry grassland and lowland heathland (collectively 'dry grassland'). Complexes comprised four 1-ha sub-treatments: repeat-cultivation, first-time-cultivation, one-year-old fallow and two-year-old fallow. We examined responses of vascular plants; spiders; true bugs; ground, rove and 'other' beetles; bees and wasps; ants; and true flies on treatment complexes and 21 4-ha untreated controls. Sampling gave 132,251 invertebrates from 877 species and 28,846 plant observations from 167 species.</p> <p>Resampling and rarefaction analyses showed shallow- and deep-cultivation both doubled priority species richness (pooling sub-treatments within complexes) compared to controls. Priority spider, ground beetle, other beetle, and true bug richness were greater on both treatments than controls. Responses were strongest for those priority dry-open-habitat associated invertebrates initially predicted (by biodiversity auditing) to benefit from heavy physical-disturbance.</p> <p>Assemblage composition (pooling non-priority and priority species) varied between sub-treatments for plants, ants, true bugs, spiders, ground, rove and other beetles; but only one-year-old fallowed deep-cultivation increased priority richness across multiple taxa.</p> <p>Treatments produced similar biodiversity responses across various dry grassland 'habitats' that differed in plant composition, allowing simplified management guidance.</p> <p><em>Synthesis and applications</em>. Our landscape-scale experiment confirmed the considerable biodiversity value of interventions inspired by history and informed by systematic multi-taxa analysis of ecological requirements across priority biota. Since assemblage composition varied between sub-treatments, providing heterogeneity in management will support the widest suite of species. Crucially, the intended recipients responded most strongly, suggesting biodiversity audits could successfully inform interventions within other systems.</p>
Integrating REBT and ACT : An intervention study for managing academic self-handicapping behaviour among young adults
<p>The data consists of pre and post interventions scores of 25 participants who were selected based on their ratings on Academic self -handicapping scale (Geetika & Gupta, 2020) . Thirteen participants were randomly assigned to the experimental group which was exposed to 8 hours of intervention that combined REBT and ACT. Twelve participants were randomly assigned to the control group and did not receive any intervention. The pre-intervention scores on Academic self -handicapping scale (Geetika & Gupta, 2020) were assessed at the beginning of the intervention and at two weeks since the beginning of the intervention. Sub scale scores on Behavioural and Claimed self -handicapping were recorded and overall score by adding the two were also obtained. </p>
Verification of the effects of a YouTube-based home-based (self-managed intervention) training system developed for frailty prevention―A pilot study ―
<p><em>Background and Objectives</em>: Resistance training is considered the most effective intervention for increasing older people’s muscle mass and strength. We devised a self-administered training system (squat + balance training, sukubara®) that incorporates a new low-load exercise. This study hypothesizes that introducing sukubara® affects skeletal muscle mass and physical function positively, and we first conducted a preliminary verification in healthy non-elderly participants.</p> <p><em>Materials and Methods</em>: This study’s participants were non-elderly healthy hospital personnel. Applicants were randomly assigned to two groups, a resistance training group that performed an exercise program (sukubara®) and a control group that did not, and they received a 12-week intervention. This study’s primary endpoint was change in skeletal muscle mass; the secondary endpoints were knee extension strength and one-leg standing time with eyes closed.</p> <p><em>Results</em>: An analysis of Tthe 18 participants (10 in the resistance training group and 8 in the control group), who were 18 analyzed this study’s results was performed. The results of changes in variables between both groups during the intervention period were as follows: skeletal muscle mass, knee extension strength, and one-leg standing time were significantly improved or tended to be significantly higher in a resistance training group than in a control group. <em>Conclusions</em>: A self-administered training system (sukubara®) incorporating low-load exercise resulted in muscle hypertrophy and improvement in physical function.</p>
Passive acoustic monitoring indicates Barred Owls are established in northern coastal California and management intervention is warranted
<p>Barred Owls (<em>Strix varia</em>) have recently expanded westward from eastern North America, contributing to substantial declines in Northern Spotted Owls (<em>Strix occidentalis caurina</em>). Passive acoustic monitoring (PAM) represents a potentially powerful approach for tracking range expansions like the Barred Owl's, but further methods development is needed to ensure that PAM-informed occupancy models meaningfully reflect population processes. Focusing on the leading edge of the Barred Owl range expansion in coastal California, we used a combination of PAM data, GPS-tagging, and active surveys to (1) estimate breeding home range size, (2) identify patterns of vocal activity that reflect resident occupancy, and (3) estimate resident occupancy rates. Mean breeding season home range size (452 ha) was reasonably consistent with the size of cells (400 ha) sampled with autonomous recording units (ARUs). Nevertheless, false-positive acoustic detections of Barred Owls frequently occurred within cells not containing an activity center such that site occupancy estimates derived using all detected vocalizations (0.61) were unlikely to be representative of resident occupancy. However, the proportion of survey nights with confirmed vocalizations (VN) and the number of ARUs within a sampling cell with confirmed vocalizations (VU) were indicative of Barred Owl residency. Moreover, the false positive error rate could be reduced for occupancy analyses by establishing thresholds of VN and VU to define detections, although doing so increased false negative error rates in some cases. Using different thresholds of VN and VU, we estimated resident occupancy to be 0.29–0.44, which indicates that Barred Owls have become established in the region but also that timely lethal removals could still help prevent the extirpation of Northern Spotted Owls. Our findings provide a scalable framework for monitoring Barred Owl populations throughout their expanded range and, more broadly, a basis for converting site occupancy to resident occupancy in PAM programs. </p>
Come As You Are - Assessing the Efficacy of a Nurse Case Management HIV Prevention and Care Intervention Among Homeless Youth
ClinicalTrials.gov study NCT03910218. IPD Sharing: NO. Countries: 1. Publications: 2.
Promoting Benzodiazepine Cessation Through an Electronically-delivered Patient Self-management Intervention
ClinicalTrials.gov study NCT04572750. IPD Sharing: NO. Countries: 1. Publications: 3.
Cognitive Behavioral Therapy and Real-Time Pain Management Intervention for Sickle Cell Via Mobile Applications
ClinicalTrials.gov study NCT04419168. IPD Sharing: NO. Countries: 1. Publications: 3.
Interventions to Manage Food Insecurity and Inappropriate Feeding Practices Related to the COVID-19 Pandemic
ClinicalTrials.gov study NCT04801134. IPD Sharing: NO. Countries: 1. Publications: 8.
Lifestyle Intervention for Weight Gain Management for Patients With Schizophrenia
ClinicalTrials.gov study NCT01368406. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Tele-SSM: A Telephone Technology Innovation in the Intervention on Supported Self-management for Depression in Vietnam
ClinicalTrials.gov study NCT06456775. IPD Sharing: YES. Countries: 1. Publications: 14.
Dementia Caregiver Chronic Grief Management: A Live Online Video Intervention
ClinicalTrials.gov study NCT03593070. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Moms@Home: A Storytelling-based Mobile Health Intervention to Improve Blood Pressure Management in Pregnancy
ClinicalTrials.gov study NCT06835959. IPD Sharing: YES. Countries: 1. Publications: 10.
Immersive Virtual Reality Intervention for Non-Opioid Pain Management: A Randomized Controlled Trial
ClinicalTrials.gov study NCT02887989. IPD Sharing: NO. Countries: 1. Publications: 1.
Physical Literacy-Based Intervention for Chronic Disease Management
ClinicalTrials.gov study NCT06325306. IPD Sharing: YES. Countries: 1. Publications: 1.
Leadership for Recovery: Evaluation of an Intervention Programme for First-line Healthcare Managers
ClinicalTrials.gov study NCT06678347. IPD Sharing: NO. Countries: 1. Publications: 4.
Development of a Dietary Intervention Model Based on Genetic Data as an Implementation of a Healthy Lifestyle in the Management of Systemic Lupus Erythematosus Patients
ClinicalTrials.gov study NCT07183007. IPD Sharing: NO. Countries: 1. Publications: 1.
ScienceDex guides
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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.