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91,407 results for “Effectiveness”
Source code and data from: Foraging personalities modify effects of habitat fragmentation on biodiversity
<p><span>Habitat loss undeniably poses a substantial threat to biodiversity, but whether fragmentation per se drives the loss of species is still widely debated. While negative consequences from fragmentation are often anticipated, many empirical studies report positive effects. However, the intrinsic mechanisms governing species' persistence in fragmented landscapes are not yet understood. In this study, we investigated consistent personality-dependent differences in foraging behavior among individuals as a possible mechanism underlying the discrepancy of reported fragmentation effects. </span><span>We </span><span>devised a mechanistic individual-based model simulating the home range behavior of a competitive small mammal community based on the availability of a shared resource. Thereby, an individual's risk-taking behavior dictates its foraging decisions at risky habitat edges, an inherent property of fragmentation per se. Our simulations show that differences in risk-taking while foraging are potentially a further mechanism contributing to reconciling the fragmentation debate. The first scenario considering risk-seeking communities showed a neutral response towards fragmentation, while the second scenario featuring risk-avoiding communities confirmed the negative effects of fragmentation. Notably, the third scenario, simulating behaviorally diverse communities including risk-avoiding and risk-seeking individuals, demonstrated a positive influence of fragmentation on biodiversity. Intraspecific differences in behavior could also enhance the temporal species coexistence (coviability) of communities threatened by an ongoing habitat loss. Our study highlights the importance of recognizing the behavioral composition of populations and communities for estimating fragmentation effects, because differences in risk-taking can influence the coping abilities of animal communities in light of fragmentation.</span></p>
The effect of a temperature‐sensitive prophage on the evolution of virulence in an opportunistic bacterial pathogen
<p>Viruses are key actors of ecosystems and have major impacts on global biogeochemical cycles. Prophages deserve particular attention as they are ubiquitous in bacterial genomes and can enter a lytic cycle when triggered by environmental conditions. We explored how temperature affects the interactions between prophages and other biological levels by using an opportunistic pathogen, the bacterium <em>Serratia marcescens</em>, that harbours several prophages and that had undergone an evolution experiment under several temperature regimes. We found that the release of one of the prophages was temperature-sensitive and malleable to evolutionary changes. We further discovered that the virulence of the bacterium in an insect model also evolved and was positively correlated with phage release rates. We determined through analysis of genetic and epigenetic data that changes in the bacterial outer cell wall structure possibly explain this phenomenon. We hypothezise that the temperature-dependent phage release rate acted as a selection pressure on <em>S. marcescens</em> and that it resulted in modified bacterial virulence in the insect host. Our study system illustrates how viruses can mediate the influence of abiotic environmental changes to other biological levels and thus be involved in ecosystem feedback loops.</p>
Investigating the "two-hit hypothesis": effects of prenatal maternal immune activation and adolescent cannabis use on neurodevelopment in mice
<p>Prenatal exposure to maternal immune activation (MIA) and chronic adolescent cannabis use have both been identified as environmental risk factors for neuropsychiatric disorders. However, most individuals exposed to a single risk factor do not typically develop major mental illness, which suggests that multiple exposures may be required for illness onset. Here, we examine whether combined exposure to prenatal MIA and adolescent delta-9-tetrahydrocannabinol (THC), the main psychoactive component of cannabis, lead to enduring neuroanatomical and behavioural changes in adult offspring, potentially reflecting changes in humans indicative of mental illness. </p> <p>Mice were prenatally exposed to a viral mimetic, poly I:C (5mg/kg), or vehicle at gestational day (GD)9, and then postnatally exposed to chronic THC (5mg/kg) or vehicle by intraperitoneal injections during adolescent development (postnatal day [PND]28-45). Longitudinal in vivo whole-brain magnetic resonance imaging (MRI) was performed pre-treatment, PND25, post-treatment, PND50, and in adulthood, PND85, followed by a series of behavioural tests aimed at assessing anxiety-like and locomotor, social, and sensorimotor gating behaviour. Post-mortem assessment of cannabinoid (CB)1 and 2 receptor expressing cells was performed in developmentally altered regions identified by MRI (anterior cingulate and somatosensory cortices, striatum, and hippocampus). We hypothesized that there would be differential, but synergistic effects of each exposure.</p> <p>Briefly, we found subtle deviations in neurodevelopmental trajectory and subthreshold anxiety-like behaviours were observed in mice exposed to both risk factors. Sex-dependent effects were observed in patterns of shared brain-behaviour covariation, suggesting that exposure to MIA and THC may affect males and females in different ways. Density of CB1 and CB2 receptor positive cells was significantly decreased in all regions assessed for all mice exposed to either one or both risk factors, relative to controls.</p> <p>These findings suggest that there may be a cumulative effect of risk factor exposure on gross neuroanatomical and behavioural development, and that the endocannabinoid system may be sensitive to both prenatal MIA, adolescent THC, or the combination. For full details, see our publication: .</p> <p>In this dataset, you will find a total of 243 preprocessed structural MRIs (in MINC format) acquired at postnatal day ~25, ~50, and ~85 in mice exposed to poly I:C or vehicle control (0.9% sterile saline) at GD9, and then postnatally treated with vehicle or THC from PND 28-45. These are T1-weighted structural images at 100 micron isotropic resolution acquired on a 7 Tesla Bruker Biospec 70/30; matrix size of 180 x 160 x 90; 14.5 minutes, 20 degrees and TE/TR of 4.5/20 ms (2 averages, ~14 minutes). Anesthesia was induced with 3% isoflurane in oxygen and a (0.075 mg/kg bolus) dexmedetomidine injection. Anesthesia was maintained during the scan between 1.5-0.5% isoflurane, and a constant infusion of dexmedetomidine (0.05mg/kg/h continuous mg/kg during scan). T1-weighted scans were preprocessed by stripping native coordinates, flipping left-right to maintain fidelity, denoising, correcting inhomogeneities in the bias field using the N4 algorithm, and registering in LSQ6 alignment (i.e. 6 degrees of freedom are allowed for imagine alignment: translations and rotations along x, y, and z dimensions). The demographics information for each animal is included in the demographics.csv file. </p> <p>Behavioural tests were performed following the postnatal day 85 scans in all animals with a 2 day rest period. These include: open field test, three chambered social approach, and prepulse inhibition. The data for all of these tests is presented in its own individual .csv spreadsheet.</p> <p>Included in this data set are the structural MRIs in MINC format, the behavioural .csv data, and a readme.txt file providing further detail on the data structure and content, and on how to interpret the data column titles. DICOMS are also available for the structural MRI data, as are the raw (not-preprocessed) MINC files, available upon request to the authors. </p> <p>Finally, the authors would like to acknowledge the funding bodies that supported the completion of this work including the Canadian Institute for Health Research, the Fonds de Recherche du Québec en Santé, and the Healthy Brains for Healthy Lives at McGill University.</p>
Dataset of pollinator functional traits and interaction networks in neotropical mangroves: effects of patch size and surrounding land use
<p>This is the dataset of the manuscript entitled "Pollinator functional traits and interaction networks in neotropical mangroves: effects of patch size and surrounding land use", which was submitted for publication. The dataset include the functional traits of 162 insect pollinator species and 315 interactions with the mangrove species <em>Avicennia germinans, Conocarpus erectus, Laguncularia racemosa,</em> and <em>Rhizophora</em> <em>mangle</em>. The manuscript evaluates the effects of mangrove patch size and surrounding land use on pollinator functional diversity and plant-pollinator interactions in seven mangrove patches from the Colombian Caribbean region. Data variables are pollinator order, family, species, functional traits (pollinator guilds, body size, feeding preference, sociality, and nesting site) and frequency, interacting mangrove species, mangrove patch name, coordinates and size (ha), surrounding land use areas (urban areas, croplands, conserved dry forest, degraded vegetation areas, beach and water) and landscape diversity (Shannon H').</p>
Direct and indirect phenotypic effects on sociability indicate potential to evolve
<p class="MsoNormal">The decision to leave or join a group is important as group size influences many aspects of organisms' lives and their fitness. This tendency to socialise with others, sociability, should be influenced by genes carried by focal individuals (direct genetic effects) and by genes in partner individuals (indirect genetic effects), indicating the trait's evolution could be slower or faster than expected. However, estimating these genetic parameters is difficult. Here, in a laboratory population of the cockroach <em>Blaptica dubia</em>, I estimate phenotypic parameters for sociability: repeatability (<em><span>R</span></em>) and repeatable influence (<em><span>RI</span></em>), which indicate whether direct and indirect genetic effects respectively are likely. I also estimate the interaction coefficient (<em><span>Ψ</span></em><em>)</em>, which quantifies how strongly a partner's trait influences the phenotype of the focal individual and is key in models for the evolution of interacting phenotypes. Focal individuals were somewhat repeatable for sociability across a three-week period (<em><span>R</span></em> = 0.080), and partners also had marginally consistent effects on focal sociability (<em><span>RI</span></em> = 0.053). The interaction coefficient was non-zero, although in the opposite sign for the sexes; males preferred to associate with larger individuals (<em><span>Ψ</span></em><sub>male </sub>= -0.129) while females preferred to associate with smaller individuals (<em><span>Ψ</span></em><sub>female</sub><strong><sub> </sub></strong>= 0.071). Individual sociability was consistent between dyadic trials and in social networks of groups. These results provide phenotypic evidence that direct and indirect genetic effects have limited influence on sociability, with perhaps the most evolutionary potential stemming from heritable effects of the body mass of partners. Sex-specific interaction coefficients may produce sexual conflict and the evolution of sexual dimorphism in social behaviour.</p>
Dataset for paper "Mitigating the effect of errors in source parameters on seismic (waveform) inversion"
<p>Dataset corresponding to the journal article "Mitigating the effect of errors in source parameters on seismic (waveform) inversion" by Blom, Hardalupas and Rawlinson, accepted for publication in Geophysical Journal International. In this paper, we demonstrate the effect or errors in source parameters on seismic tomography, with a particular focus on (full) waveform tomography. We study effect both on forward modelling (i.e. comparing waveforms and measurements resulting from a perturbed vs. unperturbed source) and on seismic inversion (i.e. using a source which contains an (erroneous) perturbation to invert for Earth structure. These data were obtained using Salvus, a state-of-the-art (though proprietary) 3-D solver that can be used for wave propagation simulations (Afanasiev et al., GJI 2018).</p> <p>This dataset contains:</p> <ul> <li>The entire Salvus project. This project was prepared using Salvus version 0.11.x and 0.12.2 and should be fully compatible with the latter.</li> <li>A number of Jupyter notebooks used to create all the figures, set up the project and do the data processing.</li> <li>A number of Python scripts that are used in above notebooks.</li> <li>two conda environment .yml files: one with the complete environment as used to produce this dataset, and one with the environment as supplied by Mondaic (the Salvus developers), on top of which I installed basemap and cartopy.</li> <li>An overview of the inversion configurations used for each inversion experiment and the name of hte corresponding figures: inversion_runs_overview.ods / .csv .</li> <li>Datasets corresponding to the different figures. <ul> <li>One dataset for Figure 1, showing the effect of a source perturbation in a real-world setting, as previously used by Blom et al., Solid Earth 2020</li> <li>One dataset for Figure 2, showing how different methodologies and assumptions can lead to significantly different source parameters, notably including systematic shifts. This dataset was kindly supplied by Tim Craig (Craig, 2019).</li> <li>A number of datasets (stored as pickled Pandas dataframes) derived from the Salvus project. We have computed: <ul> <li>travel-time arrival predictions from every source to all stations (df_stations...pkl)</li> <li>misfits for different metrics for both P-wave centered and S-wave centered windows for all components on all stations, comparing every time waveforms from a reference source against waveforms from a perturbed source (df_misfits_cc.28s.pkl)</li> <li>addition of synthetic waveforms for different (perturbed) moment tenors. All waveforms are stored in HDF5 (.h5) files of the ASDF (adaptable seismic data format) type</li> </ul> </li> </ul> </li> </ul> <p>How to use this dataset:</p> <ul> <li>To set up the conda environment: <ol> <li>make sure you have anaconda/miniconda</li> <li>make sure you have access to Salvus functionality. This is not absolutely necessary, but most of the functionality within this dataset relies on salvus. You can do the analyses and create the figures without, but you'll have to hack around in the scripts to build workarounds.</li> <li>Set up Salvus / create a conda environment. This is best done following the instructions on the Mondaic website. Check the changelog for breaking changes, in that case download an older salvus version.</li> <li>Additionally in your conda env, install basemap and cartopy: <pre><code class="language-bash">conda-env create -n salvus_0_12 -f environment.yml conda install -c conda-forge basemap conda install -c conda-forge cartopy</code></pre> </li> <li> <p>Install LASIF (https://github.com/dirkphilip/LASIF_2.0) and test. The project uses some lasif functionality.</p> </li> <li> <p> </p> </li> <li> <p> </p> </li> </ol> </li> <li>To recreate the figures: This is extremely straightforward. Every figure has a corresponding Jupyter Notebook. Suffices to run the notebook in its entirety. <ul> <li>Figure 1: separate notebook, Fig1_event_98.py</li> <li>Figure 2: separate notebook, Fig2_TimCraig_Andes_analysis.py</li> <li>Figures 3-7: Figures_perturbation_study.py</li> <li>Figures 8-10: Figures_toy_inversions.py</li> </ul> </li> <li>To recreate the dataframes in DATA: This can be done using the example notebook Create_perturbed_thrust_data_by_MT_addition.py and Misfits_moment_tensor_components.M66_M12.py . The same can easily be extended to the position shift and other perturbations you might want to investigate.</li> <li>To recreate the complete Salvus project: This can be done using: <ul> <li>the notebook Prepare_project_Phil_28s_absb_M66.py (setting up project and running simulations)</li> <li>the notebooks Moment_tensor_perturbations.py and Moment_tensor_perturbation_for_NS_thrust.py</li> <li>For the inversions: using the notebook Inversion_SS_dip.M66.28s.py as an example. See the overview table inversion_runs_overview.ods (or .csv) as to naming conventions.</li> </ul> </li> </ul> <p> </p> <p>References:</p> <ul> <li>Michael Afanasiev, Christian Boehm, Martin van Driel, Lion Krischer, Max Rietmann, Dave A May, Matthew G Knepley, Andreas Fichtner, Modular and flexible spectral-element waveform modelling in two and three dimensions, <em>Geophysical Journal International</em>, Volume 216, Issue 3, March 2019, Pages 1675–1692, <a href="https://doi.org/10.1093/gji/ggy469">https://doi.org/10.1093/gji/ggy469</a></li> <li>Nienke Blom, Alexey Gokhberg, and Andreas Fichtner, Seismic waveform tomography of the central and eastern Mediterranean upper mantle, <em>Solid Earth</em>, Volume 11, Issue 2, 2020, Pages 669–690, 2020, <a href="https://doi.org/10.5194/se-11-669-2020">https://doi.org/10.5194/se-11-669-2020</a></li> <li>Tim J. Craig, Accurate depth determination for moderate-magnitude earthquakes using global teleseismic data. <em>Journal of Geophysical Research: Solid Earth</em>, 124, 2019, Pages 1759– 1780. <a href="https://doi.org/10.1029/2018JB016902">https://doi.org/10.1029/2018JB016902</a></li> </ul> <p> </p>
Data and code – Effects of climate on salmonid productivity: A global meta-analysis across freshwater ecosystems
<p>Salmonids are of immense socio-economic importance in much of the world but are threatened by climate change. This has generated a substantial literature documenting effects of climate variation on salmonid productivity in freshwater ecosystems, but there has been no global quantitative synthesis across studies. We conducted a systematic review and meta-analysis to gain quantitative insight into key factors shaping the effects of climate on salmonid productivity, ultimately collecting 1,321 correlations from 156 studies, representing 23 species across 24 countries. Fisher's Z was used as the standardized effect size, and a series of weighted mixed-effects models were compared to identify covariates that best explained variation in effects. Patterns in climate effects were complex, and were driven by spatial (latitude, elevation), temporal (time-period, age-class), and biological (range, habitat type, anadromy) variation within and among study populations. These trends were often consistent with predictions based on salmonid thermal tolerances. Namely, warming and decreased precipitation tended to reduce productivity when high temperatures challenged upper thermal limits, while opposite patterns were common when cold temperatures limited productivity. Overall, variable climate impacts on salmonids suggest that future declines in some locations may be counterbalanced by gains in others. In particular, we suggest that future warming should (1) increase salmonid productivity at high latitudes and elevations (especially >60° and >1,500m), (2) reduce productivity in populations experiencing hotter and dryer growing season conditions, (3) favor non-native over native salmonids, and (4) impact lentic populations less negatively than lotic ones. These patterns should help conservation and management organizations identify populations most vulnerable to climate change, which can then be prioritized for protective measures. Our framework enables broad inferences about future productivity that can inform decision-making under climate change for salmonids and other taxa, but more widespread, standardized, and hypothesis-driven research is needed to expand current knowledge.</p>
Experimental corridor effects on ant seed dispersal
<p>Habitat fragmentation impacts ecosystems worldwide through habitat loss, reduced connectivity, and edge effects. Yet, these landscape factors are often confounded, leaving much to be investigated about their relative effects, especially on species interactions. In a landscape experiment, we investigated the consequences of connectivity and edge effects for seed dispersal by ants. We found that ants dispersed seeds farther in habitat patches connected by corridors, but only in patch centers. We did not see an effect on the total number of seeds moved or the rate ants detected seeds. Furthermore, we did not see any differences in ant community composition across patch types, suggesting that shifts in ant behavior or other factors increased ant seed dispersal in patches connected by corridors. Long distance seed dispersal by ants that requires an accumulation of short distance dispersal events over generations may be an underappreciated mechanism through which corridors increase plant diversity. The following data were made publicly available to go along with our paper in <em>Ecosphere</em> (Burt et al. 2022). Please read metadata file for additional information.</p>
Large effect loci mediate rapid adaptation of salmon body size after river regulation
<p>Understanding the potential of natural populations to adapt to altered environments is becoming increasingly relevant in evolutionary research. Currently, our understanding of adaptation to human alteration of the environment is hampered by lack of knowledge on the genetic basis of traits, lack of time series, and little or no information on changes in optimal trait values. Here we used time series data spanning nearly a century to investigate how body mass of Atlantic salmon (<em>Salmo salar</em>) adapts to river regulation. We found that the change in body mass followed the change in waterflow, both decreasing to ~1/3 of their original values. Allele frequency changes at two loci in the regions of <em>vgll3</em> and <em>six6 </em>predicted more than 80% of the observed body mass reduction. Modelling the adaptive dynamics revealed that the population mean lagged behind its optimum before catching up ~6 salmon generations after the initial waterflow reduction. Our results demonstrate rapid adaptation mediated by large effect loci and provide insight into the temporal dynamics of evolutionary rescue following human disturbance.</p>
Figure 1 in Effect of initial infestation on population fluctuation and spatial distribution of Panonychus citri (Acari: Tetranychidae) on Thomson navel orange in Ghaemshahr, Iran
Figure 1. Population fluctuation of Panonychus citri on Thomson navel orange in a multi-treatment experiment in in 2016. Control treatment = average number of 0.48 mite per leaf; Treatment 1 = average number of 1.12 mites per leaf, Treatment 2 = average number of 1.23 mites per leaf, Treatment 3 = average number of 6.36 mites per leaf, Treatment
Figure 4 in Effect of initial infestation on population fluctuation and spatial distribution of Panonychus citri (Acari: Tetranychidae) on Thomson navel orange in Ghaemshahr, Iran
Figure 4. Population fluctuation of Panonychus citri on Thomson navel orange in a paired-treatment experiment in 2017. Infested treatment = average number of 0.79 mite per leaf, Control treatment = average number of 0.59 mite per leaf.
Figure 2 in Effect of initial infestation on population fluctuation and spatial distribution of Panonychus citri (Acari: Tetranychidae) on Thomson navel orange in Ghaemshahr, Iran
Figure 2. Population fluctuation of Panonychus citri on Thomson navel orange in a multi-treatment experiment in in 2016. Control treatment = average number of 0.2 mite per leaf, Treatment 1 = average number of 0.3 mite per leaf, Treatment
Figure 5 in Effects of climatic parameters on Tetranychus urticae (Acari: Tetranychidae) populations based on remote sensing in the southeastern Caspian Sea
Figure 5. The relationship between UV Aerosol Index extracted from Sentinel-5 imagery and spider mite population (mean score of each window) from June 9, 2020 to September 17, 2020 (First window, May 30 to June 9 was not spider mite distribution data).
Figure 6 in Effects of climatic parameters on Tetranychus urticae (Acari: Tetranychidae) populations based on remote sensing in the southeastern Caspian Sea
Figure 6. The relationship between daily CHIRPS-precipitation and spider mite population (mean score of each window) from June 9, 2020 to September 17, 2020 (First window, May 30 to June 9 was not spider mite distribution data).
Figure 9 in Effects of climatic parameters on Tetranychus urticae (Acari: Tetranychidae) populations based on remote sensing in the southeastern Caspian Sea
Figure 9. The relationship between NDVI (10 m) provided form Sentinal-2 and density of spider mite during monitoring windows based on ANOVA for linear regression. The alphabetical letters indicate of the sequence windows from June 9, 2020 to September 17, 2020 (First window, May 30 to June 9 was not spider mite distribution data).
Figure 4 in Effects of climatic parameters on Tetranychus urticae (Acari: Tetranychidae) populations based on remote sensing in the southeastern Caspian Sea
Figure 4. Distribution maps of spider mite based on IDW model during monitoring windows, a–n are the sequence windows form June 9, 2020 to September 17, 2020 (First window, May 30 to June 9 was not spider mite population data).
Figure 8 in Effects of climatic parameters on Tetranychus urticae (Acari: Tetranychidae) populations based on remote sensing in the southeastern Caspian Sea
Figure 8. The relationship between MODIS-Evapotranspiration and spider mite population (mean score of each window) from June 9, 2020 to September 17, 2020. (First window, May 30 to June 9 was not spider mite distribution data).
Figure 3 in Effects of climatic parameters on Tetranychus urticae (Acari: Tetranychidae) populations based on remote sensing in the southeastern Caspian Sea
Figure 3. Spider mite distribution throughout Golestan province; 6 (min.) × 6 (min.) grid cells in the DMS coordinate system (yellow points indicate the monitoring fields).
Figure 2 in Sublethal effects of cyflumetofen and spirodiclofen on biological parameters of citrus red mite, Panonychus citri McGregor (Acari: Tetranychidae)
Figure 2. Age-stage specific survival rate (Sxj) of offspring from P. citri females treated with LC20 sublethal concentration of cyflumetofen and spirodiclofen compared with untreated females.
Figure 2 in Enhancing survival of Demodex folliculorum (Acari: Demodecidae) under in vitro condition: Effect of temperature and culture media
Figure 2. Microphotograph (40×) of Demodex folliculorum mites in the hair follicle of six individuals with a clinical diagnosis of blepharitis. Note the different life stages of the mites (a = adult; l = larva; n = nymph; o = ovum).
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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.