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539 results for “disentangling”
Disentangling responses to natural stressor and human impact gradients in river ecosystems across Europe
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Disentangling compound effects of changing disturbance and regeneration across temperate forest landscapes
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Long‐term trends in gastropod abundance and biodiversity: Disentangling effects of press versus pulse disturbances
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Disentangling developmental effects of play aspects in rat rough-and-tumble play
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Disentangling relationships between physiology, morphology, diet, and gut microbial diversity in American Kestrel nestlings
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Disentangling ecological and taphonomic signals in ancient food webs
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Seascape genetics in a polychaete worm: Disentangling the roles of a biogeographic barrier and environmental factors
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Data from: Disentangling the effects of temperature and rainfall on the population dynamics of Kalahari meerkats
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Disentangling complex histories of hybridisation: The genomic consequences of ancient and recent introgression in Channel Island monkeyflowers
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Disentangling the causes of age-assortative mating in bird populations with contrasting life-history strategies
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Disentangling direct and indirect drivers of farmland biodiversity at landscape scale
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Disentangling the mechanisms linking dispersal and sociality in supergene-mediated ant social forms
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Disentangling mechanical and sensory modules in the radiation of Noctilionoid bats
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Disentangling abiotic and biotic controls of age-0 Pacific herring population stability across the San Francisco Estuary
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Disentangling fragmentation effects on herbivory in understory plants of longleaf pine savanna
Habitat fragmentation affects species and their interactions through intertwined mechanisms that include changes to fragment area, shape, connectivity and distance to edge. Disentangling these pathways is a fundamental challenge of landscape ecology and will help identify ecological processes important for management of rare species or restoration of fragmented habitats. In a landscape experiment that manipulated connectivity, fragment shape, and distance to edge while holding fragment area constant, we examined how fragmentation impacts herbivory and growth of nine plant species in longleaf pine savanna. Probability of herbivory in open habitat was strongly dependent on proximity to forest edge for every species, increasing with distance to edge in six species (primarily grasses and annual forbs) and decreasing in three species (perennial forbs and a shrub). In the two species of perennial forbs, these edge effects were dependent on fragment shape; herbivory strongly decreased with distance to edge in fragments of two shapes, but not in a third shape. For most species, however, probability of herbivory was unrelated to connectivity or fragment shape. Growth was generally determined more strongly by leaf herbivory than by distance to edge, fragment shape, or connectivity. Taken together, these results demonstrate consistently strong edge effects on herbivory, one of the most important biotic factors determining plant growth and demography. Our results contrast with the generally inconsistent results of observational studies, likely because our experimental approach enabled us to tease apart landscape processes that are typically confounded.
Disentangling sources of gene tree discordance in phylogenomic datasets: testing ancient hybridizations in Amaranthaceae s.l.
<p>Gene tree discordance in large genomic datasets can be caused by evolutionary processes such as incomplete lineage sorting and hybridization, as well as model violation, and errors in data processing, orthology inference, and gene tree estimation. Species tree methods that identify and accommodate all sources of conflict are not available, but a combination of multiple approaches can help tease apart alternative sources of conflict. Here, using a phylotranscriptomic analysis in combination with reference genomes, we test a hypothesis of ancient hybridization events within the plant family Amaranthaceae s.l. that was previously supported by morphological, ecological, and Sanger-based molecular data. The dataset included seven genomes and 88 transcriptomes, 17 generated for this study. We examined gene-tree discordance using coalescent-based species trees and network inference, gene tree discordance analyses, site pattern tests of introgression, topology tests, synteny analyses, and simulations. We found that a combination of processes might have generated the high levels of gene tree discordance in the backbone of Amaranthaceae s.l. Furthermore, we found evidence that three consecutive short internal branches produce anomalous trees contributing to the discordance. Overall, our results suggest that Amaranthaceae s.l. might be a product of an ancient and rapid lineage diversification, and remains, and probably will remain, unresolved. This work highlights the potential problems of identifiability associated with the sources of gene tree discordance including, in particular, phylogenetic network methods. Our results also demonstrate the importance of thoroughly testing for multiple sources of conflict in phylogenomic analyses, especially in the context of ancient, rapid radiations. We provide several recommendations for exploring conflicting signals in such situations.</p>
Disentangling drivers of spatial autocorrelation in species distribution models
<p>Species distribution models (SDMs) are frequently used to understand the influence of site properties on species occurrence. For robust model inference, SDMs need to account for the spatial autocorrelation of virtually all species occurrence data. Current methods do not routinely distinguish between extrinsic and intrinsic drivers of spatial autocorrelation, although these may have different implications for conservation. Here, we present and test a method that disentangles extrinsic and intrinsic drivers of spatial autocorrelation using repeated observations of a species. We focus on unknown habitat characteristics and conspecific interactions as extrinsic and intrinsic drivers, respectively. We model the former with spatially correlated random effects and the latter with an autocovariate, such that the spatially correlated random effects are constant across the repeated observations whereas the autocovariate may change. We tested the performance of our model on virtual species data and applied it to observations of the corncrake Crex crex in the Netherlands. Applying our model to virtual species data revealed that it was well able to distinguish between the two different drivers of spatial autocorrelation, outperforming models with no or a single component for spatial autocorrelation. This finding was independent of the direction of the conspecific interactions (i.e., conspecific attraction versus competitive exclusion). The simulations confirmed that the ability of our model to disentangle both drivers of autocorrelation depends on repeated observations. In the case study, we discovered that the corncrake has a stronger response to habitat characteristics compared to a model that did not include spatially correlated random effects, whereas conspecific interactions appeared to be less important. This implies that future conservation efforts should primarily focus on maximizing habitat availability. Our study shows how to systematically disentangle extrinsic and intrinsic drivers of spatial autocorrelation. The method we propose can help to correctly identify the main drivers of species distributions.</p>
Data from: Disentangling the effects of geographic peripherality and habitat suitability on neutral and adaptive genetic variation in Swiss stone pine
<p><span><span><span><span><span><span><span><span><span><span><span>It is generally accepted that the spatial distribution of neutral genetic diversity within a species' native range mostly depends on effective population size, demographic history, and geographic position. However, it is unclear how genetic diversity at adaptive loci correlates with geographic peripherality or with habitat suitability within the ecological niche. Using exome-wide genomic data and distribution maps of the Alpine range, we first tested whether geographic peripherality correlates with four measures of population genetic diversity at >17,000 SNP loci in 24 Alpine populations (480 individuals) of Swiss stone pine (<i>Pinus cembra</i>) from Switzerland. To distinguish between neutral and adaptive SNP sets, we used four approaches (two gene diversity estimates, <i>F</i><sub>ST</sub> outlier test, and environmental association analysis) that search for signatures of selection. Second, we established ecological niche models for <i>P. cembra</i> in the study range and investigated how habitat suitability correlates with genetic diversity at neutral and adaptive loci. All estimates of neutral genetic diversity decreased with geographic peripherality, but were uncorrelated with habitat suitability. However, heterozygosity (<i>H</i><sub>e</sub>) at adaptive loci based on Tajima's <i>D</i> declined significantly with increasingly suitable conditions. No other diversity estimates at adaptive loci were correlated with habitat suitability. Our findings suggest that populations at the edge of a species' geographic distribution harbour limited neutral genetic diversity due to demographic properties. Moreover, we argue that populations from suitable habitats went through strong selection processes, are thus well adapted to local conditions, and therefore exhibit reduced genetic diversity at adaptive loci compared to populations at niche margins.</span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Disentangling the steps of the infection process responsible for juvenile disease susceptibility
<p>1. Juveniles of diverse host taxa, including vertebrates, invertebrates and plants, are more susceptible to parasitic infections than adults, a phenomenon still poorly understood.</p> <p>2. To shed light on the mechanisms underlying this host age effect, we investigated its expression during different steps of the infection process of a bacterial parasite of the planktonic crustacean <i>Daphnia</i>. To do so, we infected juvenile and adult <i>Daphnia magna</i> with the bacterium <i>Pasteuria ramosa</i> and monitored parasite development in experiments specific to different steps of the infection process.</p> <p>3. We did not find an effect of host age on parasite attachment, but found a strong increase in the penetration time of the parasite into hosts with increasing age. Host age at exposure also affected within-host parasite development, which was delayed in old adult <i>D. magna</i> in comparison to young adults and juveniles. Furthermore, parasite clearance was observed only in old adults, suggesting that old hosts have a better immune response than young adults and juveniles.</p> <p>4. Our results reveal a step-specific effect of host age on disease progression, giving support for different hypotheses explaining high juvenile disease susceptibility. While these mechanisms are likely to work independently from each other, earlier steps of the infection process influence later steps, and thus may respond more readily to selection than later steps.</p> <p>5. Our results also highlight that clearance can occur both during early parasite establishment and surprisingly at progressed infection phases, an undocumented phenomenon in invertebrates, where late infections are often considered chronic, lasting until host death.</p>
Data from: When are extinctions simply bad luck? rarefaction as a framework for disentangling selective and stochastic extinctions
1. A key challenge in conservation biology is that not all species are equally likely to go extinct when faced with a disturbance. Traditionally, differences in species extinction risk are considered a form of extinction selectivity, a nanrondom process by which species' extinction risks are associated with their traits. While selectivity clearly contributes to varation in extinction among taxa, it is also clear that rare species are more likely to go extinct than are common species. While obvious, this law of extinction suggests that random chance, operating on species abundance, plays an important role in the extinction process. Unless ecologists and conservation biologists can disentangle random and nonrandom extinction processes, then the prediction and prevention of future extinctions will continue to be an elusive challenge. 2. We suggest that a modified version of a common null model procedure, rarefaction, can be used to disentangle the influence of stochastic species loss from selective nonrandom processes. To this end we applied a rarefaction based null model to three published data sets to characterize the influence of species rarity in driving biodiversity loss following three disturbance events: i) disease-associated bat declines; ii) disease-associated amphibian declines; and iii) habitat loss and invasive species-associated gastropod declines. For each case study, we used rarefaction to generate null expectations of stochastic biodiversity loss and species-specific extinction probabilities. 3. In each of our case studies we find evidence for random and nonrandom (selective) extinctions. Our findings highlight the importance of explicitly considering that some species extinctions are the result of stochastic processes, i.e., bad luck. 4. Policy Implications If there is a first law of extinction, it is that rare species are most likely than common species to go extinct. We suggest that taking this law into account in analyses of extinction risk is critical to the identification of selective extinctions. Our results suggest that rarefaction can be used to identify nonrandom decline, extirpation, and extinction events and provide an important baseline comparison point for future extinction analyses.12-Jul-2019
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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.