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8,038 results for “validation.”
Development and validation of a postoperative delirium prediction model for patients admitted to an intensive care unit in China: a prospective study
<p>Objectives: We aimed to develop <span class="il">and</span> validate <span class="il">a</span> <span class="il">postoperative</span> <span class="il">delirium</span> (POD) <span class="il">prediction</span> model for patients admitted to the intensive care unit (ICU).</p> <p>Design: <span class="il">A</span> prospective study was conducted.</p> <p>Setting: The study was conducted in the surgical, cardiovascular surgical, <span class="il">and</span> trauma surgical ICUs <span class="il">of</span> an affiliated hospital <span class="il">of</span> <span class="il">a</span> medical university in Heilongjiang Province, China.</p> <p>Participants: This study included 400 patients (≥18 years old) admitted to the ICU after surgery.</p> <p>Primary <span class="il">and</span> secondary outcome measures: The primary outcome measure was <span class="il">postoperative</span> <span class="il">delirium</span> assessment during ICU stay.</p> <p>Results: The model was developed using 300 consecutive ICU patients <span class="il">and</span> was validated using 100 patients from the same ICUs. The model was based on five risk factors: Physiological <span class="il">and</span> Operative Severity Score for the Enumeration <span class="il">of</span> Mortality <span class="il">and</span> Morbidity; acid-base disturbance; <span class="il">and</span> history <span class="il">of</span> coma, diabetes, or hypertension. The model had an area under the receiver operating characteristics curve <span class="il">of</span> 0.852 (95% confidence interval: 0.802–0.902), Youden index <span class="il">of</span> 0.5789, sensitivity <span class="il">of</span> 70.73%, <span class="il">and</span> specificity <span class="il">of</span> 87.16%. The Hosmer-Lemeshow goodness <span class="il">of</span> fit was 5.203 (P = 0.736). At <span class="il">a</span> cut-off <span class="il">of</span> 24.5%, the sensitivity <span class="il">and</span> specificity were 71% <span class="il">and</span> 69%, respectively.</p> <p>Conclusions: The model, which used readily available data, exhibited high predictive value regarding risk <span class="il">of</span> intensive care unit <span class="il">postoperative</span> <span class="il">delirium</span> (ICU-POD) at admission. Use <span class="il">of</span> this model may facilitate better implementation <span class="il">of</span> preventive treatments <span class="il">and</span> nursing measures.</p>
Data from: Validation of an algorithm for identifying MS cases in administrative health claims datasets
Objective: To develop a valid algorithm for identifying multiple sclerosis (MS) cases in administrative health claims (AHC) datasets. Methods: We used 4 AHC datasets from the Veterans Administration (VA), Kaiser Permanente Southern California (KPSC), Manitoba (Canada), and Saskatchewan (Canada). In the VA, KPSC, and Manitoba, we tested the performance of candidate algorithms based on inpatient, outpatient, and disease-modifying therapy (DMT) claims compared to medical records review using sensitivity, specificity, positive and negative predictive values, and interrater reliability (Youden J statistic) both overall and stratified by sex and age. In Saskatchewan, we tested the algorithms in a cohort randomly selected from the general population. Results: The preferred algorithm required ≥3 MS-related claims from any combination of inpatient, outpatient, or DMT claims within a 1-year time period; a 2-year time period provided little gain in performance. Algorithms including DMT claims performed better than those that did not. Sensitivity (86.6%–96.0%), specificity (66.7%–99.0%), positive predictive value (95.4%–99.0%), and interrater reliability (Youden J = 0.60–0.92) were generally stable across datasets and across strata. Some variation in performance in the stratified analyses was observed but largely reflected changes in the composition of the strata. In Saskatchewan, the preferred algorithm had a sensitivity of 96%, specificity of 99%, positive predictive value of 99%, and negative predictive value of 96%. Conclusions: The performance of each algorithm was remarkably consistent across datasets. The preferred algorithm required ≥3 MS-related claims from any combination of inpatient, outpatient, or DMT use within 1 year. We recommend this algorithm as the standard AHC case definition for MS.
Data from: Validation of the hospital frailty risk score in a tertiary care hospital in Switzerland: results of a prospective, observational study
Objectives: Recently, the Hospital Frailty Risk Score based on a derivation and validation study in the United Kingdom has been proposed as a low-cost, systematic screening tool to identify older, frail patients who are at greater risk of adverse outcomes and for whom a frailty-attuned approach might be useful. We aimed to validate this Score in an independent cohort in Switzerland. Design: Secondary analysis of a prospective, observational study (TRIAGE study). Setting: One 600-bed tertiary care hospital in Aarau, Switzerland Participants: Consecutive medical inpatients aged 75 years or older that presented to the emergency department or were electively admitted between October 2015 and April 2018. Primary and secondary outcome measures: The primary endpoint was all-cause 30-day mortality. Secondary endpoints were length of hospital stay, hospital readmission, functional impairment, and quality of life measures. We used multivariate regression analyses. Results: Of 4957 included patients, 3150 (63.5%) were classified as low risk, 1663 (33.5%) intermediate risk, and 144 (2.9%) high risk for frailty. Compared to the low-risk group, patients in the moderate risk and high-risk groups had increased risk for 30-day mortality (odds ratio [OR] 2.53, 95%CI 2.09 to 3.06, P<0.001 and OR 4.40, 95%CI 2.94 to 6.57, P<0.001) with overall moderate discrimination (area under the ROC curve 0.66). The results remained robust after adjustment for important confounders. Similarly, we found longer length of hospital stay, more severe functional impairment and a lower quality of life in higher risk group patients. Conclusion: Our data confirms the prognostic value of the Hospital Frailty Risk Score to identify older, frail people at risk for mortality and adverse outcomes in an independent patient population. Trial registration number: ClinicalTrials.gov; Identifier: NCT01768494
Community science validates climate suitability projections from ecological niche modeling
<p><span>Climate change poses an intensifying threat to many bird species, and projections of future climate suitability provide insight into how species may shift their distributions in response. Climate suitability is characterized using ecological niche models (ENMs), which correlate species occurrence data with current environmental covariates and project future distributions using the modeled relationships together with climate predictions. Despite their widespread adoption, ENMs rely on several assumptions that are rarely validated <i>in situ </i>and can be highly sensitive to modeling decisions, precluding their reliability in conservation decision-making. Using data from a novel, large-scale community science program, we developed dynamic occupancy models to validate near-term climate suitability projections for bluebirds and nuthatches in summer and winter. We estimated occupancy, colonization, and extinction dynamics across species' ranges in the United States in relation to projected climate suitability in the 2020s, and used a Gibbs variable selection approach to quantify evidence of species-climate relationships. We also included a Bird Conservation Region strata-level random effect to examine among-strata variation in occupancy that may be attributable to land-use and ecoregional differences. Across species and seasons, we found strong evidence that initial occupancy and colonization were positively related to 2020 climate suitability, illustrating an independent validation of projections from ENMs across a large geographic area. </span><span>Random strata effects revealed that occupancy probabilities were generally higher than average in core areas and lower than average in peripheral areas of species' ranges, and served as a first step in identifying spatial patterns of occupancy from these community science data. </span><span>Our findings lend much-needed support to the use of ENM projections for addressing questions about potential climate-induced changes in species' occupancy dynamics. More broadly, </span>our work highlights the value of community scientist observations for ground-truthing projections from statistical models and for refining our understanding of the processes shaping species' distributions under a changing climate.</p>
Data from: Bearding the scorpion in his den: desert isopods take risks to validate their 'landscape of fear' assessment
Animals balance the risk of predation against other vital needs by adjusting their spatial behavior to match spatiotemporal variation in predation risk. To map this 'landscape of fear', prey use evolutionary rules of thumbs that are associated with the activity and hunting efficiency of predators. In addition, prey acquire perceptual information about the presence, identity, and state of potential predators and use these cues to focus their acute anti-predatory responses. Our goal was to explore if and how prey also use such perceptual information that decays with time to update their spatiotemporal risk assessment. We placed scorpions in freshly dug burrows and recorded the isopods' spatial activity and their defense behavior upon encountering the burrows straight after settling the scorpions and seven days later. To corroborate our understanding, we also examined the isopods' detailed reactions towards deserted scorpion burrows. The isopods reacted defensively to scorpion burrows during their first encounter. After seven days, proportionally more isopods approached the scorpion burrows on their way out for foraging and fewer isopods encountered it on their way back. No changes in the spatial activity were observed towards deserted burrows. In addition, on the eighth day, more isopods entered the risky area near the scorpion burrows when leaving their own burrow than on the first encounter. The results suggest that isopods used predator cues to readjust the 'landscape of fear'. Yet, rather than avoiding the dangerous areas altogether, the isopods implemented risky inspection behavior, validating whether the danger is actual. Our findings imply that inspection behavior toward predators can be used for future planning of prey spatial activity, offsetting possible 'information decay costs'.
Data from: Development and validation of an expert-based habitat suitability model to support boreal caribou conservation
The long-term persistence of boreal caribou (Rangifer tarandus caribou) is threatened by the negative impacts of human activities, including industrial development. The vast geographical distribution and behavioural variability of caribou warranted the development and validation of an appropriate conservation tool for managers in eastern Canada. We developed a habitat suitability model for boreal caribou in Québec by integrating expert knowledge into a hierarchical analysis. We elicited responses from 14 experts on caribou ecology to determine the best spatiotemporal scales of application, the relative importance of habitat variables, the zone of influence of human infrastructure, and the parameterization of the model. Based on this input, we built the model using 8 habitat categories and 3 human infrastructure variables. Experts identified mature conifer-dominated forests and open lichen woodlands as the most important vegetation categories for boreal caribou, whereas density of and proximity to paved roads, forest roads, and mines decreased habitat quality. We mapped the resulting model over the entire province of Québec (up to the northern forest allocation limit), and validated it using independent GPS telemetry datasets acquired in 3 distinct regions. Our model predicted that habitat suitability was highest in the northeastern part of our study area, where timber harvest activities and roads were virtually absent. Conversely, southern parts of Québec were generally unsuitable for boreal caribou. Our habitat suitability model is among the first tools for boreal caribou conservation available to wildlife and land managers in Québec.
Data from: Landscape connectivity for wildlife: development and validation of multi-species linkage maps
The ability to identify regions of high functional connectivity for multiple wildlife species is of conservation interest with respect to forest management and corridor planning. We present a method that does not require independent, field-collected data, is insensitive to the placement of source and destination sites (nodes) for modeling connectivity, and does not require the selection of a focal species. In the first step of our approach, we created a cost surface that represented permeability of the landscape to movement for a suite of species. We randomly selected nodes around the perimeter of the buffered study area and used circuit theory to connect pairs of nodes. When the buffer was removed, the resulting current density map represented, for each grid cell, the probability of use by moving animals. We found that using nodes that were randomly located around the perimeter of the buffered study area was less biased by node placement than randomly selecting nodes within the study area. We also found that a buffer of ≥ 20% of the study area width was sufficient to remove the effects of node placement on current density. We tested our method by creating a map of connectivity in the Algonquin to Adirondack region in eastern North America, and we validated the map with independently collected data. We found that amphibians and reptiles were more likely to cross roads in areas of high current density, and fishers (Pekania [Martes] pennanti) used areas with high current density within their home ranges. Our approach provides an efficient and cost-effective method of predicting areas with relatively high functional connectivity.
Data from: DNA metabarcoding multiplexing and validation of data accuracy for diet assessment: application to omnivorous diet
Ecological understanding of the role of consumer-resource interactions in natural food webs is limited by the difficulty of accurately and efficiently determining the complex variety of food types animals have eaten in the field. We developed a method based on DNA metabarcoding multiplexing and next-generation sequencing to uncover different taxonomic groups of organisms from complex diet samples. We validated this approach on 91 faeces of a large omnivorous mammal, the brown bear, using DNA metabarcoding markers targeting the plant, vertebrate, and invertebrate components of the diet. We included internal controls in the experiments and performed PCR replication for accuracy validation in post-sequencing data analysis. Using our multiplexing strategy, we significantly simplified the experimental procedure and accurately and concurrently identified different prey DNA corresponding to the targeted taxonomic groups, with ≥60% of taxa of all diet components identified to genus/species level. The systematic application of internal controls and replication was a useful and simple way to evaluate the performance of our experimental procedure, standardize the selection of sequence filtering parameters for each marker data, and validate the accuracy of the results. Our general approach can be adapted to the analysis of dietary samples of various predator species in different ecosystems, for a number of conservation and ecological applications entailing large-scale population level diet assessment through cost effective screening of multiple DNA metabarcodes, and the detection of fine dietary variation among samples or individuals and of rare food items.
Data from: Validating genome-wide association candidates controlling quantitative variation in nodulation
Genome-wide association (GWA) studies offer the opportunity to identify genes that contribute to naturally occurring variation in quantitative traits. However, GWA relies exclusively on statistical association, so functional validation is necessary to make strong claims about gene function. We used a combination of gene-disruption platforms (Tnt1 retrotransposons, hairpin RNA-interference constructs, and CRISPR/Cas9 nucleases) together with randomized, well-replicated experiments to evaluate the function of genes that an earlier GWA study in Medicago truncatula had identified as candidates contributing to variation in the symbiosis between legumes and rhizobia. We evaluated ten candidate genes found in six clusters of strongly associated single nucleotide polymorphisms, selected on the basis of their strength of statistical association, proximity to annotated gene models, and root or nodule expression. We found statistically significant effects on nodule production for three candidate genes, each validated in two independent mutants. Annotated functions of these three genes suggest their contributions to quantitative variation in nodule production occur through processes not previously connected to nodulation, including phosphorous supply and salicylic acid-related defense response. These results demonstrate the utility of GWA combined with reverse mutagenesis technologies to discover and validate genes contributing to naturally occurring variation in quantitative traits. The results highlight the potential for GWA to complement forward genetics in identifying the genetic basis of ecologically and economically important traits.
Data from: Proxies in practice: calibration and validation of multiple indices of animal abundance
The abundance of individuals in a population is a fundamental metric in basic and applied ecology, but sampling protocols yielding precise and unbiased estimates of abundance are often cost prohibitive. Proxies of abundance are therefore common, but require calibration and validation. There are many ways to calibrate a proxy, and it is not obvious which will perform best. We use data from eight populations of Chinook salmon (Oncorhynchus tshawytscha) on the Oregon coast where multiple proxies of abundance were obtained contemporaneously with independent mark-recapture estimates. We combined multiple proxy values associated with a single level of abundance into a univariate index and then calibrated that index to mark-recapture estimates using several different techniques. We tested our calibration methods using leave-one-out cross validation and simulation. Our cross-validation analysis did not definitively identify a single best calibration technique for all populations, but we could identify consistently inferior approaches. The simulations suggested that incorporating the known mark-recapture uncertainty into the calibration technique added bias and imprecision. Cross validation techniques should be used to test multiple methods of calibrating multiple proxies to an estimate of abundance. Critical uncertainties with the application of calibrated proxies still exist, and cost-benefit analysis should be performed to help identify optimal monitoring designs.
Data from: An updated global dataset for diet preferences in terrestrial mammals: testing the validity of extrapolation
1. Diet is a key trait of an organism's life history that influences a broad spectrum of ecological and evolutionary processes. Kissling et al. (2014) compiled a species-specific dataset of diet preferences of mammals for 38% of a total of 5364 terrestrial mammalian species assessed for the International Union for Conservation of Nature's Red List, to facilitate future studies. The authors imputed dietary data for the remaining 62% by using extrapolation from phylogenetic relatives. 2. We collected dietary information for 1261 mammalian species for which data were extrapolated by Kissling et al. (2014), in order to evaluate the success with which such extrapolation can predict true diets. 3. The extrapolation method devised by Kissling et al. (2014) performed well for broad dietary categories (consumers of plants and animals). However, the method performed inconsistently, and sometimes poorly, for finer dietary categories, varying in accuracy in both dietary categories and mammalian orders. 4. The results of the extrapolation performance serve as a cautionary tale. Given the large variation in extrapolation performance, we recommend a more conservative approach for inferring mammalian diets, whereby dietary extrapolation is implemented only when there is a high degree of phylogenetic conservatism for dietary traits. Phylogenetic comparative methods can be used to detect and measure phylogenetic signal in diet. If data for species are needed, then only the broadest feeding categories should be used. This would ensure a greater level of accuracy and provide a more robust dataset for further ecological and evolutionary analysis.
Data from: Establishing macroecological trait datasets: digitalization, extrapolation, and validation of diet preferences in terrestrial mammals worldwide
Ecological trait data are essential for understanding the broad-scale distribution of biodiversity and its response to global change. For animals, diet represents a fundamental aspect of species' evolutionary adaptations, ecological and functional roles, and trophic interactions. However, the importance of diet for macroevolutionary and macroecological dynamics remains little explored, partly because of the lack of comprehensive trait datasets. We compiled and evaluated a comprehensive global dataset of diet preferences of mammals ("MammalDIET"). Diet information was digitized from two global and cladewide data sources and errors of data entry by multiple data recorders were assessed. We then developed a hierarchical extrapolation procedure to fill-in diet information for species with missing information. Missing data were extrapolated with information from other taxonomic levels (genus, other species within the same genus, or family) and this extrapolation was subsequently validated both internally (with a jack-knife approach applied to the compiled species-level diet data) and externally (using independent species-level diet information from a comprehensive continentwide data source). Finally, we grouped mammal species into trophic levels and dietary guilds, and their species richness as well as their proportion of total richness were mapped at a global scale for those diet categories with good validation results. The success rate of correctly digitizing data was 94%, indicating that the consistency in data entry among multiple recorders was high. Data sources provided species-level diet information for a total of 2033 species (38% of all 5364 terrestrial mammal species, based on the IUCN taxonomy). For the remaining 3331 species, diet information was mostly extrapolated from genus-level diet information (48% of all terrestrial mammal species), and only rarely from other species within the same genus (6%) or from family level (8%). Internal and external validation showed that: (1) extrapolations were most reliable for primary food items; (2) several diet categories ("Animal," "Mammal," "Invertebrate," "Plant," "Seed," "Fruit," and "Leaf") had high proportions of correctly predicted diet ranks; and (3) the potential of correctly extrapolating specific diet categories varied both within and among clades. Global maps of species richness and proportion showed congruence among trophic levels, but also substantial discrepancies between dietary guilds. MammalDIET provides a comprehensive, unique and freely available dataset on diet preferences for all terrestrial mammals worldwide. It enables broad-scale analyses for specific trophic levels and dietary guilds, and a first assessment of trait conservatism in mammalian diet preferences at a global scale. The digitalization, extrapolation and validation procedures could be transferable to other trait data and taxa.
Data from: Testing the validity of functional response models using molecular gut content analysis for prey choice in soil predators
Analysis of predator - prey interactions is a core concept of animal ecology, explaining structure and dynamics of animal food webs. Measuring the functional response, i.e. the intake rate of a consumer as a function of prey density, is a powerful method to predict the strength of trophic links and assess motives of prey choice, particularly in arthropod communities. However, due to their reductionist set-up, functional responses, which are based on laboratory feeding experiments, may not display field conditions, possibly leading to skewed results. Here, we tested the validity of functional responses of centipede predators and their prey by comparing them with empirical gut content data from field-collected predators. Our predator - prey system included lithobiid and geophilomorph centipedes, abundant and widespread predators of forest soils and their soil-dwelling prey. First, we calculated the body size-dependent functional responses of centipedes using a published functional response model in which we included natural prey abundances and animal body masses. This allowed us to calculate relative proportions of specific prey taxa in the centipede diet. In a second step, we screened field-collected centipedes for DNA of eight abundant soil-living prey taxa and estimated their body size-dependent proportion of feeding events. We subsequently compared empirical data for each of the eight prey taxa, on proportional feeding events with functional response-derived data on prey proportions expected in the gut, showing that both approaches significantly correlate in five out of eight predator - prey links for lithobiid centipedes but only in one case for geophilomorph centipedes. Our findings suggest that purely allometric functional response models, which are based on predator-prey body size ratios are too simple to explain predator - prey interactions in a complex system such as soil. We therefore stress that specific prey traits, such as defence mechanisms, must be considered for accurate predictions.
Data from: Sturgeon conservation genomics: SNP discovery and validation using RAD sequencing
Caviar-producing sturgeons belonging to the genus Acipenser are considered to be one of the most endangered species groups in the world. Continued overfishing in spite of increasing legislation, zero catch quotas and extensive aquaculture production have led to the collapse of wild stocks across Europe and Asia. The evolutionary relationships among Adriatic, Russian, Persian and Siberian sturgeons are complex because of past introgression events and remain poorly understood. Conservation management, traceability and enforcement suffer a lack of appropriate DNA markers for the genetic identification of sturgeon at the species, population and individual level. This study employed RAD sequencing to discover and characterize single nucleotide polymorphism (SNP) DNA markers for use in sturgeon conservation in these four tetraploid species over three biological levels, using a single sequencing lane. Four population meta-samples and eight individual samples from one family were barcoded separately before sequencing. Analysis of 14.4 Gb of paired-end RAD data focused on the identification of SNPs in the paired-end contig, with subsequent in silico and empirical validation of candidate markers. Thousands of putatively informative markers were identified including, for the first time, SNPs that show population-wide differentiation between Russian and Persian sturgeons, representing an important advance in our ability to manage these cryptic species. The results highlight the challenges of genotyping-by-sequencing in polyploid taxa, while establishing the potential genetic resources for developing a new range of caviar traceability and enforcement tools.
Data from: Species discovery and validation in a cryptic radiation of endangered primates: coalescent-based species delimitation in Madagascar's mouse lemurs
Implementation of the coalescent model in a Bayesian framework is an emerging strength in genetically based species delimitation studies. By providing an objective measure of species diagnosis, these methods represent a quantitative enhancement to the analysis of multilocus data, and complement more traditional methods based on phenotypic and ecological characteristics. Recognized as two species 20 years ago, mouse lemurs (genus Microcebus) now comprise more than 20 species, largely diagnosed from mtDNA sequence data. With each new species description, enthusiasm has been tempered with scientific scepticism. Here, we present a statistically justified and unbiased Bayesian approach towards mouse lemur species delimitation. We perform validation tests using multilocus sequence data and two methodologies: (i) reverse-jump Markov chain Monte Carlo sampling to assess the likelihood of different models defined a priori by a guide tree, and (ii) a Bayes factor delimitation test that compares different species-tree models without a guide tree. We assess the sensitivity of these methods using randomized individual assignments, which has been used in bpp studies, but not with Bayes factor delimitation tests. Our results validate previously diagnosed taxa, as well as new species hypotheses, resulting in support for three new mouse lemur species. As the challenge of multiple researchers using differing criteria to describe diversity is not unique to Microcebus, the methods used here have significant potential for clarifying diversity in other taxonomic groups. We echo previous studies in advocating that multiple lines of evidence, including use of the coalescent model, should be trusted to delimit new species.
Data from: Validating the power of mitochondrial metagenomics for community ecology and phylogenetics of complex assemblages
1. The biodiversity of mixed-species samples of arthropods can be characterized by shotgun sequencing of bulk genomic DNA and subsequent bioinformatics assembly of mitochondrial genomes. Here, we tested the power of mitochondrial metagenomics by conducting Illumina sequencing on mixtures of >2600 individuals of leaf beetles (Chrysomelidae) from 10 communities. 2. Patterns of species richness, community dissimilarity and biomass were assessed from matches of reads against three reference databases, including (i) a custom set of mitogenomes generated for 156 species (89% of species in the study); (ii) mitogenomes obtained by the de novo assembly of sequence reads from the real-world communities; and (iii) a custom set of DNA barcode (cox1-5′) sequences. 3. Species detection against the custom-built reference genomes was very high (>90%). False presences were rare against mitogenomes but slightly higher against the barcode references. False absences were mainly due to the incompleteness of the reference databases and, thus, more prevalent in the de novo data set. Biomass (abundance × body length) and read numbers were strongly correlated, demonstrating the potential of mitochondrial metagenomics for studies of species abundance. 4. A phylogenetic tree from the mitogenomes showed high congruence with known relationships in Chrysomelidae. Patterns of taxonomic and phylogenetic dissimilarity between sites were highly consistent with data from morphological identifications. 5. The power of mitochondrial metagenomics results from the possibility of rapid assembly of mitogenomes from mixtures of specimens and the use of read counts for accurate estimates of key parameters of biodiversity directly from community samples.
Data from: Hypothesis-driven and field-validated method to prioritize fragmentation mitigation efforts in road projects
The active field of connectivity conservation has provided numerous methods to identify wildlife corridors with the aim of reducing the ecological effect of fragmentation. Nevertheless, these methods often rely on untested hypotheses of animal movements, usually fail to generate fine-scale predictions of road crossing sites, and do not allow managers to prioritize crossing sites for implementing road fragmentation mitigation measures. We propose a new method that addresses these limitations. We illustrate this method with data from southwestern Gabon (central Africa). We used stratified random transect surveys conducted in two seasons to model the distribution of African forest elephant (Loxodonta cyclotis), forest buffalo (Syncerus caffer nanus), and sitatunga (Tragelaphus spekii) in a mosaic landscape along a 38.5 km unpaved road scheduled for paving. Using a validation data set of recorded crossing locations, we evaluated the performance of three types of models (local suitability, local least-cost movement, and regional least-cost movement) in predicting actual road crossings for each species, and developed a unique and flexible scoring method for prioritizing road sections for the implementation of road fragmentation mitigation measures. With a data set collected in <10 weeks of fieldwork, the method was able to identify seasonal changes in animal movements for buffalo and sitatunga that shift from a local exploitation of the site in the wet season to movements through the study site in the dry season, whereas elephants use the entire study area in both seasons. These three species highlighted the need to use species- and season-specific modeling of movement. From these movement models, the method ranked road sections for their suitability for implementing fragmentation mitigation efforts, allowing managers to adjust priority thresholds based on budgets and management goals. The method relies on data that can be obtained in a period compatible with environmental impact assessment constraints, and is flexible enough to incorporate other potential movement models and scoring criteria. This approach improves upon available methods and can help inform prioritization of road and other linear infrastructure segments that require impact mitigation methods to ensure long-term landscape connectivity.
Data from: Development and validation of rapid environmental DNA (eDNA) detection methods for bog turtle (Glyptemys muhlenbergii)
Bog turtles Glyptemys muhlenbergii are listed as Species of Greatest Conservation Need (SGCN) for wildlife action plans in every state it occurs and multi-state efforts are underway to better characterize extant populations and prioritize restoration efforts. However, traditional sampling methods can be ineffective due to the turtle's wetland habitat, small size, and burrowing nature. Molecular methods, such as qPCR, provide the ability to overcome this challenge by effectively quantifying minute amounts of turtle DNA left behind in its environment (eDNA). Developing such methods for bog turtles has proved difficult partly because of the high sequence similarity between bog turtles and closely-related, cohabitating species, most often wood turtles ( Glyptemys insculpta ). Additionally, substrates containing bog turtle eDNA are often rich in organics or other substances that frequently inhibit both DNA extraction and qPCR amplification. Here, we describe the development and validation of a qPCR assay, BT3, targeting the mitochondrial cytochrome oxidase I gene that correctly identifies bog turtles with 100% specificity and sensitivity when tested on 201 blood samples collected from six species over a wide geographic range. We also developed a full-process internal control employing a genetically modified strain of Caenorhabditis elegans to improve DNA extraction methods, limit false negative results due to qPCR inhibition, and measure total DNA recovery from each sample. Using the internal control, we found that DNA recovery varied by over an order of magnitude between samples and likely explains the lack of bog turtle detection in some cases. Methods presented herein are highly-specific and may offer a more cost effective, non-invasive tool to supplement bog turtle population assessments in the eastern United States. Poor or differential DNA recovery, which remains unmeasured in the vast majority of eDNA studies, significantly reduced the ability to detect bog turtle in their natural environment.
Data from: Multiplex preamplification PCR and microsatellite validation allows accurate single nucleotide polymorphism (SNP) genotyping of historical fish scales
Incorporating historical tissues into the study of ecological, conservation, and management questions can broaden the scope of population genetic research by enhancing our understanding of evolutionary processes and anthropogenic influences on natural populations. Genotyping historical and low-quality samples has been plagued by challenges associated with low amounts of template DNA and the potential for preexisting DNA contamination among samples. We describe a two-step process designed to (i) accurately genotype large numbers of historical low-quality scale samples in a high-throughput format and (ii) screen samples for preexisting DNA contamination. First, we describe how an efficient multiplex preamplification PCR of 45 single nucleotide polymorphisms (SNPs) can generate highly accurate genotypes with low failure and error rates in subsequent SNP genotyping reactions of individual historical scales from sockeye salmon (Oncorhynchus nerka). Second, we demonstrate how the method can be modified for the amplification of microsatellite loci to detect preexisting DNA contamination. A total of 760 individual historical scale and 182 contemporary fin clip samples were genotyped and screened for contamination. Genotyping failure and error rates were exceedingly low and similar for both historical and contemporary samples. Preexisting contamination in 21% of the historical samples was successfully identified by screening the amplified microsatellite loci. The potential for automation, low failure and error rates, and ability to multiplex both the preamplification and subsequent genotyping reactions combine to make the protocol ideally suited for efficiently genotyping large numbers of potentially contaminated low-quality sources of DNA.
Microphone Comparison Array Validation Dataset
<p>Data generated as part of research to determine an appropriate method to make audio recordings suitable for use in comparing the perceptual characteristics imparted by microphones. Data comprise audio files, listening test interfaces and MATLAB code.</p> <p><strong>References</strong></p> <p>BBC SNN (2015): A.Pearce. T.Brookes, M.Dewhirst, "Timbral differences between microphones", BBC Sound Now & Next Technology Fair, London, UK, 19-20 May 2015</p> <p>AES139 (2015): A.Pearce. T.Brookes, M.Dewhirst, "Validation of experimental methods to record stimuli for microphone comparisons", Audio Eng.Soc. 139th Convention, New York, USA, 29 Oct - 1 Nov 2015</p>
ScienceDex guides
Understand access before you commit
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.