Find research datasets worth reusing

Data from: The importance of using topographic features to predict climate-resilient habitat for migratory forest landbirds: an example for the Rusty Blackbird, Olive-sided Flycatcher, and Canada Warbler

Maintaining a functionally-connected network of high-quality habitat is one of the most effective responses to biodiversity loss. However, the spatial distribution of suitable habitat may shift over time in response to climate change. Taxa such as migratory forest landbirds are already undergoing climate-driven range shifts. Therefore, patches of climate-resilient habitat (also known as "climate refugia") are especially valuable from a conservation perspective. Here, we performed maximum entropy (Maxent) species distribution modeling to predict suitable and potentially climate-resilient habitat in Nova Scotia, Canada, for three migratory forest landbirds: Rusty Blackbird (Euphagus carolinus), Olive-sided Flycatcher (Contopus cooperi), and Canada Warbler (Cardellina canadensis). We used a reverse stepwise elimination technique to identify covariates that influence habitat suitability for the target species at broad scales, including abiotic (topographic control of moisture and nutrient accumulation) and biotic (forest characteristics) covariates. As topography should be relatively unaffected by a changing climate and helps regulate the structure and composition of forest habitat, we posit that the inclusion of appropriate topographic features may support the identification of climate-resilient habitat. Of all covariates, Depth to water table was the most important predictor of relative habitat suitability for the Rusty Blackbird and Canada Warbler, with both species showing a strong association with wet areas. Mean canopy height was the most important predictor for the Olive-sided Flycatcher, whereby the species was associated with taller trees. Our models, which comprise the finest scale species distribution models available for these species in this region, further indicated that, for all species, habitat (1) remains relatively abundant and well distributed in Nova Scotia and (2) is often located in wet lowlands (a climate-resilient topographic landform). These findings suggest that opportunities remain to conserve breeding habitat for these species despite changing temperature and precipitation regimes.

Data from: Glucocorticoid levels predict subsequent social tactic in females of a facultatively social mammal

Facultatively social species, in which individuals can switch between group- and solitary-living tactics, offer an opportunity to shed light on proximate mechanisms underlying alternative life histories. Promising hormonal mediators of social tactic include glucocorticoids, which control energy allocation and are negatively related to body condition, and testosterone which regulates numerous social behaviours. Here, we investigated hormonal profiles associated with social tactic in eight generations of free-living female striped mice (Rhabdomys pumilio). Females are group living during the non-breeding season but live solitarily or with female kin in communally-breeding groups during the breeding season. We tested whether females' baseline levels of corticosterone (a glucocorticoid) and testosterone measured during the breeding season differed before and after females left the group (i.e. when they switched from a group-living to solitary social tactic), and whether the hormone levels of females that remained group living differed before and after their nestmates became solitary. We also asked whether seasonal variation in hormone levels was associated with social tactic. During the breeding season, corticosterone levels were lower in solitary than group-living females both before and after solitary females left the group, and did not differ in solitary females before and after leaving. In group-living females, corticosterone was higher after their nestmates became solitary than before they left. Testosterone levels did not differ before and after females became solitary and were not associated with social tactic. Corticosterone was higher during the breeding than the non-breeding season in group-living females but did not differ between the seasons in solitary females. Testosterone levels were higher in both social tactics during the non-breeding season. We conclude that baseline corticosterone but not testosterone levels are associated with female social tactic, and that corticosterone levels at the start of the breeding season, when all females are group living, may be an indicator of subsequent tactic: solitary females' corticosterone levels were lower than those of females that remained group living even before the solitary phenotype was expressed. Glucocorticoids might therefore play a modulatory role in social organisation in facultatively social species.

Breeding season length predicts duet coordination and consistency in Neotropical wrens (Troglodytidae)

Many animals produce coordinated signals, but few are more striking than the elaborate male-female vocal duets produced by some tropical songbirds. Yet, little is known about the factors driving the extreme levels of vocal coordination between mated pairs in these taxa. We examined evolutionary patterns of duet coordination and their potential evolutionary drivers in Neotropical wrens (Troglodytidae), a songbird family well-known for highly coordinated duets. Across 23 wren species we show that the degree of coordination and precision with which pairs combine their songs into duets varies by species. This includes some species that alternate their song phrases with exceptional coordination to produce rapidly alternating duets that are highly consistent across renditions. These highly coordinated, consistent duets evolved independently in multiple wren species. Duet coordination and consistency are greatest in species with especially long breeding seasons, but neither duet coordination nor consistency are correlated with clutch size, conspecific abundance, or vegetation density. These results suggest that tightly coordinated duets play an important role in mediating breeding behaviour, possibly by signalling commitment or coalition of the pair to mates and other conspecifics.

Data for: Early-life behavior predicts first-year survival in a long-distance avian migrant

Early-life conditions have critical, long-lasting effects on the fate of individuals, yet early-life activity has rarely been linked to subsequent survival of animals in the wild. Using high-resolution GPS and body-acceleration data of 93 juvenile white storks (Ciconia ciconia), we examined the links between behavior during both pre-fledging and post-fledging (fledging-to-migration) periods and subsequent first year survival. Juvenile daily activity (based on overall dynamic body acceleration) showed repeatable between-individual variation, the juveniles' pre and post-fledging activity levels were correlated, and both were positively associated with subsequent survival. Daily activity increased gradually throughout the post-fledging period, and the relationship between post-fledging activity and survival was stronger in individuals who increased their daily activity level faster (an interaction effect). We suggest that high activity profiles signified individuals with increased pre-migratory experience, higher individual quality and perhaps more proactive personality, which could underlie their superior survival rates. The duration of individuals' fledging-to-migration periods had a hump-shaped relationship with survival: higher survival was associated with intermediate rather than short or long durations. Short durations reflect lower pre-migratory experience, whereas very long ones were associated with slower increases in daily activity level which possibly reflects slow behavioral development. In accordance with previous studies, heavier nestlings and those that hatched and migrated earlier had increased survival. Using extensive tracking data, our study exposed new links between early-life attributes and survival, suggesting that early activity profiles in migrating birds can explain variation in first-year survival.

Data from: Machine learning improves predictions of agricultural nitrous oxide (N2O) emissions from intensively managed cropping systems

The potent greenhouse gas nitrous oxide (N2O) is accumulating in the atmosphere at unprecedented rates largely due to agricultural intensification, and cultivated soils contribute ~60% of the agricultural flux. Empirical models of N2O fluxes for intensively managed cropping systems are confounded by highly variable fluxes and limited geographic coverage; process-based biogeochemical models are rarely able to predict daily to monthly emissions with > 20% accuracy even with site-specific calibration. Here we show the promise for machine learning (ML) to significantly improve field-level flux predictions, especially when coupled with a cropping systems model to simulate unmeasured soil parameters. We used sub-daily N2O flux data from six years of automated flux chambers installed in a continuous corn rotation at a site in the upper U.S. Midwest (~3000 sub-daily flux observations), supplemented with weekly to biweekly manual chamber measurements (~1100 daily fluxes), to train an ML model that explained 65-89% of daily flux variance with very few input variables –soil moisture, days after fertilization, soil texture, air temperature, soil carbon, precipitation, and N fertilizer rate. When applied to a long-term test site not used to train the model, the model explained 38% of the variation observed in weekly to biweekly manual chamber measurements from corn, and 51% upon coupling the ML model with a cropping systems model that predicted daily soil N availability. This represents a 2-3 times improvement over conventional process-based models and with substantially fewer input requirements. This coupled approach offers promise for better predictions of agricultural N2O emissions and thus more precise global models and more effective agricultural mitigation interventions.

Supplementary material 1 from: Bustamante RO, Alves L, Goncalves E, Duarte M, Herrera I (2020) A classification system for predicting invasiveness using climatic niche traits and global distribution models: application to alien plant species in Chile. NeoBiota 63: 127-146. https://doi.org/10.3897/neobiota.63.50049

Table S1. Exotic species located in Quadrant 1 (see Figure 3) and impacts on biodiversity, agriculture and cattle raisng

Co-infection best predicts respiratory viral infection in a wild host

1) The dynamics of directly transmitted pathogens in natural populations are likely to result from the combined effects of host traits, pathogen biology and interactions among pathogens within a host. Discovering how these factors work in concert to shape variation in pathogen dynamics in natural host – multi‐pathogen systems is fundamental to understanding population health. 2) Here, we describe temporal variation in incidence and then elucidate the effect of hosts trait, season, and pathogen co‐occurrence on host infection risk using one of the most comprehensive studies of co‐infection in a wild population: a suite of seven directly‐transmitted, viral and bacterial, respiratory infections from a four‐year study of 200 free‐ranging African buffalo (Syncerus caffer). 3) Incidence of upper respiratory infections was common throughout the study – five out of the seven pathogens appeared to be consistently circulating throughout our study population. One pathogen exhibited clear outbreak dynamics in our final study year and another was rarely detected. 4) Co‐infection was also common in this system. The strongest indicator of pathogen occurrence for respiratory viruses was, in fact, the presence of other viral respiratory infections. Host traits had minimal effects on odds of pathogen occurrence but did modify pathogen‐pathogen associations. In contrast, only season predicted bacterial pathogen occurrence. 5) Though a combination of environmental, behavioral, and physiological factors work together to shape disease dynamics, we found pathogen associations best determined infection risk. Our study demonstrates that, in absence of very fine‐scale data, the intricate changes among these factors are best represented by co‐infection.

opencc-zeroDec 2019View details →

Transitions in paternal social status predict patterns of offspring growth and metabolic transcription

Parental effects occur when changes in the parental phenotype or environment cause changes to offspring phenotype. While some parental effects are triggered in response to an environmental cue in a time-locked fashion, other parental effects persist even after the cue has been removed, suggesting multiple timescales of action. For parental effects to serve as reliable signals of current environmental conditions, they should be reversible, such that when the cue changes, offspring phenotypes change in accordance. Social hierarchy is a prevalent feature of the environment, and current parental social status could signal the environment in which offspring will be born. Here, we sought to address parental effects of social status and their timescale of action in mice. We show that competition in seminatural environments affects offspring growth rate. Although dominant males are not heavier than nondominant or control males, they produce faster growing offspring, particularly sons. The timing, effect-size, and sex-specificity of this association are modulated by maternal social experience. We show that a change in paternal social status is sufficient to modulate offspring weight: from one breeding cycle to the next, status-ascending males produce heavier sons than before, and status-descending males produce lighter sons than before. Current paternal status is also highly predictive of liver transcription in sons, including molecular pathways controlling oxidative phosphorylation and iron metabolism. These results are consistent with a parental effect of social experience, although alternative explanations are considered. In summary, changes in paternal social status are associated with changes in offspring growth and metabolism.

opencc-zeroApr 2020View details →

Predicting human complexity perception of real-world scenes

Perceptual load is a well-established determinant of attentional engagement in a task. So far, perceptual load has typically been manipulated by increasing either the number of task-relevant items or the perceptual processing demand (e.g. conjunction vs. feature tasks). The tasks used often involved rather simple visual displays (e.g. letters or single objects). How can perceptual load be operationalised for richer, real-world images? A promising proxy is the visual complexity of an image. However, current predictive models for visual complexity have limited applicability to diverse real-world images. Here we modelled visual complexity using a deep convolutional neural network trained to learn perceived ratings of visual complexity. We presented 53 observers with 4000 images from the PASCAL VOC dataset, obtaining 75,020 2AFC paired comparisons across observers. Image visual complexity scores were obtained using the TrueSkill algorithm. A CNN with weights pre-trained on an object recognition task predicted complexity ratings with r=0.83. In contrast, feature-based models as used in the literature, working on image statistics such as entropy, edge density and JPEG compression ratio, only achieved r = 0.70. Thus, our model offers a promising method to quantify the perceptual load of real-world scenes through visual complexity.

Most cultural importance indices do not predict species cultural keystone status

The use of quantitative indices to quantify the importance of a plant species to human societies is widespread. While quantifi- cation may yield support for standardized methodologies and facilitate generalizations, it is important to examine the potential limitations of these indices. Moreover, because these indices are calculated at the species level, failure to control for phylogenetic relatedness in predictive models may yield misleading conclusions. We test if commonly used cultural importance indices predict species cultural keystone status among the Shipibo-Konibo community of Paoyhan in the Peruvian Amazon. Eleven of the 12 indices were correlated with each other indicating most cultural importance indices are redundant. Most indices did not predict species cultural keystone status. Phylogenetic control improved our models indicating a significant part of the predictive power of even the best index was explained by species shared evolutionary history. Our findings highlight the need for the cautious use of cultural importance indices to infer species cultural keystone status. Newly developed indices should be tested for correlation with existing indices to avoid redundancy.

opencc-by-4.0Apr 2020View details →

Predicted carbon-13 NMR data of Natural Products (PNMRNP)

PNMRNP is an SDF file that reports the structure, properties and classification of 211,280 natural products. The starting point of this work (January 2019) was <a href="https://github.com/oolonek/ISDB/tree/master/Data/dbs">ISDB</a> which contains csv files of the UNPD data base (Gu J et al., PLOS ONE 2013, 8, e62839, doi:10.1371/journal.pone.0062839) and which are packaged with the ISDB mass spectrometry fragmentation database (Allard PM et al., Anal. Chem. 2016, 88, 6, 3317-3323, doi: 10.1021/acs.analchem.5b04804). Starting from InChI strings of compounds, 2D structures with configuration data were produced mainly from the <a href="http://www.rdkit.org">RDKit cheminformatic toolkit</a>. <a href="http://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi">Pubchem</a> identifiers were searched for the compounds and used as keys to give names and synonyms to molecules. Carbon atoms in molecular structures were associated to 13C NMR chemical shift values using <a href="https://nmrshiftdb.nmr.uni-koeln.de">nmrshiftdb2</a>. A three-level classification of compounds according to sub-structure presence is proposed. A compound may be classified as a terpene (level 1) as a sesquiterpene (level 2) and as an eudesmane (level 3). Version 2 of PNMRNP includes the classification of organic compounds according to <a href="http://classyfire.wishartlab.com">ClassyFire</a>. Version 3 of PNMRNP includes 13C NMR chemical shifts calculated using ACD/Labs C+H NMR Predictors and DB.

Remotely sensed forest understory density and nest predator occurrence interact to predict suitable breeding habitat and the occurrence of a resident boreal bird species

Habitat suitability models (HSM) based on remotely sensed data are useful tools in conservation work. However, they typically use species occurrence data rather than robust demographic variables, and their predictive power is rarely evaluated. These shortcomings can result in misleading guidance for conservation. Here, we develop and evaluate a HSM based on correlates of long term breeding success of an open nest building boreal forest bird, the Siberian jay. In our study site in northern Sweden, nest failure of this permanent resident species is driven mainly by visually hunting corvids that are associated with human settlements. Parents rely on understory nesting cover as protection against these predators. Accordingly, our HSM includes a light detection and ranging (LiDAR) based metric of understory density around the nest and the distance of the nest to the closest settlement to predict breeding success. It reveals that a high understory density 15-80 m around nests is associated with increased breeding success in territories close to settlements (<1.5 km). Farther away from human settlements breeding success is highest at nest sites with a more open understory providing a favourable warmer microclimate. We validated this HSM by comparing the predicted breeding success with landscape-wide census data on Siberian jay occurrence. The correlation between breeding success and occurrence was strong up to 40 km around the study site. However, the HSM appears to overestimate breeding success in regions with a milder climate, and therefore higher corvid numbers. Our findings suggest that maintaining patches of small diameter trees may provide a cost-effective way to restore the breeding habitat for Siberian jays up to 1.5 km from human settlements. This distance is expected to increase in the warmer, southern, and coastal range of the Siberian jay where the presence of other corvids is to a lesser extent restricted to settlements.

The datasets used in "RNA secondary structure prediction using deep learning with thermodynamic integration"

The datasets used in "RNA secondary structure prediction using deep learning with thermodynamic integration"

Raw data for predicting sample success for large-scale ancient DNA studies on marine mammals

In recent years, non-human ancient DNA studies have begun to focus on larger sample sizes and whole genomes, offering the potential to reveal exciting and hitherto unknown answers to ongoing biological and archaeological questions. However, one major limitation to the feasibility of such studies is the substantial financial and time investments still required during sample screening, due to uncertainty regarding successful sample selection. This study investigates the effect of a wide range of sample properties including latitude, sample age, skeletal element, collagen preservation, and context on endogenous content and DNA damage profiles for 317 ancient and historic pinniped samples collected from across the North Atlantic. Using generalised linear and mixed-effect models, we found that a range of factors affected DNA preservation within each of the species under consideration. The most important findings were that endogenous content varied significantly according to context, the type of skeletal element, the collagen content and collection year. There also appears to be an effect of the sample's geographic origin, with samples from the Arctic generally showing higher endogenous content and lower damage rates. Both latitude and sample age were found to have significant relationships with damage levels, but only for walrus samples. Sex, ontogenetic age and extraction material preparation were not found to have any significant relationship with DNA preservation. Overall, the skeletal element and sample context were found to be the most influential factors and should therefore be considered when selecting samples for large-scale ancient genome studies.

Probabilistic Approach for the Fatigue Strength Prediction of Polymers

The dominating factor in the fatigue of structures made from fiber reinforced polymers (FRP), for example wind turbine blades, is the polymer matrix. Traditionally, experimental stress-life data of polymers is approximated via a linear double-log Basquin model. Recently, the non-linear stress-life formulation by Stüssi was found to provide a better fit of the experimental data with a substantially reduced standard deviation. Moreover, a non-linear constant-life formulation, as proposed by Boerstra, for example, can enhance the representation of the mean stress effect compared to state-of-the-art linear models, i.e., the modified Goodman relation. To this end, we incorporated Stüssi’s model into the Boerstra relation to take account of the mean stress effects of an epoxy. This stress-life formulation was then enhanced with the Weibull probability function. The probabilistic-stress-life model provided a good approximation of the fatigue performance as a function of the stress ratio on the basis of an experimental data set. Finally, we suggested a step-wise engineering approach to derive the permissible stress-life with a view to practical design purposes. The procedure increased the reliability of the fatigue design evaluation compared to the state-of-the-art methodologies. Slides: <a href="https://doi.org/10.5281/zenodo.4443149">https://doi.org/10.5281/zenodo.4443149</a> Paper: <a href="https://doi.org/10.2514/6.2021-1289">https://doi.org/10.2514/6.2021-1289</a>

Predictions results of DeepViral

Predictions results of the DeepViral paper, available at https://doi.org/10.1101/2020.04.22.055095. All files are tab separated, with 6 fields: <virus protein> <human protein> <virus taxon> <virus family> <predicted score> <label based on HPIDB> 1. The prediction results for 14 virus families based on the LOFO experiments. Each method was evaluated 5 times, i.e., indexed 0-4. <ul> <li>The baseline methods: RCNN - preds_rcnn_i.txt and RF+Doc2Vec - preds_rf_i.txt</li> <li>The DeepViral variants: <ul> <li>pheno: seq + viral embedding</li> <li>go: seq + human embedding</li> <li>seq and joint as described</li> </ul> </li> </ul> 2. The prediction results for 4 virus species based on the LOSO experiments. Each method was evaluated 5 times, i.e., indexed 0-4. <ul> <li>t - test virus; v - validation virus. Taxon IDs are based on NCBI Taxonomy.</li> <li>species - LOSO; family - LOFO</li> </ul>

Male-biased sexual selection, but not sexual dichromatism, predicts speciation in birds

Sexual selection is thought to shape phylogenetic diversity by affecting speciation or extinction rates. However, the net effect of sexual selection on diversification is hard to predict, because many of the hypothesised effects on speciation or extinction have opposing signs and uncertain magnitudes. Theoretical work also suggests that the net effect of sexual selection on diversification should depend strongly on ecological factors, though this prediction has seldom been tested. Here, we test whether variation in sexual selection can predict speciation and extinction rates across passerine birds (up to 5,812 species, covering most genera) and whether this relationship is mediated by environmental factors. Male-biased sexual selection, and specifically sexual size dimorphism, predicted two of the three measures of speciation rates that we examined. The link we observed between sexual selection and speciation was independent of environmental variability, though species with smaller ranges had higher speciation rates. There was no association between any proxies of sexual selection and extinction rate. Our findings support the view that male-biased sexual selection, as measured by frequent predictors of male-male competition, has shaped diversification in the largest radiation of birds.

Data from: Demographic traits improve predictions of spatiotemporal changes in community resilience to drought

<ol> <li>Communities are increasingly threatened by extreme weather events. The cumulative effects of such events are typically investigated by assessing community resilience, i.e., the extent to which affected communities can achieve pre-event states. However, a mechanistic understanding of the processes underlying resilience is frequently lacking and requires linking various measures of resilience to demographic responses within natural communities.</li> <li>Using 13 years of data from a shrub community that experienced a severe drought in 2005, we use generalized additive models to investigate temporal changes in three measures of resilience. We assess whether community-weighted, species-specific demographic traits such as longevity and reproductive output can predict changes in resilience and how the performance of these traits compares to more commonly used plant functional traits such as leaf area or root dry-matter content.</li> <li>We find significant spatiotemporal variation in community dynamics after the drought. Overall, demographic traits are better at predicting absolute and relative resilience in total plant cover, but functional traits outperform demographic traits when resilience in community composition is assessed. All resilience measures show non-linear and context-specific responses to demographic and functional traits; and these responses depend strongly on the severity of initial drought impact.</li> <li> Synthesis: Our work demonstrates that a full picture of the mechanisms underlying community responses to drought requires the assessment of numerous species-specific characteristics (including demographic and functional traits) and how these characteristics differentially affect complementary measures of community changes through time. </li> </ol>

Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study

Dataset of prediction of osteoporosis using APCT, radiomics and machine learning analysis.