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Data from: Improving spatial predictions of taxonomic, functional and phylogenetic diversity
1. In this study, we compare two community modelling approaches to determine their ability to predict the taxonomic, functional and phylogenetic properties of plant assemblages along a broad elevation gradient and at a fine resolution. The first method is the standard stacking individual species distribution modelling (SSDM) approach, which applies a simple environmental filter to predict species assemblages. The second method couples the SSDM and macroecological modelling (MEM - SSDM-MEM) approaches to impose a limit on the number of species co-occurring at each site. Because the detection of diversity patterns can be influenced by different levels of phylogenetic or functional trees, we also examine whether performing our analyses from broad to more exact structures in the trees influences the performance of the two modelling approaches when calculating diversity indices. 2. We found that coupling the SSDM with the MEM improves the predictions for the diversity facets compared with those of the SSDM alone. The accuracy of the SSDM predictions for the diversity indices varied greatly along the elevation gradient, and when considering broad to more exact structure in the functional and phylogenetic trees, the SSDM-MEM predictions were more stable. 3. SSDM-MEM moderately but significantly improved the prediction of taxonomic diversity, which was mainly driven by the corrected number of predicted species. The performance of both modelling frameworks increased when predicting the functional and phylogenetic diversity indices. In particular, fair predictions of the taxonomic composition by SSDM-MEM led to increasingly accurate predictions of the functional and phylogenetic indices, suggesting that the compositional errors were associated with species that were functionally or phylogenetically close to the correct ones; this did not always hold for the SSDM predictions. 4. Synthesis. In this study, we tested the use of a recently published approach that couples species distribution and macroecological models to provide the first predictions of the distribution of multiple facets of plant diversity: taxonomic, functional and phylogenetic. Moderate but significant improvements were obtained; thus, our results open promising avenues for improving the predictions of different facets of biodiversityacross broad environmental gradients when functional and phylogenetic information is available.
Genomic prediction applied to multiple traits and environments in second season maize hybrids
<p>Genomic selection has become a reality in plant breeding programs with the reduction in genotyping costs. Especially in maize breeding programs, it emerges as a promising tool for predicting hybrid performance. The dynamics of a commercial breeding program involve the evaluation of several traits simultaneously in a large set of target environments. Therefore, multi-trait multi-environment (MTME) genomic prediction models can leverage these data sets by exploring the correlation between traits and Genotype-by-Environment (G×E) interaction. Herein, we assess predictive abilities of univariate and multivariate genomic prediction models in a maize breeding program. To this end, we used data from 415 maize hybrids evaluated in four years of second season field trials for the traits grain yield, number of ears and grain moisture. Genotypes of these hybrids were inferred <i>in silico</i> based on their parental inbred lines using Single Nucleotide Polymorphisms (SNPs) markers obtained via genotyping-by-sequencing (GBS). Because genotypic information was available for only 257 hybrids, we used the genomic and pedigree relationship matrices to obtain the <b>H</b> matrix for all 415 hybrids. Our results demonstrated that in the single-environment context the use of multi-trait models was always superior in comparison to their univariate counterparts. Besides that, although MTME models were not particularly successful in predicting hybrid performance in untested years, they improved the ability to predict the performance of hybrids that had not been evaluated in any environment. However, the computational requirements of this kind of model could represent a limitation to its practical implementation and further investigation is necessary.</p>
Data from: Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genotyping platform
Background Genomic selection (GS) can offer unprecedented gains, in terms of cost efficiency and generation turnover, to forest tree selective breeding; especially for late expressing and low heritability traits. Here, we used: 1) exome capture as a genotyping platform for 1372 Douglas-fir trees representing 37 full-sib families growing on three sites in British Columbia, Canada and 2) height growth and wood density (EBVs), and deregressed estimated breeding values (DEBVs) as phenotypes. Representing models with (EBVs) and without (DEBVs) pedigree structure. Ridge regression best linear unbiased predictor (RR-BLUP) and generalized ridge regression (GRR) were used to assess their predictive accuracies over space (within site, cross-sites, multi-site, and multi-site to single site) and time (age-age/ trait-trait). Results The RR-BLUP and GRR models produced similar predictive accuracies across the studied traits. Within-site GS prediction accuracies with models trained on EBVs were high (RR-BLUP: 0.79–0.91 and GRR: 0.80–0.91), and were generally similar to the multi-site (RR-BLUP: 0.83–0.91, GRR: 0.83–0.91) and multi-site to single-site predictive accuracies (RR-BLUP: 0.79–0.92, GRR: 0.79–0.92). Cross-site predictions were surprisingly high, with predictive accuracies within a similar range (RR-BLUP: 0.79–0.92, GRR: 0.78–0.91). Height at 12 years was deemed the earliest acceptable age at which accurate predictions can be made concerning future height (age-age) and wood density (trait-trait). Using DEBVs reduced the accuracies of all cross-validation procedures dramatically, indicating that the models were tracking pedigree (family means), rather than marker-QTL LD. Conclusions While GS models' prediction accuracies were high, the main driving force was the pedigree tracking rather than LD. It is likely that many more markers are needed to increase the chance of capturing the LD between causal genes and markers.
Data from: Predictive modelling of habitat selection by marine predators with respect to the abundance and depth distribution of pelagic prey
1. Understanding the ecological processes that underpin species distribution patterns is a fundamental goal in spatial ecology. However, developing predictive models of habitat use is challenging for species that forage in marine environments, as both predators and prey are often highly mobile and difficult to monitor. Consequently, few studies have developed resource selection functions for marine predators based directly on the abundance and distribution of their prey. 2. We analysed contemporaneous data on the diving locations of two seabird species, the shallow-diving Peruvian Booby (Sula variegata) and deeper diving Guanay Cormorant (Phalacrocorax bougainvilliorum), and the abundance and depth distribution of their main prey, Peruvian anchoveta (Engraulis ringens). Based on this unique data set, we developed resource selection functions to test the hypothesis that the probability of seabird diving behaviour at a given location is a function of the relative abundance of prey in the upper water column. 3. For both species, we show that the probability of diving behaviour is mostly explained by the distribution of prey at shallow depths. While the probability of diving behaviour increases sharply with prey abundance at relatively low levels of abundance, support for including abundance in addition to the depth distribution of prey is weak, suggesting that prey abundance was not a major factor determining the location of diving behaviour during the study period. 4. The study thus highlights the importance of the depth distribution of prey for two species of seabird with different diving capabilities. The results complement previous research that points towards the importance of oceanographic processes that enhance the accessibility of prey to seabirds. The implications are that locations where prey is predictably found at accessible depths may be more important for surface foragers, such as seabirds, than locations where prey is predictably abundant. 5. Analysis of the relative importance of abundance and accessibility is essential for the design and evaluation of effective management responses to reduced prey availability for seabirds and other top predators in marine systems.
Data from: Depletion of heterogeneous source species pools predicts future invasion rates
Predicting how increasing rates of global trade will result in new establishments of potentially damaging invasive species is a question of critical importance to the development of national and international policies aimed at minimizing future invasions. Centuries of historical movement and establishment of invading species may have depleted the supply of species available for future invasions, and it has been suggested that the problem of invasions will diminish as a result of this. However, the extent to which source pool depletion affects future invasions remains unclear. Here we describe a mechanistic model that captures the simultaneous effects of depletion of source species pools along with increases in pathway rates (e.g. imports) to predict future numbers of new invasions. We assume that the distribution of species abundance within invasion pathways is positively skewed, which is modelled using a log-normal distribution. Given their high propagule pressure, the most abundant species are likely to invade first, while the many rare species are likely to invade only under high pathway volumes. We apply this model to the case study of bark beetle, Scolytinae, invasions in the USA. Source species pools in Europe and Asia (225 and 655 species of Scolytinae, respectively) are much larger than numbers that have historically established (16 and 32). Parameterization of the model indicates a highly skewed species abundance distribution in the pathway and this is confirmed by species frequencies in port inspection records, thus explaining why only a small fraction of species has historically invaded. Forecasts from the model indicate that with increasing rates of imports, more species from these regions are likely to invade in the future despite the depletion of the most abundant species from source species pools. Previous statistical models tend to underestimate future establishments in the presence of increasing import rates due to their failure to account for key underlying mechanisms. Policy implications. The mechanistic model developed here is widely applicable for predicting future invasions of all taxa and provides insights into how increases in rates of imports counteract the species pool depletion effect, resulting in the continued establishment of new species.
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: A new theory of plant-microbe nutrient competition resolves inconsistencies between observations and model predictions
Terrestrial plants assimilate anthropogenic CO2 through photosynthesis and synthesizing new tissues. However, sustaining these processes requires plants to compete with microbes for soil nutrients, which therefore calls for an appropriate understanding and modeling of nutrient competition mechanisms in Earth System Models (ESMs). Here, we survey existing plant-microbe competition theories and their implementations in Earth System Models (ESMs). We found no consensus regarding the representation of nutrient competition and that observational and theoretical support for current implementations are weak. To reconcile this situation, we applied the Equilibrium Chemistry Approximation (ECA) theory to plant-microbe nitrogen competition in a detailed grassland 15N tracer study and found that competition theories in current ESMs fail to capture observed patterns and the ECA prediction simplifies the complex nature of nutrient competition and quantitatively matches the 15N observations. Since plant carbon dynamics are strongly modulated by soil nutrient acquisition, we conclude that (1) predicted nutrient limitation effects on terrestrial carbon accumulation by existing ESMs may be biased and (2) our ECA-based approach may improve predictions by mechanistically representing plant-microbe nutrient competition.
Data from: Community structure of a Neotropical bat fauna as revealed by stable isotope analysis: Not all species fit neatly into predicted guilds
Neotropical bat communities are among the most diverse mammal communities in the world, and a better understanding of these assemblages may permit inferences about how so many species coexist. While broad trophic guilds (e.g., frugivore, insectivore) of bats are recognized, details of diet and similarities among species remain largely unknown. We used stable isotope ratios of carbon (δ13C) and nitrogen (δ15N) to characterize the community structure of a diverse Neotropical bat fauna from Belize to test predictions of niche theory and the competitive exclusion principle. We predicted that (1) interspecific variation in isotopic overlap would be greater within guilds than between guilds, and (2) no two sympatric populations would have isotopic niches that overlap completely, unless there is variation along some other axis (e.g., temporal, spatial). We additionally tested body size as an explanatory metric of potential overlap, and predicted that larger-bodied animals would have greater niche breadths. Results suggest that while guild-level characterizations of communities is at least somewhat informative, there are multiple examples of intra- and inter-guild species pairs with significantly overlapping isotopic niches, suggesting that, counter to predictions, they may compete for resources. Understanding the trophic structure of animal communities is fundamental to conservation and management of endangered species and ecosystems and important for evolutionary studies, and stable isotope analyses can provide key insights as well as informing hypotheses of the diet of species that are not well known.
Data from: Low-load pathogen spillover predicts shifts in skin microbiome and survival of a terrestrial-breeding amphibian
Wildlife disease dynamics are strongly influenced by the structure of host communities and their symbiotic microbiota. Conspicuous amphibian declines associated with the waterborne fungal pathogen Batrachochytrium dendrobatidis (Bd) have been observed in aquatic-breeding frogs globally. However, less attention has been given to cryptic terrestrial-breeding amphibians that have also been declining in tropical regions. By experimentally manipulating multiple tropical amphibian assemblages harboring natural microbial communities, we tested whether Bd spillover from naturally infected aquatic-breeding frogs could lead to Bd amplification and mortality in our focal terrestrial-breeding host: the pumpkin toadlet Brachycephalus pitanga. We also tested whether the strength of spillover could vary depending on skin bacterial transmission within host assemblages. Terrestrial-breeding toadlets acquired lethal spillover infections from neighboring aquatic hosts and experienced dramatic but generally non-protective shifts in skin bacterial composition primarily attributable to their Bd infections. In contrast, aquatic-breeding amphibians maintained mild Bd infections and higher survival, with shifts in bacterial microbiomes that were unrelated to Bd infections. Our results indicate that Bd spillover from even mildly infected aquatic-breeding hosts may lead to dysbiosis and mortality in terrestrial-breeding species, underscoring the need to further investigate recent population declines of terrestrial-breeding amphibians in the tropics.
Data from: Applying generalised allometric regressions to predict live body mass of tropical and temperate arthropods
1. The ecological implications of body size extend from the biology of individual organisms to ecosystem–level processes. Measuring body mass for high numbers of invertebrates can be logistically challenging, making length-mass regressions useful for predicting body mass with minimal effort. However, standardised sets of scaling relationships covering a large range in body length, taxonomic groups, and multiple geographical regions are scarce. 2. We collected 6212 arthropods from 19 higher-level taxa in both temperate and tropical locations to compile a comprehensive set of linear models relating live body mass to a range of predictor variables. We measured live weight (hereafter, body mass), body length and width of each individual and conducted linear regressions to predict body mass using body length, body width, taxonomic group and geographic region. Additionally, we quantified prediction discrepancy when using parameters from arthropods of a different geographic region. 3. Incorporating body width into taxon- and region-specific length-mass regressions yielded the highest prediction accuracy for body mass. Using regression parameters from a different geographic region increased prediction discrepancy, causing over- or underestimation of body mass depending on geographical origin and whether body width was included. 4. We present a comprehensive range of parameters for predicting arthropod body mass and provide guidance for selecting optimal scaling relationships. Given the importance of body mass for functional invertebrate ecology and the paucity of adequate regressions to predict arthropod body mass from different geographical regions, our study provides a long-needed resource for quantifying live body mass in invertebrate ecology research.
Data from: Cumulative early life adversity predicts longevity in wild baboons
In humans and other animals, harsh circumstances in early life predict morbidity and mortality in adulthood. Multiple adverse conditions are thought to be especially toxic, but this hypothesis has rarely been tested in a prospective, longitudinal framework, especially in long-lived mammals. Here we use prospective data on 196 wild female baboons to show that cumulative early adversity predicts natural adult lifespan. Females who experience ≥3 sources of early adversity die a median of 10 years earlier than females who experience ≤1 adverse circumstances (median lifespan is 18.5 years). Females who experience the most adversity are also socially isolated in adulthood, suggesting that social processes partially explain the link between early adversity and adult survival. Our results provide powerful evidence for the developmental origins of health and disease and indicate that close ties between early adversity and survival arise even in the absence of health habit and health care-related explanations.
Data from: Directed flight and optimal airspeeds: homeward-bound gulls react flexibly to wind yet fly slower than predicted
Birds in flight are proposed to adjust their body orientation (heading) and airspeed to wind conditions adaptively according to time and energy constraints. Airspeeds in goal-directed flight are predicted to approach or exceed maximum-range airspeeds, which minimize transport costs (energy expenditure per unit distance) and should increase in headwinds and crosswinds. Diagnosis of airspeed adjustment is however obscured by uncertainty regarding birds' goal-directions, transport costs, interrelations with orientation strategy and the attainability of predicted behaviour. To address these issues, we tested whether gulls minimized transport costs through adjustment of airspeed and heading to wind conditions during extended inbound flight over water (180–360 km) to their breeding colony, and introduce a methodology to assess transport (energy) efficiency given wind conditions. Airspeeds, heading, flight mode and energy expenditure were estimated using GPS tracking, accelerometer and wind data. Predicted flight was determined by simulating each trip according to maximum-range airspeeds and various orientation strategies. Gulls employed primarily flapping flight (93%), and negotiated crosswinds flexibly to exploit both high altitude tailwinds and coastal soaring opportunities. We demonstrate that predicted airspeeds in heavy crosswinds depend strongly on orientation strategy and presumed preferred direction. Measured airspeeds increased with headwind and crosswind similarly to maximum-range airspeeds based on full compensation for wind drift, yet remained ∼ 30% lower than predicted by all strategies, resulting in slower and 30–35% costlier flight. Interestingly, more energy could be saved through adjustment of airspeed (median 40%) than via orientation strategy (median 4%). Therefore, despite remarkably flexible reaction to wind at sea, these gulls evidently minimized neither time nor energy expenditure. However, airspeeds were possibly over-predicted by current aerodynamic models. This study emphasizes the importance of accounting for orientation strategy when assessing airspeed adjustments to wind and indicates that either the cost or adaptive 'currency' of extended flight among gulls may require revision.
Data from: Accurate genomic predictions for chronic wasting disease in U.S. white-tailed deer
<p>The geographic expansion of chronic wasting disease (CWD) in U.S. white-tailed deer (<em>Odocoileus virginianus</em>) has been largely unabated by best management practices, diagnostic surveillance, and depopulation of positive herds. Using a custom Affymetrix Axiom® single nucleotide polymorphism (SNP) array, we demonstrate that both differential susceptibility to CWD, and natural variation in disease progression, are moderately to highly heritable ( among farmed U.S. white-tailed deer, and that loci other than <em>PRNP</em> are involved. Genome-wide association analyses using 123,987 quality filtered SNPs for a geographically diverse cohort of 807 farmed U.S. white-tailed deer (n = 284 CWD positive; n = 523 CWD non-detect) confirmed the prion gene (<em>PRNP</em>; G96S) as a large-effect risk locus (<em>P</em>-value < 6.3E-11), as evidenced by the estimated proportion of phenotypic variance explained (PVE ≥ 0.05), but also demonstrated that more phenotypic variance was collectively explained by loci other than <em>PRNP</em>.<em> </em>Genomic best linear unbiased prediction (GBLUP; n = 123,987 SNPs) with <em>k</em>-fold cross validation (<em>k</em> = 3; <em>k</em> = 5) and random sampling (n = 50 iterations) for the same cohort of 807 farmed U.S. white-tailed deer produced mean genomic prediction accuracies ≥ 0.81; thereby providing the necessary foundation for exploring a genomically-estimated CWD eradication program.</p>
Beak morphology predicts apparent survival of crossbills: due to selective survival or selective dispersal?
<p>Dozens of morphologically differentiated populations, subspecies and species of crossbills (genus Loxia) exist. It has been suggested that this divergence is due to variation in the conifer cones that each population specialises upon, requiring a specific beak size to efficiently separate the cone scales. If so, apparent survival should depend on beak size. To test this hypothesis, we undertook multievent capture-recapture modelling for 6,844 individuals monitored during 27 years in a Pyrenean Common crossbill (L. curvirostra) population in a forest of mountain pine (Pinus uncinata). Apparent survival was indeed related to beak width, resulting in stabilizing selection around an optimum that was close to the observed mean beak width, indicating that local crossbill beak morphology is adapted to the conifer they feed upon. Both natural selection (selective mortality) and selective emigration of maladapted individuals may explain our findings. As is often the case in capture-recapture analyses but rarely recognised, we could not formally decompose apparent survival into selective mortality versus selective permanent emigration. Nonetheless, there are several indications that selective permanent emigration should not be fully excluded. First, natural selection by itself would have to be unusually strong compared to other empirical estimates to create the observed pattern of apparent survival. Second, the observed mean beak width was a bit lower than the estimated optimum beak width. This can be explained by immigration of crossbills with smaller beaks originating from southern populations, which may subsequently have left the study area permanently in response to low food intake. This is in line with a detected transient effect in the data, yet apparently little influx from crossbills from northern Europe. When permanent emigration is phenotypically selective this will have ecological and evolutionary consequences, so this possibility deserves more attention in general.</p>
Data from: Prediction and attenuation of seasonal spillover of parasites between wild and domestic ungulates in an arid mixed-use system
1.Transmission of parasites between host species affects host population dynamics, interspecific competition, and ecosystem structure and function. In areas where wild and domestic herbivores share grazing land, management of parasites in livestock may affect or be affected by sympatric wildlife due to cross-species transmission. 2.We develop a novel method for simulating transmission potential based on both biotic and abiotic factors in a semi-arid system in Botswana. Optimal timing of antiparasitic treatment in livestock is then compared under a variety of alternative host scenarios, including seasonally migrating wild hosts. 3.In this region, rainfall is the primary driver of seasonality of transmission, but wildlife migration leads to spatial differences in the effectiveness of treatment in domestic animals. Additionally, competent migratory wildlife hosts move parasites across the landscape. 4.Simulated transmission potential matches observed patterns of clinical disease in the study area. Increased wildlife contact is correlated with a decrease in disease, suggesting that noncompetent wild hosts may attenuate transmission by removing infective parasite larvae from livestock pasture. 5.Optimising the timing of treatment according to within-year rainfall patterns was considerably more effective than treating at a standard time of year. By targeting treatment in this way, efficient control can be achieved, mitigating parasite spillover from wildlife where it does occur. 6.Synthesis and applications: This model of parasite transmission potential enables evidence-based management of parasite spillover between wild and domestic species in a spatio-temporally dynamic system. It can be applied in other mixed-use systems to mitigate parasite transmission under altered climate scenarios or changes in host ranges.
Data from: Species traits and abundances predict metrics of plant–pollinator network structure, but not pairwise interactions
Plant–pollinator mutualistic networks represent the ecological context of foraging (for pollinators) and reproduction (for plants and some pollinators). Plant–pollinator visitation networks exhibit highly conserved structural properties across diverse habitats and species assemblages. The most successful hypotheses to explain these network properties are the neutrality and biological constraints hypotheses, which posit that species interaction frequencies can be explained by species relative abundances, and trait mismatches between potential mutualists respectively. However, previous network analyses emphasize the prediction of metrics of qualitative network structure, which may not represent stringent tests of these hypotheses. Using a newly documented temporally explicit alpine plant–pollinator visitation network, we show that metrics of both qualitative and quantitative network structure are easy to predict, even by models that predict the identity or frequency of species interactions poorly. A variety of phenological and morphological constraints as well as neutral interactions successfully predicted all network metrics tested, without accurately predicting species observed interactions. Species phenology alone was the best predictor of observed interaction frequencies. However, all models were poor predictors of species pairwise interaction frequencies, suggesting that other aspects of species biology not generally considered in network studies, such as reproduction for dipterans, play an important role in shaping plant–pollinator visitation network structure at this site. Future progress in explaining the structure and dynamics of mutualistic networks will require new approaches that emphasize accurate prediction of species pairwise interactions rather than network metrics, and better reflect the biology underlying species interactions.
Data from: Are pollination "syndromes" predictive? Asian dalechampia fit neotropical models
Using pollination-syndrome parameters and pollinator correlations with floral phenotype from the neotropics, we predicted that Dalechampia bidentata Blume (Euphorbiaceae) in southern China would be pollinated by female resin-collecting bees between 12 and 20 mm in length. Observations in southwestern Yunnan Province, China, revealed pollination by resin-collecting, female Megachile (Callomegachile) nr. faceta Bingham (Hymenoptera: Megachilidae). These bees, at 14 mm in length, were in the predicted size range, confirming the utility of syndromes and models developed in distant regions. Phenotypic-selection analyses and estimation of adaptive surfaces and adaptive accuracies together suggest that the blossoms of D. bidentata are well adapted to pollination by their most common floral visitors.
Data from: Out-of-sample predictions from plant–insect food webs: robustness to missing and erroneous trophic interaction records
With increasing biotic introductions, there is a great need for predictive tools to anticipate which new trophic interactions will develop and which will not. Phylogenetic constraint of interactions in both native and novel food webs can make some novel interactions predictable. However, many food webs are sparsely sampled, or may include inaccurate interactions. In such cases, it is unclear whether modeling methods are still useful to anticipate novel interactions. We ran bootstrap simulations of host-use models on a Lepidoptera–plant data set to remove native trophic records or add erroneous records in order to observe the effect of missing or erroneous data on the prediction of interactions with novel plants. We found that the model was robust to a large amount of missing interaction records, but lost predictive power with the addition of relatively few erroneous interaction records. The loss of predictive power with missing records was due to inaccuracy in estimating phylogenetic distance between native and novel hosts. Removal of interaction records proportionally to their encounter frequency in the field had little effect on the loss of predictive power. Host-use models may have immediate value for predicting novel interactions from large, but sparsely sampled databases of trophic interactions.
Data from: Gut microbiota signatures predict host and microbiota responses to dietary interventions in obese individuals
Background: Interactions between the diet and intestinal microbiota play a role in health and disease, including obesity and related metabolic complications. There is great interest to use dietary means to manipulate the microbiota to promote health. Currently, the impact of dietary change on the microbiota and the host metabolism is poorly predictable and highly individual. We propose that the responsiveness of the gut microbiota may depend on its composition, and associate with metabolic changes in the host. Methodology: Our study involved three independent cohorts of obese adults (n = 78) from Belgium, Finland, and Britain, participating in different dietary interventions aiming to improve metabolic health. We used a phylogenetic microarray for comprehensive fecal microbiota analysis at baseline and after the intervention. Blood cholesterol, insulin and inflammation markers were analyzed as indicators of host response. The data were divided into four training set – test set pairs; each intervention acted both as a part of a training set and as an independent test set. We used linear models to predict the responsiveness of the microbiota and the host, and logistic regression to predict responder vs. non-responder status, or increase vs. decrease of the health parameters. Principal Findings: Our models, based on the abundance of several, mainly Firmicute species at baseline, predicted the responsiveness of the microbiota (AUC = 0.77–1; predicted vs. observed correlation = 0.67–0.88). Many of the predictive taxa showed a non-linear relationship with the responsiveness. The microbiota response associated with the change in serum cholesterol levels with an AUC of 0.96, highlighting the involvement of the intestinal microbiota in metabolic health. Conclusion: This proof-of-principle study introduces the first potential microbial biomarkers for dietary responsiveness in obese individuals with impaired metabolic health, and reveals the potential of microbiota signatures for personalized nutrition.
Data from: Accurate predictions of coexistence in natural systems require the inclusion of facilitative interactions and environmental dependency
1. Coexistence between plant species is well known to depend on the outcomes of species interactions within an environmental context. The incorporation of environmental variation into empirical studies of coexistence are rare, however, due to the complex experiments needed to do so and the lack of feasible modelling approaches for determining how environmental factors alter specific coexistence mechanisms. 2. In this paper, we present a simple modelling framework for assessing how variation in species interactions across environmental gradients impact on niche overlap and fitness differences, two core determinants of coexistence. We use a novel formulation of an annual plant population dynamics model that allows for competitive and facilitative species interactions, and for variation in the strength and direction of these interactions across environmental gradients. Using this framework, we examine outcomes of plant-plant interactions between four commonly co-occurring annual plant species from Western Australian woodlands. We then determine how niche overlap and fitness differences between these species vary across three environmental gradients previously identified as important for structuring diversity patterns in this system: soil phosphorus, shade and water. 3. We found facilitation to be a wide-spread phenomenon and that interactions between most species pairs shift between competitive and facilitative across multiple environmental gradients. Environmental conditions also altered the strength, direction and relative variation of both niche overlap and fitness differences in non-linear and unpredictable ways. Synthesis We provide a simple framework for incorporating environmental heterogeneity into explorations of coexistence mechanisms. Our findings highlight the importance of the environment in determining the outcome of species interactions and the potential for pairwise coexistence between species. The prevalence of facilitation in our system indicates a need to improve current theoretical frameworks of coexistence to include non-competitive interactions, and ways of translating these effects into explicit predictions of coexistence. Our study also suggests a need for further research into determining which factors result in consistent responses of niche overlap and fitness differences to environmental variation. Such information will improve our ability to predict outcomes of coexistence, invasion events and responses of whole communities to future environmental change.
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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.