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13,064 results for “predictions”
Data from: How to evaluate community predictions without thresholding?
<ol> <li>Stacked species distribution models (S-SDM) provide a tool to make spatial predictions about communities by first modelling individual species and then stacking the modelled predictions to form assemblages. The evaluation of the predictive performance is usually based on a comparison of the observed and predicted community properties (e.g., species richness, composition). However, the most available and widely used evaluation metrics require the thresholding of single species' predicted probabilities of occurrence to obtain binary outcomes (i.e., presence/absence). This binarisation can introduce unnecessary bias and error.</li> <li>Herein, we present and demonstrate the use of several groups of new or rarely used evaluation approaches and metrics for both species richness and community composition that do not require thresholding but instead directly compare the predicted probabilities of occurrences of species to the presence/absence observations in the assemblages.</li> <li> <i>Community AUC</i>, which is based on traditional AUC, measures the ability of a model to differentiate between species presences or absences at a given site according to their predicted probabilities of occurrence. Summing the probabilities gives the expected species richness and allows the estimation of the <i>probability that the observed species richness is not different from the expected species richness based on the</i><i> </i><i>species' probabilities of occurrence</i>. The traditional Sørensen and Jaccard similarity indices (which are based on presences/absences) were adapted to <i>maxSørensen</i> and <i>maxJaccard</i> and to <i>probSørensen</i> and <i>probJaccard</i> (which use probabilities directly). A further approach (<i>improvement over null</i><i> </i><i>models</i>) compared the predictions based on S-SDMs with the expectations from the null models to estimate the improvement in both species richness and composition predictions. Additionally, all metrics can be described against the environmental conditions of sites (e.g., elevation) to highlight the abilities of models to detect the variation in the strength of the community assembly processes in different environments.</li> <li>These metrics offer an unbiased view of the performance of community predictions compared to metrics that requiring thresholding. As such, they allow more straightforward comparisons of model performance among studies (i.e., they are not influenced by any subjective thresholding decisions).</li> </ol>
Dataset for Ongole at al. - Functional traits predict tree-level phenological strategies in a mesic Indian savanna
<p>Leaf phenology influences terrestrial primary production by determining the period of carbon uptake. While dominant leaf habit (evergreen/deciduous) is predictable at large scales based on environmental factors, there is substantial variability in the timing of key events such as leaf flush and senescence at smaller site-level scales. How much of this variability is explained by species functional traits related to a plant's carbon economy? We monitored leaf phenology for 113 individual trees of eleven dominant species in an Indian mesic savanna. Specifically, we related leaf functional traits and wood density to the (i) timing of leaf flush, leaf maturity, peak canopy and senescence, (ii) duration of leaf deployment, (iii) duration from start of senescence to leaflessness, and (iv) time to attainment of peak mature canopy following leaf flush. We expected species that use resources conservatively (low specific leaf area (SLA), high leaf dry matter content, low leaf nitrogen and high wood density) to senesce later and retain their leaves longer. For all species, leaflessness was most pronounced in early dry season and leaf flushing occurred in the late dry season. Species with high leaf carbon and dry matter content showed earlier leaf maturation, attained peak mature canopies sooner and deployed leaves longer. Species with high SLA, leaf nitrogen and low wood density showed earlier senescence but relationships were weak. In this savanna, phenology at fine scales was indeed associated with species functional traits relating to carbon investment, but these relationships were strong only when intra-specific variation in phenology was low.</p>
Data from: Androgens predict parasitism in female meerkats: a new perspective on a classic trade-off
The immunocompetence handicap hypothesis posits that androgens in males can be a 'double-edged sword', actively promoting reproductive success, while also negatively impacting health. Because there can be both substantial androgen concentrations in females and significant androgenic variation among them, particularly in species portraying female social dominance over males or intense female–female competition, androgens might also play a role in mediating female health and fitness. We examined this hypothesis in the meerkat (Suricata suricatta), a cooperatively breeding, social carnivoran characterized by aggressively mediated female social dominance and extreme rank-related reproductive skew. Dominant females also have greater androgen concentrations and harbour greater parasite loads than their subordinate counterparts, but the relationship between concurrent androgen concentrations and parasite burdens is unknown. We found that a female's faecal androgen concentrations reliably predicted her concurrent state of endoparasitism irrespective of her social status: parasite species richness and infection by Spirurida nematodes, Oxynema suricattae, Pseudandrya suricattae and coccidia were greater with greater androgen concentrations. Based on gastrointestinal parasite burdens, females appear to experience the same trade-off in the costs and benefits of raised androgens as do the males of many species. This trade-off presumably represents a health cost of sexual selection operating in females.
Data from: Do genomics and sex predict migration in a partially migratory salmonid fish, Oncorhynchus mykiss?
Partial migration is a common phenomenon wherein populations include migratory and resident individuals. Whether an individual migrates or not has important ecological and management implications, particularly within protected populations. Within partially migratory populations of O. mykiss, migration is highly correlated with a specific genomic region, but it is unclear how well this region predicts migration at the individual level. Here, we relate sex and life history genotype, determined using >400 SNPs on the migratory-linked genomic region, to life history expression of marked juvenile O. mykiss from two tributaries to the South Fork Eel River, northern California. Most resident fish were resident-genotypes (57% resident, 37% heterozygous, 6% migratory genotype) and male (78%). Most migratory fish were female (62%), but were a mixture of genotypes (30% resident, 45% heterozygous, 25% migratory genotype). Sex was more strongly correlated with life history expression than genotype, but the best-supported model included both. Resident genotypes regularly migrated, highlighting the importance of conserving the full suite of life history and genetic diversity in partially migratory populations.
Microbial associations and spatial proximity predict North American moose (Alces alces) gastrointestinal community composition
<ol> <li>Microbial communities are increasingly recognised as crucial for animal health. However, our understanding of how microbial communities are structured across wildlife populations is poor. Mechanisms such as interspecific associations are important in structuring free-living communities, but we still lack an understanding of how important interspecific associations are in structuring gut microbial communities in comparison to other factors such as host characteristics or spatial proximity of hosts.</li> </ol> <p> </p> <ol> <li>Here we ask how gut microbial communities are structured in a population of North American moose (<i>Alces alces</i>). We identify key microbial interspecific associations within the moose gut and quantify how important they are relative to key host characteristics, such as body condition, for predicting microbial community composition.</li> </ol> <p> </p> <ol> <li>We sampled gut microbial communities from 55 moose in a population experiencing decline due to a myriad of factors, including pathogens and malnutrition. We examined microbial community dynamics in this population utilizing novel graphical network models that can explicitly incorporate spatial information.</li> </ol> <p> </p> <ol> <li>We found that interspecific associations were the most important mechanism structuring gut microbial communities in moose and detected both positive and negative associations. Models only accounting for associations between microbes had higher predictive value compared to models including moose sex, evidence of previous pathogen exposure, or body condition. Adding spatial information on moose location further strengthened our model and allowed us to predict microbe occurrences with ~90% accuracy.</li> </ol> <p> </p> <ol> <li>Collectively, our results suggest that microbial interspecific associations coupled with host spatial proximity are vital in shaping gut microbial communities in a large herbivore. In this case, previous pathogen exposure and moose body condition were not as important in predicting gut microbial community composition. The approach applied here can be used to quantify interspecific associations and gain a more nuanced understanding of the spatial and host factors shaping microbial communities in non-model hosts.</li> </ol>
Data from: Phenotype-environment matching predicts both positive and negative effects of intraspecific variation
Natural populations can vary considerably in their genotypic and/or phenotypic diversity. Differences in this intraspecific diversity can have important consequences for contemporary ecological dynamics, but the direction and magnitude of these effects appear inconsistent across studies and systems. Here we proposed and tested the hypothesis that context-dependent ecological effects of altering phenotypic variance are predictable and arise from the relationship between a population's mean phenotype and the local environmental optimum. By factorially manipulating the mean and variance of a key host trait in environments with and without a lethal parasite, we demonstrate that increasing phenotypic variance can have beneficial effects for host populations (e.g. smaller disease epidemics), but only when the population's initial phenotype was poorly-matched to the local environment. When phenotypes were initially well-suited to environmental conditions, in contrast, greater phenotypic variance led to larger disease epidemics. Significant reductions in individual susceptibility occurred in both contexts over time, but the mechanisms leading to those reductions differed; strong selection was caused by either a 'suboptimal' trait mean and insufficient trait variance, or a 'near-optimal' trait mean and too much trait variance. Increasing intraspecific variation is clearly not always beneficial for populations, instead producing predictable ecological and evolutionary effects that depend on environmental context and biological interactions.
Data from: Genomic analysis and prediction within a US public collaborative winter wheat regional testing nursery
The development of inexpensive, whole-genome profiling enables a transition to allele-based breeding using genomic prediction models. These models consider alleles shared between lines to predict phenotypes and select new lines based on estimated breeding values. This approach can leverage highly-unbalanced datasets common to breeding programs. The Southern Regional Performance Nursery (SRPN) is a public nursery established by the USDA-ARS in 1931 to characterize performance and quality of near-release wheat varieties from breeding programs in the US Central Plains. New entries are submitted annually and can be reentered only once. The trial is grown at more than 30 locations each year and lines are evaluated for grain yield, disease resistance, and agronomic traits. Overall genetic gain is measured across years by including common check cultivars for comparison. We have generated whole-genome profiles via genotyping-by-sequencing for 939 SPRN entries dating back to 1992. We measured the diversity within the nursery and have explored its potential use as a GS training population. GS prediction models across years (average r= 0.33) outperformed year-to-year phenotypic correlation for yield (r=0.27) for a majority of the years evaluated, suggesting that genomic selection has the potential to outperform low heritability selection on yield in these highly variable environments. We also examined the predictability of programs using both program-specific and whole-set training populations. Generally, the predictability of a program was similar with both approaches. These results suggest that wheat breeding programs can collaboratively leverage the immense datasets that are generated from regional testing networks.
Data from: Varyingly hungry caterpillars: predictive models and foliar chemistry suggest how to eat a rainforest
A long-term goal in evolutionary ecology is to explain the incredible diversity of insect herbivores and patterns of plant host use in speciose groups like tropical Lepidoptera. Here we used standardised food-web data, multigene phylogenies of both trophic levels and plant chemistry data to model interactions between Lepidoptera larvae (caterpillars) from two lineages (Geometridae and Pyraloidea) and plants in species-rich lowland rainforest in New Guinea. Model parameters were used to make and test blind predictions for two hectares of exhaustively sampled forest. For pyraloids we relied on phylogeny alone and predicted 54% of species level interactions, translating to 79% of all trophic links for individual insects, by sampling insects from only 15% of local woody plant diversity. The phylogenetic distribution of host plant associations in polyphagous geometrids was less conserved, reducing accuracy. In a truly quantitative food-web only 40% of pair-wise interactions were described correctly in geometrids. Polyphenol oxidative activity (but not protein precipitation capacity), was important for understanding the occurrence of geometrids (but not pyraloids) across their hosts. When both foliar chemistry and plant phylogeny were included, we predicted geometrid-plant occurrence with 89% concordance. Such models help to test macroevolutionary hypotheses at the community level.
Data from: Eco-evolutionary feedbacks predict the time course of rapid life history evolution
Organisms can change their environment and, in so doing, change the selection they experience and how they evolve. Population density is one potential mediator of such interactions because high population densities can impact the ecosystem and reduce resource availability. At present, such interactions are best known from theory and laboratory experiments. Here we quantify the importance of such interactions in nature by transplanting guppies from a stream where they co-occur with predators into tributaries that previously lacked both guppies and predators. If guppies evolve solely because of the immediate reduction in mortality rate, the strength of selection and rate of evolution should be greatest at the outset then decline as the population adapts to its new environment. If indirect effects caused by the increase in guppy population density in the absence of predation prevail, then there should be a lag in guppy evolution because time is required for them to modify their environment. The duration of this lag is predicted to be associated with the environmental modification caused by guppies. We observed a lag in life history evolution associated with increases in population density and altered ecology. How guppies evolved matched predictions derived from evolutionary theory that incorporates such density effects.
Predictability of temporal variation in climate and the evolution of seasonal polyphenism in tropical butterflies
<p>Phenotypic plasticity in heterogeneous environments can provide tight environment-phenotype matching. However, the pre-requisite is a reliable environmental cue(s) that enables organisms to use current environmental information to induce the development of a phenotype with high fitness in a forthcoming environment. Here we quantify predictability in the timing of precipitation and temperature change to examine how this is associated with seasonal polyphenism in tropical Mycalesina butterflies. Seasonal precipitation in the tropics typically results in distinct selective environments, the wet- and dry seasons, and changes in temperature can be a major environmental cue. We sampled communities of Mycalesina butterflies from two seasonal and one aseasonal location. Quantifying environmental predictability using wavelet analysis and Colwell's indices confirmed a strong periodicity of precipitation over a 12-month period at both seasonal locations compared to the aseasonal one. However, temperature seasonality and periodicity differed between the two seasonal locations. We further show that: (1) most females from both seasonal locations synchronise their reproduction with the seasons by breeding in the wet season but arresting reproduction in the dry season. In contrast, all species breed throughout the year in the aseasonal location, and (2) species from the seasonal locations, but not those from the aseasonal location, exhibited polyphenism in wing pattern traits (eyespot size). We conclude that seasonal precipitation and its predictability are primary factors shaping the evolution of polyphenism in Mycalesina butterflies, and populations or species secondarily evolve local adaptations for cue use that depend on the local variation in the environment.</p>
Data from: Abiotic proxies for predictive mapping of near-shore benthic assemblages: implications for marine spatial planning
Marine spatial planning (MSP) should assist managers in guiding human activities towards sustainable practices and in minimizing user-conflicts in our oceans. A necessary first step is to quantify spatial patterns of marine assemblages in order to understand the ecosystem's structure, function, and services. However, the large spatial scale, high economic value, and density of human activities in near-shore habitats often makes quantifying this component of marine ecosystems especially daunting. To address this challenge, we developed an assessment method that employs abiotic proxies to rapidly characterize marine assemblages in near-shore benthic environments with relatively high resolution. We evaluated this assessment method along 300 km of the State of Maine's coastal shelf (< 100m depth)—a zone where high densities of buoyed lobster traps typically preclude extensive surveys by towed sampling gear (i.e., otter trawls). During the summer months of 2010-2013, we implemented a stratified-random survey using a small remotely operated vehicle that allowed us to work around lobster buoys and to quantify all benthic megafauna to species. Stratifying by substrate, depth, and coastal water masses, we found that abiotic variables explained a significant portion of variance (37- 59%) in benthic species composition, diversity, biomass and economic value. Generally, the density, diversity, and biomass of assemblages significantly increased with the substrate complexity (i.e., from sand-mud to ledge). The diversity, biomass and economic value of assemblages also decreased significantly with increasing depth. Lastly demersal fish densities, sessile invertebrate densities, species diversity, and assemblage biomass increased from east to west, while the abundance of mobile invertebrates and economic value decreased, corresponding mainly to the contrasting water-mass characteristics of the Maine Coastal Current system (i.e., summertime current direction, speed, and temperature). Integrating modeled predictions with existing GIS layers for abiotic conditions allowed us to scale up important assemblage attributes to define key foundational ecological principles of MSP and to find priority regions where some bottom-disturbing activities would have minimal impact to benthic assemblages. We conclude that abiotic proxies can be strong forcing functions for the assembly of marine communities and therefore useful tools for spatial extrapolations of marine assemblages in congested (heavily used) near-shore habitats.
Exploratory analysis using machine learning of predictive factors for falls in persons with type 2 diabetes: A Longitudinal Study
<p>The risk of falls in elderly individuals with diabetes was reported to be 1.5 - 3 times higher than in those without diabetes. However, it is not clear what risk factors are strongly related to falls in those with diabetes. In this study, we aimed to investigate the status of falls and to identify important risk factors for falls in persons with type 2 diabetes (T2D) including the non-elderly. Participants were 316 persons with T2D who were admitted to the University of Tsukuba Hospital for treatment of diabetes. They were assessed for medical history, laboratory data and physical capabilities during the hospitalization and were given a questionnaire on falls one year after discharge. Two different statistical models, logistic regression and random forest classifier, were used to investigate important predictors of falls. The response rate to the survey was 72%; of the 226 respondents, there were 129 males and 97 females (median age 62 years). The fall rate during the first year after discharge was 19% and increased with age; fall rates were 17% for those <60 years, 20% for those aged 60 – 69 years and 24% for those ≥70 years. Logistic regression revealed that knee extension strength (β= -0.698, P = 0.002), fasting C-peptide (F-CPR) level (β= 0.492, P = 0.009) and dorsiflexion strength (β= -0.432, P = 0.047) were independent predictors of falls. The random forest classifier placed knee extension strength (covariate importance = 0.304), grip strength (0.234), F-CPR level (0.232) and dorsiflexion strength (0.230) in the top 4 important variables for falls. The rate of falls in persons with T2D was high even in middle age. Lower extremity muscle weakness as well as elevated F-CPR levels and reduced grip strength were shown to be important risk factors for falls in T2D.</p>
Data from: Mid-winter temperatures, not spring temperatures, predict breeding phenology in the European starling Sturnus vulgaris
In many species, empirical data suggest that temperatures less than 1 month before breeding strongly influence laying date, consistent with predictions that short lag times between cue and response are more reliable, decreasing the chance of mismatch with prey. Here we show in European starlings (Sturnus vulgaris) that mid-winter temperature ca 50–90 days before laying (8 January–22 February) strongly (r2 = 0.89) predicts annual variation in laying date. Mid-winter temperature also correlated highly with relative clutch size: birds laid later, but laid larger clutches, in years when mid-winter temperatures were lower. Despite a high degree of breeding synchrony (mean laying date 5–13 April = ±4 days; 80% of nests laid within 4.8 days within year), European starlings show strong date-dependent variation in clutch size and productivity, but this appears to be mediated by a different temporal mechanism for integration of supplemental cue (temperature) information. We suggest the relationship between mid-winter temperature and breeding phenology might be indirect with both components correlating with a third factor: temperature-dependent development of the starling's insect (tipulid) prey. Mid-winter temperatures might set the trajectory of growth and final biomass of tipulid larvae, with this temperature cue providing starlings with information on breeding season prey availability (though exactly how remains unknown).
Data from: Does metabolism constrain bird and mammal ranges and predict shifts in response to climate change?
Mechanistic approaches for predicting the ranges of endotherms are needed to forecast their responses to environmental change. We test whether physiological constraints on maximum metabolic rate and the factor by which endotherms can elevate their metabolism (metabolic expansibility) influence cold range limits for mammal and bird species. We examine metabolic expansibility at the cold range boundary (MECRB) and whether species' traits can predict variability in MECRB and then use MECRB as an initial approach to project range shifts for 210 mammal and 61 bird species. We find evidence for metabolic constraints: the distributions of metabolic expansibility at the cold range boundary peak at similar values for birds (2.7) and mammals (3.2). The right skewed distributions suggest some species have adapted to elevate or evade metabolic constraints. Mammals exhibit greater skew than birds, consistent with their diverse thermoregulatory adaptations and behaviors. Mammal and bird species that are small and occupy low trophic levels exhibit high levels of MECRB. Mammals with high MECRB tend to hibernate or use torpor. Predicted metabolic rates at the cold range boundaries represent large energetic expenditures (>50% of maximum metabolic rates). We project species to shift their cold range boundaries poleward by an average of 3.9° latitude by 2070 if metabolic constraints remain constant. Our analysis suggests that metabolic constraints provide a viable mechanism for initial projections of the cold range boundaries for endotherms. However, errors and approximations in estimating metabolic constraints (e.g., acclimation responses) and evasion of these constraints (e.g., torpor/hibernation, microclimate selection) highlight the need for more detailed, taxa‐specific mechanistic models. Even coarse considerations of metabolism will likely lead to improved predictions over exclusively considering thermal tolerance for endotherms.
Genetic variance for behavioural 'predictability' of stress response
<p>Genetic factors underpinning phenotypic variation are required if natural selection is to result in adaptive evolution. However, evolutionary and behavioural ecologists typically focus on variation among individuals in their average trait values, and seek to characterise genetic contributions to this. As a result, less attention has been paid to if and how genes could contribute towards within-individual variance, or trait "predictability". In fact, phenotypic 'predictability' can vary among individuals, and emerging evidence from livestock genetics suggests this can be due to genetic factors. Here we test this empirically using repeated measures of a behavioural stress response trait in a pedigreed population of wild-type guppies. We ask (1) whether individuals differ in behavioural predictability, and (2) whether this variation is heritable and so evolvable under selection. Using statistical methodology from the field of quantitative genetics, we find support for both hypotheses and also show evidence of a genetic correlation structure between the behavioural trait mean and individual predictability. We show that investigating sources of variability in trait predictability is statistically tractable, and can yield useful biological interpretation. We conclude that, if widespread, genetic variance for 'predictability' will have major implications for the evolutionary causes and consequences of phenotypic variation. </p>
Severe inbreeding depression is predicted by the "rare allele load" in Mimulus guttatus
<p>Most flowering plants are hermaphroditic and experience strong pressures to evolve self‐pollination (automatic selection, reproductive assurance). Inbreeding depression (ID) can oppose selection for selfing, but it remains unclear if ID is typically strong enough to maintain outcrossing. To measure the full cost of sustained inbreeding on fitness, and its genomic basis, we planted highly homozygous, fully genome‐sequenced inbred lines of yellow monkeyflower (<i>Mimulus guttatus)</i> in the field next to outbred plants from crosses between the same lines. The cost of full homozygosity is severe: 65% for survival, 86% for lifetime seed production. Accounting for the unmeasured effect of lethal and sterile mutations, we estimate that the average fitness of fully inbred genotypes is only 3–4% that of outbred competitors. The genome sequence data provides no indication of simple overdominance, but the number of rare alleles carried by a line, especially within rare allele clusters nonrandomly distributed across the genome, is a significant negative predictor of fitness measurements. These findings are consistent with a deleterious allele model for ID. High variance in rare allele load among lines and the genomic distribution of rare alleles both suggest that migration might be an important source of deleterious alleles to local populations.</p>
Data from: Immune stability predicts tuberculosis infection risk in a wild mammal
Immunity is one of the most variable phenotypic traits in animals; however, some individuals may show less fluctuation in immune traits, resulting in stable patterns of immune variation over time. It is currently unknown whether immune variation has consequences for infectious disease risk. In this study, we identified moderately stable immune traits in wild African buffalo and asked whether the stability of these traits affected bovine tuberculosis (TB) infection risk. We found that adaptive immune traits such as the level of IFN-γ released after white blood cell stimulation, the number of circulating lymphocytes, and the level of antibodies against bovine adenovirus-3 were moderately repeatable (i.e. stable) over time, whereas parameters related to innate immunity either had low repeatability (circulating eosinophil numbers) or were not repeatable (e.g. neutrophil numbers, plasma bacteria killing capacity). Intriguingly, individuals with more repeatable IFN-γ and lymphocyte levels were at a significantly higher risk of acquiring TB infection. In stark contrast, average IFN-γ and lymphocyte levels were poor predictors of TB risk, indicating that immune variability rather than absolute response level better captured variation in disease susceptibility. This work highlights the important and under-appreciated role of immune variability as a predictor of infection risk.
Data from: Predictable adaptive trajectories of sexual coloration in the wild: evidence from replicate experimental guppy populations
The question of whether populations evolve predictably and consistently under similar selective regimes is fundamental to understanding how adaptation proceeds in the wild. We address this question with a replicated evolution experiment focused upon male sexual coloration in guppies (Poecilia reticulata). Fish were transplanted from a single high predation population in the Guanapo River to four replicate, guppy‐free low predation headwater streams. Two streams had their canopies thinned to adjust the setting under which male coloration is displayed and perceived. We assessed evolutionary divergence using second‐generation lab‐bred offspring of fish sampled four to six years following translocation. A prior experiment of the same design, performed in an adjacent drainage, resulted in the evolution of more extensive orange, black and iridescent markings. We however found evidence for expansion only in structural coloration (iridescent blue/green), no change in orange, and a reduction in black. This response amplifies earlier findings for Guanapo fish, revealing that trajectories of color elaboration differ among drainages. We also found that color phenotypes evolved more greatly at the thinned‐canopy sites. Our findings support the predictability of sexual trait evolution in the wild, and underscore the importance of signaling conditions and ornamental starting points in shaping adaptive trajectories.
Consistent measures of oxidative balance predict survival but not reproduction in a long-distance migrant
1. Physiological processes, including those that disrupt oxidative balance, have been proposed as key to understanding fundamental life history trade-offs. Yet examination of changes in oxidative balance within wild animals across time, space and major life history challenges remain uncommon. For example, migration presents substantial physiological challenges for individuals, and data on migratory individuals would provide crucial context for exposing the importance of relationships between oxidative balance and fitness outcomes. 2. Here we examined the consistency of commonly used measures of oxidative balance in longitudinally sampled free-living individuals of a long-lived, long-distance migrant, the Brent goose Branta bernicla hrota over periods of months to years. 3. Although inter-individual and temporal variation in measures of oxidative balance were substantial, we found high consistency in measures of lipid peroxidation and circulating non-enzymatic antioxidants in longitudinally sampled individuals. This suggests the potential for the existence of individual oxidative phenotypes. 4. Given intra-individual consistency, we then examined how these physiological measures relate to survival and reproductive success across all sampled individuals. Surprisingly, lower survival was predicted for individuals with lower levels of damage, with no measured physiological metric associated with reproductive success. 5. Our results demonstrate that snapshot measurements of a consistent measure of oxidative balance can inform our understanding of differences in a key demographic trait. However, the positive relationship between oxidative damage and survival emphasises the need to investigate relationships between the oxidative system and fitness outcomes in other species undergoing similar physiologically challenging lifecycles. This would highlight the extent to which variation in such traits and resource allocation trade-offs is a result of adaptation to different life history strategies. 30-Mar-2020
Ecological patterns of root nodule diversity in cultivated and wild rooibos populations: a community prediction approach
<p>There is interest in understanding the factors behind the biogeography of root-associated bacteria due to the joint effects that plant host, climate, and soil conditions can have on bacterial diversity. For legume crops with remaining wild populations, this is of even more importance, because the effects of cropping on undisturbed root-associated bacterial communities can be addressed. Here, we used a community prediction approach to describe the diversity of the root nodule bacterial communities of rooibos (<i>Aspalathus linearis</i>), an endemic legume crop from South Africa. The goal was to reveal whether patterns of root nodule community composition in paired cultivated and wild rooibos populations could be related to geographical distance, plant traits, and plant population type (i.e. cultivated or uncultivated). We identified a core of dominant and widespread <i>Mesorhizobium</i> ZOTUs that each defined one of 4 different root nodule community classes. Rooibos cultivation impacted root nodule bacterial diversity at regional and local scales, while the geographical origin of the root nodule communities was the strongest predictor of root nodule community structure. Beyond impacts of cultivation on root nodule bacterial diversity, this study suggests a mixture of dispersal limitation and ecological drift regionally, and selection by different plant populations locally, define the biogeography of rooibos root nodule bacterial communities. </p>
ScienceDex guides
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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.