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13,064 results for “predictions”
Data from: Predicting global population connectivity and targeting conservation action for snow leopard across its range
Movements of individuals within and among populations help to maintain genetic variability and population viability. Therefore, understanding landscape connectivity is vital for effective species conservation. The snow leopard is endemic to mountainous areas of Central Asia and occurs within 12 countries. We assess potential connectivity across the species' range to highlight corridors for dispersal and genetic flow between populations, prioritizing research and conservation action for this wide-ranging, endangered top-predator. We used resistant kernel modeling to assess snow leopard population connectivity across its global range. We developed an expert-based resistance surface that predicted cost of movement as functions of topographical complexity and land cover. The distribution of individuals was simulated as a uniform density of points throughout the currently accepted global range. We modeled population connectivity from these source points across the resistance surface using three different dispersal scenarios that likely bracket the lifetime movements of individual snow leopard: 100km, 500km and 1000km. The resistant kernel models produced predictive surfaces of dispersal frequency across the snow leopard range for each distance scenario. We evaluated the pattern of connectivity in each of these scenarios and identified potentially important movement corridors and areas where connectivity might be impeded. The models predicted two regional populations, in the north and south of the species range respectively, and revealed a number of potentially important connecting areas. Discrepancies between model outputs and observations highlight unsurveyed areas of connected habitat that urgently require surveying to improve understanding of the global distribution and ecology of snow leopard, and target land management actions to prevent population isolation. The connectivity maps provide a strong basis for directed research and conservation action, and usefully direct the attention of policy makers.
Data from: Using predator-prey theory to predict outcomes of broad-scale experiments to reduce apparent competition
Apparent competition is an important process influencing many ecological communities. We used predator-prey theory to predict outcomes of ecosystem experiments aimed at mitigating apparent competition by reducing primary prey. Simulations predicted declines in secondary prey following reductions in primary prey because predators consumed more secondary prey until predator numbers responded to reduced prey densities. Losses were exacerbated by a higher carrying capacity of primary prey and a longer lag time of the predator's numerical response, but a gradual reduction in primary prey was less detrimental to the secondary prey. We compared predictions against two field experiments where endangered woodland caribou (Rangifer tarandus caribou) were victims of apparent competition. First, when deer (Odocoileus sp.) declined suddenly following a severe winter, cougar (Puma concolor) declined with a 1–2-year lag, yet in the interim more caribou were killed by cougars, and caribou populations declined by 40%. Second, when moose (Alces alces) were gradually reduced using a management experiment, wolf (Canis lupus) populations declined but did not shift consumption to caribou, and the largest caribou subpopulation stabilized. The observed contrasting outcomes of sudden versus gradual declines in primary prey supported theoretical predictions. Combining theory with field studies clarified how to manage communities to mitigate endangerment caused by apparent competition that affects many taxa.
Data from: Female-biased gape and body-size dimorphism in the New World watersnakes (tribe: Thamnophiini) oppose predictions from Rensch's Rule
Sexual size dimorphism (SSD) is ubiquitous across animals with female bias most prominent in snakes and other ectothermic organisms. To understand how SSD evolves across species, Rensch's Rule predicts that in taxa where males are larger, SSD increases with body size. In contrast, where females are larger, SSD decreases with body size. While this rule holds for many taxa, it may be ambiguous for others, particularly ectothermic vertebrates. Importantly, this rule suggests that the outcomes of SSD over phylogenetic time scales depends on the direction of dimorphism predicated on the difference in reproductive efforts between males and females. Here we examine SSD in the context of Rensch's Rule in Thamnophiini, the garter and waternsakes, a prominent group composing the North American snake biota. Using a dated phylogeny, measurements of gape, body and tail size, we show that these snakes do not follow Rensch's Rule, but rather female-biased SSD increases with body size. We in turn find that this allometry is most pronounced with gape and is correlated with both neonate and litter size, suggesting that acquiring prey of increased size may be directly related to fecundity selection. These changes in SSD are not constrained to any particular clade; we find no evidence of phylogenetic shifts in those traits showing SSD. We suggest several ways forward to better understand the anatomical units of selection for SSD and modularity
Data from: Hyaluronan and N-ERC/mesothelin as key biomarkers in a specific two-step model to predict pleural malignant mesothelioma
Purpose: Diagnosis of malignant mesothelioma is challenging. The first available diagnostic material is often an effusion and biochemical analysis of soluble markers may provide additional diagnostic information. This study aimed to establish a predictive model using biomarkers from pleural effusions, to allow early and accurate diagnosis. Patients and Methods: Effusions were collected prospectively from 190 consecutive patients at a regional referral centre. Hyaluronan, N-ERC/mesothelin, C-ERC/mesothelin, osteopontin, syndecan-1, syndecan-2, and thioredoxin were measured using ELISA and HPLC. A predictive model was generated and validated using a second prospective set of 375 effusions collected consecutively at a different referral centre. Results: Biochemical markers significantly associated with mesothelioma were hyaluronan (odds ratio, 95% CI: 8.82, 4.82–20.39), N-ERC/mesothelin (4.81, 3.19–7.93), CERC/mesothelin (3.58, 2.43–5.59) and syndecan-1 (1.34, 1.03–1.77). A two-step model using hyaluronan and N-ERC/mesothelin, and combining a threshold decision rule with logistic regression, yielded good discrimination with an area under the ROC curve of 0.99 (95% CI: 0.97–1.00) in the model generation dataset and 0.83 (0.74–0.91) in the validation dataset, respectively. Conclusions: A two-step model using hyaluronan and N-ERC/mesothelin predicts mesothelioma with high specificity. This method can be performed on the first available effusion and could be a useful adjunct to the morphological diagnosis of mesothelioma.
Data from: Bioclimatic envelope models predict a decrease in tropical forest carbon stocks with climate change in Madagascar
1. Recent studies have underlined the importance of climatic variables in determining tree height and biomass in tropical forests. Nonetheless, the effects of climate on tropical forest carbon stocks remain uncertain. In particular, the application of process-based dynamic global vegetation models have led to contrasting conclusions regarding the potential impact of climate change on tropical forest carbon storage. 2. Using a correlative approach based on a bioclimatic envelope model and data from 1771 forest plots inventoried during the period 1996-2013 in Madagascar over a large climatic gradient, we show that temperature seasonality, annual precipitation and mean annual temperature are key variables in determining forest aboveground carbon density. 3. Taking into account the explicative climate variables, we obtained an accurate (R2 = 70% and RMSE = 40 Mg.ha-1) forest carbon map for Madagascar at 250 m resolution for the year 2010. This national map was more accurate than previously published global carbon maps (R 2 ≤ 26% and RMSE ≥ 63 Mg.ha −1 ). 4. Combining our model with the climatic projections for Madagascar from seven IPCC CMIP5 global climate models following the RCP 8.5, we forecast an average forest carbon stock loss of 17% (range: 7-24%) by the year 2080. For comparison, a spatially homogeneous deforestation of 0.5% per year on the same period would lead to a loss of 30% of the forest carbon stock. 5. Synthesis: Our study shows that climate change is likely to induce a decrease in tropical forest carbon stocks. This loss could be due to a decrease in the average tree size and to shifts in tree species distribution, with the selection of small-statured species. In Madagascar, climate-induced carbon emissions might be, at least, of the same order of magnitude as emissions associated to anthropogenic deforestation.
Data from: Human circulating antibody-producing B cell as a predictive measure of mucosal immunity to poliovirus
Background: The "gold standard" for assessing mucosal immunity after vaccination with poliovirus vaccines consists in measuring virus excretion in stool after challenge with oral poliovirus vaccine (OPV). This testing is time and resource intensive, and development of alternative methods is a priority for accelerating polio eradication. We therefore evaluated circulating antibody-secreting cells (ASCs) as a potential means to evaluate mucosal immunity to poliovirus vaccine. Methods: 199 subjects, aged 10 years, and previously immunized repeatedly with OPV, were selected. Subjects were assigned to receive either a booster dose of inactivated poliovirus vaccine (IPV), bivalent OPV (bOPV), or no vaccine. Using a micro-modified whole blood-based ELISPOT assay designed for field setting, circulating poliovirus type-specific IgA- and IgG-ASCs, including gut homing ?4?7+ ASCs, were enumerated on days 0 and 7 after booster immunization. In addition, serum samples collected on days 0, 28 and 56 were tested for neutralizing antibody titers against poliovirus types 1, 2, and 3. Stool specimens were collected on day 28 (day of bOPV challenge), and on days 31, 35 and 42 and processed for poliovirus isolation. Results: An IPV dose elicited blood IgA- and IgG-ASC responses in 84.8 to 94.9% of subjects, respectively. In comparison, a bOPV dose evoked corresponding blood ASC responses in 20.0 to 48.6% of subjects. A significant association was found between IgA- and IgG-ASC responses and serum neutralizing antibody titers for poliovirus type 1, 2, 3 (p<0.001). In the IPV group, ?4?7+ ASCs accounted for a substantial proportion of IgA-ASCs and the proportion of subjects with a positive ?4?7+ IgA-ASC response to poliovirus types 1, 2 and 3 was 62.7%, 89.8% and 45.8%, respectively. A significant association was observed between virus excretion and ?4?7+ IgA- and/or IgG-ASC responses to poliovirus type 3 among immunized children; however, only a weak association was found for type 1 poliovirus. Discussion: Our results suggest that virus-specific blood ASCs, especially for type 3 poliovirus, can serve as surrogate of mucosal immunity after vaccination. Further studies are needed to evaluate the duration of such memory responses and to assess the programmatic utility of this whole blood-based mucosal ASC testing for the polio eradication program.
Data from: The influence of data resolution on predicted distribution and estimates of extent of current protection of three 'listed' deep-sea habitats
Modelling approaches have the potential to significantly contribute to the spatial management of the deep-sea ecosystem in a cost effective manner. However, we currently have little understanding of the accuracy of such models, developed using limited data, of varying resolution. The aim of this study was to investigate the performance of predictive models constructed using non-simulated (real world) data of different resolution. Predicted distribution maps for three deep-sea habitats were constructed using MaxEnt modelling methods using high resolution multibeam bathymetric data and associated terrain derived variables as predictors. Model performance was evaluated using repeated 75/25 training/test data partitions using AUC and threshold-dependent assessment methods. The overall extent and distribution of each habitat, and the percentage contained within an existing MPA network were quantified and compared to results from low resolution GEBCO models. Predicted spatial extent for scleractinian coral reef and Syringammina fragilissima aggregations decreased with an increase in model resolution, whereas Pheronema carpenteri total suitable area increased. Distinct differences in predicted habitat distribution were observed for all three habitats. Estimates of habitat extent contained within the MPA network all increased when modelled at fine scale. High resolution models performed better than low resolution models according to threshold-dependent evaluation. We recommend the use of high resolution multibeam bathymetry data over low resolution bathymetry data for use in modelling approaches. We do not recommend the use of predictive models to produce absolute values of habitat extent, but likely areas of suitable habitat. Assessments of MPA network effectiveness based on calculations of percentage area protection (policy driven conservation targets) from low resolution models are likely to be fit for purpose.
Benchmarking parametric and machine learning models for genomic prediction of complex traits
<p>The usefulness of genomic prediction in crop and livestock breeding programs has prompted efforts to develop new and improved genomic prediction algorithms, such as artificial neural networks and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of datasets and models. Using data of 18 traits across six plant species with different marker densities and training population sizes, we compared the performance of six linear and six non-linear algorithms. First, we found that hyperparameter selection was necessary for all non-linear algorithms and that feature selection prior to model training was critical for artificial neural networks when the markers greatly outnumbered the number of training lines. Across all species and trait combinations, no one algorithm performed best, however predictions based on a combination of results from multiple algorithms (i.e. ensemble predictions) performed consistently well. While linear and non-linear algorithms performed best for a similar number of traits, the performance of non-linear algorithms vary more between traits. Although artificial neural networks did not perform best for any trait, we identified strategies (i.e. feature selection, seeded starting weights) that boosted their performance to near the level of other algorithms. Our results highlight the importance of algorithm selection for the prediction of trait values.</p>
Data from: Social capital predicts corruption risk in towns
Corruption is a social plague: gains accrue to small groups, while its costs are borne by everyone. Significant variation in its level between and within countries suggests a relationship between social structure and the prevalence of corruption, yet, large-scale empirical studies thereof have been missing due to lack of data. In this paper, we relate the structural characteristics of social capital of settlements with corruption in their local governments. Using datasets from Hungary, we quantify corruption risk by suppressed competition and lack of transparency in the settlement's awarded public contracts. We characterize social capital using social network data from a popular online platform. Controlling for social, economic and political factors, we find that settlements with fragmented social networks, indicating an excess of bonding social capital has higher corruption risk, and settlements with more diverse external connectivity, suggesting a surplus of bridging social capital is less exposed to corruption. We interpret fragmentation as fostering in-group favouritism and conformity, which increase corruption, while diversity facilitates impartiality in public life and stifles corruption.
SAMPL7 Blind Challenge : Quantum-Mechanical Prediction of Partition Coefficients and Acid Dissociation Constants for Small Drug-like Molecules
<p>Global minima structures of <em>N</em>-acyl sulfonamides and sulfonamides derivatives (B3LYP-D3/6-311+G(d,p)) in gas phase.</p>
Data from: LiDAR and RGB-image analysis to predict hairy vetch biomass in breeding nurseries
Hairy vetch is a fall seeded annual legume that can be used as a forage and cover crop. As a cover crop, it can provide numerous ecosystem services, such as soil erosion reduction, carbon sequestration, and pollinator habitat, but also agronomic services such as weed suppression and N fixation via soil rhizobium species. To improve cover crop function, traits such as biomass production are especially relevant, making it a first priority trait for cover crop breeders. However, direct phenotypic methods for biomass production are destructive. Breeders have thus relied on subjective, visual scoring methods for biomass, which are generally correlative, but are not quantitative or absolute. In this study, we evaluated two low-cost remote sensing tools, LiDAR and RGB-image analysis, for their effectiveness at predicting biomass in vivo. We evaluated these tools in two common forage breeding scenarios, spaced-plant and sward-plot nurseries, at three Minnesota locations following the winter of 2016/2017. Ground cover, determined from RGB image binarization using the Canopeo application, had a significant and linear relationship with above-ground biomass in spaced-plants (R2=0.93), and sward-plots (R2=0.89). Once the image area became saturated with vegetative pixels, a near-exponential relationship with biomass would occur. Because of the low-growth habit of hairy vetch, RGB image analysis was more appropriate at lower plant densities, such as spaced-plant nurseries. LiDAR measures of sward-plot height were also linearly and strongly related to dry-matter biomass in sward-plots (R2=0.80). The dimensionality of LiDAR sensing gave it greater predictive ability at higher plant densities, where RGB analysis could not detect vertical increases in biomass production. Lastly, we combined RGB and LiDAR data to predict sward-plot biomass in a multiple mixed-effect regression model. By doing so, we were able to explain more biomass variation than with use of either phenotypic tool as a single predictor (R2=0.94).
Data from: Tropical dung beetle morphological traits predict functional traits and show intra-specific differences across land uses
1. Functional traits and functional diversity measures are increasingly being used to examine land use effects on biodiversity and community assembly rules. 2. Morphological traits are frequently derived from a mean value of many individuals, and used directly as functional traits. However, this approach overlooks the importance of intraspecific differences. 3. We collected morphometric data from over 1700 individuals of 12 species of dung beetle to establish whether morphological measurements can be used as predictors of behavioral traits. We also compared morphology among individuals collected from different land uses to identify if intra-specific differences in morphology vary among land use types. 4. We show that leg and eye measurements can be used to predict dung beetle nesting behavior and period of activity, and used this information to confirm the previously unresolved nesting behavior for Synapsis ritsemae. 5. We found intra-specific differences in morphological traits across different land use types. Phenotypic plasticity was found for traits associated with dispersal (wing aspect ratio and wing loading) and reproductive capacity (abdomen size). 6. The ability to predict behavioral functional traits from morphology is useful where the behavior of dung beetles cannot be directly observed, especially in tropical environments where the ecology of many species is poorly understood. 7. There have been very few studies investigating variability in animal traits. We provide evidence that land use change can cause phenotypic plasticity in tropical dung beetle species. Our results reinforce recent calls for intraspecific variation in traits to receive more attention within community ecology.
Data from: Predicting ecological responses in a changing ocean: the effects of future climate uncertainty
Predicting how species will respond to climate change is a growing field in marine ecology, yet knowledge of how to incorporate the uncertainty from future climate data into these predictions remains a significant challenge. To help overcome it, this review separates climate uncertainty into its three components (scenario uncertainty, model uncertainty, and internal model variability) and identifies four criteria that constitute a thorough interpretation of an ecological response to climate change in relation to these parts (awareness, access, incorporation, communication). Through a literature review, the extent to which the marine ecology community has addressed these criteria in their predictions was assessed. Despite a high awareness of climate uncertainty, articles favoured the most severe emission scenario, and only a subset of climate models were used as input into ecological analyses. In the case of sea surface temperature, these models can have projections unrepresentative against a larger ensemble mean. Moreover, 91% of studies failed to incorporate the internal variability of a climate model into results. We explored the influence that the choice of emission scenario, climate model, and model realisation can have when predicting the future distribution of the pelagic fish, Electrona antarctica. Future distributions were highly influenced by the choice of climate model, and in some cases, internal variability was important in determining the direction and severity of the distribution change. Increased clarity and availability of processed climate data would facilitate more comprehensive explorations of climate uncertainty, and increase in the quality and standard of marine prediction studies.
Data from: Predicting the cover and richness of intertidal macroalgae in remote areas: a case study in the Antarctic Peninsula
1. Antarctica is an iconic region for scientific explorations as it is remote and a critical component of the global climate system. Recent climate change causes dramatic retreat of ice in Antarctica with associated impacts to its coastal ecosystem. These anthropogenic impacts have a potential to increase habitat availability for Antarctic intertidal assemblages. Assessing the extent and ecological consequences of these changes requires us to develop accurate biotic baselines and quantitative predictive tools. 2. In this study, we demonstrated that satellite based remote sensing, when used jointly with in-situ ground-truthing and machine learning algorithms, provides a powerful tool to predict the cover and richness of intertidal macroalgae. 3. The salient finding was that the Sentinel-based remote sensing described a significant proportion of variability in the cover and richness of Antarctic macroalgae. The highest performing models were for macroalgal richness and the cover of brown and green algae as opposed to the model of red algal cover. 4. When expanding the geographical range of the ground-truthing, even involving only a few sample points, it becomes possible to potentially map other Antarctic intertidal macroalgal habitats and monitor their dynamics. This is a significant milestone as logistical constraints are an integral part of the Antarctic expeditions. The method has also a potential in other remote coastal areas where extensive in-situ mapping is not feasible.
Data from: Can terrestrial laser scanners (TLSs) and hemispherical photographs predict tropical dry forest succession with liana abundance?
Tropical dry forests (TDFs) are ecosystems with long drought periods, a mean temperature of 25 °C, a mean annual precipitation that ranges from 900 to 2000 mm, and that possess a high abundance of deciduous species (trees and lianas). What remains of the original extent of TDFs in the Americas remains highly fragmented and at different levels of ecological succession. It is estimated that one of the main fingerprints left by global environmental and climate change in tropical environments is an increase in liana coverage. Lianas are non-structural elements of the forest canopy that eventually kill their host trees. In this paper we evaluate the use of a terrestrial laser scanner (TLS) in combination with hemispherical photographs (HPs) to characterize changes in forest structure as a function of ecological succession and liana abundance. We deployed a TLS and HP system in 28 plots throughout secondary forests of different ages and with different levels of liana abundance. Using a canonical correlation analysis (CCA), we addressed how the VEGNET, a terrestrial laser scanner, and HPs could predict TDF structure. Likewise, using univariate analyses of correlations, we show how the liana abundance could affect the prediction of the forest structure. Our results suggest that TLSs and HPs can predict the differences in the forest structure at different successional stages but that these differences disappear as liana abundance increases. Therefore, in well known ecosystems such as the tropical dry forest of Costa Rica, these biases of prediction could be considered as structural effects of liana presence. This research contributes to the understanding of the potential effects of lianas in secondary dry forests and highlights the role of TLSs combined with HPs in monitoring structural changes in secondary TDFs.
Data from: Interspecific interactions through 2 million years: are competitive outcomes predictable?
Ecological interactions affect the survival and reproduction of individuals. However, ecological interactions are notoriously difficult to measure in extinct populations, hindering our understanding of how the outcomes of interactions such as competition vary in time and influence long-term evolutionary changes. Here, the outcomes of spatial competition in a temporally continuous community over evolutionary timescales are presented for the first time. Our research domain is encrusting cheilostome bryozoans from the Wanganui Basin of New Zealand over a ca 2 Myr time period (Pleistocene to Recent). We find that a subset of species can be identified as consistent winners, and others as consistent losers, in the sense that they win or lose interspecific competitive encounters statistically more often than the null hypothesis of 50%. Most species do not improve or worsen in their competitive abilities through the 2 Myr period, but a minority of species are winners in some intervals and losers in others. We found that conspecifics tend to cluster spatially and interact more often than expected under a null hypothesis: most of these are stand-off interactions where the two colonies involved stopped growing at edges of encounter. Counterintuitively, competitive ability has no bearing on ecological dominance.
Data from: Fitness in invasive social wasps: the role of variation in viral load, immune response and paternity in predicting nest size and reproductive output
Within any one habitat, the relative fitness of organisms in a population can vary substantially. Social insects like the common wasp are among the most successful invasive animals, but show enormous variation in nest size and other fitness-related traits. Some of this variation may be caused by pathogens such as viruses that can have serious consequences in social insects, which range from reduced productivity to colony death. Both individual immune responses and colony-level traits such as genetic diversity are likely to influence effects of pathogen infections on colony fitness. Here we investigate how infections with Kashmir Bee Virus (KBV), immune response and intracolony genetic diversity (due to queen polyandry) affect nest size in the invasive common wasp, Vespula vulgaris. We show that KBV is highly prevalent in wasps and expression of antiviral immune genes is significantly increased with higher viral loads across individuals. Patriline membership within a nest did not influence KBV susceptibility or immune response. A permutational MANCOVA revealed that polyandry, viral load, and expression of the immune gene Dicer were significant predictors of variation in nest size. High intracolony genetic diversity due to polyandry has previously been hypothesized to improve colony-level resistance to parasites and pathogens. Consistent with this hypothesis, we observed genetically diverse colonies to be significantly larger and to produce more queens, although this effect was not driven by the pathogen we investigated. Invasive wasps clearly suffer from pathogens and expend resources, as indicated here by elevated immune gene expression, toward reducing pathogen-impact on colony fitness.
Data from: Left cortical specialization for visual letter strings predicts rudimentary knowledge of letter-sound association in preschoolers
Reading, one of the most important cultural inventions of human society, critically depends on posterior brain areas of the left hemisphere in proficient adult readers. In children, this left hemispheric cortical specialization for letter strings is typically detected only after approximately 1 y of formal schooling and reading acquisition. Here, we recorded scalp electrophysiological (EEG) brain responses in 5-y-old (n = 40) prereaders presented with letter strings appearing every five items in rapid streams of pseudofonts (6 items per second). Within 2 min of recording only, letter strings evoked a robust specific response over the left occipito-temporal cortex at the predefined frequency of 1.2 Hz (i.e., 6 Hz/5). Interindividual differences in the amplitude of this electrophysiological response are significantly related to letter knowledge, a preschool predictor of later reading ability. These results point to the high potential of this rapidly collected behavior-free measure to assess reading ability in developmental populations. These findings were replicated in a second experiment (n = 26 preschool children), where familiar symbols and line drawings of objects evoked right-lateralized and bilaterally specific responses, respectively, showing the specificity of the early left hemispheric dominance for letter strings. Collectively, these findings indicate that limited knowledge of print in young children, before formal education, is sufficient to develop specialized left lateralized neuronal circuits, thereby pointing to an early onset and rapid impact of left hemispheric reentrant sound mapping on posterior cortical development.
Data from: Early-life telomere length predicts lifespan and lifetime reproductive success in a wild bird
Poor conditions during early development can initiate trade-offs that favour current survival at the expense of somatic maintenance and subsequently, future reproduction. However, the mechanisms that link early and late life-history are largely unknown. Recently it has been suggested that telomeres, the nucleoprotein structures at the terminal end of chromosomes, could link early-life conditions to lifespan and fitness. In wild purple-crowned fairy-wrens, we combined measurements of nestling telomere length (TL) with detailed life-history data to investigate whether early-life TL predicts fitness prospects. Our study differs from previous studies in the completeness of our fitness estimates in a highly philopatric population. The association between TL and survival was age-dependent with early-life TL having a positive effect on lifespan only among individuals that survived their first year. Early-life TL was not associated with the probability or age of gaining a breeding position. Interestingly, early-life TL was positively related to breeding duration, contribution to population growth and lifetime reproductive success because of their association with lifespan. Thus, early-life TL, which reflects growth, accumulated early-life stress and inherited TL, predicted fitness in birds that reached adulthood but not noticeably among fledglings. These findings suggest that a lack of investment in somatic maintenance during development particularly affects late life performance. This study demonstrates that factors in early-life are related to fitness prospects through lifespan, and suggests that the study of telomeres may provide insight into the underlying physiological mechanisms linking early- and late-life performance and trade-offs across a lifetime.
Selection for increased male size predicts variation in sexual size dimorphism among fish species
<p><a name="_Hlk23424265">Variation in the degree of sexual size dimorphism (SSD) among taxa is generally considered to arise from differences in the relative intensity of male-male competition and fecundity selection. One might predict, therefore, that SSD will vary systematically with: 1) the intensity of sexual selection for increased male size, and 2) the intensity of fecundity selection for increased female size. To test these two fundamental hypotheses, we conducted a phylogenetic comparative analysis of SSD in fish. Specifically, using </a>records of body length at first sexual maturity from FishBase, we quantified variation in the magnitude and direction of SSD in >600 diverse freshwater and marine fish species, from sticklebacks to sharks. Although female-biased SSD was common, and thought to be driven primarily by fecundity selection, variation in SSD was not dependent on either the allometric scaling of reproductive-energy output or fecundity in female fish. Instead, systematic patterns based on habitat and life history characteristics associated with varying degrees of male-male competition and paternal care, strongly suggest that adaptive variation in SSD is driven by the intensity of sexual selection for increased male size.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.