Find research datasets worth reusing

Data from: Invasive alien plants benefit more from clonal integration in heterogeneous environments than natives

What confers invasive alien plants a competitive advantage over native plants remains open to debate. Many of the world's worst invasive alien plants are clonal and able to share resources within clones (clonal integration), particularly in heterogeneous environments. Here, we tested the hypothesis that clonal integration benefits invasive clonal plants more than natives and thus confers invasives a competitive advantage. We selected five congeneric and naturally co-occurring pairs of invasive alien and native clonal plants in China, and grew pairs of connected and disconnected ramets under heterogeneous light, soil nutrient and water conditions that are commonly encountered by alien plants during their invasion into new areas. Clonal integration increased biomass of all plants in all three heterogeneous resource environments. However, invasive plants benefited more from clonal integration than natives. Consequently, invasive plants produced more biomass than natives. Our results indicate that clonal integration may confer invasive alien clonal plants a competitive advantage over natives. Therefore, differences in the ability of clonal integration could potentially explain, at least partly, the invasion success of alien clonal plants in areas where resources are heterogeneously distributed.

Data from: Scale-dependent role of demography and dispersal on the distribution of populations in heterogeneous landscapes

Both dispersal and local demographic processes determine a population's distribution among habitats of varying quality, yet most theory, experiments, and field studies have focused on the former. We use a generic model to show how both processes contribute to a population's distribution, and how the relative importance of each mechanism depends on scale. In contrast to studies only considering habitat-dependent dispersal, we show that predictions of ideal free distribution (IFD) theory are relevant even at landscape scales, where the assumptions of IFD theory are violated. This is because scales that inhibit one process, promote the other's ability to drive populations to the IFD. Furthermore, because multiple processes can generate IFDs, the pattern alone does not specify a causal mechanism. This is important because populations with IFDs generated by dispersal or demography respond much differently to shifts in resource distributions.

Data from: Quantitative genetic variance in experimental fly populations evolving with or without environmental heterogeneity

Heterogeneous environments are typically expected to maintain more genetic variation in fitness within populations than homogeneous environments. However, the accuracy of this claim depends on the form of heterogeneity as well as the genetic basis of fitness traits and how similar the assay environment is to the environment of past selection. Here we measure quantitative genetic variance for three traits important for fitness using replicated experimental populations of Drosophila melanogaster evolving under four selective regimes: constant salt-enriched medium (Salt), constant cadmium-enriched medium (Cad), and two heterogeneous regimes that vary either temporally (Temp) or spatially (Spatial). As theory predicts, we found that Spatial populations tend to harbor more genetic variation than Temp populations or those maintained in a constant environment that is the same as the assay environment. Contrary to expectation, Salt populations tend to have more genetic variation than Cad populations in both assay environments. We discuss the patterns for quantitative genetic (QG) variances across regimes in relation to previously reported data on genome-wide sequence diversity. For some traits, the QG patterns are similar to the diversity patterns of ecological selected SNPs whereas the QG patterns for some other traits resembled that of neutral SNPs.

opencc-zeroDec 2017View details →

Data from: The importance of individual heterogeneity for interpreting faecal glucocorticoid metabolite levels in wildlife studies

1. Being a non-invasive and inexpensive method, the analysis of faecal corticosteroid metabolites (FCM) is increasingly being applied in wildlife research. Various environmental factors have been shown to influence FCM levels, but most studies did not account for inter-individual variance, which we hypothesized may substantially affect the results. 2. We combined FCM analysis with genetic analysis to identify the sex and individual of samples collected in three consecutive winters, with repeated samples per individual, across the entire range of an endangered population of capercaillie (Tetrao urogallus) in Southwestern Germany. Using generalized additive mixed models, we modelled FCM levels as a function of sex, season and environmental covariates at two spatial scales (sampling location and home range scales). We compared two models: one including information on the individual animal, and the other excluding this information (i.e. naïve model) to assess the influence of individual effects on the results obtained. 3. Most of the variance (44.0% and 45.1% at the sampling and home range scale, respectively) was explained by the inter-individual differences, and only very little (4.0% and 5.1%, respectively) by the environmental predictors. When ignoring individual effects, the model results changed considerably, with other, previously non-informative predictors, becoming significant. 4. In the full models, accounting for inter-individual variance, no effect was found of weather conditions, at either scale. FCM levels were negatively correlated with habitat quality, at the sampling location, whereas human recreation at the home range scale led to elevated FCM levels. In the naïve models, two additional predictors appeared significant: one weather variable at local scales and two at home range scale. In all models, seasonal FCM patterns differed significantly between males and females. 5. Synthesis and applications. Our results highlight the importance of considering the effects of individual heterogeneity when studying FCM in wildlife research, as ignoring information on the individual might lead to erroneous conclusions. Combining FCM analyses with genetic analyses can be an efficient approach to adequately address this issue.21-Feb-2018

opencc-zeroDec 2013View details →

Data from: Heterogeneity in individual quality and reproductive trade-offs within species

Interspecifically, a reasonable body of evidence supports a trade-off between offspring size and number. However, at the intraspecific level, a whole manner of phenotypic correlations between offspring size and number are observed. These correlations may be predicted when heterogeneity in resource availability, or quality, is considered. Making the assumption that maternal size is a proxy for resource availability, we meta-analytically quantified four phenotypic reproductive correlations within numerous species: (1) maternal size and offspring size, (2) maternal size and offspring number, (3) offspring number and offspring size, and (4) offspring number and offspring size after controlling for maternal size. Within species, maternal size showed a positive correlation with both offspring size and number. Despite this consistency, no correlation between offspring size and number was found. After controlling for maternal size, however, offspring size and number showed a significant negative correlation. A phylogenetic component of our analysis accounted for little heterogeneity in the data, suggesting that our findings show remarkable consistency across taxa. Overall, our results support an observable phenotypic trade-off between offspring size and number. However, this analysis also highlights the importance of considering quality when examining trade-offs, a task that is not always straightforward as quality is context dependant.

opencc-zeroDec 2012View details →

Data from: Quantifying the effects of migration and mutation on adaptation and demography in spatially heterogeneous environments

How do mutation and gene flow influence population persistence, niche expansion, and local adaptation in spatially heterogeneous environments? In this article, we analyse a demographic and evolutionary model of adaptation to an environment containing two habitats in equal frequencies, and we bridge the gap between different theoretical frameworks. Qualitatively, our model yields four qualitative types of outcomes: (i) global extinction of the population (ii) adaptation to one habitat only, but also adaptation to both habitats with (iii) specialized phenotypes, or (iv) with generalized phenotypes; and we determine the conditions under which each equilibrium is reached. We derive new analytical approximations for the local densities and the distributions of traits in each habitat under a migration--selection--mutation balance, compute the equilibrium values of the means, variances and asymmetries of the local distributions of phenotypes, and contrast the effects of migration and mutation on the evolutionary outcome. We then check our analytical results by solving our model numerically, and also assess their robustness in the presence of demographic stochasticity. While increased migration results in a decrease in local adaptation, mutation in our model does not influence the values of the local mean traits. Yet, both migration and mutation can have dramatic effects on population size and even lead to metapopulation extinction when selection is strong. Niche expansion, the ability for the population to adapt to both habitats, can also be prevented by small migration rates and a reduced evolutionary potential characterised by rare mutation events of small effects; but niche expansion is otherwise the most likely outcome. Although our results are derived under the assumption of clonal reproduction, we finally show and discuss the links between our model and previous quantitative genetics models.

Data from: Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes

The spatial structure of the environment (e.g., the configuration of habitat patches) may play an important role in determining the strength of local adaptation. However, previous studies of habitat heterogeneity and local adaptation have largely been limited to simple landscapes, which poorly represent the multi-scale habitat structure common in nature. Here, we use simulations to pursue two goals: (1) we explore how landscape heterogeneity, dispersal ability, and selection affect the strength of local adaptation, and (2) we evaluate the performance of several genotype-environment association (GEA) methods for detecting loci involved in local adaptation. We found that the strength of local adaptation increased in spatially aggregated selection regimes, but remained strong in patchy landscapes when selection was moderate to strong. Weak selection resulted in weak local adaptation that was relatively unaffected by landscape heterogeneity. In general, the power of detection methods closely reflected levels of local adaptation. False positive rates (FPRs), however, showed distinct differences across GEA methods based on levels of population structure. The univariate GEA approach had high FPRs (up to 55%) under limited dispersal scenarios, due to strong isolation by distance. By contrast, multivariate, ordination-based methods had uniformly low FPRs (0-2%), suggesting these approaches can effectively control for population structure. Specifically, constrained ordinations had the best balance of high detection and low FPRs, and will be a useful addition to the GEA toolkit. Our results provide both theoretical and practical insights into the conditions that shape local adaptation and how these conditions impact our ability to detect selection.

opencc-zeroDec 2012View details →

Data from: Spatial heterogeneity in the strength of plant-herbivore interactions under predation risk: the tale of bison foraging in wolf country

Spatial heterogeneity in the strength of trophic interactions is a fundamental property of food web spatial dynamics. The feeding effort of herbivores should reflect adaptive decisions that only become rewarding when foraging gains exceed 1) the metabolic costs, 2) the missed opportunity costs of not foraging elsewhere, and 3) the foraging costs of anti-predator behaviour. Two aspects of these costs remain largely unexplored: the link between the strength of plant-herbivore interactions and the spatial scale of food-quality assessment, and the predator-prey spatial game. We modeled the foraging effort of free-ranging plains bison (Bison bison bison) in winter, within a mosaic of discrete meadows. Spatial patterns of bison herbivory were largely driven by a search for high net energy gains and, to a lesser degree, by the spatial game with grey wolves (Canis lupus). Bison decreased local feeding effort with increasing metabolic and missed opportunity costs. Bison herbivory was most consistent with a broad-scale assessment of food patch quality, i.e., bison grazed more intensively in patches with a low missed opportunity cost relative to other patches available in the landscape. Bison and wolves had a higher probability of using the same meadows than expected randomly. This co-occurrence indicates wolves are ahead in the spatial game they play with bison. Wolves influenced bison foraging at fine scale, as bison tended to consume less biomass at each feeding station when in meadows where the risk of a wolf's arrival was relatively high. Also, bison left more high-quality vegetation in large than small meadows. This behavior does not maximize their energy intake rate, but is consistent with bison playing a shell game with wolves. Our assessment of bison foraging in a natural setting clarifies the complex nature of plant-herbivore interactions under predation risk, and reveals how spatial patterns in herbivory emerge from multi-scale landscape heterogeneity.

opencc-zeroDec 2018View details →

Data from: Ecological specialization in populations adapted to constant versus heterogeneous environments

Populations vary in their degree of ecological specialization. An intuitive, but often untested, hypothesis is that populations evolving under greater environmental heterogeneity will evolve to be less specialized. How important is environmental heterogeneity in explaining among-population variation in specialization? We assessed juvenile viability of 20 Drosophila melanogaster populations evolving under one of four regimes: (i) a salt-enriched environment, (ii) a cadmium-enriched environment, (iii) a temporally varying environment, and (iv) a spatially varying environment. Juvenile viability was tested in both the original selective environments and a set of novel environments. In both the original and novel environments, populations from the constant cadmium regime had the lowest average viability and the highest variance in viability across environments but populations from the other three regimes were similar. Our results suggest that variation in specialization among these populations is most simply explained as a pleiotropic by-product of adaptation to specific environments rather than resulting from a history of exposure to environmental heterogeneity.

Data from: Modeling site heterogeneity with posterior mean site frequency profiles accelerates accurate phylogenomic estimation

Proteins have distinct structural and functional constraints at different sites that lead to site-specific preferences for particular amino acid residues as the sequences evolve. Heterogeneity in the amino acid substitution process between sites is not modeled by commonly used empirical amino acid exchange matrices. Such model misspecification can lead to artefacts in phylogenetic estimation such as long-branch attraction. Although sophisticated site-heterogeneous mixture models have been developed to address this problem in both Bayesian and maximum likelihood (ML) frameworks, their formidable computational time and memory usage severely limits their use in large phylogenomic analyses. Here we propose a posterior mean site frequency (PMSF) method as a rapid and efficient approximation to full empirical profile mixture models for ML analysis. The PMSF approach assigns a conditional mean amino acid frequency profile to each site calculated based on a mixture model fitted to the data using a preliminary guide tree. These PMSF profiles can then be used for in-depth tree-searching in place of the full mixture model. Compared with widely used empirical mixture models with k classes, our implementation of PMSF in IQ-TREE (http://www.iqtree.org) speeds up the computation by approximately k /1.5-fold and requires a small fraction of the RAM. Furthermore, this speedup allows, for the first time, full nonparametric bootstrap analyses to be conducted under complex site-heterogeneous models on large concatenated data matrices. Our simulations and empirical data analyses demonstrate that PMSF can effectively ameliorate long-branch attraction artefacts. In some empirical and simulation settings PMSF provided more accurate estimates of phylogenies than the mixture models from which they derive.

opencc-zeroDec 2017View details →

Data from: Heterogeneous and efficient transesterification of Jatropha curcas L. seed oil to produce biodiesel catalyzed by nano-sized SO42-/TiO2

Developing high-efficiency hetero-catalysts for transesterification reaction is of great importance in the production of biodiesel from Jatropha curcas L. seed oil (JO). Here, we synthesized a series of sulfated TiO2 by treating with varying H2SO4 concentration (xSO42-/TiO2) and TiO2 catalysts and applied to the transesterification of JO. Furthermore, these heterostructures were characterized by many characterization methods including XRD, FT–IR, N2–adsorption, SEM, TEM, TG, py-IR and NH3-TPD, and their catalytic performance were investigated under various operating conditions. The results reveal that both the Brønsted and Lewis acid sites are presented in the xSO42-/TiO2 catalysts, while only Lewis-type sites are observed in the TiO2 catalyst. And the acid intensity, surface area and mesoporous volume of catalysts are improved obviously after treating with sulfuric acid of TiO2. Then the xSO42-/TiO2 catalysts exhibit much higher catalytic activity than TiO2 catalyst, which is attributed to the larger surface area and mesoporous volume and stronger acidity. Furthermore, the reusability behavior of 1.5SO42-/TiO2 catalyst in transesterification of JO was also studied.

Data from: Should I change or should I go? phenotypic plasticity and matching habitat choice in the adaptation to environmental heterogeneity

It can be challenging for organisms to achieve a good match between their phenotypic characteristics and environmental requirements that vary in space and time. The evolution of adaptive phenotypes can result from genetic differentiation at the population level. Individuals, however, could also change their phenotype (adaptive plasticity) or select an environment because it matches with their phenotype (matching habitat choice). It is poorly known under which conditions these different solutions to environmental heterogeneity evolve and whether they operate together. Using an individual-based simulation model, we assessed which solutions evolved depending on degree of temporal variation, costs of multiple underlying traits, and order of dispersal and development. Population genetic divergence was superseded by plasticity or matching habitat choice as temporal variation increased. Plasticity and matching habitat choice were limited by their trait costs, even when this involved only a part of the underlying traits. Independent of the order of dispersal and development, plasticity evolved more commonly than matching habitat choice, in part because the match a phenotype can achieve by matching habitat choice is limited by the types of environments available. Our results explain the apparent relative rarity of matching habitat choice in nature. At the same time, our results can be used to look for matching habitat choice in those biological systems where the conditions for other solutions seem unfavorable.

Data from: Genome-wide association analysis for blood lipid traits measured in three pig populations revealed a substantial level of genetic heterogeneity

Serum lipids are associated with myocardial infarction and cardiovascular disease in humans. Here we dissected the genetic architecture of blood lipid traits by applying genome-wide association studies (GWAS) in 1,256 pigs from Laiwu, Erhualian and Duroc × (Landrace × Yorkshire) populations, and a meta-analysis of GWAS in more than 2,400 pigs from five diverse populations. A total of 22 genomic loci surpassing the suggestive significance level were detected on 11 pig chromosomes (SSC) for six blood lipid traits. Meta-analysis of GWAS identified 5 novel loci associated with blood lipid traits. Comparison of GWAS loci across the tested populations revealed a substantial level of genetic heterogeneity for porcine blood lipid levels. We further evaluated the causality of nine polymorphisms nearby or within the APOB gene on SSC3 for serum LDL-C and TC levels. Of the 9 polymorphisms, an indel showed the most significant association with LDL-C and TC in Laiwu pigs. But the significant association was not identified in the White Duroc × Erhualian F2 resource population, in which the QTL for LDL-C and TC was also detected on SSC3. This indicates that population-specific signals may exist for the SSC3 QTL. Further investigations are warranted to validate this assumption.

Data from: A spatial theory for characterizing predator–multiprey interactions in heterogeneous landscapes

Trophic interactions in multiprey systems can be largely determined by prey distributions. Yet, classic predator–prey models assume spatially homogeneous interactions between predators and prey. We developed a spatially informed theory that predicts how habitat heterogeneity alters the landscape-scale distribution of mortality risk of prey from predation, and hence the nature of predator interactions in multiprey systems. The theoretical model is a spatially explicit, multiprey functional response in which species-specific advection–diffusion models account for the response of individual prey to habitat edges. The model demonstrates that distinct responses of alternative prey species can alter the consequences of conspecific aggregation, from increasing safety to increasing predation risk. Observations of threatened boreal caribou, moose and grey wolf interacting over 378 181 km2 of human-managed boreal forest support this principle. This empirically supported theory demonstrates how distinct responses of apparent competitors to landscape heterogeneity, including to human disturbances, can reverse density dependence in fitness correlates.

Data from: Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media

In this study, we apply four Monte Carlo simulation methods, namely, Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo and multilevel quasi-Monte Carlo to the problem of uncertainty quantification in the estimation of the average travel time during the transport of particles through random heterogeneous porous media. We apply the four methodologies to a model problem where the only input parameter, the hydraulic conductivity, is modelled as a log-Gaussian random field by using direct Karhunen–Loéve decompositions. The random terms in such expansions represent the coefficients in the equations. Numerical calculations demonstrating the effectiveness of each of the methods are presented. A comparison of the computational cost incurred by each of the methods for three different tolerances is provided. The accuracy of the approaches is quantified via the mean square error.

opencc-zeroDec 2015View details →

Data from: Roles of pathogens on replacement of tree seedlings in heterogeneous light environments in a temperate forest: a reciprocal seed sowing experiment

1. In forest communities, the Janzen–Connell (J-C) hypothesis proposes that species diversity is maintained by non-competitive distance- and/or density-dependent seedling mortality caused by host-specific natural enemies. However, the effects of pathogen associations from nearby conspecifics vs. heterospecifics remain unknown in spatially heterogeneous light environments. 2. Seeds of hardwood species Cornus controversa (Cornus) and Prunus grayana (Prunus) were sown beneath 6–7 Cornus and Prunus adults in both the forest understory (FU) and in gaps (Gap) created by felling all woody vegetation near the focal adults. Seedling growth, mortality, killing agents (e.g. pathogens that cause damping-off and leaf diseases), and root infection by arbuscular mycorrhizal fungi (AMF) were investigated. 3. We found strong habitat effects on the expression of soil fungi beneath both tree species. Seedling mortality caused by soil-borne damping-off pathogens was greater in FU than in Gap, and AMF infection, which enhanced relative seedling growth rate, was greater in Gap than in FU. Seedling mortality caused by damping-off pathogens did not differ between Cornus and Prunus seedlings beneath the adults of conspecific or heterospecific adults in both FU and Gap, suggesting little distance-dependence or host preference in the fungus. 4. Beneath the adults of Cornus and Prunus, the most prevalent leaf diseases were zonate leaf blight and angular leaf spot caused by the airborne pathogenic fungi Haradamyces foliicola and Phaeoisariopsis pruni-grayanae, respectively. Although these pathogens attacked the seedlings of both species, conspecific seedlings (i.e. home) showed more severe leaf damage, earlier leaf shedding and/or less defensive behaviour (cell wall defence) relative to heterospecific seedlings (i.e. away), suggesting negative distance-dependent attack (i.e. host preference) for these leaf diseases. As a result, greater seedling mortality was observed for conspecific seedlings under both FU and Gap treatments. 5. Synthesis. In the temperate forest, the J-C hypothesis is largely mediated through the strong negative influence of airborne leaf diseases rather than through soil-borne damping-off pathogens. We found that airborne diseases demonstrated distance-dependent host preferences, which led to greater conspecific seedling damage regardless of environmental light conditions.

opencc-zeroMay 2019View details →

Data from: Effect of detection heterogeneity in occupancy-detection models: an experimental test of time-to-first-detection methods

Imperfect detection can bias estimates of site occupancy in ecological surveys but can be corrected by estimating detection probability. Time-to-first-detection (TTD) occupancy models have been proposed as a cost-effective survey method that allows detection probability to be estimated from single site visits. Nevertheless, few studies have validated the performance of occupancy-detection models by creating a situation where occupancy is known, and model outputs can be compared with the truth. We tested the performance of TTD occupancy models in the face of detection heterogeneity using an experiment based on standard survey methods to monitor koala (Phascolarctos cinereus) populations in Australia. Known numbers of koala faecal pellets were placed under trees, and observers, uninformed as to which trees had pellets under them, carried out a TTD survey. We fitted five TTD occupancy models to the survey data, each making different assumptions about detectability, to evaluate how well each estimated the true occupancy status. Relative to the truth, all five models produced strongly biased estimates, overestimating detection probability and underestimating the number of occupied trees. Despite this, goodness-of-fit tests indicated that some models fitted the data well, with no evidence of model misfit. Hence, TTD occupancy models that appear to perform well with respect to the available data may be performing poorly. The reason for poor model performance was unaccounted for heterogeneity in detection probability, which is known to bias occupancy-detection models. This poses a problem because unaccounted for heterogeneity could not be detected using goodness-of-fit tests and was only revealed because we knew the experimentally determined outcome. A challenge for occupancy-detection models is to find ways to identify and mitigate the impacts of unobserved heterogeneity, which could unknowingly bias many models.

opencc-zeroDec 2012View details →

Data from: A branch-heterogeneous model of protein evolution for efficient inference of ancestral sequences

Most models of nucleotide or amino acid substitution used in phylogenetic studies assume that the evolutionary process has been homogeneous across lineages and that composition of nucleotides or amino acids has remained the same throughout the tree. These oversimplified assumptions are refuted by the observation that compositional variability characterizes extant biological sequences. Branch-heterogeneous models of protein evolution that account for compositional variability have been developed, but are not yet in common use because of the large number of parameters required, leading to high computational costs and potential overparameterization. Here, we present a new branch-nonhomogeneous and nonstationary model of protein evolution that captures more accurately the high complexity of sequence evolution. This model, henceforth called Correspondence and likelihood analysis (COaLA), makes use of a correspondence analysis to reduce the number of parameters to be optimized through maximum likelihood, focusing on most of the compositional variation observed in the data. The model was thoroughly tested on both simulated and biological data sets to show its high performance in terms of data fitting and CPU time. COaLA efficiently estimates ancestral amino acid frequencies and sequences, making it relevant for studies aiming at reconstructing and resurrecting ancestral amino acid sequences. Finally, we applied COaLA on a concatenate of universal amino acid sequences to confirm previous results obtained with a nonhomogeneous Bayesian model regarding the early pattern of adaptation to optimal growth temperature, supporting the mesophilic nature of the Last Universal Common Ancestor.

opencc-zeroDec 2010View details →

Data from: Resource heterogeneity, soil fertility, and species diversity: effects of clonal species on plant communities

Spatial heterogeneity in soil resources is widely thought to promote plant species coexistence, and this mechanism figures prominently in resource-ratio models of competition. However, most experimental studies have found that nutrient enhancements depress diversity regardless of whether nutrients are uniformly or heterogeneously applied. This mismatch between theory and empirical pattern is potentially due to an interaction between plant size and the scale of resource heterogeneity. Clonal plants that spread vegetatively via rhizomes or stolons can grow large and may integrate across resource patches, thus reducing the positive effect of small-scale resource heterogeneity on plant species richness. Many rhizomatous clonal species respond strongly to increased soil fertility, and they have been hypothesized to drive the descending arm of the hump-shaped productivity-diversity relationship in grasslands. We tested whether clonals reduce species richness in a grassland community by manipulating nutrient heterogeneity, soil fertility, and the presence of rhizomatous clonal species in a 6-year field experiment. We found strong and consistent negative effects of clonals on species richness. These effects were greatest at high fertility and when soil resources were applied at a scale at which rhizomatous clonals could integrate across resource patches. Thus, we find support for the hypothesis that plant size and resource heterogeneity interact to determine species diversity.