mizer: an R package for multispecies, trait-based and community size spectrum ecological modelling.
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ABSTRACT:
SUBMITTER: Scott F
PROVIDER: S-EPMC4384942 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Food webs map feeding interactions among species, providing a valuable tool for understanding and predicting community dynamics. Using species' body sizes is a promising avenue for parameterizing food-web models, but such approaches have not yet been able to fully recover observed community dynamics. Such discrepancies suggest that traits other than body size also play important roles. For example, differences in species' use of microhabitat or non-consumptive effects of intraguild predators may affect dynamics in ways not captured by body size. In Laubmeier et al. (2018), we developed a dynamic food-web model incorporating microhabitat and non-consumptive predator effects in addition to body size, and used simulations to suggest an optimal sampling design of a mesocosm experiment to test the model. Here, we perform the mesocosm experiment to generate empirical time-series of insect herbivore and predator abundance dynamics. We minimize least squares error between the model and time-series to determine parameter values of four alternative models, which differ in terms of including vs excluding microhabitat use and non-consumptive predator-predator effects. We use both statistical and expert-knowledge criteria to compare the models and find including both microhabitat use and non-consumptive predator-predator effects best explains observed aphid and predator population dynamics, followed by the model including microhabitat alone. This ranking suggests that microhabitat plays a larger role in driving population dynamics than non-consumptive predator-predator effects, although both are clearly important. Our results illustrate the importance of additional traits alongside body size in driving trophic interactions. They also point to the need to consider trophic interactions and population dynamics in a wider community context, where non-trophic impacts can dramatically modify the interplay between multiple predators and prey. Overall, we demonstrate the potential for utilizing traits beyond body size to improve trait-based models and the value of iterative cycling between theory, data and experiment to hone current insights into how traits affect food-web dynamics.
| S-EPMC9302725 | biostudies-literature
Project description:Ecological networks of two interacting guilds of species, such as flowering plants and pollinators, are common in nature, and studying their structure can yield insights into their resilience to environmental disturbances. Here we develop analytical methods for exploring the strengths of interactions within bipartite networks consisting of two guilds of phylogenetically related species. We then apply these methods to investigate the resilience of a plant-pollinator community to anticipated climate change. The methods allow the statistical assessment of, for example, whether closely related pollinators are more likely to visit plants with similar relative frequencies, and whether closely related pollinators tend to visit closely related plants. The methods can also incorporate trait information, allowing us to identify which plant traits are likely responsible for attracting different pollinators. These questions are important for our study of 14 prairie plants and their 22 insect pollinators. Over the last 70 years, six of the plants have advanced their flowering, while eight have not. When we experimentally forced earlier flowering times, five of the six advanced-flowering species experienced higher pollinator visitation rates, whereas only one of the eight other species had more visits; this network thus appears resilient to climate change, because those species with advanced flowering have ample pollinators earlier in the season. Using the methods developed here, we show that advanced-flowering plants did not have a distinct pollinator community from the other eight species. Furthermore, pollinator phylogeny did not explain pollinator community composition; closely related pollinators were not more likely to visit the same plant species. However, differences among pollinator communities visiting different plants were explained by plant height, floral color, and symmetry. As a result, closely related plants attracted similar numbers of pollinators. By parsing out characteristics that explain why plants share pollinators, we can identify plant species that likely share a common fate in a changing climate.
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Project description:Selection and drift are universally accepted as the cornerstones of evolutionary changes. Recent theories extend this view to ecological changes, arguing that any change in species composition is driven by deterministic fitness differences among species (enhancing selection) and/or stochasticity in birth and death rates of individuals within species (enhancing drift). These forces have contrasting effects on the predictability of ecological dynamics, and thus understanding the factors affecting their relative importance is crucial for understanding ecological dynamics. Here we test the hypothesis that dispersal increases the relative importance of ecological selection by increasing the effective size of the community (i.e., the size relevant for competitive interactions and drift). According to our hypothesis, dispersal increases the effective size of the community by mixing individuals from different localities. This effect diminishes the relative importance of demographic stochasticity, thereby reducing drift and increasing the relative importance of selective forces as drivers of species composition. We tested our hypothesis, which we term the "effective community size" hypothesis, using two independent experiments focusing on annual plants: a field experiment in which we manipulated the magnitude of dispersal and a mesocosm experiment in which we directly manipulated the effective size of the communities. Both experiments, as well as related model simulations, were consistent with the hypothesis that increasing dispersal increases the role of selective forces as drivers of species composition. This finding has important implications for our understanding of the fundamental forces affecting community dynamics, as well as the management of species diversity, particularly in patchy and fragmented environments.
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Project description:Evaluation of reproducibility and accuracy of 16S rRNA gene sequecing using mock communities
| PRJNA390244 | ENA
Project description:Several types of size-based models have been developed to model the size spectra of marine communities, in which abundance scales strongly with body size, using continuous differential equations. In this study, we develop a size-structured matrix model, which can be seen as a discretization of the Mckendrick-von Foerster equation, to simulate the dynamics of marine communities. The developed model uses a series of simple body size power functions to describe the basic processes of predator-prey interactions, reproduction, metabolism, and non-predation mortality based on the principle of mass balance. Linear size spectra with slopes of approximately -1 are successfully reproduced by this model. Several examples of numerical simulations are provided to demonstrate the model's behavior. Size spectra with cut-offs and/or waves are also found for certain parameter values. The matrix model is flexible and can be freely manipulated by users to answer different questions and is executed over a shorter numerical calculation running time than continuous models.
| S-EPMC5991710 | biostudies-literature
Project description:A global ecological restoration agenda has led to ambitious programs in environmental policy to mitigate declines in biodiversity and ecosystem services. Current restoration programs can incompletely return desired ecosystem service levels, while resilience of restored ecosystems to future threats is unknown. It is therefore essential to advance understanding and better utilize knowledge from ecological literature in restoration approaches. We identified an incomplete linkage between global change ecology, ecosystem function research, and restoration ecology. This gap impedes a full understanding of the interactive effects of changing environmental factors on the long-term provision of ecosystem functions and a quantification of trade-offs and synergies among multiple services. Approaches that account for the effects of multiple changing factors on the composition of plant traits and their direct and indirect impact on the provision of ecosystem functions and services can close this gap. However, studies on this multilayered relationship are currently missing. We therefore propose an integrated restoration agenda complementing trait-based empirical studies with simulation modeling. We introduce an ongoing case study to demonstrate how this framework could allow systematic assessment of the impacts of interacting environmental factors on long-term service provisioning. Our proposed agenda will benefit restoration programs by suggesting plant species compositions with specific traits that maximize the supply of multiple ecosystem services in the long term. Once the suggested compositions have been implemented in actual restoration projects, these assemblages should be monitored to assess whether they are resilient as well as to improve model parameterization. Additionally, the integration of empirical and simulation modeling research can improve global outcomes by raising the awareness of which restoration goals can be achieved, due to the quantification of trade-offs and synergies among ecosystem services under a wide range of environmental conditions.
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Project description:Species trait data have been used to predict and infer ecological processes and the responses of biological communities to environmental changes. It has also been suggested that, in lieu of trait, data niche differences can be inferred from phylogenetic distance. It remains unclear how variation in trait data may influence the strength and character of ecological inference. Using species-level trait data in community ecology assumes intraspecific variation is small in comparison with interspecific variation. Intraspecific variation across species ranges or within populations may lead to variability in trait data derived from different scales (i.e., local or regional) and methods (i.e., mean or maximum values). Variation in trait data across species can affect community-level relationships. I examined variability in body size, a key trait often measured across taxa. I collected 12 metrics of fish species length (including common and maximum values) for 40 species from literature, online databases, museum collections, and field data. I then tested whether different metrics of fish length could consistently predict observed species range boundary shifts and the impacts of an introduced predator on inland lake fish communities across Ontario, Canada. I also investigated whether phylogenetic signal, an indicator of niche-conservativism, changed among measures. I found strong correlations between length metrics and limited variation across metrics. Accordingly, length was a consistently significant predictor of the response of fish communities to environmental change. Additionally, I found significant evidence of phylogenetic signal in fish length across metrics. Limited variation in length across metrics (within species), in comparison with variation within metrics (across species), made fish species length a reliable predictor at a community-level. When considering species-level trait data from different sources, researchers should examine the potential influence of intraspecific trait variation on data derived by different metrics and at different scales.
| S-EPMC5016654 | biostudies-literature
Project description:Implementation of an ecosystem approach to fisheries requires advice on trade-offs among fished species and between fisheries yields and biodiversity or food web properties. However, the lack of explicit representation, analysis and consideration of uncertainty in most multispecies models has limited their application in analyses that could support management advice.We assessed the consequences of parameter uncertainty by developing 78 125 multispecies size-structured fish community models, with all combinations of parameters drawn from ranges that spanned parameter values estimated from data and literature. This unfiltered ensemble was reduced to 188 plausible models, the filtered ensemble (FE), by screening outputs against fish abundance data and ecological principles such as requiring species' persistence.Effects of parameter uncertainty on estimates of single-species management reference points for fishing mortality (FMSY, fishing mortality rate providing MSY, the maximum sustainable yield) and biomass (BMSY, biomass at MSY) were evaluated by calculating probability distributions of estimated reference points with the FE. There was a 50% probability that multispecies FMSY could be estimated to within ±25% of its actual value, and a 50% probability that BMSY could be estimated to within ±40% of its actual value.Signal-to-noise ratio was assessed for four community indicators when mortality rates were reduced from current rates to FMSY. The slope of the community size spectrum showed the greatest signal-to-noise ratio, indicating that it would be the most responsive indicator to the change in fishing mortality F. Further, the power of an ongoing international monitoring survey to detect predicted responses of size spectrum slope was higher than for other size-based metrics.Synthesis and applications: Application of the ensemble model approach allows explicit representation of parameter uncertainty and supports advice and management by (i) providing uncertainty intervals for management reference points, (ii) estimating working values of reference points that achieve a defined reduction in risk of not breaching the true reference point, (iii) estimating the responsiveness of population, community, food web and biodiversity indicators to changes in F, (iv) assessing the performance of indicators and monitoring programmes and (v) identifying priorities for data collection and changes to model structure to reduce uncertainty.
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Project description:High-throughput measurements of DNA methylation are increasingly becoming a mainstay of biomedical investigations. While the methylation status of individual cytosines can sometimes be informative, several recent papers have shown that the functional role of DNA methylation is better captured by a quantitative analysis of the spatial variation of methylation across a genomic region.Here, we present BPRMeth, a Bioconductor package that quantifies methylation profiles by generalized linear model regression. The original implementation has been enhanced in two important ways: we introduced a fast, variational inference approach that enables the quantification of Bayesian posterior confidence measures on the model, and we adapted the method to use several observation models, making it suitable for a diverse range of platforms including single-cell analyses and methylation arrays.http://bioconductor.org/packages/BPRMeth.Supplementary data are available at Bioinformatics online.
| S-EPMC6041802 | biostudies-other
Project description:Evolutionary change is a characteristic of living organisms and forms one of the ways in which species adapt to changed conditions. However, most ecological models do not incorporate this ubiquitous phenomenon. We have developed a model that takes a 'phenotypic gambit' approach and focuses on changes in the frequency of phenotypes (which differ in timing of breeding and fecundity) within a population, using, as an example, seasonal breeding. Fitness per phenotype calculated as the individual's contribution to population growth on an annual basis coincide with the population dynamics per phenotype. Simplified model variants were explored to examine whether the complexity included in the model is justified. Outputs from the spatially implicit model underestimated the number of individuals across all phenotypes. When no phenotype transitions are included (i.e. offspring always inherit their parent's phenotype) numbers of all individuals are always underestimated. We conclude that by using a phenotypic gambit approach evolutionary dynamics can be incorporated into individual based models, and that all that is required is an understanding of the probability of offspring inheriting the parental phenotype.
| S-EPMC3733718 | biostudies-literature