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Register a new userBenthic inputs as predictors of seagrass (Posidonia oceanica) fish farm-induced decline
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Fish farms represent a growing source of disturbance to shallow benthic ecosystems like seagrass meadows. Despite some existing insights on the mechanisms underlying decline, efficient tools to quantitatively predict the response of benthic communities to fish farm effluents have not yet been developed. We explored relationships of fish farm organic and nutrient input rates to the sediments with population dynamics of the key seagrass species (Posidonia oceanica) in deep meadows growing around four Mediterranean Sea bream and Sea bass fish farms. We performed 2 annual shoot censuses on permanent plots at increasing distance from cages. Before each census we measured sedimentation rates adjacent to the plots using benthic sediment traps. High shoot mortality rates were recorded near the cages, up to 20 times greater than at control sites. Recruitment rates remained similar to undisturbed meadows and could not compensate mortality, leading to rapid seagrass decline within the first 100 meters from cages. Seagrass mortality increased with total (R2= 0.47, p< 0.0002), organic matter (R2= 0.36, p= 0.001), nitrogen (R2= 0.34, p= 0.002) and phosphorus (R2= 0.58, p< 3 x 10-5) sedimentation rates. P. oceanica decline accelerated above a phosphorus loading threshold of 50 mg m-2 day-1. Benthic sedimentation rates seem a powerful predictor of seagrass mortality from fish farming, integrating local hydrodynamics, waste effluents variability and several environmental mechanisms, fuelled by organic inputs and leading to seagrass loss. Coupling direct measurements of benthic sedimentation rates with dynamics of key species is proposed as an efficient way to predict and minimize fish farm impacts to benthic communities.
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The sequence of amino acids in a protein is believed to determine its native state structure, which in turn is related to the functionality of the protein. In addition, information pertaining to evolutionary relationships is contained in homologous sequences. One powerful method for inferring these sequence attributes is through comparison of a query sequence with reference sequences that contain significant homology and whose structure, function, and/or evolutionary relationships are already known. In spite of decades of concerted work, there is no simple framework for deducing structure, function, and evolutionary (SF&E) relationships directly from sequence information alone, especially when the pair-wise identity is less than a threshold figure ~25% [1,2]. However, recent research has shown that sequence identity as low as 8% is sufficient to yield common structure/function relationships and sequence identities as large as 88% may yet result in distinct structure and function [3,4]. Starting with a basic premise that protein sequence encodes information about SF&E, one might ask how one could tease out these measures in an unbiased manner. Here we present a unified framework for inferring SF&E from sequence information using a knowledge-based approach which generates phylogenetic profiles in an unbiased manner. We illustrate the power of phylogenetic profiles generated using the Gestalt Domain Detection Algorithm Basic Local Alignment Tool (GDDA-BLAST) to derive structural domains, functional annotation, and evolutionary relationships for a host of ion-channels and human proteins of unknown function. These data are in excellent accord with published data and new experiments. Our results suggest that there is a wealth of previously unexplored information in protein sequence.
In this paper, decision theory was used to derive Bayes and minimax decision rules to estimate allelic frequencies and to explore their admissibility. Decision rules with uniformly smallest risk usually do not exist and one approach to solve this problem is to use the Bayes principle and the minimax principle to find decision rules satisfying some general optimality criterion based on their risk functions. Two cases were considered, the simpler case of biallelic loci and the more complex case of multiallelic loci. For each locus, the sampling model was a multinomial distribution and the prior was a Beta (biallelic case) or a Dirichlet (multiallelic case) distribution. Three loss functions were considered: squared error loss (SEL), Kulback-Leibler loss (KLL) and quadratic error loss (QEL). Bayes estimators were derived under these three loss functions and were subsequently used to find minimax estimators using results from decision theory. The Bayes estimators obtained from SEL and KLL turned out to be the same. Under certain conditions, the Bayes estimator derived from QEL led to an admissible minimax estimator (which was also equal to the maximum likelihood estimator). The SEL also allowed finding admissible minimax estimators. Some estimators had uniformly smaller variance than the MLE and under suitable conditions the remaining estimators also satisfied this property. In addition to their statistical properties, the estimators derived here allow variation in allelic frequencies, which is closer to the reality of finite populations exposed to evolutionary forces.
An evolutionary tree is a cascade of bifurcations starting from a single common root, generating a growing set of daughter species as time goes by. Species here is a general denomination for biological species, spoken languages or any other entity evolving through heredity. From the N currently alive species within a clade, distances are measured through pairwise comparisons made by geneticists, linguists, etc. The larger is such a distance for a pair of species, the older is their last common ancestor. The aim is to reconstruct the past unknown bifurcations, i.e. the whole clade, from the knowledge of the N(N-1)/2 quoted distances taken for granted. A mechanical method is presented, and its applicability discussed.
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R. Klages (Max Planck Institute for the Physics of Complex Systems
, n Dresden
, School of Mathematical Sciences
2018
This book chapter introduces to the problem to which extent search strategies of foraging biological organisms can be identified by statistical data analysis and mathematical modeling. A famous paradigm in this field is the Levy Flight Hypothesis: It states that under certain mathematical conditions Levy flights, which are a key concept in the theory of anomalous stochastic processes, provide an optimal search strategy. This hypothesis may be understood biologically as the claim that Levy flights represent an evolutionary adaptive optimal search strategy for foraging organisms. Another interpretation, however, is that Levy flights emerge from the interaction between a forager and a given (scale-free) distribution of food sources. These hypotheses are discussed controversially in the current literature. We give examples and counterexamples of experimental data and their analyses supporting and challenging them.
The availability of a large number of assembled genomes opens the way to study the evolution of syntenic character within a phylogenetic context. The DeCo algorithm, recently introduced by B{e}rard et al. allows the computation of parsimonious evolutionary scenarios for gene adjacencies, from pairs of reconciled gene trees. Following the approach pioneered by Sturmfels and Pachter, we describe how to modify the DeCo dynamic programming algorithm to identify classes of cost schemes that generates similar parsimonious evolutionary scenarios for gene adjacencies, as well as the robustness to changes to the cost scheme of evolutionary events of the presence or absence of specific ancestral gene adjacencies. We apply our method to six thousands mammalian gene families, and show that computing the robustness to changes to cost schemes provides new and interesting insights on the evolution of gene adjacencies and the DeCo model.
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