Origin, sequence stratigraphy and depositional environment of an upper Ordovician (Hirnantian) deglacial black shale, Jordan

The upper Ordovician succession of Jordan was located 60°S, less than 100 km from the Hirnantian ice sheet margin. New graptolite dates indicate glaciation ended in Jordan in the late Hirnantian (persculptus Biozone). The succession records two glacial advances within the Ammar Formation and the subsequent deglaciations. Organic-rich black shales (Batra Formation) form part of the final deglacial transgressive succession that in-filled an existing low stand glacial continental shelf topography. The base of the black shale is coincident with the maximum flooding surface. During transgression, interfluves and sub-basin margins were breached and black shale deposition expanded rapidly across the region. The top of the black shales coincides with peak highstand. The “expanding puddle model” (sensu Wignall) for black shale deposition, adapted for the peri-glacial setting, provides the best explanation for this sequence of events.

We propose a hypothesis in which anoxic conditions were initiated beneath the halocline in a salinity stratified water column; a fresher surface layer resulted from ice meltwater generated during early deglaciation. During the initial stages of marine incursion, nutrients in the monimolimnion were isolated from the euphotic zone by the halocline. Increasing total organic carbon (TOC) and δ¹³C_org up section indicates the organic carbon content of the shales was controlled mainly by increasing bioproductivity in the mixolimnion (the Strakhov model). Mixolimnion nutrient levels were sustained by a continual and increasing supply of meltwater-derived nutrients, modulated by obliquity changes in high latitude insolation. Anoxia was sustained over tens to hundreds of thousands of years. The formation of black shales on the north Gondwana shelf was little different to those observed in modern black shale environments, suggesting that it was the nature of the Ordovician seas that pre-disposed them to anoxia.     

Marriage Timing over the Generations

Strong relationships have been hypothesized between the timing of marriage and the familial environment of the couple. Sociologists have identified various mechanisms via which the age at marriage in the parental generation might be related to the age at marriage of the children. In our paper we study this relationship for historical populations. We use a dataset consisting of several hundreds of thousands of marriages contracted in three of the 11 Dutch provinces between 1812 and 1922. We identified the generational links between the brides, grooms, their parents, and grandparents. We studied (a) whether there is a relationship between ages at marriage of (grandfathers) fathers and sons, and ages at marriage of (grandmothers) mothers and daughters and (b) whether this relationship might be explained by social class. We find evidence for a clear effect of the family on age at marriage and substantial intergenerational transmission. The impact that the family of origin has on age at entry into marriage can partly be attributed to social class. We also observed positive effects of grandparents’ age at marriage on their offspring’s age at marriage.

Mitochondrial sequence data and Dipsacales phylogeny: mixed models, partitioned Bayesian analyses, and model selection

Phylogenetic analyses of chloroplast DNA sequences, morphology, and combined data have provided consistent support for many of the major branches within the angiosperm clade Dipsacales. Here we use sequences from three mitochondrial loci to test the existing broad scale phylogeny and in an attempt to resolve several relationships that have remained uncertain. Parsimony, maximum likelihood, and Bayesian analyses of a combined mitochondrial data set recover trees broadly consistent with previous studies, although resolution and support are lower than in the largest chloroplast analyses. Combining chloroplast and mitochondrial data results in a generally well-resolved and very strongly supported topology but the previously recognized problem areas remain. To investigate why these relationships have been difficult to resolve we conducted a series of experiments using different data partitions and heterogeneous substitution models. Usually more complex modeling schemes are favored regardless of the partitions recognized but model choice had little effect on topology or support values. In contrast there are consistent but weakly supported differences in the topologies recovered from coding and non-coding matrices. These conflicts directly correspond to relationships that were poorly resolved in analyses of the full combined chloroplast-mitochondrial data set. We suggest incongruent signal has contributed to our inability to confidently resolve these problem areas.     

Seasonal variation in detectability of butterflies surveyed with Pollard walks

Monitoring protocols should be designed to maximize the probability of detecting target species with limited resources. Most species are imperfectly detected, hence, they will often be overlooked at sites where they actually occur, resulting in false-negative errors (i.e. false absences). Uncertain detection of target species has profound implications for conservation, but can be dealt with by using adequate survey designs and statistical models. Butterflies often are monitored with repeated, fixed-route transect counts (Pollard walks). Even though this survey method is widely used in temperate regions, its efficiency in terms of detection probability has never been rigorously assessed in part owing to a lack of suitable analysis methods. Here, I use site-occupancy models to explore the seasonal patterns in detection probability of four California butterflies using Pollard walks. In an effort to inventory the butterfly fauna in two natural areas in the eastern foothills of the Santa Cruz mountains (California), I surveyed twelve 250 m long transects weekly for 22 weeks. I estimated the detection probability (the probability of recording a species during a single transect walk, given it is present) of four species. The probability of detecting each species depended mostly on the monitoring week. Average detection probability across the season was 64% for Cercyonis pegala, 56% for Limenitis lorquini, 76% for Euphydryas chalcedona, and 50% for Lycaena arota. Based on the mean detection probability, I then inferred the number of visits necessary to be statistically confident that a given species was indeed absent from a transect where it was not observed (i.e. obtaining a false absence rate <5%). Knowledge of detection probabilities is fundamental to the optimal design of monitoring programs and the interpretation of their results. The methods applied in this study provide an efficient and evidence-based method to optimally allocate butterfly monitoring resources across space (number of transects) and time (number and timing of visits).     

Crop model based QTL analysis across environments and QTL based estimation of time to floral induction and flowering in <Emphasis Type="Italic">Brassica oleracea</Emphasis>

Studying quantitative traits is complicated due to genotype by environment interactions. One strategy to overcome these difficulties is to combine quantitative trait loci (QTL) and ecophysiological models, e.g. by identifying QTLs for the response curves of adaptive traits to influential environmental factors. A B. oleracea DH-population segregating for time to flowering was cultivated at different temperature regimes. Composite interval mapping was carried out on the three parameters of a model describing time to flowering as a function of temperature, i.e. on the intercept and slope of the response of time to floral induction to temperature and on the duration from transition to flowering. The additive effects of QTLs detected for the parameters have been used to estimate time to floral induction and flowering in the B. oleracea DH-population. The combined QTL and crop model explained 66% of the phenotypic variation for time to floral induction and 56% of the phenotypic variation for time to flowering. Estimation of time to floral induction and flowering based on environment specific QTLs explained 61 and 41% of the phenotypic variation. Results suggest that flowering time can be predicted effectively by coupling QTL and crop models and that using crop modelling tools for QTL analysis increases the power of QTL detection.     

Predicting global change impacts on plant species’ distributions: Future challenges

Given the rate of projected environmental change for the 21st century, urgent adaptation and mitigation measures are required to slow down the on-going erosion of biodiversity. Even though increasing evidence shows that recent human-induced environmental changes have already triggered species’ range shifts, changes in phenology and species’ extinctions, accurate projections of species’ responses to future environmental changes are more difficult to ascertain. This is problematic, since there is a growing awareness of the need to adopt proactive conservation planning measures using forecasts of species’ responses to future environmental changes.

There is a substantial body of literature describing and assessing the impacts of various scenarios of climate and land-use change on species’ distributions. Model predictions include a wide range of assumptions and limitations that are widely acknowledged but compromise their use for developing reliable adaptation and mitigation strategies for biodiversity. Indeed, amongst the most used models, few, if any, explicitly deal with migration processes, the dynamics of population at the “trailing edge” of shifting populations, species’ interactions and the interaction between the effects of climate and land-use.

In this review, we propose two main avenues to progress the understanding and prediction of the different processes occurring on the leading and trailing edge of the species’ distribution in response to any global change phenomena. Deliberately focusing on plant species, we first explore the different ways to incorporate species’ migration in the existing modelling approaches, given data and knowledge limitations and the dual effects of climate and land-use factors. Secondly, we explore the mechanisms and processes happening at the trailing edge of a shifting species’ distribution and how to implement them into a modelling approach. We finally conclude this review with clear guidelines on how such modelling improvements will benefit conservation strategies in a changing world.     

Interlinking hare and lynx dynamics using a century’s worth of annual data

The classic fur trade records on Canadian lynx (Lynx canadensis) have rarely been analysed in direct conjunction with data on its principal prey, the snowshoe hare (Lepus americanus). Comparable long-term data for hare exist only for a region south of Hudson Bay. We fitted a bivariate log-linear time-series model to this hare and lynx data to disentangle the within- and between-population interactions of these species. To reduce problems with fur returns being non-normal and non-linearly related to abundance, we transformed the fur returns to a normal distribution based on sample quantiles. The estimated effect on next year’s lynx abundance of a 1% increase in current hare abundance was a 0.23% (SE = 0.05) increase in lynx. Conversely, a 1% increase in current lynx abundance corresponded to a 0.46% (SE = 0.12) decrease in next year’s hare abundance. This contrasts with some earlier studies. However, these studies mixed hare data from south of Hudson Bay with lynx totals for all of Canada. Despite this asymmetry of interaction strengths, coefficients of determination were similar for hare versus lynx and lynx versus hare, because hare abundance varies more than lynx. Both species showed clear intraspecific density-dependence of about equal strength. A 1% increase in current abundance increased next year’s abundance by about 0.75%. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Why equalising trade-offs aren't always neutral

Equalising trade-offs, such as seed mass vs. number, have been invoked to reconcile neutral theory with observed differences between species. This is an appealing explanation for the dramatic seed size variation seen within guilds of otherwise similar plants: under size-symmetric competition, where resource capture is proportional to mass, the outcome of competition should be insensitive to whether species produce many small seeds or few large ones. However, under this assumption, stochastic variation in seed rain leads to exclusion of all but the smallest-seeded species. Thus stochasticity in seed arrivals, a process that was previously thought to generate drift, instead results in deterministic competitive exclusion. A neutral outcome is possible under one special case of a more general equalising framework, where seed mass affects survival but not competition. Further exploration of the feasibility of neutral trade-offs is needed to understand the respective roles of neutrality and niche structure in community dynamics.     

Group additive regression models for genomic data analysis

One important problem in genomic research is to identify genomic features such as gene expression data or DNA single nucleotide polymorphisms (SNPs) that are related to clinical phenotypes. Often these genomic data can be naturally divided into biologically meaningful groups such as genes belonging to the same pathways or SNPs within genes. In this paper, we propose group additive regression models and a group gradient descent boosting procedure for identifying groups of genomic features that are related to clinical phenotypes. Our simulation results show that by dividing the variables into appropriate groups, we can obtain better identification of the group features that are related to the phenotypes. In addition, the prediction mean square errors are also smaller than the component-wise boosting procedure. We demonstrate the application of the methods to pathway-based analysis of microarray gene expression data of breast cancer. Results from analysis of a breast cancer microarray gene expression data set indicate that the pathways of metalloendopeptidases (MMPs) and MMP inhibitors, as well as cell proliferation, cell growth, and maintenance are important to breast cancer-specific survival.