Reconstructing local population dynamics in noisy metapopulations--the role of random catastrophes and Allee effects.

Reconstructing the dynamics of populations is complicated by the different types of stochasticity experienced by populations, in particular if some forms of stochasticity introduce bias in parameter estimation in addition to error. Identification of systematic biases is critical when determining whether the intrinsic dynamics of populations are stable or unstable and whether or not populations exhibit an Allee effect, i.e., a minimum size below which deterministic extinction should follow. Using a simulation model that allows for Allee effects and a range of intrinsic dynamics, we investigated how three types of stochasticity--demographic, environmental, and random catastrophes--affect our ability to reconstruct the intrinsic dynamics of populations. Demographic stochasticity aside, which is only problematic in small populations, we find that environmental stochasticity--positive and negative environmental fluctuations--caused increased error in parameter estimation, but bias was rarely problematic, except at the highest levels of noise. Random catastrophes, events causing large-scale mortality and likely to be more common than usually recognized, caused immediate bias in parameter estimates, in particular when Allee effects were large. In the latter case, population stability was predicted when endogenous dynamics were actually unstable and the minimum viable population size was overestimated in populations with small or non-existent Allee effects. Catastrophes also generally increased extinction risk, in particular when endogenous Allee effects were large. We propose a method for identifying data points likely resulting from catastrophic events when such events have not been recorded. Using social spider colonies (Anelosimus spp.) as models for populations, we show that after known or suspected catastrophes are accounted for, reconstructed growth parameters are consistent with intrinsic dynamical instability and substantial Allee effects. Our results are applicable to metapopulation or time series data and are relevant for predicting extinction in conservation applications or the management of invasive species.

Testing a 'genes-to-ecosystems' approach to understanding aquatic-terrestrial linkages.

A 'genes-to-ecosystems' approach has been proposed as a novel avenue for integrating the consequences of intraspecific genetic variation with the underlying genetic architecture of a species to shed light on the relationships among hierarchies of ecological organization (genes → individuals → communities → ecosystems). However, attempts to identify genes with major effect on the structure of communities and/or ecosystem processes have been limited and a comprehensive test of this approach has yet to emerge. Here, we present an interdisciplinary field study that integrated a common garden containing different genotypes of a dominant, riparian tree, Populus trichocarpa, and aquatic mesocosms to determine how intraspecific variation in leaf litter alters both terrestrial and aquatic communities and ecosystem functioning. Moreover, we incorporate data from extensive trait screening and genome-wide association studies estimating the heritability and genes associated with litter characteristics. We found that tree genotypes varied considerably in the quality and production of leaf litter, which contributed to variation in phytoplankton abundances, as well as nutrient dynamics and light availability in aquatic mesocosms. These 'after-life' effects of litter from different genotypes were comparable to the responses of terrestrial communities associated with the living foliage. We found that multiple litter traits corresponding with aquatic community and ecosystem responses differed in their heritability. Moreover, the underlying genetic architecture of these traits was complex, and many genes contributed only a small proportion to phenotypic variation. Our results provide further evidence that genetic variation is a key component of aquatic-terrestrial linkages, but challenge the ability to predict community or ecosystem responses based on the actions of one or a few genes.

An analytical and structural database provides a strategy for sequencing O-glycans from microgram quantities of glycoproteins.

A sensitive, rapid, quantitative strategy has been developed for O-glycan analysis. A structural database has been constructed that currently contains analytical parameters for more than 50 glycans, enabling identification of O-glycans at the subpicomole level. The database contains the structure, molecular weight, and both normal and reversed-phase HPLC elution positions for each glycan. These observed parameters reflect the mass, three-dimensional shape, and hydrophobicity of the glycans and, therefore, provide information relating to linkage and arm specificity as well as monosaccharide composition. Initially the database was constructed by analyzing glycans released by mild hydrazinolysis from bovine serum fetuin, synthetic glycopeptides, human glycophorin A, and serum IgA1. The structures of the fluorescently labeled sugars were determined from a combination of HPLC data, mass spectrometric composition and mass fragmentation data, and exoglycosidase digestions. This approach was then applied to human neutrophil gelatinase B and secretory IgA, where 18 and 25 O-glycans were identified, respectively, and the parameters of these glycans were added to the database. This approach provides a basis for the analysis of subpicomole quantities of O-glycans from normal levels of natural glycoproteins.