Egg coloration in ring-billed gulls (<Emphasis Type="Italic">Larus delawarensis</Emphasis>): a test of the sexual signaling hypothesis

Although many avian eggs appear to be cryptically colored, many species also lay vibrant blue green eggs. This seemingly conspicuous coloration has puzzled biologists since Wallace, as natural selection should favor reduced egg visibility to minimize predation pressure. The sexual signaling hypothesis posits that blue green egg coloration serves as a signal of female quality and that males exert post-mating sexual selection on this trait by investing more in the nests of females laying more intensely blue green eggs. This hypothesis has received mixed support to date, and most previous studies have been conducted in cavity-nesting species where male evaluation of his partner’s egg coloration, relative to that of other females, may be somewhat limited. In this study, we test the sexual signaling hypothesis in colonially nesting ring-billed gulls (Larus delawarensis) where males have ample opportunity to assess their mate’s egg coloration relative to that of other females. We used correlational data and an experimental manipulation to test four assumptions and predictions of the sexual signaling hypothesis: (1) blue green pigmentation should be limiting to females; (2) extent of blue green egg coloration should relate to female quality; (3) extent of blue green egg coloration should relate to offspring quality; and (4) males should provide more care to clutches with higher blue green chroma. Our data provide little support for these predictions of the sexual signaling hypothesis in ring-billed gulls. In light of this and other empirical data, we encourage future studies to consider additional hypotheses for the evolution of blue green egg coloration.     

A constrained least-squares approach to combine bottom-up and top-down CO2 flux estimates

Terrestrial CO₂ flux estimates are obtained from two fundamentally different methods generally termed bottom-up and top-down approaches. Inventory methods are one type of bottom-up approach which uses various sources of information such as crop production surveys and forest monitoring data to estimate the annual CO₂ flux at locations covering a study region. Top-down approaches are various types of atmospheric inversion methods which use CO₂ concentration measurements from monitoring towers and atmospheric transport models to estimate CO₂ flux over a study region. Both methods can also quantify the uncertainty associated with their estimates. Historically, these two approaches have produced estimates that differ considerably. The goal of this work is to construct a statistical model which sensibly combines estimates from the two approaches to produce a new estimate of CO₂ flux for our study region. The two approaches have complementary strengths and weaknesses, and our results show that certain aspects of the uncertainty associated with each of the approaches are greatly reduced by combining the methods. Our model is purposefully simple and designed to take the two approaches’ estimates and measures of uncertainty at ‘face value’. Specifically, we use a constrained least-squares approach to appropriately weigh the estimates by the inverse of their variance, and the constraint imposes agreement between the two sources. Our application involves nearly 18,000 flux estimates for the upper midwest United States. The constrained dependencies result in a non-sparse covariance matrix, but computation requires only minutes due to the structure of the model.     

Sampling animal association networks with the gambit of the group

Ecologists increasingly use network theory to examine animal association patterns. The gambit of the group (GoG) is a simple and useful assumption for accumulating the data necessary for a network analysis. The gambit of the group implies that each animal in a group is associating with every other individual in that group. Sampling is an important issue for networks in wild populations collected assuming GoG. Due to time, effort, and resource constraints and the difficulty of tracking animals, sampled data are usually a subset of the actual network. Ecologists often use association indexes to calculate the frequency of associations between individuals. These indexes are often transformed by applying a filter to produce a binary network. We explore GoG sampling using model networks. We examine assortment at the level of the group by a single dichotomous trait, along with many other network measures, to examine the effect of different sampling regimes, and choice of filter on the accuracy and precision with which measures are estimated. We find strong support for the use of weighted, rather than filtered, network measures and show that different filters have different effects depending on the nature of the sampling. We make several practical recommendations for ecologists planning GoG sampling.     

Validating graph-based connectivity models with independent presence–absence and genetic data sets

Habitat connectivity is a key objective of current conservation policies and is commonly modeled by landscape graphs (i.e., sets of habitat patches [nodes] connected by potential dispersal paths [links]). These graphs are often built based on expert opinion or species distribution models (SDMs) and therefore lack empirical validation from data more closely reflecting functional connectivity. Accordingly, we tested whether landscape graphs reflect how habitat connectivity influences gene flow, which is one of the main ecoevolutionary processes. To that purpose, we modeled the habitat network of a forest bird (plumbeous warbler [Setophaga plumbea]) on Guadeloupe with graphs based on expert opinion, Jacobs’ specialization indices, and an SDM. We used genetic data (712 birds from 27 populations) to compute local genetic indices and pairwise genetic distances. Finally, we assessed the relationships between genetic distances or indices and cost distances or connectivity metrics with maximum-likelihood population-effects distance models and Spearman correlations between metrics. Overall, the landscape graphs reliably reflected the influence of connectivity on population genetic structure; validation R² was up to 0.30 and correlation coefficients were up to 0.71. Yet, the relationship among graph ecological relevance, data requirements, and construction and analysis methods was not straightforward because the graph based on the most complex construction method (species distribution modeling) sometimes had less ecological relevance than the others. Cross-validation methods and sensitivity analyzes allowed us to make the advantages and limitations of each construction method spatially explicit. We confirmed the relevance of landscape graphs for conservation modeling but recommend a case-specific consideration of the cost-effectiveness of their construction methods. We hope the replication of independent validation approaches across species and landscapes will strengthen the ecological relevance of connectivity models.     

Special issue on contamination,remediation and health for pollutants in natural aquatic,soil, sediments and atmospheric environments

Environmental Geochemistry and Health -     

Market responses to hurricanes

This paper uses one of the strongest hurricanes to hit the US, Andrew in 1992, to define a quasi-random experiment that permits estimation of the responses of housing values to information about new hurricanes. Lee County, Florida did not experience damage from Andrew. The storm was a “near-miss.” We hypothesize that Andrew conveyed risk information to homeowners in the county. A difference-in-differences (DND) framework identifies the effect of this information on property values in areas likely to experience significant storm damage. The DND findings indicate at least a 19 percent decline in property values.