Animal social networks: an introduction

Network analysis has a long history in the mathematical and social sciences and the aim of this introduction is to provide a brief overview of the potential that it holds for the study of animal behaviour. One of the most attractive features of the network paradigm is that it provides a single conceptual framework with which we can study the social organisation of animals at all levels (individual, dyad, group, population) and for all types of interaction (aggressive, cooperative, sexual etc.). Graphical tools allow a visual inspection of networks which often helps inspire ideas for testable hypotheses. Network analysis itself provides a multitude of novel statistical tools that can be used to characterise social patterns in animal populations. Among the important insights that networks have facilitated is that indirect social connections matter. Interactions between individuals generate a social environment at the population level which in turn selects for behavioural strategies at the individual level. A social network is often a perfect means by which to represent heterogeneous relationships in a population. Probing the biological drivers for these heterogeneities, often as a function of time, forms the basis of many of the current uses of network analysis in the behavioural sciences. This special issue on social networks brings together a diverse group of practitioners whose study systems range from social insects over reptiles to birds, cetaceans, ungulates and primates in order to illustrate the wide-ranging applications of network analysis. This contribution is part of the special issue “Social Networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau and R. James).     

Coregionalization analysis with a drift for multi-scale assessment of spatial relationships between ecological variables 1. Estimation of drift and random components

In two articles, we present ‘coregionalization analysis with a drift’ (CRAD), a method to assess the multi-scale variability of and relationships between ecological variables from a multivariate spatial data set. In phase I of CRAD (the first article), a deterministic drift component representing the large-scale pattern and a random component modeled as a second-order stationary process are estimated for each variable separately. In phase II (this article), a linear model of coregionalization (LMC) is fitted by estimated generalized least squares to the direct and cross experimental variograms of residuals (i.e., after the removal of estimated drifts). Structural correlations and coefficients of determination at smaller scales are then computed from the estimated coregionalization matrices, while the estimated drifts are used to calculate pseudo coefficients at large scale. The performance of five procedures in estimating correlations and coefficients of determination was compared using a Monte Carlo study. In four CRAD procedures, drift estimation was based on local polynomials of order 0, 1, 2 (L₀, L₁, L₂) or a global polynomial with forward selection of the basis functions; the fifth procedure was coregionalization analysis (CRA), in which large-scale patterns were modeled as a supplemental component in the LMC. In bivariate and multivariate analyses, the uncertainty in the estimation of correlations and coefficients of determination could be related to the interference between spatial components within a bounded sampling domain. In the bivariate case, most procedures provided acceptable estimates of correlations. In regionalized redundancy analysis, uncertainty was highest for CRA, while L₁ provided the best results overall. In a forest ecology example, the identification of scale-specific correlations between plant species diversity and soil and topographical variables illustrated the potential of CRAD to provide unique insight into the functioning of complex ecosystems.     

Seasonal changes in movements and habitat preferences of the scalloped hammerhead shark (Sphyrna lewini) while refuging near an oceanic island

Movements and habitat preferences of sharks relative to a central location are widely documented for many species; however, the reasons for such behaviors are currently unknown. Do movements vary spatially or temporally or between individuals? Do sharks have seasonal habitat and environmental preferences or simply perform movements at random at any time of the year? To help understand requirements for the designation of critical habitats for an endangered top predator and to develop zoning and management plans for key habitats, we examined vertical and horizontal movements, and determined habitat and environmental preferences of scalloped hammerhead sharks (Sphyrna lewini). We tracked seven hammerheads for 19–96 h at Wolf Island (1.38ºN, 91.82ºW) between 2007 and 2009 using ultrasonic transmitters with depth and temperature sensors, and we profiled temperature through the water column. Movements of individual hammerheads fell in two classes: constrained (remaining near the island) and dispersive (moving offshore to pelagic environments). The central activity space or kernel off the southeast side of Wolf Island was small and common to most, but the area varied among individuals (mean ± SE 0.25 ± 0.2 km²), not exceeding 0.6 km² for any of the sharks, and not changing significantly between seasons. In general, hammerheads showed preference for the up-current habitat on the eastern side of Wolf Island in both the warm and cold seasons. However, the depth of sharks varied with season, apparently in response to seasonal changes in the vertical structure of temperature. Hammerheads performed frequent vertical excursions above the thermocline during offshore movements and, in general, were observed to prefer temperatures of 23–26 °C found above the thermocline. At times, though individuals moved into the thermocline and made brief dives below it. Our results provided evidence that hammerheads (1) are highly selective of location (i.e., habitat on up-current side of island) and depth (i.e., top of the thermocline) while refuging, where they may carry out essential activities such as cleaning and thermoregulation, and (2) perform exploratory vertical movements by diving the width of the mixed layer and occasionally diving below the thermocline while moving offshore, most likely for foraging.     

Growth response of an early successional assemblage of coralline algae and benthic diatoms to ocean acidification

The sustained absorption of anthropogenically released atmospheric CO₂ by the oceans is modifying seawater carbonate chemistry, a process termed ocean acidification (OA). By the year 2100, the worst case scenario is a decline in the average oceanic surface seawater pH by 0.3 units to 7.75. The changing seawater carbonate chemistry is predicted to negatively affect many marine species, particularly calcifying organisms such as coralline algae, while species such as diatoms and fleshy seaweed are predicted to be little affected or may even benefit from OA. It has been hypothesized in previous work that the direct negative effects imposed on coralline algae, and the direct positive effects on fleshy seaweeds and diatoms under a future high CO₂ ocean could result in a reduced ability of corallines to compete with diatoms and fleshy seaweed for space in the future. In a 6-week laboratory experiment, we examined the effect of pH 7.60 (pH predicted to occur due to ocean acidification just beyond the year 2100) compared to pH 8.05 (present day) on the lateral growth rates of an early successional, cold-temperate species assemblage dominated by crustose coralline algae and benthic diatoms. Crustose coralline algae and benthic diatoms maintained positive growth rates in both pH treatments. The growth rates of coralline algae were three times lower at pH 7.60, and a non-significant decline in diatom growth meant that proportions of the two functional groups remained similar over the course of the experiment. Our results do not support our hypothesis that benthic diatoms will outcompete crustose coralline algae under future pH conditions. However, while crustose coralline algae were able to maintain their presence in this benthic rocky reef species assemblage, the reduced growth rates suggest that they will be less capable of recolonizing after disturbance events, which could result in reduced coralline cover under OA conditions.     

Price controls and banking in emissions trading: An experimental evaluation

We present results from laboratory emissions markets designed to investigate the effects of price controls and permit banking on limiting permit price risk. While both instruments reduce between-period price volatility and within-period price dispersion, combining price controls and permit banking yields important benefits. Banking alone produces high permit prices in earlier periods that fall over time, but the combined policy produces lower initial prices and lower volatility. However, banking, price controls, and the combination all produce higher between-period emissions volatility. Hence, for emissions markets that seek to control flow pollutants with strictly convex damages, efforts to limit permit price risk can result in higher expected damage.     

A new meta-analysis on the WTP/WTA disparity

This study reports a new meta-analysis of papers that elicit willingness-to-pay (WTP) and willingness-to-accept compensation (WTA) measures of value for the same good. We investigate the effects of type of good and several survey-design features on the WTP/WTA disparity, measured as the logarithm of the ratio of mean WTA to mean WTP. Confirming Horowitz and McConnell?s (2002) pioneering meta-analysis, we find the disparity is smaller for ordinary private goods than for public and non-market goods, that it is not solely an artifact of using hypothetical transactions or other weak experimental or survey methods, and that it is smaller for studies using student subjects. In addition, we find that the disparity is smaller when subjects have experience valuing the good in real markets or through repeated experimental trials. In contrast to Horowitz and McConnell, we find the disparity is significantly smaller in studies using incentive-compatible elicitation mechanisms. The disparity is smaller in more recent studies, an effect that is attributable only in part to changes in study characteristics.     

Predicting arsenic concentrations in groundwater of San Luis Valley,Colorado: implications for individual-level lifetime exposure assessment

Consumption of inorganic arsenic in drinking water at high levels has been associated with chronic diseases. Risk is less clear at lower levels of arsenic, in part due to difficulties in estimating exposure. Herein we characterize spatial and temporal variability of arsenic concentrations and develop models for predicting aquifer arsenic concentrations in the San Luis Valley, Colorado, an area of moderately elevated arsenic in groundwater. This study included historical water samples with total arsenic concentrations from 595 unique well locations. A longitudinal analysis established temporal stability in arsenic levels in individual wells. The mean arsenic levels for a random sample of 535 wells were incorporated into five kriging models to predict groundwater arsenic concentrations at any point in time. A separate validation dataset (n = 60 wells) was used to identify the model with strongest predictability. Findings indicate that arsenic concentrations are temporally stable (r = 0.88; 95 % CI 0.83–0.92 for samples collected from the same well 15–25 years apart) and the spatial model created using ordinary kriging best predicted arsenic concentrations (ρ = 0.72 between predicted and observed validation data). These findings illustrate the value of geostatistical modeling of arsenic and suggest the San Luis Valley is a good region for conducting epidemiologic studies of groundwater metals because of the ability to accurately predict variation in groundwater arsenic concentrations.     

Bridging gaps in demographic analysis with phylogenetic imputation

Phylogenetically informed imputation methods have rarely been applied to estimate missing values in demographic data but may be a powerful tool for reconstructing vital rates of survival, maturation, and fecundity for species of conservation concern. Imputed vital rates could be used to parameterize demographic models to explore how populations respond when vital rates are perturbed. We used standardized vital rate estimates for 50 bird species to assess the use of phylogenetic imputation to fill gaps in demographic data. We calculated imputation accuracy for vital rates of focal species excluded from the data set either singly or in combination and with and without phylogeny, body mass, and life-history trait data. We used imputed vital rates to calculate demographic metrics, including generation time, to validate the use of imputation in demographic analyses. Covariance among vital rates and other trait data provided a strong basis to guide imputation of missing vital rates in birds, even in the absence of phylogenetic information. Mean NRMSE for null and phylogenetic models differed by <0.01 except when no vital rates were available or for vital rates with high phylogenetic signal (Pagel's λ > 0.8). In these cases, including body mass and life-history trait data compensated for lack of phylogenetic information: mean normalized root mean square error (NRMSE) for null and phylogenetic models differed by <0.01 for adult survival and <0.04 for maturation rate. Estimates of demographic metrics were sensitive to the accuracy of imputed vital rates. For example, mean error in generation time doubled in response to inaccurate estimates of maturation time. Accurate demographic data and metrics, such as generation time, are needed to inform conservation planning processes, for example through International Union for Conservation of Nature Red List assessments and population viability analysis. Imputed vital rates could be useful in this context but, as for any estimated model parameters, awareness of the sensitivities of demographic model outputs to the imputed vital rates is essential.     

Solution of fully-coupled shallow water equations and contaminant transport using a primitive-variable Riemann method

A Riemann-solver scheme, using primitive variables rather than conserved variables, is configured and tuned for the solution of the fully-coupled two-dimensional shallow water and contaminant transport equations. This scheme is based on the unstructured finite volume discretization using primitive-variable Roe-flux approximation with an entropy fix. The primitive-variable flux associated with the exact source-term balancing is well-behaved and well-balanced for both still-water and dry regions with arbitrary bed topography. Second-order accuracy is used in space and time. The present study uses a nonlinear implicit scheme based on Newton-iterative algorithm for the time integration. In order to show the accuracy of the scheme, numerical results are verified by different test cases for contaminant advection and diffusion. A scenario of contaminant transport in a complex geometry with wet and dry elements is also simulated to demonstrate that the present work can be implemented on practical applications involving flooding and contaminant transport.