Network ecology in dynamic landscapes.

Network ecology is an emerging field that allows researchers to conceptualize and analyse ecological networks and their dynamics. Here, we focus on the dynamics of ecological networks in response to environmental changes. Specifically, we formalize how network topologies constrain the dynamics of ecological systems into a unifying framework in network ecology that we refer to as the 'ecological network dynamics framework'. This framework stresses that the interplay between species interaction networks and the spatial layout of habitat patches is key to identifying which network properties (number and weights of nodes and links) and trade-offs among them are needed to maintain species interactions in dynamic landscapes. We conclude that to be functional, ecological networks should be scaled according to species dispersal abilities in response to landscape heterogeneity. Determining how such effective ecological networks change through space and time can help reveal their complex dynamics in a changing world.

How spatial heterogeneity of cover affects patterns of shrub encroachment into mesic grasslands.

We used a multi-method approach to analyze the spatial patterns of shrubs and cover types (plant species, litter or bare soil) in grassland-shrubland ecotones. This approach allows us to assess how fine-scale spatial heterogeneity of cover types affects the patterns of Cytisus balansae shrub encroachment into mesic mountain grasslands (Catalan Pyrenees, Spain). Spatial patterns and the spatial associations between juvenile shrubs and different cover types were assessed in mesic grasslands dominated by species with different palatabilities (palatable grass Festuca nigrescens and unpalatable grass Festuca eskia). A new index, called RISES ("Relative Index of Shrub Encroachment Susceptibility"), was proposed to calculate the chances of shrub encroachment into a given grassland, combining the magnitude of the spatial associations and the surface area for each cover type. Overall, juveniles showed positive associations with palatable F. nigrescens and negative associations with unpalatable F. eskia, although these associations shifted with shrub development stage. In F. eskia grasslands, bare soil showed a low scale of pattern and positive associations with juveniles. Although the highest RISES values were found in F. nigrescens plots, the number of juvenile Cytisus was similar in both types of grasslands. However, F. nigrescens grasslands showed the greatest number of juveniles in early development stage (i.e. height<10 cm) whereas F. eskia grasslands showed the greatest number of juveniles in late development stages (i.e. height>30 cm). We concluded that in F. eskia grasslands, where establishment may be constrained by the dominant cover type, the low scale of pattern on bare soil may result in higher chances of shrub establishment and survival. In contrast, although grasslands dominated by the palatable F. nigrescens may be more susceptible to shrub establishment; current grazing rates may reduce juvenile survival.

Utility of computer simulations in landscape genetics.

Population genetics theory is primarily based on mathematical models in which spatial complexity and temporal variability are largely ignored. In contrast, the field of landscape genetics expressly focuses on how population genetic processes are affected by complex spatial and temporal environmental heterogeneity. It is spatially explicit and relates patterns to processes by combining complex and realistic life histories, behaviours, landscape features and genetic data. Central to landscape genetics is the connection of spatial patterns of genetic variation to the usually highly stochastic space-time processes that create them over both historical and contemporary time periods. The field should benefit from a shift to computer simulation approaches, which enable incorporation of demographic and environmental stochasticity. A key role of simulations is to show how demographic processes such as dispersal or reproduction interact with landscape features to affect probability of site occupancy, population size, and gene flow, which in turn determine spatial genetic structure. Simulations could also be used to compare various statistical methods and determine which have correct type I error or the highest statistical power to correctly identify spatio-temporal and environmental effects. Simulations may also help in evaluating how specific spatial metrics may be used to project future genetic trends. This article summarizes some of the fundamental aspects of spatial-temporal population genetic processes. It discusses the potential use of simulations to determine how various spatial metrics can be rigorously employed to identify features of interest, including contrasting locus-specific spatial patterns due to micro-scale environmental selection.

A nonlinear regression approach to test for size-dependence of competitive ability.

An individual's competitive ability is often dependent on its size, but the methods commonly used to analyze plant competition experiments generally assume that the outcome of interactions are size independent. A method for the analysis of experiments with paired competition treatments based on nonlinear regression with a power function is presented. This method allows straightforward tests of whether a competitive interaction is size dependent, and for the significance of experimental treatments. The method is applied to three example data sets: (1) an experiment where pairs of plants were grown with and without competition at five fertilization levels, (2) an experiment where the fecundity of two snail species were compared between environments at two densities, and (3) an addition series experiment where two plant species were grown in proportional mixtures at several densities. Competitive ability was size-dependent in two of these examples, which demonstrates that a wide range of ecologically important information can be lost when the assumption of size-dependence is ignored. Regression with a power curve should always be used to test whether competitive interactions are size independent, and for the further analysis of size-dependent interactions.

Measuring the effect of an abiotic stress on competition.

Using recently developed solution culture techniques, the effect of a non-resource abiotic stress, nickel toxicity, was tested on intraspecific nutrient competition among wheat. The choice of an appropriate statistical model was of paramount importance in interpreting these effects. We argue that a multiplicative model is more appropriate for experiments on interactions of competition and abiotic stress. By such an analysis, nickel had no relative effect on the ability of competition to reduce plant size in two experiments, and caused a small reduction in competition in another. These results are contrary to other reports of the effect of a non-resource abiotic stress on competition and appear to be due to an increased demand for nutrients in the presence of toxic levels of nickel. The effects of an abiotic stress on competition may thus be specitic to the stress and not generalized across all abiotic stresses.