Defining patch mosaic functional types to predict invasion patterns in a forest landscape.

Alien plant invasions contribute significantly to global changes by often affecting biodiversity and ecosystem processes. Operational methods for identifying landscape attributes that promote or constrain plant invasions are urgently needed to predict their future spread and manage them efficiently. We combined landscape and functional ecology concepts to define patch mosaic functional types (PMFTs) as groups of cells showing the same response to a plant invasion in a heterogeneous forest landscape. The invasion of a European temperate forest by the American black cherry (Prunus serotina) has been chosen as a case study. A set of variables was collected, mapped using a Geographic Information System, and analyzed with multivariate analyses to correlate landscape traits with Prunus serotina abundance in each cell of a grid overlaid on the forest. A risk index was derived and mapped for three invasion levels: seedling colonization, tree establishment, and ecosystem invasion. Five PMFTs were identified and characterized by a set of traits related to soil properties, land use, disturbance, and invasion history. Scots pine plantations on podzols were the most invasible, while cells dominated by hydromorphic or calcareous soils were the most resistant. Most colonized patch mosaics provided suitable conditions for future establishment and invasion. Being strongly spatially connected, suitable patches provide corridors for Prunus serotina to colonize new parts of the forest. Conversely, the most resistant PMFTs were spatially agglomerated in the south of the forest and could act as a barrier. Colonization, establishment, and invasion risk maps were finally obtained by combining partial risks associated with each landscape trait at the cell scale. Within a heterogeneous landscape, we defined and organized PMFTs into a hierarchy, according to their associated risk for colonization, establishment, or invasion by a given invasive species. Each hierarchical level should be associated with a management strategy aiming at reducing one or more partial risk. Monitoring safe areas, extending cutting rotations, harvesting recently colonized stands tree by tree, promoting a multilayered understory vegetation, cutting down reproducing alien trees, favoring shade-tolerant, fast-growing, native tree species, removing alien trees at the leading edge, and proposing soil enrichment or irrigation in heavily invaded areas are recommended.     

Multiscale codependence analysis: an integrated approach to analyze relationships across scales

The spatial and temporal organization of ecological processes and features and the scales at which they occur are central topics to landscape ecology and metapopulation dynamics, and increasingly regarded as a cornerstone paradigm for understanding ecological processes. Hence, there is need for computational approaches which allow the identification of the proper spatial or temporal scales of ecological processes and the explicit integration of that information in models. For that purpose, we propose a new method (multiscale codependence analysis, MCA) to test the statistical significance of the correlations between two variables at particular spatial or temporal scales. Validation of the method (using Monte Carlo simulations) included the study of type I error rate, under five statistical significance thresholds, and of type II error rate and statistical power. The method was found to be valid, in terms of type I error rate, and to have sufficient statistical power to be useful in practice. MCA has assumptions that are met in a wide range of circumstances. When applied to model the river habitat of juvenile Atlantic salmon, MCA revealed that variables describing substrate composition of the river bed were the most influential predictors of parr abundance at 0.4-4.1 km scales whereas mean channel depth was more influential at 200-300 m scales. When properly assessed, the spatial structuring observed in nature may be used purposefully to refine our understanding of natural processes and enhance model representativeness.     

Studying dispersal at the landscape scale: efficient combination of population surveys and capture-recapture data

Researchers often rely on capture-mark-recapture (CMR) data to study animal dispersal in the wild. Yet their spatial coverage often does not encompass the entire dispersal range of the study individuals, sometimes producing misleading results. Information contained in population surveys and variation in population spatial structure can be used to overcome this issue. We build an integrated model in a multisite context in which CMR data are only collected at a subset of sites, but numbers of breeding pairs are counted at all sites. In a Black-headed Gull Chroicocephalus ridibundus population, the integrated-modeling approach induces an increase in precision for the demographic parameters of interest (variances, on average, were decreased by 20%) and provides a more precise extrapolation of results from the CMR data to the whole population. Patterns of condition-dependent dispersal are therefore made easier to detect, and we obtain evidence for colony-size dependence in recruitment, dispersal, and breeding success. These results suggest that first-time breeders disperse to small colonies in order to recruit earlier. The exchange of experienced breeders between colonies appears as a main determinant of the observed variation in colony sizes.     

Forward selection of explanatory variables

This paper proposes a new way of using forward selection of explanatory variables in regression or canonical redundancy analysis. The classical forward selection method presents two problems: a highly inflated Type I error and an overestimation of the amount of explained variance. Correcting these problems will greatly improve the performance of this very useful method in ecological modeling. To prevent the first problem, we propose a two-step procedure. First, a global test using all explanatory variables is carried out. If, and only if, the global test is significant, one can proceed with forward selection. To prevent overestimation of the explained variance, the forward selection has to be carried out with two stopping criteria: (1) the usual alpha significance level and (2) the adjusted coefficient of multiple determination (Ra(2)) calculated using all explanatory variables. When forward selection identifies a variable that brings one or the other criterion over the fixed threshold, that variable is rejected, and the procedure is stopped. This improved method is validated by simulations involving univariate and multivariate response data. An ecological example is presented using data from the Bryce Canyon National Park, Utah, U.S.A.