Dissolved organic carbon in runoff and tile-drain water under corn and forage fertilized with hog manure

Dissolved organic carbon (DOC) export from soils can play a significant role in soil C cycling and in nutrient and pollutant transport. However, information about DOC losses from agricultural soils as influenced by management practices is scarce. We compared the effects of mineral fertilizer (MF) and liquid hog manure (LHM) applications on the concentration and molecular size of DOC released in runoff and tile-drain water under corn (Zea mays L.) and forage cropping systems. Runoff and tile-drain water samples were collected during a 2-mo period (October to December 1998) and DOC concentration was measured. Characterization of DOC was performed by tangential ultrafiltration with nominal cut-offs at 3 and 100 kDa. Mean concentration of DOC in runoff water (12.7 mg DOC L(-1)) was higher than in tile-drain water (6.5 mg DOC L(-1)). Incorporation of corn residues increased the DOC concentration by 6- to 17-fold in surface runoff, but this effect was short-lived. In runoff water, the relative size of the DOC molecules increased when corn residues and LHM were applied probably due to partial microbial breakdown of these organic materials and to a faster decomposition or preferential adsorption of the small molecules. The DOC concentration in tile-drain water was slightly higher under forage (7.5 mg DOC L(-1)) than under corn (5.4 mg DOC L(-1)) even though the application rates of LHM were higher in corn plots. We suggest that preferential flow facilitated the migration of DOC to tile drains in forage plots. In conclusion, incorporation of corn residues and LHM increased the concentration of DOC and the relative size of the molecules in surface runoff water, whereas DOC in tile-drain water was mostly influenced by the cropping system with relatively more DOC and larger molecules under forage than corn.     

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.