Landscape metrics as functional traits in plants: perspectives from a glacier foreland

Spatial patterns of vegetation arise from an interplay of functional traits, environmental characteristics and chance. The retreat of glaciers offers exposed substrates which are colonised by plants forming distinct patchy patterns. The aim of this study was to unravel whether patch-level landscape metrics of plants can be treated as functional traits. We sampled 46 plots, each 1 m × 1 m, distributed along a restricted range of terrain age and topsoil texture on the foreland of the Nardis glacier, located in the South-Eastern Alps, Italy. Nine quantitative functional traits were selected for 16 of the plant species present, and seven landscape metrics were measured to describe the spatial arrangement of the plant species' patches on the study plots, at a resolution of 1 cm × 1 cm. We studied the relationships among plant communities, landscape metrics, terrain age and topsoil texture. RLQ-analysis was used to examine trait-spatial configuration relationships. To assess the effect of terrain age and topsoil texture variation on trait performance, we applied a partial-RLQ analysis approach. Finally, we used the fourth-corner statistic to quantify and test relationships between traits, landscape metrics and RLQ axes. Floristically-defined relevé clusters differed significantly with regard to several landscape metrics. Diversity in patch types and size increased and patch size decreased with increasing canopy height, leaf size and weight. Moreover, more compact patch shapes were correlated with an increased capacity for the conservation of nutrients in leaves. Neither plant species composition nor any of the landscape metrics were found to differ amongst the three classes of terrain age or topsoil texture. We conclude that patch-level landscape metrics of plants can be treated as species-specific functional traits. We recommend that existing databases of functional traits should incorporate these type of data.

Exploration of Spatial Scale Sensitivity in Geographic Cellular Automata

Cellular automata (CA) are individual-based models in which states, time, and space are discrete. Spatiotemporal dynamics emerge from the simple and local interactions of the cells. When using CA in a geographic context, nontrivial questions have to be answered about the choice of spatial scale, namely cell size and neighbourhood configuration. However, the spatial scale decisions involved in the elaboration of geographic cellular automata (GCA) are often made arbitrarily or in relation to data availability. The objective of this study is to evaluate the sensitivity of GCA to spatial scale. A stochastic GCA was built to model land-cover change in the Maskoutains region (Quebec, Canada). The transition rules were empirically derived from two Landsat-TM (30 m resolution) images taken in 1999 and 2002 that have been resampled to four resolutions (100, 200, 500, 1000 m). Six different neighbourhood configurations were considered (Moore, Von Neumann, and circular approximations of 2, 3, 4, and 5 cell radii). Simulations were performed for each of the thirty spatial scale scenarios. Results show that spatial scale has a considerable impact on simulation dynamics in terms of both land-cover area and spatial structure. The spatial scale domains present in the results reveal the nonlinear relationships that link the spatial scale components to the simulation results.

Navigation performance in virtual environments varies with fractal dimension of landscape

Fractal geometry has been used to describe natural and built environments, but has yet to be studied in navigational research. In order to establish a relationship between the fractal dimension (D) of a natural environment and humans' ability to navigate such spaces, we conducted two experiments using virtual environments that simulate the fractal properties of nature. In Experiment 1, participants completed a goal-driven search task either with or without a map in landscapes that varied in D. In Experiment 2, participants completed a map-reading and location-judgment task in separate sets of fractal landscapes. In both experiments, task performance was highest at the low-to-mid range of D, which was previously reported as most preferred and discriminable in studies of fractal aesthetics and discrimination, respectively, supporting a theory of visual fluency. The applicability of these findings to architecture, urban planning, and the general design of constructed spaces is discussed.

Self-organization of developing embryo using scale-invariant approach

BACKGROUND

Self-organization is a fundamental feature of living organisms at all hierarchical levels from molecule to organ. It has also been documented in developing embryos.

METHODS

In this study, a scale-invariant power law (SIPL) method has been used to study self-organization in developing embryos. The SIPL coefficient was calculated using a centro-axial skew symmetrical matrix (CSSM) generated by entering the components of the Cartesian coordinates; for each component, one CSSM was generated. A basic square matrix (BSM) was constructed and the determinant was calculated in order to estimate the SIPL coefficient. This was applied to developing C. elegans during early stages of embryogenesis. The power law property of the method was evaluated using the straight line and Koch curve and the results were consistent with fractal dimensions (fd). Diffusion-limited aggregation (DLA) was used to validate the SIPL method.

RESULTS AND CONCLUSION

The fractal dimensions of both the straight line and Koch curve showed consistency with the SIPL coefficients, which indicated the power law behavior of the SIPL method. The results showed that the ABp sublineage had a higher SIPL coefficient than EMS, indicating that ABp is more organized than EMS. The fd determined using DLA was higher in ABp than in EMS and its value was consistent with type 1 cluster formation, while that in EMS was consistent with type 2.

Weather-driven model indicative of spatiotemporal power laws

In the traditional Drossel-Schwabl forest fire model (DS model), the frequency distributions of fire size and fire interval follow a power law and an exponential law, respectively. However, it is found that the frequency-interval distribution of actual forest fires is not exponential, but a power law with periodical fluctuations which may be caused by the daily cycle of weather parameters. Therefore, a weather driven forest fire model (WD model) is built considering actual hourly weather records, with which the fire igniting probability is calculated. The simulation results indicate that the frequency-interval distribution of the WD model agrees with that of actual forest fire data and, at the same time, the frequency-size distributions of the WD and the DS models are in accordance with each other. In the further analysis of the temporal property of weather data, it is found that the change of weather data also exhibits a power-law relation with periodic fluctuations, implying that the external driving from weather parameters is the essential reason for the power-law distribution of fire intervals. The results suggest that natural systems may be coupled with each other and that the decoupling of systems is important to identifying system characteristics.

Models, data and mechanisms: quantifying wildfire regimes

The quantification of wildfire regimes, especially the relationship between the frequency with which events occur and their size, is of particular interest to both ecologists and wildfire managers. Recent studies in cellular automata (CA) and the fractal nature of the frequency-area relationship they produce has led some authors to ask whether the power-law frequency-area statistics seen in the CA might also be present in empirical wildfire data. Here, we outline the history of the debate regarding the statistical wildfire frequency-area models suggested by the CA and their confrontation with empirical data. In particular, the extent to which the utility of these approaches is dependent on being placed in the context of self-organized criticality (SOC) is examined. We also consider some of the other heavy-tailed statistical distributions used to describe these data. Taking a broadly ecological perspective we suggest that this debate needs to take more interest in the mechanisms underlying the observed power-law (or other) statistics. From this perspective, future studies utilizing the techniques associated with CA and statistical physics will be better able to contribute to the understanding of ecological processes and systems.

Characterizing wildfire regimes in the United States

Wildfires statistics for the conterminous United States (U.S.) are examined in a spatially and temporally explicit manner. We use a high-resolution data set consisting of 88,916 U.S. Department of Agriculture Forest Service wildfires over the time period 1970-2000 and consider wildfire occurrence as a function of ecoregion (land units classified by climate, vegetation, and topography), ignition source (anthropogenic vs. lightning), and decade. For the conterminous U.S., we (i) find that wildfires exhibit robust frequency-area power-law behavior in 18 different ecoregions; (ii) use normalized power-law exponents to compare the scaling of wildfire-burned areas between ecoregions, finding a systematic change from east to west; (iii) find that wildfires in the eastern third of the U.S. have higher power-law exponents for anthropogenic vs. lightning ignition sources; and (iv) calculate recurrence intervals for wildfires of a given burned area or larger for each ecoregion, allowing for the classification of wildfire regimes for probabilistic hazard estimation in the same vein as is now used for earthquakes.