International audienceAim: Vegetation exhibiting landscape-scale regular spatial patterns has been reported for arid and semi-arid areas world-wide. Recent theories state that such structures are bound to low-productivity environments and result from a self-organization process. Our objective was to test this relationship between periodic pattern occurrence and environmental factors at a global scale and to parametrize a predictive distribution model. Location: Arid and semi-arid areas world-wide. Methods: We trained an empirical predictive model (Maxent) for the occurrence of periodic vegetation patterns, based on environmental predictors and known occurrences verified on Landsat satellite images. Results: This model allowed us to discover previously unreported pattern locations, and to report the first ever examples of spotted patterns in natural systems. Relationships to the main environmental drivers are discussed. Main conclusions: These results confirm that periodic patterned vegetations are ubiquitous at the interface between arid and semi-arid regions. Self-organized patterning appears therefore to be a biome-scale response to environmental conditions, including soil and topography. The set of correlations between vegetation patterns and their environmental conditions presented in this study will need to be reproduced in future modelling attempts

International audienceSpatially periodic vegetation patterns in arid to semi-arid regions have inspired numerous mechanistic models in the last decade. All embody a common principle of self-organization and make concordant, hence robust, predictions on how environmental factors may modulate the morphological properties of these patterns. Such an array of predictions still needs to be corroborated by synchronic and diachronic field observations on a large scale. Using Fourier-based texture analysis of satellite imagery, we objectively categorized the typical morphologies of periodic patterns and their characteristic scales (wavelength) over extensive areas in Sudan. We then analyzed the environmental domain and the modulation of patterns morphologies at different dates to test the theoretical predictions within a single synthetic and quantitative study. Our results show that, below a critical slope gradient which depends on the aridity level, pattern morphologies vary in space in relation to the decrease of mean annual rainfall in a sequence consistent with the predictions of self-organization models: gaps, labyrinths and spots with increasing wavelengths. Moreover, the same dynamical sequence was observed over time during the Sahelian droughts of the 1970s and 1980s. For a given morphology, the effect of aridity is to increase the pattern wavelength. Above the critical slope gradient, we observed a pattern of parallel bands oriented along the contour lines (the so called tiger-bush). The wavelength of these bands displayed a loose inverse correlation with the slope. These results highlight the pertinence of self-organization theory to explain and possibly predict the dynamics of these threatened ecosystem

International audienceDense vegetation bands aligned to contour levels and alternating at regular intervals with relatively barren interbands have been reported at the margins of all tropical deserts. Since their discovery in the 1950s, it has been supposed that these vegetation bands migrate upslope, forming a space-time cyclic pattern. Evidence to date has been relatively sparse and indirect, and observations have remained conflicting. Unequivocal photographic evidence of upslope migration (a few decimeters per year) is provided here for three independent dryland areas exhibiting periodic banded pattern: (1) the U.S. northeastern Chihuahuan Desert, (2) the Somalian Haud, and (3) the Mediterranean steppes of eastern Morocco. Migration speeds, averaged through time and space using Fourier cross-spectral analysis, are shown to be directly proportional to pattern scale (wavelength). A sequence of aerial photographs of the Chihuahuan Desert showed that migration was not continuous, but intermittent in response to fluctuating weather regimes. The rates at which bands expanded upslope and contracted downslope were better predicted by the change in annual rainfall than by its average level. However, the migration of banded patterns cannot be considered as systematic because in our observations of three other banded systems located in the Somalian Haud, central Australia, and western New South Wales, migration was undetectable at the available image resolution. In each of the six sites under study, the modal value of band orientation axes was verified to be approximately orthogonal to the steepest slope. Our results underscore the importance of taking both the spatial structure and the past climate sequence into account for understanding vegetation dynamics in arid to semiarid ecosystems. In addition, we show how Fourier spectral analysis applied to historical series of optical images can serve to quantify landscape dynamics at a decadal time scale

The conversion of landscapes by human activities results in widespread changes in landscape spatial structure. Regardless of the type of land conversion, there appears to be a limited number of common spatial configurations that result from such land transformation processes. Some of these configurations are considered optimal or more desirable than others. Based on pattern geometry, we define ten processes responsible for pattern change: aggregation, attrition, creation, deformation, dissection, enlargement, fragmentation, perforation, shift, and shrinkage. A novelty in this contribution is the inclusion of transformation processes causing expansion of the land cover of interest. Consequently, we propose a decision tree algorithm that enables detection of these processes, based on three parameters that have to be determined before and after the transformation of the landscape: area, perimeter length, and number of patches of the focal landscape class. As an example, the decision tree algorithm is applied to determine the transformation processes of three divergent land cover change scenarios: deciduous woodland degradation in Cadiz Township (Wisconsin, USA) 1831-1950, canopy gap formation in a terra firme rain forest at the Tiputini Biodiversity Station (Amazonian Ecuador) 1997-1998, and forest regrowth in Petersham Township (Massachusetts, USA) 1830-1985. The examples signal the importance of the temporal resolution of the data, since long-term pattern conversions can be subdivided in stadia in which particular pattern components are altered by specific transformation processes.

The identification of a universal law that can predict the spatiotemporal structure of any entity at any scale has long been pursued. Thermodynamics have targeted this goal, and the concept of entropy has been widely applied for various disciplines and purposes, including landscape ecology. Within this discipline, however, the uses of the entropy concept and its underlying assumptions are various and are seldom described explicitly. In addition, the link between this concept and thermodynamics is unclear. The aim of this paper is to review the various interpretations and applications of entropy in landscape ecology and to sort them into clearly defined categories. First, a retrospective study of the concept genesis from thermodynamics to landscape ecology was conducted. Then, 50 landscape ecology papers that use or discuss entropy were surveyed and classified by keywords, variables and metrics identified as related to entropy. In particular, the thermodynamic component of entropy in landscape ecology and its various interpretations related to landscape structure and dynamics were considered. From the survey results, three major definitions (i.e., spatial heterogeneity, the unpredictability of pattern dynamics and pattern scale dependence) associated with the entropy concept in landscape ecology were identified. The thermodynamic interpretations of these definitions are based on different theories. The thermodynamic interpretation of spatial heterogeneity is not considered relevant. The thermodynamic interpretation related to scale dependence is also questioned by complexity theory. Only unpredictability can be thermodynamically relevant if appropriate measurements are used to test it.