Diversity is a concept central to ecology, and its measurement is essential for any study of ecosystem health. But summarizing this complex and multidimensional concept in a single measure is problematic. Dozens of mathematical indices have been proposed for this purpose, but these can provide contradictory results leading to misleading or incorrect conclusions about a community's diversity. In this review, we summarize the key conceptual issues underlying the measurement of ecological diversity, survey the indices most commonly used in ecology, and discuss their relative suitability. We advocate for indices that: (i) satisfy key mathematical axioms; (ii) can be expressed as so-called effective numbers; (iii) can be extended to account for disparity between types; (iv) can be parameterized to obtain diversity profiles; and (v) for which an estimator (preferably unbiased) can be found so that the index is useful for practical applications.

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Infiltration measurements in arid, stony soils are notoriously variable in visually homogeneous areas, and have been reported to be influenced by embedded stone fragments. This study aimed to identify the influence of rock fragment contents, orientation, and position within a small arid watershed on hydraulic conductivity in northern Chile. Two different measurement techniques were used, a single‐ring infiltrometer with constant ponding head and a tension infiltrometer, which were applied at both an undisturbed field site (44 locations along three transects) and on the disturbed <2‐mm soil fraction from the same locations. Variations in saturated and unsaturated hydraulic conductivities were observed when using different calculation methods, adding to the observed variability. For saturated conditions, only small differences in conductivities were observed between two calculation methods, whereas unsaturated hydraulic conductivities calculated by five different methods showed more important variations. Stone fragment content correlated significantly with both saturated and unsaturated conductivities, probably due to a positive correlation between stone content and coarse lacunar pore space. Slope orientations with higher amounts of stone fragments gave higher infiltration rates, as well as transects with steeper slopes and more, but smaller, rock fragments. When evaluating differences in infiltration rates observed along three transects in the watershed, variability could be mainly attributed to stone fragment content influences.

[1] The classical determination of the soil water retention curve (SWRC) by measuring soil water content q at different matric potentials y using undisturbed soil samples is time consuming and expensive. Furthermore, undisturbed soil sampling can be an intricate task when coarse soil fragments (>2 mm) are present. The objective of this study was to test whether tension infiltrometry could be used to estimate the SWRC of stony soils and to investigate to what extent the coarse fragments affected the SWRC. Tension infiltrometer measurements were conducted at 44 sites with stony soils in arid Chile. Soil water retention curves obtained through inverse modeling were compared with laboratory-determined water retention (q, y) data pairs. Differences were found to be small, confirming the applicability of the inverse modeling method. Rock fragments had a significant indirect influence on water retention for matric potentials higher than À0.30 kPa, which could be attributed to their direct influence on pore size distribution.

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Key message: Pattern recognition has become an important tool to aid in the identification and classification of timber species. In this context, the focus of this work is the classification of wood species using texture characteristics of transverse cross-section images obtained by microscopy. The results show that this approach is robust and promising. Context: Considering the lack of automated methods for wood species classification, machine vision based on pattern recognition might offer a feasible and attractive solution because it is less dependent on expert knowledge, while existing databases containing high-quality microscopy images can be exploited. Aims: This work focuses on the automated classification of 1221 micro-images originating from 77 commercial timber species from the Democratic Republic of Congo. Methods: Microscopic images of transverse cross-sections of all wood species are taken in a standardized way using a magnification of 25x. The images are represented as texture feature vectors extracted using local phase quantization or local binary patterns and classified by a nearest neighbor classifier according to a triplet of labels ( species, genus, family). Results: The classification combining both local phase quantization and linear discriminant analysis results in an average success rate of approximately 88% at species level, 89% at genus level and 90% at family level. The success rate of the classification method is remarkably high. More than 50% of the species are never misclassified or only once. The success rate is increasing from the species, over the genus to the family level. Quite often, pattern recognition results can be explained anatomically. Species with a high success rate show diagnostic features in the images used, whereas species with a low success rate often have distinctive anatomical features at other microscopic magnifications or orientations than those used in our approach. Conclusion: This work demonstrates the potential of a semi-automated classification by resorting to pattern recognition. Semi-automated systems like this could become valuable tools complementing conventional wood identification