The goal of this thesis is to understand how children and adults scale distance. My preliminary work has shown that young children can accurately scale distances along a single dimension (i.e., length) even when the magnitude of the scale difference is very large. In these studies, 4- and 5-year-olds and adults first saw a location marked on a narrow mat placed on the floor of one testing space. They then reproduced that location on another narrow mat that was either the same length (i.e., the memory task) or a different length (i.e., the memory + scaling task) placed on the floor of an adjacent testing space. These experiments illustrated that both children and adults had more difficulty scaling up than scaling down (i.e., had more difficulty going from a small to a large mat than from a large to a small mat). In the present thesis, I used this difference between scaling up and scaling down as a tool to examine the processes underlying the ability to scale distance more generally. I predicted that the difficulty children and adults have scaling up can be attributed to mapping relative distances onto spaces that are too large to be viewed from a single vantage point. Experiment 1 demonstrated that although a visible boundary dividing a large space influenced how children and adults remember locations, scaling up was still more difficult than scaling down. Experiments 2 and 3 examined the influence of absolute size on mapping relative distance. When the absolute size of the test space was reduced, scaling up was no longer more difficult than scaling down. In contrast, when the absolute size was large, both scaling up and scaling down were more difficult, illustrating the importance of absolute size in using relative distance to scale. These findings suggest that when the absolute size of the space is large, children and adults have more difficulty using multiple edges of the space to accurately scale distance. More generally, these experiments underscore how the cognitive system and task structure interact to give rise to the ability to use relative distance to scale.

Previous research has shown a link between the spatial words children use and their performance on spatial reasoning tasks. There is a dearth of measures of spatial language, especially those that focus on a specific type of word. This dissertation introduces three studies, using two measures of dimensional adjective comprehension, one in English and one in Spanish. Study one found that bilingual children’s knowledge of dimensional adjectives in one language is not predictive of their performance on dimensional adjectives in the other language, but that general vocabulary within a language predicts performance in that language. This study also showed that within a pair of polar opposite terms (e.g., long and short) children are more likely to know the term describing the big dimension and not the small dimension than vice versa. The second study found that the number of dimensional concepts children comprehend predicts how well they perform on a spatial scaling test, controlling for age and general vocabulary. The final study failed to find a link between dimensional adjective knowledge and performance on the children’s mental transformation task. These findings might have important implications for early education, showing that supporting children’s understanding of language might have an impact on their spatial reasoning.

This dissertation focuses on the challenge of soil hydraulic parameter scaling in soil hydrology and related applications in general; and, in particular, the upscaling of these parameters to provide effective values at coarse scales. Soil hydraulic properties are required for many hydrological and ecological models at their representative scales. Prediction accuracy of these models is highly dependent on the quality of the model input parameters. However, measurement of parameter data at all such required scales is impractical as that would entail huge outlays of finance, time and effort. Hence, alternate methods of estimating the soil hydraulic parameters at the scales of interest are necessary. Two approaches to bridge this gap between the measurement and application scales for soil hydraulic parameters are presented in this dissertation. The first one is a stochastic approach, based on artificial neural networks (ANNs) applied within a Bayesian framework. ANNs have been used before to derive soil hydraulic parameters from other more easily measured soil properties at matching scales. Here, ANNs were applied with different training and simulation scales. This concept was further extended to work within a Bayesian framework in order to provide estimates of uncertainty in such parameter estimations. Use of ancillary information such as elevation and vegetation data, in addition to the soil physical properties, were also tested. These multiscale pedotransfer function methods were successfully tested with numerical and field studies at different locations and scales. Most upscaling efforts thus far ignore the effect of the topography on the upscaled soil hydraulic parameter values. While this flat-terrain assumption is acceptable at coarse scales of a few hundred meters, at kilometer scales and beyond, the influence of the physical features cannot be ignored. anew upscaling scheme which accounts for variations in topography within a domain was developed to upscale soil hydraulic parameters to hill-slope (kilometer) scales. The algorithm was tested on different synthetically generated topographic configurations with good results. Extending the methodology to field conditions with greater complexities also produced good results. A comparison of different recently developed scaling schemes showed that at hill-slope scales, inclusion of topographic information produced better estimates of effective soil hydraulic parameters at that scale.

Spatially explicit ecological models rely on spatially exhaustive data layers that have scales appropriate to the ecological processes of interest. Such data layers are often categorical raster maps derived from high-resolution, remotely sensed data that must be scaled to a lower spatial resolution to make them compatible with the scale of ecological analysis. Statistical functions commonly used to aggregate categorical data are majority-, nearest-neighbor- and random-rule. For heterogeneous landscapes and large scaling factors, however, use of these functions results in two critical issues: (1) ignoring large portions of information present in the high-resolution grid cells leads to high and uncontrolled loss of information in the scaled dataset; and (2) maintaining classes from the high-resolution dataset at the lower spatial resolution assumes validity of the classification scheme at the low-resolution scale, failing to represent recurring mixes of heterogeneous classes present in the low-resolution grid cells. The proposed new scaling algorithm resolves these issues, aggregating categorical data while simultaneously controlling for information loss by generating a non-hierarchical, representative, classification system valid at the aggregated scale. Implementing scaling parameters, that control class-label precision effectively reduced information loss of scaled landscapes as class-label precision increased. In a neutral-landscape simulation study, the algorithm consistently preserved information at a significantly higher level than the other commonly used algorithms. When applied to maps of real landscapes, the same increase in information retention was observed, and the scaled classes were detectable from lower-resolution, remotely sensed, multi-spectral reflectance data with high accuracy. The framework developed in this research facilitates scaling-parameter selection to address trade-offs among information retention, label fidelity, and spectral detectability of scaled classes. When generating high spatial resolution land-cover maps, quantifying effects of sampling intensity, feature-space dimensionality and classifier method on overall accuracy, confidence estimates, and classifier efficiency allowed optimization of the mapping method. Increase in sampling intensity boosted accuracies in a reasonably predictable fashion. However, adding a second image acquired when ground conditions and vegetation phenology differed from those of the first image had a much greater impact, increasing classification accuracy even at low sampling intensities, to levels not reached with a single season image.

Les patrons de diversité caractérisent la structure de la diversité des communautés, c'est-à-dire sa valeur, sa distribution et son changement dans l'espace et le temps. Leur étude peut amener des informations importantes sur les processus écologiques qui en sont à l'origine. Cependant de nombreuses hypothèses de travail sont faites lors de leur analyse. L'idée générale de cette thèse est qu'en remettant en cause ces hypothèses, un certain nombre de développements liés aux indices de diversité et aux modèles nuls deviennent possibles et permettent de mieux comprendre les processus écologiques à l'origine des patrons de diversité fonctionnelle ou phylogénétique. Le premier chapitre est consacré à l'étude des patrons de diversité fonctionnelle des communautés végétales alpines à de multiples échelles spatiales et organisationnelles. Le second chapitre s'intéresse aux perspectives méthodologiques amenés par les nombres de Hill. Dans le dernier chapitre, on s'intéresse aux enjeux méthodologiques d'un nouveau type de données de communautés : l'ADN environnemental. / Patterns of community diversity refers to the structure of diversity, i.e. its quantification, its distribution and its turnover in space and time. Its study is likely to shed the light on the assembly rules that determined the structure of communities. However, numerous ecological assumptions are often made when studying diversity patterns. What motivated the work was the perspective that by relaxing these assumptions, a number of developments linked to diversity indices and null models are possible and can help to understand the impact of multiple ecological processes on phylogenetic and functional diversity patterns. In a first part we studied the pattern of functional diversity of alpine plant communities as a function of spatial and organizational scales. In the second part, we studied the methodological perspectives brought by the Hill numbers. In a third part, we addressed the main methodological issues of a new type of community data: environmental DNA.

Biodiversity is determined by a myriad of complex processes acting at different scales. Given the current rates of biodiversity loss and change, it is of paramount importance that we improve our understanding of the underlying structure of ecological communities. In this thesis, I focused on Species Abundance Distributions (SAD), as a synthetic measure of biodiversity and community structure, and on Beta (β) diversity patterns, as a description of the spatial variation of species composition. I systematically assessed the effect of scale on both these patterns, analysing a broad range of community data, including different taxa and habitats, from the terrestrial, marine and freshwater realms. Knowledge of the scaling properties of abundance and compositional patterns must be fully integrated in biodiversity research if we are to understand biodiversity and the processes underpinning it, from local to global scales. SADs depict the relative abundance of the species present in a community. Although typically described by unimodal logseries or lognormal distributions, empirical SADs can also exhibit multiple modes. However, the existence of multiple modes in SADs has largely been overlooked, assumed to be due to sampling errors or a rare pattern. Thus, we do not know how prevalent multimodality is, nor do we have an understanding of the factors leading to this pattern. Here, I provided the first global empirical assessment of the prevalence of multimodality across a wide range of taxa, habitats and spatial extents. I employed an improved method combining two model selection tools, and (conservatively) estimated that ~15% of the communities were multimodal with strong support. Furthermore, I showed that the pattern is more common for communities at broader spatial scales and with greater taxonomic diversity (i.e. more phylogenetically diverse communities, since taxonomic diversity was measured as number of families). This suggests a link between multimodality and ecological heterogeneity, broadly defined to incorporate the spatial, environmental, taxonomic and functional variability of ecological systems. Empirical understanding of how spatial scale affects SAD shape is still lacking. Here, I established a gradient in spatial scale spanning several orders of magnitude by decomposing the total extent of several datasets into smaller subsets. I performed an exploratory analysis of how SAD shape is affected by area sampled, species richness, total abundance and taxonomic diversity. Clear shifts in SAD shape can provide information about relevant ecological and spatial mechanisms affecting community structure. There was a clear effect of area, species richness and taxonomic diversity in determining SAD shape, while total abundance did not exhibit any directional effect. The results supported the findings of the previous analysis, with a higher prevalence of multimodal SADs for larger areas and for more taxonomically diverse communities, while also suggesting that species spatial aggregation patterns can be linked to SAD shape. On the other hand, there was a systematic departure from the predictions of two important macroecological theories for SAD across scales, specifically regarding logseries distributions being selected only for smaller scales and when species richness and number of families were proportionally much smaller than the total extent. β diversity quantifies the variation in species composition between sites. Although a fundamental component of biodiversity, its spatial scaling properties are still poorly understood. Here, I tested if two conceptual types of β diversity showed systematic variation with scale, while also explicitly accounting for the two β diversity components, turnover and nestedness (species replacement vs species richness differences). I provided the first empirical analysis of β diversity scaling patterns for different taxa, revealing remarkably consistent scaling curves. Total β diversity and turnover exhibit a power law decay with log area, while nestedness is largely insensitive to scale changes. For the distance decay of similarity analysis, while area sampled affected the overall dissimilarity values, rates of similarity were consistent across large variations in sampled area. Finally, in both these analyses, turnover was the main contributor to compositional change. These results suggest that species are spatially aggregated across spatial scales (from local to regional scales), while also illustrating that substantial change in community structure might occur, despite species richness remaining relatively stable. This systematic and comprehensive analysis of SAD and community similarity patterns highlighted spatial scale, ecological heterogeneity and species spatial aggregation patterns as critical components underlying the results found. This work expanded the range of scales at which both theories deriving SAD and community similarity studies have been developed and tested (from local plots to continents). The results here showed strong departures from two important macroecological theories for SAD at different scales. In addition, the overall findings in this thesis clearly indicate that unified theories of biodiversity (or assuming a set of synthetic minimal assumptions) are unable to accommodate the variability in SADs shape across spatial scales reported here, and cannot fully reproduce community similarity patterns across scales. Incorporating more realistic assumptions, or imposing scale dependent assumptions, may prove to be a fruitful avenue for ecological research regarding the scaling properties of SAD and community similarity patterns. This will allow deriving new predictions and improving the ability of theoretical models to incorporate the variability in abundance and similarity patterns across scales.

The use of audio and video communication has increased exponentially over the last decade and has gone from speech over GSM to HD resolution video conference between continents on mobile devices. As the use becomes more widespread the interest in delivering high quality media increases even on devices with limited resources. This includes both development and enhancement of the communication chain but also the topic of objective measurements of the perceived quality. The focus of this thesis work has been to perform enhancement within speech encoding and video decoding, to measure influence factors of audio and video performance, and to build methods to predict the perceived video quality. The audio enhancement part of this thesis addresses the well known problem in the GSM system with an interfering signal generated by the switching nature of TDMA cellular telephony. Two different solutions are given to suppress such interference internally in the mobile handset. The first method involves the use of subtractive noise cancellation employing correlators, the second uses a structure of IIR notch filters. Both solutions use control algorithms based on the state of the communication between the mobile handset and the base station. The video enhancement part presents two post-filters. These two filters are designed to improve visual quality of highly compressed video streams from standard, block-based video codecs by combating both blocking and ringing artifacts. The second post-filter also performs sharpening. The third part addresses the problem of measuring audio and video delay as well as skewness between these, also known as synchronization. This method is a black box technique which enables it to be applied on any audiovisual application, proprietary as well as open standards, and can be run on any platform and over any network connectivity. The last part addresses no-reference (NR) bitstream video quality prediction using features extracted from the coded video stream. Several methods have been used and evaluated: Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Least Square Support Vector Machines (LS-SVM), showing high correlation with both MOS and objective video assessment methods as PSNR and PEVQ. The impact from temporal, spatial and quantization variations on perceptual video quality has also been addressed, together with the trade off between these, and for this purpose a set of locally conducted subjective experiments were performed.