Alexander Braun

Drivers and Spatio-Temporal Extent of Hyporheic Patch Variation: Implications for Sampling

The hyporheic zone in stream ecosystems is a heterogeneous key habitat for species across many taxa. Consequently, it attracts high attention among freshwater scientists, but generally applicable guidelines on sampling strategies are lacking. Thus, the objective of this study was to develop and validate such sampling guidelines. Applying geostatistical analysis, we quantified the spatio-temporal variability of parameters, which characterize the physico-chemical substratum conditions in the hyporheic zone. We investigated eight stream reaches in six small streams that are typical for the majority of temperate areas. Data was collected on two occasions in six stream reaches (development data), and once in two additional reaches, after one year (validation data). In this study, the term spatial variability refers to patch contrast (patch to patch variance) and patch size (spatial extent of a patch). Patch contrast of hyporheic parameters (specific conductance, pH and dissolved oxygen) increased with macrophyte cover (r2 = 0.95, p<0.001), while patch size of hyporheic parameters decreased from 6 to 2 m with increasing sinuosity of the stream course (r2 = 0.91, p<0.001), irrespective of the time of year. Since the spatial variability of hyporheic parameters varied between stream reaches, our results suggest that sampling design should be adapted to suit specific stream reaches. The distance between sampling sites should be inversely related to the sinuosity, while the number of samples should be related to macrophyte cover.

Transamination Governs Nitrogen Isotope Heterogeneity of Amino Acids in Rats

The nitrogen isotope composition (δ¹⁵N) of different amino acids carries different dietary information. We hypothesized that transamination and de novo synthesis create three groups that largely explain their dietary information. Rats were fed with ¹⁵N-labeled amino acids. The redistribution of the dietary ¹⁵N labels among the muscular amino acids was analyzed. Subsequently, the labeling was changed and the nitrogen isotope turnover was analyzed. The amino acids had a common nitrogen half-life of ∼20 d, but differed in δ¹⁵N. Nontransaminating and essential amino acids largely conserved the δ¹⁵N of the source and, hence, trace the origin in heterogeneous diets. Nonessential and nontransaminating amino acids showed a nitrogen isotope composition between their dietary composition and that of their de novo synthesis pool, likely indicating their fraction of de novo synthesis. The bulk of amino acids, which are transaminating, derived their N from a common N pool and hence their δ¹⁵N was similar.

Dietary protein content affects isotopic carbon and nitrogen turnover

RATIONALE Isotopic turnover quantifies the metabolic renewal process of elements in organs and excreta. Knowledge of the isotopic turnover of animal organs and excreta is necessary for diet reconstruction via stable isotope analysis, as used in animal ecology, palaeontology and food authentication. Effects of dietary protein content on the isotopic carbon and nitrogen turnover (i.e. delay, representing the time between ingestion and start of renewal, and half-life) are unknown for most mammalian organs and excreta. METHODS To examine the effect of dietary protein content on turnover (delay and turnover rate), we fed 18 rats either a diet at protein maintenance or above protein maintenance, and quantified their isotopic carbon and nitrogen turnover in ten organs and excreta. These included the excreta faeces and urine, the visceral organs blood plasma, liver, kidney, lung and spleen, the cerebral tissue brain, and the muscular tissues heart and muscle. For data analysis, we used piecewise linear/non-linear exponential modelling that allows quantifying delay and turnover rate simultaneously. RESULTS Delays were ~0.5 days for carbon and nitrogen turnover and were not affected by dietary protein content. Half-lives during the following reaction progress were in the range of 1 to 45 days, increasing from excreta to visceral organs to muscular and cerebral organs. Rats fed the higher protein amount had 30% shorter nitrogen half-lives, and 20% shorter carbon half-lives. CONCLUSIONS The renewal times of organs and excreta depend on the dietary protein content. Hence, isotopic diet reconstruction is confronted with variation in half-lives within the same organ or excrement, altering the time window through which information can be perceived.

Forward Modeling of Fluctuating Dietary 13C Signals to Validate 13C Turnover Models of Milk and Milk Components from a Diet-Switch Experiment

Isotopic variation of food stuffs propagates through trophic systems. But, this variation is dampened in each trophic step, due to buffering effects of metabolic and storage pools. Thus, understanding of isotopic variation in trophic systems requires knowledge of isotopic turnover. In animals, turnover is usually quantified in diet-switch experiments in controlled conditions. Such experiments usually involve changes in diet chemical composition, which may affect turnover. Furthermore, it is uncertain if diet-switch based turnover models are applicable under conditions with randomly fluctuating dietary input signals. Here, we investigate if turnover information derived from diet-switch experiments with dairy cows can predict the isotopic composition of metabolic products (milk, milk components and feces) under natural fluctuations of dietary isotope and chemical composition. First, a diet-switch from a C3-grass/maize diet to a pure C3-grass diet was used to quantify carbon turnover in whole milk, lactose, casein, milk fat and feces. Data were analyzed with a compartmental mixed effects model, which allowed for multiple pools and intra-population variability, and included a delay between feed ingestion and first tracer appearance in outputs. The delay for milk components and whole milk was ∼12 h, and that of feces ∼20 h. The half-life (t½) for carbon in the feces was 9 h, while lactose, casein and milk fat had a t½ of 10, 18 and 19 h. The 13C kinetics of whole milk revealed two pools, a fast pool with a t½ of 10 h (likely representing lactose), and a slower pool with a t½ of 21 h (likely including casein and milk fat). The diet-switch based turnover information provided a precise prediction (RMSE ∼0.2 ‰) of the natural 13C fluctuations in outputs during a 30 days-long period when cows ingested a pure C3 grass with naturally fluctuating isotope composition.

Automated progress monitoring based on photogrammetric point clouds and precedence relationship graphs

Construction progress monitoring is an essential but time-consuming work on all construction sites. This research introduces a method to facilitate the asplanned versus as-built comparison through image based monitoring. A dense point cloud is reconstructed from the images that is compared to an existing 4D building information model (BIM). However, due to the numerous obstructions found on a construction site, only a minority of building elements can be detected directly. In this paper, we discuss how the detection results are significantly refined and enriched by using additional spatial and temporal information gained from the 4D BIM. In this regard, a precedence relationship graph is derived which helps to identify occluded elements and enhance the detection algorithm.

BIM-Based Progress Monitoring

On-site progress monitoring is essential for keeping track of the ongoing work on construction sites. Currently, this task is a manual, time-consuming activity. BIM-based progress monitoring facilitates the automated comparison of the actual state of construction with the planned state for the early detection of deviations in the construction process. In this chapter, we discuss an approach where the actual state of the construction site is captured using photogrammetric surveys. From these recordings, dense point clouds are generated by the fusion of disparity maps created with semi-global-matching (SGM). These are matched against the target state provided by a 4D Building Information Model. For matching the point cloud and the model, the distances between individual points of the cloud and a component’s surface are aggregated using a regular cell grid. For each cell, the degree of coverage is determined. Based on this, a confidence value is computed which serves as a basis for detecting the existence of a respective component. Additionally, process- and dependency-relations provided by the BIM model are taken into account to further enhance the detection process.