Consequences of increasing nitrogen:phosphorus ratios for pelagic lake food webs

Lake ecosystems are faced with increasing depositions of nitrogen (N), deriving from anthropogenic activities such as the use of fertilizers and fossil fuels. The consequences are, however, still far from being understood. Since the great majority of pre-alpine lakes are limited in phosphorus (P), one would not immediately expect that an increasing N input would have serious consequences for these lake ecosystems. However, there is evidence that the consequences of rising N depositions, especially for the trophic systems of these lakes, are more far-reaching than would be initially assumed. To investigate the influence of increasing N loading in pre-alpine lakes, I conducted various mesocosm and microcosm experiments and large-scale monitoring of 11 pre-alpine lakes. The mesocosm experiments were performed in three pre-alpine lakes in southern Germany. The natural phyto- and zooplankton communities and juvenile whitefish (Coregonus spec.) were exposed to different N concentrations or to gradients of dissolved inorganic nitrogen to total phosphorus (DIN:TP) ratios. These experiments were carried out in the spring, immediately after ice-break (March–May), for periods between 63 and 76 days. The aim of the microcosm experiments was to estimate the toxic thresholds of ammonium or ammonia (NH4+ or NH3) for the survival of a Daphnia hyalina complex. In these microcosm experiments, Daphnia were exposed to a NH4+ gradient with NH4+ concentrations between 0–87.8 mg L-1. In contrast, the large-scale lake-monitoring included 11 pre-alpine lakes in southern Germany. These lakes were located between Lake Königssee in the east and Lake Constance in the west of southern Germany. Over a period of three years, the lakes were sampled twice a year for nutrient concentrations, and different parameters concerning phytoplankton-, zooplankton- and whitefish communities (including whitefish of all age classes). I found that increasing N concentrations had significant effects on organisms from different trophic levels in the pelagic food-webs of pre-alpine lakes. Phytoplankton communities responded with measurable qualitative effects to increasing N inputs (changes in community composition, in biomass stoichiometry and in biochemical composition). In contrast, zooplankton communities and whitefish were both, qualitatively but also quantitatively affected. Observed effects on zooplankton levels were changes in community composition and decreasing Daphnia abundances. With increasing N, whitefish showed a decrease in growth and a lowered condition factor. These findings indicate a transfer of N-derived effects from the primary producers at the base of pelagic food-webs through all trophic levels up to top consumers such as planktivorous whitefish. However, rising N concentrations had no toxic effects on Daphnia survival, neither in the mesocosm experiments nor in natural lakes. For the survival of the Daphnia clone used in the microcosm experiments, I found a toxic threshold concentration of ammonium of NH4+ = 4.22 mg L-1. This is more than two times higher than the artificially established ammonium concentrations in the mesocosms (NH4+max = 1.88 mg L-1) and over 13 times higher than the natural ammonium concentrations found in the monitored lakes (NH4+max = 0.32 mg L-1). In conclusion, my results emphasize the importance of increasing N loading for organisms in pre-alpine lake ecosystems. This is especially true as the observed N-derived effects in pre-alpine lakes were clearly measurable, although the productivity of those lakes is known to be predominantly P-limited. Furthermore, the paradigm that P concentrations alone determine the functioning and transfer efficiency of lake food-webs comes into question. I suggest that for future lake-management programs not only the P concentrations but also N enrichment and consequent N:P ratios should be considered

Learning Counterfactual Representations for Estimating Individual Dose-Response Curves

Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are either focused on estimating average dose-response curves, or limited to settings with only two treatments that do not have an associated dosage parameter. Here, we present a novel machine-learning approach towards learning counterfactual representations for estimating individual dose-response curves for any number of treatments with continuous dosage parameters with neural networks. Building on the established potential outcomes framework, we introduce performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual dose-response curves. Our experiments show that the methods developed in this work set a new state-of-the-art in estimating individual dose-response

Toward an Objective Measurement of AI Literacy

Humans multitudinously interact with Artificial Intelligence (AI) as it permeates every aspect of contemporary professional and private life. The socio-technical competencies of humans, i.e., their AI literacy, shape human-AI interactions. While academia does explore AI literacy measurement, current literature exclusively approaches the topic from a subjective perspective. This study draws on a well-established scale development procedure employing ten expert interviews, two card-sorting rounds, and a between-subject comparison study with 88 participants in two groups to define, conceptualize, and empirically validate an objective measurement instrument for AI literacy. With 16 items, our developed instrument discriminates between an AI-literate test and a control group. Furthermore, the structure of our instrument allows us to distinctly assess AI literacy aspects. We contribute to IS education research by providing a new instrument and conceptualizing AI literacy, incorporating critical themes from the literature. Practitioners may employ our instrument to assess AI literacy in their organizations

The Explanation Matters: Enhancing AI Adoption in Human Resource Management

Artificial intelligence (AI) has ubiquitous applications in companies, permeating multiple business divisions like human resource management (HRM). Yet, in these high-stakes domains where transparency and interpretability of results are of utmost importance, the black-box characteristic of AI is even more of a threat to AI adoption. Hence, explainable AI (XAI), which is regular AI equipped with or complemented by techniques to explain it, comes in. We present a systematic literature review of n=62 XAI in HRM papers. Further, we conducted an experiment among a German sample (n=108) of HRM personnel regarding a turnover prediction task with or without (X)AI-support. We find that AI-support leads to better task performance, self-assessment accuracy and response characteristics toward the AI, and XAI, i.e., transparent models allow for more accurate self-assessment of one’s performance. Future studies could enhance our research by employing local explanation techniques on real-world data with a larger and international sample

On Efficiency of Artifact Lookup Strategies in Digital Forensics

In recent years different strategies have been proposed to handle the problem of ever-growing digital forensic databases. One concept to deal with this data overload is data reduction, which essentially means to separate the wheat from the chaff, e.g., to filter in forensically relevant data. A prominent technique in the context of data reduction are hash-based solutions. Data reduction is achieved because hash values (of possibly large data input) are much smaller than the original input. Today\u27s approaches of storing hash-based data fragments reach from large scale multithreaded databases to simple Bloom filter representations. One main focus was put on the field of approximate matching, where sorting is a problem due to the fuzzy nature of the approximate hashes. A crucial step during digital forensic analysis is to achieve fast query times during lookup (e.g., against a blacklist), especially in the scope of small or ordinary resource availability. However, a comparison of different database and lookup approaches is considerably hard, as most techniques partially differ in considered use-case and integrated features, respectively. In this work we discuss, reassess and extend three widespread lookup strategies suitable for storing hash-based fragments: (1) Hash database for hash-based carving (hashdb), (2) hierarchical Bloom filter trees (hbft) and (3) flat hash maps (fhmap). We outline the capabilities of the different approaches, integrate new extensions, discuss possible features and perform a detailed evaluation with a special focus on runtime efficiency. Our results reveal major advantages for fhmap in case of runtime performance and applicability. Hbft showed a comparable runtime efficiency in case of lookups, but hbft suffers from pitfalls with respect to extensibility and maintenance. Finally, hashdb performs worst in case of a single core environment in all evaluation scenarios. However, hashdb is the only candidate which offers full parallelization capabilities, transactional features, and a Single-level storage

Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions

Seasonal forecasts have the potential to substantially improve water management particularly in water-scarce regions. However, global seasonal forecasts are usually not directly applicable as they are provided at coarse spatial resolutions of at best 36 km and suffer from model biases and drifts. In this study, we therefore apply a bias-correction and spatial-disaggregation (BCSD) approach to seasonal precipitation, temperature and radiation forecasts of the latest long-range seasonal forecasting system SEAS5 of the European Centre for Medium-Range Weather Forecasts (ECMWF). As reference we use data from the ERA5-Land offline land surface rerun of the latest ECMWF reanalysis ERA5. Thereby, we correct for model biases and drifts and improve the spatial resolution from 36 km to 0.1∘. This is performed for example over four predominately semi-arid study domains across the world, which include the river basins of the Karun (Iran), the São Francisco River (Brazil), the Tekeze–Atbara river and Blue Nile (Sudan, Ethiopia and Eritrea), and the Catamayo–Chira river (Ecuador and Peru). Compared against ERA5-Land, the bias-corrected and spatially disaggregated forecasts have a higher spatial resolution and show reduced biases and better agreement of spatial patterns than the raw forecasts as well as remarkably reduced lead-dependent drift effects. But our analysis also shows that computing monthly averages from daily bias-corrected forecasts particularly during periods with strong temporal climate gradients or heteroscedasticity can lead to remaining biases especially in the lowest- and highest-lead forecasts. Our SEAS5 BCSD forecasts cover the whole (re-)forecast period from 1981 to 2019 and include bias-corrected and spatially disaggregated daily and monthly ensemble forecasts for precipitation, average, minimum, and maximum temperature as well as for shortwave radiation from the issue date to the next 215 d and 6 months, respectively. This sums up to more than 100 000 forecasted days for each of the 25 (until the year 2016) and 51 (from the year 2017) ensemble members and each of the five analyzed variables. The full repository is made freely available to the public via the World Data Centre for Climate at https://doi.org/10.26050/WDCC/SaWaM_D01_SEAS5_BCSD (Domain D01, Karun Basin (Iran), Lorenz et al., 2020b), https://doi.org/10.26050/WDCC/SaWaM_D02_SEAS5_BCSD (Domain D02: São Francisco Basin (Brazil), Lorenz et al., 2020c), https://doi.org/10.26050/WDCC/SaWaM_D03_SEAS5_BCSD (Domain D03: basins of the Tekeze–Atbara and Blue Nile (Ethiopia, Eritrea, Sudan), Lorenz et al., 2020d), and https://doi.org/10.26050/WDCC/SaWaM_D04_SEAS5_BCSD (Domain D04: Catamayo–Chira Basin (Ecuador, Peru), Lorenz et al., 2020a). It is currently the first publicly available daily high-resolution seasonal forecast product that covers multiple regions and variables for such a long period. It hence provides a unique test bed for evaluating the performance of seasonal forecasts over semi-arid regions and as driving data for hydrological, ecosystem or climate impact models. Therefore, our forecasts provide a crucial contribution for the disaster preparedness and, finally, climate proofing of the regional water management in climatically sensitive regions

Genetic Analysis of Putative Familial Relationships in a Captive Chimpanzee (Pan troglodytes) Population

Twelve autosomal dinucleotide repeat loci were analyzed in chimpanzees genomes by DNA amplification using primers designed for analysis of human loci. The markers span the entire length of human chromosomes 21 and 22. Nine markers were polymorphic in chimpanzee as well, with a somewhat comparable level of polymorphism and allele size range. Even in the presence of very limited information and in spite of missing samples, it was possible to reconstruct a complex pedigree and to provide molecular data that corroborate family relationships that were deduced from cage history and behavioral data. The conclusions were further supported by mitochondrial DNA analysis. The data presented in this report show that the extremely abundant source of human markers may be exploited to validate, with molecular evidence, hypotheses on individual relationship or alleged pedigrees, based upon behavioral observations