simecol: An Object-Oriented Framework for Ecological Modeling in R

The simecol package provides an open structure to implement, simulate and share ecological models. A generalized object-oriented architecture improves readability and potential code re-use of models and makes simecol-models freely extendable and simple to use. The simecol package was implemented in the S4 class system of the programming language R. Reference applications, e.g. predator-prey models or grid models are provided which can be used as a starting point for own developments. Compact example applications and the complete code of an individual-based model of the water flea Daphnia document the efficient usage of simecol for various purposes in ecological modeling, e.g. scenario analysis, stochastic simulations and individual based population dynamics. Ecologists are encouraged to exploit the abilities of simecol to structure their work and to use R and object-oriented programming as a suitable medium for the distribution and share of ecological modeling code.

Dr. Strangelove, or how I learned to stop worrying and love the drone: A review of current debates on drone applications

Drohnen werden inzwischen in vielen und sehr unterschiedlichen Kontexten verwendet. Aus dem Blickwinkel der Technikfolgenabschätzung scheint es daher sinnvoll, den Umfang der momentanen und zukünftigen Nutzung von Drohnen und daraus resultierende Implikationen näher zu beleuchten und eine Bestandsaufnahme durchzuführen. Darüber hinaus sollen die voraussichtlichen Pfade der weiteren Technikentwicklung, relevante Akteure und deren Interessenslage sowie zukünftige Anwendungspotenziale und Einsatzfelder analysiert werden.Drones are nowadays used in many and very different contexts. From the technology assessment perspective, it therefore seems reasonable to shed more light on the extent of the current and future use of drones and the resulting consequences. In addition, the expected paths of further technological development, relevant actors and their interests as well as potential future applications and fields of use should be analyzed

Lake Sevan. Past, present, and future state of a unique alpine lake

Lake Sevan, a large, deep, alpine lake in the Lesser Caucasus is the focus of this Special Issue of the Journal of Limnology. It was an outstanding ecosystem 100 years ago characterised by excellent water quality, rich biodiversity with a high level of endemism, wide-ranging beds of macrophytes along the shores and a productive and sustainable fish production. Due to its beauty, natural history, and contributions to social and economic welfare it is also a cultural heritage for the Armenian Nation including its large diaspora

Contemporary community composition, spatial distribution patterns, and biodiversity characteristics of zooplankton in large alpine Lake Sevan, Armenia

We studied the quantitative composition, spatial distribution, and temporal dynamics of the zooplankton community of the alpine Lake Sevan, Armenia, the largest surface water in the Caucasus region. This article is providing a long-term information and fills the research gap of multiyear data on zooplankton, as the previous research on zooplankton provided only snapshots of the community, and a consistent assessment over multiple years was missing. However, an initial mini-review of historical studies indicated that zooplankton biomass and fish abundance were undergoing large fluctuations, indicating the importance of top-down control. We analysed 239 samples from the period 2016-2019 from 32 sampling sites in Lake Sevan and recorded 37 species of meso- and macrozooplankton (Rotifers, Copepods, Cladocera). Biomass fluctuations were high with peaking biomasses in 2016 and lowest biomasses in 2018, yearly averaged biomass varied about one order of magnitude. Variability over time was hence much higher than spatial variability. The pelagic habitat at the deepest part of the lake showed the highest diversity and biomasses but contrasts between sampling sites remained smaller than changes from year to year or seasonally. Many samples were dominated by a single species, and these key species explain observed biomass dynamics to a wide extent. We applied hierarchical clustering in order to identify phenological groups that appear to show similar patterns of occurrence. This clustering resulted in 6 groups where of 5 groups just consisting of one single species and these 5 key species were the Cladocerans Daphnia magna, Daphnia hyalina, Diaphanosoma sp. as well as the calanoids Arctodiaptomus bacillifer and Acanthodiaptomus denticornis. The most important species in Lake Sevan’s zooplankton during the observation period was D. magna, which reached high biomasses in 2016 and 2017 but then suddenly almost disappeared in 2018 and 2019. When there were more D. magna present, the water became clearer, which was measured using Secchi depth. This shows that these large water fleas effectively controlled the amount of phytoplankton in the water. Daphnia magna, in turn, managed to dominate zooplankton community only during times of extremely low fish biomass indicating strong top-down control of this large Cladoceran by fish. Both observations together imply a strong trophic linkage between fish, zooplankton, and phytoplankton and provide evidence for trophic cascades in Lake Sevan. Besides the novel insights into zooplankton community dynamics of this unique lake of high socio-economical, cultural, and ecological importance, our study also points to potential management opportunities for eutrophication control by biomanipulation, as well as our investigation allows us to conclude that probably biotic factors were more important than abiotic factors in explaining the observed changes and dynamics within the plankton community

Regulatory ecotoxicity testing of nanomaterials – proposed modifications of OECD test guidelines based on laboratory experience with silver and titanium dioxide nanoparticles

Conservation Biolog

Sources of skill in lake temperature, discharge and ice-off seasonal forecasting tools

Despite high potential benefits, the development of seasonal forecasting tools in the water sector has been slower than in other sectors. Here we assess the skill of seasonal forecasting tools for lakes and reservoirs set up at four sites in Australia and Europe. These tools consist of coupled hydrological catchment and lake models forced with seasonal meteorological forecast ensembles to provide probabilistic predictions of seasonal anomalies in water discharge, temperature and ice-off. Successful implementation requires a rigorous assessment of the tools' predictive skill and an apportionment of the predictability between legacy effects and input forcing data. To this end, models were forced with two meteorological datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF), the seasonal forecasting system, SEAS5, with 3-month lead times and the ERA5 reanalysis. Historical skill was assessed by comparing both model outputs, i.e. seasonal lake hindcasts (forced with SEAS5), and pseudo-observations (forced with ERA5). The skill of the seasonal lake hindcasts was generally low although higher than the reference hindcasts, i.e. pseudo-observations, at some sites for certain combinations of season and variable. The SEAS5 meteorological predictions showed less skill than the lake hindcasts. In fact, skilful lake hindcasts identified for selected seasons and variables were not always synchronous with skilful SEAS5 meteorological hindcasts, raising questions on the source of the predictability. A set of sensitivity analyses showed that most of the forecasting skill originates from legacy effects, although during winter and spring in Norway some skill was coming from SEAS5 over the 3-month target season. When SEAS5 hindcasts were skilful, additional predictive skill originates from the interaction between legacy and SEAS5 skill. We conclude that lake forecasts forced with an ensemble of boundary conditions resampled from historical meteorology are currently likely to yield higher-quality forecasts in most cases.publishedVersio

Forecasting water temperature in lakes and reservoirs using seasonal climate prediction

ABSTRACT: Seasonal climate forecasts produce probabilistic predictions of meteorological variables for subsequent months. This provides a potential resource to predict the influence of seasonal climate anomalies on surface water balance in catchments and hydro-thermodynamics in related water bodies (e.g., lakes or reservoirs). Obtaining seasonal forecasts for impact variables (e.g., discharge and water temperature) requires a link between seasonal climate forecasts and impact models simulating hydrology and lake hydrodynamics and thermal regimes. However, this link remains challenging for stakeholders and the water scientific community, mainly due to the probabilistic nature of these predictions. In this paper, we introduce a feasible, robust, and open-source workflow integrating seasonal climate forecasts with hydrologic and lake models to generate seasonal forecasts of discharge and water temperature profiles. The workflow has been designed to be applicable to any catchment and associated lake or reservoir, and is optimized in this study for four catchment-lake systems to help in their proactive management. We assessed the performance of the resulting seasonal forecasts of discharge and water temperature by comparing them with hydrologic and lake (pseudo)observations (reanalysis). Precisely, we analysed the historical performance using a data sample of past forecasts and reanalysis to obtain information about the skill (performance or quality) of the seasonal forecast system to predict particular events. We used the current seasonal climate forecast system (SEAS5) and reanalysis (ERA5) of the European Centre for Medium Range Weather Forecasts (ECMWF). We found that due to the limited predictability at seasonal time-scales over the locations of the four case studies (Europe and South of Australia), seasonal forecasts exhibited none to low performance (skill) for the atmospheric variables considered. Nevertheless, seasonal forecasts for discharge present some skill in all but one case study. Moreover, seasonal forecasts for water temperature had higher performance in natural lakes than in reservoirs, which means human water control is a relevant factor affecting predictability, and the performance increases with water depth in all four case studies. Further investigation into the skillful water temperature predictions should aim to identify the extent to which performance is a consequence of thermal inertia (i.e., lead-in conditions).This is a contribution of the WATExR project (watexr.eu/), which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by MINECO-AEI (ES), FORMAS (SE), BMBF (DE), EPA (IE), RCN (NO), and IFD (DK), with co-funding by the European Union (Grant 690462 ). MINECO-AEI funded this research through projects PCIN- 2017-062 and PCIN-2017-092. We thank all water quality and quantity data providers: Ens d’Abastament d’Aigua Ter-Llobregat (ATL, https://www.atl.cat/es ), SA Water ( https://www.sawater.com. au/ ), Ruhrverband ( www.ruhrverband.de ), NIVA ( www.niva.no ) and NVE ( https://www.nve.no/english/ ). We acknowledge the contribution of the Copernicus Climate Change Service (C3S) in the production of SEAS5. C3S provided the computer time for the generation of the re-forecasts for SEAS5 and for the production of the ocean reanalysis (ORAS5), used as initial conditions for the SEAS5 re-forecasts

Sensors in the stream: the high-frequency wave of the present

New scientific understanding is catalysed by novel technologies that enhance measurement precision, resolution or type, and that provide new tools to test and develop theory. Over the last 50 years, technology has transformed the hydrologic sciences by enabling direct measurements of watershed fluxes (evapotranspiration, streamflow) at time scales and spatial extents aligned with variation in physical drivers. High frequency water quality measurements, increasingly obtained by in-situ water quality sensors, are extending that transformation. Widely available sensors for some physical (temperature) and chemical (conductivity, dissolved oxygen) attributes have become integral to aquatic science, and emerging sensors for nutrients, dissolved CO2, turbidity, algal pigments, and dissolved organic matter are now enabling observations of watersheds and streams at timescales commensurate with their fundamental hydrological, energetic, elemental, and biological drivers. Here we synthesize insights from emerging technologies across a suite of applications, and envision future advances, enabled by sensors, in our ability to understand, predict, and restore watershed and stream systems

Exploring, exploiting and evolving diversity of aquatic ecosystem models: A community perspective

Here, we present a community perspective on how to explore, exploit and evolve the diversity in aquatic ecosystem models. These models play an important role in understanding the functioning of aquatic ecosystems, filling in observation gaps and developing effective strategies for water quality management. In this spirit, numerous models have been developed since the 1970s. We set off to explore model diversity by making an inventory among 42 aquatic ecosystem modellers, by categorizing the resulting set of models and by analysing them for diversity. We then focus on how to exploit model diversity by comparing and combining different aspects of existing models. Finally, we discuss how model diversity came about in the past and could evolve in the future. Throughout our study, we use analogies from biodiversity research to analyse and interpret model diversity. We recommend to make models publicly available through open-source policies, to standardize documentation and technical implementation of models, and to compare models through ensemble modelling and interdisciplinary approaches. We end with our perspective on how the field of aquatic ecosystem modelling might develop in the next 5–10 years. To strive for clarity and to improve readability for non-modellers, we include a glossary