Applied statistics in field and semi-field studies with bees

Field and semi-field studies are important tools in the ecotoxicological risk assessment of plant protection products for bees (honey bees, bumblebees and solitary bees). While these studies represent far more realistic conditions than laboratory tests, they also present a challenge for the analysis and interpretation due to the large and complex datasets. Therefore, in order to correctly answer the underlying ecotoxicological questions, it is crucial that these studies are not only thoroughly planned and conducted, it is also important that they are subjected to adequate statistical analysis. Our aim is to provide a better understanding on how to conduct and interpret statistical analyses in field and semi-field studies with bees made for regulatory purposes. An overview of how study design and statistics should be aligned with each other is given including the specific challenges of (semi-) field trials, as for instance how to address the problem of pseudoreplication if hives are regarded as experimental units. Different statistical tools are compared and their suitability for different data types and questions are discussed. Generalized Linear (Mixed) Models (GLMMs) are evaluated in more detail as they provide a flexible and robust tool for the analysis of honey bee (semi-) field data. Furthermore, some more light is shed on what p-values really tell us, how they can help to interpret data and how they should not be misinterpreted.Field and semi-field studies are important tools in the ecotoxicological risk assessment of plant protection products for bees (honey bees, bumblebees and solitary bees). While these studies represent far more realistic conditions than laboratory tests, they also present a challenge for the analysis and interpretation due to the large and complex datasets. Therefore, in order to correctly answer the underlying ecotoxicological questions, it is crucial that these studies are not only thoroughly planned and conducted, it is also important that they are subjected to adequate statistical analysis. Our aim is to provide a better understanding on how to conduct and interpret statistical analyses in field and semi-field studies with bees made for regulatory purposes. An overview of how study design and statistics should be aligned with each other is given including the specific challenges of (semi-) field trials, as for instance how to address the problem of pseudoreplication if hives are regarded as experimental units. Different statistical tools are compared and their suitability for different data types and questions are discussed. Generalized Linear (Mixed) Models (GLMMs) are evaluated in more detail as they provide a flexible and robust tool for the analysis of honey bee (semi-) field data. Furthermore, some more light is shed on what p-values really tell us, how they can help to interpret data and how they should not be misinterpreted

Higher TIER bumble bees and solitary bees recommendations for a semi-field experimental design

The publication of the proposed EFSA risk assessment guidance document of plant protection products for pollinators highlighted that there are no study designs for non-Apis pollinators available. Since no official guidelines exist for semi-field testing at present, protocols were proposed by the ICPPR non-Apis working group and two years of ring-testing were conducted in 2016 and 2017 to develop a general test set-up. The ringtest design was based on the draft EFSA guidance document, OEPP/EPPO Guideline No. 170 and results of discussions regarding testing solitary bees and bumble bees during the meetings of the ICPPR non-Apis workgroup. Ring-tests were conducted with two different test organisms, one representative of a social bumble bee species (Bombus terrestris L; Hymenoptera, Apidae) and one representative of a solitary bee species (Osmia bicornis L; Hymenoptera, Megachilidae). The species are common species in Europe, commercially available and widely used for pollination services. Several laboratories participated in the higher-tier ring tests. 15 semi-field tests were conducted with bumble bees and 16 semi-field tests were done with solitary bees in 2016 and 2017. Two treatment groups were always included in the ringtests: an untreated control (water treated) and the treatment with dimethoate as a toxic reference item (optional other i.e. brood-affecting substances fenoxycarb or diflubenzuron). The toxic reference items were chosen based on their mode of action and long term experience in honey bee testing. A summary of the ringtest results will be given and the recommendations for the two semi-field test designs will be presented.The publication of the proposed EFSA risk assessment guidance document of plant protection products for pollinators highlighted that there are no study designs for non-Apis pollinators available. Since no official guidelines exist for semi-field testing at present, protocols were proposed by the ICPPR non-Apis working group and two years of ring-testing were conducted in 2016 and 2017 to develop a general test set-up. The ringtest design was based on the draft EFSA guidance document, OEPP/EPPO Guideline No. 170 and results of discussions regarding testing solitary bees and bumble bees during the meetings of the ICPPR non-Apis workgroup. Ring-tests were conducted with two different test organisms, one representative of a social bumble bee species (Bombus terrestris L; Hymenoptera, Apidae) and one representative of a solitary bee species (Osmia bicornis L; Hymenoptera, Megachilidae). The species are common species in Europe, commercially available and widely used for pollination services. Several laboratories participated in the higher-tier ring tests. 15 semi-field tests were conducted with bumble bees and 16 semi-field tests were done with solitary bees in 2016 and 2017. Two treatment groups were always included in the ringtests: an untreated control (water treated) and the treatment with dimethoate as a toxic reference item (optional other i.e. brood-affecting substances fenoxycarb or diflubenzuron). The toxic reference items were chosen based on their mode of action and long term experience in honey bee testing. A summary of the ringtest results will be given and the recommendations for the two semi-field test designs will be presented

Habitat and Season Effects on Small Mammal Bycatch in Live Trapping

Trapping small mammals is frequently used to study the dynamics, demography, behavior and presence of pathogens. When only particular small mammal species are in the focus of interest, all other species are unnecessary bycatch. We analyzed data from extensive live trapping campaigns conducted over the last decade in Germany, following a consistent standard trapping protocol that resulted in about 18,500 captures of small mammals. Animals were trapped with Ugglan multiple capture traps in grassland, forest and margin habitat. Trap success and the proportion of bycatch were about 30% when target species were common voles (Microtus arvalis) in grassland and common voles and bank voles (Clethrionomys glareolus) in margins and forests. This was more pronounced in spring and along margins. Species mentioned in the early warning list according to the Red List Germany were higher in numbers and proportion in spring and in grassland. The results will help to avoid periods with enhanced presence of bycatch, including endangered species (if the purpose of the study allows) or to pay particular attention in certain seasons and habitats when the occurrence of bycatch is most likely

Nitrifikation und Denitrifikation im Boden in Abhängigkeit von Sauerstoff und mikrobieller Aktivität - Entwicklung, Analyse, Parametrisierung und Anwendung eines gekoppelten Simulationsmodells

Stickstoff ist ein lebenswichtiger Bestandteil der aller Organismen. Dazu gehören auch Pflanzen, die der Ernährung von Tier und Mensch dienen. Terrestrische Pflanzen nehmen Stickstoff über die Wurzeln und damit aus dem Boden auf. Die verschiedenen Stickstoffverbindungen sind durch biogeochemische Prozesse miteinander verknüpft. Zusammen bilden diese Prozesse den Stickstoffkreislauf. Mikrobielle Nitrifikation und Denitrifikation gehören zu den häufig in Grünlandböden vorkommenden Prozessen. Die Aktivität der Mikroorganismen und damit die Geschwindigkeit und Intensität, mit welcher die Transformationsprozesse ablaufen, sind stark abhängig von den vorliegenden Umweltbedingungen. Insbesondere der mikrobiell im Boden verfügbare Sauerstoff spielt hier eine bedeutende Rolle: Während die Nitrifikation von seiner Anwesenheit abhängt, wird die Denitrifikation durch ihn gehemmt. Die Denitrifizierer dagegen können, aber müssen nicht Sauerstoff verwenden. Dadurch entsteht eine gekoppelte, nicht lineare Abhängigkeit der Prozesse vom Sauerstoffgehalt.Der Sauerstoffgehalt im Boden schwankt durch natürliche Faktoren, wie Niederschlagsereignisse, aber ändert sich auch mit der Landnutzung. Die Übergänge zwischen aeroben und anaeroben Bedingungen sind dabei fließend, so dass eine strikte Trennung der beiden Prozesse in der Modellierung nicht sinnvoll ist. In meiner Dissertation im Rahmen des Teilprojekts „InDiLaNi“ im DFG Schwerpunktprogramm „Biodiversitätsexploratorien“ entwickelte ich ein Simulationsmodell für eine gekoppelte Nitrifikations- und Denitrifikationsdynamik. Diese Dynamik ist abhängig vom Sauerstoffgehalt und den Abundanzen der Nitrifizierer und Denitrifizierer. Das Modell brachte dabei Erkenntnisse über den Einfluss von Sauerstoff auf die Nitrifikations-Denitrifikationsdynamik, dient jedoch nicht der Abschätzung von tatsächlichen Stickstoffkonzentrationen im Boden. Mittels eines rein aeroben und eines strikt anaeroben Sauerstoffszenarios wurde das Modell auf seine Gültigkeit überprüft. Aerobe Bedingungen führten zu einer Nitrifikationsdynamik und anaerobe Bedingungen zu einer Denitrifikationsdynamik. Ein transientes Szenario, welches unter Annahme eines niedrigen Sauerstoffgehaltes simuliert wurde, zeigte die Verknüpfung beider Prozesse. Eine Sensitivitätsanalyse diente der Eingrenzung der Modellparameter auf diejenigen, welche den größten Einfluss auf die Modelldynamik haben. Über einen Zeitraum von mehr als 1000 Stunden zeigte sich dabei, dass besonders die Wachstums- und Sterberaten der Nitrifizierer und Denitrifizierer Einfluss auf die Modelldynamik haben. Mit Hilfe experimentellen Daten sollten einige Modellparameter angepasst werden. Dies erwies sich als schwierig, da die Datengrundlage nicht zu einer wirklichen Parameteroptimierung ausreichte und so lediglich eine Anpassung per Hand erfolgen konnte. Dabei konnte mit leichten Veränderungen der Umsatzgeschwindigkeiten der Teilprozesse die modellierte Dynamik dem experimentellen Verlauf angenähert werden. Durch die Biodiversitätsexploratorien standen mir Zeitreihen für den Bodenwassergehalt und die Bodentemperatur zur Verfügung. Aus der Reihe für den Bodenwassergehalt konnte ich Sauerstoffgehalte berechnen und so das Modell mit variablem Sauerstoffgehalt nutzen. Zusätzlich konnte ich den Einfluss von Temperatur und Bodenwassergehalt auf das mikrobielle Wachstum einbringen. Dabei zeigte sich, dass die Sauerstoffgehalte in den Böden im aeroben Bereich lagen, so dass vor allen Dingen die Nitrifikationsdynamik zu beobachten war. Der Einfluss der Temperatur führte zu einer leichten Verlangsamung der Prozesse, da sie immer etwas unter dem mikrobiellen Optimum lag. Weiterhin konnte ich aufgrund von Informationen aus den Biodiversitätsexploratorien Dünge- und Beweidungsereignisse zu modellieren. Durch den Eintrag von Ammonium und Nitrat durch Mineraldünger werden Transformationsprozesse angestoßen und es kommt es zu starken Veränderungen in den Stickstoffkonzentrationen im Boden. Durch Beweidung erfolgt ein Ammoniumeintrag über den gesamten Beweidungszeitraum. Diese externen Einträge sind so stark, dass sie alle anderen, eventuell vorher ablaufenden Stickstoffumwandlungsprozesse überdecken

AlgalFertilizer project:Algae deliering nutrients from phosphorous-rich media to soil and wheat

As an application-oriented part of the AlgalFertilizer Project, we cultivated Chlorella vulgaris, IPPAS C1 in 3.6 m3 suspension in V-shaped plastic bag photobioreactors (NovaGreen GmbH) in Jülich, Germany. The culture was grown in batch regime in a P-rich medium. The thus produced algal biomass was analyzed for nutrient content with special regard to phosphorus and used as fertilizer to grow wheat in a pot experiment under controlled phosphorus levels on different substrates: sand and nutrient-poor ‘Null-Erde’ for controlled nutrient levels as well as a nutrient-rich substrate (Dachstaudensubstrat, SoMi 513). The algal biomass was mixed into the soil substrates either in form of spray-dried powder or as a suspension of fresh cells obtained by centrifugation to estimate P-exchange properties of pre-treated and fresh biomass. Aiming at comparison to standard Hoagland mineral fertilizer, we applied algae at two different P-levels: high, corresponding to 45 kg(P)/ha and low, corresponding to 4.5 kg(P)/ha. 150 pots were prepared for the experiment. Each experimental variant was represented by 10 pots of identical substrate and fertilization treatment. One pre-germinated seed of wheat (Triticum aestivum, var. Scirocco) was placed in each pot. The plants were placed in a greenhouse on a robotic platform that randomized position of individual pots and also ensured uniform watering. Individual plants were moved regularly with their pots into an imaging system that served to quantify the plant growth. We also analyzed the dynamics of nutrients in the soil. The results of the AlgalFertilizer experiment lead us to conclude that microalgae are not only very potent in sequestering phosphorus from nutrient rich streams (Part 1 of this presentation) but that algal biomass can be used as a very effective fertilizer and ameliorant of nutrient-poor soils (Part 2 of this presentation)