Can space-for-time-substitution surveys represent zooplankton biodiversity patterns and their relationship to environmental drivers?

Space-for-Time-Substitution surveys (SFTS) are commonly used to describe zooplankton community dynamics and to determine lake ecosystem health. SFTS surveys typically combine single point observations from many lakes to evaluate the response of zooplankton community structure and dynamics (e.g., species abundance and biomass, diversity, demographics and modeled rate processes) to spatial gradients in hypothesized environmental drivers (e.g., temperature, nutrients, predation), in lieu of tracking such responses over long time scales. However, the reliability and reproducibility of SFTS zooplankton surveys have not yet been comprehensively tested against empirically-based community dynamics from longterm monitoring efforts distributed worldwide. We use a recently compiled global data set of more than 100 lake zooplankton time series to test whether SFTS surveys can accurately capture zooplankton diversity, and the hypothesized relationship with temperature, using simulated SFTS surveys of the time series data. Specifically, we asked: (1) to what degree can SFTS surveys capture observed biodiversity dynamics; (2) how does timing and duration of sampling affect detected biodiversity patterns; (3) does biodiversity ubiquitously increase with temperature across lakes, or vary by climate zone or lake type; and (4) do results from SFTS surveys produce comparable biodiversity-temperature relationship(s) to empirical data within and among lakes? Testing biodiversity-ecosystem function (BEF) relationships, and the drivers of such relationships, requires a solid data basis. Our work provides a global perspective on the design and usefulness of (long-term) zooplankton monitoring programs and how much confidence we can place in the zooplankton biodiversity patterns observed from SFTS surveys

Nitrogen mineralized in anaerobiosis as indicator of soil aggregate stability

Monitoring soil health status is imperative to pursue sustainable agriculture. Aggregate stability (AS) is fundamental to define several soil functions and, therefore, physical soil health. The objectives of thisworkwere to (i) evaluate the effect of contrasting cropping systems on AS, soil (SOC) and particulate (POC) organic carbon, and anaerobic nitrogen (AN) both in bulk soil and in macroaggregates (MA), and (ii) assess the relationship between AS and AN both in bulk soil and in MA to facilitate soil physical health monitoring. Aggregate stability, AN, SOC and POC were evaluated at three depths (0–5, 5–20, and 0–20 cm) in a Mollisol of the Southeastern Argentinean Pampas under a long-term experiment of cropping systems (crop-pasture rotations under conventional tillage [CT] and no-tillage [NT]). Bulk-soil SOC and POC contents and AN showed the effect of cropping systems, especially the effect of crop-pasture rotation and at 0–5 cm depth. However, NT did not lead to SOC sequestration except at 0–5 cm depth. In turn, pastures in the rotation and NT improved AS. Bulk-soil AN explained 75, 41, and 71% of AS at 0–5, 5–20, and 0–20 cm depths, respectively, and provides an indication of AS status. Instead, AN in MA did not explain bulk-soil AS changes as much as bulk-soil AN, except at 0–5 cm depth. Therefore, it is not worth determining AN in MA. However, routine bulk-soil AN determination at 0–20 cm depth by producers to diagnose nitrogen soil fertility would also provide an additional valuable indication of AS status

Assessment of nitrogen diagnosis methods in sunflower

Nitrogen deficiency can severely limit sunflower (Helianthus annuus L.) grain yield and quality. Our objective was to evaluate N diagnosis methods based on: (a) pre-plant soil nitrate-nitrogen (NO3––N) test (PPSNT) and soil N mineralized in short-term anaerobic incubation (Nan), (b) Greenness index (GI) and the normalized difference vegetation index (NDVI) measured at 6 (V6) and 12 (V12) leaves, and (c) grain nitrogen concentration (Nc). Seventeen experiments were carried out between 2010 and 2019 in Argentina, evaluating nine N rates (0, 30, 40, 60, 80, 90, 120, 150, and 160 kg N ha–1). The GI, NDVI, N sufficiency index and relative normalized difference vegetation index (NDVIr) were determined at V6 and V12 growth stages. On average, yield response to N was 492 kg ha–1 and Nc response was 0.25% in 9 and 11 responsive experiments, respectively. The inclusion of Nan improved the PPSNT diagnosis method. The critical N availability (PPSNT + fertilizer N) threshold was 115 kg N ha–1 for experiments with low Nan (60 mg kg–1). The NDVIr at V12 allowed monitoring the crop N status with a 0.95 critical threshold. The Nc adequately diagnosed N deficiencies and the critical threshold was 2.26%. Also, Nc was predicted from the ratio between N availability and grain yield (R2 = .39). Our results would allow to better estimate N availability to recommend adequate N fertilizer rates for sunflower aiming to optimize grain yield and quality, and minimize the economic and environmental cost of fertilization.EEA BalcarceFil: Tovar Hernandez, Sergio. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Diovisalvi, Natalia. Laboratorio de Suelos Fertilab; Argentina.Fil: Carciochi, Walter Daniel. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Carciochi, Walter Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Izquierdo, Natalia. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Izquierdo, Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Sainz Rozas, Hernán René. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina.Fil: Sainz Rozas, Hernán René. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Sainz Rozas, Hernán René. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: García, Fernando. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Reussi Calvo, Nahuel Ignacio. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Reussi Calvo, Nahuel Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Reussi Calvo, Nahuel Ignacio. Laboratorio de Suelos Fertilab; Argentina