When Is a Principal Charged With an Agent’s Knowledge?

Question: Detecting species presence in vegetation and making visual assessment of abundances involve a certain amount of skill, and therefore subjectivity. We evaluated the magnitude of the error in data, and its consequences for evaluating temporal trends. Location: Swedish forest vegetation. Methods: Vegetation data were collected independently by two observers in 342 permanent 100-m2 plots in mature boreal forests. Each plot was visited by one observer from a group of 36 and one of two quality assessment observers. The cover class of 29 taxa was recorded, and presence/absence for an additional 50. Results: Overall, one third of each occurrence was missed by one of the two observers, but with large differences among species. There were more missed occurrences at low abundances. Species occurring at low abundance when present tended to be frequently overlooked. Variance component analyses indicated that cover data on 5 of 17 species had a significant observer bias. Observer-explained variance was < 10% in 15 of 17 species. Conclusion: The substantial number of missed occurrences suggests poor power in detecting changes based on presence/absence data. The magnitude of observer bias in cover estimates was relatively small, compared with random error, and therefore potentially analytically tractable. Data in this monitoring system could be improved by a more structured working model during field work.Original publication: Milberg, P., Bergstedt, J., Fridman, J., Odell, G & Westerberg, L., Systematic and random variation in vegetation monitoring data, 2008, Journal of Vegetation Science, (19), 633-644. http://dx.doi.org/10.3170/2008-8-18423. Copyright: Opulus Press, http://www.opuluspress.se/index.ph

Relative influence of shredders and fungi on leaf litter decomposition along a river altitudinal gradient

We compared autumn decomposition rates of European alder leaves at four sites along the Lasset–Hers River system, southern France, to test whether changes in litter decomposition rates from upstream (1,300 m elevation) to downstream (690 m) could be attributed to temperature-driven differences in microbial growth, shredder activity, or composition of the shredder community. Alder leaves lost 75–87% of original mass in 57 days, of which 46–67% could be attributed to microbial metabolism and 8–29% to shredder activity, with no trend along the river. Mass loss rates in both fine-mesh (excluding shredders) and coarse-mesh (including shredders) bags were faster at warm, downstream sites (mean daily temperature 7–8°C) than upstream (mean 1–2°C), but the differ- ence disappeared when rates were expressed in heat units to remove the temperature effect. Mycelial biomass did not correlate with mass loss rates. Faster mass loss rates upstream, after temperature correction, evidently arise from more efficient shredding by Nemourid stoneflies than by the Leuctra-dominated assemblage downstream. The influence of water temperature on decomposition rate is therefore expressed both directly, through microbial metabolism, and indirectly, through the structure of shredder commu- nities. These influences are evident even in cold water where temperature variation is small

Are current dynamic water quality models too complex? A comparison of a new parsimonious phosphorus model, SimplyP, and INCA-P

Catchment-scale water quality models are becoming increasingly popular tools for exploring the potential effects of land management, land use change and climate change on water quality. However, the dynamic, catchment-scale nutrient models in common usage are complex, with many uncertain parameters requiring calibration, limiting their usability and robustness. A key question is whether this complexity is justified. To explore this, we have developed a parsimonious P model, SimplyP, incorporating a coupled rainfall-runoff model and a biogeochemical model able to simulate streamflow, suspended sediment, particulate and dissolved P dynamics. The model’s complexity is compared in a small rural catchment in northeast Scotland. For three land use classes, less than six SimplyP model parameters must be determined through calibration alone, the rest may be based on measurements; INCA-P has around 40 unmeasurable parameters. Despite simpler process-representation, SimplyP produced a slightly better dissolved P simulation during both calibration and validation, and produced similar long-term projections in response to changes in land management. Results support the hypothesis that INCA-P is overly complex for the study catchment. We hope our findings will help prompt wider model comparison exercises, as well as debate amongst the water quality modelling community as to whether today's models are fit for purpose. Simpler models such as SimplyP have the potential to be useful management and research tools, building blocks for future model development (prototype code is freely available), or benchmarks against which more complex models could be evaluated

Pine afforestation changes more strongly community structure than ecosystem functioning in grassland mountain streams

In the past decades, afforestation of grassland landscapes has gained importance both as an economic activity and a mechanism to mitigate anthropogenic carbon emissions. This study evaluates the effect of pine afforestation on grassland streams analyzing changes in two integrative ecological indicators: leaf litter breakdown and primary production. We compare those results with changes in structural attributes of benthic biota (primary producers and invertebrates). Six contiguous first-order streams were selected in the upper basin of the Ctalamochita river (Córdoba, Argentina): three reference streams draining grasslands and three streams draining Pinus elliottii afforestations. Two in situ experiments were performed to compare leaf litter breakdown and primary production between grassland and afforested streams. Additionally, invertebrate assemblages in leaf litter and riffles, and periphyton standing stock were sampled and assessed. Nine out of 26 structural indicators showed differences between stream types but indicators measuring changes at the basal level of the food web (i.e. detritus and primary producers) were less sensitive than those recording changes in consumers. Our attempt to measure direction and magnitude of changes on stream functioning following afforestation was halted by our simple implemented methodology (i.e. leaf pack method for leaf litter decay and biofilm accrual on natural stone substrates for primary production assessments); only 1 out of 4 indicators differed. We argue that the lack of strong differences in elemental measurements of primary production and needle decay between afforested and grassland streams resulted from compensating opposing forces controlling such processes, i.e. higher grazing vs. higher sunlight in grassland streams and higher shredding vs. lower microbial decomposition mediated by lower temperature in afforested streams. Attributes related to the invertebrate compartment showed the highest sensitivity to afforestation, emphasizing their value as biological indicators of stream ecological integrity.Fil: Principe, Romina Elizabeth. Universidad Nacional de Rio Cuarto. Facultad de Ciencias Exactas Fisicoquimicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Márquez, Javier Andrés. Universidad Nacional de Rio Cuarto. Facultad de Ciencias Exactas Fisicoquimicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cibils Martina, Luciana. Universidad Nacional de Rio Cuarto. Facultad de Ciencias Exactas Fisicoquimicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Jobbagy Gampel, Esteban Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Luis. Instituto de Matemática Aplicada de San Luis; ArgentinaFil: Albariño, Ricardo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación En Biodiversidad y Medioambiente; Argentina. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche. Laboratorio de Fotobiologia; Argentin