Creation and preservation of vegetation patterns by grazing

Structural patterns of tall stands ("tussock") and short stands ("lawn") are observed in grazed vegetation throughout the world. Such structural vegetation diversity influences plant and animal diversity. A possible mechanism for the creation and preservation of such patterns is a positive feedback between grazing and plant palatability. Although some theoretical studies have addressed this point in a non-spatial setting, the spatial consequences of this feedback mechanism on the stability and spatial characteristics of vegetation structure patterns have not been studied. We addressed this issue by analyzing a spatially explicit individual-based plant-grazer simulation model, based on published empirical relations and the assumption of optimal foraging. In the model, the selection by the grazer of short stands (that have a higher energy content and are more palatable) is affected by traveling costs and the spatial organization of swards. Nevertheless, the most selected biomass in this type of short stands was the optimal biomass predicted by cropping and digestion constraints. As a result of the optimal foraging strategy, the grazers displayed Levy-flight traveling behavior during the simulations with characteristic exponent mu approximate to 2. Patterns of short and tall stands created by grazing were preserved for at least a decade. Even in seasonal habitat, the spatial organization of the patterns remained relatively constant, despite fluctuations in the area of short stands. Heterogeneity of initial vegetation increased heterogeneity of the grazing-induced pattern, but did not affect its stability. The area of short stands that was preserved by grazing scaled with the herbivore mass to the power 0.4 and with the carrying capacity of the vegetation to the power -0.75. Patterns of tall and short stands can be created and perpetuated by optimally grazing ruminants, irrespective of possible underlying soil patterns. The simulations generate predictions for the stability and spatial characteristics of such structural vegetation patterns. (C) 2008 Elsevier B.V. All rights reserved

Increased N affects P uptake of eight grassland species: The role of root surface phosphatase activity

Increased N deposition may change species composition in grassland communities by shifting them to P limitation. Interspecific differences in P uptake traits might be a crucial yet poorly understood factor in determining the N effects. To test the effects of increased N supply (relative to P), we conducted two greenhouse fertilization experiments with eight species from two functional groups (grasses, herbs), including those common in P and N limited grasslands. We investigated plant growth and P uptake from two P sources, orthophosphate and not-readily available P (bound-P), under different N supply levels. Furthermore, to test if the N effects on P uptake was due to N availability alone or altered N:P ratio, we examined several uptake traits (root-surface phosphatase activity, specific root length (SRL), root mass ratio (RMR)) under varying N:P supply ratios. Only a few species (M. caerulea, A. capillaris, S. pratensis) could take up a similar amount of P from bound-P to that from orthophosphate. These species had neither higher SRL, RMR, phosphatase activity per unit root (Paseroot), nor higher total phosphatase activity (Pasetot: Paseroot times root mass), but higher relative phosphatase activity (Paserel: Pasetot divided by biomass) than other species. The species common from P-limited grasslands had high Paserel. P uptake from bound-P was positively correlated with Pasetot for grasses. High N supply stimulated phosphatase activity but decreased RMR and SRL, resulting in no increase in P uptake from bound-P. Paseroot was influenced by N:P supply ratio, rather than by only N or P level, whereas SRL and RMR was not dominantly influenced by N:P ratio. We conclude that increased N stimulates phosphatase activity via N:P stoichiometry effects, which potentially increases plant P uptake in a species-specific way. N deposition, therefore, may alter plant community structure not only by enhancing productivity, but also by favouring species with traits that enable them to persist better under P limited conditions.</p

Appendix B. The Markov chain Synthetic Turbulence Generation (STG) model.

The Markov chain Synthetic Turbulence Generation (STG) model

Appendix A. Calculation of the Markov chain dispersion process.

Calculation of the Markov chain dispersion process

Linking field experiments to long-term simulation of impacts of nitrogen deposition on heathlands and moorlands

The results from three long-term field manipulation studies of the impacts of increased nitrogen deposition (0–120 kg N ha–1 yr–1) on lowland and upland heathlands in the UK were compared, to test if common responses are observed. Consistent increases in Calluna foliar N content and decreases in litter C:N ratios were found across all sites, while increases in N leaching were not observed at any site over the range 0–80 kg ha–1 yr–1. However, the response of Calluna biomass did vary between sites, possibly reflecting site differences in nutrient status and management histories. Five versions of a simulation model of heathland responses to N were developed, each reflecting different assumptions about the fate and turnover of soil N. Model outputs supported the deduction from mass balance calculations at two of the field sites that N additions have resulted in an increase in immobilisation; the latter was needed to prevent the model overestimating measured N leaching. However, this version of the model significantly underestimated Calluna biomass. Model versions, which included uptake of organic N by Calluna and re-mobilisation of N from the soil organic store provided some improvement in the fit between modelled and field biomass data, but re-mobilisation also led to an overestimation of N leaching. Quantification of these processes and their response to increased N deposition are therefore critical to interpreting experimental data and predicting the long-term impacts of atmospheric deposition on heathlands and moorlands

Validation of a Prognostic Multivariable Prediction Model for Insufficient Clinical Response to Methotrexate in Early Rheumatoid Arthritis and Its Clinical Application in Evidencio

Introduction: Methotrexate (MTX) constitutes the first-line therapy in rheumatoid arthritis (RA), yet approximately 30% of the patients do not benefit from MTX. Recently, we reported a prognostic multivariable prediction model for insufficient clinical response to MTX at 3 months of treatment in the treatment in the Rotterdam Early Arthritis Cohort (tREACH), including baseline predictors: Disease activity score 28 (DAS28), Health Assessment Questionnaire (HAQ), erythrocyte folate, single-nucleotide polymorphisms (SNPs; ABCB1, ABCC3), smoking, and BMI. The purpose of the current study was (1) to externally validate the model and (2) to enhance the model’s clinical applicability. Methods: Erythrocyte folate and SNPs were assessed in 91 early disease-modifying antirheumatic drug (DMARD)-naïve RA patients starting MTX in the external validation cohort (U-Act-Early). Insufficient response (DAS28 > 3.2) was determined after 3 months and non-response after 6 months of therapy. The previously developed prediction model was considered successfully validated in the U-Act-Early (validation cohort) if the area under the curve (AUC) of the receiver operating characteristic (ROC) was not significantly lower than in the tREACH (derivation cohort). Results: The AUCs in U-Act-Early at three and 6 months were 0.75 (95% CI 0.64–0.85) and 0.71 (95% CI 0.60–0.82) respectively, similar to the tREACH. Baseline DAS28 > 5.1 and HAQ > 0.6 were the strongest predictors. The model was simplified by excluding the SNPs, while still classifying 73% correctly. Furthermore, interaction terms between BMI and HAQ and BMI and erythrocyte folate significantly improved the model increasing correct classification to 75%. Results were successfully implemented in Evidencio online platform assisting clinicians in shared decision-making to intensify treatment when appropriate. Conclusions: We successfully externally validated our recently reported prediction model for MTX non-response and enhanced its clinical application thus enabling its evaluation in a clinical trial. Trial Registration: The U-Act-Early is registered at ClinicalTrials.gov. number: NCT01034137. tREACH is registered retrospectively at ISRCTN registry, number: ISRCTN26791028 at 23 August 2007