Ecosystem development in roadside grasslands: biotic control, plant-soil interactions, and dispersal limitations

Roadside grasslands undergoing secondary succession are abundant, and represent ecologically meaningful examples of novel, human-created ecosystems. Interactions between plant and soil communities (hereafter plant-soil interactions) are of major importance in understanding the role of biotic control in ecosystem functioning, but little is known about these links in the context of ecosystem restoration and succession. The assessment of the key biotic communities and interactions driving ecosystem development will help practitioners to better allocate the limited resources devoted to roadside grassland restoration. We surveyed roadside grasslands from three successional stages (0-2, 7-9, and >20 years) in two Mediterranean regions of Spain. Structural equation modeling was used to evaluate how interactions between plants, biological soil crusts (BSCs), and soil microbial functional diversity (soil microorganisms) affect indicators of ecosystem development and restoration: plant similarity to the reference ecosystem, erosion control, and soil C storage and N accumulation. Changes in plant community composition along the successional gradient exerted the strongest influence on these indicators. High BSC cover was associated with high soil stability, and high soil microbial functional diversity from late-successional stages was associated with high soil fertility. Contrary to our expectations, the indirect effects of plants, mediated by either BSCs or soil microorganisms, were very weak in both regions, suggesting a minor role for plant-soil interactions upon ecosystem development indicators over long periods. Our results suggest that natural vegetation dynamics effectively improved ecosystem development within a time frame of 20 years in the grasslands evaluated. They also indicate that this time could be shortened if management actions focus on: (1) maintaining well-conserved natural areas close to roadsides to enhance plant compositional changes towards late-successional stages, (2) increasing BSC cover in areas under strong erosion risk, to avoid soil loss, and (3) enhancing soil microbial functional diversity in resource-limited areas, to enhance soil C and N accumulation.     

“Winter” aggregations, colony cycle, and seasonal phenotypic change in the paper wasp Polistes versicolor in subtropical Brazil

Social wasps from temperate zones have clear annual colony cycles, and the young queens hibernate during winter. In the subtropics, the only previously reported evidence for the existence of “hibernation” is the facultative winter aggregations of females during harsh climate conditions. As in temperate-zone species analyzed so far, we show in this study that in the paper wasp, Polistes versicolor, a subtropical species, body size increases as an unfavorable season approaches. Our morphological studies indicate that larger females come from winter aggregations—that is, they are new queens. Multivariate analyses indicate that size is the only variable analyzed that shows a relationship to the differences. Given the absence of a harsh climate, we suggest that the occurrence of winter aggregations in tropical P. versicolor functions to allow some females to wait for better environmental conditions to start a new nest, rather than all being obliged to start a new nest as soon as they emerge.     

Learning Machines Applied to Potential Forest Distribution

The clearing of forests to obtain land for pasture and agriculture and the replacement of autochthonous species by other faster-growing varieties of trees for timber have both led to the loss of vast areas of forest worldwide. At present, many developed countries are attempting to reverse these effects, establishing policies for the restoration of older woodland systems. Reforestation is a complex matter, planned and carried out by experts who need objective information regarding the type of forest that can be sustained in each area. This information is obtained by drawing up feasibility models constructed using statistical methods that make use of the information provided by morphological and environmental variables (height, gradient, rainfall, etc.) that partially condition the presence or absence of a specific kind of forestation in an area. The aim of this work is to construct a set of feasibility models for woodland located in the basin of the River Liébana (NW Spain), to serve as a support tool for the experts entrusted with carrying out the reforestation project. The techniques used are multilayer perceptron neural networks and support vector machines. Their results will be compared to the results obtained by traditional techniques (such as discriminant analysis and logistic regression) by measuring the degree of fit between each model and the existing distribution of woodlands. The interpretation and problems of the feasibility models are commented on in the Discussion section.     

A Comparison of Bayesian Methods for Uncertainty Analysis in Hydraulic and Hydrodynamic Modeling

We evaluate and compare the performance of Bayesian Monte Carlo (BMC), Markov chain Monte Carlo (MCMC), and the Generalized Likelihood Uncertainty Estimation (GLUE) for uncertainty analysis in hydraulic and hydrodynamic modeling (HHM) studies. The methods are evaluated in a synthetic 1D wave routing exercise based on the diffusion wave model, and in a multidimensional hydrodynamic study based on the Environmental Fluid Dynamics Code to simulate estuarine circulation processes in Weeks Bay, Alabama. Results show that BMC and MCMC provide similar estimates of uncertainty. The posterior parameter densities computed by both methods are highly consistent, as well as the calibrated parameter estimates and uncertainty bounds. Although some studies suggest that MCMC is more efficient than BMC, our results did not show a clear difference between the performance of the two methods. This seems to be due to the low number of model parameters typically involved in HHM studies, and the use of the same likelihood function. In fact, for these studies, the implementation of BMC results simpler and provides similar results to MCMC. The results of GLUE are, on the other hand, less consistent to the results of BMC and MCMC in both applications. The posterior probability densities tend to be flat and similar to the uniform priors, which can result in calibrated parameter estimates centered in the parametric space.     

Error Autocorrelation and Linear Regression for Temperature‐Based Evapotranspiration Estimates Improvement1

Medeiros, Patrick Valverde, Francisco Fernando Noronha Marcuzzo, Cristián Youlton, and Edson Wendland, 2012. Error Autocorrelation and Linear Regression for Temperature‐Based Evapotranspiration Estimates Improvement. Journal of the American Water Resources Association (JAWRA) 48(2): 297‐305. DOI: 10.1111/j.1752‐1688.2011.00614.x Abstract: Estimates of evapotranspiration on a local scale is important information for agricultural and hydrological practices. However, equations to estimate potential evapotranspiration based only on temperature data, which are simple to use, are usually less trustworthy than the Food and Agriculture Organization (FAO)‐Penman‐Monteith standard method. The present work describes two correction procedures for potential evapotranspiration estimates by temperature, making the results more reliable. Initially, the standard FAO‐Penman‐Monteith method was evaluated with a complete climatologic data set for the period between 2002 and 2006. Then temperature‐based estimates by Camargo and Jensen‐Haise methods have been adjusted by error autocorrelation evaluated in biweekly and monthly periods. In a second adjustment, simple linear regression was applied. The adjusted equations have been validated with climatic data available for the Year 2001. Both proposed methodologies showed good agreement with the standard method indicating that the methodology can be used for local potential evapotranspiration estimates.     

The potential of animal manure management pathways toward a circular economy: a bibliometric analysis

Environmental Science and Pollution Research - Improper disposal of animal waste is responsible for several environmental problems, causing eutrophication of lakes and rivers, nutrient overload in...