Classification of Azolla spp., section Azolla

Summary Azolla accessions (section Azolla) from the germplasm collections of the International Rice Research Institute and Washington State University were fingerprinted and classified by enzyme electrophoresis and leaf trichome morphology. A. filiculoides was enzymatically distinctive and also reliably identified by its prominent one-celled trichomes. Neotropical accessions labelled as A. filiculoides proved to be members of other species. Two groups of isolates were designated A. rubra, but those from Japan were identified as A. filiculoides. The A. rubra of Australia-New Zealand was biochemically unique and possessed less protuberant trichomes than A. filiculoides. A. microphylla, A. mexicana, and A. caroliniana were phenetically similar, but a. microphylla was identifiable from the others in the banding patterns of certain enzymes. A. mexicana and A. caroliniana were closely related enzymatically. The two-celled leaf trichomes of these three species were similar in size and shape.     

Land-use history has a stronger impact on soil microbial community composition than aboveground vegetation and soil properties

The response of soil microbial communities following changes in land-use is governed by multiple factors. The objectives of this study were to investigate (i) whether soil microbial communities track the changes in aboveground vegetation during succession; and (ii) whether microbial communities return to their native state over time. Two successional gradients with different vegetation were studied at the W. K. Kellogg Biological Station, Michigan. The first gradient comprised a conventionally tilled cropland (CT), mid-succession forest (SF) abandoned from cultivation prior to 1951, and native deciduous forest (DF). The second gradient comprised the CT cropland, early-succession grassland (ES) restored in 1989, and long-term mowed grassland (MG). With succession, the total microbial PLFAs and soil microbial biomass C consistently increased in both gradients. While bacterial rRNA gene diversity remained unchanged, the abundance and composition of many bacterial phyla changed significantly. Moreover, microbial communities in the relatively pristine DF and MG soils were very similar despite major differences in soil properties and vegetation. After >50 years of succession, and despite different vegetation, microbial communities in SF were more similar to those in mature DF than in CT. In contrast, even after 17 years of succession, microbial communities in ES were more similar to CT than endpoint MG despite very different vegetation between CT and ES. This result suggested a lasting impact of cultivation history on the soil microbial community. With conversion of deciduous to conifer forest (CF), there was a significant change in multiple soil properties that correlated with changes in microbial biomass, rRNA gene diversity and community composition. In conclusion, history of land-use was a stronger determinant of the composition of microbial communities than vegetation and soil properties. Further, microbial communities in disturbed soils apparently return to their native state with time.     

Rapid wastage of Alaska glaciers and their contribution to rising sea level

We have used airborne laser altimetry to estimate volume changes of 67 glaciers in Alaska from the mid-1950s to the mid-1990s. The average rate of thickness change of these glaciers was -0.52 m/year. Extrapolation to all glaciers in Alaska yields an estimated total annual volume change of -52 +/- 15 km3/year (water equivalent), equivalent to a rise in sea level (SLE) of 0.14 +/- 0.04 mm/year. Repeat measurements of 28 glaciers from the mid-1990s to 2000-2001 suggest an increased average rate of thinning, -1.8 m/year. This leads to an extrapolated annual volume loss from Alaska glaciers equal to -96 +/- 35 km3/year, or 0.27 +/- 0.10 mm/year SLE, during the past decade. These recent losses are nearly double the estimated annual loss from the entire Greenland Ice Sheet during the same time period and are much higher than previously published loss estimates for Alaska glaciers. They form the largest glaciological contribution to rising sea level yet measured.     

A comparison of popular approaches to optimize landscape resistance surfaces

Landscape Ecology - Landscape resistance surfaces are often used to address questions related to movement, dispersal, or population connectivity. However, modeling landscape resistance is...     

Multi-model comparison on the effects of climate change on tree species in the eastern U.S.: results from an enhanced niche model and process-based ecosystem and landscape models

Context

Species distribution models (SDM) establish statistical relationships between the current distribution of species and key attributes whereas process-based models simulate ecosystem and tree species dynamics based on representations of physical and biological processes. TreeAtlas, which uses DISTRIB SDM, and Linkages and LANDIS PRO, process-based ecosystem and landscape models, respectively, were used concurrently on four regional climate change assessments in the eastern Unites States.

Objectives

We compared predictions for 30 species from TreeAtlas, Linkages, and LANDIS PRO, using two climate change scenarios on four regions, to derive a more robust assessment of species change in response to climate change.

Methods

We calculated the ratio of future importance or biomass to current for each species, then compared agreement among models by species, region, and climate scenario using change classes, an ordinal agreement score, spearman rank correlations, and model averaged change ratios.

Results

Comparisons indicated high agreement for many species, especially northern species modeled to lose habitat. TreeAtlas and Linkages agreed the most but each also agreed with many species outputs from LANDIS PRO, particularly when succession within LANDIS PRO was simulated to 2300. A geographic analysis showed that a simple difference (in latitude degrees) of the weighted mean center of a species distribution versus the geographic center of the region of interest provides an initial estimate for the species’ potential to gain, lose, or remain stable under climate change.

Conclusions

This analysis of multiple models provides a useful approach to compare among disparate models and a more consistent interpretation of the future for use in vulnerability assessments and adaptation planning.

     

Assessing wild bees in perennial bioenergy landscapes: effects of bioenergy crop composition,landscape configuration,and bioenergy crop area

Context

Wild bee populations are currently under threat, which has led to recent efforts to increase pollinator habitat in North America. Simultaneously, U.S. federal energy policies are beginning to encourage perennial bioenergy cropping (PBC) systems, which have the potential to support native bees.

Objectives

Our objective was to explore the potentially interactive effects of crop composition, total PBC area, and PBC patches in different landscape configurations.

Methods

Using a spatially-explicit modeling approach, the Lonsdorf model, we simulated the impacts of three perennial bioenergy crops (PBC: willow, switchgrass, and prairie), three scenarios with different total PBC area (11.7, 23.5 and 28.8% of agricultural land converted to PBC) and two types of landscape configurations (PBC in clustered landscape patterns that represent realistic future configurations or in dispersed neutral landscape models) on a nest abundance index in an Illinois landscape.

Results

Our modeling results suggest that crop composition and PBC area are particularly important for bee nest abundance, whereas landscape configuration is associated with bee nest abundance at the local scale but less so at the regional scale.

Conclusions

Strategies to enhance wild bee habitat should therefore emphasize the crop composition and amount of PBC.

     

Using unsupervised learning techniques to assess interactions among complex traits in soybeans

Soybean yield components and agronomic traits are connected through physiological pathways that impose tradeoffs through genetic and environmental constraints. Our primary aim is to assess the interdependence of soybean traits by using unsupervised machine learning techniques to divide phenotypic associations into environmental and genetic associations. This study was performed on large scale, jointly analyzing 14 quantitative traits in a large multi-parental population designed for genetic studies. We collected phenotypes from 2012 to 2015 from a soybean nested association panel with 40 families of approximately 140 individuals each. Pearson and Spearman correlations measured phenotypic associations. A multivariate mixed linear model provided genotypic and environmental correlations. To evaluate relationships among traits, the study used principal component and undirected graphical models from phenotypic, genotypic, and environmental correlation matrices. Results indicate that high phenotypic correlation occurs when traits display both genetic and environmental correlations. In genetic terms, length of reproductive period, node number, and canopy coverage play important roles in determining yield potential. Optimal grain yield production occurs when the growing environment favors faster canopy closure and extended reproductive length. Environmental associations found among yield components give insight into the nature of yield component compensation. The use of unsupervised learning methods provides a good framework for investigating interactions among various quantitative traits and defining target traits for breeding.     

How climate change might affect tree regeneration following fire at northern latitudes: a review

New Forests - Climate change is projected to increase fire severity and frequency in the boreal forest, but it could also directly affect post-fire recruitment processes by impacting seed...     

Application of Portfolio Theory for the optimal choice of dairy veterinary management programs

Portfolio theory, an established technique in the selection of stocks and financial instruments for investment purposes, was used as a means of selecting an economic mix of veterinary interventions in a dairy herd. The expected return on investment in six veterinary services and the associated standard deviations (as measures of risk) were calculated using computer simulation modeling techniques. Mathematical programming techniques were applied to identify the optimal combinations of veterinary interventions which maximize expected financial returns while minimizing risk. Parametric analysis was used to show how risk (standard deviation) increases as the required expected return is increased along the efficient risk-return frontier of these optimal combinations. This exercise emphasizes the importance of regarding risk in addition to expected return when considering investment in veterinary services.