Christine Lamanna

Microbes on mountainsides: Contrasting elevational patterns of bacterial and plant diversity

The study of elevational diversity gradients dates back to the foundation of biogeography. Although elevational patterns of plant and animal diversity have been studied for centuries, such patterns have not been reported for microorganisms and remain poorly understood. Here, in an effort to assess the generality of elevational diversity patterns, we examined soil bacterial and plant diversity along an elevation gradient. To gain insight into the forces that structure these patterns, we adopted a multifaceted approach to incorporate information about the structure, diversity, and spatial turnover of montane communities in a phylogenetic context. We found that observed patterns of plant and bacterial diversity were fundamentally different. While bacterial taxon richness and phylogenetic diversity decreased monotonically from the lowest to highest elevations, plants followed a unimodal pattern, with a peak in richness and phylogenetic diversity at mid-elevations. At all elevations bacterial communities had a tendency to be phylogenetically clustered, containing closely related taxa. In contrast, plant communities did not exhibit a uniform phylogenetic structure across the gradient: they became more overdispersed with increasing elevation, containing distantly related taxa. Finally, a metric of phylogenetic beta-diversity showed that bacterial lineages were not randomly distributed, but rather exhibited significant spatial structure across the gradient, whereas plant lineages did not exhibit a significant phylogenetic signal. Quantifying the influence of sample scale in intertaxonomic comparisons remains a challenge. Nevertheless, our findings suggest that the forces structuring microorganism and macroorganism communities along elevational gradients differ.

Functional trait space and the latitudinal diversity gradient

Significance We present a conceptual framework for testing theories for the latitudinal gradient of species richness in terms of variation in functional diversity at the alpha, beta, and gamma scales. We compared ecological community theory with large-scale observational data of tree species richness and functional diversity. We found that the patterns of functional trait diversity are not consistent with any one theory of species diversity. These conflicting results indicate that none of the broad classes of biodiversity theory considered here is alone able to explain the latitudinal gradient of species diversity in terms of functional trait space. The processes causing the latitudinal gradient in species richness remain elusive. Ecological theories for the origin of biodiversity gradients, such as competitive exclusion, neutral dynamics, and environmental filtering, make predictions for how functional diversity should vary at the alpha (within local assemblages), beta (among assemblages), and gamma (regional pool) scales. We test these predictions by quantifying hypervolumes constructed from functional traits representing major axes of plant strategy variation (specific leaf area, plant height, and seed mass) in tree assemblages spanning the temperate and tropical New World. Alpha-scale trait volume decreases with absolute latitude and is often lower than sampling expectation, consistent with environmental filtering theory. Beta-scale overlap decays with geographic distance fastest in the temperate zone, again consistent with environmental filtering theory. In contrast, gamma-scale trait space shows a hump-shaped relationship with absolute latitude, consistent with no theory. Furthermore, the overall temperate trait hypervolume was larger than the overall tropical hypervolume, indicating that the temperate zone permits a wider range of trait combinations or that niche packing is stronger in the tropical zone. Although there are limitations in the data, our analyses suggest that multiple processes have shaped trait diversity in trees, reflecting no consistent support for any one theory.

How climate-smart is conservation agriculture (CA)? – its potential to deliver on adaptation, mitigation and productivity on smallholder farms in southern Africa

Climate resilient cropping systems are required to adapt to the increasing threats of climate change projected for Southern Africa and to better manage current climate variability. Conservation agriculture (CA) has been proposed among technologies that are climate-smart. For a cropping system to be labelled “climate-smart” it has to deliver three benefits: a) adapt to the effects of climate and be of increased resilience; b) mitigate climate effects by sequestering carbon (C) and reducing greenhouse gas emissions (GHG); and c) sustainably increase productivity and income. Research on smallholder farms from Southern Africa was analysed to assess if CA can deliver on the three principles of climate-smart agriculture. Results from Southern Africa showed that CA systems have a positive effect on adaptation and productivity, but its mitigation potential lags far behind expectations. CA systems maintain higher infiltration rates and conserve soil moisture, which helps to overcome seasonal dry-spells. Increased productivity and profitability were recorded although a lag period of 2–5 cropping seasons is common until yield benefits become significant. Immediate economic benefits such as reduced labour requirements in some systems will make CA more attractive in the short term to farmers who cannot afford to wait for several seasons until yield benefits accrue. The available data summarizing the effects of CA on soil organic C (SOC) and reductions in greenhouse gases, are often contradictory and depend a great deal on the agro-ecological environment and the available biomass for surface residue retention. There is an urgent need for more research to better quantify the mitigation effects, as the current data are scanty. Possible co-interventions such as improved intercropping/relay cropping systems, agroforestry and other tree-based systems may improve delivery of mitigation benefits and need further exploration.

Controls on radial growth of mountain big sagebrush and implications for climate change

ABSTRACT. Mountain big sagebrush (Artemisia tridentata Nutt. ssp. vaseyana) covers large areas in arid regions of western North America. Climate-change models predict a decrease in the range of sagebrush, but few studies have examined details of predicted changes on sagebrush growth and the potential impacts of these changes on the community. We analyzed effects of temperature, precipitation, and snow depth on sagebrush annual ring width for 1969 to 2007 in the Gunnison Basin of Colorado. Temperature at all times of year except winter had negative correlations with ring widths; summer temperature had the strongest negative relationship. Ring widths correlated positively with precipitation in various seasons except summer; winter precipitation had the strongest relationship with growth. Maximum snow depth also correlated positively and strongly with ring width. Multiple regressions showed that summer temperature and either winter precipitation or maximum snow depth, which recharges deeper soil horizons, are both important in controlling growth. Overall, water stress and perhaps especially maximum snow depth appear to limit growth of this species. With predicted increases in temperature and probable reduced snow depth, sagebrush growth rates are likely to decrease. If so, sagebrush populations and cover may decline, which may have substantial effects on community composition and carbon balance.

New approaches for delineating n-dimensional hypervolumes

1.Hutchinsons n-dimensional hypervolume concept underlies many applications in contemporary ecology and evolutionary biology. Estimating hypervolumes from sampled data has been an ongoing challenge due to conceptual and computational issues. 2.We present new algorithms for delineating the boundaries and probability density within n-dimensional hypervolumes. The methods produce smooth boundaries that can fit data either more loosely (Gaussian kernel density estimation) or more tightly (one-classification via support vector machine). Further, the algorithms can accept abundance-weighted data, and the resulting hypervolumes can be given a probabilistic interpretation and projected into geographic space. 3.We demonstrate the properties of these methods on a large dataset that characterizes the functional traits and geographic distribution of thousands of plants. The methods are available in version ≥2.0.6 of the hypervolume R package. 4.These new algorithms provide: (i) a more robust approach for delineating the shape and density of n-dimensional hypervolumes; (ii) more efficient performance on large and high-dimensional datasets; and (iii) improved measures of functional diversity and environmental niche breadth. This article is protected by copyright. All rights reserved.

A Qualitative Evaluation of CSA Options in Mixed Crop-Livestock Systems in Developing Countries

The mixed crop-livestock systems of the developing world will become increasingly important for meeting the food security challenges of the coming decades. The synergies and trade-offs between food security, adaptation, and mitigation objectives are not well studied, however. Comprehensive evaluations of the costs and benefits, and the synergies and trade-offs, of different options in developing-country mixed systems do not exist as yet. Here we summarise what we know about the climate smartness of different alternatives in the mixed crop-livestock systems in developing countries, based on published literature supplemented by a survey of experts. We discuss constraints to the uptake of different interventions and the potential for their adoption, and highlight some of the technical and policy implications of current knowledge and knowledge gaps.