Clarence Lehman

Human-caused environmental change: Impacts on plant diversity and evolution

Human-caused environmental changes are creating regional combinations of environmental conditions that, within the next 50 to 100 years, may fall outside the envelope within which many of the terrestrial plants of a region evolved. These environmental modifications might become a greater cause of global species extinction than direct habitat destruction. The environmental constraints undergoing human modification include levels of soil nitrogen, phosphorus, calcium and pH, atmospheric CO2, herbivore, pathogen, and predator densities, disturbance regimes, and climate. Extinction would occur because the physiologies, morphologies, and life histories of plants limit each species to being a superior competitor for a particular combination of environmental constraints. Changes in these constraints would favor a few species that would competitively displace many other species from a region. In the long-term, the “weedy” taxa that became the dominants of the novel conditions imposed by global change should become the progenitors of a series of new species that are progressively less weedy and better adapted to the new conditions. The relative importance of evolutionary versus community ecology responses to global environmental change would depend on the extent of regional and local recruitment limitation, and on whether the suite of human-imposed constraints were novel just regionally or on continental or global scales.

Bioenergy and Wildlife: Threats and Opportunities for Grassland Conservation

Demand for land to grow corn for ethanol increased in the United States by 4.9 million hectares between 2005 and 2008, with wide-ranging effects on wildlife, including habitat loss. Depending on how biofuels are made, additional production could have similar impacts. We present a framework for assessing the impacts of biofuels on wildlife, and we use this framework to evaluate the impacts of existing and emerging biofuels feedstocks on grassland wildlife. Meeting the growing demand for biofuels while avoiding negative impacts on wildlife will require either biomass sources that do not require additional land (e.g., wastes, residues, cover crops, algae) or crop production practices that are compatible with wildlife. Diverse native prairie offers a potential approach to bioenergy production (including fuel, electricity, and heat) that is compatible with wildlife. Additional research is required to assess the compatibility of wildlife with different composition, inputs, and harvest management approaches, and to address concerns over prairie yields versus the yields of other biofuel crops.

DOES METABOLIC THEORY APPLY TO COMMUNITY ECOLOGY? IT'S A MATTER OF SCALE

al. (2004), there were moments of insight, of enthusiastic consensus, and of strongly divergent opinion. We agreed that the empirical relations and scaling theory of Brown et al. (2004) hold great appeal because of their power to abstract and simplify some of the complexity of nature. The earth harbors several million species, each having unique aspects to its morphology, physiology, and life history. A fundamental goal of science is to simplify and explain such complexity. Brown et al. (2004) do just this. They have documented robust patterns relating the body size and temperature of species to their basal metabolic rate; plotted on loglog scales, these empirical functions are well fit by straight lines. Moreover, they have used these scaling relations to make numerous predictions about other patterns and processes, thus greatly extending an approach that already had been shown to have considerable power (e.g., Huxley 1932, McMahon and Bonner 1983, Peters 1983, May 1986). One question that generated considerable debate among us was whether metabolic scaling theory represents a fundamental mechanism that has shaped life on earth, or whether it is a description of correlated patterns of as yet poorly known causes. Brown et al. (2004) hypothesize that scaling relations have a fundamental basis that comes from the universality of metabolic activation energy and of the fractal branching networks that determine resource distribution within

An efficient assignment of drainage direction over flat surfaces in raster digital elevation models

In processing raster digital elevation models (DEMs) it is often necessary to assign drainage directions over flats-that is, over regions with no local elevation gradient. This paper presents an approach to drainage direction assignment which is not restricted by a flats shape, number of outlets, or surrounding topography. Flow is modeled by superimposing a gradient away from higher terrain with a gradient towards lower terrain resulting in a drainage field exhibiting flow convergence, an improvement over methods which produce regions of parallel flow. This approach builds on previous work by Garbrecht and Martz (1997), but presents several important improvements. The improved algorithm guarantees that flats are only resolved if they have outlets. The algorithm does not require iterative application; a single pass is sufficient to resolve all flats. The algorithm presents a clear strategy for identifying flats and their boundaries. The algorithm is not susceptible to loss of floating-point precision. Furthermore, the algorithm is efficient, operating in O(N) time whereas the older algorithm operates in O ( N 3 / 2 ) time. In testing, the improved algorithm ran 6.5 times faster than the old for a 100i?100 cell flat and 69 times faster for a 700i?700 cell flat. In tests on actual DEMs, the improved algorithm finished its processing 38-110 times sooner while running on a single processor than a parallel implementation of the old algorithm did while running on 16 processors. The improved algorithm is an optimal, accurate, easy-to-implement drop-in replacement for the original. Pseudocode is provided in the paper and working source code is provided in the Supplemental Materials. HighlightsWe present an improved algorithm to model ow directions in at regions of DEMs.The algorithm works regardless of the topography surrounding the at region.The algorithm produces convergent ows away from higher and towards lower terrain.The algorithm has a number of sanity checks which guarantee correct output.The algorithm works in O(N) time and supplants an older O(N3\2) time algorithm.

Response to Comment on "Carbon-Negative Biofuels from Low-Input High-Diversity Grassland Biomass"

We discovered that biofuels from low-input high-diversity mixtures of native perennial prairie plants grown on degraded soil can provide similar bioenergy gains and greater greenhouse gas benefits than current corn ethanol produced from crops grown in monoculture on fertile soil with high inputs. Russelle et al.s technical concerns are refuted by a substantial body of research on prairie ecosystems and managed perennial grasslands.

Fluctuating Selection in the Moran.

Contrary to classical population genetics theory, experiments demonstrate that fluctuating selection can protect a haploid polymorphism in the absence of frequency dependent effects on fitness. Using forward simulations with the Moran model, we confirm our analytical results showing that a fluctuating selection regime, with a mean selection coefficient of zero, promotes polymorphism. We find that increases in heterozygosity over neutral expectations are especially pronounced when fluctuations are rapid, mutation is weak, the population size is large, and the variance in selection is big. Lowering the frequency of fluctuations makes selection more directional, and so heterozygosity declines. We also show that fluctuating selection raises dn/ds ratios for polymorphism, not only by sweeping selected alleles into the population, but also by purging the neutral variants of selected alleles as they undergo repeated bottlenecks. Our analysis shows that randomly fluctuating selection increases the rate of evolution by increasing the probability of fixation. The impact is especially noticeable when the selection is strong and mutation is weak. Simulations show the increase in the rate of evolution declines as the rate of new mutations entering the population increases, an effect attributable to clonal interference. Intriguingly, fluctuating selection increases the dn/ds ratios for divergence more than for polymorphism, a pattern commonly seen in comparative genomics. Our model, which extends the classical neutral model of molecular evolution by incorporating random fluctuations in selection, accommodates a wide variety of observations, both neutral and selected, with economy.

Short-term harvesting of biomass from conservation grasslands maintains plant diversity

High yields are a priority in managing biomass for renewable energy, but the environmental impacts of various feedstocks and production systems should be equally considered. Mixed‐species, perennial grasslands enrolled in conservation programs are being considered as a source of biomass for renewable energy. Conservation grasslands are crucial in sustaining native biodiversity throughout the US Upper Midwest, and the effects of biomass harvest on biodiversity are largely unknown. We measured the effect of late‐season biomass harvest on plant community composition in conservation grasslands in three regions of Minnesota, USA from 2009 to 2012. Temporal trends in plant species composition within harvested grasslands were compared to unharvested grasslands using mixed effects models. A before‐after control‐impact approach using effect sizes was applied to focus on pre‐ and postharvest conditions. Production‐scale biomass harvest did not affect plant species richness, species or functional group diversity, nor change the relative abundance of the main plant functional groups. Differences in the relative abundances of plant functional groups were observed across locations; and at some locations, changed through time. The proportion of non‐native species remained constant, while the proportion of noxious weeds decreased through time in both harvested and unharvested grasslands at the central location. Ordination revealed patterns in species composition due to location, but not due to harvest treatment. Therefore, habitat and bioenergy characteristics related to grassland plant communities are not expected to change due to short‐term or intermittent late‐season biomass harvest.

Effects of Grassland Biomass Harvest on Nesting Pheasants and Ducks

Abstract Grasslands enrolled in conservation programs provide important habitat for nesting game birds and waterfowl, but conservation grasslands have been targeted as a source of biomass for bioenergy and this could impact nesting birds. We studied the effects of biomass harvest on nest success and density using 109 blue-winged teal (Anas discors), mallard (Anas platyrhynchos), and ring-necked pheasant (Phasianus colchicus) nests found in southwestern Minnesota during 2009 (pretreatment) and 2010 (posttreatment). Grassland biomass was harvested in late autumn of 2009 with production-scale machinery. Harvest treatments included controls (0% biomass removal), partial harvest (50 or 75% biomass removal), and full harvest (100% biomass removal) from 8 ha plots. Nest success averaged 31% and was not influenced by biomass harvest. Daily survival rates were greater for nests located closer to wetlands. Estimated total nest density (0.42 nests ha−1; corrected for survivorship) was similar across harvest treatments, but within-plot analysis revealed nest density was greater in unharvested refuge regions. Estimated nest density was positively correlated with vegetation height and the spatial extent of wetlands surrounding each plot. Harvesting relatively small-scale patches of conservation grasslands in late autumn does not appear to be detrimental to nesting ducks and pheasants the following spring, but managers should consider leaving unharvested refuges near wetlands when harvesting large continuous tracts.

Supervised and unsupervised machine learning approaches to classifying chimpanzee vocalizations

Quantitative tools for classifying vocal repertoires have been constantly evolving with developments in machine learning and speech recognition research as well as increasing computing power. There are two main methodological considerations in classifying vocalizations: (i) choosing the classification technique and (ii) choosing the features for classification. Current state-of-the-art classification techniques are artificial neural networks (ANNs), support vector machines (SVMs), and ensemble methods like random forests (RFs). Current state-of-the-art features from speech recognition research include mel frequency cepstral coefficients (MFCCs). Bioacoustics researchers have applied these tools to problems including individual-, species-, and call-type identification, and vocal repertoire classification. However, researchers studying non-human primate vocalizations have only recently started adopting these approaches and none have applied them to study chimpanzee vocalizations. Here, we analyze vocalizations recorded in Gombe National Park, Tanzania. First, we use supervised classification techniques (ANNs, SVMs, and RFs) that involve training the models based on predefined call-types to evaluate the classification accuracy. Second, we use unsupervised techniques (that do not require prior knowledge of call-types), namely, K-means clustering, and self-organizing neural networks to identify discrete call types. We discuss the results from both supervised and unsupervised techniques and their strengths over traditional methods.Quantitative tools for classifying vocal repertoires have been constantly evolving with developments in machine learning and speech recognition research as well as increasing computing power. There are two main methodological considerations in classifying vocalizations: (i) choosing the classification technique and (ii) choosing the features for classification. Current state-of-the-art classification techniques are artificial neural networks (ANNs), support vector machines (SVMs), and ensemble methods like random forests (RFs). Current state-of-the-art features from speech recognition research include mel frequency cepstral coefficients (MFCCs). Bioacoustics researchers have applied these tools to problems including individual-, species-, and call-type identification, and vocal repertoire classification. However, researchers studying non-human primate vocalizations have only recently started adopting these approaches and none have applied them to study chimpanzee vocalizations. Here, we analyze vocalizati...

2. Biodiversity, Composition, and Ecosystem Processes: Theory and Concepts

PART 1 Empirical Progress 2. Biodiversity, Composition, and Ecosystem Processes: Theory and Concepts David Tilman and Clarence Lehman 9 Introduction 9 Definitions of Diversity 11 Problems Related to Experiments and Observations 14 Diversity, Productivity, and Resource Dynamics 15 Sampling Effect Models 16 Niche Differentiation Models 23 Diversity and Stability 29 Measures of Stability 29 Components of Temporal Stability 30 Diversity and Temporal Stability in Multispecies Models 34 Summary 39 Acknowledgments 41