Sean T. Hammond

The Malthusian-Darwinian dynamic and the trajectory of civilization.

Two interacting forces influence all populations: the Malthusian dynamic of exponential growth until resource limits are reached, and the Darwinian dynamic of innovation and adaptation to circumvent these limits through biological and/or cultural evolution. The specific manifestations of these forces in modern human society provide an important context for determining how humans can establish a sustainable relationship with the finite Earth.

Insights into plant size-density relationships from models and agricultural crops

There is general agreement that competition for resources results in a tradeoff between plant mass, M, and density, but the mathematical form of the resulting thinning relationship and the mechanisms that generate it are debated. Here, we evaluate two complementary models, one based on the space-filling properties of canopy geometry and the other on the metabolic basis of resource use. For densely packed stands, both models predict that density scales as M−3/4, energy use as M0, and total biomass as M1/4. Compilation and analysis of data from 183 populations of herbaceous crop species, 473 stands of managed tree plantations, and 13 populations of bamboo gave four major results: (i) At low initial planting densities, crops grew at similar rates, did not come into contact, and attained similar mature sizes; (ii) at higher initial densities, crops grew until neighboring plants came into contact, growth ceased as a result of competition for limited resources, and a tradeoff between density and size resulted in critical density scaling as M−0.78, total resource use as M−0.02, and total biomass as M0.22; (iii) these scaling exponents are very close to the predicted values of M−3/4, M0, and M1/4, respectively, and significantly different from the exponents suggested by some earlier studies; and (iv) our data extend previously documented scaling relationships for trees in natural forests to small herbaceous annual crops. These results provide a quantitative, predictive framework with important implications for the basic and applied plant sciences.

COMPUTER SIMULATIONS OF PLANT BIODIVERSITY IN STABLE AND UNSTABLE ENVIRONMENTS: A TEST OF THE NEUTRAL BIODIVERSITY THEORY

The Neutral Biodiversity Theory (NBT) argues that community-level patterns are not determined by trait differences among species, but rather by demographic stochasticity. If true, deterministic patterns of species co-existence, transient or otherwise, should not occur. We tested this critical prediction using a spatially explicit, reiterative algorithm (SERA) that incorporates demographic stochasticity to examine how artificial plant species compete for light and space. An additional level of ecological realism was added by simulating species competition in heterogeneous and disturbed environments. Across 108 simulations, small niche differences influenced interspecific competition and persistence, particularly in spatiotemporally heterogeneous environments. One species re-emerged as the dominant, and the identities of subdominant and rare species remained constant. Although stochastic processes played an important role, the results of these simulations are inconsistent with NBT, i.e., very small niche differences dictated patterns even when all species obey the same simple physical rules.

Assessing Scaling Relationships: Uses, Abuses, and Alternatives

Premise of research. Workers have relied on fitting a straight line to logarithmically transformed data to determine biological scaling relationships without testing the assumption that error is normal and additive on the logarithmic scale. Methodology. We review the history of this practice, the pros and cons of log transformation, and the use of model Type I and II regression protocols. Using standard statistical protocols and the Akaike Information Criterion, we then evaluate linear and nonlinear models applied to a large interspecific data set and a smaller intraspecific data set to reexamine the hypothesis called diminishing returns, which states that the surface areas of mature leaves may fail to increase one-to-one (isometrically) as lamina dry mass increases. Pivotal results. The error structures of both data sets were multiplicative and lognormal and thus complied with a linear model, which obtained log-log linear lines with slopes less than 1; i.e., the data were consistent with the hypothesis of diminishing returns. Conclusions. History shows that log transformation has always been a controversial practice. However, the extent to which linear or nonlinear models comply with a particular data set is generally transparent using standard statistical protocols (e.g., analysis of residuals). Previous scaling analyses using log-transformed data therefore are likely generally valid. Nevertheless, the error structure in every data set should be assessed to determine whether linear or nonlinear regression models are appropriate. Reliable algorithms are available for this purpose.

A macroecological analysis of SERA derived forest heights and implications for forest volume remote sensing.

Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H100, and Loreys height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H100 and Loreys, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 102–106 plants/hectare and heights 6–49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Loreys height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H100.

Modeling Forest Self-assembly Dynamics Using Allometric and Physical First Principles

Computer models are used by ecologists for studying a broad range of research questions, from long-term forest dynamics to the functional traits that theoretically give one species an advantage over others. Despite their increasing popularity, these models have been criticized for simulating complex biological phenomena, involving numerous biotic and abiotic variables, using seemingly overly simplistic computational approaches. In this article, we review the usefulness and limitations of spatially explicit individual-based models for forested ecosystems by focusing on the attributes of a recent model, called SERA (for spatially explicit reiterative algorithm), that employs seven allometric formulas and a few physical principles. Despite its simplicity, SERA successfully predicts forest self-assembly and dynamics. It also predicts phenomena that are not part of its mathematical structure. Because of this, SERA simulations can be used to explore the consequences of experimentally manipulating plant communities in ways that cannot be achieved using real communities.