Partitioning direct and indirect human-induced effects on carbon sequestration of managed coniferous forests using model simulations and forest inventories

Temperate forest ecosystems have recently been identified as an important net sink in the global carbon budget. The factors responsible for the strength of the sinks and their permanence, however, are less evident. In this paper, we quantify the present carbon sequestration in Thuringian managed coniferous forests. We quantify the effects of indirect human‐induced environmental changes (increasing temperature, increasing atmospheric CO₂ concentration and nitrogen fertilization), during the last century using BIOME‐BGC, as well as the legacy effect of the current age‐class distribution (forest inventories and BIOME‐BGC). We focused on coniferous forests because these forests represent a large area of central European forests and detailed forest inventories were available. The model indicates that environmental changes induced an increase in biomass C accumulation for all age classes during the last 20 years (1982–2001). Young and old stands had the highest changes in the biomass C accumulation during this period. During the last century mature stands (older than 80 years) turned from being almost carbon neutral to carbon sinks. In high elevations nitrogen deposition explained most of the increase of net ecosystem production (NEP) of forests. CO₂ fertilization was the main factor increasing NEP of forests in the middle and low elevations. According to the model, at present, total biomass C accumulation in coniferous forests of Thuringia was estimated at 1.51 t C ha^?1 yr^?1 with an averaged annual NEP of 1.42 t C ha^?1 yr^?1 and total net biome production of 1.03 t C ha^?1 yr^?1 (accounting for harvest). The annual averaged biomass carbon balance (BCB: biomass accumulation rate‐harvest) was 1.12 t C ha^?1 yr^?1 (not including soil respiration), and was close to BCB from forest inventories (1.15 t C ha^?1 yr^?1). Indirect human impact resulted in 33% increase in modeled biomass carbon accumulation in coniferous forests in Thuringia during the last century. From the forest inventory data we estimated the legacy effect of the age‐class distribution to account for 17% of the inventory‐based sink. Isolating the environmental change effects showed that these effects can be large in a long‐term, managed conifer forest.     

Herbarium collections and field data-based plant diversity maps for Burkina Faso

A map of plant species diversity in Burkina Faso is presented based on field observations and specimen data from the Ouagadougou University Herbarium (OUA) and the Herbarium Senckenbergianum (FR). A map of collecting intensity and field observations illustrates centres of botanical research activities in Burkina Faso. To overcome problems associated with biased sampling intensity, distributions of species have been modelled and extrapolated to maps of vascular plant diversity, life forms and diversity of four selected families (Poaceae, Cyperaceae, Dioscoreaceae and Rubiaceae). The area of most intensive collection and observation is around Gorom‐Gorom and Fada N’Gourma. Modelled diversity generally increases towards the south, as does the proportion of phanerophytes, lianas and hemicryptophytes, while the opposite trend is observed for therophytes. Poaceae diversity is highly correlated with total vascular plant diversity, making the family especially suitable as an indicator for overall plant diversity. Cyperaceae are rather evenly distributed throughout the country, Dioscoreaceae are restricted to the Sudanian Zone. Rubiaceae have their highest diversities in the very south. Our approach can be transferred to areas with a similar database, certainly to other areas within West Africa. Future research should focus on distribution data for rare species, enabling our approach to evaluate the West African system of protected areas.     

Regional Estimate of Nitric Oxide Emissions Following Woody Encroachment: Linking Imaging Spectroscopy and Field Studies

Woody encroachment, a spatially explicit process of land-cover change, is known to affect the biophysical and biogeochemical properties of ecosystems. However, little information is available on the impacts of woody encroachment on N oxide emissions from savanna regions. We combined hyperspectral remote sensing and field measurements to quantify spatial patterns and estimate regional fluxes of soil N oxide emissions as they covary with vegetation cover and soil type across a semiarid rangeland in north Texas. Soil nitric oxide (NO) emissions were highly correlated with Prosopis canopy cover, allowing the extrapolation of NO fluxes from hyperspectral observations of woody cover. NO emissions were highly variable, ranging from 0 to 550 kg NO-N km^–2 y^–1 across the region, with the lowest emissions from shallow clay soils and highest from deeper upland clay loams. An estimate of annual NO emissions based on remotely derived Prosopis cover, temperature, and precipitation was 160 kg NO-N km^–2 y^–1, almost twice that of the value derived from traditional averaging of field measurements. We conclude that relationships between NO emissions and remotely sensed structure and composition are advantageous for quantifying NO emissions at the regional scale. This study also provides new insight into the role of woody encroachment on biogeochemical processes that are highly variable and otherwise difficult to measure at the regional scale.     

A new statistical approach for assessing similarity of species composition with incidence and abundance data

The classic Jaccard and Sørensen indices of compositional similarity (and other indices that depend upon the same variables) are notoriously sensitive to sample size, especially for assemblages with numerous rare species. Further, because these indices are based solely on presence–absence data, accurate estimators for them are unattainable. We provide a probabilistic derivation for the classic, incidence‐based forms of these indices and extend this approach to formulate new Jaccard‐type or Sørensen‐type indices based on species abundance data. We then propose estimators for these indices that include the effect of unseen shared species, based on either (replicated) incidence‐ or abundance‐based sample data. In sampling simulations, these new estimators prove to be considerably less biased than classic indices when a substantial proportion of species are missing from samples. Based on species‐rich empirical datasets, we show how incorporating the effect of unseen shared species not only increases accuracy but also can change the interpretation of results.     

Assessment of the Automobile Assembly Paint Process for Energy, Environmental, and Economic Improvement

A coat of paint adds considerable value to an automobile. In addition to consuming up to 60% of the energy needed by automobile assembly plants, however, the painting process also creates both economic and environmental impacts. This study investigated the degree of cost and environmental impact improvement that can be expected when modifications are considered for existing paint processes through heat integration. In order to accomplish this goal, a mathematical model was created to describe the energy use, costs, and environmental impacts from energy consumption in an automobile assembly painting facility. The model agrees with measured energy consumption data for process heating and electricity demand to within about 15% for one Michigan truck facility from which model input parameters were obtained. Thermal pinch analysis determined an energy conservation target of 58% of paint process energy demand. A heat exchanger network optimization study was conducted in order to determine how closely the network design could achieve this target. The resulting heat exchanger network design was profitable based on a discounted cash flow analysis and may achieve reductions in total corporate energy consumption of up to 16% if implemented corporatewide at a major automobile manufacturer.     

Place prioritization for biodiversity conservation using probabilistic surrogate distribution data

We analyse optimal and heuristic place prioritization algorithms for biodiversity conservation area network design which can use probabilistic data on the distribution of surrogates for biodiversity. We show how an Expected Surrogate Set Covering Problem (ESSCP) and a Maximal Expected Surrogate Covering Problem (MESCP) can be linearized for computationally efficient solution. For the ESSCP, we study the performance of two optimization software packages (XPRESS and CPLEX) and five heuristic algorithms based on traditional measures of complementarity and rarity as well as the Shannon and Simpson indices of α‐diversity which are being used in this context for the first time. On small artificial data sets the optimal place prioritization algorithms often produced more economical solutions than the heuristic algorithms, though not always ones guaranteed to be optimal. However, with large data sets, the optimal algorithms often required long computation times and produced no better results than heuristic ones. Thus there is generally little reason to prefer optimal to heuristic algorithms with probabilistic data sets.     

Bayesian inference in ecology

Bayesian inference is an important statistical tool that is increasingly being used by ecologists. In a Bayesian analysis, information available before a study is conducted is summarized in a quantitative model or hypothesis: the prior probability distribution. Bayes’ Theorem uses the prior probability distribution and the likelihood of the data to generate a posterior probability distribution. Posterior probability distributions are an epistemological alternative to P‐values and provide a direct measure of the degree of belief that can be placed on models, hypotheses, or parameter estimates. Moreover, Bayesian information‐theoretic methods provide robust measures of the probability of alternative models, and multiple models can be averaged into a single model that reflects uncertainty in model construction and selection. These methods are demonstrated through a simple worked example. Ecologists are using Bayesian inference in studies that range from predicting single‐species population dynamics to understanding ecosystem processes. Not all ecologists, however, appreciate the philosophical underpinnings of Bayesian inference. In particular, Bayesians and frequentists differ in their definition of probability and in their treatment of model parameters as random variables or estimates of true values. These assumptions must be addressed explicitly before deciding whether or not to use Bayesian methods to analyse ecological data.