Effect of Classification Procedure on the Performance of Numerically Defined Ecological Regions

Ecological regionalizations define geographic regions exhibiting relative homogeneity in ecological (i.e., environmental and biotic) characteristics. Multivariate clustering methods have been used to define ecological regions based on subjectively chosen environmental variables. We developed and tested three procedures for defining ecological regions based on spatial modeling of a multivariate target pattern that is represented by compositional dissimilarities between locations (e.g., taxonomic dissimilarities). The procedures use a “training dataset” representing the target pattern and models this as a function of environmental variables. The model is then extrapolated to the entire domain of interest. Environmental data for our analysis were drawn from a 400 m grid covering all of Switzerland and consisted of 12 variables describing climate, topography and lithology. Our target patterns comprised land cover composition of each grid cell that was derived from interpretation of aerial photographs. For Regionalization 1 we used conventional cluster analysis of the environmental variables to define 60 hierarchically organized levels comprising from 5 to 300 regions. Regionalization 1 provided a base-case for comparison with the model-based regionalizations. Regionalization 2, 3 and 4 also comprised 60 hierarchically organized levels and were derived by modeling land cover composition for 4000 randomly selected “training” cells. Regionalization 2 was based on cluster analysis of environmental variables that were transformed based on a Generalized Dissimilarity Model (GDM). Regionalization 3 and 4 were defined by clustering the training cells based on their land cover composition followed by predictive modeling of the distribution of the land cover clusters using Classification and Regression Tree (CART) and Random Forest (RF) models. Independent test data (i.e. not used to train the models) were used to test the discrimination of land cover composition at all hierarchical levels of the regionalizations using the classification strength (CS) statistic. CS for all the model-based regionalizations was significantly higher than for Regionalization 1. Regionalization 3 and 4 performed significantly better than Regionalization 2 at finer hierarchical levels (many regions) and Regionalization 4 performed significantly better than Regionalization 3 for coarse levels of detail (few regions). Compositional modeling can significantly increase the performance of numerically defined ecological regionalizations. CART and RF-based models appear to produce stronger regionalizations because discriminating variables are able to change at each hierarchic level.     

Strong Influence of Variable Treatment on the Performance of Numerically Defined Ecological Regions

Numerical clustering has frequently been used to define hierarchically organized ecological regionalizations, but there has been little robust evaluation of their performance (i.e., the degree to which regions discriminate areas with similar ecological character). In this study we investigated the effect of the weighting and treatment of input variables on the performance of regionalizations defined by agglomerative clustering across a range of hierarchical levels. For this purpose, we developed three ecological regionalizations of Switzerland of increasing complexity using agglomerative clustering. Environmental data for our analysis were drawn from a 400 m grid and consisted of estimates of 11 environmental variables for each grid cell describing climate, topography and lithology. Regionalization 1 was defined from the environmental variables which were given equal weights. We used the same variables in Regionalization 2 but weighted and transformed them on the basis of a dissimilarity model that was fitted to land cover composition data derived for a random sample of cells from interpretation of aerial photographs. Regionalization 3 was a further two-stage development of Regionalization 2 where specific classifications, also weighted and transformed using dissimilarity models, were applied to 25 small scale “sub-domains” defined by Regionalization 2. Performance was assessed in terms of the discrimination of land cover composition for an independent set of sites using classification strength (CS), which measured the similarity of land cover composition within classes and the dissimilarity between classes. Regionalization 2 performed significantly better than Regionalization 1, but the largest gains in performance, compared to Regionalization 1, occurred at coarse hierarchical levels (i.e., CS did not increase significantly beyond the 25-region level). Regionalization 3 performed better than Regionalization 2 beyond the 25-region level and CS values continued to increase to the 95-region level. The results show that the performance of regionalizations defined by agglomerative clustering are sensitive to variable weighting and transformation. We conclude that large gains in performance can be achieved by training classifications using dissimilarity models. However, these gains are restricted to a narrow range of hierarchical levels because agglomerative clustering is unable to represent the variation in importance of variables at different spatial scales. We suggest that further advances in the numerical definition of hierarchically organized ecological regionalizations will be possible with techniques developed in the field of statistical modeling of the distribution of community composition.     

University engagement and regional sustainability initiatives: some Danish experiences

With an outset in ‘sustainability’ defined as a continuous process requiring balance between (the emergence of) problems and our ability and capability to solve them using four distinct capitals available, i.e. natural, production, human and intellectual, and social capital, a model of universities as factories is used. The model suggests that the most notable products of a university being graduates, and knowledge in the form of books, journal articles etc., i.e. human and intellectual capital. As a consequence, these are also the most important impacts from a university. Using the case of Aalborg University, Denmark, the linkages (actual, potential and missing) between academic work in research and education and sustainability initiatives are explored. The focus is both on the university's core activities as a provider of research and education and on the university's activities as an organisation, i.e. its own operation and its accounting for this. It is suggested that systematic engagement activities could benefit both the university's ability to manage internal university processes (by learning from its peers) and its ability to produce the right graduates and knowledge. As a consequence, the main societal benefit would be the ability to better cope with emerging problems endangering the sustainable development of our societies. It is concluded that Aalborg University with its long tradition for project-based and problem-oriented learning in fact has every opportunity to be able to lead by example and become the link to sustainability.     

Spermatophore characteristics in bushcrickets vary with parasitism and remating interval

Male bushcrickets provide females with a nuptial gift, a spermatophore, which is transferred to females at mating. The spermatophore consists of a gelatinous mass, the spermatophylax, and the sperm-containing ampulla. Male spermatophore size is positively correlated with insemination rate and female refractory period and therefore with male reproductive success. In this study, we examined spermatophylax weight, ampulla weight and sperm number in males of Poecilimon mariannae parasitized by the acoustically orienting fly Therobia leonidei. We show that in parasitized males, spermatophylax weight decreases with the level of parasitism. In line with the hypothesis that parasitism is a cost to reproduction, we found that spermatophylax weight was reduced at remating. In contrast, the replenishment of the spermatophylax in unparasitized males was complete after 2 days and was increased no further after 3 days. Both sperm number and ampulla weight showed an increase over time since last mating and sperm production was estimated at a constant rate of 500,000 per day in all individuals, regardless of parasitism. The allocation of investment in components of the spermatophore varies greatly with parasitism and remating. Both factors had rather independent effects on spermatophore constitution, revealing functional constraints acting on spermatophore characteristics in bushcrickets, which are important for understanding the selection pressures working on its components. Received: 13 September 1999 / Received in revised form: 4 February 2000 / Accepted: 13 March 2000     

On metapopulation resistance to drift and extinction

The spatial configuration of metapopulations (numbers, sizes, and localization of patches) affects their ability to resist demographic extinction and genetic drift, but sometimes with opposite effects. Small and isolated patches, for instance, contribute marginally to demography but may play a large role in genetics by maintaining a sizeable amount of genetic variance among demes. In source-sink systems, similarly, connectivity may be beneficial in terms of effective size, but detrimental in terms of survival, by lowering the reproductive value of source populations. How to reconcile these opposite effects? Here we propose an analytical framework that integrates fixation time (ability to resist genetic drift) and extinction time (ability to resist demographic extinction) into a single index of resistance, measuring the ability of a metapopulation to maintain its demo-genetic integrity. We then illustrate with numerical examples how conflicting demands may be resolved.     

One-dimensional simulation of solute transfer in saturated–unsaturated porous media using the discontinuous finite elements method

A one-dimensional transport model for simulating water flow and solute transport in homogeneous–heterogeneous, saturated–unsaturated porous media is presented. The model is composed of a combination of accurate numerical algorithms for solving the nonlinear Richard's and advection–dispersion equations (ADE). The mixed form of Richard's equation is solved using a standard finite element method (FEM) with primary variable switching. The transport equation is solved using operator splitting, with the discontinuous finite element method (DFE) for discretization of the advective term. A slope limiting procedure for DFE avoids numerical instabilities but creates very limited numerical dispersion for high Peclet numbers. An implicit finite differences scheme (FD) is used for the dispersive term.The unsaturated flow and transport model (Wamos-T) is applied to a variety of rigorous problems including transient flow, heterogeneous medium and abrupt variations of velocity in magnitude and direction due to time-varying boundary conditions. It produces accurate and mass-conservative solutions for a very large range of grid Peclet numbers. The Wamos-T model is a good and robust alternative for the simulation of mass transport in unsaturated domain.     

Biochar—One way forward for soil carbon in offset mechanisms in Africa?

The Kyoto Protocol's Clean Development Mechanism (CDM) has had relatively little success in Africa due to a number of factors. Increases in agricultural soil carbon have strong benefits for soil health as well as potential for carbon sequestration, but such projects are currently excluded from the CDM and other offset mechanisms. Small-scale biochar systems with net emission reductions may hold a key for Africa to engage with the international offset mechanisms and open the door to soil carbon sequestration projects.     

Evolution and frequency (1970–2007) of combined temperature–precipitation modes in the Spanish mountains and sensitivity of snow cover

Snow cover in Spanish mountains is crucial for ensuring water availability in spring and summer months, for the success of winter tourism or for the maintenance of biodiversity in mountain ecosystems. A changing climate may affect the volume of snow cover even in high mountains, where weather conditions are usually favorable for snow accumulation. In this paper, we aim to investigate the evolution (1970–2007) of combined precipitation–temperature modes in the Spanish mountains, and the sensitivity of the snowpack to their occurrence. The climatic database “Spain02” and snow thickness data for Spanish mountains were used for this purpose. Results showed that the frequency of dry-warm and wet-warm days has increased over time in all mountain ranges, while the frequency of the “cold” modes has decreased. The thickness of the snowpack in the Pyrenees has also decreased and its evolution is negatively correlated with the frequency of dry-warm days, and positively correlated with the frequency of dry-cold and wet-cold days. This work constitutes the first approach that relates the evolution of climatic conditions favorable or unfavorable for snow accumulation and the evolution of the snowpack in Spanish mountains.     

The CPCAT as a novel tool to overcome the shortcomings of NOEC/LOEC statistics in ecotoxicology: a simulation study to evaluate the statistical power

Species reproduction is an important determinant of population dynamics. As such, this is an important parameter in environmental risk assessment. The closure principle computational approach test (CPCAT) was recently proposed as a method to derive a NOEC/LOEC for reproduction count data such as the number of juvenile Daphnia. The Poisson distribution used by CPCAT can be too restrictive as a model of the data-generating process. In practice, the generalized Poisson distribution could be more appropriate, as it allows for inequality of the population mean \(\mu\) and the population variance \(\sigma ^2\). It is of fundamental interest to explore the statistical power of CPCAT and the probability of determining a regulatory relevant effect correctly. Using a simulation, we varied between Poisson distribution (\(\mu =\sigma ^2\)) and generalized Poisson distribution allowing for over-dispersion (\(\mu <\sigma ^2\)) and under-dispersion (\(\mu >\sigma ^2\)). The results indicated that the probability of detecting the LOEC/NOEC correctly was \(\ge 0.8\) provided the effect was at least 20% above or below the mean level of the control group and mean reproduction of the control was at least 50 individuals while over-dispersion was missing. Specifically, under-dispersion increased, whereas over-dispersion reduced the statistical power of the CPCAT. Using the well-known Hampel identifier, we propose a simple and straight forward method to assess whether the data-generating process of real data could be over- or under-dispersed.