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.     

Permanent sample plots for natural tropical forests: A rationale with special emphasis on Central Africa

Permanent sample plots (PSP), where trees are individually and permanently marked, have received increased interest in Central Africa as a tool to monitor vegetation changes. Although techniques for mounting PSP in tropical forests are well known, their planning still deserves attention. This study aims at defining a rationale for determining the size and number of replicates for setting up PSP in mixed tropical forests. It considers PSP as a sampling plan to estimate a target quantity with its associated margin of error. The target quantity considered here is the stock recovery rate, which is a key parameter for forest management in Central Africa. It is computed separately for each commercial species. The number of trees to monitor for each species defines the margin of error on the stock recovery rate. The size and number of replicated plots is obtained as the solution of an optimization problem that consists in minimizing the margin of error for every species while ensuring that the mounting cost remains below a given threshold. This rationale was applied using the data from the M’Baïki experimental site in the Central African Republic. It showed that the stock recovery rate is a highly variable quantity, and that the typical cost that forest managers are prone to devote to PSP leads to high margins of error. It also showed that the size and number of replicated plots is related to the spatial pattern of trees: clustered or spatially heterogeneous patterns favor many small plots, whereas regular or spatially homogeneous patterns favor few large plots.     

Existential threat and compliance with pro-environmental norms

Recent research on the effects of personal threat suggests that perceived threat might enhance pro-environmental behavior when pro-environmental norms are in focus. In three experiments we found support for the latter assumption, showing that mortality salience and salience of pro-environmental norms interacted in predicting pro-environmental attitudes and information search (Study 1), sustainable behavior in a forest management game (Study 2), as well as pro-environmental intentions and behavior (Study 3). Specifically, mortality salience increased pro-environmental conduct only when pro-environmental norms were salient. Moreover, norm salience only had an effect on pro-environmental attitudes and behavior when the threat of personal mortality was salient. We discuss the implications of these results for both terror management theory and the promotion of pro-environmental behaviors.     

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.     

Linking intra-seasonal variations in climate and tree-ring δC: A functional modelling approach

Stable carbon isotopic composition (δ¹³C) in tree rings is a widely recognized tool for climate reconstruction, and several works suggest that seasonal information can be extracted from intra-ring δ¹³C variations. In this study, we explored the link between climate and intra-seasonal oak ring δ¹³C using a process-based modelling approach. The ISOCASTANEA model was developed to compute the seasonal dynamics of tree-ring δ¹³C for deciduous species from half-hourly climatic data by accounting for photosynthetic discrimination and carbon translocation and allocation at the tree scale and in tree rings.The model was applied from March 2005 to December 2007 in a 150-year-old deciduous oak forest. Canopy photosynthesis and stomatal conductance were calibrated using H₂O and CO₂ fluxes measured by the eddy flux technique, and simulated δ¹³C values were compared to seasonal patterns of total organic matter δ¹³C measured in tree rings for 2006 and 2007 at the same site. With the inclusion of carbon translocation and with regard to ¹³C enrichment of starch compared to soluble sugars, the model can reasonably simulate the intra-seasonal and inter-annual variability of tree-ring δ¹³C using the same parameter values for 2006 and 2007. The amplitude of the seasonal carbon isotope pattern in tree rings was influenced by both photosynthetic and post-photosynthetic processes (starch enrichment and reserve use). The δ¹³C variations in the early part of the ring, i.e., mainly in the earlywood, were related mostly to carbohydrate metabolism, although diluted information about environmental conditions during the previous year could also be found. The last part of the ring, consisting mainly of latewood, was found to be a good recorder of current-year environmental conditions, in particular relative humidity, at a fine temporal resolution when the growth rate was high. The sensitivity of the δ¹³C in the early part of the ring to carbohydrate metabolism suggests that intra-ring δ¹³C could be used to explore the relationship between tree decline or mortality and carbohydrate deficiency.     

Non-biological inhibition-based sensing (NIBS) demonstrated for the detection of toxic arsenic compounds

This work demonstrates the success of a recently developed technique in chemical amplification, non-biological inhibition-based sensing (NIBS), for the detection of toxic arsenic compounds. Screening for toxic arsenic compounds is especially important due to their prevalence in wastewater and water sources. The detection method presented in this work amplifies the chemical response of toxic arsenic compounds by developing a sensor chemistry where the analyte inhibits, rather than enhances, the rate of a catalytic reaction. This technique mimics the work done with enzyme inhibition; however, using non-biological molecules allows for selective detection without the shelf-life issue associated with biological molecules. Using NIBS we find that we can enhance the sensitivity of the system by two orders of magnitude with no apparent loss in selectivity. This work demonstrates the versatility of NIBS, showing that the technique can be of general use for the detection of toxic compounds.     

Assessment of nitrate pollution in the Grand Morin aquifers (France): combined use of geostatistics and physically based modeling

The objective of this work is to combine several approaches to better understand nitrate fate in the Grand Morin aquifers (2700 km(2)), part of the Seine basin. cawaqs results from the coupling of the hydrogeological model newsam with the hydrodynamic and biogeochemical model of river ProSe. cawaqs is coupled with the agronomic model Stics in order to simulate nitrate migration in basins. First, kriging provides a satisfactory representation of aquifer nitrate contamination from local observations, to set initial conditions for the physically based model. Then associated confidence intervals, derived from data using geostatistics, are used to validate cawaqs results. Results and evaluation obtained from the combination of these approaches are given (period 1977-1988). Then cawaqs is used to simulate nitrate fate for a 20-year period (1977-1996). The mean nitrate concentrations increase in aquifers is 0.09 mgN L(-1)yr(-1), resulting from an average infiltration flux of 3500 kgN.km(-2)yr(-1).