Shifts in species and phylogenetic diversity between sapling and tree communities indicate negative density dependence in a lowland rain forest

1. As trees in a given cohort progress through ontogeny, many individuals die. This risk of mortality is unevenly distributed across species because of many processes such as habitat filtering, interspecific competition and negative density dependence. Here, we predict and test the patterns that such ecological processes should inscribe on both species and phylogenetic diversity as plants recruit from saplings to the canopy. 2. We compared species and phylogenetic diversity of sapling and tree communities at two sites in French Guiana. We surveyed 2084 adult trees in four 1-ha tree plots and 943 saplings in sixteen 16-m2 subplots nested within the tree plots. Species diversity was measured using Fisher's alpha (species richness) and Simpson's index (species evenness). Phylogenetic diversity was measured using Faith's phylogenetic diversity (phylogenetic richness) and Rao's quadratic entropy index (phylogenetic evenness). The phylogenetic diversity indices were inferred using four phylogenetic hypotheses: two based on rbcLa plastid DNA sequences obtained from the inventoried individuals with different branch lengths, a global phylogeny available from the Angiosperm Phylogeny Group, and a combination of both. 3. Taxonomic identification of the saplings was performed by combining morphological and DNA barcoding techniques using three plant DNA barcodes (psbA-trnH, rpoC1 and rbcLa). DNA barcoding enabled us to increase species assignment and to assign unidentified saplings to molecular operational taxonomic units. 4. Species richness was similar between saplings and trees, but in about half of our comparisons, species evenness was higher in trees than in saplings. This suggests that negative density dependence plays an important role during the sapling-to-tree transition. 5. Phylogenetic richness increased between saplings and trees in about half of the comparisons. Phylogenetic evenness increased significantly between saplings and trees in a few cases (4 out of 16) and only with the most resolved phylogeny. These results suggest that negative density dependence operates largely independently of the phylogenetic structure of communities. 6. Synthesis. By contrasting species richness and evenness across size classes, we suggest that negative density dependence drives shifts in composition during the sapling-to-tree transition. In addition, we found little evidence for a change in phylogenetic diversity across age classes, suggesting that the observed patterns are not phylogenetically constrained.

Thermal energetics of the New-Guinean moss-forest rat (Rattus niobe) in comparison with other tropical murid rodents.

The thermal energetics of rodents from cool, wet tropical highlands are poorly known. Metabolic rate, body temperature and thermal conductance were measured in the moss-forest rat, Rattus niobe (Rodentia), a small murid endemic to the highlands of New Guinea. These data were evaluated in the context of the variation observed in the genus Rattus and among tropical murids. In 7 adult R. niobe, basal metabolic rate (BMR) averaged 53.6±6.6mLO2h(-1), or 103% of the value predicted for a body mass of 42.3±5.8g. Compared to other species of Rattus, R. niobe combines a low body temperature (35.5±0.6°C) and a moderately low minimal wet thermal conductance cmin (5.88±0.7mLO2h(-1)°C(-1), 95% of predicted) with a small size, all of which lead to reduced energy expenditure in a constantly cool environment. The correlations of mean annual rainfall and temperature, altitude and body mass with BMR, body temperature and cmin were analyzed comparatively among tropical Muridae. Neither BMR, nor cmin or body temperature correlated with ambient temperature or altitude. Some of the factors which promote high BMR in higher latitude habitats, such as seasonal exposure to very low temperature and short reproductive season, are lacking in wet montane tropical forests. BMR increased with rainfall, confirming a pattern observed among other assemblages of mammals. This correlation was due to the low BMR of several desert adapted murids, while R. niobe and other species from wet habitats had a moderate BMR.

Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI.

BACKGROUND AND PURPOSE: MCI was recently subdivided into sd-aMCI, sd-fMCI, and md-aMCI. The current investigation aimed to discriminate between MCI subtypes by using DTI. MATERIALS AND METHODS: Sixty-six prospective participants were included: 18 with sd-aMCI, 13 with sd-fMCI, and 35 with md-aMCI. Statistics included group comparisons using TBSS and individual classification using SVMs. RESULTS: The group-level analysis revealed a decrease in FA in md-aMCI versus sd-aMCI in an extensive bilateral, right-dominant network, and a more pronounced reduction of FA in md-aMCI compared with sd-fMCI in right inferior fronto-occipital fasciculus and inferior longitudinal fasciculus. The comparison between sd-fMCI and sd-aMCI, as well as the analysis of the other diffusion parameters, yielded no significant group differences. The individual-level SVM analysis provided discrimination between the MCI subtypes with accuracies around 97%. The major limitation is the relatively small number of cases of MCI. CONCLUSIONS: Our data show that, at the group level, the md-aMCI subgroup has the most pronounced damage in white matter integrity. Individually, SVM analysis of white matter FA provided highly accurate classification of MCI subtypes.

Improving generalized regression analysis for the spatial prediction of forest communities

Aim This study used data from temperate forest communities to assess: (1) five different stepwise selection methods with generalized additive models, (2) the effect of weighting absences to ensure a prevalence of 0.5, (3) the effect of limiting absences beyond the environmental envelope defined by presences, (4) four different methods for incorporating spatial autocorrelation, and (5) the effect of integrating an interaction factor defined by a regression tree on the residuals of an initial environmental model. Location State of Vaud, western Switzerland. Methods Generalized additive models (GAMs) were fitted using the grasp package (generalized regression analysis and spatial predictions, http://www.cscf.ch/grasp). Results Model selection based on cross-validation appeared to be the best compromise between model stability and performance (parsimony) among the five methods tested. Weighting absences returned models that perform better than models fitted with the original sample prevalence. This appeared to be mainly due to the impact of very low prevalence values on evaluation statistics. Removing zeroes beyond the range of presences on main environmental gradients changed the set of selected predictors, and potentially their response curve shape. Moreover, removing zeroes slightly improved model performance and stability when compared with the baseline model on the same data set. Incorporating a spatial trend predictor improved model performance and stability significantly. Even better models were obtained when including local spatial autocorrelation. A novel approach to include interactions proved to be an efficient way to account for interactions between all predictors at once. Main conclusions Models and spatial predictions of 18 forest communities were significantly improved by using either: (1) cross-validation as a model selection method, (2) weighted absences, (3) limited absences, (4) predictors accounting for spatial autocorrelation, or (5) a factor variable accounting for interactions between all predictors. The final choice of model strategy should depend on the nature of the available data and the specific study aims. Statistical evaluation is useful in searching for the best modelling practice. However, one should not neglect to consider the shapes and interpretability of response curves, as well as the resulting spatial predictions in the final assessment.

State power and protected areas: Dynamics and contradictions of forest conservation in Madhya Pradesh, India

The traditionally coercive and state-controlled governance of protected areas for nature conservation in developing countries has in many cases undergone change in the context of widespread decentralization and liberalization. This article examines an emerging "mixed" (coercive, community- and market-oriented) conservation approach in managed-resource protected areas and its effects on state power through a case study on forest protection in the central Indian state of Madhya Pradesh. The findings suggest that imperfect decentralization and partial liberalization resulted in changed forms, rather than uniform loss, of state power. A forest co-management program paradoxically strengthened local capacity and influence of the Forest Department, which generally maintained its territorial and knowledge-based control over forests and timber management. Furthermore, deregulation and reregulation enabled the state to withdraw from uneconomic activities but also implied reduced place-based control of non-timber forest products. Generally, the new policies and programs contributed to the separation of livelihoods and forests in Madhya Pradesh. The article concludes that regulatory, community- and market-based initiatives would need to be better coordinated to lead to more effective nature conservation and positive livelihood outcomes.

Old ferricrete landscape dismantling in Central Africa rain forest zone: formation of the present downslope iron accumulations.

Present downslope iron accumulations were investigated in the rainforest zone in southern Cameroon. Six clay and Fe-hydroxide dominated patterns have been identified and occur on the lower part of hill slopes. They can be subdivided in three different sequences, related to gentle, moderate or steep slopes. They are discontinuous with respect to the dismantling zone of the old ferricrete cap formed at Cretaceous period. They show a gradual development from a soft Fe-crust (carapace) to a vesicular facies that will, with time, cover the whole landscape again.

Do PES Improve the Governance of Forest Restoration?

Payments for Environmental Services (PES) are praised as innovative policy instruments and they influence the governance of forest restoration efforts in two major ways. The first is the establishment of multi-stakeholder agencies as intermediary bodies between funders and planters to manage the funds and to distribute incentives to planters. The second implication is that specific contracts assign objectives to land users in the form of conditions for payments that are believed to increase the chances for sustained impacts on the ground. These implications are important in the assessment of the potential of PES to operate as new and effective funding schemes for forest restoration. They are analyzed by looking at two prominent payments for watershed service programs in Indonesia-Cidanau (Banten province in Java) and West Lombok (Eastern Indonesia)-with combined economic and political science approaches. We derive lessons for the governance of funding efforts (e.g., multi-stakeholder agencies are not a guarantee of success; mixed results are obtained from a reliance on mandatory funding with ad hoc regulations, as opposed to voluntary contributions by the service beneficiary) and for the governance of financial expenditure (e.g., absolute need for evaluation procedures for the internal governance of farmer groups). Furthermore, we observe that these governance features provide no guarantee that restoration plots with the highest relevance for ecosystem services are targeted by the PES

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Example-based support vector machine for drug concentration analysis

Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction results compared with pharmacokinetic model. The two proposed example-based SVM methods demonstrate that the individual features help to increase the accuracy in the predictions of drug concentration with a reduced library of training data.