“Decision-making” in conflicts of wild chimpanzees (<Emphasis Type="Italic">Pan troglodytes</Emphasis>): an extension of the Relational Model

>We examined the "decision-making" process of aggressive interactions within a community of wild chimpanzees (Pan troglodytes verus) in the Taï National Park, Côte d'Ivoire (West Africa). Costs and benefits were investigated for 876 dyadic aggressive interactions among 18 adults (including 4 independent adolescents) of either sex. An extended version of the Relational Model was developed to describe the dynamics of the "decision-making" process in Taï chimpanzees, which suggests that the net benefit determines the occurrence of conflicts. Both sexes fought more frequently for the resources that were most important to them, food for females and social contexts for males. Individuals used two different strategies according to their likelihood of winning the aggressive interaction, determined by the dominance relationship of the conflict partners. Dominant initiators had longer and more intense aggressive interactions, but they limited their social disadvantages by fighting non-cooperative partners. Subordinate initiators had shorter and less intense aggressive interactions, but risked more social costs, which they could reduce afterwards by reconciliation. Both strategies included a positive overall net benefit. The extended Relational Model fits the complexity of wild chimpanzee conflicts and allows for more flexibility in the "decision-making" compared to the original version.Communicated by E. Sterck     

Bayesian entropy for spatial sampling design of environmental data

We develop a spatial statistical methodology to design national air pollution monitoring networks with good predictive capabilities while minimizing the cost of monitoring. The underlying complexity of atmospheric processes and the urgent need to give credible assessments of environmental risk create problems requiring new statistical methodologies to meet these challenges. In this work, we present a new method of ranking various subnetworks taking both the environmental cost and the statistical information into account. A Bayesian algorithm is introduced to obtain an optimal subnetwork using an entropy framework. The final network and accuracy of the spatial predictions is heavily dependent on the underlying model of spatial correlation. Usually the simplifying assumption of stationarity, in the sense that the spatial dependency structure does not change location, is made for spatial prediction. However, it is not uncommon to find spatial data that show strong signs of nonstationary behavior. We build upon an existing approach that creates a nonstationary covariance by a mixture of a family of stationary processes, and we propose a Bayesian method of estimating the associated parameters using the technique of Reversible Jump Markov Chain Monte Carlo. We apply these methods for spatial prediction and network design to ambient ozone data from a monitoring network in the eastern US.     

A fuzzy expert system for soil characterization

As soil is a natural resource not always renewable, the risk characterization of contaminated soils is an issue of great interest. Artificial Intelligence (AI), based on Decision Support Systems (DSSs), has been developed for a wide range of applications in contaminated soil management. Decision trees have already shown to be easy to interpret and able to treat large scale applications. Fuzzy logic gives an improvement in the perturbations and the variance of the training data, due to the elasticity of fuzzy set formalism. In this study, we have developed a classificatory tool applied to characterize contaminated soil in function of human and environmental risks. Knowledge engineering for constructing the Soil Risk Characterization Decision Support System (SRC-DSS) involves three stages: knowledge acquisition, conceptual design and system implementation. A total of 26 parameters were divided into three groups to facilitate the configuration of the expert system: source attributes, transfer vector attributes, and local properties. Sixteen case studies were evaluated with the SRC-DSS. In comparison with other techniques, the results of the current study have shown that SRC-DDS is an excellent tool to classify and characterize soils according to the associated risk.     

Improving land use inference by factorizing mobile phone call activity matrix

Land use is shaped by human activities. Traditional methods of measuring land uses (e.g. surveys and remote sensing techniques) often have difficulties in capturing human activities. The near-ubiquitous coverage of mobile phones opens up a new way to investigate land use through human activities. We propose to analyze land use by characterizing human activity patterns based on the aggregated call volume, and apply non-negative matrix factorization to identify fundamental behavioral classes. Using tower-based call data from Dakar, Senegal, we discover two fundamental land use patterns: commercial/business/industrial (C/B/I) and residential. Then, the land use of the reception area of each cell tower can be inferred based on the weights obtained for each basis vector. To evaluate the proposed approach, the results are compared with two points-of-interest (POI) data sets obtained from OpenStreetMap and Facebook’s Graph API. We have found that a majority of POIs like embassies, offices, and hotels are located in the predicted C/B/I areas; specifically, there is a strong positive correlation between estimated land use weights and the number of related POIs. Furthermore, we have shown analyzing 24-h call pattern matrix can track daily land use changes.     

Statistical matching for conservation science

The awareness of the need for robust impact evaluations in conservation is growing and statistical matching techniques are increasingly being used to assess the impacts of conservation interventions. Used appropriately matching approaches are powerful tools, but they also pose potential pitfalls. We outlined important considerations and best practice when using matching in conservation science. We identified 3 steps in a matching analysis. First, develop a clear theory of change to inform selection of treatment and controls and that accounts for real-world complexities and potential spillover effects. Second, select the appropriate covariates and matching approach. Third, assess the quality of the matching by carrying out a series of checks. The second and third steps can be repeated and should be finalized before outcomes are explored. Future conservation impact evaluations could be improved by increased planning of evaluations alongside the intervention, better integration of qualitative methods, considering spillover effects at larger spatial scales, and more publication of preanalysis plans. Implementing these improvements will require more serious engagement of conservation scientists, practitioners, and funders to mainstream robust impact evaluations into conservation. We hope this article will improve the quality of evaluations and help direct future research to continue to improve the approaches on offer.     

Computer Inference Of Network Of Ecological Interactions From Sampling Data

Both direct and indirect ecological interactions may occur in an ecosystem with large numbers of taxa. Traditional food web technique is a popular tool to measure the quality and health of the environment. Much of works must be done before constructing a food web for an ecosystem especially with many taxa. This food web is generally specific for some ecological interactions and fixed for a set of given species. It is therefore not an effective method for dynamic and prompt assessment of environment. Ecological interactions and their interactive intensity may be detected by sampling biological taxa in the field and by detecting various between-taxa distances or similarities. Network may clearly exhibit the complex interactions among biological taxa. Statistic tests on various distance or similarity measures and computer designs are required to infer the {network. We develop an algorithm and software to infer the network of direct or indirect ecological interactions in ecosystem. It is a prompt and effective tool in monitoring and assessment of the environment. A redundant network may be inferred and drawn by computer based on the statistic tests on sampling data or the pathway information given in HTML file. Dominant taxa may be found in the network. In total of 16 distance and similarity measures, including Euclidean distance, Manhattan distance, Pearson correlation, partial correlation, point correlation, linkage coefficients, Jaccard coefficient etc., are provided to detect taxa pairs with significant parametric or nonparametric similarities, based on randomization tests and ordinary statistic tests. Criteria to use distance and similarity measures are discussed.     

A process-convolution approach to modelling temperatures in the North Atlantic Ocean

This paper develops a process-convolution approach for space-time modelling. With this approach, a dependent process is constructed by convolving a simple, perhaps independent, process. Since the convolution kernel may evolve over space and time, this approach lends itself to specifying models with non-stationary dependence structure. The model is motivated by an application from oceanography: estimation of the mean temperature field in the North Atlantic Ocean as a function of spatial location and time. The large amount of this data poses some difficulties; hence computational considerations weigh heavily in some modelling aspects. A Bayesian approach is taken here which relies on Markov chain Monte Carlo for exploring the posterior distribution.     

A guide to qualitative attribution methods for evaluation in conservation

Knowledge of what conservation interventions improve biodiversity outcomes, and in which circumstances, is imperative. Experimental and quasi-experimental methods are increasingly used to establish causal inference and build the evidence base on the effectiveness of interventions, but their ability to provide insight into how and under what conditions an intervention should be implemented to improve biodiversity outcomes faces limitations. A suite of attribution methods that leverage qualitative methods for causal inference is available but underutilized in conversation impact evaluation. This article provides a guide to 5 such qualitative attribution methods: contribution analysis, process tracing, realist evaluation, qualitative comparative analysis, and most significant change. It defines and introduces each method and then illustrates how they could be applied through a case study of community conservancies in Namibia. This guide provides examples of how qualitative attribution methods can advance knowledge of what works, in which contexts, and why in biodiversity conservation.     

基于贝叶斯网络的煤矿事故人因推理

煤矿事故的不可重现性决定了事故原因的调查具有很强的不确定性,如何通过事故发生后的相关信息提高事故深层次原因调查的准确性是非常重要的。将HFACS与贝叶斯网络相结合,以煤矿事故HFACS分析结果为样本,通过卡方检验和让步比分析建立人因的贝叶斯网络因果图,进一步利用最大似然估计算法确定了煤矿事故人因的贝叶斯网络参数。最后,以双柳煤业顶板事故的调查信息为证据推理导致煤矿事故发生的深层次原因,提高事故原因调查的准确性,从而验证模型的有效性。