Using fuzzy logic to design fermentation media: A comparison to neural networks and factorial design

Summary In analysis of oil accumulation by the yeast Rhodotorula gracilis, fuzzy logic and neural networks were shown to be able to significantly reduce the number of experiments required in designing fermentation media. Fuzzy logic performed similarly, although slightly less accurately, than neural networks in predicting the outcome of shake flask experiments. In some instances having too many fuzzy rules decreased the accuracy of the technique, and further work is required to determine the criteria for achieving optimum performance from the fuzzy logic model. Overall fuzzy logic is a viable and useful tool for designing fermentation media.     

Using topographic networks to build a representation of consciousness

The subject of consciousness has intrigued both psychologists and neuroscientists for many years. Recently, following many recent advances in the emerging field of cognitive neuroscience, there is the possibility that this fundamental process may soon be explained. In particular, there have been dramatic insights gained into the mechanisms of attention, cognition and perception in recent decades. Here, simple network models are proposed which are used to create a representation of consciousness. The models are inspired by the structure of the thalamus and all incorporate topographic layers in their structure. Operation of the models allows filtering of the information reaching the representation according to (1) modality and/or (2) sub-modality, in addition several of the models allowing filtering at the topographic level. The models presented have different structures and employ different integrative mechanisms to produce gating or amplification at different levels; the resultant representations of consciousness are discussed.     

Application of fuzzy logic to the Apgar scoring system

Apgar fuzzy expert system (AFES) applied fuzzy logic to the Apgar Scoring System (APG). We prepared the AFESs for both an inexperienced obstetrician (resident A) and an experience obstetrician (specialist B) separately. We then compared their evaluations of the same 80 neonates at 1 min after birth using both the AFES and the APG. Analysis of the relationship of the general evaluation by the APG (scores below 6 or over 7) and the AFES (bad or good) to the umbilical arterial blood gases (pH below or above 7.20) showed that the sensitivities were 0.36 for the APG evaluation by resident A, 0.50 for the APG evaluation by specialist B, 0.60 for the AFES rating by resident A, and 0.71 for the AFES rating by specialist B. The specificities were 0.94, 0.95, 0.97 and 0.97, respectively. These results demonstrated that the AFES was capable of reflecting the recognition of the examiner.     

An ecological example of the application of projection pursuit to compositional data

A modern statistical technique for the exploratory analysis of multivariate data, called projection pursuit, is described. The method consists of the extraction and display of informative one- or two-dimensional projections of the multivariate dataset. Projection pursuit has much scope for application to ecological datasets. The example in this paper concerns element concentrations in soil samples from a coastal embayment on the east coast of Australia. The application attempts to search for patterns in the soils which could be related to vegetation patterns. The compositional nature of the dataset adds a novel element to the methodology.     

Surreptitious sympatry: Exploring the ecological and genetic separation of two sibling species

Climate change is having profound impacts on animal populations, and shifts in geographic range are predicted in response. Shifts that result in range overlap between previously allopatric congeneric species may have consequences for biodiversity through interspecific competition, hybridization, and genetic introgression. Harbor seals (Phoca vitulina) and spotted seals (Phoca largha) are parapatric sibling species and areas of co‐occurrence at the edges of their range, such as Bristol Bay, Alaska, offer a unique opportunity to explore ecological separation and discuss potential consequences of increased range overlap resulting from retreating sea ice. Using telemetry and genetic data from 14 harbor seals and six spotted seals, we explored the ecological and genetic separation of the two species by comparing their utilization distributions, distance from haul‐out, dive behavior (e.g., depth, duration, focus), and evidence of hybridization. Firstly, we show that harbor and spotted seals, which cannot be visually distinguished definitively in all cases, haul‐out together side by side in Bristol Bay from late summer to early winter. Secondly, we observed subtle rather than pronounced differences in ranging patterns and dive behavior during this period. Thirdly, most spotted seals in this study remained close to shore in contrast to what is known of the species in more northern areas, and lastly, we did not find any evidence of hybridization. The lack of distinct ecological separation in this area of sympatry suggests that interspecific competition could play an important role in the persistence of these species, particularly if range overlap will increase as a result of climate‐induced range shifts and loss of spotted seal pagophilic breeding habitat. Our results also highlight the added complexities in monitoring these species in areas of suspected overlap, as they cannot easily be distinguished without genetic analysis. Predicted climate‐induced environmental change will likely influence the spatial and temporal extent of overlap in these two sibling species. Ultimately, this may alter the balance between current isolating mechanisms with consequences for species integrity and fitness.     

Segmentation of protein surfaces using fuzzy logic

An algorithm has been developed that can be used to divide triangulated molecular surfaces into distinct domains on the basis of physical and topographical molecular properties. Domains are defined by a certain degree of homogeneity concerning one of these properties. The method is based on fuzzy logic strategies, thus taking into consideration the smooth changes of the properties considered along complex macromolecular surfaces. Scalar qualities assigned to every node point on a triangulated surface are translated into linguistic variables, which can then be processed using a special fuzzy dissimilarity operator.Possible applications are demonstrated using surface segmentation for properties like electrostatic potential, lipophilicity and shape for the analysis of serine proteinase substrate/inhibitor specificity.     

A fuzzy logic model of fracture healing

A quantitative biomechanical model describes the tissue transformation during healing of a transverse osteotomy of a sheep metatarsal. The model predicts bridging of the bone ends through cartilage, followed by the growth of a callus cuff, and finally, the resorption of callus after ossification of the interfragmentary gap. We suggest bone density or the modulus of elasticity do not sufficiently characterize healing tissue for predictive purposes. In addition to the stimulus reflected by strain energy density we introduce a new osteogenic factor based upon stress gradients and which predicts areas of a high osteogenic capacity. Our model distinguishes three basic types of tissue, namely bone, cartilage and fibrous tissue. A fuzzy controller is proposed to model the tissue reaction. A set of fuzzy rules derived from medical knowledge has been implemented to describe tissue transformation such as intramembraneous or chondral ossification, atrophy or destruction. Fuzzy logic is able to model tissue transformation processes within the numerical simulation of remodeling processes. This approach improves the simulation tools and affords the potential to optimize planning of animal experiments and conduct parametric studies.     

Evaluating the ecological status of rivers using an index of ecological distance: An application to diatom communities

Community structure changes with pollution or stress. In the Water Framework Directive, high ecological status through biological parameters is defined as a slight or minor deviation from the reference community, while the good status is defined as a small deviation.To assess the importance of this deviation, and then to measure the degradation of ecological status along a river, an index based on the concept of “ecological distance” between species was created and called ecological distance index (EDI). It was tested on diatom data from a pilot watershed (the Garonne river basin, South-West France).The results show a good correlation between the EDI and the IPS (Indice de Pollusensibilité Spécifique – specific pollusensitivity index – a diatom-based biotic index) ecological ratios, which means first that the EDI is a valuable indicator of ecological status, and that it can account for ecoregional specificities. This index can be applied to any communities (macro-invertebrates, fish, etc.), since: (i) typical reference communities are found for each river type; (ii) species are characterised then ranked by pollution sensitivity values.     

Using fuzzy logic to determine the vulnerability of marine species to climate change

Marine species are being impacted by climate change and ocean acidification, although their level of vulnerability varies due to differences in species' sensitivity, adaptive capacity and exposure to climate hazards. Due to limited data on the biological and ecological attributes of many marine species, as well as inherent uncertainties in the assessment process, climate change vulnerability assessments in the marine environment frequently focus on a limited number of taxa or geographic ranges. As climate change is already impacting marine biodiversity and fisheries, there is an urgent need to expand vulnerability assessment to cover a large number of species and areas. Here, we develop a modelling approach to synthesize data on species‐specific estimates of exposure, and ecological and biological traits to undertake an assessment of vulnerability (sensitivity and adaptive capacity) and risk of impacts (combining exposure to hazards and vulnerability) of climate change (including ocean acidification) for global marine fishes and invertebrates. We use a fuzzy logic approach to accommodate the variability in data availability and uncertainties associated with inferring vulnerability levels from climate projections and species' traits. Applying the approach to estimate the relative vulnerability and risk of impacts of climate change in 1074 exploited marine species globally, we estimated their index of vulnerability and risk of impacts to be on average 52 ± 19 SD and 66 ± 11 SD, scaling from 1 to 100, with 100 being the most vulnerable and highest risk, respectively, under the ‘business‐as‐usual' greenhouse gas emission scenario (Representative Concentration Pathway 8.5). We identified 157 species to be highly vulnerable while 294 species are identified as being at high risk of impacts. Species that are most vulnerable tend to be large‐bodied endemic species. This study suggests that the fuzzy logic framework can help estimate climate vulnerabilities and risks of exploited marine species using publicly and readily available information.     

Approaches to integrating genetic data into ecological networks

As molecular tools for assessing trophic interactions become common, research is increasingly focused on the construction of interaction networks. Here, we demonstrate three key methods for incorporating DNA data into network ecology and discuss analytical considerations using a model consisting of plants, insects, bats and their parasites from the Costa Rica dry forest. The simplest method involves the use of Sanger sequencing to acquire long sequences to validate or refine field identifications, for example of bats and their parasites, where one specimen yields one sequence and one identification. This method can be fully quantified and resolved and these data resemble traditional ecological networks. For more complex taxonomic identifications, we target multiple DNA loci, for example from a seed or fruit pulp sample in faeces. These networks are also well resolved but gene targets vary in resolution and quantification is difficult. Finally, for mixed templates such as faecal contents of insectivorous bats, we use DNA metabarcoding targeting two sequence lengths (157 and 407 bp) of one gene region and a MOTU, BLAST and BIN association approach to resolve nodes. This network type is complex to generate and analyse, and we discuss the implications of this type of resolution on network analysis. Using these data, we construct the first molecular‐based network of networks containing 3,304 interactions between 762 nodes of eight trophic functions and involving parasitic, mutualistic and predatory interactions. We provide a comparison of the relative strengths and weaknesses of these data types in network ecology.     

Fuzzy clustering of fuzzy ecological data

Besides the problem of searching for effective methods for data analysis there are some additional problems with handling data of high uncertainty. Uncertainty problems often arise in an analysis of ecological data, e.g. in the cluster analysis of ecological data. Conventional clustering methods based on Boolean logic ignore the continuous nature of ecological variables and the uncertainty of ecological data. That can result in misclassification or misinterpretation of the data structure. Clusters with fuzzy boundaries reflect better the continuous character of ecological features. But the problem is, that the common clustering methods (like the fuzzy c-means method) are only designed for treating crisp data, that means they provide a fuzzy partition only for crisp data (e.g. exact measurement data). This paper presents the extension and implementation of the method of fuzzy clustering of fuzzy data proposed by Yang and Liu [Yang, M.-S. and Liu, H-H, 1999. Fuzzy clustering procedures for conical fuzzy vector data. Fuzzy Sets and Systems, 106, 189-200.]. The imprecise data can be defined as multidimensional fuzzy sets with not sharply formed boundaries (in the form of the so-called conical fuzzy vectors). They can then be used for the fuzzy clustering together with crisp data. That can be particularly useful when information is not available about the variances which describe the accuracy of the data and probabilistic approaches are impossible. The method proposed by Yang has been extended and implemented for the Fuzzy Clustering System EcoFucs developed at the University of Kiel. As an example, the paper presents the fuzzy cluster analysis of chemicals according to their ecotoxicological properties. The uncertainty and imprecision of ecotoxicological data are very high because of the use of various data sources, various investigation tests and the difficulty of comparing these data. The implemented method can be very helpful in searching for an adequate partition of ecological data into clusters with similar properties.     

Control model of human stance using fuzzy logic

 A control model of human stance is proposed based on knowledge from behavioral experiments and physiological systems. The proposed model is based on the control of global variables specific to body orientation and alignment, rather than on the control of the body’s center of mass within the base of support. Furthermore, the proposed control model is not based on purely inverted pendulum body mechanics where only motion at one joint is controlled, as for instance the ankle. In the proposed model, the degrees of freedom are controlled by using reciprocal and synergistic muscle actions at multiple joints. The control model is based on three sets of different global variables which act in parallel: (1) limb length and its derivative, (2) limb orientation and its derivative, and (3) trunk attitude and its derivative. An important feature of the control model is the use of fuzzy logic, which enables us to model experimental findings and physiological knowledge in a meaningful and explicit way using fuzzy if-then rules. In the control model, 36 fuzzy if-then rules are implemented and applied using a four-linked segment model consisting of a trunk, thigh, shank and foot. Uni- and biarticular limb muscles and trunk muscles are represented as torque actuators at each individual joint. In the model, three sets of global variables act in parallel and make corrective and coordinated responses to internal, self-induced perturbations. The data show that the use of global variables and fuzzy logic successfully enables us to model human standing with sway about a point of equilibrium. Small changes in, for example, total body sway are comparable to those seen during natural sway in human stance. The selected controllers—limb length, limb orientation and trunk attitude—seem to be appropriate for human stance control. Received: 30 October 1996/Accepted in revised form: 7 April 1997     

The dimensionality of ecological networks

How many dimensions (trait‐axes) are required to predict whether two species interact? This unanswered question originated with the idea of ecological niches, and yet bears relevance today for understanding what determines network structure. Here, we analyse a set of 200 ecological networks, including food webs, antagonistic and mutualistic networks, and find that the number of dimensions needed to completely explain all interactions is small ( < 10), with model selection favouring less than five. Using 18 high‐quality webs including several species traits, we identify which traits contribute the most to explaining network structure. We show that accounting for a few traits dramatically improves our understanding of the structure of ecological networks. Matching traits for resources and consumers, for example, fruit size and bill gape, are the most successful combinations. These results link ecologically important species attributes to large‐scale community structure.     

An invitation to ecological economics

The emerging interdisciplinary field of ecological economics should be a recognized research priority. Only through a combination of sound ecology and good economics can we hope to manage our exploitation of the biosphere in a manner that is both sustainable and efficient. This article is an invitation to ecologists to use economic tools and to participate in ecological economic debate. To this end, we review basic ecological economic concepts and discuss how the field has arisen, what benefits it offers, and what challenges it must overcome.     

The analysis of convergence in ecological indicators: An application to the Mediterranean fisheries

Ecological indicators are increasingly used to examine the evolution of natural ecosystems and the impacts of human activities. Assessing their trends to develop comparative analyses is essential. We introduce the analysis of convergence, a novel approach to evaluate the dynamic and trends of ecological indicators and predict their behavior in the long-term. Specifically, we use a non-parametric estimation of Gaussian kernel density functions and transition probability matrix integrated in the R software. We validate the performance of our methodology through a practical application to three different ecological indicators to study whether Mediterranean countries converge towards similar fisheries practices. We focus on how distributions evolve over time for the Marine Trophic Index, the Fishing in Balance Index and the Expansion Factor during 1950–2010. Results show that Mediterranean countries persist in their fishery behaviors throughout the time series, although a tendency towards similar negative effects on the ecosystem is apparent in the long-term. This methodology can be easily reproduced with different indicators and/or ecosystems in order to analyze ecosystem dynamics.     

基于景观格局和连接度评价的生态网络方法优化与应用

景观生态学中常凭借最小累积阻力模型构建目标种生态网络,以提升破碎栖息地间的景观连接度,缓解生境破碎化负面影响.但传统最小累积阻力生态网络方法缺乏对生态网络的效用验证,对研究地的景观结构变化与生态过程的影响认识不足.本研究运用景观格局指数与连接度概率指数,定量评价生态网络构建前后的研究地景观结构与连接度特征,并以崇左白头叶猴栖息地生态网络为例,详尽叙述此生态网络方法的优化与应用过程.通过对白头叶猴栖息地斑块进行辨认、踏脚石斑块识别,对研究地用地类型进行划分并进行阻力赋值,运用最小累积阻力模型生成了20条白头叶猴栖息地生态网络廊道;然后利用景观结构指数与连接度概率指数结合的方法,对生成的生态网络结构和功能连接度进行评价.结果表明: 凭借最小累积阻力模型构建的目标种生态网络,能有效提升栖息地生境的完整性和连续性,降低总体破碎化水平,并改善生境质量.同时,该生态网络构建能提升生境景观的结构连接度与功能连接度,且两方面的连接度变化在结果上具有极显著的一致性(R²=98.3%,P<0.01).生态网络带来的景观结构方面变化与功能连接度的关联性不强,两种指数间的相互关系不如结构与功能的内在关系显著.