Resolving the Conflicts Between Biodiversity Conservation and Socioeconomic Development in China: Fuzzy Clustering Approach

Resolving the conflicts between biodiversity conservation and socioeconomic development is a global pursuit for the long-run prospects of the human species. Based on Wenchuan County, a typical county in southwestern China, a group of 20 indicators quantifying regional biodiversity and socioeconomic development was established to classify and evaluate the county area spatially. A fuzzy c-means clustering (FCM) algorithm was used as the classification method. Three indices including BD, DL and DR characterizing the value of biodiversity, the level and rate of socioeconomic development of the delineated regions were formulated. The results indicated that Wenchuan County was optimally classified into 4 types of regions (region I to IV). The area percentages of the regions vary widely from 4.3 to 65.7%. The sequences of the regions on biodiversity, socioeconomic development level, and socioeconomic development rate were, respectively, IV > II > III > I, I > III > II > IV and III >I >II >IV. The spatial strategy on coordinating biodiversity conservation and regional development is to develop mainly from the east(I, II, III) and to conserve mainly in the west(IV). Eco-industry, such as eco-tourism and eco-agriculture, need to be emphasized in the process of regional development. The quantitative methods used here may have a wide applicability.     

A unifying theory for methods of systematic analysis

A unifying theory for systematic analysis states that a number of methods should be used jointly to cope with various kinds of data; also that groups should be as consistent as possible, be made with least information loss, and where needed, be polythetic. A test of relationship, homogeneity, can use various kinds of data. It can take account of the internal variation of aggregate items such as genera. It can give due emphasis to smaller clusters that have likely important contexts of external items. It helps in analysing trends, cores and hazes in dendrograms. A proposed detector for formal groups can be based on measures of isolation, identifiability and inclusiveness. Non-mathematical, inter-item reaction tests such as hybridization and serology can also be used in grouping. All relationship data are used polythetically to reveal natural groups. This leads to a unified informational concept for taxa. This is more useful than the biological species concept that is restricted to inter-breeding data. All the methods appear to be analogues of the powerful human grouping instinct. The resulting compatibility is important as precise methods are needed mainly when the data are too complex for the mind to use reliably. Cladograms can be made by self-graded deweighting of homogeneity and agglomerative clustering. Unlike classical cladistics this can reveal any polythetic group. Finding the derived states for making cladograms is often much too hypothetical for a fully cladistic approach to be properly precise. Instead, where the evidence is weak, a milder strength of graded deweighting is used for the cladistic properties, which help to show relationships along with the others. Axiomatic failures of other classes of grouping methods are discussed. Unavoidable remnants of instinctive processing lower the precision of all the methods. The Uniter computer program, based on the theory, is tested with finely graded values of artificially ‘evolved’ items and with coarsely coded cladistic data. The results show that with natural data, the program should act as a fairly sensitive probe of past evolutionary branching. Another test shows how specimens from species complexes can be grouped and how distinctions between groups are analysed.     

Multi-criteria clustering in genotype-environment interaction problems

Summary Significant genotype-environment interactions in an ANOVA can be found for a number of reasons: one is the differences in the among-environments variances for each genotype, another is the differences in the ordering of the environments by each genotype. Using conditional clustering, groups may be formed in which the means, variances and patterns are used simultaneously but separately to decide on group homogeneity. Contribution No. I-685 from the Engineering and Statistical Research Institute     

A new dissimilarity measure and a new optimality criterion in phytosociological classification

A new dissimilarity measure, Uppsala dissimilarity, is proposed. It is a Manhattan-type measure in between the Canberra and Gower measures, based on the differences between scores in relevés compared, but it also takes both the sums of scores and the difference between maximum and minimum score into account. The measure is considered realistic for phytosociological material.A new optimality criterion has been developed after unsatisfactory results had been obtained with the DOL criterion (Popma et al. 1983) which was developed previously by our group. Problems with DOL were especially met when the criterion was applied to the distribution of only one species over the cluster array obtained. The new criterion takes both internal cluster homogeneity and between-cluster dissimilarity into account. Between-cluster dissimilarity is calculated for all other clusters and not only for the nearest neighbour, as in DOL. The new criterion has both an unweighted form: SOM, and a form with weighting for cluster size: SWOM.This new criterion was successfully applied to the evaluation of the sharpness of distribution of individual species over cluster arrays, under the name of SIM: species indication measure and SWIM, species weighted indication measure.The measures were applied to some test data. Differences between the unweighted and weighted forms were found which could not be easily interpreted.Some remarks are made on the coherence of d-SAHN and h-SAHN approaches in agglomerative clustering within the new strategy proposed.Abbreviations DOL = Detection of Optimal Level - S(W)IM = Species (Weighted) Indication Measure - S(W)OM = Standardized (Weighted) Optimality Measure - UD = Uppsala Dissimilarity measure - WPGMA = Weighted Pair-Group Method Average linking clustering - SAHN = Sequential Agglomerative Hierarchical Non-overlapping clustering     

Monitoring plant community change: an application of quadrat classification + discriminant analysis

A procedure for monitoring plant community change was described using data from 189 quadrats (each 0.09 m² in area) from or near 11 Carex exserta meadow sites in the high Sierra Nevada, California, USA. Initially the quadrats were agglomerated into five clusters by the flexible clustering strategy (beta=–0.25) with the standard absolute distance resemblance function. Data for each quadrat were cover percentages for C. exserta, other plants, litter, soil, gravel, and rock. The five clusters appeared to define a cover gradient, from quadrats with mostly gravel and rock to those with mostly C. exserta, and were accordingly designated pioneer, low seral, mid-seral, high seral, and climax.Classification functions (from discriminant analysis) are used with values of the variables to classify individual quadrats on sites used to monitor change. A site is characterized at repeated observations by the proportions of quadrats in each class. Within-class (low seral vs. low seral) rather than between-class (pioneer vs. low seral) tests are made for presence of change. Confidence intervals for differences in proportions of quadrats or individual quadrat probabilities of class membership are computed. If the confidence intervals do not cover zero, values for time one versus time two differ significantly.     

Latent group detection in functional partially linear regression models

In this paper, we propose a functional partially linear regression model with latent group structures to accommodate the heterogeneous relationship between a scalar response and functional covariates. The proposed model is motivated by a salinity tolerance study of barley families, whose main objective is to detect salinity tolerant barley plants. Our model is flexible, allowing for heterogeneous functional coefficients while being efficient by pooling information within a group for estimation. We develop an algorithm in the spirit of the K-means clustering to identify latent groups of the subjects under study. We establish the consistency of the proposed estimator, derive the convergence rate and the asymptotic distribution, and develop inference procedures. We show by simulation studies that the proposed method has higher accuracy for recovering latent groups and for estimating the functional coefficients than existing methods. The analysis of the barley data shows that the proposed method can help identify groups of barley families with different salinity tolerant abilities.     

Estimating tree-based dynamic treatment regimes using observational data with restricted treatment sequences

A dynamic treatment regime (DTR) is a sequence of decision rules that provide guidance on how to treat individuals based on their static and time-varying status. Existing observational data are often used to generate hypotheses about effective DTRs. A common challenge with observational data, however, is the need for analysts to consider “restrictions” on the treatment sequences. Such restrictions may be necessary for settings where (1) one or more treatment sequences that were offered to individuals when the data were collected are no longer considered viable in practice, (2) specific treatment sequences are no longer available, or (3) the scientific focus of the analysis concerns a specific type of treatment sequences (eg, “stepped-up” treatments). To address this challenge, we propose a restricted tree–based reinforcement learning (RT-RL) method that searches for an interpretable DTR with the maximum expected outcome, given a (set of) user-specified restriction(s), which specifies treatment options (at each stage) that ought not to be considered as part of the estimated tree-based DTR. In simulations, we evaluate the performance of RT-RL versus the standard approach of ignoring the partial data for individuals not following the (set of) restriction(s). The method is illustrated using an observational data set to estimate a two-stage stepped-up DTR for guiding the level of care placement for adolescents with substance use disorder.     

Pattern-based clustering of daily weigh-in trajectories using dynamic time warping

“Smart”-scales are a new tool for frequent monitoring of weight change as well as weigh-in behavior. These scales give researchers the opportunity to discover patterns in the frequency that individuals weigh themselves over time, and how these patterns are associated with overall weight loss. Our motivating data come from an 18-month behavioral weight loss study of 55 adults classified as overweight or obese who were instructed to weigh themselves daily. Adherence to daily weigh-in routines produces a binary times series for each subject, indicating whether a participant weighed in on a given day. To characterize weigh-in by time-invariant patterns rather than overall adherence, we propose using hierarchical clustering with dynamic time warping (DTW). We perform an extensive simulation study to evaluate the performance of DTW compared to Euclidean and Jaccard distances to recover underlying patterns in adherence time series. In addition, we compare cluster performance using cluster validation indices (CVIs) under the single, average, complete, and Ward linkages and evaluate how internal and external CVIs compare for clustering binary time series. We apply conclusions from the simulation to cluster our real data and summarize observed weigh-in patterns. Our analysis finds that the adherence trajectory pattern is significantly associated with weight loss.     

Toward a hypothesis-free understanding of how phosphorylation dynamically impacts protein turnover

The turnover measurement of proteins and proteoforms has been largely facilitated by workflows coupling metabolic labeling with mass spectrometry (MS), including dynamic stable isotope labeling by amino acids in cell culture (dynamic SILAC) or pulsed SILAC (pSILAC). Very recent studies including ours have integrated themeasurement of post-translational modifications (PTMs) at the proteome level (i.e., phosphoproteomics) with pSILAC experiments in steady state systems, exploring the link between PTMs and turnover at the proteome-scale. An open question in the field is how to exactly interpret these complex datasets in a biological perspective. Here, we present a novel pSILAC phosphoproteomic dataset which was obtained during a dynamic process of cell starvation using data-independent acquisition MS (DIA-MS). To provide an unbiased “hypothesis-free” analysis framework, we developed a strategy to interrogate how phosphorylation dynamically impacts protein turnover across the time series data. With this strategy, we discovered a complex relationship between phosphorylation and protein turnover that was previously underexplored. Our results further revealed a link between phosphorylation stoichiometry with the turnover of phosphorylated peptidoforms. Moreover, our results suggested that phosphoproteomic turnover diversity cannot directly explain the abundance regulation of phosphorylation during cell starvation, underscoring the importance of future studies addressing PTM site-resolved protein turnover.