Morphological selectivity of the Permian-Triassic ammonoid mass extinction

Ammonoids suffered a diversity bottleneck during the Permian-Triassic mass extinction (PTME) and experienced a rapid diversification in the Early Triassic. However, the kinds of ammonoids that were more likely to survive the PTME and that fueled subsequent diversification are still poorly known. We compiled a comprehensive morphological data set and used the nonmetric multidimensional scaling method to reveal the impact of the PTME on the morphological selectivity of ammonoids. Our results show that postextinction taxa occupied a quite different morphospace when compared with the pre-extinction assemblages. The survivors were mainly smooth and weakly ornamented forms, while the late Permian species were dominated by coarsely ornamented forms. Contrary to previously recognized nonselective patterns, these results suggest a morphological selectivity of the Permian-Triassic crisis. Newcomers in the Griesbachian were mainly compressed and smooth forms. This morphological shift from the coarsely ornamented ammonoids dominating the Changhsingian to the smooth ammonoids dominating the Griesbachian possibly suggests an ecological turnover of ammonoids during the PTME.

Wireless Network Indoor Positioning Method Using Nonmetric Multidimensional Scaling and RSSI in the Internet of Things Environment

Aiming at the problem that the indoor target location algorithm based on received signal strength (RSSI) in the IoT environment is susceptible to interference and large fluctuations, an indoor localization algorithm combining RSSI and nonmetric multidimensional scaling (NMDS) is proposed (RSSI- NMDS). First, Gaussian filtering is performed on the received plurality of sets of RSSI signals to eliminate abnormal fluctuations of the RSSI. Then, based on the RSSI data, the dissimilarity matrix is constructed, and the relative coordinates of the nodes in the low-dimensional space are obtained by NMDS solution. Finally, according to the actual coordinates of the reference node, the coordinate transformation is performed by the planar four-parameter model, and the position of the node in the actual coordinate system is obtained. The simulation results show that the proposed method has strong anti-RSSI perturbation and high positioning accuracy.

The Use of the Incomplete Tetrad Method for Measuring the Similarities in Nonmetric Multidimensional Scaling

Research background: So far, many methods of direct measurement of similarity in multidimensional scaling have been developed (e.g. ranking, sorting, pairwise comparison and others). The method selection affects the subjective feelings of the respondents, i.e. fatigue, weariness resulting from making numerous assessments, or difficulties in expressing similarity assessments.Purpose: In the proposed method, for all four-element sets (tetrads) of objects a respondent is asked to pick out the most similar and the least similar pair. Because the number of tetrads increases very rapidly with the number of objects, the aim of the study is to indicate the possibility of measuring similarities based on the reduced number of tetrads.Research methodology: In order to make scaling results independent from respondents’ subjective effects the analysis was made on the basis of the given distance matrix. To construct perceptual maps based on tetrads, multidimensional scaling with the use of the MINISSA program was performed. The quality of matching the resulting points configuration to the configuration determined based on the distance matrix was tested by a Procrustes statistic.Results: It was demonstrated that the choice of the incomplete set of tetrads has no significant effect on the results of multidimensional scaling, even when all pairs of objects in tetrads cannot be presented equally frequently.Novelty: An original method for calculating similarities in nonmetric multidimensional scaling.

Sampling fossil floras for the study of insect herbivory: how many leaves is enough?

Despite the great importance of plant–insect interactions to the functioning of terrestrial ecosystems, many temporal gaps exist in our knowledge of insect herbivory in deep time. Subsampling of fossil leaves, and subsequent extrapolation of results to the entire flora from which they came, is practiced inconsistently and according to inconsistent, often arbitrary criteria. Here we compare herbivory data from three exhaustively sampled fossil floras to establish guidelines for subsampling in future studies. The impact of various subsampling routines is evaluated for three of the most common metrics of insect herbivory: damage type diversity, nonmetric multidimensional scaling, and the herbivory index. The findings presented here suggest that a minimum fragment size threshold of 1 cm2 always yields accurate results and that a higher threshold of 2 cm2 should yield accurate results for plant hosts that are not polyphyletic form taxa. Due to the structural variability of the plant hosts examined here, no other a priori subsampling strategy yields consistently accurate results. The best approach may be a sequential sampling routine in which sampling continues until the 100 most recently sampled leaves have caused no change to the mean value or confidence interval for damage type diversity and have caused minimal or no change to the herbivory index. For nonmetric multidimensional scaling, at least 1000 cm2 of leaf surface area should be examined and prediction intervals should be generated to verify the relative positions of all points. Future studies should evaluate the impact of subsampling routines on floras that are collected based on different criteria, such as angiosperm floras for which the only specimens collected are those that are at least 50 % complete.