The Presence–Absence Situation and Its Impact on the Assemblage Structure and Interspecific Relations of Pronophilina Butterflies in the Venezuelan Andes (Lepidoptera: Nymphalidae)

Assemblage structure and altitudinal patterns of Pronophilina, a species-rich group of Andean butterflies, are compared in El Baho and Monte Zerpa, two closely situated and ecologically similar Andean localities. Their faunas differ only by the absence of Pedaliodes ornata Grose-Smith in El Baho. There are, however, important structural differences between the two Pronophilina assemblages. Whereas there are five co-dominant species in Monte Zerpa, including P. ornata, Pedaliodes minabilis Pyrcz is the only dominant with more than half of all the individuals in the sample in El Baho. The absence of P. ornata in El Baho is investigated from historical, geographic, and ecological perspectives exploring the factors responsible for its possible extinction including climate change, mass dying out of host plants, and competitive exclusion. Although competitive exclusion between P. ornata and P. minabilis is a plausible mechanism, considered that their ecological niches overlap, which suggests a limiting influence on each other’s populations, the object of competition was not identified, and the reason of the absence of P. ornata in El Baho could not be established. The role of spatial interference related to imperfect sexual behavioral isolation is evaluated in maintaining the parapatric altitudinal distributions of three pairs of phenotypically similar and related species of Pedaliodes, Corades, and Lymanopoda

Rigid Transformations for Stabilized Lower Dimensional Space to Support Subsurface Uncertainty Quantification and Interpretation

Subsurface datasets inherently possess big data characteristics such as vast volume, diverse features, and high sampling speeds, further compounded by the curse of dimensionality from various physical, engineering, and geological inputs. Among the existing dimensionality reduction (DR) methods, nonlinear dimensionality reduction (NDR) methods, especially Metric-multidimensional scaling (MDS), are preferred for subsurface datasets due to their inherent complexity. While MDS retains intrinsic data structure and quantifies uncertainty, its limitations include unstabilized unique solutions invariant to Euclidean transformations and an absence of out-of-sample points (OOSP) extension. To enhance subsurface inferential and machine learning workflows, datasets must be transformed into stable, reduced-dimension representations that accommodate OOSP. Our solution employs rigid transformations for a stabilized Euclidean invariant representation for LDS. By computing an MDS input dissimilarity matrix, and applying rigid transformations on multiple realizations, we ensure transformation invariance and integrate OOSP. This process leverages a convex hull algorithm and incorporates loss function and normalized stress for distortion quantification. We validate our approach with synthetic data, varying distance metrics, and real-world wells from the Duvernay Formation. Results confirm our method's efficacy in achieving consistent LDS representations. Furthermore, our proposed "stress ratio" (SR) metric provides insight into uncertainty, beneficial for model adjustments and inferential analysis. Consequently, our workflow promises enhanced repeatability and comparability in NDR for subsurface energy resource engineering and associated big data workflows.Comment: 30 pages, 17 figures, Submitted to Computational Geosciences Journa

Mitigation of Spatial Nonstationarity with Vision Transformers

Spatial nonstationarity, the location variance of features' statistical distributions, is ubiquitous in many natural settings. For example, in geological reservoirs rock matrix porosity varies vertically due to geomechanical compaction trends, in mineral deposits grades vary due to sedimentation and concentration processes, in hydrology rainfall varies due to the atmosphere and topography interactions, and in metallurgy crystalline structures vary due to differential cooling. Conventional geostatistical modeling workflows rely on the assumption of stationarity to be able to model spatial features for the geostatistical inference. Nevertheless, this is often not a realistic assumption when dealing with nonstationary spatial data and this has motivated a variety of nonstationary spatial modeling workflows such as trend and residual decomposition, cosimulation with secondary features, and spatial segmentation and independent modeling over stationary subdomains. The advent of deep learning technologies has enabled new workflows for modeling spatial relationships. However, there is a paucity of demonstrated best practice and general guidance on mitigation of spatial nonstationarity with deep learning in the geospatial context. We demonstrate the impact of two common types of geostatistical spatial nonstationarity on deep learning model prediction performance and propose the mitigation of such impacts using self-attention (vision transformer) models. We demonstrate the utility of vision transformers for the mitigation of nonstationarity with relative errors as low as 10%, exceeding the performance of alternative deep learning methods such as convolutional neural networks. We establish best practice by demonstrating the ability of self-attention networks for modeling large-scale spatial relationships in the presence of commonly observed geospatial nonstationarity

Notes on the identity of the male paralectotype of Thecla heodes and description of a new species : Strymon cryptodes sp. nov. from northern Peru (Lepidoptera: Lycaenidae)

A new species, Strymon cryptodes sp. nov. is described from puna grasslands of northern Peru. Its phenotype corresponds with the male paralectotype of a congeneric species originally described as Thecla heodes Druce, 1909, which, in consequence, turns out to be a mixture of two biological species. Males of S. heodes have conspicuous scent patches, absent in S. cryptodes sp. nov. The new species is known from four individuals recently collected in the vicinity of the city of Cajamarca and two historical specimens from other localities in the department of Cajamarca. Based on wing colour patterns and other morphological characters S. cryptodes sp. nov. is placed in the Strymon istapa species-group defined by Robbins & Nicolay (2002), although the presence or absence of male androconial patch and genitalia brush organ are demonstrated not to be a valid diagnostic infrageneric character

The Early Stages of Pedaliodes poesia (Hewitson, 1862) in Eastern Ecuador (Lepidoptera: Satyrinae: Pronophilina)

We describe the immature stages Pedaliodes poesia Hewitson, 1862 from northeastern Ecuador. Chusquea scandens (Poaceae, Bambusoidea) is the larval food plant. Eggs are laid singly or in pairs on the bottom side of host plant leaves. The duration of the egg, larval, and pupal stages, combined, is 99–107 days

Optimal Placement of Public Electric Vehicle Charging Stations Using Deep Reinforcement Learning

The placement of charging stations in areas with developing charging infrastructure is a critical component of the future success of electric vehicles (EVs). In Albany County in New York, the expected rise in the EV population requires additional charging stations to maintain a sufficient level of efficiency across the charging infrastructure. A novel application of Reinforcement Learning (RL) is able to find optimal locations for new charging stations given the predicted charging demand and current charging locations. The most important factors that influence charging demand prediction include the conterminous traffic density, EV registrations, and proximity to certain types of public buildings. The proposed RL framework can be refined and applied to cities across the world to optimize charging station placement.Comment: 25 pages with 12 figures. Shankar Padmanabhan and Aidan Petratos provided equal contributio

Reconstruction of three-dimensional porous media using generative adversarial neural networks

To evaluate the variability of multi-phase flow properties of porous media at the pore scale, it is necessary to acquire a number of representative samples of the void-solid structure. While modern x-ray computer tomography has made it possible to extract three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often experimentally not feasible. We present a novel method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image datasets. We show, by using an adversarial learning approach for neural networks, that this method of unsupervised learning is able to generate representative samples of porous media that honor their statistics. We successfully compare measures of pore morphology, such as the Euler characteristic, two-point statistics and directional single-phase permeability of synthetic realizations with the calculated properties of a bead pack, Berea sandstone, and Ketton limestone. Results show that GANs can be used to reconstruct high-resolution three-dimensional images of porous media at different scales that are representative of the morphology of the images used to train the neural network. The fully convolutional nature of the trained neural network allows the generation of large samples while maintaining computational efficiency. Compared to classical stochastic methods of image reconstruction, the implicit representation of the learned data distribution can be stored and reused to generate multiple realizations of the pore structure very rapidly.Comment: 21 pages, 20 figure

Phylogeny and systematics of the "Pronophila clade," with 2 new genera to resolve the formerly polyphyletic genus Pseudomaniola (Lepidoptera: Nymphalidae: Satyrinae)

Analysis of a target enrichment molecular dataset confirms the monophyly of the Neotropical montane butterfly group known as the Pronophila Westwood clade, 1 of 2 major lineages of the satyrine subtribe Pronophilina. The Pronophila clade comprises 18-20 recognized genera and some 125 species. Within this group, the genus Pseudomaniola Röber appears as paraphyletic, and is split here into 3 genera, Pseudomaniola sensu novum with 6 species, including 4 previously considered as subspecies of P. phaselis (Hewitson), the monobasic Fahraeusia Pyrcz n. gen. for Catargynnis asuba Thieme, n. comb., and Boyeriana Pyrcz, Espeland & Willmott n. gen., with 9 species. The adults of all 3 genera can be recognized by their wing color patterns, but the strongest synapomorphies are found in the genitalia, especially those of the male, supporting the above systematic de cisions. Notable differences are also found in scale organization and morphology. A divergence time analysis suggests that Fahraeusia diverged from Pseudomaniola + Boyeriana in the mid-Miocene, around 12 Mya, and the subsequent separation of the last 2 genera occurred at the start of the Pliocene at around 5 Mya