Habitat Characterization of Five Rare Insects in Michigan (Lepidoptera: Hesperiidae, Riodinidae, Satyridae; Homoptera: Cercopidae)

Over 80 species of insects are listed as endangered, threatened, or special concern under Michigan\u27s endangered species act. For the majority of these species, detailed habitat information is scant or difficult to interpret. We describe the habitat of five insect species that are considered rare in Michigan: Lepyronia angulifera (Cercopidae), Prosapia ignipectus (Cercopidae), Oarisma poweshiek (Hesperiidae), Calephelis mutica (Riodinidae), and Neonympha mitchellii mitchellii (Satyridae). Populations of each species were only found within a fraction of the plant communities deemed suitable based upon previous literature. Furthermore, individuals of each species were observed to be closely affiliated with just a few vegetation associations within larger plant communities. Restriction of these species to particular microhabitats was determined to be, in part, due to ecological or behavioral specialization of each insect species. We believe that the most holistic man­agement and conservation practices for these rare insects in Michigan should focus on protecting the integrity of both the plant community and the micro- habitat upon which these species depend

Patch-Scale Movement Dynamics in the Iowa Grassland Butterflies \u3ci\u3eSpeyeria Cybele\u3c/i\u3e and \u3ci\u3eMegisto Cymela\u3c/i\u3e (Lepidoptera: Nymphalidae)

An understanding of the movement dynamics of invertebrates can be critical to their conservation, especially when managing relatively small, isolated habitats. Most studies of butterfly movement have focused on metapopulation dynamics at relatively large spatial scales, and the results from these studies may not translate well for patchy populations within a single nature preserve. In this work we use individual mark and recapture (IMR) methods to follow the movements of two species of butterfly, Megisto cymela (Cramer) and Speyeria cybele F. (Lepidoptera: Nymphalidae) within a 240 hectare forest and grassland preserve in central Iowa, USA. Significant redistribution was seen in both species, with 55.7% of S. cybele and 31.1% of M. cymela undergoing interpatch movement. Median movement rates during the study were 105 m/day for S. cybele and 38 m/day for M. cymela, with the top decile moving at a rate of over five times these values. This movement did not appear to be random. S. cybele exhibited directed movement towards patches with high nectaring potential, although not all such patches were selected. M. cymela aggregated in particular prairie patches, especially those with high edge to area ratios, although the reason for aggregation is not clear

Twilight an evening love song

https://digitalcommons.library.umaine.edu/mmb-vp/2608/thumbnail.jp

Learning the Designer's Preferences to Drive Evolution

This paper presents the Designer Preference Model, a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the user's design style to better assess the tool's procedurally generated content with respect to that user's preferences. Through this approach, we aim for increasing the user's agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking. We describe the details of this novel solution, as well as its implementation in the MI-CC tool the Evolutionary Dungeon Designer. We present and discuss our findings out of the initial tests carried out, spotting the open challenges for this combined line of research that integrates MI-CC with Procedural Content Generation through Machine Learning.Comment: 16 pages, Accepted and to appear in proceedings of the 23rd European Conference on the Applications of Evolutionary and bio-inspired Computation, EvoApplications 202

"Intraspecific aggregation decreases local species diversity of arthropods"

Keith S. Summerville ia a professor of Environmental Science in the Department of Environmental Science and Policy at Drake University. He can be contacted at [email protected]. The aggregation model of species coexistence predicts that insect species diversity within a community is maintained by intraspecific aggregation among resource patches. An untested corollary of this prediction is that diversity within resource patches should decrease with increasing intraspecific aggregation. The recently derived species–aggregation relationship provides a general formulation of this prediction: as intraspecific aggregation increases within a geographic area, the species richness within samples of the area decreases. We tested this prediction by compiling and analyzing 76 data sets of arthropod species distribution and abundance. For each data set, we determined the mean amount of intra- and interspecific aggregation and three types of within-sample or local species diversity: species richness, evenness, and dominance. Using regression, we found a negative relationship between intraspecific aggregation and all three types of local diversity. Intraspecific aggregation explained a significant percentage of the variation in species diversity, typically between 20% and 60%. By comparison, interspecific aggregation usually explained <1% of the variation in species diversity. Our study provides empirical support for the species–aggregation relationship as a general macroecological pattern that emerges from intraspecific aggregation

Patch-Scale Movement Dynamics in the Iowa Grassland Butterflies \u3ci\u3eSpeyeria Cybele\u3c/i\u3e and \u3ci\u3eMegisto Cymela\u3c/i\u3e (Lepidoptera: Nymphalidae)

An understanding of the movement dynamics of invertebrates can be critical to their conservation, especially when managing relatively small, isolated habitats. Most studies of butterfly movement have focused on metapopulation dynamics at relatively large spatial scales, and the results from these studies may not translate well for patchy populations within a single nature preserve. In this work we use individual mark and recapture (IMR) methods to follow the movements of two species of butterfly, Megisto cymela (Cramer) and Speyeria cybele F. (Lepidoptera: Nymphalidae) within a 240 hectare forest and grassland preserve in central Iowa, USA. Significant redistribution was seen in both species, with 55.7% of S. cybele and 31.1% of M. cymela undergoing interpatch movement. Median movement rates during the study were 105 m/day for S. cybele and 38 m/day for M. cymela, with the top decile moving at a rate of over five times these values. This movement did not appear to be random. S. cybele exhibited directed movement towards patches with high nectaring potential, although not all such patches were selected. M. cymela aggregated in particular prairie patches, especially those with high edge to area ratios, although the reason for aggregation is not clear

Increasing generality in machine learning through procedural content generation

Procedural Content Generation (PCG) refers to the practice, in videogames and other games, of generating content such as levels, quests, or characters algorithmically. Motivated by the need to make games replayable, as well as to reduce authoring burden, limit storage space requirements, and enable particular aesthetics, a large number of PCG methods have been devised by game developers. Additionally, researchers have explored adapting methods from machine learning, optimization, and constraint solving to PCG problems. Games have been widely used in AI research since the inception of the field, and in recent years have been used to develop and benchmark new machine learning algorithms. Through this practice, it has become more apparent that these algorithms are susceptible to overfitting. Often, an algorithm will not learn a general policy, but instead a policy that will only work for a particular version of a particular task with particular initial parameters. In response, researchers have begun exploring randomization of problem parameters to counteract such overfitting and to allow trained policies to more easily transfer from one environment to another, such as from a simulated robot to a robot in the real world. Here we review the large amount of existing work on PCG, which we believe has an important role to play in increasing the generality of machine learning methods. The main goal here is to present RL/AI with new tools from the PCG toolbox, and its secondary goal is to explain to game developers and researchers a way in which their work is relevant to AI research

Garden and landscape-scale correlates of moths of differing conservation status: significant effects of urbanization and habitat diversity

Moths are abundant and ubiquitous in vegetated terrestrial environments and are pollinators, important herbivores of wild plants, and food for birds, bats and rodents. In recent years, many once abundant and widespread species have shown sharp declines that have been cited by some as indicative of a widespread insect biodiversity crisis. Likely causes of these declines include agricultural intensification, light pollution, climate change, and urbanization; however, the real underlying cause(s) is still open to conjecture. We used data collected from the citizen science Garden Moth Scheme (GMS) to explore the spatial association between the abundance of 195 widespread British species of moth, and garden habitat and landscape features, to see if spatial habitat and landscape associations varied for species of differing conservation status. We found that associations with habitat and landscape composition were species-specific, but that there were consistent trends in species richness and total moth abundance. Gardens with more diverse and extensive microhabitats were associated with higher species richness and moth abundance; gardens near to the coast were associated with higher richness and moth abundance; and gardens in more urbanized locations were associated with lower species richness and moth abundance. The same trends were also found for species classified as increasing, declining and vulnerable under IUCN (World Conservation Union) criteria