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      Potential Distribution of Six North American Higher-Attine Fungus-Farming Ant (Hymenoptera: Formicidae) Species

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          Abstract

          Ants are among the most successful insects in Earth’s evolutionary history. However, there is a lack of knowledge regarding range-limiting factors that may influence their distribution. The goal of this study was to describe the environmental factors (climate and soil types) that likely impact the ranges of five out of the eight most abundant Trachymyrmex species and the most abundant Mycetomoellerius species in the United States. Important environmental factors may allow us to better understand each species’ evolutionary history. We generated habitat suitability maps using MaxEnt for each species and identified associated most important environmental variables. We quantified niche overlap between species and evaluated possible congruence in species distribution. In all but one model, climate variables were more important than soil variables. The distribution of M. turrifex (Wheeler, W.M., 1903) was predicted by temperature, specifically annual mean temperature (BIO1), T. arizonensis (Wheeler, W.M., 1907), T. carinatus, and T. smithi Buren, 1944 were predicted by precipitation seasonality (BIO15), T. septentrionalis (McCook, 1881) were predicted by precipitation of coldest quarter (BIO19), and T. desertorum (Wheeler, W.M., 1911) was predicted by annual flood frequency. Out of 15 possible pair-wise comparisons between each species’ distributions, only one was statistically indistinguishable ( T. desertorum vs T. septentrionalis). All other species distribution comparisons show significant differences between species. These models support the hypothesis that climate is a limiting factor in each species distribution and that these species have adapted to temperatures and water availability differently.

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          Multiorganismal insects: diversity and function of resident microorganisms.

          All insects are colonized by microorganisms on the insect exoskeleton, in the gut and hemocoel, and within insect cells. The insect microbiota is generally different from microorganisms in the external environment, including ingested food. Specifically, certain microbial taxa are favored by the conditions and resources in the insect habitat, by their tolerance of insect immunity, and by specific mechanisms for their transmission. The resident microorganisms can promote insect fitness by contributing to nutrition, especially by providing essential amino acids, B vitamins, and, for fungal partners, sterols. Some microorganisms protect their insect hosts against pathogens, parasitoids, and other parasites by synthesizing specific toxins or modifying the insect immune system. Priorities for future research include elucidation of microbial contributions to detoxification, especially of plant allelochemicals in phytophagous insects, and resistance to pathogens; as well as their role in among-insect communication; and the potential value of manipulation of the microbiota to control insect pests.
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            ENMTools: a toolbox for comparative studies of environmental niche models

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              Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias

              MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one “virtual” derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases.
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                Author and article information

                Contributors
                Role: Subject Editor
                Journal
                J Insect Sci
                J. Insect Sci
                jis
                Journal of Insect Science
                Oxford University Press (US )
                1536-2442
                November 2019
                19 December 2019
                19 December 2019
                : 19
                : 6
                : 24
                Affiliations
                [1 ] Department of Biology, University of Texas at Tyler , Tyler, TX, USA
                [2 ] Section of Integrative Biology, University of Texas at Austin , Austin, TX, USA
                [3 ] Department of Computer Science, Rowan University , Glassboro, NJ, USA
                Author notes
                Corresponding author, e-mail: sfsenula@ 123456gmail.com

                These authors contributed equally.

                Author information
                http://orcid.org/0000-0001-7225-8957
                Article
                iez118
                10.1093/jisesa/iez118
                6921375
                31854452
                cd70c218-a8ca-4c5b-9385-bf7ae723781b
                © The Author(s) 2019. Published by Oxford University Press on behalf of Entomological Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 03 December 2018
                : 18 November 2019
                Page count
                Pages: 11
                Funding
                Funded by: National Science Foundation 10.13039/100000001
                Award ID: DEB-1354629
                Award ID: IOS 1552822
                Categories
                Research

                Entomology
                maxent,attine,texas,ecological niche modeling,temperature
                Entomology
                maxent, attine, texas, ecological niche modeling, temperature

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