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Odonata: Coenagrionidae) Temporal differentiation in environmental niche modeling of Nearctic narrow-winged damselflies (Odonata: Coenagrionidae) Emily L Sandall Corresp., 1 , Andrew R Deans 1 1 Frost Entomological Museum, Department of Entomology, Pennsylvania State University, University Park, PA, United States Corresponding Author: Emily L Sandall Email address: [email protected] Narrow-winged damselflies (Odonata: Coenagrionidae) can be observed in a variety of habitats, by both professional collectors and amateur odonatologists. Their abundance and ease of recognition has resulted in a large amount of occurrence data, which can be used to establish species distribution maps through environmental niche modeling. Distributional models often aim to maximize the quantity of occurrence points and environmental variables to relate to the distribution, neglecting both the quality and overlap of these two datasets when generating the models. In order to examine the effects of temporal data and environmental variables influencing change in species distributions, we used occurrence data for twelve species of Coenagrionidae damselflies to generate niche models separated by time periods of specimen collection. Our study examines environmental niche models generated for four time periods for each of these coenagrionid species: Amphiagrion abbreivatum (Selys,1876), Enallagma civile (Hagen,1861), Chromagrion conditum (Hagen in Selys, 1876), Nehalennia gracilis Morse, 1895, Enallagma hageni (Walsh, 1863), Hesperagrion heterodoxum (Selys, 1868), Nehalennia irene (Hagen, 1861), Argia moesta (Hagen, 1861), Ischnura ramburii (Selys, 1850), Argia tibialis (Rambur, 1842), Argia translata Hagen in Selys, 1865, and Argia vivida Hagen in Selys, 1865. The best supported models in each analysis were generated with occurrences of specimens collected from the 1970s to 2000s, and we used occurrence data outside of this range, from the 1800s to 2017, to compare the consistency of model predictions based on specimens of different time periods. In this approach, combining traditional environmental niche modeling and analysis of the specimen records themselves, we have found that ranges for narrow-winged damselflies expand over time, with increase in distributional coverage and decrease in model strength without temporal overlap between occurrences and environmental variables. PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27261v1 | CC BY 4.0 Open Access | rec: 7 Oct 2018, publ: 7 Oct 2018 1 Temporal differentiation in environmental 2 niche modeling of Nearctic narrow-winged 3 damselflies (Odonata: Coenagrionidae) 1 1 4 Emily L. Sandall and Andrew R. Deans 1 5 Frost Entomological Museum, Department of Entomology, Pennsylvania State 6 University, University Park, PA 7 Corresponding author: 8 Emily L. Sandall 9 Email address: [email protected] 10 ABSTRACT 11 Narrow-winged damselflies (Odonata: Coenagrionidae) can be observed in a variety of habitats, by both 12 professional collectors and amateur odonatologists. Their abundance and ease of recognition has resulted 13 in a large amount of occurrence data, which can be used to establish species distribution maps through 14 environmental niche modeling. Distributional models often aim to maximize the quantity of occurrence 15 points and environmental variables to relate to the distribution, neglecting both the quality and overlap of 16 these two datasets when generating the models. In order to examine the effects of temporal data and 17 environmental variables influencing change in species distributions, we used occurrence data for twelve 18 species of Coenagrionidae damselflies to generate niche models separated by time periods of specimen 19 collection. Our study examines environmental niche models generated for four time periods for each of 20 these coenagrionid species: Amphiagrion abbreivatum (Selys,1876), Enallagma civile (Hagen,1861), 21 Chromagrion conditum (Hagen in Selys, 1876), Nehalennia gracilis Morse, 1895, Enallagma hageni 22 (Walsh, 1863), Hesperagrion heterodoxum (Selys, 1868), Nehalennia irene (Hagen, 1861), Argia moesta 23 (Hagen, 1861), Ischnura ramburii (Selys, 1850), Argia tibialis (Rambur, 1842), Argia translata Hagen in 24 Selys, 1865, and Argia vivida Hagen in Selys, 1865. The best supported models in each analysis were 25 generated with occurrences of specimens collected from the 1970s to 2000s, and we used occurrence 26 data outside of this range, from the 1800s to 2017, to compare the consistency of model predictions 27 based on specimens of different time periods. In this approach, combining traditional environmental niche 28 modeling and analysis of the specimen records themselves, we have found that ranges for narrow-winged 29 damselflies expand over time, with increase in distributional coverage and decrease in model strength 30 without temporal overlap between occurrences and environmental variables. 31 INTRODUCTION 32 Linking current and past occurrences on a large scale, to document species distribution over time requires 33 that specimens be identified and accessible. The digitization of natural history collections (Graham et al., 34 2004; Page et al., 2015) has liberated the occurrence and taxonomic determination data of millions of 35 insect specimens. Some of these insects have captured the attention of naturalists for centuries, resulting 36 in extensive datasets that allow for a thorough analysis of shifting distributions, outbreaks, or other 37 population changes (Peterson et al., 2005; Estrada-Pena˜ et al., 2013). Odonata, commonly known as 38 dragonflies and damselflies, is one such order with extensive representation in natural history collections 39 throughout time (Bybee et al., 2016). Maximal amounts of occurrence data are used to establish species 40 distributions typically without thorough examination of individual specimen’s temporal data. For many 41 taxa, the quantity of digital data available may not be great enough to carry out such analyses. Taxa 42 with widespread collection and digitization efforts enable analysis of data associated with the specimen 43 occurrences in order to build environmental niche models of distribution. In doing so, we can identify 44 how concept of species distribution changes as specimen occurrences are documented. 45 The addition of recent observations and citizen science projects to digital repositories extends the reach 46 of occurrence data beyond the walls of a single museum or individual researcher (Graham et al., 2004; PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27261v1 | CC BY 4.0 Open Access | rec: 7 Oct 2018, publ: 7 Oct 2018 47 Anderson et al., 2006). While these data have multiple uses and implications, from conservation policy 48 to invasive species monitoring, the occurrence data attached to physical specimens in collections and 49 museums and that of specimen occurrences recorded digitally in online repositories typically exemplify 50 differing levels of data quality and completeness (Wieczorek et al., 2012; Parr et al., 2012). An exploration 51 of the occurrence data’s spatial and temporal components can demonstrate distributional and collecting 52 changes over time (Soberon´ and Peterson, 2004; Ward, 2012). 53 Loss in insect quantity and diversity has been demonstrated in research of protected habitats (Hallmann 54 et al., 2017), pollinators (Potts et al., 2010), and aquatic insects (Collins and McIntyre, 2017). Since the 55 dawn of industrialization and urbanization, climatic factors have had a large impact on the presence and 56 quality of aquatic habitats, especially affecting the diversity of macroinvertebrates that rely on water to 57 complete their life cycles (Finch et al., 2006; Jones and Clark, 1987). Species distribution models of 58 aquatic insects, such as Plecoptera, indicate that analyses of historical species ranges can aid in identifying 59 hotspots of biodiversity for future monitoring or collection in water bodies (Cao et al., 2013). 60 Damselflies, the suborder Zygoptera, in addition to dragonflies, the suborder Epiprocta, make up the 61 Odonata. Their ancient and widely successful lineage can be found in a variety of habitats throughout 62 the world (Collins and McIntyre, 2015). As predators of other insects and small organisms in both their 63 aquatic and terrestrial life stages, odonates can greatly influence the overall niche composition of their 64 habitats (Corbet, 1990). Considered to be bioindicators, their sensitivity to environmental fluctuations 65 makes them ideal for studies of evolutionary ecology (Sahlen´ and Ekestubbe, 2001). Because the diversity 66 of Odonata is relatively well-understood and species-level identification is possible at a range of expertise 67 levels, dragonflies and damselflies can be monitored for broader distributional questions (Carle et al., 68 2008). Damselflies do not as often disperse through flight as dragonflies, whose abilities enable them in 69 some cases to migrate thousands of kilometers (May, 2013; Wikelski et al., 2006). Damselflies are also 70 sensitive to environmental changes, but are less frequently examined as bioindicators than other aquatic 71 insects (Jaeschke et al., 2013). Given these traits, climatic factors may affect aquatic habitats, influencing 72 damselfly distributions over generations. 73 Since females deposit eggs in water or aquatic plants and larvae live in a range of aquatic habitats, 74 successful emergence as adults depends on water quality (Kalkman et al., 2008). Species of Coenagrion- 75 idae typically have wide distributions throughout the
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