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      Climatic Niche Model for Overwintering Monarch Butterflies in a Topographically Complex Region of California

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          Abstract

          We use climatic conditions that are associated with known monarch butterfly overwintering groves in California to build a Maxent model, and focus on the fine scale probability of overwintering grove occurrence in a topographically complex region of the state (Santa Barbara County). Grove locations are known from recent and historical surveys and a long-term citizen science database. The climatic niche model performs well, predicting that overwintering habitat is most likely to occur along the coast and at low elevations, as shown by empirical data. We then use climatic variables in conjunction with climate change scenarios to model the future location of overwintering habitat, and find a substantial shift in the predicted distribution. Under a plausible scenario, the probability of occurrence of overwintering habitat directly reflects elevation, with coastal regions having a reduced probability relative to today, and higher elevation sites increasing in probability. Under a more extreme scenario, high probability sites are only located along ridgelines and in mountaintop regions of the county. This predicted shift in distribution is likely to have management implications, as sites that currently lack monarchs may become critical to conservation in the future. Our results suggest that estimating the size of the western overwintering population in the future will be problematic, unless annual counts compensate for a shift in the distribution and a potential change in the number and location of occupied sites.

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          Poleward shifts in geographical ranges of butterfly species associated with regional warming

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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

                Journal
                Insects
                Insects
                insects
                Insects
                MDPI
                2075-4450
                20 November 2018
                December 2018
                : 9
                : 4
                : 167
                Affiliations
                [1 ]Biological Sciences Department, Cal Poly State University, San Luis Obispo, CA 93407, USA; afishe08@ 123456gmail.com (A.F.); ksaniee@ 123456calpoly.edu (K.S.); charisvdh@ 123456gmail.com (C.v.d.H.)
                [2 ]Althouse and Meade Inc., 1602 Spring St., Paso Robles, CA 93446, USA; jessicag@ 123456althouseandmeade.com (J.G.); dan@ 123456althouseandmeade.com (D.M.)
                Author notes
                [* ]Correspondence: fvillabl@ 123456calpoly.edu ; Tel.: +1-805-748-1014
                Article
                insects-09-00167
                10.3390/insects9040167
                6316322
                30463305
                c00c5b3b-2d37-47c6-b391-a0a8d5758082
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 11 October 2018
                : 15 November 2018
                Categories
                Article

                monarch butterfly,overwintering,climatic niche model,species distribution model,western monarch thanksgiving count,maxent

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