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Effects of Temperature, Phenology, and Geography on Butterfly Population Dynamics under Climate Change.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Effects of Temperature, Phenology, and Geography on Butterfly Population Dynamics under Climate Change./
作者:	Kiekebusch, Elsita Maria.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	148 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-11, Section: B.
Contained By:	Dissertations Abstracts International81-11B.
標題:	Biology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27820122
ISBN:	9781658439923

Effects of Temperature, Phenology, and Geography on Butterfly Population Dynamics under Climate Change.
Kiekebusch, Elsita Maria.

Effects of Temperature, Phenology, and Geography on Butterfly Population Dynamics under Climate Change. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 148 p.

Source: Dissertations Abstracts International, Volume: 81-11, Section: B.

Thesis (Ph.D.)--North Carolina State University, 2020.

This item must not be sold to any third party vendors.

Global climate change caused by anthropogenic greenhouse gas emissions is increasing the risk of species extinctions worldwide. Ectotherms are likely to be particularly vulnerable because their basic physiological functions such as development and reproduction are strongly influenced by external temperature. To discern the potential magnitude of future species declines, it is thus vitally important to gain a mechanistic understanding of how temperature determines population responses. Some of these mechanisms include the effects of temperature on survival and reproduction, the effects of temperature on developmental rates, and geographical variation in temperature. I chose two butterfly species, the Appalachian Brown (Satyrodes appalachia) and the Saint Francis' Satyr (Neonympha mitchellii francisci) to answer the following questions: 1) How will projected warming affect population growth rates of S. appalachia? 2) How will temperature and phenology combine to affect growth rates of S. appalachia? 3) How do S. appalachia survival rate responses to warming compare across a mid-latitude species' range? 4) How can machine learning be used to optimize models to predict phenology of N. mitchellii francisci under different emissions scenarios? I carried out field warming experiments at all annual life stages of S. appalachia to fit functions describing survival and reproductive rates under a range of increased temperatures. I developed a population model based on these vital rate responses and downscaled climate datasets to project future population growth rates under the RCP 8.5 "business as usual" emissions scenario. The model projected that annual growth rates will shift from growing to shrinking in the 2060s, and when predation was incorporated into the model, shrinking occurred by the late 2020s. My findings suggest population declines for a non-rare species under a higher emissions scenario. I carried out field experiments to evaluate the timing of annual life stages relative to the critical photoperiod triggering the onset of winter diapause. I combined these measures with downscaled climate datasets to project annual ratios of S. appalachia individuals developing directly into a third generation versus going into diapause. Incorporation of these into the above population model revealed that the indirect effect of temperature on phenology had a positive effect on population growth that behaved antagonistically to the negative direct effects of temperature. Under RCP 8.5, the model projected that the annual growth rate will remain above one before 2020, but as temperatures continue to increase throughout the 21st Century, the negative direct effects of temperature outweigh the positive indirect effects and the population growth rates shift from growing to shrinking. I evaluated juvenile survival rate responses to temperature of S. appalachia populations from northern and southern range limits. I compared these responses to future projected temperatures at both locations. I found no difference between the survival rates of individuals from the two populations. Comparison of projected survival rates suggested that southern populations have already surpassed optima for thermal demographic response, while northern populations may surpass optima by the middle of the century under RCP 8.5. I used machine learning algorithms to optimize degree day models predicting the annual emergence of N. mitchellii francisci. I validated the use of several algorithms and identified the RandomForest algorithm as the best classifier. Using downscaled climate data, RandomForest projected a decreasing mean date of annual first emergence, with projected advances of approximately 1.4 and 2.1 days per decade for RCP 4.5 and RCP 8.5 respectively. My findings suggest that mechanistic field-based approaches are imperative to predict future effects of climate change. My methods are applicable to a wide range of ectothermic organisms with complex lifecycles. Under higher emissions, my results highlight a critical window of time in the early 21st Century for conservation action.

ISBN: 9781658439923Subjects--Topical Terms:

522710
Biology.
Subjects--Index Terms:

Temperature

Effects of Temperature, Phenology, and Geography on Butterfly Population Dynamics under Climate Change.
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500 $a Source: Dissertations Abstracts International, Volume: 81-11, Section: B.
500 $a Advisor: Haddad, Nicholas M.
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506 $a This item must not be sold to any third party vendors.
520 $a Global climate change caused by anthropogenic greenhouse gas emissions is increasing the risk of species extinctions worldwide. Ectotherms are likely to be particularly vulnerable because their basic physiological functions such as development and reproduction are strongly influenced by external temperature. To discern the potential magnitude of future species declines, it is thus vitally important to gain a mechanistic understanding of how temperature determines population responses. Some of these mechanisms include the effects of temperature on survival and reproduction, the effects of temperature on developmental rates, and geographical variation in temperature. I chose two butterfly species, the Appalachian Brown (Satyrodes appalachia) and the Saint Francis' Satyr (Neonympha mitchellii francisci) to answer the following questions: 1) How will projected warming affect population growth rates of S. appalachia? 2) How will temperature and phenology combine to affect growth rates of S. appalachia? 3) How do S. appalachia survival rate responses to warming compare across a mid-latitude species' range? 4) How can machine learning be used to optimize models to predict phenology of N. mitchellii francisci under different emissions scenarios? I carried out field warming experiments at all annual life stages of S. appalachia to fit functions describing survival and reproductive rates under a range of increased temperatures. I developed a population model based on these vital rate responses and downscaled climate datasets to project future population growth rates under the RCP 8.5 "business as usual" emissions scenario. The model projected that annual growth rates will shift from growing to shrinking in the 2060s, and when predation was incorporated into the model, shrinking occurred by the late 2020s. My findings suggest population declines for a non-rare species under a higher emissions scenario. I carried out field experiments to evaluate the timing of annual life stages relative to the critical photoperiod triggering the onset of winter diapause. I combined these measures with downscaled climate datasets to project annual ratios of S. appalachia individuals developing directly into a third generation versus going into diapause. Incorporation of these into the above population model revealed that the indirect effect of temperature on phenology had a positive effect on population growth that behaved antagonistically to the negative direct effects of temperature. Under RCP 8.5, the model projected that the annual growth rate will remain above one before 2020, but as temperatures continue to increase throughout the 21st Century, the negative direct effects of temperature outweigh the positive indirect effects and the population growth rates shift from growing to shrinking. I evaluated juvenile survival rate responses to temperature of S. appalachia populations from northern and southern range limits. I compared these responses to future projected temperatures at both locations. I found no difference between the survival rates of individuals from the two populations. Comparison of projected survival rates suggested that southern populations have already surpassed optima for thermal demographic response, while northern populations may surpass optima by the middle of the century under RCP 8.5. I used machine learning algorithms to optimize degree day models predicting the annual emergence of N. mitchellii francisci. I validated the use of several algorithms and identified the RandomForest algorithm as the best classifier. Using downscaled climate data, RandomForest projected a decreasing mean date of annual first emergence, with projected advances of approximately 1.4 and 2.1 days per decade for RCP 4.5 and RCP 8.5 respectively. My findings suggest that mechanistic field-based approaches are imperative to predict future effects of climate change. My methods are applicable to a wide range of ectothermic organisms with complex lifecycles. Under higher emissions, my results highlight a critical window of time in the early 21st Century for conservation action.
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653 $a Geography
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653 $a Climate change
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