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Improving Species Distribution Models with Bias Correction and Geographically Weighted Regression : = Tests of Virtual Species and Past and Present Distributions in North American Deserts.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Improving Species Distribution Models with Bias Correction and Geographically Weighted Regression :/
其他題名:	Tests of Virtual Species and Past and Present Distributions in North American Deserts.
作者:	Inman, Richard.
面頁冊數:	1 online resource (208 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
Contained By:	Dissertation Abstracts International79-09B(E).
標題:	Macroecology. -
電子資源:	click for full text (PQDT)
ISBN:	9780355909548

Improving Species Distribution Models with Bias Correction and Geographically Weighted Regression : = Tests of Virtual Species and Past and Present Distributions in North American Deserts.
Inman, Richard.

Improving Species Distribution Models with Bias Correction and Geographically Weighted Regression :Tests of Virtual Species and Past and Present Distributions in North American Deserts. - 1 online resource (208 pages)

Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.

Thesis (Ph.D.)--Arizona State University, 2018.

Includes bibliographical references

This work investigates the effects of non-random sampling on our understanding of species distributions and their niches. In its most general form, bias is systematic error that can obscure interpretation of analytical results by skewing samples away from the average condition of the system they represent. Here I use species distribution modelling (SDM), virtual species, and multiscale geographically weighted regression (MGWR) to explore how sampling bias can alter our perception of broad patterns of biodiversity by distorting spatial predictions of habitat, a key characteristic in biogeographic studies. I use three separate case studies to explore: 1) How methods to account for sampling bias in species distribution modeling may alter estimates of species distributions and species-environment relationships, 2) How accounting for sampling bias in fossil data may change our understanding of paleo-distributions and interpretation of niche stability through time (i.e. niche conservation), and 3) How a novel use of MGWR can account for environmental sampling bias to reveal landscape patterns of local niche differences among proximal, but non-overlapping sister taxa. Broadly, my work shows that sampling bias present in commonly used federated global biodiversity observations is more than enough to degrade model performance of spatial predictions and niche characteristics. Measures commonly used to account for this bias can negate much loss, but only in certain conditions, and did not improve the ability to correctly identify explanatory variables or recreate species-environment relationships. Paleo-distributions calibrated on biased fossil records were improved with the use of a novel method to directly estimate the biased sampling distribution, which can be generalized to finer time slices for further paleontological studies. Finally, I show how a novel coupling of SDM and MGWR can illuminate local differences in niche separation that more closely match landscape genotypic variability in the two North American desert tortoise species than does their current taxonomic delineation.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355909548Subjects--Topical Terms:

1184624
Macroecology.
Index Terms--Genre/Form:

554714
Electronic books.

Improving Species Distribution Models with Bias Correction and Geographically Weighted Regression : = Tests of Virtual Species and Past and Present Distributions in North American Deserts.
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520 $a This work investigates the effects of non-random sampling on our understanding of species distributions and their niches. In its most general form, bias is systematic error that can obscure interpretation of analytical results by skewing samples away from the average condition of the system they represent. Here I use species distribution modelling (SDM), virtual species, and multiscale geographically weighted regression (MGWR) to explore how sampling bias can alter our perception of broad patterns of biodiversity by distorting spatial predictions of habitat, a key characteristic in biogeographic studies. I use three separate case studies to explore: 1) How methods to account for sampling bias in species distribution modeling may alter estimates of species distributions and species-environment relationships, 2) How accounting for sampling bias in fossil data may change our understanding of paleo-distributions and interpretation of niche stability through time (i.e. niche conservation), and 3) How a novel use of MGWR can account for environmental sampling bias to reveal landscape patterns of local niche differences among proximal, but non-overlapping sister taxa. Broadly, my work shows that sampling bias present in commonly used federated global biodiversity observations is more than enough to degrade model performance of spatial predictions and niche characteristics. Measures commonly used to account for this bias can negate much loss, but only in certain conditions, and did not improve the ability to correctly identify explanatory variables or recreate species-environment relationships. Paleo-distributions calibrated on biased fossil records were improved with the use of a novel method to directly estimate the biased sampling distribution, which can be generalized to finer time slices for further paleontological studies. Finally, I show how a novel coupling of SDM and MGWR can illuminate local differences in niche separation that more closely match landscape genotypic variability in the two North American desert tortoise species than does their current taxonomic delineation.
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