Research Mentor(s)

Merrill Peterson

Affiliated Department

Biology

Sort Order

05

Start Date

14-5-2015 10:00 AM

End Date

14-5-2015 2:00 PM

Keywords

Climate change, Natural history data, Bioinformatics, GLMM

Document Type

Event

Abstract

Climate change is altering the distribution, behavior, and migration patterns of many species. Typically, these responses are documented studies in which standardized methods are used to collect population or behavioral data over several years. Multi-decade studies are rare and few predate the recent dramatic increase in global temperatures, limiting our ability to understand long-term consequences of climate change. Natural history (NH) collections offer a potential solution; they hold a wealth of species occurrence documentation spanning from decades to centuries. However, because the sampling of natural history collectors is spatially and temporally haphazard, it remains unclear whether NH data is useful for examining the effects of climate change. We investigated whether statistical methods could be developed for NH specimen data, focusing on the large PNW Moth database. Moths are good candidates for this research because collectors have sampled them in the region for over a century. We selected a test case species with >700 database records and assessed whether annual shifts in flight date, correcting for the effects of latitude and elevation, could be detected with generalized linear mixed-effects models. We found that this approach has great promise for detecting temporal shifts, despite the non-standardized sampling inherent in NH data.

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May 14th, 10:00 AM May 14th, 2:00 PM

Can Collection Specimen Data Reveal Temporal Shifts Due to Climate Change?

Biology

Climate change is altering the distribution, behavior, and migration patterns of many species. Typically, these responses are documented studies in which standardized methods are used to collect population or behavioral data over several years. Multi-decade studies are rare and few predate the recent dramatic increase in global temperatures, limiting our ability to understand long-term consequences of climate change. Natural history (NH) collections offer a potential solution; they hold a wealth of species occurrence documentation spanning from decades to centuries. However, because the sampling of natural history collectors is spatially and temporally haphazard, it remains unclear whether NH data is useful for examining the effects of climate change. We investigated whether statistical methods could be developed for NH specimen data, focusing on the large PNW Moth database. Moths are good candidates for this research because collectors have sampled them in the region for over a century. We selected a test case species with >700 database records and assessed whether annual shifts in flight date, correcting for the effects of latitude and elevation, could be detected with generalized linear mixed-effects models. We found that this approach has great promise for detecting temporal shifts, despite the non-standardized sampling inherent in NH data.

 

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