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SUMMARY:A Bayesian Approach to Linking Ecological Data
LOCATION:Biology 136
TZID:America/Denver
DTSTART:20260428T160000
UID:2026-05-13-07-24-57@natsci.colostate.edu
DTSTAMP:20260513T072457
Description:Dr. Andee Kaplan\, Associate Professor of Statistics. Dr. Kapla
 n will present groundbreaking research on applying Bayesian hierarchical m
 odels to ecological data\, addressing challenges in record linkage across 
 overlapping datasets.\n\nIn this seminar\, Dr. Kaplan will introduce innov
 ative methods for linking ecological data sources when individual identiti
 es are unknown. Her work focuses on two fascinating applications: estimati
 ng sea otter abundance using overlapping aerial images in Glacier Bay\, Al
 aska\, and modeling growth-size curves for conifer species using overlappi
 ng LiDAR scans in the Upper Gunnison Watershed. These approaches provide v
 aluable insights into ecological inference\, including the effects of topo
 graphic covariates on conifer growth in the Southern Rocky Mountains.\n\nE
 vent Details:\nSpeaker: Dr. Andee Kaplan\nTitle: A Bayesian Approach to Li
 nking Ecological Data\nDate: Tuesday\, April 28th\, 2026\nTime: 4:00 PM\nL
 ocation: BIO 136\n\nHosted by Kim Hoke\, Professor\, CSU Department of Bio
 logy\nLight refreshments while supplies last.\n\nVisit our website for mo
 re information on our seminars and follow us on social media for more anno
 uncements from Biology.\n\n 	\nInstagram: @csubio\n 	\nTwitter/X: @csubiol
 ogy\n 	\nFacebook: Department of Biology at Colorado State University\n\nW
 e look forward to seeing you there!\n\nAbstract\n\"It has become increasin
 gly common for data containing records about overlapping individuals to be
  distributed across multiple sources\, making it necessary to identify whi
 ch records refer to the same individual. The goal of record linkage is to 
 estimate this unknown structure in the absence of a unique identifiable at
 tribute. While this task is commonly used to link social science and offic
 ial statistics data\, it can also be useful to link overlapping ecological
  data sets. We introduce a Bayesian hierarchical record linkage model moti
 vated by two tasks in ecological inference using overlapping aerial data s
 ources. The first is a hierarchical framework to achieve abundance estimat
 ion using overlapping aerial images of sea otters in Glacier Bay\, Alaska 
 in which the individuals can occur in multiple images. The second is a two
 -stage approach to estimate individual growth-size curves for conifer spec
 ies using overlapping LiDAR scans of the Upper Gunnison Watershed\, which 
 allows assessment of the impact of key topographic covariates on the growt
 h behavior of conifer species in the Southern Rocky Mountains (USA). In bo
 th scenarios\, we have overlapping individuals with unknown identity and a
  record linkage model is introduced to facilitate large scale inference.\"
 \n\n 4:00 pm
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