au-ecoscience-2023
Seminar at Dept Ecoscience, Aarhus University, March 8th 2023
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Seminar at Dept Ecoscience, Aarhus University, March 8th 2023
Basic Info
- Host: GitHub
- Owner: gavinsimpson
- License: cc-by-4.0
- Language: HTML
- Default Branch: main
- Homepage: https://gavinsimpson.github.io/au-ecoscience-2023/
- Size: 7.89 MB
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- Stars: 4
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 1
Created almost 3 years ago
· Last pushed almost 3 years ago
Metadata Files
Readme
License
Citation
README.md
Department of Ecoscience Seminar
Quantifying trends in ecological data using GAMs
Seminar at Dept Ecoscience, Aarhus University, March 8th 2023
Gavin Simpson
Rendered slidedeck: https://gavinsimpson.github.io/au-ecoscience-2023/
Owner
- Name: Gavin Simpson
- Login: gavinsimpson
- Kind: user
- Location: Denmark
- Company: Aarhus University
- Website: fromthebottomoftheheap.net
- Twitter: ucfagls
- Repositories: 194
- Profile: https://github.com/gavinsimpson
Citation (CITATION.cff)
title: "Quantifying trends in ecological data using GAMs"
authors:
- family-names: Simpson
given-names: Gavin
orcid: "https://orcid.org/0000-0002-9084-8413"
cff-version: 1.2.0
contact:
- affiliation: "Department of Animal and Veterinary Sciences, Aarhus University"
email: "ucfagls@gmail.com"
family-names: Simpson
given-names: Gavin
date-released: "2023-03-08"
doi: "10.5281/zenodo.7706659"
version: "1.0"
keywords:
- "biodiversity"
- "time series"
- "generalised additive models"
- "trends"
- "penalized splines"
- "lym borreliosis"
license: "CC-BY-4.0"
license-url: "https://creativecommons.org/licenses/by/4.0/legalcode"
abstract: "Climate change and other human-caused environmental disturbance
may lead to declines in biodiversity. Recently, a number of studies have
collated large data sets of monitoring time series for selected ecosystem
or organism groups and used these data sets to estimate trends in
biodiversity, with many studies identifying large declines in biodiversity
across a number of organisms or ecosystems. These results are not without
controversy however; data selection and quality issues, as well as
questions over statistical methodology have lead to vigorous debate.
Typically, trends in biodiversity are estimated using linear effects, via
generalized linear mixed (or hierarchical) models to account for site-to-site
heterogeneity in temporal trends. Additionally, year-to-year variation may
enhance or mask estimated losses or gains in biodiversity over time if the
first observation year in a given series is unusually rich or depauperate.
Using year random effects has been suggested as a mechanism to account for
this potential bias. An alternative way to model trends in biodiversity
time series is using penalized splines for the trends, leading to
hierarchical generalized additive models (HGAMs). Here, I introduce HGAMs
and penalized splines and their use for modelling biodiversity trends, and
reanalyze a forest arthropod diversity time series data set. Additionally,
I analyse a time series weekly cases of Lyme borreliosis in Norway, to
investigate changes in the seasonal incidence and timing of peak cases of
Lyme disease."
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