title: "Quantifying trends in biodiversity with generalized additive models"
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 Science, Aarhus University"
    email: "ucfagls@gmail.com"
    family-names: Simpson
    given-names: Gavin
date-released: "2022-08-18"
doi: "10.5281/zenodo.7003948"
version: "1.0"
keywords:
  - "biodiversity"
  - "time series"
  - "generalised additive models"
  - "trends"
  - "penalized splines"
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.

  HGAMs were fitted to the forest arthropod data (150 plots, 9 years per plot)
  that included a variety of decompositions of biodiversity trends. Of the
  these, two models had clearly superior predictive ability: (1) a model with
  region-specific trends plus plot-specific random smooths (AIC 15120), and (2)
  as model (1) plus year random effects to model the average year-to-year
  effects (AIC 15122). Both models represent significant improvements over a
  GLMM with linear trends plus random linear slopes per plot and year-to-year
  effects (AIC 15467). The main difference between the two HGAMs is the
  complexity of the region-specific trends: model (2) had simpler regional
  trends, closer to linear, than model (1), due to the additional year-to-year
  effect modelling variation ascribed to the regional trends in model (1).

  This shows that there may be several ways to decompose trends in biodiversity
  data that are equally good at describing those underlying trends. Here, the
  bias in the estimated linear trends due to many series starting in 'good'
  years can be removed either via the year-to-year random effect or region
  specific smooths, both of which capture the overall tendency for some years
  to have greater arthropod abundance than others."