Methods
Population time series data
We obtain populations monitored to extirpation from two sources: i) the
Living Planet database (LPD)
(http://www.livingplanetindex.org/data_portal),
containing annual population abundance data for over 25000 vertebrate
populations between 1950-2019 and ii) from previously published work on
the extinction vortex (Fagan & Holmes 2006). A diverse range of methods
to monitor population abundance are included in the LPD, with the caveat
to inclusion in the dataset being that monitoring should be reputable,
appropriate for the species and consistent through time. A detailed
outline of inclusion criteria for the populations in the LPD are
provided by Loh et al. (2005). In some cases, complete censuses of the
population were carried out, whereas in others population abundance was
monitored using indirect indicators. In the absence of evidence to the
contrary, regardless of the method used in population monitoring, we
assume that indices of population abundance are representative of the
true population size at any given point in time.
We define extirpation as a population declining to a zero-abundance
count at the end of the time series and identify populations from the
LPD that showed this. Zero-abundance counts occurring before the end of
the time series might indicate a relatively low species detectability
and, correspondingly, a high rate of observation error (Brook et al.
2006). To minimize the possibility of including populations that were
not actually extinct and to avoid inflating annual variation in
population abundance, we omit time series where zero counts occurred and
were followed by subsequent observations. In addition, we only consider
populations where the time between the penultimate abundance count and
the zero-abundance count (signifying extirpation) was no more than one
year, so that we can ascertain the exact year in which the population
went extinct. Furthermore, to avoid introducing possible bias from short
time series, we only include time series with at least 10 counts of
population abundance.
Based on these filtering criteria, we produce a dataset of 55 population
extirpations of 52 different species, including two elasmobranchs, five
actinopterygians, one amphibian, one reptile, nine mammals and 34 birds.
Our dataset of time series has a mean length of 15.98 (±6.65) years.
Life history data
We compile life history data for all species in this dataset from
various life history databases (Myhrvold et al. 2015; Oliveira et al.
2017; Froese & Pauly 2000), extracting data on log-transformed (base
10) adult body mass in kg. Additionally, where possible, we collate up
to six other traits indicative of life history speed: maximum longevity,
female maturity, incubation time, fledgling age, number of litters per
year and litter/clutch size. We log-transform (base 10) these trait
values and investigate their relationship with body mass using linear
models.
LMM/GLMMs
We perform all statistical analyses using R version 3.6.1 (R Core Team,
2019). We perform three statistical analyses to investigate how
population dynamics change in the region of an extinction event. For
each analysis we use linear or generalized linear mixed effects models
(LMMs/GLMMs) in the ‘nlme’ (Pinheiro et al. 2019) and ‘glmmTMB’ (Brooks
et al. 2017) packages respectively, to account for context-specific
factors that could mask the effect size of fixed effects on the response
variables. We account for the nested random effects of our data using a
mixed modelling framework, with population nested inside species, nested
inside units, nested inside data type. This accounts for the
site-specific effects on the population dynamics, the potential effects
of relatedness at the species level and the potential effects of units
of measurement. These are nested within data type, which is consistent
across species and populations, but may vary between species and units.
We present full descriptions of the fixed and nested random effects used
in our LMMs/GLMMs in Table 1. We also normalize our fixed effects to
enable easier comparison of the relative importance of each variable in
the models. As avian taxa are overwhelmingly represented in the dataset
(65.38% of species), we perform our analyses on all populations
together, as well as two subsets: i) only avian populations and ii) only
non-avian populations. For each analysis, we employ AIC-based model
selection to identify the best fitting model using the ‘MuMIn’ package
(Bartón 2019).