Improving assessments of data-limited populations using life-history theory

1. Predicting how populations may respond to climate change and
anthropogenic pressures requires detailed knowledge of demographic traits,
such as survival and reproduction. However, the availability of these data
varies greatly across space and taxa. Therefore, it is common practice to
conduct population assessments by filling in missing values from surrogate
species or other populations of the same species. Using these independent
surrogate values concurrently with observed data neglects the life‐history
trade‐offs that connect the different aspects of a population's
demography. Consequently, this approach introduces biases that could
ultimately lead to erroneous management decisions. 2. We use a Bayesian
hierarchical framework to combine fragmented multi‐population data with
established life‐history theory and reconstruct population‐specific
demographic data across a substantial part of a species breeding range. We
apply our analysis to a long‐lived colonial species, the black‐legged
kittiwake Rissa tridactyla, that is classified as globally Vulnerable and
is highly threatened by increasing anthropogenic pressures, including
offshore renewable energy development. We then use a projection analysis
to examine how the reconstructed demographic parameters may improve
population assessments, compared to models that combine observed data with
independent surrogate values. 3. Demographic parameters reconstructed
using a hierarchical framework can be utilised in a range of population
modelling approaches. They can also be used as reference estimates to
assess whether independent surrogate values are likely to over or
underestimate missing demographic parameters. We show that surrogate
values from independent sources are often used to fill in missing
parameters that have large potential demographic impact, and that
resulting biases are driven in unpredictable directions thus precluding
assessments from being consistently precautionary. 4. Synthesis and
applications. Our study dramatically increases the spatial coverage of
population‐specific demographic data for black‐legged kittiwakes. The
reconstructed demographic parameters presented can also be used
immediately to reduce uncertainty in the consenting process for offshore
wind development in the United Kingdom and Ireland. More broadly, we show
that the reconstruction approach used here provides a new avenue for
improving evidence‐based management and policy action for animal and plant
populations with fragmented and error prone demographic data. 22-Mar-2021