A framework for mapping the distribution of seabirds by integrating tracking, demography and phenology

1. The identification of geographic areas where the densities of animals
are highest across their annual cycles is a crucial step in conservation
planning. In marine environments, however, it can be particularly
difficult to map the distribution of species, and the methods used are
usually biased towards adults, neglecting the distribution of other
life-history stages even though they can represent a substantial
proportion of the total population. 2. Here we develop a methodological
framework for estimating population-level density distributions of
seabirds, integrating tracking data across the main life-history stages
(adult breeders and non-breeders, juveniles and immatures). We incorporate
demographic information (adult and juvenile/immature survival, breeding
frequency and success, age at first breeding) and phenological data
(average timing of breeding and migration) to weight distribution maps
according to the proportion of the population represented by each
life-history stage.   3. We demonstrate the utility of this
framework by applying it to 22 species of albatrosses and petrels that are
of conservation concern due to interactions with fisheries. Because
juveniles, immatures and non-breeding adults account for 47–81% of all
individuals of the populations analysed, ignoring the distributions of
birds in these stages leads to biased estimates of overlap with threats,
and may misdirect management and conservation efforts. Population-level
distribution maps using only adult distributions underestimated exposure
to longline fishing effort by 18–42%, compared with overlap scores based
on data from all life-history stages. 4. Synthesis and applications. Our
framework synthesises and improves on previous approaches to estimate
seabird densities at sea, is applicable for data-poor situations, and
provides a standard and repeatable method that can be easily updated as
new tracking and demographic data become available. We provide scripts in
the R language and a Shiny app to facilitate future applications of our
approach. We recommend that where sufficient tracking data are available,
this framework be used to assess overlap of seabirds with at-sea threats
such as overharvesting, fisheries bycatch, shipping, offshore industry and
pollutants. Based on such an analysis, conservation interventions could be
directed towards areas where they have the greatest impact on populations.