  Species distribution models (SDMs) are key tools in ecology, conservation and
management of natural resources. They are commonly trained by scientific survey
data but, since surveys are expensive, there is a need for complementary
sources of information to train them. To this end, several authors have
proposed to use expert elicitation since local citizen and substance area
experts can hold valuable information on species distributions. Expert
knowledge has been incorporated within SDMs, for example, through informative
priors. However, existing approaches pose challenges related to assessment of
the reliability of the experts. Since expert knowledge is inherently subjective
and prone to biases, we should optimally calibrate experts' assessments and
make inference on their reliability. Moreover, demonstrated examples of
improved species distribution predictions using expert elicitation compared to
using only survey data are few as well. In this work, we propose a novel
approach to use expert knowledge on species distribution within SDMs and
demonstrate that it leads to significantly better predictions. First, we
propose expert elicitation process where experts summarize their belief on a
species occurrence proability with maps. Second, we collect survey data to
calibrate the expert assessments. Third, we propose a hierarchical Bayesian
model that combines the two information sources and can be used to make
predictions over the study area. We apply our methods to study the distribution
of spring spawning pikeperch larvae in a coastal area of the Gulf of Finland.
According to our results, the expert information significantly improves species
distribution predictions compared to predictions conditioned on survey data
only. However, experts' reliability also varies considerably, and even
generally reliable experts had spatially structured biases in their
assessments.
