  Understanding and predicting interactions between predators and prey and
their environment are fundamental for understanding food web structure,
dynamics, and ecosystem function in both terrestrial and marine
ecosystems.Thus, estimating the conditional associations between species and
their environments is important for exploring connections or cooperative links
in the ecosystem, which in turn can help to clarify such causal relationships.
For this purpose, a relevant and practical statistical method is required to
link presence/absence observations with biomass, abundance, and physical
quantities obtained as continuous real values.These data are sometimes sparse
in oceanic space and too short as time series data. To meet this challenge, we
provide an approach based on applying categorical data analysis to
present/absent observations and real-number data.This approach consists of a
two-step procedure for categorical data analysis:1) finding the appropriate
threshold to discretize the real-number data for applying an independent
test;and 2) identifying the best conditional probability model to investigate
the possible associations among the data based on a statistical information
criterion.We conduct a simulation study to validate our proposed approach.
Furthermore, the approach is applied to two datasets: 1) one collected during
an international synoptic krill survey in the Scotia Sea west of the Antarctic
Peninsula to investigate associations among krill, fin whale (Balaenoptera
physalus),surface temperature, depth, slope in depth, and temperature gradient;
2) the other collected by ecosystem surveys conducted during August-September
in 2014 - 2017 to investigate associations among common minke whales, the
predatory fish Atlantic cod, and their main prey groups in Arctic Ocean waters
to the west and north of Svalbard, Norway.
