Local environment and sampling bias drive parasite prevalence estimates
in freshwater fish communities
Abstract
Parasite occurrence and infection estimates vary through time and space,
making understanding the underlying drivers highly complex. Comparative
studies based on empirical data must consider the factors of variation
involved in estimating infection metrics in natural populations to make
appropriate and reliable comparisons. Using a multi-scale approach, we
explored the sources of variation in the estimation of infection
prevalence, focusing on black spot disease in littoral freshwater fish
communities sampled across 15 lakes in Québec, Canada. Our results show
that infection prevalence is spatially heterogeneous across the
landscape with evidence of infection hotspots and coldspots.
Method-related sampling biases led to significant variations in
prevalence estimates and spatial patterns of disease occurrence. Our
results also indicated that low sampling efforts tend to overestimate
the prevalence of infection in the landscape, and that the sampling
effort required to estimate an accurate infection prevalence depends on
the sampling method employed. Physico-chemical characteristics of the
sites and local fish community structure were found to be the best
drivers of infection at smaller spatial scales. Furthermore, our results
suggest dilution effects due to obstruction and compatibility barriers
limit the survival of the free-living cercaria parasite lifestage.
Several relationships between infection prevalence and environmental
drivers revealed non-linearity, suggesting complex interactions.
Examining infection prevalence data at various spatial scales revealed
method-induced biases, sampling effort effect and environment driven
relationships underscoring the importance of context-dependencies and
scale-dependencies in studies on host-parasite interactions.