The performance of field sampling for parasite detection in a wild passerine

Parasites can impact the behavior of animals and alter the interplay with ecological factors in their environment. Studying the effects that parasites have on animals thus requires accurate estimates of infections in individuals. However, quantifying parasites can be challenging due to several factors. Laboratory techniques, physiological fluctuations, methodological constraints, and environmental influences can introduce measurement errors, in particular when screening individuals in the wild. These issues are pervasive in ecological studies where it is common to sample study subjects only once. Such factors should be carefully considered when choosing a sampling strategy, yet presently there is little guidance covering the major sources of error. In this study, we estimate the reliability and sensitivity of different sampling practices at detecting two internal parasites—Serratospiculoides amaculata and Isospora sp.—in a model organism, the great tit Parus major. We combine field and captive sampling to assess whether individual parasite infection status and load can be estimated from single field samples, using different laboratory techniques—McMaster and mini-FLOTAC. We test whether they vary in their performance, and quantify how sample processing affects parasite detection rates. We found that single field samples had elevated rates of false negatives. By contrast, samples collected from captivity over 24 h were highly reliable (few false negatives) and accurate (repeatable in the intensity of infection). In terms of methods, we found that the McMaster technique provided more repeatable estimates than the mini-FLOTAC for S. amaculata eggs, and both techniques were largely equally suitable for Isospora oocysts. Our study shows that field samples are likely to be unreliable in accurately detecting the presence of parasites and, in particular, for estimating parasite loads in songbirds. We highlight important considerations for those designing host–parasite studies in captive or wild systems giving guidance that can help select suitable methods, minimize biases, and acknowledge possible limitations.publishe

Machine learning reveals cryptic dialects that explain mate choice in a songbird

Culturally transmitted communication signals – such as human language or bird song – can change over time through cultural drift, and the resulting dialects may consequently enhance the separation of populations. However, the emergence of song dialects has been considered unlikely when songs are highly individual-specific, as in the zebra finch (Taeniopygia guttata). Here we show that machine learning can nevertheless distinguish the songs from multiple captive zebra finch populations with remarkable precision, and that ‘cryptic song dialects’ predict strong assortative mating in this species. We examine mating patterns across three consecutive generations using captive populations that have evolved in isolation for about 100 generations. We cross-fostered eggs within and between these populations and used an automated barcode tracking system to quantify social interactions. We find that females preferentially pair with males whose song resembles that of the females’ adolescent peers. Our study shows evidence that in zebra finches, a model species for song learning, individuals are sensitive to differences in song that have hitherto remained unnoticed by researchers.publishe