No abstract

In order to assess how the last sea level rise affected the Aegean archipelago, we quantified the magnitude and rate of geographic change for the Aegean islands during the last sea-level-rise episode (21 kyr BP-present) with a spatially explicit geophysical model. An island-specific Area-Distance-Change (ADC) typology was constructed, with higher ADC values representing a higher degree of change. The highest fragmentation rates of the Aegean archipelago occurred in tandem with the largest rates of sea-level-rise occurring between 17 kyr and 7 kyr ago. Sea-level rise resulted in an area loss for the Aegean archipelago of approximately 70%. Spatiotemporal differences in sea-level changes across the Aegean Sea and irregular bathymetry produced a variety of island surface-area loss responses, with area losses ranging from 20% to N90% per island. In addition, sea-level rise led to increased island isolation, increasing distances of islands to continents to N200% for some islands. We discuss how rates of area contractions and distance increases may have affected biotas, their evolutionary history and genetics. Five testable hypotheses are proposed to guide future research. We hypothesize that islands with higher ADC-values will exhibit higher degrees of community hyper-saturation, more local extinctions, larger genetic bottlenecks, higher genetic diversity within species pools, more endemics and shared species on continental fragments and higher z-values of the power-law species-area relationship. The developed typology and the quantified geographic response to sea-level rise of continental islands, as in the Aegean Sea, present an ideal research framework to test biogeographic and evolutionary hypotheses assessing the role of rates of area and distance change affecting biota.

Studying the epidemiology of schistosomiasis—the most prevalent gastropod-borne human disease and an economic burden for the livestock industry—relies on adequate monitoring tools. Here we describe a molecular assay for detecting human and animal African schistosome species in their planorbid gastropod host (xenomonitoring) using a two-step approach. First, schistosome infections are detected and discriminated from other trematode infections using a multiplex polymerase chain reaction (PCR) that includes a trematode-specific marker (in 18S rDNA), a Schistosoma genus-specific marker (in internal transcribed spacer 2 [ITS2]) and a general gastropod marker (in 18S rDNA) as an internal control. Upon Schistosoma sp. detection, a second multiplex PCR is performed to discriminate among Schistosoma haematobium, Schistosoma mansoni, Schistosoma mattheei and Schistosoma bovis/Schistosoma curassoni/Schistosoma guineensis using markers of differential lengths in the cytochrome c oxidase subunit 1 (COX1) gene. The specificity of these assays was validated with adult worms, naturally infected gastropods and human urine and stool samples. Sensitivity was tested on experimentally infected snail specimens that were sacrificed 10 and 40 days post-infection in order to mimic a natural prepatent and mature infection, respectively. The assay provides a diagnostic tool to support the xenomonitoring of planorbid gastropods for trematode infections in a One Health context, with a focus on the transmission monitoring of schistosomiasis.

Schistosomiasis affects over 700 million people globally. 90% of the infected live in sub-Saharan Africa, where the trematode species Schistosoma mansoni and S. haematobium transmitted by intermediate hosts (IH) of the gastropod genera Biomphalaria and Bulinus are the major cause of the human disease burden. Understanding the factors influencing the distribution of the IH is vital towards the control of human schistosomiasis. We explored the applicability of a machine learning algorithm, random forest, to determine significant predictors of IH distribution and their variation across different geographic scales in crater lakes in western Uganda. We found distinct variation in the potential controls of IH snail distribution among the two snail genera as well as across different geographic scales. On the larger scale, geography, diversity of the associated mollusk fauna and climate are important predictors for the presence of Biomphalaria, whereas mollusk diversity, water chemistry and geography mainly control the occurrence of Bulinus. Mollusk diversity and geography are relevant for the presence of both genera combined. On the scale of an individual crater lake field, Biomphalaria is solely controlled by geography, while mollusk diversity is most relevant for the presence of Bulinus. Our study demonstrates the importance of combining a comprehensive set of predictor variables, a method that allows for variable selection and a differentiated assessment of different host genera and geographic scale to reveal relevant predictors of distribution. The results of our study contribute to making realistic predictions of IH snail distribution and schistosomiasis prevalence and can help in supporting strategies towards controlling the disease.