Disparate dispersal limitation in Geomalacus slugs unveiled by the shape and slope of the genetic–spatial distance relationship

Long‐term dispersal ability is a key species’ trait constraining species ranges and thus large‐scale biodiversity patterns. Here we infer the long‐term dispersal abilities of three Geomalacus (Gastropoda, Pulmonata) species from their range‐wide genetic–spatial distance relationships. This approach follows recent advances in statistical modelling of the analogous pattern at the community level: the distance decay in assemblage similarity. While linear relationships are expected for species with high long‐term dispersal abilities, asymptotic relationships are expected for those with more restricted mobility. We evaluated three functional forms (linear, negative exponential and power‐law) for the relationship between genetic distance (computed from mitochondrial cox1 sequences, n = 701) and spatial distance. Range fragmentation at present time and at the Last Glacial Maximum was also estimated based on the projection of climatic niches. The power‐law function best fit the relationship between genetic and spatial distances, suggesting strong dispersal limitation and long‐term population isolation in all three species. However, the differences in slope and explained variance pointed to disparities in dispersal ability among these weak dispersers. Phylogeographic patterns of Geomalacus species are thus largely driven by the same major process (i.e. dispersal limitation), operating at different strengths. This strong dispersal limitation results in geographic clustering of genetic diversity that makes these species highly vulnerable to genetic erosion due to climate changThe authors were supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (ERDF) through grant CGL2016‐76637‐P and fellowship IJCI‐2014‐20881 to CG‐RS

DNA Barcoding of Recently Diverged Species: Relative Performance of Matching Methods

Recently diverged species are challenging for identification, yet they are frequently of special interest scientifically as well as from a regulatory perspective. DNA barcoding has proven instrumental in species identification, especially in insects and vertebrates, but for the identification of recently diverged species it has been reported to be problematic in some cases. Problems are mostly due to incomplete lineage sorting or simply lack of a ‘barcode gap’ and probably related to large effective population size and/or low mutation rate. Our objective was to compare six methods in their ability to correctly identify recently diverged species with DNA barcodes: neighbor joining and parsimony (both tree-based), nearest neighbor and BLAST (similarity-based), and the diagnostic methods DNA-BAR, and BLOG. We analyzed simulated data assuming three different effective population sizes as well as three selected empirical data sets from published studies. Results show, as expected, that success rates are significantly lower for recently diverged species (∼75%) than for older species (∼97%) (P<0.00001). Similarity-based and diagnostic methods significantly outperform tree-based methods, when applied to simulated DNA barcode data (P<0.00001). The diagnostic method BLOG had highest correct query identification rate based on simulated (86.2%) as well as empirical data (93.1%), indicating that it is a consistently better method overall. Another advantage of BLOG is that it offers species-level information that can be used outside the realm of DNA barcoding, for instance in species description or molecular detection assays. Even though we can confirm that identification success based on DNA barcoding is generally high in our data, recently diverged species remain difficult to identify. Nevertheless, our results contribute to improved solutions for their accurate identification

Application of a Random Walk Model to Geographic Distributions of Animal Mitochondrial DNA Variation

In rapidly evolving molecules, such as animal mitochondrial DNA, mutations that delineate specific lineages may not be dispersed at sufficient rates to attain an equilibrium between genetic drift and gene flow. Here we predict conditions that lead to nonequilibrium geographic distributions of mtDNA lineages, test the robustness of these predictions and examine mtDNA data sets for consistency with our model. Under a simple isolation by distance model, the variance of an mtDNA lineage's geographic distribution is expected be proportional to its age. Simulation results indicated that this relationship is fairly robust. Analysis of mtDNA data from natural populations revealed three qualitative distributional patterns: (1) significant departure of lineage structure from equilibrium geographic distributions, a pattern exhibited in three rodent species with limited dispersal; (2) nonsignificant departure from equilibrium expectations, exhibited by two avian and two marine fish species with potentials for relatively long-distance dispersal; and (3) a progression from nonequilibrium distributions for younger lineages to equilibrium distributions for older lineages, a condition displayed by one surveyed avian species. These results demonstrate the advantages of considering mutation and genealogy in the interpretation of mtDNA geographic variation

Foundation.

Address correspondence to Joseph E. Neigel at the addres

Differential host mortality explains the effect of high temperature on the prevalence of a marine pathogen

<div><p>Infectious diseases threaten marine populations, and the extent of their impacts is often assessed by prevalence of infection (the proportion of infected individuals). Changes in prevalence are often attributed to altered rates of transmission, although the rates of birth, recovery, and mortality also determine prevalence. The parasitic dinoflagellate <i>Hematodinium perezi</i> causes a severe, often fatal disease in blue crabs. It has been speculated that decreases in prevalence associated with high temperatures result from lower rates of infection. We used field collections, environmental sensor data, and high-temperature exposure experiments to investigate the factors that change prevalence of infections in blue crab megalopae (post-larvae). These megalopae migrate from offshore waters, where temperatures are moderate, to marshes where temperatures may be extremely high. Within a few days of arriving in the marsh, the megalopae metamorphose into juvenile crabs. We found a strong negative association between prevalence of <i>Hematodinium</i> infection in megalopae and the cumulative time water temperatures in the marsh exceeded 34°C over the preceding two days. Temperatures this high are known to be lethal for blue crabs, suggesting that higher mortality of infected megalopae could be the cause of reduced prevalence. Experimental exposure of megalopae from the marsh to a temperature of 34°C resulted in higher mortality for infected than uninfected individuals, and decreased the prevalence of infection among survivors from 18% to 3%.</p></div

Effect of temperature and salinity on prevalence of <i>H</i>. <i>perezi</i> in blue crab megalopae.

<p>(<i>A</i>) Hourly water temperature at Coastwide Reference Monitoring System (CRMS) station 0581 near the RWR marsh site (yellow), nearshore buoy 42051 (blue) and offshore buoy 42050 (green) for 40 days prior to collection of megalopae at RWR on Aug. 11, 2015. Red line indicates high-temperature threshold of 35°C, red-shaded area indicates 2-day period prior to megalopae collection. (<i>B</i>) Megalopae were collected from marshes at Rockefeller Wildlife Refuge (RWR), Freshwater City Locks (FWC), Louisiana Universities Marine Consortium (LUM), and Grand Isle Marine Laboratory (GIL). Temperature and salinity data from CRMS sites 0178 (at GIL), 0347 (at LUM), 0581 (at RWR) and 0633 (at FWC). Temperature data from buoys at stations 42050 (TABS F; green) and 42051 (TABS R; blue). (<i>C</i>) Prevalence, as percentage of megalopae from which a portion of the <i>H</i>. <i>perezi</i> 18S rRNA gene was PCR-amplified, plotted against temperature (°C) at collection time and location. (<i>D</i>) Prevalence plotted against salinity (ppt) at collection time and location. (<i>E</i>) Log-likelihoods and (<i>F</i>) slopes from logistic regression models that predict log odds ratio of infection from proportion of hourly marsh temperatures that exceeded each threshold, with variable threshold temperatures and time intervals. Increasing log-likelihood values (lighter color) indicate models with combinations of threshold temperature and time intervals with greater support. Steeper downward slopes (darker colors) indicate models in which thermal stress has a larger negative effect on prevalence of infection.</p

Heat treatment experiment.

<p>(<i>A</i>) Total (live + dead) prevalence of <i>H</i>. <i>perezi</i> compared between control (blue) and heat-treatment (red) groups (<i>χ</i>² = 0.06, d.f. = 1, <i>P</i> = 0.81). (<i>B</i>) Overall percent mortality of megalopae compared between control (blue) and heat-treatment (red) groups (<i>χ</i>² = 13.10, d.f. = 1, <i>P</i> = 0.0003). (<i>C</i>) Percent mortality compared between infected (+) and uninfected (<b>-</b>) megalopae in control (blue) groups (<i>χ</i>² = 0.23, d.f. = 1, <i>P</i> = 0.63) and heat-treatment (red) groups (<i>χ</i>² = 19.7, d.f. = 1, <i>P</i> < 0.00001). Bar graphs show mean values for control and treatment tanks (3 tanks for each group with 20 individuals per tank). Error bars represent standard error. For raw data see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0187128#pone.0187128.s006" target="_blank">S4 Table</a>.</p

<i>SI</i> model of prevalence.

<p>Boxes represent groups that differ in disease status, with numbers of <i>S</i> susceptible and <i>I</i> infected individuals. Prevalence is the fraction of infected individuals <i>I</i>/(<i>I</i>+<i>S</i>). Arrows represent transitions with rates for birth (<i>b</i>), infection (<i>i</i>), recovery (<i>r</i>), and mortality (<i>m</i>).</p