The historical reconstruction of distribution of the genus <i>Halecium</i> (Hydrozoa: Haleciidae): a biological signal of ocean warming?

<p>The distribution of 130 nominal species of the genus <i>Halecium</i>, based on published records, has been mapped for the first time in a comprehensive set of marine ecoregions, to analyse their distribution. Most <i>Halecium</i> species are found at mid- and high latitudes, with some overlaps in distribution ranges across regions. The species richness of <i>Halecium</i> is strongly related to the latitudinal gradient, with maximal diversity at polar and temperate latitudes. Previous detailed studies in the Mediterranean Sea show that large <i>Halecium</i> species of coldwater affinity have regressed or disappeared in recent years, probably due to global warming. Worldwide, however, the overall species richness of <i>Halecium</i> has not changed along the latitudinal gradient over recent decades, with some changes in species composition at temperate-tropical latitudes in both hemispheres, even though the majority of the species that have not been recorded for more than 50 years are of coldwater affinity. The genus can be considered an indicator for biological responses to climate changes for the Mediterranean Sea, but the available distribution data do not allow extending this possibility to the rest of the world. A focused evaluation on the distribution of <i>Halecium</i> species with the addition of new field data might reinforce the picture stemming from the present analysis.</p

Biomass standing stocks, time series.

<p>Colored bar show the intertidal (green) and subtidal (blue) realized biomass stock estimated from the different scenarios for the present extension of the basin. Broken-line bars on the years 1968 and 1983 include the area that was cut-off from the beginning of the Oesterdam works in 1979 (25 km² between 1968 and 1983 and 12 km² between 1983 and 1986). Empty bars on the years 2010 and 2100 show the result of the scenarios simulated removing the Delta Works.</p

Models of the 0.975<i>^th</i> quantile, response surfaces.

<p>Models of the maximal biomass, when extrapolated in the explanatory variable space, give a description of the species potential niche consistent with the Liebig's Law.</p

Median values of the explanatory variables on different year-scenarios.

<p>Circles represent the median values predicted for the available years-scenarios by the hydrodynamic model. Triangles represent the values predicted for the years 2010 and 2100 removing the Delta Works (NDW).</p

Models of the 0.975<i>^th</i> quantile, habitat suitability.

<p>Once extrapolated to realistic scenarios, the response surface shown in 3 are useful to produce clearly interpretable habitat suitability maps. In the figure we show as example the output for the 1968, 2010 and 2100 scenarios.</p

Complete distribution model <i>vs</i> Model of the maxima.

<p>Example for <i>C. edule</i>, year 2010. Map produced by sampling from the complete quantile distribution models (A) are able to represent the realistic scatter around (mainly below) the response surface shown in (B). To help the reader in appreciating the fine mosaic of points in (A) we restricted the map to a smaller portion of the basin and we used a logarithmic scale for plotting the estimated values.</p

Areas of the total, subtidal and intertidal surface for the different scenarios.

<p>Areas of the total, subtidal and intertidal surface for the different scenarios.</p

Models validation. Ratio between observed and predicted values.

<p>To validate our forecast for each of the modeled quantiles, the whole dataset was sampled with replacement. Due to sampling with replacement, some observations are repeated and others remain unpicked. The model was fitted on the sampled observation (training dataset) and used to predict the unpicked ones (validation dataset). The random sampling-fitting-predicting procedure was iterated 5000 times and repeated for each one of the forecast quantiles. To make predicted (quantiles) and realized values comparable each other, we discretized them in 10 homogeneous classes based on the predicted values. For each of the classes, the correspondent sample quantile of the observed data was calculated. To finally asses the validity of the model, observed and predicted quantiles were plotted against each other and checked for linear correlation. The four quantiles for species showed as examples in the graphs were selected among those predicting occurrence (<i>e.g.</i>, up to the 35<i>^th</i> quantile for <i>S. armiger</i>, up to the 78<i>^th</i> quantile for <i>L. conchilega</i> <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089131#pone-0089131-t004" target="_blank">Table 4</a>). The other quantiles generally follow the same trends. The black broken line represent the 1∶1 ratio.</p

Target species occurrences.

<p>Target species occurrences.</p

Reworked sediment

Peak Suspended Sediment Concentration (SSC, g/L) measured in the flume experiments using OBS. The OBS data has been calibrated by gravimetric analysis