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Seasonal sea surface chlorophyll a from 2002 to 2012 and primary production data from 2006 to 2010 were extracted. Chlorophyll a was derived from Aqua-MODIS (Moderate Resolution Imaging Spectroradiometer) Case I was processed by the Remote Sensing Unit at the Bedford Institute of Oceanography (RSU-BIO). Annual and seasonal averages were computed, with seasons delimited by the following day of year ranges: days 91–181 (spring), 182–273 (summer), and 274–365 (fall). Seasonal (spring, summer, fall) and annual primary production layers were also used in the model, the details of which are summarized in.
For each variable type (e.g., bottom temperature), four different statistical quantities were calculated across its temporal data range: minimum, maximum, mean and range (difference between minimum and maximum). This was done by averaging the minimum, maximum, or mean values between all years of the dataset. All variables except depth and slope were then spatially interpolated across the study area using ordinary kriging in ArcMap 10.2.2 software to create continuous data surfaces with a ~1 km grid size. |
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Seasonal sea surface chlorophyll a from 2002 to 2012 and primary production data from 2006 to 2010 were extracted. Chlorophyll a was derived from Aqua-MODIS (Moderate Resolution Imaging Spectroradiometer) Case I was processed by the Remote Sensing Unit at the Bedford Institute of Oceanography (RSU-BIO). Annual and seasonal averages were computed, with seasons delimited by the following day of year ranges: days 91–181 (spring), 182–273 (summer), and 274–365 (fall). Seasonal (spring, summer, fall) and annual primary production layers were also used in the model, the details of which are summarized in.
For each variable type (e.g., bottom temperature), four different statistical quantities were calculated across its temporal data range: minimum, maximum, mean and range (difference between minimum and maximum). This was done by averaging the minimum, maximum, or mean values between all years of the dataset. All variables except depth and slope were then spatially interpolated across the study area using ordinary kriging in ArcMap 10.2.2 software to create continuous data surfaces with a ~1 km grid size. |
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Fisheries and Oceans Canada, Bedford Institute of Oceanography, P.O. Box 1006, Dartmouth, NS, Canada B2Y 4A2 |
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5000 |
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[] |
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<DIV STYLE="text-align:Left;"><DIV><DIV><P STYLE="margin:0 0 0 0;"><SPAN>Emerald Basin on the Scotian Shelf off Nova Scotia, Canada, is home to a globally unique</SPAN><SPAN /><SPAN>aggregation of the glass sponge </SPAN><SPAN STYLE="font-style:italic;"><SPAN>Vazella pourtalesi</SPAN></SPAN><SPAN>, first documented in the region in 1889.</SPAN><SPAN /><SPAN>In 2009, Fisheries and Oceans Canada (DFO) implemented two Sponge Conservation</SPAN><SPAN /><SPAN>Areas to protect these sponge grounds from bottom fishing activities. Together, the two conservation</SPAN><SPAN /><SPAN>areas encompass 259 km</SPAN><SPAN><SPAN>2</SPAN></SPAN><SPAN>. In order to ascertain the degree to which the sponge</SPAN><SPAN /><SPAN>grounds remain unprotected, we modelled the presence probability and predicted range distribution</SPAN><SPAN /><SPAN>of </SPAN><SPAN STYLE="font-style:italic;"><SPAN>V</SPAN></SPAN><SPAN><SPAN>. </SPAN></SPAN><SPAN STYLE="font-style:italic;"><SPAN>pourtalesi </SPAN></SPAN><SPAN>on the Scotian Shelf using random forest modelling on presence</SPAN><SPAN /><SPAN>absence</SPAN><SPAN /><SPAN>records. With a high degree of accuracy the random forest model predicted the</SPAN><SPAN /><SPAN>highest probability of occurrence of </SPAN><SPAN STYLE="font-style:italic;"><SPAN>V</SPAN></SPAN><SPAN><SPAN>. </SPAN></SPAN><SPAN STYLE="font-style:italic;"><SPAN>pourtalesi </SPAN></SPAN><SPAN>in the inner basins on the central Scotian</SPAN><SPAN /><SPAN>Shelf, with lower probabilities at the shelf break and in the Fundian and Northeast Channels.</SPAN><SPAN /><SPAN>Bottom temperature was the most important determinant of its distribution in the model.</SPAN><SPAN /><SPAN>Although the two DFO Sponge Conservation Areas protect some of the more significant</SPAN><SPAN /><SPAN>concentrations of </SPAN><SPAN STYLE="font-style:italic;"><SPAN>V</SPAN></SPAN><SPAN><SPAN>. </SPAN></SPAN><SPAN STYLE="font-style:italic;"><SPAN>pourtalesi</SPAN></SPAN><SPAN>, much of its predicted distribution remains unprotected (over</SPAN><SPAN /><SPAN>99%). Examination of the hydrographic conditions in Emerald Basin revealed that the </SPAN><SPAN STYLE="font-style:italic;"><SPAN>V</SPAN></SPAN><SPAN>.</SPAN><SPAN /><SPAN STYLE="font-style:italic;">pourtalesi </SPAN><SPAN>sponge grounds are associated with a warmer and more saline water mass compared</SPAN><SPAN /><SPAN>to the surrounding shelf. Reconstruction of historical bottom temperature and salinity</SPAN><SPAN /><SPAN>in Emerald Basin revealed strong multi-decadal variability, with average bottom temperatures</SPAN><SPAN /><SPAN>varying by 8</SPAN><SPAN><SPAN>˚</SPAN></SPAN><SPAN>C. We show that this species has persisted in the face of this climatic variability,</SPAN><SPAN /><SPAN>possibly indicating how it will respond to future climate change.</SPAN></P></DIV></DIV></DIV> |
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<DIV STYLE="text-align:Left;"><DIV><DIV><P><A href="http://open.canada.ca/en/open-government-licence-canada"><SPAN><SPAN>http://open.canada.ca/en/open-government-licence-canada</SPAN></SPAN></A></P></DIV></DIV></DIV> |
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pp_sum_max_avg |
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| tags:
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["Nova Scotia","Canada","Scotian Shelf","Emerald Basin","Environmental Predictor Layers","Spatial Interpolation","Ordinary Kriging","Temperature","Salinity","Current Speed","Slope","Sea Surface Chlorophyll a","Primary Production","Vazella pourtalesi"] |
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en-CA |
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150000000 |
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