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I have a file containing the data from bottom trawl survey. There are 102 draw's points to each with associated coordinates (Lon,Lat), for every set I calculated the density (DI N / 1km2) of the predator (merlmerDI N/1km2) and the density of its preferred prey (other columns) with the same coordinates...

I would pull out a spatial overlap index that quantifies a number of affinity for the presence / absence (using the value of DI N / 1km2) for the predator with its prey in order to justify the preferential choice of a prey than another (which will basically be a choice of presence in that same area shared ). I had found the Moran Index ( precisely Bivariate Moran's I) that returns a simple number (1) when there is high spatial correlation ... once compared the data comes out a Moran's bi-variate scatter plot.

I should compare the hake (predator) separately with each of its preys and to see so many index how many the preys are.

Can someone help me? I don't know if it is right to use this index. Some or any idea?

Year    PrHN°   Latitude    Longitude   Haul depth (m)  Swept area (km2)    merlmerDI N/1km2    tractraDI N/1km2    engrenDI N/1km2 sardpilDI N/1km2    papelonDI N/1km2
2004    1       37,5370     12,6067     51              0,044               137                 69                  0               891                 0
2004    2       37,5433     12,8518     34              0,043               743                 0                   0               2067                0
2004    3       37,4757     12,9192     51              0,045               841                 376                 1350            5754                0
2004    4       37,3212     12,9258     310             0,076               4299                949                 0               0                   12223
2004    5       37,2868     12,8012     214             0,098               1729                366                 0               0                   4027
2004    6       37,1255     12,9703     331             0,103               77                  29                  0               0                   2563
2004    7       37,0010     12,8058     391             0,099               192                 0                   0               0                   6891
2004    8       37,0298     12,7738     388             0,103               156                 0                   0               0                   5040
2004    9       37,2212     12,6082     158             0,049               2347                7000                0               0                   3768
2004    10      37,3883     12,5287     151             0,045               2467                1102                0               0                   5023
2004    11      37,2632     13,2430     130             0,049               2788                10298               0               0                   66304
2004    12      37,1952     13,3478     136             0,048               952                 16612               0               0                   21412
2004    13      37,2642     13,4077     40              0,045               270                 112                 270             8764                0
2004    14      37,2472     13,4677     34              0,045               539                 0                   0               16854               0
2004    15      37,1348     13,6887     26              0,045               22                  3461                0               12135               0
2004    16      36,9882     13,0683     337             0,101               99                  50                  0               0                   3044
2004    17      37,0145     13,4638     619             0,102               10                  10                  0               0                   79
2004    18      37,0800     13,5803     96              0,045               516                 314                 516             426                 7063
2004    19      36,9162     13,6578     655             0,084               0                   0                   0               0                   95
2004    20      36,8105     13,3932     413             0,102               108                 0                   0               0                   2626
2004    22      36,5673     13,9302     586             0,103               29                  0                   0               0                   652
Andrew T.
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Virginia Carrozzi
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  • First, are your data spatial data read as numbers or factors if you use a decimal comma? Second, what have you tried so far? – nya Jul 06 '16 at 09:30
  • data are numbers (decimal comma is a error processing from .csv, its just like example). Frankly I do not know exactly how to base the analysis... i know that i have to find one index to compare the density of predator with the density of each preys to see how many spatial correlation, or percentage of overlap is present between the predator and any prey. I saw this Moran's Index could be appropriate, and there are some packages in R like spdep GeoXp ncf... I'm looking for a way to to have this type of analysis. – Virginia Carrozzi Jul 06 '16 at 10:05
  • Okay. See if this helps: http://stackoverflow.com/questions/21039681/spatial-autocorrelation-analysis-global-morans-i-in-r – nya Jul 06 '16 at 12:17

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