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The Donut Selectivity Model for Fish Fee Start Model Read Background el for Fish Feeding Enter Predator Name Predator Enter Number of Prey 7 Note Major Factors or Considerations of the Predator none Done Redo Please use your mouse to move from textbox to textbox Then click on the appropriate button Once you are entirely finished, you will want to save your spreadsheet with a new name Please Enter Prey Names And Relative Abundances (Decimal Format; e.g If used, the sum should =1. If not known, just le 1 1 1 1 1 Prey 1 Prey 2 Prey 3 Prey 4 Prey 5 Prey 6 Prey 7 1 1 Please use your mouse to m Then click on the appropria If you have more than 25 prey items, you'll need to aggregate them s (Decimal Format; e.g. 0.5) 1. If not known, just leave all as 1 Done Redo Back To Start Please use your mouse to move from textbox to textbox Then click on the appropriate button Predator Name Predator Location, details, etc. Predator none Rank Matrix Taxa Prey 1 Prey 2 Prey 3 Prey 4 Prey 5 Prey 6 Prey 7 Rel Abundance 1 1 1 1 1 1 1 Overlap (Oij) 1 1 1 1 1 1 1 (Rijm) Detection Number of Prey 7 Summation Sum of ranks should = 0 28 Finish Ranking Redo Ranking PLEASE FILL IN RANKS, with 1 as the highest Reaction Capture Ingestion Icing Taxa Prey 1 Prey 2 Prey 3 Prey 4 Prey 5 Prey 6 Prey 7 Proportion Matrix Overlap 1 1 1 1 1 1 1 Detection #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! 0 0 0 0 (Pijm) Reaction Capture #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Ingestion Icing #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Product (Sij) #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Taxa Prey 1 Prey 2 Prey 3 Prey 4 Prey 5 Prey 6 Prey 7 RPA Model #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Summation #DIV/0! Summation #DIV/0! View Graph Null Ambient (Ai) 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Null Selectivity 14.3% 14.3% 14.3% 14.3% 14.3% 14.3% 14.3% 700.0% 100.0% Back to Start Back to Ranks If I told you we could predict fish prey preference, and maybe even diet compos with limited data and information, would you believe me? Actually, that is exactly what this model is intended to do. Start Model Description of the Rank Proportion Algorithm (RP 1 2 3 4 Assess general characteristics of a predator j Determine all possible prey N If feasible, determine relative abundance A (numerical or mass, ranging from 0-1) If feasible, determine spatio-temporal (x, y, z, & t) overlap Oij between the predato If steps 3 & 4 possible, then can do diet composition If not possible, then can only prey selectivity, set all abundance and overlaps to 1 5 6 7 8 Determine number of factors M to evaluate Usually four steps of the predation process plus an “icing” factor Rank each prey i for each factor m, ranging from 1 to N (1 being the highest and s Calculate an adjusted (inverse) rank R’ijm to account for ranking highest prey as # Rijm ( N 1) Rijm 9 Generate rank proportions for each prey and factor, Pijm P ijm R ijm N i1 R ijm 10 Multiply rank proportions across all M factors for each prey i to develop a preferen S ij M Pijm m 1 S ij Dij M Pijm m 1 11 Calculate diet composition proxy Dij if abundance and overlap are available (Sij* A 12 Calculate RPA estimate of diet composition (or, using just S’s, for preference), D’i D 'ij D i 1 N ij aybe even diet composition, Start Model See Example n Algorithm (RPA ) or mass, ranging from 0-1) of all prey ap Oij between the predator j and each prey i (scaled from 0-1) nd overlaps to 1 (1 being the highest and so forth), Rijm r ranking highest prey as #1 rey i to develop a preference proxy, Sij verlap are available (Sij* Ai* Oij) st S’s, for preference), D’ij Return Start Model Let's say we have a big gaped, visually oriented piscivore. Let's also say that we have two fish prey, where one is less savvy at predator avoidance and the other is much better at it. Then we would fill in the following rank matrix as such: Taxa Fish 1 (The wimp) Fish 2 (The savvy fish) Summation Rank Matrix (Rijm ) Overlap (Oij ) Detection Reaction 1 2 1 1 3 Capture 1 2 3 1 2 3 Ingestion 1 2 3 Icing 1 2 3 Which would produce the following proportion matrix: Proportion Matrix Taxa Fish 1 (The wimp) Fish 2 (The savvy fish) Overlap (Pijm) Capture Ingestion Icing Detection Reaction 1 0.333333 0.666666667 1 0.666667 0.333333333 0.66666667 0.666667 0.666667 0.33333333 0.333333 0.333333 And finally, given our spatial overlap and an equal relative abundance (Null Ambient), we would get the following RPA Model output for predicted diet composition: Taxa Fish 1 (The wimp) Fish 2 (The savvy fish) Product (Sij ) 0.03292181 0.00411523 RPA Model 88.9% 11.1% Null Ambient (Ai) Selectivity Null 50% 50% 50% 50% Summation 0.03703704 See Link (in review), xxxx; for further de uch better at it. See Link (in review), xxxx; for further details

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