Skip to main content

Advertisement

Table 2 Summary information for candidate models summarizing the probability of detecting a transmitter at 1000 m as a function of month and transmitter location (receiver line or transmitter)

From: Probability of acoustic transmitter detections by receiver lines in Lake Huron: results of multi-year field tests and simulations

Candidate model df LL AIC ΔAIC Akaike weight R 2
\(DP_{{1000_{is} }} = \alpha + f_{\text{line}} \left( {{\text{month}}_{s} } \right) + {\text{tag}}_{i} + \varepsilon_{is}\) 14 −526.74 1081.49 0.0 0.980 0.63
\(DP_{{1000_{is} }} = \alpha + f\left( {\text{month}_{s} } \right) + {\text{tag}}_{i} + \varepsilon_{is}\) 11 −533.66 1089.32 7.8 0.020 0.57
\(DP_{{1000_{is} }} = \alpha + f_{\text{tag}} \left( {\text{month}_{s} } \right) + {\text{tag}}_{i} + \varepsilon_{is}\) 18 −530.36 1096.73 15.24 0.000 0.59
\(DP_{{1000_{is} }} = \alpha + f_{\text{line}} \left( {\text{month}_{s} } \right) + {\text{line}} + \varepsilon_{is}\) 10 −541.55 1103.11 21.62 0.000 0.54
\(DP_{{1000_{is} }} = \alpha + f\left( {\text{month}_{s} } \right) + {\text{line}} + \varepsilon_{is}\) 7 −547.85 1109.71 28.22 0.000 0.51
\(DP_{{1000_{is} }} = \alpha + f_{\text{tag}} \left( {\text{month}_{s} } \right) + {\text{line}} + \varepsilon_{is}\) 14 −543.79 1115.57 34.08 0.000 0.53
\(DP_{{1000_{is} }} = \alpha + f_{\text{line}} \left( {\text{month}_{s} } \right) + \varepsilon_{is}\) 7 −590.51 1195.02 113.53 0.000 0.01
\(DP_{{1000_{is} }} = \alpha + f\left( {\text{month}_{s} } \right) + \varepsilon_{is}\) 4 −596.57 1201.14 119.66 0.000 0.00
\(DP_{{1000_{is} }} = \alpha + f_{\text{tag}} \left( {\text{month}_{s} } \right) + \varepsilon_{is}\) 11 −590.62 1203.23 121.75 0.000 0.00
  1. DP 1000 is the probability of detecting an acoustic transmission at 1000-m transmitter–receiver spacing. Subscript i represents individual transmitters (n = 8), and subscript s is the unit of time. Each line represented a geographic location in Lake Huron and included 2 acoustic transmitters (tag) located approximately 250 m apart. Residual error (ɛ) represents residual error unaccounted by model. The variable month was represented numerically as the month of the observation during the experimental trial. All models included an AR1 autocorrelation structure to account for autocorrelation structure in data. Model degrees of freedom (df), log likelihood (LL), Akaike information criterion (AIC), delta AIC, Akaike weight, and estimated coefficient of determination (R 2) are summarized for each candidate model