Hobbs_Hilborn_supplement1.txt -- Contains data collected at Isle Royal National Park (Michigan, USA) during the winter (January and February) from 1971–2001 and prey dependent model predictions and calculations.

Hobbs_Hilborn_supplement2.txt -- Contains data collected at Isle Royal National Park (Michigan, USA) during the winter (January and February) from 1971–2001 and ratio dependent model predictions and calculations.

Hobbs_Hilborn_supplement3.txt -- Contains data collected at Isle Royal National Park (Michigan, USA) during the winter (January and February) from 1971–2001 and predator dependent model predictions and calculations.

Hobbs_Hilborn_supplement4.txt -- Contains estimates of model selection statistics for the prey dependent, ratio dependent, and predator dependent models.

Hobbs_Hilborn_supplement.xls -- A spreadsheet showing details of the computation. The data files listed above can be downloaded into a spreadsheet of your choice.

Hobbs_Hilborn_supplement1.txtcontains data collected at Isle Royal National Park (Michigan, USA) during the winter (January and February) from 1971–2001 and prey dependent model predictions and calculations.The file is tab delimited and contains 8 columns and 94 rows. The columns correspond to the variables listed below, and each row corresponds to a yearly observation, prey dependent model prediction, or calculation.

-- TABLE: Please see in attached file. --

Hobbs_Hilborn_supplement1To make sure the file was downloaded properly, compare the column sums to the following figures:

-- TABLE: Please see in attached file. --There are also calculations based on the data that are indexed in a column to their left (2 columns, 7 rows). These indices and the calculations they index are:

-- TABLE: Please see in attached file. --

Hobbs_Hilborn_supplement2.txtcontains data collected at IsleRoyalNational Park (Michigan, USA) during the winter (January and February) from 1971–2001 and ratio dependent model predictions and calculations.The file is tab delimited and contains 9 columns and 94 rows. The columns correspond to the variables listed below, and each row corresponds to a yearly observation, ratio dependent model prediction, or calculation.

-- TABLE: Please see in attached file. --

Hobbs_Hilborn_supplement2To make sure the file was downloaded properly, compare the column sums to the following figures:

-- TABLE: Please see in attached file. --There are also calculations based on the data that are indexed in a column to their left (2 columns, 7 rows). These indices and the calculations they index are:

-- TABLE: Please see in attached file. --

Hobbs_Hilborn_supplement3.txtcontains data collected at IsleRoyalNational Park (Michigan, USA) during the winter (January and February) from 1971–2001 and predator dependent model predictions or calculations.The file is tab delimited and contains 10 columns and 94 rows. The columns correspond to the variables listed below, and each row corresponds to a yearly observation, predator dependent model prediction, or calculation.

-- TABLE: Please see in attached file. --

Hobbs_Hilborn_supplement3To make sure the file was downloaded properly, compare the column sums to the following figures:

-- TABLE: Please see in attached file. --There are also calculations based on the data that are indexed in a column to their left (2 columns, 7 rows. These indices and the calculations they index are:

-- TABLE: Please see in attached file. --

Hobbs_Hilborn_supplement4.txtcontains estimates of model selection statistics for the prey dependent, ratio dependent, and predator dependent models.The file is tab delimited and contains 5 columns and 4 rows. The columns correspond to the variables listed below, and each row corresponds to a model type.

-- TABLE: Please see in attached file. --

Hobbs_Hilborn_supplement4To make sure the file was downloaded properly, compare the column sums to the following figures:

-- TABLE: Please see in attached file. --

InstructionsTo reproduce the results presented in the paper, see the descriptions of the indexed calculations shown above. To obtain the parameter estimates, use a nonlinear optimization routine to find the values of model parameters that maximize the sum of the log likelihoods.