I am hoping someone out there can help me with some statistics as I am relatively new to coding. I have a large dataset which I want to model appropriately to answer some key biological questions. I am looking at whale shark scarring over a 9-year time frame in three populations in the Indian Ocean and the questions I want to ask are:
Does the distribution of animals in each scarring category (location/ age/ cause/ type) differ between aggregations?
Does the distribution of animals in each scarring category (location/ age/ cause/ type) change over time within an aggregation?
Do population demographics (size/sex) influence the distribution of animals in each scarring category (location/ age/ cause/ type)?
Each year has a number of known individuals who have scarring information associated with them. This presents difficulties for question one as individuals can show up more than once, making some years dependent on others. One way to overcome this would be to only model new sharks or old sharks with new injuries.
• Dependent variables: (1) Severity - Maj scar Yes/No Binary. (2) Type – Categorical (3) Location on body – Categorical. (4) Cause – Categorical.
• Samples: 997 individuals grouped in three aggregations (Djibouti, Seychelles, Maldives)
• Independent variables: Year, Sex, Size
ID Location Year Severity Age Type Location Cause Sex WS000 Maldives 2009 x x x x x U WS004 Maldives 2009 Min Healed Abrasion Dorsal x M WS005 Maldives 2009 x x x x x M WS006 Maldives 2009 Min Healed Undefined Caudal x M WS010 Maldives 2009 Min Fresh Abrasion Caudal x M
Does anyone have any insight as to which approach I should take for each question?
To move further I will give a little more of a description and explain what I hope to achieve.
So, I will be working from two datasets. For one I have removed all duplicate sightings of individuals keeping only the most major injury. I aim to use this to make comparisons between 'Aggregations' using individuals sighted in the 9 year time frame. For the other I have removed all duplicate sightings of scars so that I can make comparisons within aggregations over time. I think making these adjustments makes my data far more manageable and removes confounding in both instances.
My data set will look like this (example of the one with duplicate scars removed):
ID Aggregation Year Severity Age Type Location Cause Sex Size Number dji.2003.003 Djibouti 2012 0 Healed Amputation 2nd Dorsal M 6 2 dji.2003.003 Djibouti 2013 1 Fresh Laceration Head Boat M 6 4 dji.2003.011 Djibouti 2009 1 Healed Laceration Caudal Boat M 4 1 dji.2004.003 Djibouti 2009 0 Healed Abrasion Caudal M 4.5 1 dji.2004.003 Djibouti 2010 1 Healed Laceration Flank Boat M 4.5 4 dji.2004.003 Djibouti 2011 0 Healed Deformity Pectoral M 4 1 dji.2004.003 Djibouti 2012 0 Healed Abrasion Head M 4.5 4 dji.2004.005 Djibouti 2009 0 Healed Laceration Head Boat M 4 3 dji.2004.005 Djibouti 2010 0 Healed Nick Caudal M 4 3 dji.2004.005 Djibouti 2014 0 Healed Abrasion Caudal M 4 4 dji.2004.012 Djibouti 2010 0 M 3.5 0 dji.2004.012 Djibouti 2011 0 Healed Abrasion Flank M 4.5 4 dji.2004.012 Djibouti 2012 1 Healed Amputation Caudal Bite M 5 4 dji.2004.012 Djibouti 2013 0 Healed Bite Caudal Bite M 4 1 dji.2004.016 Djibouti 2009 0 Healed Undefined Caudal M 3.5 2 dji.2004.016 Djibouti 2013 0 Healed Bite Pectoral Bite M 4.5 4 dji.2004.018 Djibouti 2012 0 M 6 0 dji.2004.020 Djibouti 2009 0 Healed Abrasion 1st Dorsal M 6 4 dji.2004.020 Djibouti 2010 0 Healed Abrasion Caudal Boat M 4.5 4 dji.2004.020 Djibouti 2011 0 Healed Deformity Caudal M 4.5 2 dji.2004.020 Djibouti 2012 0 Healed Deformity Caudal M 4.5 4 dji.2004.020 Djibouti 2014 0 Healed Nick Caudal M 3.5 4
Aggregation - Which population the shark is from
Age - Age of the injury, 2 levels healed or fresh
Severity - Binary response: 1- Major scar, 0- Min scar or no scar
Size - Estimated size of the individual
Number - Number of injuries
So, for the comparison between
Aggregation sites what tests can I perform to see if there are differences in scarring patterns?
Then for my other data set I want to look at what factors influence scarring and do scarring patterns change over time. How can I model this? Mixed effect generalized linear model with an 'Individual ID' random effect? I am a bit stumped here.