Dear group,
I am implementing a spatial model for estimating the mean density of rainbow trout in a lake using hydroacoustic echo counts (horizontal transducer setup). My observations consist of number of trouts per insonified volume of water (m3). Using design-based estimators, we obtain mean density of 0.000458 fish/m3 for the entire lake.
Now, using sdmTMB with spatial random effects, I obtain a mean density estimate of 0.13. This is roughly three orders of magnitude larger than the previous estimate.
Inputs to the model
1-. The density is provided spatially in a regular grid.
2-. The grid was developed from X and Y (UTM) in km units.
3-. Cells of grid were only delimited for aquatic ecosystem based on a lake coastline with _add_barriermesh
4-. Based on different tests of probability distributions, i.e., tweedie, delta-gamma, delta-lognormal, delta-poisson-link-gamma, delta-poisson-link-lognormal; I selected delta-lognormal distribution
5-. Then, the model configuration:
_sdmTMB(density ~ -1 + as.factor(stratum), mesh= bspde, time= "year",
spatiotemporal= list("off","off"), spatial= list("on","on"), anisotropy= F,
family= deltalognormal (link1="logit", link2="log"), data= data)
Any feedback that helps me track this tremendous difference would be helpful and greatly appreciated
Dear group, I am implementing a spatial model for estimating the mean density of rainbow trout in a lake using hydroacoustic echo counts (horizontal transducer setup). My observations consist of number of trouts per insonified volume of water (m3). Using design-based estimators, we obtain mean density of 0.000458 fish/m3 for the entire lake. Now, using sdmTMB with spatial random effects, I obtain a mean density estimate of 0.13. This is roughly three orders of magnitude larger than the previous estimate. Inputs to the model 1-. The density is provided spatially in a regular grid. 2-. The grid was developed from X and Y (UTM) in km units. 3-. Cells of grid were only delimited for aquatic ecosystem based on a lake coastline with _add_barriermesh 4-. Based on different tests of probability distributions, i.e., tweedie, delta-gamma, delta-lognormal, delta-poisson-link-gamma, delta-poisson-link-lognormal; I selected delta-lognormal distribution 5-. Then, the model configuration: _sdmTMB(density ~ -1 + as.factor(stratum), mesh= bspde, time= "year", spatiotemporal= list("off","off"), spatial= list("on","on"), anisotropy= F, family= deltalognormal (link1="logit", link2="log"), data= data)
Any feedback that helps me track this tremendous difference would be helpful and greatly appreciated
Thanks, Braulio.