Functional Distribution of Fin Whales. (Balaenoptera physalus) in Catalonian Coasts. Daniel Patón. Ãrea de EcologÃa. Facultad de Ciencias. Campus de ...
A Generalized Logistic Regression Model of the Functional Distribution of Fin Whales (Balaenoptera physalus) physalus) in Catalonian Coasts
Daniel Patón Área de Ecología. Facultad de Ciencias. Campus de Badajoz. Universidad de Extremadura.
Natalia Amigó; Margarita Junza; Cristina Martín; Eduard Degollada Asociación EDMAKTUB, Barcelona
FIN-WHALE PROJECT This research project is focused towards the analysis of the environmental factors that affect the distribution and movement of Fin Whales in Catalonian coasts.
The project has a duration of five years and includes different interdisciplinary areas of research: identification by drones, GPS Sampling, chlorophyll measurements and
STATISTICAL MODELLING (my small contribution)
The first question: What is the size of the sampling unit?
Scale
Variogram analysis
Gyre
Scale
CONCLUSIONS: 1) The most appropriate unit size is 350 divisions (~46 km2) 2) Anisometry
The second: Models of Kernel density (XY)
Latitude
46 km2 of resolution (~350 divisions)
KernSmooth y Lattice Longitude
Latitude
18 km2 of resolution (~895 divisions)
Longitude
ANNUAL
Latitude
MARCH
Longitude
Latitude
APRIL
Longitude
Latitude
MAY
Longitude
8.0
Logarithm of Density
7.5
March
7.0 6.5 6.0
May
483 m
5.5 5.0
April
4.5 4.0 0.0
0.5
1.0
1.5
Logarithm of Scale
2.0
2
The third: Multifractal analysis of densities
Fractal dimension (Dq)
2.2
2.0
1.8
1.6
1.4
1.2
Global Data -10.0
-8.0
-6.0
-4.0
-2.0
1.0 0.0
2.0
4.0
6.0
8.0
Q momments (Relative densities of whales)
10.0
SAMPLING AREA
Generalized linear models (GLM) are flexible generalizations of ordinary linear regression with non-normal error distributions Outcome
Regression
Family in R
Default Link Function
Binary (1/0)
Logistic
binomial
(link = "logit")
Binary (1/0)
Probit
binomial
(link = "probit")
Normal
Linear
gaussian
(link = "identity")
Continuous
Exponential, chi-square, lognormal, etc…
gamma
(link = "inverse")
Normal but μ and σ unknow
Inverse gamma
inverse.gauss ian
(link = "1/mu^2")
Frequencies (0, 1, 2.. n)
Poisson
poisson
(link = "log")
Unknown
Quasi (approximations to unknow functions)
quasi
(link = "identity", variance = "constant")
Excess of variance: Dispersion parameter is not one
Approximation to binomial
quasibinomial
(link = "logit")
Excess of variance: Dispersion parameter is not one
