fishmove: Prediction of Fish Movement Parameters based on Multiple...

Description Usage Arguments Details Value Author(s) References See Also Examples


Functions to predict fish movement parameters of leptokurtic fish dispersal, based on multiple regression.





Fish species (scientific name) from speciesfishmove, used for prediction of movement parameters. If species is set, then values for length and the aspect ratio are taken from speciesfishmove and must not be provided by the user. Any additional argument on fish length and/or aspect ratio overwrites these settings. speciesfishmove originates in (Froese and Pauly, 2011).


Fish length (mm), used for prediction of movement parameters. Also a vector of several fish lengths can be provided.


Aspect ratio of the caudal fin, used for prediction of movement parameters (Pauly, 1989). A vector of several aspect ratios can be provided.


Stream order (Strahler, 1957), used for prediction of movement parameters. The default value for SO is 6. A vector of several stream orders can be provided.


Time step (days), used for prediction of movement parameters. The default value for new.time is 30 (days). A vector of several times can be provided.


Type of interval calculation. Can be either "confidence" or "prediction". The default for interval is "confidence". See predict.lm.


Number of regression runs to correct for replicates in datafishmove. The default value for rep is 50.


Number (integer) specifying a fixed seed for the random subsampling process (replicates of regression runs). By default this value is set random.


do not use.


fishmove predicts movement parameters of leptokurtic fish dispersal. The prediction is based on multiple regression on four variables (fish length, aspect ratio of the caudal fin (Pauly, 1989), stream order (Strahler, 1957) and time).

The package 'fishmove' is based on a meta-analyis of heterogeneous fish movement in rivers (see Radinger and Wolter, 2013)

The movement parameters are calculated for a stationary (sigma_stat) and for a mobile component (sigma_mob) of a fish population. These parameters in combination with a value for the share of each component can be used to describe and display leptokurtic dispersal kernels according to:

F(x) = p * (1/(2*pi*sigma_stat^2)^(1/2))*e^(-(x-mu)^2/(2*pi*sigma_stat^2)) + (1-p) * (1/(2*pi*sigma_mob^2)^(1/2))*e^(-(x-mu)^2/(2*pi*sigma_mob^2))

New values are calculated for a fitted mean and its upper and lower bound (confidence or prediction interval) based on a set of new input variables. In addition, common fish length and aspect ratio, are already provided for appr. 40 different species from (Froese and Pauly, 2011).



Array of regression parameters of the original multiple regression


predicted value, upper and lower bound for sigma_stat (movement parameter of stationary component) and sigma_mob (movement parameter of mobile component)


Johannes Radinger


Froese, R. and Pauly D. (2011) FishBase. World Wide Web electronic publication., version (12/2011).

Pauly, D. (1989) A simple index of metabolic level in fishes. Fishbyte, Newsletter of the Network of Tropical Fisheries Scientists 7, 22.

Radinger, J. and Wolter C. (2014) Patterns and predictors of fish dispersal in rivers. Fish and Fisheries. 15:456-473. DOI:

Strahler, A. N. (1957) Quantitative analysis of watershed geomorphology. Transactions of the American Geophysical Union 8, 913-920.

See Also

pdk, lm, predict.lm


	# Prediction for a selected fish species with time=365 days
	fishmove(species="Salmo trutta fario",T=365)

	# Prediction with fish length=350 mm, aspect ratio=2, stream order=4 and prediction interval
	# fishmove(L=350,AR=2,SO=4,T=365,interval="prediction")

Example output

Loading required package: ggplot2
Loading required package: plyr
Loading required package: MASS
Loading required package: boot
Predicted movement for selected parameters:
, , L=200, AR=1.25, SO=6, T=365

    sigma_stat sigma_mob
fit  106.49849  2590.035
lwr   59.79296  1658.111
upr  189.69572  4046.052

fishmove documentation built on May 1, 2019, 9:04 p.m.