**Proposed standard weight equations for brown
trout ( Salmo trutta Linnaeus, 1758) and Barbus tyberinus Bonaparte, 1839 in the River Tiber basin (**

**Angeli Valentina ^{ }, Bicchi Agnese, Carosi Antonella,
Spigonardi Maria Pia, Pedicillo Giovanni, Lorenzoni Massimo.**

^{ }

Department of
Cellular and Environmental Biology University of Perugia, via Elce di Sotto
06123,

**Abstract:** Relative weight is an index of condition that provides a measure of the
well-being of a fish population. The index is calculated on the basis of
comparison between the actual weight of a specimen and the ideal weight of a
specimen of the same species in good physiological condition (standard weight).
Two methods forcalculating the standard weight are proposed in the literature:
the RLP method and the EmP method. Although the RLP method is widely used, it has
some limitations; as it uses the weights derived from the TL/W regressions of
different populations to calculate the index, it is influenced by the size
distribution of the specimens. The main aim of our research was to work out
equations for calculating standard weight that would be valid for two species
in the River Tiber basin. To this aim, 91 (N = 18216) different populations of
brown trout (*Salmo trutta* L.) and 64
(N = 12778) different populations of *Barbus
tyberinus* were examined. A further aim was to compare the validity of the
two proposed methods (RLP and EmP) of calculating relative weight. For brown
trout, the equations calculated with regard to the River Tiber basin are as
follows: log_{10}W_{s} = -
5.197 + 3.117 log_{10}TL (RLP method); log_{10}W_{s} = - 5.203 + 3.154 log_{10}TL –
0.015 (log_{10}TL)^{2} (EmP method), where TL is the
total length. The equations calculated by means of the two methods for *Barbus tyberinus *in the River Tiber
basin are as follows: log_{10}W_{s} = – 5.072 + 3.040 log_{10}
TL (log_{10} TL) (RLP method) and log_{10}W_{s} = –
4.917 + 2.987 log_{10}TL + 0.003 (log_{10}TL)^{2} (EmP
method)*.*

**Key words: **Relative
weight (W_{r}), index of condition, RLP method, EmP method,
length-weight regression.