Tag Archives: marble trout

New pre-print

Pre-print of my last work is on biorxiv

Vital rates, source-sink dynamics, and type of competition in congeneric species

Simone Vincenzi, Dusan Jesensek, Alain J Crivelli


The estimation of vital rates and life-history traits and how they vary with habitat and population factors are central for our understanding of population dynamics, risk of extinction, and evolution of traits in natural populations. We used long-term tag-recapture data and novel statistical and modeling techniques to investigate how population and environmental factors determine variation in vital rates and population dynamics in the population of brown trout Salmo trutta L. of Upper Volaja (Western Slovenia). Alien brown trout were introduced in the stream in the 1920s and the population has been self-sustaining since then. The population of Upper Volaja has been the subject of a monitoring program that started in 2004 and is currently on going. Upper Volaja is also a sink, receiving individuals from a source population living above an impassable waterfall. We estimated the contribution of the source population on the sink population and tested the effects of temperature, population density, and early environment on variation in vital rates and life-history traits among more than 4,000 individually tagged brown trout that have been sampled since 2004. We found that fish migrating from the source population (>30% of population size) help maintain high population densities despite poor recruitment. Neither variation in density nor in temperature explained variation in survival or growth; the best model of survival for individuals older than juveniles included cohort and time effects. Fast growth of older cohorts and higher population densities in 2004-2006 suggest very low densities in early 2000s, probably due to a flood event that caused a strong reduction in population size. Higher population densities, smaller variation in growth and weaker maintenance of size hierarchies with respect to endemic marble trout suggest that exploitative competition for food is at work in brown trout and interference competition for space is operating in marble trout.

Data is here

I will provide the R code soon

Correlation between stream temperatures in Slovenian streams in which marble trout live

This is an update on my research and I will try to post more often in these last months of my Marie Curie Fellowship. Files are hosted on my github page. Data have been collected by Alain Crivelli and Dušan Jesenšek since 1996. Some info on marble trout, the conservation program, and Western Slovenian streams here below.

1. Marble trout and Western Slovenian streams

Marble trout is a freshwater resident salmonid endemic in the Adriatic basin of Slovenia. Whether there are still pure marble trout populations living in the Po river system (Northern Italy) is subject of current research. Marble trout live in streams with mean summer temperature below 15°C and winter temperature ranging from 0 to 5 °C. Marble trout spawn in November-December and offspring emerge in May-June.
The Marble Trout Conservation Program started in 1993 in the upper reaches of the Soca River basin and its tributaries - the Idrijca and Baca Rivers - in Western Slovenia. Eight pure marble trout populations, all isolated and separated from the downstream hybrid marble-brown trout zone by impassable waterfalls, live in headwater streams in the basins of Soca, Baca, and Idrijca Rivers: Huda, Lower Idrijca, Upper Idrijca (in the map below Lower and Upper Idrijca are grouped together), Lipovesck, Studenc, Svenica, Zadlascica, Trebuscica.
Other two populations (Zakojska and Gacnik) have been created by translocating the progeny of the Zadlascica (Zakojska) and Trebuscica X Lipovesck (Gacnik) in 1996 and 1998, respectively.


2. Analyses

For some of the analyses I intended to carry out (temperature-dependent survival, growth, and recruitment), it was necessary to have complete temperature records for all streams since the start of the sampling. However, there were some missing data (sometimes whole seasons/years) in evert stream. The temperature .csv files are (stream_name)_temp.csv, the first column is the Date, the second is the mean daily temperature (Temp). Start by sourcing the file Temp.r, which is reading all the temperature files and merging them together (r scripts are here).


The output temp.all.df (along with the production of a ten-panel plot with stream-specific monthly temperature boxplots for 2009-2013) is a data.frame with columns Date, Temp, Year, Month, Stream, Calc (Meas = temperature has been recorded, see below for other values) (see below).


Daily Water Temperature (C)
Date Temp Year Month Stream Calc
1996-07-04 10.98 1996 7 Zak Meas
1996-07-05 10.99 1996 7 Zak Meas
1996-07-06 11.26 1996 7 Zak Meas
1996-07-07 11.19 1996 7 Zak Meas
1996-07-08 11.06 1996 7 Zak Meas
1996-07-09 9.96 1996 7 Zak Meas
1996-07-10 9.85 1996 7 Zak Meas
1996-07-11 10.07 1996 7 Zak Meas
1996-07-12 10.51 1996 7 Zak Meas
1996-07-13 11.08 1996 7 Zak Meas

Then, I tested the correlation between stream temperatures between pair of streams (one is the target - the one with missing data - and the other is the tested). I used the temperature data of the tested stream with the highest correlation with the temperature data of the target stream to impute the missing temperature data in the tested stream.
The Temp.corr.f function tests the correlation between water temperature data recorded in different streams.

source("Temp.corr.r") # contains Temp.corr.f
Temp.tb = Temp.corr.f(temp.all.df)

The Temp.tb data.frame has columns target stream (tar), tested stream (var), correlation between stream temperature of the two streams (cor), years with common number of days with temperature recorded (common.years), years with missing data for the target stream (miss.years), and years with missing data for the target stream, but with complete data for the tested stream (miss.in.var). The years in miss.in.var can thus be used to impute the missing data.
The correlation between water temperature of streams are typically very high (mean correlation[sd] = 0.97[0.01]).

Correlation of water temperature
tar var cor years.cor common.years miss.years miss.in.var
Zak Gac 0.95 5 2001-2002-2005-2009-2013 1996-1997-1999-2000-2006-2008-2010-2011-2014 2006-2008-2010-2011
Zak Sve 0.95 3 2002-2009-2013 1996-1997-1999-2000-2006-2008-2010-2011-2014 2006-2008-2010-2011
Zak Stu 0.97 3 2005-2009-2013 1996-1997-1999-2000-2006-2008-2010-2011-2014 2006-2008-2010-2011
Zak LIdri 0.97 3 2005-2009-2013 1996-1997-1999-2000-2006-2008-2010-2011-2014 2006-2008-2010-2011
Zak UIdri 0.96 3 2003-2005-2013 1996-1997-1999-2000-2006-2008-2010-2011-2014 2006-2010-2011

In each stream, I imputed the missing data (1) using the temperature data from the tested stream with the highest correlation with the target stream and (2) by applying the best model (linear or non-linear - gam -, chosen according to best prediction) linking the water temperature data of the two streams. The r script for imputing missing data is in Temp.filling.r.


The output of the script is the data frame temp.all.df with columns: Date, Temp, Year, Month, Stream,
Calc (Meas = temperature recorded in the stream, Gac2005 = in one year (1997) we had missing data for Gac and the only acceptable data for imputing was coming from Gac in 2005, Same_as_a = Same temperature as days after (just for a few days missing), Same_as_b = Same temperature as days before (just for a few days missing), Zak2012 = in one year (1997) we had missing data for Zak and the only acceptable data for imputing was coming from Zak in 2012, Stream_name = stream whose temperature data was used to impute missing data, degree_days = degree days for the day using 5C as base temperature, Sampling_Season = Summer for June, July, September - Winter for the rest of the year). Sampling occurred either in June or September or in both.

Daily Water Temperature (C)
Date Temp Year Month Stream Calc degree_days Sampling_Season
2006-08-19 13 2006 8 Stu Meas 7.8 Summer
2006-08-20 13 2006 8 Stu Meas 7.8 Summer
2006-08-21 13 2006 8 Stu Meas 8.1 Summer
2006-08-22 13 2006 8 Stu Meas 7.6 Summer
2006-08-23 13 2006 8 Stu Meas 7.6 Summer
2006-08-24 12 2006 8 Stu Meas 7.3 Summer

Temperature data is now ready to be used to test differences in water temperature between streams, and temperature-dependent survival, growth, and recruitment.

Manuscript submitted

I recently submitted a new manuscript on vital rates and life histories in marble trout. Dense paper, lots of models, lots of results. Currently under review. Here below are the Title and Abstract.


Within and among-population variation in vital rates and population dynamics in a variable environment --- Vincenzi, Mangel, Jesensek, Garza, Crivelli.


Understanding the causes of within- and among-population differences in vital rates, life histories, and population dynamics is a central topic in ecology. In order to understand how within- and among-population variation emerge, we need long-term studies that include episodic events and contrasting environmental conditions, tag-recapture data for the estimation and characterization of individual and shared variation, and statistical models that can tease apart population-, shared-, and individual contribution to the observed variation.

We used long-term tag-recapture data and novel statistical and modeling techniques to investigate and estimate within- and among-population differences in vital rates, life histories and population dynamics of marble trout Salmo marmoratus, a narrow endemic freshwater salmonid. Only ten populations of pure marble trout still persist in Western Slovenian headwaters. Marble trout populations are also threatened by floods and landslides, which have already caused the extinction of two populations in recent years.

In particular, we estimated and determined causes of variation and trade-offs within- and among populations in growth, survival, and recruitment in response to variation in water temperature, density, sex, early conditions, and extreme events.

In all ten populations, we found that the effects of population density on traits were mostly limited to the early stages of life and that individual growth trajectories were established early in life. We found no clear effects of water temperature on survival and recruitment. Population density was variable over time in all populations, with flash floods and debris flows causing massive mortalities and threatening population persistence. Apart from flood events, variation in population density within streams was largely determined by variation in recruitment, with survival of older fish being relatively constant over time within populations, but substantially different among populations. A fast- to slow-continuum of life histories in marble trout populations seemed to emerge, with slow growth associated with higher survival at the population level, possibly determined by food conditions and age at maturity.

Our work provides unprecedented insight into the causes of variation in vital rates, life histories, and population dynamics in an endemic species that is teetering on the edge of extinction.