1 The application of electro-fishing to produce census estimates of

The Scottish Government
Marine Directorate
Scottish Fisheries Research Report
Number 67, 2007
ISSN 0308 8022
The application of electro-fishing to produce census estimates of juvenile
salmonid populations within defined areas : taking stock of the options
P. J. Bacon and A. F. Youngson
1. Aims of EF surveys of salmonid populations
2. Basic principle of capture and population estimation
2.1 The capture method
2.2 Inferring information about the populations within the sites (areas) sampled
2.3 Trends over time at the same sites using the same methods; population indices
2.4 Difficulties in inferring information about the wider population of fish in a
catchment from a sample of sites
2.5 Practical problems with statistical sub-sampling approaches
3. Comparison of different EF protocols
3.1 Presence or Absence EF
3.2 Constant Effort (timed) EF
3.3 Depletion estimates
3.4 Mark-Recapture estimates
3.5 Multiple CMR estimates of marked individuals
3.6 Remaining problems and biases
3.7 More cost-effective strategies: Hierarchical analyses
3.8 Future Prospects: Research and Management needs
3.9 Guarding against mis-interpretation
3.10 Reference sites
3.11 Dangers of density data alone
3.12 Size-at-Age information
3.13 Juvenile salmonid sizes correlate with densities
3.14 Reference sites and environmental instability (e.g climate change)
3.15 Saturation Stocking
3.16 Calibration models
4. References
5. Appendix One
6. Appendix Two
Aims of EF surveys of salmonid populations
Scottish salmon managers would like extensive data about the status of salmon stocks in Scotland’s fresh waters.
Electro-fishing (EF) surveys of juveniles offer one of the few ways of providing it, and the merits of different
approaches are briefly discussed below.
Electro-fishing is a common means of obtaining information about populations of juvenile salmonids in different
places at different times. Indeed, it is the only practicable basis for obtaining census information about juvenile
salmonids in shallow waters which are unsuited for netting. It is most effective in shallow nursery streams and
difficult, or impossible, to use effectively in deeper rivers or lakes.
This report gives an overview of the strengths and weaknesses of EF in the different applications required by
managers, with a view to identifying issues which remain to be resolved. Populations can only be accurately
described from a sample of individuals obtained from a prescribed sampling area. Sampling data may be obtained
to address any of a range of management issues. These cover such questions as: ‘Are there any juvenile salmonids
in a given location?’, ‘Is the number of juveniles changing from year to year?’ or ‘Is the location full of juveniles?’
In addition, as transporting field workers to a sampling site, setting up the equipment and catching the fish involves
substantial effort, we consider whether additional, ancillary, information, such as the ages of the fish and their
sizes, might be simultaneously gathered to provide a fuller assessment of the status of the population, which the
samples represent.
Basic principles of capture and population estimation
2.1 The capture method
Electro-fishing (EF) works by producing a voltage gradient throughout the water containing the fish to be sampled.
The voltage difference induced over the length of the fish determines its response. The effective voltage difference
depends on the length of the fish, the conductivity of the water and the distance from the fish to the anode (positive
electrode). Low voltages (at large distances) cause the fish to swim away from the anode; moderate voltages cause
the fish, critically, to swim towards the anode. EF procedures require two or three operators (for Health and Safety
requirements), and aim to attract fish from cover towards the anode, catching them in hand nets before they reach
the anode itself.
To estimate the efficiency of an EF pass, and thereby allow calculation of the total numbers of fish present, requires
multiple passes (typically three or more). Field protocols should also record the area of stream fished, so that the
numbers caught can be transformed to estimated densities (fish per square metre). If only one EF pass is undertaken,
fishing effort should be quantified by recording the time taken, as well as the area fished (allowing a catch per unit
effort to be calculated, in units of fish per minute per square metre1).
It is widely agreed that the capture efficiencies of fry and parr are different, and that these should be separately
estimated, along with their densities. We shall assume throughout that fry and parr are so distinguished, and
discuss the merits of knowing the age of captured fish more fully, below.
This document does not attempt a review of the complex technical literature. Readers interested in such further
details could usefully refer to Schnute (1983) and Bohlin T. & Cowx I. G. (1990) for removal methods, and to
Anderson (1995), Link et al (2004) and Peterson et al (2005) for mark-recapture methods.
2.2 Inferring information about the populations within the sites (areas) sampled
As EF disturbs fish, it is good practice to delimit the area of a site to be fished by stop-nets , which impede, and
ideally prevent, fish leaving or entering the area to be sampled. In shallow rivers this is usually done with a small
mesh net above and below the reach to be sampled. Fish which are near the nets may, of course, be disturbed
when the nets are installed, but once this is achieved, further entry or egress in response to the EF itself will be
However, even with nets in place, EF is not completely effective, and in some conditions nowhere near so. Capture
efficiency can vary widely given, for example, different operators, different equipment, different water qualities,
different weather conditions, or different water depths. More refined procedures, such as depletion estimates or
mark-recapture methods are needed to estimate the total number of fish likely to be located between the nets, as
opposed to the total number actually caught. The relative merits of these refinements are discussed below.
2.3 Trends over time at the same sites using the same methods; population indices
Various statistical sampling procedures exist for inferring the densities of salmonids in a wider area on the basis of
With more detailed data, e.g. from multiple passes, one may attempt more sophisticated corrections, by apportioning the total time between
fishing-time and handling time, of which the latter clearly varies with the total number of fish captured. Crisp & Crisp (2006) give an initial, but
incomplete, discussion of such refinements for multiple-pass fishings.
samples caught at a set of random and representative sample EF sites (outlined below). However, a still simpler aim
may be conceived, and measurements for single sample sites may be considered relative to some known starting
condition with a view to establishing local trends. However, in spite of its apparent simplicity, this approach is
fraught with difficulties in interpretation.
There is an important distinction in principle between these approaches. Imagine that one wishes to detect a change
over time. If one believes that there are differences in fish densities between different replicate sites, both between
the same sites in different years and between different replicates within the same habitat in the same year, then
one will get a more precise estimate of the change over time by re-sampling the same sites in different years ( so
avoiding confounding any year changes with site differences (within habitats) ). This approach will provide more
precise information about the trend over time within the sampled sites. However, we should note a down-side to
this approach. If the index sites were not fully representative of the wider catchment in the first place, then it would
be possible for the index to change in a way that was not representative of the wider population. For example, if
fish at different overall catchment densities preferentially occupied particular habitats, then a set of sites that were
not representative of all the habitats would not capture the response of the catchment population over time2 .
Unfortunately, assessment of juvenile salmonids is bedevilled both by an inability to adequately define habitats
and therefore transitional areas of interest and by a history of basing management decisions on samples from
sites that were unrepresentative, perhaps because they were chosen for the wrong reasons or because available
but inappropriate data was used opportunistically.
2.4 Difficulties in inferring information about the wider population of fish in a catchment
from a sample of sites
We have described the means of assessing the numbers of fish within the stop-nets delimiting a single site. This
estimate (and its confidence bounds) could be applied to a larger area of the same habitat if one is prepared to
assume that the sample taken is fully representative of the sampled habitat but note that the term habitat could
be defined on scales as broad as stream or river or as narrow as riffle or reach. This is rarely a prudent assumption.
Variability in ecology is so commonly considerable that, even within single habitat types (even if we could define
them sufficiently) one would often wish to assess the variation between several replicate samples within the same
habitat class, and use the mean of the replicates to extrapolate to a wider area of that, the same habitat.
We note that, if such potential habitat effects are considered to be large, then one would like to have at least two
or three replicate sites within each habitat. In short, the (minimum) total number of sites within a sub-catchment
should be around three times the number of important habitat classes , simply to ensure that the number of sites
has a reasonable chance of being fully representative of the wider catchment. However, habitat effects can still
be investigated without having to pre-define habitats or to choose EF sites with regard to a known map of such
habitat reaches within the catchment (see below).
In these circumstances, a large set of randomly located sites is probably preferable to sub-sets within habitat classes of dubious merit. However,
beware of this concept at the micro-habitat scale. If, as may well be the case for salmonids, the micro-habitats cannot be usefully defined and
measured, such concepts about potentially preferred habitats are not refutable null hypotheses (sensu Popper) and thus cannot be investigated
scientifically, as they make no testable predictions. In such situations one is probably best guided by the precepts of Ockham’s Razor, which state
that one should try and disprove the simplest hypothesis (no habitat effect) first, before investigating more complex ones.
Another basic concept behind statistical sampling is that the samples represent independent random sub-sets
of the total population. This is rarely true in observational studies in ecology. For example, many environmental
parameters of rivers will change with altitude, and thus be correlated to, and not independent from distance along
stream, stream width, stream temperature, growing season, riparian land-use and farming, eutrophication, water
pollution, water quality, distance from spawning habitat or distance from last-year’s redds.
This topic is extremely complex and beyond the scope of this document. As an illustration we note that, while three
adjacent 20 m stretches of the habitats {pool, riffle, glide} would not be independent of each other with regards to
altitude (because they are adjacent), a set of ten such triplets over a range of altitudes would be a powerful way
of distinguishing the effects of altitude and habitat on fish density because there could be ten altitude effects
unconfounded by habitat due to three common habitat measures at each elevation, and three habitat effects,
unconfounded by altitude due to ten common replicates for each habitat at common elevations.
A useful rule of thumb would be that sites within 1 km are unlikely to approach full independence from each other
simply because they will be within likely dispersal distance from the same (set of) redds the previous autumn.
Within such a 1 km stretch, sub-sections would very likely have extremely similar altitudes, temperature and (in the
absence of side-streams or major ground-water inputs) water quality. They could still, however, be of very different
habitats with respect to such important qualities as gradient, substrate and the availability of shelter.
2.5 Practical problems with statistical sub-sampling approaches
Statistical theory has a wealth of experience and recommendations about how to sample most efficiently in a wide
range of different circumstances. In brief, this theory relies on taking a set of randomly chosen sites which are,
taken together, fully representative of the overall situation one wishes to assess. Imagine, for example, that we
have a river catchment conveniently divided into the (clear and distinct) habitats classed as riffle, glide or pool. We
suspect that salmon (or trout) will be differently distributed between these three habitat types. We state our task
as being to assess the population densities (and their confidence bounds) within the entire catchment.
If we believe that habitat might be of over-riding importance as determinants of fish densities, and if and only if we
have, or can get, a measure of the frequency of the habitats in the entire catchment (either by total survey or by a
survey of randomly chosen sub-sections of river within the catchment), then we should sub-sample the catchment
by habitat classes (i.e. riffle, glide and pool). Such protocols are often referred to as stratified sampling because
they sub-sample within strata – in this context, habitat strata.
It would thus be prudent to start by taking a small number of replicates sites (say six) within each of the habitat types.
Ideally these should be chosen at random (for example (a) river sites nearest to random X,Y map coordinates or (b)
randomly chosen distances downstream from a randomly chosen starting point) in order that they are not biased
by convenience or observer preference . For example, it is a common view that actual EF sites are conveniently near
roads, especially road bridges. Another common view is that operators choose interesting sites - in other words,
sites where they are more likely to have an interesting time because they will catch more fish. Clearly such sites
are unlikely to be fully representative of the rest of the catchment, which will include sites distant from roads (and
thereby probably having different substrates and topographies), and sites that are less or, perhaps, more suitable
for fish , at least as assessed by the field operatives.
We could then calculate densities (± confidence bounds) for each habitat. Importantly we could then also both (a) test
whether the presumed habitat effects were indeed significantly different; and (b) calculate (assuming the observed
habitat differences were real but the initial sample of replicated habitats too small to show this statistically), how
many more habitat replicates would be needed to prove that the estimated differences were actually significant.
Unfortunately a consideration of (b) often indicates a requirement for impracticably large numbers. Finally, we
obtain a catchment estimate for average density by scaling up. We do this by multiplying the densities within
habitats by the areas of each habitat (and the same for their confidence intervals). If we have decided that the
habitat differences are negligible (or inestimable) then we have only a single calculation to make.
Furthermore, armed with these data, we could determine in which major habitats we should expend most
future sampling effort in order to get, for the least cost of field sampling effort, the most reliable estimate of fish
The converse possibility is that we might find that the presumed habitat differences are tiny. In which case our
sampling sites may be chosen entirely at random with regard to habitat classes for no loss of efficiency (meaning
the precision of our estimate of fish numbers for the same field-work effort and cost). This simpler approach is
often termed ‘random sampling’.
Accordingly, we appear to have a genuine dilemma for structuring field surveys for the extensive assessment of
populations of juvenile salmon. Although most operatives clearly believe habitat factors ought to be very important
for density, no one has yet defined these clearly enough to demonstrate their utility for widespread, practical juvenile
sampling and management (Godfrey 2006, see below). Moreover, a recent unpublished study of the reliability of
habitat recording showed that although different trained practitioners showed a pleasing similarity of proportions
of different habitats which they estimated to be within a test reach of river, when the positions of these presumed
stretches were compared, there was widespread disagreement.
If experienced freshwater biologists and fisheries practitioners experience this large degree of difficulty in defining
habitats , then it is probably safe to argue that the locations for representative replicate EF sites might just as well
be chosen at random (and more cheaply as no prior habitat survey would be needed). Note that this conclusion is
not the same as ignoring habitat, since habitat classes or a set of habitat co-variates could still be recorded for each
such randomly selected location , and their importance assessed in a subsequent, suitable, statistical analysis.
Comparison of different EF protocols
The discussion of a range of management uses for EF data above identifies some snags for interpreting the data.
This next section reviews refinements of the approach that have, and still are, being developed to reduce these
difficulties. This next section is structured to progressively illustrate how more sophisticated techniques can tackle
management questions such as :
Is the EF sample representative of the population present in the site at the time of capture?
Can a set of samples be made representative of a wider area and period, such as a river reach or
sub-catchment ?
Can the set of samples be obtained more cost-effectively?
The precision of EF estimates and the removal of some potential biases can be reduced by: the use of stop-nets, of
repeat fishings (EF passes) at the same site on the same date (depletion estimates) and by other more sophisticated
methods summarised below. These are presented in order of simplicity, with an outline of their strengths and
3.1 Presence or Absence EF
In brief, as absence can never be proven, effort should concentrate on looking for presence. Thus, for this aspect
and this alone, subjectively concentrating on sites just down-stream of potential redds (for example 50~200 m
downstream) and sites of likely preferred habitat to fry or parr is a good strategy. Having a quick look at several
likely good candidate sites (say 3) per km for five minutes each would probably be more robust than concentrating
on just one such site for 15 minutes. It is sensible to structure such surveys with regard to barriers that may be
impassable to adult migration.
This approach, usually involving just back-pack equipment, is intended simply to show if a species is present in a
reach of river. A typical protocol would involve, for example, single-pass EF for 5 minutes at two or three replicate sites
that appear to be well-suited to fry or parr within every 500 to 1,000 m along a river reach of interest, and especially
above and below potential barriers to adult migration. Stop-nets would not be used and area measurements are
unnecessary. The aim is simply to get a sample of the species present.
Advantages: The most basic method of establishing presence of fish (breeding fish if fry present) in a reach
of river (note fry may disperse of the order of 100 m upstream and 500 to 1000 m downstream).
Useful for obtaining tissue samples for DNA analysis. Can give a rough indication of age-structure
(especially if scale samples are analysed).
Disadvantages: Imprecise and extremely biased results for densities, size and age structure. Apart from the
presence data, results cannot be reliably compared across sites, years, equipment or operators.
3.2 Constant Effort (timed) EF
This approach would also normally involve a back-pack, no stop-nets and single-pass fishings. The aim is to sample
many sites, for example in very variable habitats, and obtain a comparable index of fish abundance at each. The
standard of comparability is simply that the operators take care to fish with the same effort. Both the time spent
fishing and the area fished should be recorded, and the index for comparison is fish per unit effort as fish per unit
time per unit area. It is necessary to fish for a sufficient period to catch enough fish to allow the required precision.
As a guide, 15 to 20 minutes wil often suffice in a wide range of circumstances.
Advantages: Quick and simple. Semi-quantitative. Can potentially be calibrated (see Hierarchical Analysis)
to improve the accuracy.
Disadvantages: Not very accurate and potentially biased. Hard to compare across sites, years, equipment
or operators. The apparent quantification may be biased by habitat effects, especially variations in
water depth and cover (e.g. over-hanging banks). Note that it is never a better method of assessing
fish, only a cheaper one (sensu Wyatt and Lacey 1999; W167)
We stress that BOTH time and area measurements should always be recorded. If the area fished is unknown, it is
impossible to use the information to infer likely numbers of fish in a wider area of catchment. Similarly, if the time
taken is unknown, then gross differences in fishing effort intensity could confound any further calculations.
The more complex methods subsequently described all attempt to remove one or more aspects of potential bias
by repeated fishing attempts in different circumstances.
3.3 Depletion estimates
These approaches are also often referred to as ‘removal estimates’ or ‘Zippin estimates’. These methods seek to
estimate the capture efficiency of the equipment and operators at the site and on the day of sampling. They thus
rely on more than single-pass fishings, and three is often the recommended minimum. They attempt to estimate
the capture efficiency by assuming fish are equally susceptible to capture on successive passes, and using the
(hopefully) declining numbers caught on each pass to estimate that efficiency.
Major assumptions of Depletion EF are that, during the capture operation:
The population is closed (i.e. no fish can leave or enter the site, or die, or recruit to the
Fishing effort is the same on each pass;
The catchability (probability of capture for a given effort) is the same on each pass;
The catchability is the same for each individual.
Note that, if the catchability declines between passes, because of either reduced effort or fish avoidance behaviour,
then the resulting removal estimates are downwardly biased, potentially quite dramatically.
The assumptions are listed in Appendix 1, together with a table of illustrative likely captures from an initial 100
fish, which is also shown as a graph. The method relies on estimating the rates at which the number of captures
declines with number of passes (see Figure A.1) and estimating (as the integral) the extra number of fish that would
have been caught if many more passes had been undertaken. It can be seen that at high capture probabilities
per pass (0.8 to 0.6), over 90% of fish are taken in the first three passes, and subsequent real captures are highly
affected by random sampling error of small numbers. Conversely, at low capture probabilities (0.2 to 0.1) the
numbers caught on each of the first three passes are extremely similar, and the estimate of the depletion slope is
accordingly imprecise. The ability of depletion methods to usefully adjust estimated fish densities is thus limited
both to a narrow single-pass capture probability range (around 0.5~0.3) and situations where large total numbers
of fish are caught (around 100). Note that Schnute (1983) suggests, as a rule of thumb, that if < 40 fish are captured,
depletion estimates are unreliable. As 100 fish is quite a lot for the population in a normal EF site (ca 100 m2 ) and
three passes is a typical maximum (NB the Environment Agency (EA) and Scottish Fisheries Coordination Centre
(SFCC) data have rather few records of even 3-pass electro-fishing, it can be seen that, in usual sampling situations,
the ability of depletion estimation to improve fish density estimates, at least when based on unique estimates of
single events, is regrettably weak. Its main potential advantage probably lies when embedded in a more complex
sampling strategy (see Hierarchic EF below).
Advantages: Depletion Estimates are the simplest way of removing the likely serious biases in density
estimates resulting from unknown probabilities of capture efficiency (for example, equipment,
water conductivity, water depth, and operators). It probably produces very much improved index
of change over time estimates in situations where habitat variation between sites in different years
does not hugely affect the capture probabilities (especially for the same site!). It can be undertaken
in a single visit on a single day (unlike Mark-Recapture).
Disadvantages: It is quite sensitive to its assumptions. Without many passes (>3) and large total numbers
of fish caught (>100) estimates of efficiency for single site-on-date assessments usually have rather
wide confidence limits (which can still be biased).
3.4 Mark-Recapture estimates
Simple Mark-recapture estimates assume, in contrast to Depletion Estimates, that the capture probabilities can
vary, and especially that, having been approached, but uncaught on the first pass, fish may then go and hide for a
long time in refuges that are much harder (impossible) to extract them from on subsequent passes. The approach
assumes, however, that these inaccessible fish are a random sample of fish on different occasions (days) and
that, between sampling occasions (days) previously captured and previously uncaught fish mix at random, and
are consequently re-caught at random.
The method’s key assumptions (basically those for the classical Lincoln Index ), are :
The population is closed {ideally, effective stop-nets are left in place over-night};
Marked and unmarked fish are equally vulnerable to capture;
Marks are retained during the sampling period {next day is quite common} and all marks on
recaptured fish are recognized;
Marked fish randomly mix with unmarked fish between capture and re-capture attempts.
Anderson (1995), Link et al (2004) and Peterson et al (2005) discuss recent developments of mark-recapture
methods and the sorts of biases they can account for. It is also possible to envisage more complex, but potentially
also less biased and more effective, protocols, which combine the merits of both removal and mark-recapture
approaches with hierarchical analytical approaches (for example, Wyatt 2003).
Advantages: The method does not assume equal capture probability between different EF passes. A number
of recent papers (for example, Rosenberger & Dunham 2005) now show that, in real situations, it
gives more accurate estimates than depletion estimation. Within (unknown) ranges of similar habitat
types, it could probably be cross-calibrated to depletion estimates to give both more robust and
more cost-effective answers.
Disadvantages: Requires re-visiting the site on different days, preferably with effective stop-nets in place
over the interval. Probably much better if based on 3-pass than 1-pass EF per day. Need to mark
(batches) of fish to assess recapture rates (for example, by using fin-clips, PanJets or PIT-tags).
3.5 Multiple CMR estimates of marked individuals
This is a highly sophisticated variant of Mark-Recapture methods, which, by following the fates of individually
marked fish, can potentially account for individual differences in capture probability (but only for those fish which
are caught initially), and estimates both mortality rates and movements between multiple sites. Unfortunately it is
costly in both tags and manpower, and for species like salmonids, whose juveniles emigrate, it is hard to distinguish
between mortality, local dispersal and emigration in a real seasonal environment.
Advantages: It provides a powerful cross-check of different estimation procedures at research sites.
Disadvantages: It is too complex and costly for widespread use.
3.6 Remaining problems and biases
None of the above methods are perfect. Difficulties remain with regard to:
Fish behaviour – individual fish (perhaps due to characteristics of their home-ranges) almost certainly have
different individual capture probabilities
Some habitats, such as deep water and over-hanging banks, are harder to electro-fish effectively, and
depletion methods in particular are likely to give biased estimates when comparing between such
Fish capture probability is significantly size-dependent for salmonid parr (Anderson, 1995, Thorley and
Bacon, in prep), and probably more so for fry (Wyatt, Bacon & Thorley in prep). The effects are small
for 3-pass EF, but size variation appreciably biases single-pass results. Because very small fry are
hard to catch, variable capture probabilities could appreciably bias the estimate of fry numbers as
well as of fry sizes, and thus the age composition of salmonids (fry versus parr ages).
3.7 More cost-effective strategies: Hierarchical analyses
Hierarchical estimates of fish densities aim to increase the value of EF information by squeezing more information
out of the same raw data. Thus they can potentially either achieve more precision for the same cost, or the same
precision for reduced cost. For example, they can test the assumption that 1-pass EF has the same capture efficiency
as the first pass of 3-pass EF (preferably within the same situations and habitats using the same gear). If this is
found to be the case (perhaps within certain broad habitat and river-depth classes) then a combination of 1-pass
and 3-pass EF can be used to obtain results of the same precision more cheaply, and thus, for example, scale-up
population estimates to sub-catchments with better cost-effectiveness. Such scaling up can be powerfully linked to
Geographic Information Systems (GIS). Such hierarchic methods can be implemented within either the frequentist or
the Bayesian statistical paradigms, although the Bayesian is especially powerful. The basic methods are described
in Wyatt (2002, 2003). Wyatt’s later paper indicates how, particularly in combination with GIS, these approaches
can potentially be used in combination with statistical sampling theory to improve the cost effectiveness of the
information obtained from EF programs. However, such savings can usually only be made if the enquiry is directed
at a single, clearly defined question). The methods can potentially be elaborated to account for other difficulties,
such as size-selective capture biases, sites or habitats within reaches, depletion versus removal bias effects, or
growth and mortality during a protracted EF campaign (as changes may occur over the course of time, for example,
over a month or so of fieldwork in summer. They may also be able to increase power by comparing across species
(for example salmon and trout) (Wyatt and Bacon, in prep; Thorley, in prep).
Advantages: can potentially both reduce biases in EF data while also increasing the cost-effectiveness of
the same EF sampling, for both scientific and management purposes.
Disadvantages: The methods are still under development. The proper design and analysis of such costeffective surveys depends on both clear questions and, presently, on data analysis software and
skills that few fisheries management groups might posses.
3.8 Future Prospects: Research and Management needs
None of the EF approaches in widespread use in Scotland is really fit for assessment purposes. Not only are current
EF methods known to be less accurate than desirable, but analysis of SFCC’s extensive habitat and fisheries data
(Godfrey 2006) has shown rather little correlation with habitat information (combined r2 values in the region of
17-24% for density and 18-30% for biomass, depending on age class and the type of data used ). Only small
proportions of this variance were explained by habitat, as opposed to environmental surrogate, variables such as
altitude. This extensive study by SFCC failed to show any large effects of recorded habitat variables on salmonid
populations. This is rather surprising in view of both (a) the widespread belief that habitats are an important
determining factor of fish densities and (b) the amount of grant-aid available to improve habitats for salmon
populations . Thus, following Ockham’s Razor, future work should either concentrate on a rigorous study to settle
this matter (as the finding is counter-intuitive) or else assume the differences are actually small compared to other
uncontrollable factors.
The discrepancy between belief, management practice and firm data may, at least in part, be due to a combination
of inadequate EF protocols and data collection, combined with insufficient environmental co-variates (temperature
and water quality parameters might well be at least as critical as habitat information, and both might be needed
together before the effects of either can be discerned) and lack of rigour in the choice of sampling locations
(especially because environmental co-variates, such as temperature, altitude and eutrophication, are intercorrelated anyway).
The research challenges to improve methods of survey design and data analysis are being addressed by FRS. The
main issues are outlined in Appendix 2.
FRS is also attempting to collect sufficient rigorous data to start to develop these concepts into practical
We note that Scottish Environment Protection Agency (SEPA) and Scottish Natural Heritage (SNH) also have
requirements under the EU’s Habitats and Water Framework Directives to understand and document the status of
fish populations in Scotland which will almost certainly require very similar approaches.
Potentially, FRS could focus and coordinate a combined approach to these common needs (of FRS, SEPA, SNH and
Scotland’s Fishery Trust biologists). An outline of an approach to collect the data necessary and to develop these
analytical concepts into a widespread and useful tool will be outlined in a future document.
3.9 Guarding against mis-interpretation
We note that unless such approaches to fishery assessment were carefully managed and monitored, there would
be scope for mis-interpretation. For example, it is likely that any index of juvenile salmon abundance would look
more favourably on allowing increased rod-fishing for adults in zones where the juvenile populations appeared
to be increasing. If the assessment method included a mixture of 3-pass and 1-pass EF, and, over the course of
time, the operators become more zealous during their 1-pass fishings, then the apparent number of juveniles
would go up. Accordingly, protocols to cross-calibrate potentially matching sets of results would be both prudent
and desirable.
3.10 Reference sites
We note that Scotland does not have an extensive network of reference sites to which novel sites could be compared
to see whether they were ‘regionally typical’ or to which cross-calibrations could be performed. Nor does it have
a protocol that, for a given set of environmental conditions (for example, temperature, altitude and water quality)
allows one to predict expected salmonid population status and look for differences from it. These deficiencies
pose problems, and we discuss below how they might be remedied (see Reference sites and calibration models
3. 11 Dangers of density data alone
This section should be read with especial consideration for the European Union’s (EU) Habitats & Water Framework
Directives (WFD).
Density data are frequently interpreted relative to reference sites. However, such reference sites are undefined
in Scotland, although existing SFCC data would aid their judicious selection for subsequent full characterisation.
Reference sites often refer to locations which are deemed pristine, and are consequently often remote from human
interference and accordingly, at high altitude. The reference conditions also often refer back in time as far as possible
(for example before1970, if records permit), before the major salmonid declines took place. In such cool, high
altitude circumstances salmonids tend to grow slowly, and have high proportions of 1, 2 and 3 year old parr. This
is not the case in warmer and richer waters, where even in Scotland salmon may smolt at 13~15 months. In such
lowland streams the density of 1+ parr can be low, simply because they grew so well in the warmer water that they
have already completed their freshwater life-cycle and gone to sea. But, compared to reference conditions, they
show sub-optimal densities. This situation needs resolving by contrasting low-lying streams that are and are not
nutrient enriched, but we don’t as yet have the data to do so.
3. 12 Size-at-age information
The fish fauna of Scotland is frequently dominated by just two species, salmon and trout. Both are migratory, and
numbers of their juveniles at particular sites are accordingly subject to wide variations, depending on adult returns
and breeding success nearby. As, for the reasons given above, density estimates often have wide confidence bounds,
assessment of fish community status based on density data alone are further impaired by this uncertainty, as well
as the two species limitation. Age and size data can ameliorate this difficulty, by utilizing the extra data on the
body-length sizes achieved at different ages, preferably in the context of a knowledge of local water temperatures,
to contrast similar densities but very different age- and size- structured populations.
3.13 Juvenile salmonid sizes correlate with densities
Recent empirical analyses by FRS has shown, both at the Girnock Burn and for sites in the Acid Waters Monitoring
Network, that for both fry and parr of salmon and trout that densities and sizes are inter-correlated. In brief, the
shapes of the size at age histograms contain information about densities as well as fish sizes. As the same EF
surveys can, and often do, obtain both sets of data (although ages from scales are not always obtained) it seems
sensible to combine them to make the best use of the information to hand.
3.14 Reference sites and environmental instability (e.g climate change)
Data from the Girnock Burn (Gurney et al, in prep) show that increased spring temperatures have changed smolt
age composition much more than they have changed smolt production, and thus, arguably, that the age structure
alone is not a reliable indication of the population status . As we know that temperatures have changed since the
calibration of (English and Welsh) reference sites (which are not typical of Scotland anyway) and that temperatures
are likely to change more over the next few decades, it would seem sensible to investigate Fish Population
Indices, which would allow us to disentangle changes caused by the habitat (to which the EU’s Habitats and Water
Frameworks Directives directly refer) from those caused by climate change. This is presumably especially desirable
in Scotland, where changes in climate are likely to move salmonid populations away from the reference conditions
of pristine, colder, upland environments.
3. 15 Saturation Stocking
The Habitats Directive and WFD require that habitats are maintained in good condition. Unfortunately, some salmon
and sea-trout populations are in decline. Thus it is possible that, in future, stocks of both will continue to decline
in fresh water, despite the freshwater habitat being potentially in perfectly good condition. To this end it would be
worthwhile developing a method which allowed managers, including fishery managers, to assess how suitable
the habitat was, even in the absence of natural spawning.
Various techniques being developed by FRS allow eggs from suitable local strains of salmon (and sea trout) to be
planted out at low densities, their hatching success assessed and the Age and Size Structure of the consequent
juvenile populations to be monitored and modelled. This offers an extremely powerful means of assessing habitat
carrying capacity in the unfortunate prevailing situation when the natural population may be well below the desired
reference conditions for a pristine situation (see below).
3.16 Calibration models
Although these empirical analyses do not yet indicate the magnitude of increased precision in describing
fish population status which might be expected to accrue from such extended Size-at-Age given temperature
analyses, the process-based modelling work of Gurney et al (in prep) gives strong grounds for believing that useful
improvements are possible, if not probable.
The main limitation to developing such improved understanding is lack of suitable data from which to calibrate the
models or empirical analyses, and thereby to serve as reference sites for the comparison of other Scottish data.
We note that such an endeavour, to calibrate salmonid population status via Size at Age given temperature, would
be enhanced by contrasting over the wider geographic scale of Scotland, England and Wales and their ensuing
climate, environment and habitat comparisons.
We note further that Scottish Government Freshwater Fisheries and Aquaculture, SEPA, SNH and the Fishery Trust’s
Biologists have all expressed interests in contributing towards such a better understanding.
Anderson, C. S. (1995). Measuring and correcting for size selection in electrofishing mark recapture experiments.
Transactions of the American Fisheries Society, 124: 663-676.
Crisp, D.T & Crisp, D.C., (2006). Problems with timed electric fishing assessment. Fisheries Management and
Ecology, 13: 211-212
Link, W. A. & Barker, R. J., (2004). Hierarchical mark–recapture models: a framework for inference about demographic
processes. Animal Biodiversity and Conservation, 27.1: 441– 449.
Godfrey, J.D. (2006). Site Condition Monitoring of Atlantic Salmon cSACs. Contract between Scottish Natural Heritage
and the Scottish Fisheries Co-ordination Centre, Final Report, December 2006
Peterson, J.T, Banish, N.P. & Thurow, R.F. (2005). Are Block Nets Necessary?: Movement of Stream-Dwelling Salmonids
in Response to Three Common Survey Methods. North American Journal of Fisheries Management, 25:732–743
Rosenberger, A.E. & Dunham, J.B. (2005) Validation of abundance estimates from mark-recapture and removal
techniques for Rainbow trout captured by electrofishing in small streams. North American Journal of Fisheries
Management, 25, 1395-1410
Schnute, J. (1983). A new approach to estimating populations by the removal method. Canadian Journal of Fisheries
and Aquatic Sciences, 40: 2153-2169.
Wyatt, R.J. & Lacey, R.F. (1999). Semi-Quantitative methods for Fisheries Classification. Water Research Council,
Technical Report W167, WRC, Swindon, p 51.
Wyatt, R.J. (2002). Estimating riverine fish population size from single- and multi-pass removal sampling using a
hierarchical model. Canadian Journal of Fisheries and Aquatic Sciences, 59(4): 659-706.
Wyatt, R.J. (2003). Mapping the abundance of riverine fish populations: integrating hierarchical Bayesian models with
a geographical information system (GIS). Canadian Journal of Fisheries and Aquatic Sciences, 60(8): 997-1006.
Appendix One
Major assumptions of Depletion EF
Major assumptions of Depletion EF are that, during the capture operation:
1. The population is closed (i.e. no fish can leave or enter the site, or be born or die);
2. Fishing effort is the same on each pass;
3. The catchability (probability of capture for a given effort) is the same on each pass;
4. The catchability is the same for each individual fish.
Note that, if the catchability declines between passes, for reasons of either reduced effort or because of
subsequent fish avoidance behaviour, then the resulting removal estimates are downwardly biased, potentially
quite dramatically.
Arithmetic example
Method : capture a sample of fish from a known area.
Basic principles: Each column of Table A.1 represents a different capture probability (0.8,0.7,...0.1 per pass,
with a 100 fish present before the first pass. The rows show the expected number of captures on each pass,
and the two summary rows the totals after three and five passes. These results are illustrated in Figure A.1. The
slope of the line allows estimation of the number of fish that would be caught were many more passes done.
Table A.1
Illustrative electro-fishing captures per EF pass, for assumed known capture probabilities
Assumed Capture probability values
EF Pass
Total @ 3
Total @ 5
Appendix Two
Statistical Challenges arising from Juvenile Salmonid Assessment studies.
ONE: Size-At-Age of known and unknown juvenile individuals comprising populations.
Data: Size-At-Age information from single (summer) surveys of multiple sub-sites censused over several years,
and with multiple samples per year (4-9, mainly in spring and summer).
Analyses: Generalised Linear Mixed Models (including hierarchies) usefully predict densities and sizes. These show
that water temperature, Altitude, River flow and both Fry and Parr densities affect fry (and probably parr) sizes.
Challenge One. The three-pass EF needed to obtain accurate density estimates is too time-consuming to get a
representative sample of what is happening to the wider population.
TWO: Single-Pass Surveys of Fry and Parr.
The use of single-pass surveys increases the range of sites sampled. This is showing encouraging results, but
uncovering further analytical challenges. Preliminary results (from 24 sites along the Girnock) demonstrate that
densities, sizes and biomasses of fry and parr change smoothly with distance along the burn. This is an ecologically
important finding. However, distance along the burn correlates significantly with many environmental factors (such
as: altitude and temperature (indeed 15/18 variables from a catchment GIS) and fish densities. It is not easy to
de-confound those inter-relationships, although ‘block stocking’ of ova (see 6) might help.
Challenge Two. Densities are collinear with altitude (but see 6) and single-pass EFs are size-biased (see 3).
THREE: Size-bias in Single Pass EFs.
Preliminarily analyses show that while 3-pass fishings are virtually unbiased (i.e. trivial bias error compared to the
stochastic error of the smallish sample sizes of fish (around 40)), single pass fishings appreciably mis-estimate
the joint size and density distributions. So neither is correct. If single-pass fishings are used, correction factors
for both densities and sizes need developing. It is likely that these corrections would be small to trivial in some
circumstances/ habitats, but large in others. Such an approach would fit very nicely into Wyatt’s ideas (2003)
of using GIS and environmental co-variables to optimise the more cost-effective estimation of fish population
descriptions for wider catchments.
Challenge Three. Devise a method to extrapolate, objectively and cost-efficiently, from survey sites to the wider
geographic regions amenable to fishery management.
The challenge here would be to combine 1-3 above to produce a Size-At-Age population assessment protocol for
salmonids that is unbiased and informative over a range of environmental situations (temperature, altitude, water
quality, habitats). Note that this would be a protocol that would need fine-tuning to novel situations, and not a
single prescription that would be equally informative and cost-effective everywhere.
FIVE: Choosing informative study sites.
In order to implement reliable surveys and advice based on 1-4 above, one would need to calibrate models
based on an informative set of sites. For this to be economically feasible, the number of sites would have to be a
minimum number. So we need to use background (GIS) environmental data (temperature, altitude, water quality,
habitats) to quantitatively assess the co-linearities, etc, between these potential driving variables, and then, at
least at regional and national scales, choose study sites that maximise information that helps de-confound those
Challenge Four. Use GIS approaches to assess colinearities between driving environmental variables and optimise
fishery study site choices to allow cost-efficient sampling.
SIX: Saturation and block ova stocking.
Experiments at the Girnock, recently extended to the Lour and Baddoch burns, show that stocking with eyed salmon
eggs at uniform high densities gives important information on both the rivers’ carrying capacities for fry and the
density-dependence of fry numbers and growth. A pilot study suggests that, by stocking in adjacent high and low
density blocks, one can manipulate field situations to help deconfound density effects from environmental factors
such as altitude and water quality.
Challenge Six. Develop analyses to cope rigorously with this complex spatial block design and block-shifting.
Past issues
No. 18
No. 1.
The Aberdeen single-anchor mooring
Payne, R.
No. 2.
The Aberdeen shallow water current meter mooring
Payne, R.
No. 19
Survey of trace elements in fish and shellfish landed at
Scottish ports 1975-76
Davies, I.M.
No. 20
A study of the sand clouds produced by trawl boards
and their possible effect on fish capture
Main, J. & Sangster, G.I.
No. 21
Orientation and energetic efficiency in the offshore
movements of returning Atlantic salmon Salmo salar L.
Smith, G.W., Hawkins, A.D., Urquhart, G.G. & Shearer,
No. 22
The theory of solid spheres as sonar calibration targets
MacLennan, D.N.
No. 23
A study of the fish capture process in a bottom trawl by
direct observations from a towed underwater vehicle
Main, J. & Sangster, G.I.
No. 3.
The Bridger version of the Gulf III high speed plankton
Adams, J.A.
No. 4.
Some parasites of plaice Pleuronectes platessa L. in
three different farm environments
MacKenzie, K., McVicar, A.H. & Waddell, I.F.
No. 5.
Simulation of vertical structure in a planktonic
Steele, J.H. & Henderson, E.W.
No. 6
The intertidal fauna of sandy beaches: a survey of the
Scottish coast
Eleftheriou, A. & McIntyre, A.D.
No. 7
A net drag formula for pelagic nets
Reid, A.J.
No. 8
The Aberdeen sedimentation trap and its moorings
Payne, R. & Davies, J.M.
No. 24
A study of separating fish from Nephrops norvegicus L.
in a bottom trawl
Main, J. & Sangster, G.I.
No. 25
Target strength measurements on metal spheres
MacLennan, D.
A study of a multi-level bottom trawl for species
separation using direct observation techniques
Main, J. & Sangster, G.I.
No. 9
A preliminary model of shear diffusion and plankton
Evans, G.T., Steele, J.H. & Kullenberg, G.E.B
No. 26
No. 10
Diving observations on the efficiency of dredges used in
the Scottish fishery for the scallop Pecten maximus (L.)
Chapman, C.J., Mason, J., Kinnear, J.A.M.
No. 27
Fish reactions to trawl gear a study comparing light and
heavy ground gear
Main, J. & Sangster, G.I.
No. 11
Comparative fishing for flatfish using a beam trawl fitted
with electric ticklers
Stewart, P.A.M.
No. 28
Guidelines for the use of biological material in first order
pollution assessment and trend monitoring
Topping, G.
No. 12
The value of direct observation techniques by divers in
fishing gear research
Main, J. & Sangster, G.I.
No. 29
TUV II: a towed wet submersible for use in fishing gear
Main, J. & Sangster, G.I.
No. 30
The United Kingdom fishery for the deep water shrimp
Pandalus borealis in the North Sea
Howard, G.
No. 13
The occurrence of pleroceroids of Diphyllobothrium spp.
in wild and cultured salmonids from the Loch Awe area
Wootten, R. & Smith, J.W.
A study of bottom trawling gear on both sand and hard
Main, J. & Sangster, G.I.
No. 31
No. 15
Hydrodynamic characteristics of trawl warps
MacLennan, D.N.
No. 16
Some parasites and diseases of blue whiting,
Micromesistius poutassou (Risso), to the north and west
of Scotland and at the Faroe Islands
MacKenzie, K.
The coastal movements of returning Atlantic salmon,
Salmo salar L.
Hawkins, A.D., Urquhart, G.G. & Shearer, W.M.
No. 14
No. 17
Changes in the respiration and blood circulation of cod,
Gadus morhua L., induced by exposure to pollutants
Johnstone, A.D.F. & Hawkins, A.D.
A selected review of hydrodynamic force coefficient data
on stranded wires used in fishing gear
Ferro, R.S.T & Hou, E.H.
No. 32
Small mesh cod-end covers
Stewart, P.A.M. & Robertson, J.H.B
No. 33
Attachments to cod-ends
Stewart, P.A.M. & Robertson, J.H.B.
No. 34
The behaviour of the Norway lobster, Nephrops
norvegicus (L.), during trawling
Main, J. & Sangster, G.I.
No. 35
Experiments on the cultivation of oysters in Scotland
Drinkwater, J. & Howell, T.R.W.
No. 36
Radio-tracking observations on Atlantic salmon
ascending the Aberdeenshire Dee
Hawkins, A.D. & Smith, G.W.
No. 52
The behaviour of adult salmon (Salmo salar L.) in the
River Tay as determined by radio telemetry
Webb, J.
No. 53
Fish populations and invertebrates in some headwaters
of the Rivers Dee and Spey, 1983-1985
Morrison, B.R.S. & Harriman, R.
No. 37
A model to assess the effect of predation by sawbill
ducks on the salmon stock of the River North Esk
Shearer, W.M., Cook, R.M., Dunkley, D.A., MacLean, J.C.
& Shelton, R.G.J.
No. 54
Persistent organochlorine contaminants in fish and
shellfish from Scottish waters
Kelly, A.G. & Campbell, D.
No. 38
The intertidal fauna of sandy beaches: a survey of the
east Scottish coast
Eleftheriou, A. & Robertson, M.R.
No. 39
Mesh selection within the cod-end of trawls: the effects
of narrowing the cod-end and shortening the extension
Robertson, J.H.B. & Ferro, R.S.T.
No. 55
The movements of sea trout smolts, Salmo trutta L., in
a Scottish west coast sea loch determined by acoustic
Johnstone, A.D.F., Walker, A.F., Urquhart, G.G. & Thorne,
16pp + appendices
No. 56
The movements and spawning behaviour of adult
salmon in the Girnock Burn, a tributary of the
Aberdeenshire Dee, 1986
Webb, J. & Hawkins, A.D.
Estimation of the daily consumption of food by fish in
the North Sea in each quarter of the year
Greenstreet, S.P.R.
No. 57
A shelf plankton model: description and testing
Henderson, E.W. & Steele, J.H.
No. 41
The movements of adult salmon within the River Spey
Laughton, R.
No. 58
Integrated Catch at Age Analysis Version 1.2
Patterson, K.R. & Melvin, G.D.
No. 42
Genetic protein variation in farmed Atlantic salmon in
Scotland: comparison of farmed strains with their wild
source populations
Youngson, A.F., Martin, S.A.M, Jordan, W.C. & Verspoor,
No. 59
A bibliography of electrophoretic studies relating to
genetic protein variation in the Atlantic salmon Salmo
salar L.
Wilson, I.F. & Verspoor, E.
No. 40
No. 43
Length/weight relationships for 88 species of fish
encountered in the North East Atlantic
Coull, K.A., Jermyn, A.S., Newton, A.W., Henderson, G.I.
& Hall, W.B.
No. 44
The movements of adult Atlantic salmon in the River Tay
Webb, J.
No. 45
Records of porbeagles landed in Scotland, with
observations on the biology, distribution and
exploitation of the species
Gauld, J.A.
No. 60
No. 46
An assessment of the scale damage to and survival
rates of young gadoid fish escaping from the cod-end of
a demersal trawl
Main, J. & Sangster, G.I.
No. 47
Force coefficients for stranded and smooth cables
Ferro, R.S.T.
No. 48
The behaviour of adult Atlantic salmon ascending the
Rivers Tay and Tummel to Pitlochry dam
Webb, J.
No. 49
The design and testing of a divided trawl for
comparative fishing experiments
Robertson, J.H.B., Shanks, A.M. & Kynoch, R.J.
No. 50
The movements of adult salmon (Salmo salar L.) in the
River Tay as determined by radio telemetry
Laughton, R.
Trace metals in fish and shellfish from Scottish waters
Brown, F.M.J. & Balls, P.W.
No. 61
Relocation of naturally-spawned salmon ova as a
countermeasure to patchiness in adult distribution at
Youngson, A.F. & McLaren, I.S.
No. 62
Catch and release: the survival and behaviour of Atlantic
salmon angled and returned to the Aberdeenshire Dee,
in spring and early summer
Webb, J.H.
No. 63
Scottish Executive locational guidelines for fish farming:
predicted levels of nutrient enhancement and benthic
Gillibrand, P.A., Gubbins, M.J., Greathead, C. & Davies,
I.M. (Web download only)
24pp + appendices
No. 64
The Development of Conservation Limits (CLs) for
Scottish Salmon Stocks: I - Establishing a method of
transporting CLs among locations
MacLean, J.C., Smith, G.W., Tulett, D. & Jackson, J. 18pp
No. 51
The distribution and growth of juvenile salmon and
trout in the major tributaries of the River Dee catchment
(Grampian region)
Shackley, P.E. & Donaghy, M.J.
No. 65
Hatchery Work in Support of Salmon Fisheries
Youngson, A.
No. 66
The Development of Conservation Limits (CLs) for
Scottish Salmon Stocks: III - Estimation and transport of
MacLean, J.C.
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