National Salmon Fry Density Model

Why do we need to understand the influence of habitat on salmon productivity?• Assessment

– Derive an expectation: Is what I see what should be there?– How many fish are required to stock different rivers?

• Management– Why are there less fish here than I would expect?– What are costs / consequences of human alterations / impacts e.g.

abstraction, impoundment, landuse change?– How do we restore or improve productivity?– Can we modify human activity to minimise negative effects?

Overview• Can be thought of as two separate models (or 2 stage process)

– Catch probability (efficiency) model– Salmon density model

• Catch probability model:– People, equipment and protocols (Organisation)– Time (Year and Day of Year)– GIS derived habitat characteristics ( e.g. Distance to Sea, Channel Width,

Upstream Catchment Area, Gradient, Landuse)– Large scale hydro-climatic controls (spatial hydrometric area effect)

• Salmon density model (using corrected abundance estimates)– Time – GIS derived habitat characteristics– Large scale hydro-climatic controls (spatial hydrometric area effect)

Capture probability model (partial effects)

Modelled capture probability vs. other approaches

• Capture probability model provides more precise estimates of abundance than sample-wise approaches (i.e. Zippin estimates at each site)

• Provides less biased estimates than assuming constant capture probability

Fry density model (partial effects)All values fish m-2

Potential value for assessment: catchment scale

• Catchment effect provides a relative measure (relative to other catchments) of performance averaged over study period providing that:

– Electrofishing sites are representative of catchment– Variation does not reflect uncharacterised habitat controls e.g. water quality

• Present model does not allow you to assess performance of catchments in particular years

• Significance of differences not tested

e.g. catchment value of 1.9 indicates EF sites with mean characteristics will have 1.9 more fry than a catchment with a value of zero

Potential value for assessment: sub-catchment

• What metric?

• Mean national expectation (for that habitat) in a good year (the year with the highest national average fry densities).

– If habitats are saturated in “good year”, then expectation is mean carrying capacity for a given habitat

Dee 2015

Potential value for assessment: Sub-catchment• Expectation for Dee in a good year• Local expectation would be a problem in catchments

that have never had adequate spawner returns (pull down expectation)

Dee 2015

Summary• A capture probability model has been developed

• A preliminary density model produced for salmon fry

• Potential for juvenile density models to contribute towards assessments of catchment health and local EF site health, but further development required

Next stage• Catch probability model includes parr and behavioural effects (between

pass differences in catch probability)

• EF data quality controlled with help of data providers (partial samples, wrong locations)

• Habitat characterisation improved with use of new R routines

• Density model to include salmon fry and parr and interactions / correlations

• In-river smoother to account for spatial correlation

• Quantify measures of catchment and site performance (significance)

Implications for data collection•Some thoughts on future data collection:

– Sampling should be strategically planned and representative of catchments

– Include 3 pass data in all catchments in all years

– If1 pass, should use same equipment and protocols as 3 pass or include specific calibration work (e.g. 1 pass using backpack rather than bank equipment, need to calibrate equipment)

– Record staff, equipment, protocols

– Consider options for quantitative mainstem sampling (e.g. boat)

Further information