A hierarchical approach to assess the effectiveness of aquatic organism passage
Principal Investigator - Andrew Whiteley – Assistant Professor, Department of Natural Resources
Conservation, UMASS and Conservation Geneticist, USDA Forest Service Northern Research Station,
Amherst, MA 01003
Co – Principal Investigators - Benjamin H. Letcher, Ecology Section Leader, USGS-BRD CAFRC, Turners
Falls, MA; Keith H. Nislow, Fish and Wildlife Habitat Relationships Team Leader, USDA Forest Service
Northern Research Station, Amherst, MA 01003, Jason Coombs, Postdoctoral Researcher, Department
of Natural Resources Conservation, UMASS
Cooperators – Mark Hudy, National Aquatic Ecologist, USDA Forest Service, Harrisonburg, VA; Steven
Roy and Dan McKinley, Green Mountain National Forest, Rutland, VT; Mark Prout, White Mountain
National Forest, Campton, NH; Michael Owen, Monongahela National Forest; Nick Schmal, USFS Region
9; John Magee, New Hampshire Fish and Game Department, Concord, NH; Kim Lutz, The Nature
Conservancy – Connecticut River Program, Northampton, MA, Colin Apse, TNC Eastern Regional Office
Progress Report FY 2010
Summary
Individual aquatic organism passage provides definitive evidence that the removal or replacement of a
barrier has been effective in achieving AOP goals. In 2010 we initiated a study designed to evaluate a
range of approaches to monitoring aquatic organism passage in eastern National Forests. In 2010, we
accomplished several key tasks including
1) Reanalysis of abundance data above- and below- predicted passable and impassable culverts in
the Monongahela National Forest
2) Conducted simulations to determine the efficacy of the sibship-splitting method for
determining fish passage using genetic data
3) Assembled list of appropriate culvert locations for sampling
4) Completed the first field season
5) Began genetic sample and data analysis
Preliminary results of the new analysis of previously-collected data and new simulations suggest that
both patterns of abundance and family spatial distribution hold promise as useful indicators of aquatic
organism passage effectiveness that are amenable to use by management agencies. Analyses of data
collected this and the next field seasons will allow us to test these ideas more thoroughly and further
development of management tools.
1. Variation in abundance as an indicator of aquatic organism passage
In cases where it is unclear whether potential barriers actually restrict the movements of individuals,
local reductions in abundance and richness might be reliable indicators of reduced immigration, and
help to both prioritize management actions (such as barrier removals), and to assess the consequences
of these actions. However the effects of barriers on local abundance are highly dependent on the overall
influence of immigration on the basic population equation (Lowe and Allendorf 2010). Even in cases
where immigration is an important determinant (i.e. in metapopulations ((Hanski, 1991)), high levels of
spatial variation and confounding effects of habitat area and habitat isolation by distance (Ewers and
Didham, 2006) can constrain both the ability to detect barrier effects and hinder the use of differences
in abundance as a monitoring and management tool.
We developed a simple method to assess the relationship between road-stream crossings and local
abundance and species richness, then tested this
approach in streams within the Monongahela National
Forest (West Virginia, USA) (Figure 1) that contain a
diverse stream fish assemblage.
20 X channel
width
Road
Upstream site
20 X channel
width buffer
20 X channel
width
20 X channel
width buffer
Downstream site
Figure 1. Map of study area and schematic of sampling protocol.
Our rationale was that while upstream and downstream differences in abundance within one or a few
sites may provide little information, differences in abundance across many sites will provide sufficient
power to detect the effects of barriers even in the face of substantial intrinsic variation.
We sampled with consistent effort and at consistent distances above and below stream crossings that
were a priori determined to be passable or impassable to fish movement. We then used mixed linear
models to test if local abundance and species richness were consistently lower upstream of crossings
predicted to be impassable relative to upstream/downstream comparisons at passable crossings.
Table 1. Fish species collected from 31 study streams during summer 2002 and 2003, Monongahela National
Forest, West Virginia. Listed in descending order of occurrence.
Common name
Scientific name
ID code Number
Number of streams with
Brook trout (wild and stocked)
of streams
passable culverts
BKT
28
12
SCL
22
15
Mottled sculpin
Salvelinus fontinalis
Cottus bairdi
Blacknose dace
Rhinichthys atratulus
BND
18
13
Fantail darter
Etheostoma flabellare
FTD
17
12
Creek chub
Semotilus atromaculatus CC
16
12
Mountain redbelly dace
Phoxinus oreas
11
8
White sucker
Catostomus commersoni WS
10
10
Rosyside dace 1
Clinostomus funduloides RSD
7
7
Central stoneroller
Campostoma anomalum STR
6
6
BNT
5
5
Brown trout (wild and stocked)1 Salmo trutta
MRD
Longnose dace
Rhinichthys cataractae
LND
4
4
New River shiner
Notropis scabriceps
NRS
3
3
Rainbow trout (stocked only)1 Onchorhynchus mykiss
RBT
3
3
Bigmouth chub
Nocomis platyrhynchus
BMC
2
2
Rock bass
Ambloplites rupestris
RB
2
2
BB
1
1
BNM
1
1
GS
1
1
Brown bullhead 1
Bluntnose minnow
Ameiurus nebulosus
Pimephales vigilax
Green sunfish
Lepomis cyanellus
Greenside darter
Etheostoma blennioides GSD
1
1
Toungetied minnow
Exoglossum laurae
1
1
1
TTM
Not native to watershed
Sampled streams contained a diverse fish assemblage including cold-, cool- and warm-water species
(Table 1). As predicted, both total abundance, and species richness were greater below then above
impassable but not passable culverts (Figure 2), indicating a strong influence of movement on stream
fish demography and community structure, and suggesting a potential monitoring tool. We will be
submitting the results of this study for publication in the next month, and further developing and
applying this approach in the field component of our effectiveness monitoring study.
Figure 3. Relationship between total fish abundance (number of individuals per sample) (upper panel)
and species richness (lower panel) downstream (x-axis) and upstream (y-axis) of road crossings in the
Mongahela National Forest. Closed circles (and corresponding dark line) are crossings predicted to be
passable, open circles (and corresponding light line) are predicted impassable crossings. DRAFT DATA –
DO NOT CITE
2. Determining the efficacy of the sibship-splitting method for determining fish passage using
genetics data.
Traditionally, conclusive evidence of movement requires the use of a capture-mark-recapture (CMR)
experimental design. Outlined briefly, individuals would be initially captured, marked with an individual
or habitat specific identifier, and returned before executing a second capture event after an elapsed
period of time to recapture marked individuals and assess movement rates and directions. However, the
advent and advancement of molecular techniques and relationship reconstruction algorithms has made
another alternative possible. Like CMR, the populations adjacent to the barrier would be sampled.
Unlike CMR, individuals would not be tagged, but instead donate a tissue sample as a source of DNA.
Molecular techniques would then acquire individual genotypes which, when run through a relationship
reconstruction algorithm, would delineate individuals into full-sibling families. Movement across a
barrier would then be determined by whether all members of a full-sibling family were on one side or
both sides of the barrier. Directionality of movement could be attained through use of a majority-rule
approach, where the side with the greatest proportion of family members is assumed to be the natal
patch, and direction of movement would be away from this side. In addition to this key assumption, a
second assumes that a parental pair reproduces on only one side of a barrier. Initial data for brook trout
(Salvelinus fontinalis) (Hudy et al. 2010) support these assumptions, but requires further assessment.
Figure 4. Results from “No Barrier” scenario for sibship splitting (left) and Structure (right). DRAFT DATA
– DO NOT CITE
To address the potential for the sibship method to identify movement we conducted a series of
simulations (parameterized with data from wild brook trout studies) over a range of observed levels of
population genetic differentiation and genetic diversity and replicating typical real world scenarios (i.e.
no barrier, new barrier, old barrier, remediated barrier; one-way, two-way, and no movement). One of
the benefits of using simulations is that all parameters are known and under the user’s control. This
allows for the testing of individual parameters while holding all others constant to see what effect
changing that value has on the outcome. Another major benefit of simulations is that they allow for
testing of a large number of scenarios in a short period of time. This enables examination of many, many
more scenarios than would otherwise be possible through field work alone because of time and budget
constraints. Preliminary outcomes have been positive, with greater than 95% accuracy for assigned
movers and directionality achieved for all scenarios. Additionally, the use of this method does not
preclude the use of traditional CMR techniques in that individual genotypes can be used as individual
identifiers. Furthermore, unlike traditional CMR techniques, this method is able to use information on
parent locations during spawning/birth to determine natal patch origin, lessening the dependence on
the majority-rule assumption. For example parental locations at spawning for brook trout would be
determined by sampling during the spawning period. However, we are trying to keep this method broad
in its applicability and so are incorporating options for other species for which parental association or
birth accessibility are viable means to acquire parentage (i.e. mammals, birds). Parents can either be
known directly or assigned genetically through relationship reconstruction algorithms. In summary, we
feel that this method holds great promise for barrier assessment in that it is able to provide estimates of
both movement rate and directionality without the time lag of traditional CMR techniques.
In the upcoming year we will be testing this concept in the field using data collected in 2010 as well as in
2011. Simulation results are currently being combined with a field test from our long-term study site
and will be submitted as a journal publication.
3. 2010 Fieldwork and Analysis
In 2010, we worked with National Forest and USGS-BRD personnel to identify a list of road crossings
which we plan to apply the hierarchical effectiveness monitoring protocol. We identified a list of 49
surveyed crossings, with 47 on eastern NF (WMNF and GMFLNF) lands and two on private lands (see
enclosed spreadsheet). This list included all NF sites where replacements are planned in the upcoming 1
– 2 years, along with a representative set of predicted passable and impassable crossings. Upon site
inspection, 13 of these sites were deemed inappropriate for application of the protocol, usually because
they were dry when visited (and unlikely to maintain fish) or had insufficient stream length to sample
before major habitat changes (such as a change in stream order) occurred. During the 2010 field season
we sampled twelve of the sites on the resultant list (Table 2).
Table 2- Sampled crossings in 2010.
Forest
Stream Name
Salmonids
Non-game
Green Mt.
Green Mt.
Sparks Brook
Joe Smith Brook
None
Slimy Sculpin
Green Mt.
Jenny Coolidge
Brook
Brook Trout
Atlantic
Salmon, Brook
Trout
Atlantic
Salmon, Brook
Trout
Slimy Sculpin
Passable
To be
replaced
Green Mt.
Green Mt.
USGS
White Mt.
White Mt.
White Mt.
Tributary of White
River
Perkins Brook
Mitchell Brook
Tributary of Upper
Ammonoosuc
Brandy Brook
Brook Trout
None
Atlantic
Salmon, Brook
Trout, Brown
Trout
Brook Trout
Brook Trout
Longnose Dace,
Slimy Sculpin
Brook Trout
Blacknose Dace,
Slimy Sculpin
None
Brook Trout
Finger Lakes
Tributary of Swift
River
Spring Creek
Finger Lakes
Hencoop Creek
Brown Trout
Finger Lakes
Tributary of
Breakneck Creek
None
Brown Trout
None
None
Blacknose Dace,
Creek Chub
Blacknose Dace,
Creek Chub
Creek Chub
In the upcoming year we will be analyzing abundance and population genetics data from the sampled
sites, and planning the sampling schedule for the 2011 field season