mussel species turnover along the river continuum · mussel species turnover along the river...

MUSSEL SPECIES TURNOVER ALONG THE RIVER CONTINUUM

Carla L. Atkinson University of Oklahoma &

Oklahoma Biological Survey

Organization of Fish and Wildlife Information Managers Meeting October 2011

Why mussels? • Dominated biomass in rivers

• Ecosystem services

BACKGROUND IMPLICATIONS RESULTS METHODS



• ~300 species in North America




• ~300 species in North America

• Highly imperiled


Freshwater mussels

Very threatened as a group

3 federally listed species in the Kiamichi and Little Rivers

River Continuum Concept in a

Nutshell

Functional feeding groups vary in a downstream direction There are also differences within functional groups!


Vannote et al. 1980

Study Area -3 rivers

-Kiamichi (18 species) -Little (16 species) -Mt. Fork (18 species)

-Mussels -28 species included in analysis -High densities (up to 100 mussels/m2)

- Landuse -Primarily forest (70-80%) -Human use (water extraction, agriculture)


Rivers of southeastern Oklahoma

Relatively pristine water

High biodiversity, but imperiled

Source: The Nature Conservancy


Methods

• Mussel data – 16 sites (collections in 1994, 2006, 2010)

– 28 species – how to describe the community?

– Bray-Curtis Ordination (Bray & Curtis 1954, Ecology) • 1st – a dissimilarity matrix is computed among sites

• 2nd – selection of 2 sites as poles (find the community separated by the greatest distance)

• Ordination is projected (along a gradient)


Predictable Species Shifts

-Bray-Curtis value is indicative of community composition

-Communities that are the same distance from the headwaters are more similar

Explain

• What are the factors that lead to shifts in community composition?

– Physiography?

– Land use?

GIS Data

• DEM – extracted watersheds for each site

• NLCD – all landuses (%forest, %urban, etc.)

• SSURGO - CaCO3, % frequently flooded

• DEM and NHD combined - stream gradient, 100 m buffers

• NAIP aerial photographs - verification


Multiple Spatial Scales

• Allan (2004) suggested the use of 3 spatial scales

– Watershed (upstream of the site)

– Buffer (100 m around all channels)

– Site (buffer scale 1 km upstream from site)


Example

• Created 100-m buffer using spatial analyst

• Measured 1 km upstream and selected the buffer for that length

3 Scales • For each scale I extracted:

– Landuse

– Information on soils

• Gradient was calculated different ways for each scale – Watershed – total change in elevation/stream

length

– Buffer – change in elevation 10 km upstream/10km

– Site – change in elevation 1km/1km


Watershed Scale

River Site

Mainstem

Distance

Downstream

(km)

Watershed

Land Area

(km2)

Gradient*

(m/km)

% of

Watershed

that is

Frequently

Flooded**

% Open

Water

%

Urban

%

Barren

%

Forest

%

Grassland/S

hrubs

%

Agriculture

%

Wetland

Kia

mic

hi

KM1 61.88 443.87 4.85 8.84 0.07 2.67 0.00 87.14 1.77 8.04 0.30

KM2 81.50 815.19 3.77 8.47 0.29 3.08 0.01 75.94 4.85 14.84 1.00

KM3 109.17 1040.14 2.91 8.29 0.28 3.14 0.01 73.51 6.26 15.44 1.35

KM4 117.39 1860.24 2.80 8.16 3.11 2.64 0.01 70.91 6.83 15.23 1.26

KM5 131.63 1946.57 2.60 8.33 3.00 2.70 0.01 71.10 6.78 15.15 1.27

KM6 152.01 2043.85 2.30 8.31 2.89 2.68 0.01 71.25 6.82 15.06 1.28

Litt

le

LM1 18.49 73.55 15.08 7.94 0.00 1.56 0.01 84.64 12.10 1.23 0.45

LM2 51.96 362.87 6.90 9.83 0.21 2.66 0.01 80.21 14.36 2.05 0.52

LM3 59.94 414.48 6.18 9.98 0.20 2.68 0.01 79.18 14.41 3.00 0.53

LM4 67.10 717.24 5.36 10.20 0.15 3.04 0.00 77.53 15.63 3.22 0.44

LM5 93.23 1055.20 4.30 11.38 0.18 3.25 0.00 77.86 15.96 2.34 0.41

Mt.

Fo

rk

MF1 43.19 437.99 2.63 5.10 0.31 4.06 0.03 76.14 4.01 15.31 0.14

MF2 53.01 565.94 2.34 5.59 0.27 3.75 0.02 78.77 4.28 12.76 0.15

MF3 62.19 673.38 2.23 5.87 0.26 3.85 0.02 78.09 4.94 12.67 0.17

MF4 71.98 831.49 2.04 5.60 0.24 3.83 0.02 78.09 6.36 11.30 0.17

MF5 76.66 1091.94 1.97 5.92 0.22 3.86 0.01 79.67 7.09 8.82 0.32

*Gradient considering the elevation and length of the entire mainstem channel

**The percentage of land area in which that the chance of flooding is more than 50 percent in any year but is less than 50 percent in

Further Analysis

• Used Akaike’s Information Criterion (AIC) approach to select model to predict community composition*

• Several multiple linear regressions generated

• Provides the best compromise between predictive power and model complexity

* See Burnham and Anderson 2001 for more information


AIC results

Watershed and buffer scale variables best at predicting mussel community composition

Scale Parameters in Model K F-value R2 AIC Δi wm

Watershed Gradient, %Open Water, %Urban 3 55.79 0.933 -84.224 0.000 0.169

Gradient, % Open Water, %Urban, %Grassland/Shrub 4 38.85 0.934 -82.416 1.809 0.068

Gradient, %Open Water, %Urban, %Frequently Flooded 3 38.76 0.934 -82.381 1.843 0.067

Buffer Gradient, %Open Water, %Grassland/Shrub 3 56.76 0.934 -84.483 0.000 0.146

Gradient, %Open Water, %Agriculture 3 51.19 0.928 -82.942 1.541 0.068

Gradient, %Open Water, %Frequently Flooded, %Agriculture 4 36.11 0.927 -82.924 1.559 0.067

Site %Urban, %Forest, %Agriculture 3 3.8 0.488 -51.647 0.000 0.042

Gradient, %Agriculture, %Wetland 3 3.75 0.484 -51.540 0.107 0.040

%Frequently Flooded, %Urban, %Forest, %Agriculture 4 3.12 0.531 -51.073 0.574 0.031


2 Most Predictive Scales – Watershed & Buffer

• Gradient best predictor in the model – geomorphic control (corroborates with Arbuckle and Downing 2002)

• %Open Water another good predictor (likely driven by Sardis Lake)


Sardis Lake Dams Jackfork Creek

Management Implications -- Water Management

-- Watershed Management

Other drivers

• Watershed Scale

– %Urban in top 3 models

• Buffer Scale

– Very variable

• Site Scale

– %Agriculture in top models

• Not included

– Fish


Why does this matter?

• Changes in hydrology and land use are influencing community composition

• Protecting site ≠ protecting the community

• Need to consider the buffer scale, and the whole watershed


New Biogeochemistry Data Corroborates

Biogeochemical signature is indicative of the watershed.

Extinction Debt?

• Mussels are long-lived and slow-growing

• Time lag between disturbance and species extinctions


2011 Drought – Little River 2011 Drought – Kiamichi River

Acknowledgements

• Vaughn lab / OK Biological Survey

• Robert S. Kerr Lab – Environmental Protection Agency

• Landowners

• Funding Sources:

– Sigma Xi

– OU College of Arts and Sciences

– OU Graduate Student Senate

– OU Zoology Dept.

“Mussels are not dismissible, even by those who have little interest in the natural world.

Their presence is a signature of healthy aquatic ecosystems, to which they contribute as living water filters.”

- E.O. Wilson

ANY QUESTIONS?

mussel species turnover along the river continuum · mussel species turnover along the river...

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