Download Influence of land use on the distribution of submerged aquatic

Influence of land use on the distribution of
Submerged Aquatic Vegetation and fish populations in the
Rappahannock River
Inia M. Soto
University of Puerto Rico at Mayaguez
To:
REU – Program at VIMS
Mentors:
Marcia Berman: Director, Comprehensive Coastal Inventory Program
Donna Bilkovic : Fisheries Biologist, Center for Coastal Resources Management
Tami Rudnicky: GIS Programmer/Analyst - CCI
August 6, 2004
Abstract
During the last decades, submerged aquatic vegetation (SAV) abundance has
been declining drastically. Submerged Aquatic Vegetation is considered an important
essential fish habitat, thus declines in SAV might have negative consequences to fish
communities. Import of nutrients and sediment into tributaries affect water clarity; and
consequently, the survival of SAV. Inputs of nutrients, pollutants and sediments are
directly related to land use practices. The purpose of this research is to evaluate
anthropogenic impacts on SAV biomass, fish communities and water quality in the lower
Rappahannock River, a major tributary of the Chesapeake Bay. We wanted to assess
associations between SAV abundance and fish diversity with respect to land use; and
evaluate river discharge as an indicator of water clarity. For the time period 1989-2002,
we calculated fish community metrics, used Geographic Information Systems (GIS) to
compute land use change, examined environmental parameters (e.g. river discharge,
temperature and precipitation), and estimated SAV biomass. We found a slight increase
in the amount of developed area, with decreases in agricultural and forested land use.
Fish species richness and abundance were inversely correlated, while species richness
and the number of dominant species were positively correlated. Environmental
parameters were not correlated with the examined fish community metrics. There was
evidence that stream flow may influence SAV biomass and distribution in this system.
Relationships among SAV biomass, land use and fish community metrics could not be
completed quantified. Examining other environmental factors and extending the study
area might provide a better understanding of the impact of human activities to SAV
abundance and fish communities. During the last decades, submerged aquatic
vegetation (SAV) abundance has been decline drastically.
Introduction
Essential fish habitat is defined to include both waters and substrate necessary
to fish for spawning, breeding, feeding, or growth maturity (16 US.C 1802(10)). One of
the most commonly recognized essential nursery habitat is submerged aquatic
vegetation (SAV), due in part to its abilities to export large quantities of carbon, nitrogen,
and phosphorous to coastal food webs (Beck et al, 2001). In addition to being a nursery
area, SAV provides oxygen, absorb wave energy and uptake nutrients (NMFS Web site,
16 U.S.C. 1801 (A)(9)). Researchers have shown evidence of higher fish abundance
and diversity in SAV habitat versus nonvegetated subaqueous bottom (Beck et al. 2001,
Hoss and Thyer 1993, Matheson et al. 1999).
The distribution of SAV has been fluctuating during the past decades. Recently,
the Bay Journal (June 2004) reported for 2003 a 30% decline in the abundance of SAV,
especially in the lower and middle Rappahannock River, compared with the four prior
years. High rainfall in 2003 caused an increase in stream flow, which delivered three
times more nitrogen, five times more phosphorus, and 11 times more sediments to the
tributaries (Bay Journal, 2004). Since SAV depend on sunlight to survive, large
quantities of sediments and particulate matter reduce water clarity causing mortality of
SAV. Also, discharge of other pollutants might be limiting factors for SAV survival.
Large inputs of nutrients, pollutants and sediments are directly related to land
use practices. Agriculture and development are accompanied by land disturbance that
increases erosion, sedimentation, and nutrient runoff to the bay (NOAA Technical report
123, 1995). During wet years, nutrient rich sediments and particulate runoff enter
streams causing turbidity, low light attenuation, and consequently a die off of SAV. The
VIMS SAV Program reports an increase in abundance of SAV during dry years
preceding 2003, suggesting lower turbidity and high light attenuation as primary driving
factors.
The NOAA Technical report 123 (1995) suggests the study of land cover change
as a tool to measure the input of nutrients, sediments, pollutants and other factors that
might control water quality and habitat disturbance. Studying change in land use
patterns over time is a good approach to understanding the impacts of land use on SAV
growth, and consequently fish habitat. A better understanding of essential fish habitat
and the factors affecting them will improve management, conservation, and decisions
related to fisheries (Beck et al. 2001).
Objectives/ Hypothesis
The objective of this research is to evaluate the anthropogenic impacts to SAV
and fish populations in the lower Rappahannock River. The analyses will compare
abundance of SAV and fish diversity with respect to land use. We hypothesize a decline
in fish diversity and abundance during years with low SAV abundance. SAV abundance
is expected to respond to changes in land use over the last several decades. Since
rivers transport sediment, nutrients, and pollutants produced by anthropogenic activity
on the upland, river discharge will be used in the absence of water quality data, to
assess water quality condition in the river. Discharge will then be compared with SAV
abundance. Low densities of SAV are expected during wet years as a consequence of
high sediment discharge. SAV distribution should increase during drought years.
Methods
This research is divided into three components: analysis of fish community, SAV
abundance and changes in land use. Each component will be analyzed individually and
the compared.
Fish community
Data from the 1989 – 2003 VIMS striped bass seine survey was used to assess
temporal patterns in abundance and diversity. The seine survey is conducted bi-weekly
between July to September using 0.25 in. mesh minnow seine in shallow water. This
project uses data collected from station 12 in the lower Rappahannock River (-76.525N/
37.6117W). In addition to station 12, fish data were compared with two upriver stations:
#21(-76.62N/ 37.75W), and #28(-76.73N/ 37.80W). Parameters used to measure fish
population were species richness, species dominance, and fish abundance. Species
richness is the total number of species minus one divided by the log abundance
(Margalef’s Index). Species dominance is equal to the number of species that
contribute to 90 percent of the total abundance. Abundance was calculated as the
natural log of the total number of fish. Data was compiled from the seine surveys using
averaged rounds for the year. A One Way ANOVA and Pearson correlation were used
to statistically analyze the results.
Study area
An Anderson (1976) level 1 land use classification (e.g. forest, agriculture and
developed areas) was applied to describe land use in the study area for three different
years: 1978, 1994, and 2002. The 1978 digital imagery, a product of the United States
Army Corps of Engineers (USACE) is natural color aerial photography scanned to 600
dpi, and scaled 1:12,000. The 1994 imagery is color infrared digital ortho quarter
quadrangles with a resolution of 1m, obtained from the U.S. Geological Survey. The
2002 digital image is ortho corrected with a resolution of 0.61m, and scaled 1:400. This
digital image was obtained from the VA Base Mapping Program.
Using Geographical information system (GIS), land use change was computed
for a 15,600 acre area surrounding the fish sampling point (station 12). ERDAS®
Imagine image processing software was used to digitize land use boundaries from the
digital imagery. ArcInfo was used to edit, label, and classify land use polygons. A
frequency analysis was applied to each year to compute the occurrence of land use
classes. Values obtained with the frequency analysis were compared and patterns of
change observed.
SAV biomass
Submerged Aquatic Vegetation delineations generated by the Department of
Biological Sciences at the Virginia Institute of Marine Science were used in this study.
SAV biomass was calculated using a monthly biomass model (Moore, 2002) where:
Monthly Biomass = Mb * Cc* Ba, and Mb = model biomass for assigned community type
(grams dry mass per square meter), Cc = photo-interpreted density class to ground
cover conversions, and Ba = bed area (m2).
The study area used in our research is primarily populated by Ruppia maritima
(Moore,2000), with a modeled biomass of 100 dgm/m 2. The photo interpreted values
were obtained by an equation presented by Moore (2000): Cc = 0.69x + 17, where x =
mid point of density classes (%). SAV density delineations have a scale assigned from 0
to 4, 0 to 100% bottom coverage respectively. The density range (as a percentage) is
assigned for each value in the density scale where: 0=0%, 1=<10%, 2=10–40%, 3=40 –
70%, and 4=70 – 100. For example, when the density scale = 1, then bottom coverage
equals 0 to 10%, and x=5; Cc = 20.45. The bed area was calculated using ArcView 3.1.
Environmental Parameters
Submerged Aquatic Vegetation biomass, fish population data, and land use
changes over time were compared with variations in temperature, precipitation, and
stream flow for similar years. Stream flow data were obtained from the USGS web site
(station number p1668000, Spotsylvania County, Virginia (38º18’30N, 77º31’46’’W)).
The drainage area for this station is 1596 squares miles. Stream flow was measured in
cubic feet per second. Temperature and precipitation data were obtained from the
National Climatic Data Center, NOAA. For 1989 to 1993 data was reported from the
station located in Fredericksburg National Park (38.19N/ 77.27W). For 1993 to 2003
data from the Fredericksburg sewage treatment plant was used (38.17N/ 77.27W).
Precipitation was measured in inches per day, and temperature was measured in
Fahrenheit degrees. Stream flow and temperature were averaged per year, and total
inches of precipitation per year were summed.
Results
Land use within the study area on the lower Rappahannock River did not vary
significantly during the period of time studied. The frequency analysis showed a slight
increase in developed areas, with a decrease in agriculture and forested land area
(Figure 1).
From the analysis of the fish survey data we found an inverse significant
correlation (r = -0.0528, p = 0.052) between species richness and fish abundance at
station 12 (Figure 2). At the same time, species richness and dominance (Figure 3)
were significantly correlated (r = 0.593, p = 0.025). During years when species richness
was low, the number of species dominanting was also low. Generally, dominant species
were schooling fish like Atlantic menhaden (Brevoortia tyrannus), Bay anchovy (Anchoa
mitchelli), spot (Leiostomus xantharus), and Atlantic silverside (Menidia menidia).
However, during years where species richness was high, these species were not
present.
The fish community metrics varied over the period of time studied. Species
richness was low during 1989, and increased in 1990. However, during the following 11
years species richness declined until 2002, when a drastic increase in occurred (Figure
2).
The comparison of fish community metrics among three stations on the
Rappahannock River (12, 21, 28) showed no significant difference (p = 0.112) in fish
abundance, and a significant difference (p = 0.0001) in species richness (Figures 4 and
5). Species richness was highest at the uppermost station (28), possibly due to salinity
regimes. Species richness at stations 12 and 28 were higher than station 21, located
mid-river.
The presence of SAV was not consistent during the period of time studied. In
1989, the biomass of SAV was 8.2 x 1010 dgm m-2, and during 1990 it declined to 7.8 x
107 dgm m-2. During the following eleven years no SAV were present in the study site.
In 2002 SAV returned with a biomass of 6.5 x 10 8 dgm m-2. This recurrence quickly
disappeared and the 2003 survey showed no SAV again. The presence of SAV during
the year 2002 coincides with an increase in species richness (Figure 6). In addition, the
extended period of no SAV correlates with the eleven years of low fish diversity.
The environmental factors (e.g. temperature, precipitation, and stream flow) did
not correlate with the species richness, dominance, and abundance (Table 1). Also,
environmental factors did not correlate among each other. However, presence of SAV
coincides with years of low river discharge. From 1999 to 2002 stream flow was very
low, and that is very important because after eleven years (1990-2001) of SAV
absence, SAV reappeared in 2002. In June 2004, the Bay Journal published a report
with data from the U.S. Geological Survey that showed a drastic increase in river
discharge compared to a 30% decline in SAV biomass as reported from the Maryland
Department of Natural Resources.
Discussion
The small size of the study area contributes to the low variation in land use
between years. Due to the increase in population and global movement of development
toward the coast, we expected a significant increase in developed area. However, the
study area seems to have an economy based on agriculture practice, which showed
only a slight decline. Other factors non-related to the study area might also be
influencing the results. For example, the aerial photographs used to calculate land use
had different resolutions and spectral properties. These differences can influence
interpretation and ultimately the designations of land use boundaries.
The presence of schooling fish in the catch increased the abundance estimates,
and at the same time decreased the diversity. The species dominance metric showed
that during years with high abundance of fish only a few species contribute to 90% of
the total abundance. Increases in diversity during 2002 suggest a possible relationship
with the presence of SAV. This point is not supported with the low diversity of fish
observed in 1989, where SAV was present. However, the exploration of additional years
indicates possible relationships may exist. An increase in diversity occurred from 1989
to 1990, when SAV declined but did not disappear. Subsequently, low fish diversity
persists over the eleven years when SAV was absent.
The low abundance of fish during years of SAV presence might be related to
environmental changes that influence conditions for SAV growth. For instance, an
increase in diversity due to SAV presence might promote an increase in predators for
the schooling fish that dominate the fish population during years of high abundance of
fish and low species richness.
The difference in species richness among stations along the river suggests the
influence of other environmental factors besides those analyzed in this research.
Neither stream flow, precipitation nor temperature correlated with fish populations. A
factor that might be driving fish diversity in the river is salinity, which varies along the
river. Other factors such as dissolved oxygen, water clarity, and nutrients might cause
variations in diversity of fish. These factors are influenced by anthropogenic impacts. In
this study, change in land use was used as a measure of anthropogenic impacts.
However, effects of land use on SAV biomass and fish communities cannot be
determined with the data obtained in our research due to the spatial size and scale of
the study area. Our results do not limit the possible relationship between SAV
abundance and consequently fish communities.
Stream flow seems to influence the presence and biomass of SAV. Low river
discharge yields low suspended sediment concentrations and enhances water clarity.
These conditions favor the growth of SAV. On the other hand, high river discharge
reduces water clarity with higher sediment loads and may induce die-off in SAV.
In conclusion, relationships among SAV biomass, land use, and fish community
metrics could not be completely quantified. Further research is necessary to understand
the relationship of these three components: land use, SAV abundance and fish
populations. This can be achieved in part by extending the study area and incorporating
land use for the entire watershed. A better understanding of conditions in the upper
watershed with respect to land use might explain responses in the lower river. The
relationship between SAV and fish communities will be better determined if future
analyses target watersheds with consistently larger SAV beds and incorporate
additional fish survey stations. The study of additional environmental factors (e.g.
salinity and dissolve oxygen) will provide critical information to understand the human
impacts to essential fish habitats.
Acknowledgements
Special thanks to Marcia Berman, Donna Bilkovic, and Tami Rudnicky for
intellectual contributions and support during the research. Thank you very much to
Dave, Harry, Tami and Sharon for helping me with the technology and instrumentation.
Thanks to the Center for Coastal Resource Management people for their support during
the research. Thanks to Linda Shaffner and Rochelle Seitz for the opportunity to
participate in the VIMS-REU program. This research was part of the VIMS-REU
summer internship funded by the National Science Foundation. Thanks to the VIMS
SAV mapping program and the Striped bass seine survey group, especially to Dr. Herb
Austin and Chris Bonzek for facilitating the use of the fish survey data. Special thank to
Dave Wilcox for orienting me about the distribution and ecology of SAV. Thanks to the
College of William and Mary and the Virginia Institute of Marine Science for allow me to
use their facilities. Thanks to the VIMS staff and personnel for offering unconditional
help when I needed it.
References
Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land
cover classification system for use with remote sensor data: U.S. Geol Survey
Circ. 964, 1- 27.
Beck, M.W, Jr. K.L.Heck, K.W. Able, and D.L. Childers. 2001. The identification,
conservation, and management of estuarine and marine nurseries for fish and
invertebrates. BioScience. 51 (8), 633 – 641.
Blankenship, K. 2004. Bay’s SAV fell off almost 30% in 2003. Bay Journal:
Alliance for the Chesapeake Bay. 14(4), 6-7.
Hardy, R.W. 1995. NOAA Coastal Change Analysis Program (C-CAP): guidance for
regional implementation. NOAA Technical Report NMFS 123. Pp. 1-92
Hoss, D. E., G.W.Thayer. 1993. The importance of habitat to the early life history of
estuarine dependent fishes. American Fisheries Society Symposium 14, 147158.
Lazzari, M. A., 2002. Epibenthic fishes and decapods crustaceans in northern estuaries:
a comparison of vegetated and unvegetated habitats in Maine. Estuaries. 25(6A),
1210-1218.
Matheson, Richard E, S.M. Sogard, and K.A. Bjorgo. 1999. Changes in Sea grass associated fish and crustacean communities on Florida Bay mud banks: the
effects of recent ecosystem changes? Estuaries. 22(2B): 534-551.
Internet Sources:
NOAA- NMFS Web Page. Essential Fish Habitat. June 23, 2004.
http://www.nmfs.noaa.gov/habitat/habitatprotection/essentialfishhabitat.htm.
VIMS SAV Mapping Lab Web page. Submerged Aquatic Vegetation (SAV) species in
Chesapeake Bay. http://www.vims.edu/bio/sav/aboutsav.html. Last modified
2/18/04.
Land Use in the Lower Rappahannock River
7000
Agriculture
Developed
Forested
Water
6000
Land use (acres)
5000
4000
6413
6361
3000
4913
6267
4905
4855
2000
3320
2837
2666
1000
1046
1481
1849
0
1978
1994
2002
YEAR
Figure 1: Land use changes in Rappahannock River from 1978 to 2002.
9
4.5
8
4
7
3.5
6
3
5
2.5
4
2
3
1.5
r = -0.0528, p = 0.052
2
LN Abundance
Average Diversity
1
0
Species richness
LN Abundance of fish
Species Richness and LN abundance in Rappahannock River Station 12
1
0.5
0
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Year (1989 - 2003)
Figure 2: Average species richness (Total # species – 1/Log total # fish) and LN
abundance (LN total number of fish per year) in Rappahannock River Station 12 from
1989 to 2003. An inverse correlation between species richness and LN abundance was
present.
Species Richness and Dominance in Rappahannock River Station 12
8
Diversity
4
Dominance
Species Richness
3.5
3
7
6
5
2.5
4
2
3
1.5
1
2
r = 0.593, p = 0.025
1
0.5
0
0
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Year (1989 - 2003)
Figure 3: Species richness and dominance (number of species that contribute to the
90% of the abundance) in Lower Rappahannock River Station 12 from 1989 to 2003. A
significant correlation between species richness and dominance was present.
Species Dominance
4.5
LN fish abundance by station
9
Ln Abundance
8
7
6
5
p = 0.112
4
28
21
12
3
Stations in Rappahannock River
Downstream -> upriver
Figure 4: LN abundance of fish for three stations in Rappahannock. No significant
difference in abundance among the stations was present.
Species Richness by Station
6
Species richness
5
4
3
2
p<0.0001
1
28
21
12
0
Stations in Rappahannock River
Downstrean -> upriver
Figure 5: Species richness for three in Rappahannock River. Station 12 is located
downstream, station 21 is mid-river, and station 28 is the upper most examined. locate
upper in the river. A significant difference among the stations was observed..
Comparison between SAV biomass and fish species richness in
Rappahannock River Station 12
4.5
1.E+10
8.19 x 10 10 dgm m-2
4
8.E+09
3.5
7.E+09
3
6.E+09
2.5
5.E+09
2
4.E+09
1.5
3.E+09
2.E+09
SAV
1
1.E+09
Diversity
0.5
Fish Species Richness
SAV Biomass (dgm m-2)
9.E+09
0
0.E+00
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2002
2003
Year (1989 - 2003)
Figure 6:
Comparison between SAV biomass and fish species richness in
Rappahannock River station 12 from 1989 to 2003.
Environmental
Parameter
Precipitation
Temperature
Stream flow
Station 12 – Rappahannock River
Pearson
Species richness
LN abundance
correlation
r - value
0.172
0.222
p - value
0.556
0.445
r - value
0.466
0.123
p - value
0.093
0.674
r - value
-0.433
0.358
p - value
0.140
0.230
Species
Dominance
-0.001
0.997
0.078
0.792
0.044
0.887
Table 1: No significant correlation between the fish population parameter and
environmental parameters in Rappahannock River station 12 was present. The
correlation coefficient and associated p-value are depicted for each parameter.