This study examined whether headwater streams located within protected areas in Maryland exhibited less impacted ecological conditions compared to adjacent unprotected streams. The study analyzed landscape features, water quality variables, and biological indicators at 148 protected areas and 474 unprotected stream sites across three regions of Maryland. Results showed that protected area streams had significantly less urban and agricultural land use and better habitat quality. While water quality differences were small, protected streams in some regions showed marginally better quality. Landscape factors at broader spatial scales influenced stream conditions more than local factors in protected areas. Therefore, the study found that protected areas can effectively filter water quality impacts from surrounding land uses in some areas of Maryland.

1. RESEARCH POSTER PRESENTATION DESIGN © 2011
www.PosterPresentations.com
Abiotic and Biotic Stream Conditions within Maryland Protected Areas:
Do Parks Act as Effective Filters of Water Quality?
The water quality and ecological conditions of headwater streams
can be greatly influenced by the surrounding landscape features.
Protected areas are important mechanisms of conservation biology,
as they help shield biodiversity from negative anthropogenic land
uses by providing habitat refuges and limiting urban and
agricultural development. Protected areas have been shown to
effectively conserve terrestrial species diversity, but how
protected areas influence aquatic systems is not well understood.
The relationship that exists between protected area land use and
stream conditions was explored in this study to determine if and
how parks influence the abiotic and biotic factors of stream
ecosystems.
INTRODUCTION
OBJECTIVES
• Land Use Land Cover (LULC) data were harvested from the MBSS
web portal from the first two rounds of sampling, spanning from
1995 to 2004. These data included upstream watershed
percentages for urban, agricultural, and forested LULC classes.
• Additional MBSS habitat variables included vegetated riparian
buffer width and physical habitat index (PHI).
METHODS: LULC and Habitat Variables RESULTS: Water Quality RESULTS: LULC-WQ Associations
CONTACT INFO
John M. Fitz, johnfitz058@gmail.com, 240-338-2172
This study was conducted to determine if headwater streams
found within various protected areas (PAs) exhibited less impacted
ecological conditions compared to adjacent non-protected
streams. Landscape features at three spatial scales, water
chemistry and nutrient variables, and biological variables were
used to compare the protected sites to the unprotected sites.
John M. Fitz, Zanvyl Krieger School of Arts and Sciences, the Johns Hopkins University
• At watershed and local scales, landscape and habitat features
surrounding streams throughout Maryland protected areas were
significantly less impacted compared to adjacent unprotected
streams.
• Despite the major differences in landscape elements across the
entire study region, water quality differences were more
commonly found in protected streams of the Appalachian Plateau
and water quality was only marginally better in protected streams
of the Piedmont and Coastal Plain provinces.
• For protected stream sites, landscape factors at broader spatial
scales were more influential than local scales in driving stream
nutrients and biota responses.
CONCLUSION
• Two-sample t-Tests (two-tailed) were used to compare the LULC,
habitat and WQ variables of PA and non-PA stream sites. The null
hypotheses (H0) were used: PA variables = non-PA variables and the
alternative hypotheses (Ha): PA variables ≠ non-PA variables.
• Simple regression analysis was also used to determine the
strength of associations between LULC and WQ variables. For
these questions, the H0 used were no positive or negative linear
relationships between predictor and response variables (i.e. slope
of linear regression equation = 0) and the Ha was slope ≠ 0.
METHODS: Statistical Analysis
• Using Geographical Information Systems (GIS) maps, 148 PAs and
474 MBSS stream sampling sites were selected from across three
physiographic regions throughout Maryland.
•An additional 474 MBSS sites were selected from surrounding
unprotected streams for the comparison. These sites were nearby,
but were not upstream or downstream of the paired protected
sites.
STUDY DESIGN : Park and Stream Site Selection
Fig. 1. Land Use-Land Cover in Maryland.
Fig. 2. Protected Areas in Maryland.
• Data for several water quality (WQ) variables were harvested
from the MBSS dataset for protected and unprotected sites and
included pH, acid neutralizing capacity (ANC), specific
conductivity, temperature, dissolved oxygen (DO), Chloride (Cl-),
and dissolved organic carbon (DOC).
• Nutrient data were also compared to include variants of
nitrogen, sulfur, and phosphorous.
•Stream Biota variables included Indices of Biological Integrity (IBI)
for fish (F-IBI) and benthic macroinvertebrates (B-IBI), species
richness for fish and macroinvertebrates, and taxa richness of
Ephemeroptera, Plectoptera, and Trichoptera (EPT).
METHODS: Water Quality Variables
RESULTS: Landscape and Habitat Features
0.24
13.47
84.81
12.22
48.87
37.71
18.19
21.69
56.35
1.09
24.62
72.87
13.79
54.65
30.44
18
27.51
50.12
0
10
20
30
40
50
60
70
80
90
% Urb
(A.P.)
% Agr
(A.P.)
% For
(A.P.)
% Urb
(Pied)
% Agr
(Pied)
% For
(Pied)
% Urb
(C.P.)
% Agr
(C.P.)
% For
(C.P.)
%LULC(watershedboundary)
Park Sites
Non-Park Sites
Percent Land Use Land Cover
Fig. 3. Watershed LULC percentages (top) and Local habitat
features (bottom) for PA and non-PA sites throughout MD.
Fig. 5. Biota response variables (species richness top and IBIs
bottom) for PA and non-PA sites throughout MD.
4.32
19.47
10.38
11.54
20.17
6.97
8.03
18.64
3.81
4.78
20.87
8.92
10.69
20.69
6.46
8.7
18.97
4.28
0
5
10
15
20
25
Fish N
(A.P.)
Benthic
N (A.P.)
EPT N
(A.P.)
Fish N
(Pied)
Benthic
N (Pied)
EPT N
(Pied)
N-Fish
(C.P.)
Benthic
N (C.P.)
EPT N
(C.P.)
SpeciesRichness
(NumberofSpecies) Park Sites Non-Park Sites
(A)
Species Richness
2.92
3.24
3.44
2.92 2.94 2.95
2.64
3.01
3.42
2.76
3.04
2.94
0
0.5
1
1.5
2
2.5
3
3.5
4
F-IBI (A.P.) B-IBI (A.P.) F-IBI (Pied) B-IBI (Pied) F-IBI (C.P.) B-IBI (C.P.)
IBIScore
(B)
Index of Biological Integrity
80.88
92.6
72.02
92.28
71.16
94.95
68.98
80.73
65.73
76.32
70.2
87.52
0
10
20
30
40
50
60
70
80
90
100
PHI (A.P.) Rip Width (A.P.) PHI (Pied) Rip Width (Pied) PHI (C.P.) Rip Width (C.P.)
PHI(indexscore)andBufferWidth(m)
Habitat Factors
Fig. 4. Water quality variables for PA and non-PA sites
throughout MD.
126.3194444
233.1341463
170.4820144
265.6598639
247.5818182
201.3286713
0
50
100
150
200
250
300
350
Appalachian Plateau Piedmont Coastal Plains
conductivity(μeqL-1)
in-situ Conductivity
ParkSites Non-ParkSites
6.781631206
7.390426829
6.555
7.113809524
7.287272727
6.722971014
0
1
2
3
4
5
6
7
8
9
Appalachian Plateau Piedmont Coastal Plains
pH(standardpHunits)
in-situ pH
Park Sites
Non-Park Sites
337.43875
773.7992711
317.7262342
804.671875
821.5104807
448.4798658
0
100
200
300
400
500
600
700
800
900
Appalachian Plateau Piedmont Coastal Plains
ANC(μeqL-1)
ANC
17.15007042
19.24756098
20.92794118
18.3047619
19.43151515
20.82101449
0
5
10
15
20
25
Appalachian Plateau Piedmont Coastal Plains
temp(degreesC)
Water Temperature
9.39
45.8
30.7730.32
46.48
35.81
0
5
10
15
20
25
30
35
40
45
50
Appalachian Plateau Piedmont Coastal Plains
Cl-(mg/L)
Chloride ions-
0.6627475
2.221979518
0.864028188
1.132749375
2.741781928
1.393126174
0
0.5
1
1.5
2
2.5
3
Appalachian Plateau Piedmont Coastal Plains
NO3-N(mgL-1)
Nitrate-Nitrogen
0.016269307
0.022690099
0.045831429
0.018895238
0.03316092
0.042892708
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0.05
Appalachian Plateau Piedmont Coastal Plains
TP(mgL-1)
Total Phosphorous
15.65115375
11.2483012
14.64613221
31.54152937
12.23423855
18.41870671
0
5
10
15
20
25
30
35
Appalachian Plateau Piedmont Coastal Plains
SO4-S(mgL-1)
Sulfate-Sulfur
(A)
y = 0.0437x + 0.6168
R² = 0.75621
0
1
2
3
4
5
6
7
0.00 20.00 40.00 60.00 80.00 100.00
TN(mg/L)
% agriculture (park-scale)
(B)
y = -0.1037x + 11.215
R² = 0.18379
0
5
10
15
20
25
0.00 50.00 100.00 150.00
EPTrichness
% agriculture (park-scale)
(A)
y = 0.2513x + 8.4564
R² = 0.42952
0
5
10
15
20
25
30
35
40
45
50
0 20 40 60 80
SO4(mg/L)
% urban (watershed-scale)
(B)
y = -0.1142x + 8.0187
R² = 0.31556
0
2
4
6
8
10
12
14
16
18
0 20 40 60 80
EPTrichness
% urban (watershed-scale)
(A)
y = 0.0751x - 0.6948
R² = 0.3628
0
2
4
6
8
10
12
14
16
0 20 40 60 80
NO3-N(mg/L)
% agriculture (watershed-scale)
(B)
y = -0.013x + 2.9758
R² = 0.12408
0
1
2
3
4
5
6
0 20 40 60 80
B-IBIscore
% urban (park-scale)
Fig. 6. Strongest landscape-WQ associations of the
Appalachian Plateau (top), Piedmont (middle), and Coastal
Plain (bottom).
METHODS: Landscape and Habitat Variables
ACKNOWLEDGEMENTS
This project was completed as my Master’s thesis while at the
JHU. I would like to thank Eileen McGurty and the other excellent
faculty members of the Environmental Sciences/Policy program
and particularly Todd Lookingbill for serving as my advisor. I would
also like to thank Michael Kashiwagi and Dan Boward from MD-
DNR, and J.B. Churchill from the Appalachian Lab-UMD.